From fab98ca172de92489f01c21373543db7b1e6f418 Mon Sep 17 00:00:00 2001 From: GitHub Action Date: Sun, 18 Aug 2024 04:56:39 +0000 Subject: [PATCH] Updated datasets 2024-08-18 UTC --- aws_open_datasets.json | 701 +++++++++++++++++++++-------------------- aws_open_datasets.tsv | 111 +++---- gee_catalog.json | 170 +++++----- gee_catalog.tsv | 170 +++++----- nasa_cmr_catalog.json | 663 ++++++++++++++++++++++---------------- nasa_cmr_catalog.tsv | 51 +-- 6 files changed, 1008 insertions(+), 858 deletions(-) diff --git a/aws_open_datasets.json b/aws_open_datasets.json index d97d799..e5eb0ff 100644 --- a/aws_open_datasets.json +++ b/aws_open_datasets.json @@ -2379,7 +2379,7 @@ "segmentation" ], "Explore": [ - "[Browse Bucket](https://s3.amazonaws.com/bobsrepository/index.html)" + "[Browse Bucket](https://bobsrepository.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -23767,8 +23767,8 @@ }, { "Name": "OpenAlex dataset", - "Description": "Openalex Entities decomposed to tab-separated columnar files for backward compatibility with Microsoft Academic Graph", - "ARN": "arn:aws:s3:::openalex-mag-format", + "Description": "OpenAlex Entities in JSON Lines format", + "ARN": "arn:aws:s3:::openalex", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://docs.openalex.org", @@ -23784,7 +23784,7 @@ "aws-pds" ], "Explore": [ - "[Browse Bucket](https://openalex-mag-format.s3.amazonaws.com/browse.html)" + "[Browse Bucket](https://openalex.s3.amazonaws.com/browse.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -23793,8 +23793,8 @@ }, { "Name": "OpenAlex dataset", - "Description": "OpenAlex Entities in JSON Lines format", - "ARN": "arn:aws:s3:::openalex", + "Description": "Openalex Entities decomposed to tab-separated columnar files for backward compatibility with Microsoft Academic Graph", + "ARN": "arn:aws:s3:::openalex-mag-format", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://docs.openalex.org", @@ -23810,7 +23810,7 @@ "aws-pds" ], "Explore": [ - "[Browse Bucket](https://openalex.s3.amazonaws.com/browse.html)" + "[Browse Bucket](https://openalex-mag-format.s3.amazonaws.com/browse.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -23978,10 +23978,10 @@ }, { "Name": "OpenStreetMap on AWS", - "Description": "Imagery and metadata", - "ARN": "arn:aws:s3:::osm-pds", + "Description": "New data notifications", + "ARN": "arn:aws:sns:us-east-1:800218804198:New_File", "Region": "us-east-1", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://github.com/awslabs/open-data-docs/tree/main/docs/osm-pds", "Contact": "https://github.com/mojodna/osm-pds-pipelines/issues", "ManagedBy": "Pacific Atlas", @@ -24002,10 +24002,10 @@ }, { "Name": "OpenStreetMap on AWS", - "Description": "New data notifications", - "ARN": "arn:aws:sns:us-east-1:800218804198:New_File", + "Description": "Imagery and metadata", + "ARN": "arn:aws:s3:::osm-pds", "Region": "us-east-1", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://github.com/awslabs/open-data-docs/tree/main/docs/osm-pds", "Contact": "https://github.com/mojodna/osm-pds-pipelines/issues", "ManagedBy": "Pacific Atlas", @@ -24127,8 +24127,8 @@ }, { "Name": "Orcasound - bioacoustic data for marine conservation", - "Description": "Live-streamed orca audio data (HLS)", - "ARN": "arn:aws:s3:::streaming-orcasound-net", + "Description": "Labeled audio data for ML model development", + "ARN": "arn:aws:s3:::acoustic-sandbox", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/orcasound/orcadata/wiki", @@ -24195,8 +24195,8 @@ }, { "Name": "Orcasound - bioacoustic data for marine conservation", - "Description": "Labeled audio data for ML model development", - "ARN": "arn:aws:s3:::acoustic-sandbox", + "Description": "Live-streamed orca audio data (HLS)", + "ARN": "arn:aws:s3:::streaming-orcasound-net", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/orcasound/orcadata/wiki", @@ -24304,8 +24304,8 @@ }, { "Name": "Oxford Nanopore Technologies Benchmark Datasets", - "Description": "Oxford Nanopore Open Datasets", - "ARN": "arn:aws:s3:::ont-open-data", + "Description": "CpG dinucleotides frequently occur in high-density clusters called CpG islands (CGI) and >60% of human genes have their promoters embedded within CGIs Determining the methylation status of cytosines within CpGs is of substantial biological interest: alterations in methylation patterns within promoters is associated with changes in gene expression and disease states such as cancer Exploring methylation differences between tumour samples and normal samples can help to elucidate mechanisms associated with tumour formation and development Nanopore sequencing enables direct detection of methylated cytosines (eg at CpG sites), without the need for bisulfite conversionOxford Nanopore\u2019s Adaptive Sampling offers a flexible method to enrich regions of interest (eg CGIs) by depleting off-target regions during the sequencing run itself with no upfront sample manipulation Here we introduce Reduced Representation Methylation Sequencing (RRMS) to target 310 Mb of the human genome including regions which are highly enriched for CpGs including ~28,000 CpG islands, ~50,600 shores and ~42,700 shelves as well as ~21,600 promoter regions", + "ARN": "arn:aws:s3:::ont-open-data/rrms_2022.07", "Region": "eu-west-1", "Type": "S3 Bucket", "Documentation": "https://labs.epi2me.io/dataindex/", @@ -24360,8 +24360,8 @@ }, { "Name": "Oxford Nanopore Technologies Benchmark Datasets", - "Description": "Using nanopore sequencing, researchers have directly identified DNA and RNA base modifications at nucleotide resolution, including 5-methylycytosine, 5-hydroxymethylcytosine, N6-methyladenosine, 5-bromodeoxyuridine in DAN; and N6-methyladenosine in RNA, with detection of other natural or synthetic epigenetic modifications possible through training basecalling algorithms One of the most widespread genomic modifications is 5-methylcytosine (5mC), which most frequently occurs at dinucleotides Compared to whole-genome bisulfite sequencing, the traditional method of 5mC detection, nanopore technology can offer many advantagesThe following cell lines/DNA samples were obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research: GM24385", - "ARN": "arn:aws:s3:::ont-open-data/gm24385_mod_2021.09/extra_analysis/bonito_remora", + "Description": "Oxford Nanopore Open Datasets", + "ARN": "arn:aws:s3:::ont-open-data", "Region": "eu-west-1", "Type": "S3 Bucket", "Documentation": "https://labs.epi2me.io/dataindex/", @@ -24388,8 +24388,8 @@ }, { "Name": "Oxford Nanopore Technologies Benchmark Datasets", - "Description": "CpG dinucleotides frequently occur in high-density clusters called CpG islands (CGI) and >60% of human genes have their promoters embedded within CGIs Determining the methylation status of cytosines within CpGs is of substantial biological interest: alterations in methylation patterns within promoters is associated with changes in gene expression and disease states such as cancer Exploring methylation differences between tumour samples and normal samples can help to elucidate mechanisms associated with tumour formation and development Nanopore sequencing enables direct detection of methylated cytosines (eg at CpG sites), without the need for bisulfite conversionOxford Nanopore\u2019s Adaptive Sampling offers a flexible method to enrich regions of interest (eg CGIs) by depleting off-target regions during the sequencing run itself with no upfront sample manipulation Here we introduce Reduced Representation Methylation Sequencing (RRMS) to target 310 Mb of the human genome including regions which are highly enriched for CpGs including ~28,000 CpG islands, ~50,600 shores and ~42,700 shelves as well as ~21,600 promoter regions", - "ARN": "arn:aws:s3:::ont-open-data/rrms_2022.07", + "Description": "Using nanopore sequencing, researchers have directly identified DNA and RNA base modifications at nucleotide resolution, including 5-methylycytosine, 5-hydroxymethylcytosine, N6-methyladenosine, 5-bromodeoxyuridine in DAN; and N6-methyladenosine in RNA, with detection of other natural or synthetic epigenetic modifications possible through training basecalling algorithms One of the most widespread genomic modifications is 5-methylcytosine (5mC), which most frequently occurs at dinucleotides Compared to whole-genome bisulfite sequencing, the traditional method of 5mC detection, nanopore technology can offer many advantagesThe following cell lines/DNA samples were obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research: GM24385", + "ARN": "arn:aws:s3:::ont-open-data/gm24385_mod_2021.09/extra_analysis/bonito_remora", "Region": "eu-west-1", "Type": "S3 Bucket", "Documentation": "https://labs.epi2me.io/dataindex/", @@ -24611,8 +24611,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2018", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2018", + "Description": "original 256 kHz audio recordings year 2021", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2021", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -24643,8 +24643,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2025", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2025", + "Description": "decimated 16 kHz audio recordings", + "ARN": "arn:aws:s3:::pacific-sound-16khz", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -24675,8 +24675,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2016", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2016", + "Description": "original 256 kHz audio recordings year 2025", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2025", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -24707,8 +24707,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2017", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2017", + "Description": "original 256 kHz audio recordings year 2024", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2024", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -24739,8 +24739,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2019", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2019", + "Description": "original 256 kHz audio recordings year 2022", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2022", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -24771,8 +24771,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2020", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2020", + "Description": "original 256 kHz audio recordings year 2015", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2015", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -24803,8 +24803,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2021", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2021", + "Description": "original 256 kHz audio recordings year 2016", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2016", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -24835,8 +24835,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2022", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2022", + "Description": "original 256 kHz audio recordings year 2023", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2023", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -24867,8 +24867,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2023", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2023", + "Description": "original 256 kHz audio recordings year 2018", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2018", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -24899,8 +24899,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2024", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2024", + "Description": "original 256 kHz audio recordings year 2019", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2019", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -24931,8 +24931,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2015", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2015", + "Description": "original 256 kHz audio recordings year 2020", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2020", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -24963,8 +24963,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "decimated 2 kHz audio recordings", - "ARN": "arn:aws:s3:::pacific-sound-2khz", + "Description": "machine learning models", + "ARN": "arn:aws:s3:::pacific-sound-models", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -24995,8 +24995,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "decimated 16 kHz audio recordings", - "ARN": "arn:aws:s3:::pacific-sound-16khz", + "Description": "original 256 kHz audio recordings year 2017", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2017", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -25027,8 +25027,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "machine learning models", - "ARN": "arn:aws:s3:::pacific-sound-models", + "Description": "decimated 2 kHz audio recordings", + "ARN": "arn:aws:s3:::pacific-sound-2khz", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -25080,8 +25080,8 @@ }, { "Name": "Pancreatic Cancer Organoid Profiling", - "Description": "WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation,RNA-Seq Splice Junction Quantification", - "ARN": "arn:aws:s3:::gdc-organoid-pancreatic-phs001611-2-controlled", + "Description": "RNA-Seq Gene Expression Quantification", + "ARN": "arn:aws:s3:::gdc-organoid-pancreatic-phs001611-2-open", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v1.p1", @@ -25100,14 +25100,14 @@ ], "Explore": null, "RequesterPays": null, - "ControlledAccess": "https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v1.p1", + "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "Pancreatic Cancer Organoid Profiling", - "Description": "RNA-Seq Gene Expression Quantification", - "ARN": "arn:aws:s3:::gdc-organoid-pancreatic-phs001611-2-open", + "Description": "WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation,RNA-Seq Splice Junction Quantification", + "ARN": "arn:aws:s3:::gdc-organoid-pancreatic-phs001611-2-controlled", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v1.p1", @@ -25126,7 +25126,7 @@ ], "Explore": null, "RequesterPays": null, - "ControlledAccess": null, + "ControlledAccess": "https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v1.p1", "AccountRequired": null, "Host": null }, @@ -25224,8 +25224,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Nodal Seismometer Continuous Data", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac/", + "Description": "PoroTomo Nodal Seismometer Sweep Data", + "ARN": "arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac_sweep/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -25241,7 +25241,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac_sweep%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -25250,8 +25250,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Nodal Seismometer Sweep Data", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac_sweep/", + "Description": "PoroTomo Nodal Seismometer Continuous Data", + "ARN": "arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -25267,7 +25267,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac_sweep%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -25276,8 +25276,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Nodal Seismometer Field Notes and Metadata", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_metadata/", + "Description": "HSDS PoroTomo domains", + "ARN": "arn:aws:s3:::nrel-pds-hsds/nrel/porotomo/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -25293,7 +25293,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_metadata%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fporotomo%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -25302,8 +25302,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Datasets", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/", + "Description": "PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in HDF5 format", + "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASV/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -25319,7 +25319,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASV%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -25328,8 +25328,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in SEG-Y format", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/", + "Description": "PoroTomo Nodal Seismometer Field Notes and Metadata", + "ARN": "arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_metadata/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -25345,7 +25345,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_metadata%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -25354,8 +25354,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data Resampled in Time MATLAB Files", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/Resampled/", + "Description": "PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in HDF5 format", + "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASH/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -25371,7 +25371,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2FResampled%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASH%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -25406,8 +25406,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in HDF5 format", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASH/", + "Description": "PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data Resampled in Time MATLAB Files", + "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/Resampled/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -25423,7 +25423,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASH%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2FResampled%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -25432,8 +25432,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in HDF5 format", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASV/", + "Description": "PoroTomo Datasets", + "ARN": "arn:aws:s3:::nrel-pds-porotomo/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -25449,7 +25449,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASV%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo)" ], "RequesterPays": null, "ControlledAccess": null, @@ -25458,8 +25458,8 @@ }, { "Name": "PoroTomo", - "Description": "HSDS PoroTomo domains", - "ARN": "arn:aws:s3:::nrel-pds-hsds/nrel/porotomo/", + "Description": "PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in SEG-Y format", + "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -25475,7 +25475,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fporotomo%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -25508,8 +25508,8 @@ }, { "Name": "Prefeitura Municipal de S\u00e3o Paulo (PMSP) LiDAR Point Cloud", - "Description": "S\u00e3o Paulo city's 3D LiDAR - Entwine Point Tiles", - "ARN": "arn:aws:s3:::ept-m3dc-pmsp", + "Description": "S\u00e3o Paulo city's 3D LiDAR - LAZ Files", + "ARN": "arn:aws:s3:::laz-m3dc-pmsp", "Region": "sa-east-1", "Type": "S3 Bucket", "Documentation": "https://github.com/geoinfo-smdu/M3DC", @@ -25535,8 +25535,8 @@ }, { "Name": "Prefeitura Municipal de S\u00e3o Paulo (PMSP) LiDAR Point Cloud", - "Description": "S\u00e3o Paulo city's 3D LiDAR - LAZ Files", - "ARN": "arn:aws:s3:::laz-m3dc-pmsp", + "Description": "S\u00e3o Paulo city's 3D LiDAR - Entwine Point Tiles", + "ARN": "arn:aws:s3:::ept-m3dc-pmsp", "Region": "sa-east-1", "Type": "S3 Bucket", "Documentation": "https://github.com/geoinfo-smdu/M3DC", @@ -25810,8 +25810,8 @@ }, { "Name": "PyEnvs and CallArgs", - "Description": "PyEnvs", - "ARN": "arn:aws:s3:::pyenvs-and-callargs/pyenvs/", + "Description": "CallArgs", + "ARN": "arn:aws:s3:::pyenvs-and-callargs/callargs/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/amazon-research/function-call-argument-completion", @@ -25831,8 +25831,8 @@ }, { "Name": "PyEnvs and CallArgs", - "Description": "CallArgs", - "ARN": "arn:aws:s3:::pyenvs-and-callargs/callargs/", + "Description": "PyEnvs", + "ARN": "arn:aws:s3:::pyenvs-and-callargs/pyenvs/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/amazon-research/function-call-argument-completion", @@ -26442,10 +26442,10 @@ }, { "Name": "Registry of Open Data on AWS", - "Description": "Registry of Open Data on AWS", - "ARN": "arn:aws:s3:::registry.opendata.aws/roda/ndjson/", + "Description": "SNS topic for object create events", + "ARN": "arn:aws:sns:us-east-1:652627389412:roda-object_created", "Region": "us-east-1", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://github.com/awslabs/open-data-registry#how-are-datasets-added-to-the-registry", "Contact": "opendata@amazon.com", "ManagedBy": "[Amazon Web Services](https://aws.amazon.com/)", @@ -26464,10 +26464,10 @@ }, { "Name": "Registry of Open Data on AWS", - "Description": "SNS topic for object create events", - "ARN": "arn:aws:sns:us-east-1:652627389412:roda-object_created", + "Description": "Registry of Open Data on AWS", + "ARN": "arn:aws:s3:::registry.opendata.aws/roda/ndjson/", "Region": "us-east-1", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://github.com/awslabs/open-data-registry#how-are-datasets-added-to-the-registry", "Contact": "opendata@amazon.com", "ManagedBy": "[Amazon Web Services](https://aws.amazon.com/)", @@ -26513,10 +26513,10 @@ }, { "Name": "SILAM Air Quality", - "Description": "Surface Zarr files", - "ARN": "arn:aws:s3:::fmi-opendata-silam-surface-zarr", + "Description": "Notifications for new zarr surface data", + "ARN": "arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-silam-surface-zarr", "Region": "eu-west-1", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3", "Contact": "avoin-data@fmi.fi", "ManagedBy": "[Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/)", @@ -26530,9 +26530,7 @@ "air quality", "meteorological" ], - "Explore": [ - "[Browse Bucket](https://fmi-opendata-silam-surface-zarr.s3.amazonaws.com/index.html)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -26565,10 +26563,10 @@ }, { "Name": "SILAM Air Quality", - "Description": "Notifications for new zarr surface data", - "ARN": "arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-silam-surface-zarr", + "Description": "Surface Zarr files", + "ARN": "arn:aws:s3:::fmi-opendata-silam-surface-zarr", "Region": "eu-west-1", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3", "Contact": "avoin-data@fmi.fi", "ManagedBy": "[Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/)", @@ -26582,7 +26580,9 @@ "air quality", "meteorological" ], - "Explore": null, + "Explore": [ + "[Browse Bucket](https://fmi-opendata-silam-surface-zarr.s3.amazonaws.com/index.html)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -26774,10 +26774,10 @@ }, { "Name": "Safecast", - "Description": "New air and radiation measurement payloads", - "ARN": "arn:aws:sns:us-west-2:985752656544:ingest-measurements-prd", - "Region": "us-west-2", - "Type": "SNS Topic", + "Description": "Bulk exports of air and radiation measurements", + "ARN": "arn:aws:s3:::safecast-opendata-public-us-east-1", + "Region": "us-east-1", + "Type": "S3 Bucket", "Documentation": "https://github.com/Safecast/safecastapi/wiki/Data-Sets", "Contact": "https://groups.google.com/forum/#!forum/safecast-devices", "ManagedBy": "[Safecast](https://safecast.org/)", @@ -26791,7 +26791,9 @@ "geospatial", "radiation" ], - "Explore": null, + "Explore": [ + "[Browse Bucket](https://safecast-opendata-public-us-east-1.s3.amazonaws.com/index.html)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -26799,10 +26801,10 @@ }, { "Name": "Safecast", - "Description": "Bulk exports of air and radiation measurements", - "ARN": "arn:aws:s3:::safecast-opendata-public-us-east-1", - "Region": "us-east-1", - "Type": "S3 Bucket", + "Description": "New air and radiation measurement payloads", + "ARN": "arn:aws:sns:us-west-2:985752656544:ingest-measurements-prd", + "Region": "us-west-2", + "Type": "SNS Topic", "Documentation": "https://github.com/Safecast/safecastapi/wiki/Data-Sets", "Contact": "https://groups.google.com/forum/#!forum/safecast-devices", "ManagedBy": "[Safecast](https://safecast.org/)", @@ -26816,9 +26818,7 @@ "geospatial", "radiation" ], - "Explore": [ - "[Browse Bucket](https://safecast-opendata-public-us-east-1.s3.amazonaws.com/index.html)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -26924,8 +26924,8 @@ }, { "Name": "Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD)", - "Description": "Spatial transcriptomics data files in a public bucket", - "ARN": "arn:aws:s3:::sea-ad-spatial-transcriptomics", + "Description": "Single cell profiling (transcriptomics and epigenomics) data files in a public bucket", + "ARN": "arn:aws:s3:::sea-ad-single-cell-profiling", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://portal.brain-map.org/explore/seattle-alzheimers-disease/seattle-alzheimers-disease-brain-cell-atlas-download?edit&language=en", @@ -26951,7 +26951,7 @@ "transcriptomics" ], "Explore": [ - "[Browse Bucket](https://sea-ad-spatial-transcriptomics.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://sea-ad-single-cell-profiling.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26960,8 +26960,8 @@ }, { "Name": "Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD)", - "Description": "Single cell profiling (transcriptomics and epigenomics) data files in a public bucket", - "ARN": "arn:aws:s3:::sea-ad-single-cell-profiling", + "Description": "Spatial transcriptomics data files in a public bucket", + "ARN": "arn:aws:s3:::sea-ad-spatial-transcriptomics", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://portal.brain-map.org/explore/seattle-alzheimers-disease/seattle-alzheimers-disease-brain-cell-atlas-download?edit&language=en", @@ -26987,7 +26987,7 @@ "transcriptomics" ], "Explore": [ - "[Browse Bucket](https://sea-ad-single-cell-profiling.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://sea-ad-spatial-transcriptomics.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -27062,10 +27062,10 @@ }, { "Name": "Sentinel-1", - "Description": "GRD in a Requester Pays S3 bucket", - "ARN": "arn:aws:s3:::sentinel-s1-l1c", + "Description": "SNS topic for notification of new scenes, can subscribe with Lambda", + "ARN": "arn:aws:sns:eu-central-1:214830741341:SentinelS1L1C", "Region": "eu-central-1", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html", "Contact": "https://forum.sentinel-hub.com/c/aws-sentinel", "ManagedBy": "[Sinergise](https://www.sinergise.com/)", @@ -27081,20 +27081,18 @@ "cog", "synthetic aperture radar" ], - "Explore": [ - "[STAC V1.0.0 endpoint](https://sentinel-s1-l1c-stac.s3.amazonaws.com/)" - ], - "RequesterPays": true, + "Explore": null, + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "Sentinel-1", - "Description": "SNS topic for notification of new scenes, can subscribe with Lambda", - "ARN": "arn:aws:sns:eu-central-1:214830741341:SentinelS1L1C", + "Description": "S3 Inventory files for L1C and CSV", + "ARN": "arn:aws:s3:::sentinel-inventory/", "Region": "eu-central-1", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html", "Contact": "https://forum.sentinel-hub.com/c/aws-sentinel", "ManagedBy": "[Sinergise](https://www.sinergise.com/)", @@ -27118,8 +27116,8 @@ }, { "Name": "Sentinel-1", - "Description": "S3 Inventory files for L1C and CSV", - "ARN": "arn:aws:s3:::sentinel-inventory/", + "Description": "GRD in a Requester Pays S3 bucket", + "ARN": "arn:aws:s3:::sentinel-s1-l1c", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html", @@ -27137,8 +27135,10 @@ "cog", "synthetic aperture radar" ], - "Explore": null, - "RequesterPays": null, + "Explore": [ + "[STAC V1.0.0 endpoint](https://sentinel-s1-l1c-stac.s3.amazonaws.com/)" + ], + "RequesterPays": true, "ControlledAccess": null, "AccountRequired": null, "Host": null @@ -27254,8 +27254,8 @@ }, { "Name": "Sentinel-2", - "Description": "Zipped archives for each L1C product with 3 day retention period, in Requester Pays bucket", - "ARN": "arn:aws:s3:::sentinel-s2-l1c-zips", + "Description": "S3 Inventory files for L2A and CSV", + "ARN": "arn:aws:s3:::sentinel-inventory/sentinel-s2-l2a", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/sentinel-s2-l1c/readme.html) and [Sentinel-2 L2A](https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html).", @@ -27274,15 +27274,15 @@ "stac" ], "Explore": null, - "RequesterPays": true, + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "Sentinel-2", - "Description": "Zipped archives for each L2A product with 3 day retention period, in Requester Pays bucket", - "ARN": "arn:aws:s3:::sentinel-s2-l2a-zips", + "Description": "Level 2A scenes and metadata, in Requester Pays S3 bucket", + "ARN": "arn:aws:s3:::sentinel-s2-l2a", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/sentinel-s2-l1c/readme.html) and [Sentinel-2 L2A](https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html).", @@ -27300,7 +27300,9 @@ "disaster response", "stac" ], - "Explore": null, + "Explore": [ + "[STAC V1.0.0 endpoint](https://sentinel-s2-l2a-stac.s3.amazonaws.com/)" + ], "RequesterPays": true, "ControlledAccess": null, "AccountRequired": null, @@ -27308,8 +27310,8 @@ }, { "Name": "Sentinel-2", - "Description": "Level 2A scenes and metadata, in Requester Pays S3 bucket", - "ARN": "arn:aws:s3:::sentinel-s2-l2a", + "Description": "S3 Inventory files for L1C and CSV", + "ARN": "arn:aws:s3:::sentinel-inventory/sentinel-s2-l1c", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/sentinel-s2-l1c/readme.html) and [Sentinel-2 L2A](https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html).", @@ -27327,18 +27329,16 @@ "disaster response", "stac" ], - "Explore": [ - "[STAC V1.0.0 endpoint](https://sentinel-s2-l2a-stac.s3.amazonaws.com/)" - ], - "RequesterPays": true, + "Explore": null, + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "Sentinel-2", - "Description": "S3 Inventory files for L1C and CSV", - "ARN": "arn:aws:s3:::sentinel-inventory/sentinel-s2-l1c", + "Description": "Level 1C scenes and metadata, in Requester Pays S3 bucket", + "ARN": "arn:aws:s3:::sentinel-s2-l1c", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/sentinel-s2-l1c/readme.html) and [Sentinel-2 L2A](https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html).", @@ -27356,16 +27356,21 @@ "disaster response", "stac" ], - "Explore": null, - "RequesterPays": null, + "Explore": [ + "[Earth Search STAC L1C Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l1c)", + "[Earth Search STAC Browser L1C Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l1c)", + "[STAC V1.0.0 endpoint](https://sentinel-s2-l1c-stac.s3.amazonaws.com/)", + "[Earth Viewer by Element 84](https://viewer.aws.element84.com/)" + ], + "RequesterPays": true, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "Sentinel-2", - "Description": "Level 1C scenes and metadata, in Requester Pays S3 bucket", - "ARN": "arn:aws:s3:::sentinel-s2-l1c", + "Description": "Zipped archives for each L2A product with 3 day retention period, in Requester Pays bucket", + "ARN": "arn:aws:s3:::sentinel-s2-l2a-zips", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/sentinel-s2-l1c/readme.html) and [Sentinel-2 L2A](https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html).", @@ -27383,12 +27388,7 @@ "disaster response", "stac" ], - "Explore": [ - "[Earth Search STAC L1C Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l1c)", - "[Earth Search STAC Browser L1C Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l1c)", - "[STAC V1.0.0 endpoint](https://sentinel-s2-l1c-stac.s3.amazonaws.com/)", - "[Earth Viewer by Element 84](https://viewer.aws.element84.com/)" - ], + "Explore": null, "RequesterPays": true, "ControlledAccess": null, "AccountRequired": null, @@ -27396,10 +27396,10 @@ }, { "Name": "Sentinel-2", - "Description": "New scene notifications for L1C, can subscribe with Lambda", - "ARN": "arn:aws:sns:eu-west-1:214830741341:NewSentinel2Product", - "Region": "eu-west-1", - "Type": "SNS Topic", + "Description": "Zipped archives for each L1C product with 3 day retention period, in Requester Pays bucket", + "ARN": "arn:aws:s3:::sentinel-s2-l1c-zips", + "Region": "eu-central-1", + "Type": "S3 Bucket", "Documentation": "Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/sentinel-s2-l1c/readme.html) and [Sentinel-2 L2A](https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html).", "Contact": "https://forum.sentinel-hub.com/c/aws-sentinel", "ManagedBy": "[Sinergise](https://www.sinergise.com/)", @@ -27416,16 +27416,16 @@ "stac" ], "Explore": null, - "RequesterPays": null, + "RequesterPays": true, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "Sentinel-2", - "Description": "New scene notifications for L2A, can subscribe with Lambda", - "ARN": "arn:aws:sns:eu-central-1:214830741341:SentinelS2L2A", - "Region": "eu-central-1", + "Description": "New scene notifications for L1C, can subscribe with Lambda", + "ARN": "arn:aws:sns:eu-west-1:214830741341:NewSentinel2Product", + "Region": "eu-west-1", "Type": "SNS Topic", "Documentation": "Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/sentinel-s2-l1c/readme.html) and [Sentinel-2 L2A](https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html).", "Contact": "https://forum.sentinel-hub.com/c/aws-sentinel", @@ -27450,10 +27450,10 @@ }, { "Name": "Sentinel-2", - "Description": "S3 Inventory files for L2A and CSV", - "ARN": "arn:aws:s3:::sentinel-inventory/sentinel-s2-l2a", + "Description": "New scene notifications for L2A, can subscribe with Lambda", + "ARN": "arn:aws:sns:eu-central-1:214830741341:SentinelS2L2A", "Region": "eu-central-1", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/sentinel-s2-l1c/readme.html) and [Sentinel-2 L2A](https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html).", "Contact": "https://forum.sentinel-hub.com/c/aws-sentinel", "ManagedBy": "[Sinergise](https://www.sinergise.com/)", @@ -27477,8 +27477,8 @@ }, { "Name": "Sentinel-2 Cloud-Optimized GeoTIFFs", - "Description": "S3 Inventory files for L1C and CSV", - "ARN": "arn:aws:s3:::sentinel-cogs-inventory", + "Description": "Level 2A scenes and metadata", + "ARN": "arn:aws:s3:::sentinel-cogs", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/cirrus-geo/cirrus-earth-search", @@ -27497,16 +27497,19 @@ "cog", "stac" ], - "Explore": null, - "RequesterPays": null, + "Explore": [ + "[Earth Search STAC L2A Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)", + "[STAC Browser L2A Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)" + ], + "RequesterPays": false, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "Sentinel-2 Cloud-Optimized GeoTIFFs", - "Description": "Level 2A scenes and metadata", - "ARN": "arn:aws:s3:::sentinel-cogs", + "Description": "S3 Inventory files for L1C and CSV", + "ARN": "arn:aws:s3:::sentinel-cogs-inventory", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/cirrus-geo/cirrus-earth-search", @@ -27525,11 +27528,8 @@ "cog", "stac" ], - "Explore": [ - "[Earth Search STAC L2A Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)", - "[STAC Browser L2A Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)" - ], - "RequesterPays": false, + "Explore": null, + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null @@ -27591,8 +27591,8 @@ }, { "Name": "Sentinel-3", - "Description": "Sentinel-3 Near Real Time Data (NRT) format", - "ARN": "arn:aws:s3:::meeo-s3/NRT/", + "Description": "Sentinel-3 Cloud Optimized GeoTIFF (COG) format", + "ARN": "arn:aws:s3:::meeo-s3-cog/", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Description.md", @@ -27611,7 +27611,9 @@ "cog", "stac" ], - "Explore": null, + "Explore": [ + "[STAC V1.0.0 endpoint](https://meeo-s3.s3.amazonaws.com/)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -27619,8 +27621,8 @@ }, { "Name": "Sentinel-3", - "Description": "Sentinel-3 Not Time Critical (NTC) format", - "ARN": "arn:aws:s3:::meeo-s3/NTC/", + "Description": "Sentinel-3 Short Time Critical (STC) format", + "ARN": "arn:aws:s3:::meeo-s3/STC/", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Description.md", @@ -27647,8 +27649,8 @@ }, { "Name": "Sentinel-3", - "Description": "Sentinel-3 Short Time Critical (STC) format", - "ARN": "arn:aws:s3:::meeo-s3/STC/", + "Description": "Sentinel-3 Not Time Critical (NTC) format", + "ARN": "arn:aws:s3:::meeo-s3/NTC/", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Description.md", @@ -27675,8 +27677,8 @@ }, { "Name": "Sentinel-3", - "Description": "Sentinel-3 Cloud Optimized GeoTIFF (COG) format", - "ARN": "arn:aws:s3:::meeo-s3-cog/", + "Description": "Sentinel-3 Near Real Time Data (NRT) format", + "ARN": "arn:aws:s3:::meeo-s3/NRT/", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Description.md", @@ -27695,9 +27697,7 @@ "cog", "stac" ], - "Explore": [ - "[STAC V1.0.0 endpoint](https://meeo-s3.s3.amazonaws.com/)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -27705,8 +27705,8 @@ }, { "Name": "Sentinel-5P Level 2", - "Description": "Sentinel-5p Near Real Time Data (NRTI) NetCDF format", - "ARN": "arn:aws:s3:::meeo-s5p/NRTI/", + "Description": "Sentinel-5p Off Line Data (OFFL) NetCDF format", + "ARN": "arn:aws:s3:::meeo-s5p/OFFL/", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Description.md", @@ -27733,8 +27733,8 @@ }, { "Name": "Sentinel-5P Level 2", - "Description": "Sentinel-5p Cloud Optimised GeoTIFF (COGT) TIFF format", - "ARN": "arn:aws:s3:::meeo-s5p/COGT/", + "Description": "Sentinel-5p Near Real Time Data (NRTI) NetCDF format", + "ARN": "arn:aws:s3:::meeo-s5p/NRTI/", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Description.md", @@ -27753,9 +27753,7 @@ "cog", "stac" ], - "Explore": [ - "[STAC V1.0.0 endpoint](https://meeo-s5p.s3.amazonaws.com/index.html?t=catalogs)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -27763,8 +27761,8 @@ }, { "Name": "Sentinel-5P Level 2", - "Description": "Sentinel-5p Reprocessed Data (RPRO) NetCDF format", - "ARN": "arn:aws:s3:::meeo-s5p/RPRO/", + "Description": "Sentinel-5p Cloud Optimised GeoTIFF (COGT) TIFF format", + "ARN": "arn:aws:s3:::meeo-s5p/COGT/", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Description.md", @@ -27783,7 +27781,9 @@ "cog", "stac" ], - "Explore": null, + "Explore": [ + "[STAC V1.0.0 endpoint](https://meeo-s5p.s3.amazonaws.com/index.html?t=catalogs)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -27791,8 +27791,8 @@ }, { "Name": "Sentinel-5P Level 2", - "Description": "Sentinel-5p Off Line Data (OFFL) NetCDF format", - "ARN": "arn:aws:s3:::meeo-s5p/OFFL/", + "Description": "Sentinel-5p Reprocessed Data (RPRO) NetCDF format", + "ARN": "arn:aws:s3:::meeo-s5p/RPRO/", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Description.md", @@ -28078,8 +28078,8 @@ }, { "Name": "Software Heritage Graph Dataset", - "Description": "Software Heritage Graph Dataset", - "ARN": "arn:aws:s3:::softwareheritage", + "Description": "S3 Inventory files", + "ARN": "arn:aws:s3:::softwareheritage-inventory", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://docs.softwareheritage.org/devel/swh-dataset/graph/athena.html", @@ -28102,8 +28102,8 @@ }, { "Name": "Software Heritage Graph Dataset", - "Description": "S3 Inventory files", - "ARN": "arn:aws:s3:::softwareheritage-inventory", + "Description": "Software Heritage Graph Dataset", + "ARN": "arn:aws:s3:::softwareheritage", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://docs.softwareheritage.org/devel/swh-dataset/graph/athena.html", @@ -28274,6 +28274,29 @@ "AccountRequired": null, "Host": null }, + { + "Name": "Spatiam Corporation National Lab Research Announcement International Space Station Technology Demonstration", + "Description": "Spatiam Corporation NLRA ISS Technology Demonstration archive data", + "ARN": "arn:aws:s3:::spatiam-nlra-iss-experiment-open-data", + "Region": "us-east-1", + "Type": "S3 Bucket", + "Documentation": "https://spatiam-nlra-iss-experiment-open-data.s3.amazonaws.com/README.md", + "Contact": "info@spatiam.com", + "ManagedBy": "[Spatiam Corporation](https://www.spatiam.com/)", + "UpdateFrequency": "No updates envisioned after the conclusion of the experiment.", + "License": "There are no restrictions on the use of this data.", + "Tags": [ + "network traffic", + "telecommunications" + ], + "Explore": [ + "[Browse Bucket](https://spatiam-nlra-iss-experiment-open-data.s3.amazonaws.com/index.html)" + ], + "RequesterPays": null, + "ControlledAccess": null, + "AccountRequired": null, + "Host": null + }, { "Name": "Speedtest by Ookla Global Fixed and Mobile Network Performance Maps", "Description": "Parquet and Shapefiles", @@ -28430,8 +28453,8 @@ }, { "Name": "Sup3rCC", - "Description": "Sup3rCC - CONUS - MRI ESM 20 - SSP585 - r1i1p1f1", - "ARN": "arn:aws:s3:::nrel-pds-sup3rcc/conus_mriesm20_ssp585_r1i1p1f1/", + "Description": "Sup3rCC", + "ARN": "arn:aws:s3:::nrel-pds-sup3rcc/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/NREL/sup3r", @@ -28447,7 +28470,7 @@ "climate model" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_mriesm20_ssp585_r1i1p1f1%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc)" ], "RequesterPays": null, "ControlledAccess": null, @@ -28456,8 +28479,8 @@ }, { "Name": "Sup3rCC", - "Description": "Sup3rCC", - "ARN": "arn:aws:s3:::nrel-pds-sup3rcc/", + "Description": "Sup3rCC Generative Models", + "ARN": "arn:aws:s3:::nrel-pds-sup3rcc/models/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/NREL/sup3r", @@ -28473,7 +28496,7 @@ "climate model" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=models%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -28482,8 +28505,8 @@ }, { "Name": "Sup3rCC", - "Description": "Sup3rCC Generative Models", - "ARN": "arn:aws:s3:::nrel-pds-sup3rcc/models/", + "Description": "Sup3rCC - CONUS - MRI ESM 20 - SSP585 - r1i1p1f1", + "ARN": "arn:aws:s3:::nrel-pds-sup3rcc/conus_mriesm20_ssp585_r1i1p1f1/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/NREL/sup3r", @@ -28499,7 +28522,7 @@ "climate model" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=models%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_mriesm20_ssp585_r1i1p1f1%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -28743,9 +28766,9 @@ }, { "Name": "Terrain Tiles", - "Description": "Gridded elevation tiles - replication in EU region", - "ARN": "arn:aws:s3:::elevation-tiles-prod-eu", - "Region": "eu-central-1", + "Description": "Gridded elevation tiles", + "ARN": "arn:aws:s3:::elevation-tiles-prod", + "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://github.com/tilezen/joerd/tree/master/docs", "Contact": "https://github.com/tilezen/joerd/issues", @@ -28760,7 +28783,9 @@ "geospatial", "disaster response" ], - "Explore": null, + "Explore": [ + "[Browse Map](https://elevation-tiles-prod.s3.amazonaws.com/index.html)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -28768,9 +28793,9 @@ }, { "Name": "Terrain Tiles", - "Description": "Gridded elevation tiles", - "ARN": "arn:aws:s3:::elevation-tiles-prod", - "Region": "us-east-1", + "Description": "Gridded elevation tiles - replication in EU region", + "ARN": "arn:aws:s3:::elevation-tiles-prod-eu", + "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/tilezen/joerd/tree/master/docs", "Contact": "https://github.com/tilezen/joerd/issues", @@ -28785,9 +28810,7 @@ "geospatial", "disaster response" ], - "Explore": [ - "[Browse Map](https://elevation-tiles-prod.s3.amazonaws.com/index.html)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -28816,8 +28839,8 @@ }, { "Name": "The Cancer Genome Atlas", - "Description": "WXS/RNA-Seq/miRNA-Seq/ATAC-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw SomaticMutation, WXS Aggregated Somatic Mutation", - "ARN": "arn:aws:s3:::tcga-2-controlled", + "Description": "Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-SeqIsoform Expression Quantification, miRNA Expression Quantification, Genotyping Array CopyNumber Segment, Genotyping Array Masked Copy Number Segment, Genotyping Array Gene Level CopyNumber Scores, WXS Masked Somatic Mutation", + "ARN": "arn:aws:s3:::tcga-2-open", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga", @@ -28835,14 +28858,14 @@ ], "Explore": null, "RequesterPays": null, - "ControlledAccess": "https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000178.v1.p1", + "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "The Cancer Genome Atlas", - "Description": "Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-SeqIsoform Expression Quantification, miRNA Expression Quantification, Genotyping Array CopyNumber Segment, Genotyping Array Masked Copy Number Segment, Genotyping Array Gene Level CopyNumber Scores, WXS Masked Somatic Mutation", - "ARN": "arn:aws:s3:::tcga-2-open", + "Description": "WXS/RNA-Seq/miRNA-Seq/ATAC-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw SomaticMutation, WXS Aggregated Somatic Mutation", + "ARN": "arn:aws:s3:::tcga-2-controlled", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga", @@ -28860,7 +28883,7 @@ ], "Explore": null, "RequesterPays": null, - "ControlledAccess": null, + "ControlledAccess": "https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000178.v1.p1", "AccountRequired": null, "Host": null }, @@ -29419,8 +29442,8 @@ }, { "Name": "USGS 3DEP LiDAR Point Clouds", - "Description": "A Requester Pays Bucket of Raw LAZ 14 3DEP data Data in this bucket is more complete in coverage than the EPT bucket, but it is not a complete 3DEP mirror Some resources in this bucket also have incomplete and missing coordinate system information, which is why they might not be mirrored into the EPT bucket", - "ARN": "arn:aws:s3:::usgs-lidar", + "Description": "Public access Entwine Point Tiles of most resources from the ``arn:aws:s3:::usgs-lidar`` bucket", + "ARN": "arn:aws:s3:::usgs-lidar-public", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/hobu/usgs-lidar/", @@ -29437,16 +29460,18 @@ "lidar", "stac" ], - "Explore": null, - "RequesterPays": true, + "Explore": [ + "[STAC Catalog](https://usgs-lidar-stac.s3-us-west-2.amazonaws.com/ept/catalog.json)" + ], + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "USGS 3DEP LiDAR Point Clouds", - "Description": "Public access Entwine Point Tiles of most resources from the ``arn:aws:s3:::usgs-lidar`` bucket", - "ARN": "arn:aws:s3:::usgs-lidar-public", + "Description": "A Requester Pays Bucket of Raw LAZ 14 3DEP data Data in this bucket is more complete in coverage than the EPT bucket, but it is not a complete 3DEP mirror Some resources in this bucket also have incomplete and missing coordinate system information, which is why they might not be mirrored into the EPT bucket", + "ARN": "arn:aws:s3:::usgs-lidar", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/hobu/usgs-lidar/", @@ -29463,10 +29488,8 @@ "lidar", "stac" ], - "Explore": [ - "[STAC Catalog](https://usgs-lidar-stac.s3-us-west-2.amazonaws.com/ept/catalog.json)" - ], - "RequesterPays": null, + "Explore": null, + "RequesterPays": true, "ControlledAccess": null, "AccountRequired": null, "Host": null @@ -29522,10 +29545,10 @@ }, { "Name": "USGS Landsat", - "Description": "New scene notifications, Level-1 and Level-2 Scenes", - "ARN": "arn:aws:sns:us-west-2:673253540267:public-c2-notify-v2", + "Description": "Scenes and metadata", + "ARN": "arn:aws:s3:::usgs-landsat/collection02/", "Region": "us-west-2", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-data-access", "Contact": "https://answers.usgs.gov/", "ManagedBy": "[United States Geological Survey](https://www.usgs.gov)", @@ -29542,18 +29565,20 @@ "stac", "cog" ], - "Explore": null, - "RequesterPays": null, + "Explore": [ + "[STAC Catalog](https://landsatlook.usgs.gov/stac-server/collections)" + ], + "RequesterPays": true, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "USGS Landsat", - "Description": "Scenes and metadata", - "ARN": "arn:aws:s3:::usgs-landsat/collection02/", + "Description": "New scene notifications, Level-1 and Level-2 Scenes", + "ARN": "arn:aws:sns:us-west-2:673253540267:public-c2-notify-v2", "Region": "us-west-2", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-data-access", "Contact": "https://answers.usgs.gov/", "ManagedBy": "[United States Geological Survey](https://www.usgs.gov)", @@ -29570,10 +29595,8 @@ "stac", "cog" ], - "Explore": [ - "[STAC Catalog](https://landsatlook.usgs.gov/stac-server/collections)" - ], - "RequesterPays": true, + "Explore": null, + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null @@ -29691,8 +29714,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2021_02", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2021-02/", + "Description": "UniProt 2021_01", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2021-01/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -29721,8 +29744,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2021_03", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2021-03/", + "Description": "UniProt 2021_02", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2021-02/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -29751,8 +29774,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2021_04", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2021-04/", + "Description": "UniProt 2021_03", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2021-03/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -29781,8 +29804,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2022_01", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-01/", + "Description": "UniProt 2021_04", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2021-04/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -29811,8 +29834,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2022_02", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-02/", + "Description": "UniProt 2022_01", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-01/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -29841,8 +29864,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2022_04", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-04/", + "Description": "UniProt 2022_02", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-02/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -29871,8 +29894,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2021_01", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2021-01/", + "Description": "UniProt 2022_04", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-04/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -29901,8 +29924,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2023_01", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-01/", + "Description": "UniProt 2024_02", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2024-02/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -29931,8 +29954,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2023_02", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-02/", + "Description": "UniProt 2023_01", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-01/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -29961,8 +29984,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2023_03", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-03/", + "Description": "UniProt 2023_02", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-02/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -29991,8 +30014,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2023_04", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-04/", + "Description": "UniProt 2023_03", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-03/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -30021,8 +30044,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2023_05", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-05/", + "Description": "UniProt 2023_04", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-04/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -30051,8 +30074,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2024_01", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2024-01/", + "Description": "UniProt 2023_05", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-05/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -30081,8 +30104,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2024_02", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2024-02/", + "Description": "UniProt 2024_01", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2024-01/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -30111,8 +30134,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2022_05", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-05/", + "Description": "UniProt 2024_03", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2024-03/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -30141,8 +30164,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2024_03", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2024-03/", + "Description": "UniProt 2022_05", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-05/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -30200,10 +30223,10 @@ }, { "Name": "VENUS L2A Cloud-Optimized GeoTIFFs", - "Description": "Venus L2A dataset (COG) and metadata (STAC)", - "ARN": "arn:aws:s3:::venus-l2a-cogs", + "Description": "New Venus L2A dataset notifications, can subscribe with Lambda", + "ARN": "arn:aws:sns:us-east-1:794383284256:venus-l2a-cogs-object_created", "Region": "us-east-1", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://github.com/earthdaily/venus-on-aws/", "Contact": "Klaus Bachhuber - klaus.bachhuber@earthdaily.com", "ManagedBy": "[EarthDaily Analytics](https://earthdaily.com/)", @@ -30224,20 +30247,18 @@ "environmental", "land cover" ], - "Explore": [ - "[STAC Browser Venus L2A (COG) Catalog](https://radiantearth.github.io/stac-browser/#/external/venus-l2a-cogs.s3.us-east-1.amazonaws.com/catalog.json)" - ], - "RequesterPays": false, + "Explore": null, + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "VENUS L2A Cloud-Optimized GeoTIFFs", - "Description": "New Venus L2A dataset notifications, can subscribe with Lambda", - "ARN": "arn:aws:sns:us-east-1:794383284256:venus-l2a-cogs-object_created", + "Description": "Venus L2A dataset (COG) and metadata (STAC)", + "ARN": "arn:aws:s3:::venus-l2a-cogs", "Region": "us-east-1", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://github.com/earthdaily/venus-on-aws/", "Contact": "Klaus Bachhuber - klaus.bachhuber@earthdaily.com", "ManagedBy": "[EarthDaily Analytics](https://earthdaily.com/)", @@ -30258,8 +30279,10 @@ "environmental", "land cover" ], - "Explore": null, - "RequesterPays": null, + "Explore": [ + "[STAC Browser Venus L2A (COG) Catalog](https://radiantearth.github.io/stac-browser/#/external/venus-l2a-cogs.s3.us-east-1.amazonaws.com/catalog.json)" + ], + "RequesterPays": false, "ControlledAccess": null, "AccountRequired": null, "Host": null @@ -30291,8 +30314,8 @@ }, { "Name": "Vermont Open Geospatial on AWS", - "Description": "Imagery datsets are organized in this bucket as statewide file mosaics and by acquisition year (often a portion of the state in any given year) These data are available in Cloud Optimized GeoTIFF (COG) format and use the following naming convention; 1) Statewide - STATEWIDE__cm__<#BANDS>Band, 2) By Acquisition Year - _cm__<#BANDS>Band Individual tiles are also available as lossless COGs under the /_Tiles subfolder", - "ARN": "arn:aws:s3:::vtopendata-prd/Imagery", + "Description": "Elevation datsets (primarily lidar based) are organized in this bucket as statewide file mosaics and by acquisition year (often a portion of the state in any given year) These data are available in Cloud Optimized GeoTIFF (COG) format and use the following naming convention; 1) Statewide COGs - STATEWIDE__cm_, 2) By Acquisition Year - _cm_ Individual tiles are also available as lossless COGs under the /_Tiles subfolder", + "ARN": "arn:aws:s3:::vtopendata-prd/Elevation", "Region": "us-east-2", "Type": "S3 Bucket", "Documentation": "https://vcgi.vermont.gov/data-and-programs/", @@ -30316,8 +30339,8 @@ }, { "Name": "Vermont Open Geospatial on AWS", - "Description": "Landcover datsets are organized in this bucket as statewide file mosaics These data are available in Cloud Optimized GeoTIFF (COG) format and use the following naming convention STATEWIDE__cm_LANDCOVER_", - "ARN": "arn:aws:s3:::vtopendata-prd/Landcover", + "Description": "Imagery datsets are organized in this bucket as statewide file mosaics and by acquisition year (often a portion of the state in any given year) These data are available in Cloud Optimized GeoTIFF (COG) format and use the following naming convention; 1) Statewide - STATEWIDE__cm__<#BANDS>Band, 2) By Acquisition Year - _cm__<#BANDS>Band Individual tiles are also available as lossless COGs under the /_Tiles subfolder", + "ARN": "arn:aws:s3:::vtopendata-prd/Imagery", "Region": "us-east-2", "Type": "S3 Bucket", "Documentation": "https://vcgi.vermont.gov/data-and-programs/", @@ -30341,8 +30364,8 @@ }, { "Name": "Vermont Open Geospatial on AWS", - "Description": "Elevation datsets (primarily lidar based) are organized in this bucket as statewide file mosaics and by acquisition year (often a portion of the state in any given year) These data are available in Cloud Optimized GeoTIFF (COG) format and use the following naming convention; 1) Statewide COGs - STATEWIDE__cm_, 2) By Acquisition Year - _cm_ Individual tiles are also available as lossless COGs under the /_Tiles subfolder", - "ARN": "arn:aws:s3:::vtopendata-prd/Elevation", + "Description": "Landcover datsets are organized in this bucket as statewide file mosaics These data are available in Cloud Optimized GeoTIFF (COG) format and use the following naming convention STATEWIDE__cm_LANDCOVER_", + "ARN": "arn:aws:s3:::vtopendata-prd/Landcover", "Region": "us-east-2", "Type": "S3 Bucket", "Documentation": "https://vcgi.vermont.gov/data-and-programs/", @@ -30594,8 +30617,8 @@ }, { "Name": "Wind AI Bench", - "Description": "Wind AI Bench Airfoil Computational Fluid Dynamics (CFD) - 2k Shapes Data Sets", - "ARN": "arn:aws:s3:::nrel-pds-windai/aerodynamic_shapes/2D/2K_airfoils/", + "Description": "Wind AI Bench Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets] (https://dataopeneiorg/submissions/5884)", + "ARN": "arn:aws:s3:::nrel-pds-windai/wind_plant_power/floris/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/NREL/windAI_bench", @@ -30610,7 +30633,7 @@ "machine learning" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=aerodynamic_shapes%2F2D%2F2k_airfoils%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=wind_plant_power%2Ffloris%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -30644,8 +30667,8 @@ }, { "Name": "Wind AI Bench", - "Description": "Wind AI Bench Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets] (https://dataopeneiorg/submissions/5884)", - "ARN": "arn:aws:s3:::nrel-pds-windai/wind_plant_power/floris/", + "Description": "Wind AI Bench Airfoil Computational Fluid Dynamics (CFD) - 2k Shapes Data Sets", + "ARN": "arn:aws:s3:::nrel-pds-windai/aerodynamic_shapes/2D/2K_airfoils/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/NREL/windAI_bench", @@ -30660,7 +30683,7 @@ "machine learning" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=wind_plant_power%2Ffloris%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=aerodynamic_shapes%2F2D%2F2k_airfoils%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -30799,8 +30822,8 @@ }, { "Name": "YouTube 8 Million - Data Lakehouse Ready", - "Description": "Original YT8M *tfrecords File structure info can be found here", - "ARN": "arn:aws:s3:::aws-roda-ml-datalake/yt8m/", + "Description": "Lakehouse ready YT8M as Glue Parquet files Install instructions here", + "ARN": "arn:aws:s3:::aws-roda-ml-datalake/yt8m_ods/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_install.md", @@ -30824,8 +30847,8 @@ }, { "Name": "YouTube 8 Million - Data Lakehouse Ready", - "Description": "Lakehouse ready YT8M as Glue Parquet files Install instructions here", - "ARN": "arn:aws:s3:::aws-roda-ml-datalake/yt8m_ods/", + "Description": "Original YT8M *tfrecords File structure info can be found here", + "ARN": "arn:aws:s3:::aws-roda-ml-datalake/yt8m/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_install.md", @@ -31165,10 +31188,10 @@ }, { "Name": "nuScenes", - "Description": "Globally cached distribution of the nuScenes Dataset Web frontend is available to browse the dataset", - "ARN": null, + "Description": "nuScenes Dataset", + "ARN": "arn:aws:s3:::motional-nuscenes", "Region": "ap-northeast-1", - "Type": "CloudFront Distribution", + "Type": "S3 Bucket", "Documentation": "https://www.nuscenes.org", "Contact": "https://www.nuscenes.org", "ManagedBy": "[Motional, Inc.](https://motional.com)", @@ -31183,18 +31206,20 @@ "transportation", "urban" ], - "Explore": null, + "Explore": [ + "[Browse Bucket](https://motional-nuscenes.s3.ap-northeast-1.amazonaws.com/index.html)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, - "Host": "https://d36yt3mvayqw5m.cloudfront.net" + "Host": null }, { "Name": "nuScenes", - "Description": "nuScenes Dataset", - "ARN": "arn:aws:s3:::motional-nuscenes", + "Description": "Globally cached distribution of the nuScenes Dataset Web frontend is available to browse the dataset", + "ARN": null, "Region": "ap-northeast-1", - "Type": "S3 Bucket", + "Type": "CloudFront Distribution", "Documentation": "https://www.nuscenes.org", "Contact": "https://www.nuscenes.org", "ManagedBy": "[Motional, Inc.](https://motional.com)", @@ -31209,20 +31234,18 @@ "transportation", "urban" ], - "Explore": [ - "[Browse Bucket](https://motional-nuscenes.s3.ap-northeast-1.amazonaws.com/index.html)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, - "Host": null + "Host": "https://d36yt3mvayqw5m.cloudfront.net" }, { "Name": "real-changesets", - "Description": "real-changesets", - "ARN": "arn:aws:s3:::real-changesets", + "Description": "New File Notification", + "ARN": "arn:aws:sns:us-west-2:877446169145:real-changesets-object_created", "Region": "us-west-2", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://github.com/osmus/osmcha-charter-project/blob/main/real-changesets-docs.md", "Contact": "team@openstreetmap.us", "ManagedBy": "OpenStreetMap US", @@ -31243,10 +31266,10 @@ }, { "Name": "real-changesets", - "Description": "New File Notification", - "ARN": "arn:aws:sns:us-west-2:877446169145:real-changesets-object_created", + "Description": "real-changesets", + "ARN": "arn:aws:s3:::real-changesets", "Region": "us-west-2", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://github.com/osmus/osmcha-charter-project/blob/main/real-changesets-docs.md", "Contact": "team@openstreetmap.us", "ManagedBy": "OpenStreetMap US", diff --git a/aws_open_datasets.tsv b/aws_open_datasets.tsv index 1b2857c..9216a2a 100644 --- a/aws_open_datasets.tsv +++ b/aws_open_datasets.tsv @@ -85,7 +85,7 @@ Australasian Genomes Data files arn:aws:s3:::koalagenomes ap-southeast-2 S3 Buck Automated Segmentation of Intracellular Substructures in Electron Microscopy (ASEM) on AWS Trained ML segmentation models for use in ASEM pipeline arn:aws:s3:::asem-project/models/ us-east-1 S3 Bucket https://open.quiltdata.com/b/asem-project tklab@tklab.hms.harvard.edu Kirchhausen Lab at Harvard Medical School Data is added as it becomes available All available datasets and models are licensed under a Creative Commons Attribut aws-pds, biology, cell biology, segmentation, microscopy, electron microscopy, computer vision, imaging Automated Segmentation of Intracellular Substructures in Electron Microscopy (ASEM) on AWS High resolution 3D cell image datasets arn:aws:s3:::asem-project/datasets/ us-east-1 S3 Bucket https://open.quiltdata.com/b/asem-project tklab@tklab.hms.harvard.edu Kirchhausen Lab at Harvard Medical School Data is added as it becomes available All available datasets and models are licensed under a Creative Commons Attribut aws-pds, biology, cell biology, segmentation, microscopy, electron microscopy, computer vision, imaging Automatic Speech Recognition (ASR) Error Robustness Datatasets with ASR Errors arn:aws:s3:::asr-error-robustness us-east-1 S3 Bucket https://github.com/anjiefang/asr-error-robustness njfn@amazon.com [Amazon](https://www.amazon.com/) N/A See https://github.com/anjiefang/asr-error-robustness amazon.science, natural language processing, deep learning, machine learning, speech recognition -Baby Open Brains (BOBs) Repository on AWS BOBs Repository data arn:aws:s3:::bobsrepository us-east-2 S3 Bucket https://bobsrepository.readthedocs.io/en/latest/ Eric Feczko (feczk001@umn.edu) & Sally M. Stoyell (stoye003@umn.edu) Masonic Institute for the Developing Brain (MIDB) Open Data Initiative The repository is updated when: (1) all brain segmentations have undergone furth CC-By Attribution 4.0 International neuroimaging, magnetic resonance imaging, neuroscience, pediatric, nifti, segmentation ['[Browse Bucket](https://s3.amazonaws.com/bobsrepository/index.html)'] +Baby Open Brains (BOBs) Repository on AWS BOBs Repository data arn:aws:s3:::bobsrepository us-east-2 S3 Bucket https://bobsrepository.readthedocs.io/en/latest/ Eric Feczko (feczk001@umn.edu) & Sally M. Stoyell (stoye003@umn.edu) Masonic Institute for the Developing Brain (MIDB) Open Data Initiative The repository is updated when: (1) all brain segmentations have undergone furth CC-By Attribution 4.0 International neuroimaging, magnetic resonance imaging, neuroscience, pediatric, nifti, segmentation ['[Browse Bucket](https://bobsrepository.s3.amazonaws.com/index.html)'] Basic Local Alignment Sequences Tool (BLAST) Databases BLAST databases with associated files in a public S3 bucket arn:aws:s3:::ncbi-blast-databases us-east-1 S3 Bucket https://github.com/ncbi/blast_plus_docs https://support.nlm.nih.gov/support/create-case/ [National Library of Medicine (NLM)](http://nlm.nih.gov/) Periodically """[NIH Genomic Data Sharing Policy](https://osp.od.nih.gov/scientific-sharing/gen" aws-pds, bioinformatics, biology, health, life sciences, genetic, genomic, transcriptomics, protein, reference index Beat Acute Myeloid Leukemia (AML) 1.0 BEATAML10-CRENOLANIB Clinical Supplement arn:aws:s3:::gdc-beataml1.0-crenolanib-phs001628-2-open us-east-1 S3 Bucket https://www.cancer.gov/about-nci/organization/ccg/blog/2019/beataml dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, life sciences, cancer, genetic, genomic, Homo sapiens, STRIDES Beat Acute Myeloid Leukemia (AML) 1.0 BEATAML10-COHORT RNA-Seq Gene Expression Quantification arn:aws:s3:::gdc-beataml1-cohort-phs001657-2-open us-east-1 S3 Bucket https://www.cancer.gov/about-nci/organization/ccg/blog/2019/beataml dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, life sciences, cancer, genetic, genomic, Homo sapiens, STRIDES @@ -874,30 +874,30 @@ Open-Meteo Weather API Database Open-Meteo Weather API Database arn:aws:s3:::ope OpenAQ Daily gzipped CSVs of global air quality measurements fetched from sources all o arn:aws:s3:::openaq-data-archive us-east-1 S3 Bucket https://openaq.org info@openaq.org [OpenAQ](https://openaq.org) Hourly Varies, depends on data provider aws-pds, air quality, cities, environmental, geospatial OpenAQ SNS topic for new objects in the openaq-data-archive bucket arn:aws:sns:us-east-1:817926761842:openaq-data-archive-object_created us-east-1 SNS Topic https://openaq.org info@openaq.org [OpenAQ](https://openaq.org) Hourly Varies, depends on data provider aws-pds, air quality, cities, environmental, geospatial OpenAQ OpenAQ API us-east-1 CloudFront Distribution https://openaq.org info@openaq.org [OpenAQ](https://openaq.org) Hourly Varies, depends on data provider aws-pds, air quality, cities, environmental, geospatial api.openaq.org -OpenAlex dataset Openalex Entities decomposed to tab-separated columnar files for backward compat arn:aws:s3:::openalex-mag-format us-east-1 S3 Bucket https://docs.openalex.org team@ourresearch.org [OurResearch](https://ourresearch.org/) Approximately monthly [CC0](https://creativecommons.org/publicdomain/zero/1.0/) graph, json, metadata, scholarly communication, aws-pds ['[Browse Bucket](https://openalex-mag-format.s3.amazonaws.com/browse.html)'] OpenAlex dataset OpenAlex Entities in JSON Lines format arn:aws:s3:::openalex us-east-1 S3 Bucket https://docs.openalex.org team@ourresearch.org [OurResearch](https://ourresearch.org/) Approximately monthly [CC0](https://creativecommons.org/publicdomain/zero/1.0/) graph, json, metadata, scholarly communication, aws-pds ['[Browse Bucket](https://openalex.s3.amazonaws.com/browse.html)'] +OpenAlex dataset Openalex Entities decomposed to tab-separated columnar files for backward compat arn:aws:s3:::openalex-mag-format us-east-1 S3 Bucket https://docs.openalex.org team@ourresearch.org [OurResearch](https://ourresearch.org/) Approximately monthly [CC0](https://creativecommons.org/publicdomain/zero/1.0/) graph, json, metadata, scholarly communication, aws-pds ['[Browse Bucket](https://openalex-mag-format.s3.amazonaws.com/browse.html)'] OpenCRAVAT OpenCRAVAT Store EU arn:aws:s3:::opencravat-store-eu-west-2 eu-west-2 S3 Bucket https://open-cravat.readthedocs.io support@opencravat.org KarchinLab, Potomac IT Group Data is mirrored daily. Update frequencies of individual annotators depend on th "License varies per-annotator. Commercial users must check the ""commercial_warnin" aws-pds, genetic, genomic, life sciences, variant annotation, sqlite, tertiary analysis OpenCRAVAT OpenCRAVAT Store US arn:aws:s3:::opencravat-store-aws us-east-1 S3 Bucket https://open-cravat.readthedocs.io support@opencravat.org KarchinLab, Potomac IT Group Data is mirrored daily. Update frequencies of individual annotators depend on th "License varies per-annotator. Commercial users must check the ""commercial_warnin" aws-pds, genetic, genomic, life sciences, variant annotation, sqlite, tertiary analysis OpenCell on AWS Live-cell confocal fluorescence microscopy images of the OpenCell library of flu arn:aws:s3:::czb-opencell us-west-2 S3 Bucket https://opencell.czbiohub.org/download opencell@czbiohub.org [Chan Zuckerberg Biohub](https://www.czbiohub.org/) This is the final version of the dataset. https://github.com/czbiohub/opencell/blob/master/LICENSE aws-pds, biology, cell biology, life sciences, imaging, cell imaging, fluorescence imaging, microscopy, computer vision, machine learning OpenEEW OpenEEW arn:aws:s3:::grillo-openeew us-east-1 S3 Bucket https://github.com/openeew/openeew hello@openeew.com [Grillo](https://grillo.io/) Approximately every 5 minutes https://github.com/openeew/openeew#license disaster response, earth observation, earthquakes, aws-pds ['[Browse Bucket](https://grillo-openeew.s3.amazonaws.com/index.html)'] OpenNeuro MRI, MEG, EEG, iEEG, and ECoG datasets from OpenNeuro arn:aws:s3:::openneuro.org us-east-1 S3 Bucket http://openneuro.org Support form at https://openneuro.org [Stanford University Center for Reproducible Neuroscience](https://reproducibili New datasets deposited every 4-6 days CC0 aws-pds, biology, imaging, life sciences, neurobiology, neuroimaging OpenProteinSet A repository of MSAs and template hits arn:aws:s3:::openfold us-east-1 S3 Bucket https://docs.google.com/document/d/1R90-VJSLQEbot7tgXF3zb068Y1ZJAmsckQ_t2sJTv2c/ https://github.com/aqlaboratory/openfold/issues OpenFold Never [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) openfold, msa, protein, protein template, protein folding, alphafold, open source software, life sciences, aws-pds -OpenStreetMap on AWS Imagery and metadata arn:aws:s3:::osm-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/osm-pds https://github.com/mojodna/osm-pds-pipelines/issues Pacific Atlas Data is updated weekly https://www.openstreetmap.org/copyright aws-pds, geospatial, mapping, osm, disaster response OpenStreetMap on AWS New data notifications arn:aws:sns:us-east-1:800218804198:New_File us-east-1 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/osm-pds https://github.com/mojodna/osm-pds-pipelines/issues Pacific Atlas Data is updated weekly https://www.openstreetmap.org/copyright aws-pds, geospatial, mapping, osm, disaster response +OpenStreetMap on AWS Imagery and metadata arn:aws:s3:::osm-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/osm-pds https://github.com/mojodna/osm-pds-pipelines/issues Pacific Atlas Data is updated weekly https://www.openstreetmap.org/copyright aws-pds, geospatial, mapping, osm, disaster response OpenSurfaces OpenSurfaces data arn:aws:s3:::labelmaterial us-east-1 S3 Bucket http://opensurfaces.cs.cornell.edu/publications/opensurfaces/ snavely@cs.cornell.edu Cornell University Static dataset (not updated) The annotations are licensed under a Creative Commons Attribution 4.0 Internatio computer vision, aws-pds ['[Browse data on project webpage](http://opensurfaces.cs.cornell.edu/)'] OpenUniverse 2024 Matched Rubin and Roman Simulations: Preview The simulated Rubin data products include raw pixel data, calibrated exposures, arn:aws:s3:::nasa-irsa-simulations/openuniverse2024/rubin/ us-east-1 S3 Bucket https://irsa.ipac.caltech.edu/data/theory/openuniverse2024 https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The OpenUniverse 2024 Data Preview has been finalized and will not be updated. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, object detection, parquet, satellite imagery, simulations, survey False False OpenUniverse 2024 Matched Rubin and Roman Simulations: Preview The simulated Roman data products include truth files listing the basic physical arn:aws:s3:::nasa-irsa-simulations/openuniverse2024/roman/ us-east-1 S3 Bucket https://irsa.ipac.caltech.edu/data/theory/openuniverse2024 https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The OpenUniverse 2024 Data Preview has been finalized and will not be updated. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, object detection, parquet, satellite imagery, simulations, survey False False Opioid Industry Documents Archive (OIDA) Data on AWS Raw data from the Opioid Industry Documents Archive (OIDA), including documents arn:aws:s3:::opioid-industry-documents-archive-dataset-bucket us-east-1 S3 Bucket https://opioid-industry-documents-archive-dataset-bucket.s3.amazonaws.com/index. opioidarchive@jh.edu Johns Hopkins University monthly https://www.industrydocuments.ucsf.edu/opioids/help/copyright/ aws-pds, archives, text analysis, txt, pharmaceutical -Orcasound - bioacoustic data for marine conservation Live-streamed orca audio data (HLS) arn:aws:s3:::streaming-orcasound-net us-west-2 S3 Bucket https://github.com/orcasound/orcadata/wiki info@orcasound.net Orcasound Typical latency is 10-120 seconds https://creativecommons.org/licenses/by-nc-sa/4.0/ aws-pds, biodiversity, biology, coastal, conservation, deep learning, ecosystems, environmental, geospatial, labeled, machine learning, mapping, oceans, open source software, signal processing -Orcasound - bioacoustic data for marine conservation Archived lossless orca audio data (FLAC) arn:aws:s3:::archive-orcasound-net us-west-2 S3 Bucket https://github.com/orcasound/orcadata/wiki info@orcasound.net Orcasound Typical latency is 10-120 seconds https://creativecommons.org/licenses/by-nc-sa/4.0/ aws-pds, biodiversity, biology, coastal, conservation, deep learning, ecosystems, environmental, geospatial, labeled, machine learning, mapping, oceans, open source software, signal processing Orcasound - bioacoustic data for marine conservation Labeled audio data for ML model development arn:aws:s3:::acoustic-sandbox us-west-2 S3 Bucket https://github.com/orcasound/orcadata/wiki info@orcasound.net Orcasound Typical latency is 10-120 seconds https://creativecommons.org/licenses/by-nc-sa/4.0/ aws-pds, biodiversity, biology, coastal, conservation, deep learning, ecosystems, environmental, geospatial, labeled, machine learning, mapping, oceans, open source software, signal processing +Orcasound - bioacoustic data for marine conservation Archived lossless orca audio data (FLAC) arn:aws:s3:::archive-orcasound-net us-west-2 S3 Bucket https://github.com/orcasound/orcadata/wiki info@orcasound.net Orcasound Typical latency is 10-120 seconds https://creativecommons.org/licenses/by-nc-sa/4.0/ aws-pds, biodiversity, biology, coastal, conservation, deep learning, ecosystems, environmental, geospatial, labeled, machine learning, mapping, oceans, open source software, signal processing +Orcasound - bioacoustic data for marine conservation Live-streamed orca audio data (HLS) arn:aws:s3:::streaming-orcasound-net us-west-2 S3 Bucket https://github.com/orcasound/orcadata/wiki info@orcasound.net Orcasound Typical latency is 10-120 seconds https://creativecommons.org/licenses/by-nc-sa/4.0/ aws-pds, biodiversity, biology, coastal, conservation, deep learning, ecosystems, environmental, geospatial, labeled, machine learning, mapping, oceans, open source software, signal processing Oregon Health & Science University Chronic Neutrophilic Leukemia Dataset RNA-Seq Gene Expression Quantification arn:aws:s3:::gdc-ohsu-cnl-phs001799-2-open us-east-1 S3 Bucket https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001799.v dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, cancer, genomic, life sciences Overture Maps Foundation Open Map Data New File Notification arn:aws:sns:us-west-2:913550007193:overturemaps-us-west-2 us-west-2 SNS Topic Documentation is available at [docs.overturemaps.org](https://docs.overturemaps. info@overturemaps.org [Overture Maps Foundation](https://overturemaps.org) Monthly Overture data is licensed under the Community Database License Agreement Permiss aws-pds, geospatial, global, mapping, osm, parquet, transportation Overture Maps Foundation Open Map Data Overture Maps Foundation Data (GeoParquet) arn:aws:s3:::overturemaps-us-west-2/release/ us-west-2 S3 Bucket Documentation is available at [docs.overturemaps.org](https://docs.overturemaps. info@overturemaps.org [Overture Maps Foundation](https://overturemaps.org) Monthly Overture data is licensed under the Community Database License Agreement Permiss aws-pds, geospatial, global, mapping, osm, parquet, transportation -Oxford Nanopore Technologies Benchmark Datasets Oxford Nanopore Open Datasets arn:aws:s3:::ont-open-data eu-west-1 S3 Bucket https://labs.epi2me.io/dataindex/ support@nanoporetech.com Oxford Nanopore Technologies Additional datasets will be added periodically. Updates and amendents will be ma Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommo aws-pds, bioinformatics, biology, fastq, fast5, genomic, life sciences, Homo sapiens, whole genome sequencing False +Oxford Nanopore Technologies Benchmark Datasets CpG dinucleotides frequently occur in high-density clusters called CpG islands ( arn:aws:s3:::ont-open-data/rrms_2022.07 eu-west-1 S3 Bucket https://labs.epi2me.io/dataindex/ support@nanoporetech.com Oxford Nanopore Technologies Additional datasets will be added periodically. Updates and amendents will be ma Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommo aws-pds, bioinformatics, biology, fastq, fast5, genomic, life sciences, Homo sapiens, whole genome sequencing False Oxford Nanopore Technologies Benchmark Datasets Nanopore sequencing data of the Genome in a Bottle samples NA24385, NA24149, and arn:aws:s3:::ont-open-data/giab_lsk114_2022.12 eu-west-1 S3 Bucket https://labs.epi2me.io/dataindex/ support@nanoporetech.com Oxford Nanopore Technologies Additional datasets will be added periodically. Updates and amendents will be ma Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommo aws-pds, bioinformatics, biology, fastq, fast5, genomic, life sciences, Homo sapiens, whole genome sequencing False +Oxford Nanopore Technologies Benchmark Datasets Oxford Nanopore Open Datasets arn:aws:s3:::ont-open-data eu-west-1 S3 Bucket https://labs.epi2me.io/dataindex/ support@nanoporetech.com Oxford Nanopore Technologies Additional datasets will be added periodically. Updates and amendents will be ma Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommo aws-pds, bioinformatics, biology, fastq, fast5, genomic, life sciences, Homo sapiens, whole genome sequencing False Oxford Nanopore Technologies Benchmark Datasets Using nanopore sequencing, researchers have directly identified DNA and RNA base arn:aws:s3:::ont-open-data/gm24385_mod_2021.09/extra_analysis/bonito_remora eu-west-1 S3 Bucket https://labs.epi2me.io/dataindex/ support@nanoporetech.com Oxford Nanopore Technologies Additional datasets will be added periodically. Updates and amendents will be ma Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommo aws-pds, bioinformatics, biology, fastq, fast5, genomic, life sciences, Homo sapiens, whole genome sequencing False -Oxford Nanopore Technologies Benchmark Datasets CpG dinucleotides frequently occur in high-density clusters called CpG islands ( arn:aws:s3:::ont-open-data/rrms_2022.07 eu-west-1 S3 Bucket https://labs.epi2me.io/dataindex/ support@nanoporetech.com Oxford Nanopore Technologies Additional datasets will be added periodically. Updates and amendents will be ma Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommo aws-pds, bioinformatics, biology, fastq, fast5, genomic, life sciences, Homo sapiens, whole genome sequencing False Ozone Monitoring Instrument (OMI) / Aura NO2 Tropospheric Column Density S3 Bucket for OMI NO2 in Cloud-Optimized GeoTiff format arn:aws:s3:::omi-no2-nasa us-west-2 S3 Bucket https://disc.gsfc.nasa.gov/datasets/OMNO2d_003/summary binita.kc@nasa.gov NASA None There are no restrictions on the use of these data. aws-pds, earth observation, geospatial, satellite imagery, air quality, atmosphere, environmental PALSAR-2 ScanSAR CARD4L (L2.2) PALSAR-2 ScanSAR CARD4L arn:aws:s3:::jaxaalos2/palsar2/L2.2/Africa/ us-west-2 S3 Bucket https://www.eorc.jaxa.jp/ALOS/en/dataset/palsar2_l22_e.htm aproject@jaxa.jp [JAXA](https://www.jaxa.jp/) Every month after 42 days observed Data is available for free under the [terms of use](https://earth.jaxa.jp/policy aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, sustainability, disaster response, synthetic aperture radar, deafrica, stac, cog False PALSAR-2 ScanSAR Flooding in Rwanda (L2.1) PALSAR-2 ScanSAR L11 & L22 arn:aws:s3:::jaxaalos2/palsar2-scansar/Rwanda/ us-west-2 S3 Bucket https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm, https://www.e aproject@jaxa.jp [JAXA](https://www.jaxa.jp/) As available. Data is available for free under the terms of use. aws-pds, agriculture, cog, deafrica, disaster response, earth observation, geospatial, natural resource, satellite imagery, stac, sustainability, synthetic aperture radar False @@ -905,40 +905,40 @@ PALSAR-2 ScanSAR Tropical Cycolne Mocha (L2.1) PALSAR-2 ScanSAR L22 arn:aws:s3:: PALSAR-2 ScanSAR Turkey & Syria Earthquake (L2.1 & L1.1) PALSAR-2 ScanSAR L11 & L22 arn:aws:s3:::jaxaalos2/palsar2-scansar/Turkey-Syria-earthquake/ us-west-2 S3 Bucket https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm, https://www.e aproject@jaxa.jp [JAXA](https://www.jaxa.jp/) As available. Data is available for free under the [terms of use](https://earth.jaxa.jp/policy aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, sustainability, disaster response, synthetic aperture radar, deafrica, stac, cog False PASS: Perturb-and-Select Summarizer for Product Reviews A collection of summaries generated by PASS for the FewSum Product Reviews datas arn:aws:s3:::pass-summary-fewsum us-east-1 S3 Bucket https://pass-summary-fewsum.s3.amazonaws.com/README.md noved@amazon.com [Amazon](https://www.amazon.com/) Not updated This data is available for anyone to use under the terms of the CDLA-Sharing lic amazon.science, natural language processing, text analysis ['[pass_generated_summaries.jsonl](https://pass-summary-fewsum.s3.amazonaws.com/pass_gen_summaries_fewsum_amazon_val_test.jsonl)'] PROJ datum grids Horizontal and vertical adjustment datasets us-east-1 CloudFront Distribution https://github.com/OSGeo/proj-datumgrid-geotiff proj@lists.osgeo.org [PROJ](https://proj.org) New grids are added when made available Per file. Under an Open Source Definition compliant license. Consult the READMEs aws-pds, geospatial, mapping cdn.proj.org -Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2018 arn:aws:s3:::pacific-sound-256khz-2018 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software +Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2021 arn:aws:s3:::pacific-sound-256khz-2021 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software +Pacific Ocean Sound Recordings decimated 16 kHz audio recordings arn:aws:s3:::pacific-sound-16khz us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2025 arn:aws:s3:::pacific-sound-256khz-2025 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software +Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2024 arn:aws:s3:::pacific-sound-256khz-2024 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software +Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2022 arn:aws:s3:::pacific-sound-256khz-2022 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software +Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2015 arn:aws:s3:::pacific-sound-256khz-2015 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2016 arn:aws:s3:::pacific-sound-256khz-2016 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software -Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2017 arn:aws:s3:::pacific-sound-256khz-2017 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software +Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2023 arn:aws:s3:::pacific-sound-256khz-2023 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software +Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2018 arn:aws:s3:::pacific-sound-256khz-2018 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2019 arn:aws:s3:::pacific-sound-256khz-2019 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2020 arn:aws:s3:::pacific-sound-256khz-2020 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software -Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2021 arn:aws:s3:::pacific-sound-256khz-2021 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software -Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2022 arn:aws:s3:::pacific-sound-256khz-2022 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software -Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2023 arn:aws:s3:::pacific-sound-256khz-2023 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software -Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2024 arn:aws:s3:::pacific-sound-256khz-2024 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software -Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2015 arn:aws:s3:::pacific-sound-256khz-2015 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software -Pacific Ocean Sound Recordings decimated 2 kHz audio recordings arn:aws:s3:::pacific-sound-2khz us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software -Pacific Ocean Sound Recordings decimated 16 kHz audio recordings arn:aws:s3:::pacific-sound-16khz us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software Pacific Ocean Sound Recordings machine learning models arn:aws:s3:::pacific-sound-models us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software +Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2017 arn:aws:s3:::pacific-sound-256khz-2017 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software +Pacific Ocean Sound Recordings decimated 2 kHz audio recordings arn:aws:s3:::pacific-sound-2khz us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software Pan-STARRS PS1 Survey PS1 DR1 and DR2 image files arn:aws:s3:::stpubdata/ps1 us-east-1 S3 Bucket https://outerspace.stsci.edu/display/PANSTARRS/ archive@stsci.edu [Space Telescope Science Institute](http://www.stsci.edu/) Never STScI hereby grants the non-exclusive, royalty-free, non-transferable, worldwide aws-pds, astronomy False -Pancreatic Cancer Organoid Profiling WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic M arn:aws:s3:::gdc-organoid-pancreatic-phs001611-2-controlled us-east-1 S3 Bucket https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, cancer, genetic, genomic, transcriptomics, whole genome sequencing, STRIDES https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v1.p1 Pancreatic Cancer Organoid Profiling RNA-Seq Gene Expression Quantification arn:aws:s3:::gdc-organoid-pancreatic-phs001611-2-open us-east-1 S3 Bucket https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, cancer, genetic, genomic, transcriptomics, whole genome sequencing, STRIDES +Pancreatic Cancer Organoid Profiling WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic M arn:aws:s3:::gdc-organoid-pancreatic-phs001611-2-controlled us-east-1 S3 Bucket https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, cancer, genetic, genomic, transcriptomics, whole genome sequencing, STRIDES https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v1.p1 PersonPath22 Source data arn:aws:s3:::tracking-dataset-eccv-2022 us-east-2 S3 Bucket https://amazon-science.github.io/tracking-dataset/personpath22.html Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 [Amazon Web Services](https://aws.amazon.com/) Periodically Creative Commons Attribution-NonCommercial 4.0 International Public License (CC amazon.science, computer vision Phrase Clustering Dataset (PCD) Phsrase Clustering Dataset (PCD) arn:aws:s3:::amazon-phrase-clustering us-west-2 S3 Bucket https://amazon-phrase-clustering.s3.amazonaws.com/readme.md Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 [Amazon](https://www.amazon.com/) Not updated This data is available for anyone to use under the terms of the CDLA-permissive amazon.science, natural language processing, json ['[phrase-clustering-dataset.json](https://amazon-phrase-clustering.s3.amazonaws.com/phrase-clustering-dataset.json)'] Physionet https://s3amazonawscom/physionet-pds/indexhtml arn:aws:s3:::physionet-pds us-east-1 S3 Bucket https://physionet.org/ contact@physionet.org [MIT Laboratory for Computational Physiology](https://lcp.mit.edu/) Not updated PhysioBank databases are made available under the ODC Public Domain Dedication a aws-pds, biology, life sciences Pohang Canal Dataset: A Multimodal Maritime Dataset for Autonomous Navigation in Restricted Waters Pohang Canal dataset arn:aws:s3:::pohang-canal-dataset us-west-2 S3 Bucket https://sites.google.com/view/pohang-canal-dataset/home morin-lab@kaist.ac.kr [MORIN](http://morin.kaist.ac.kr) Not updated [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) aws-pds, autonomous vehicles, marine navigation, robotics, computer vision, lidar -PoroTomo PoroTomo Nodal Seismometer Continuous Data arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac%2F)'] PoroTomo PoroTomo Nodal Seismometer Sweep Data arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac_sweep/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac_sweep%2F)'] +PoroTomo PoroTomo Nodal Seismometer Continuous Data arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac%2F)'] +PoroTomo HSDS PoroTomo domains arn:aws:s3:::nrel-pds-hsds/nrel/porotomo/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fporotomo%2F)'] +PoroTomo PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in HDF5 format arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASV/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASV%2F)'] PoroTomo PoroTomo Nodal Seismometer Field Notes and Metadata arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_metadata/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_metadata%2F)'] +PoroTomo PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in HDF5 format arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASH/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASH%2F)'] +PoroTomo PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in SEG-Y format arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASV/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASV%2F)'] +PoroTomo PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data Resampled in Time M arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/Resampled/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2FResampled%2F)'] PoroTomo PoroTomo Datasets arn:aws:s3:::nrel-pds-porotomo/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo)'] PoroTomo PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in SEG-Y format arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2F)'] -PoroTomo PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data Resampled in Time M arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/Resampled/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2FResampled%2F)'] -PoroTomo PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in SEG-Y format arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASV/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASV%2F)'] -PoroTomo PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in HDF5 format arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASH/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASH%2F)'] -PoroTomo PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in HDF5 format arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASV/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASV%2F)'] -PoroTomo HSDS PoroTomo domains arn:aws:s3:::nrel-pds-hsds/nrel/porotomo/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fporotomo%2F)'] Pre- and post-purchase product questions S3 bucket with dataset arn:aws:s3:::pre-post-purchase-questions us-east-1 S3 Bucket https://pre-post-purchase-questions.s3.amazonaws.com/README.txt litalku@amazon.com [Amazon](https://www.amazon.com/) Not currently being updated [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) amazon.science, natural language processing, machine learning ['[PrePostQuestions.csv](https://pre-post-purchase-questions.s3.amazonaws.com/PrePostQuestions.csv)'] -Prefeitura Municipal de São Paulo (PMSP) LiDAR Point Cloud São Paulo city's 3D LiDAR - Entwine Point Tiles arn:aws:s3:::ept-m3dc-pmsp sa-east-1 S3 Bucket https://github.com/geoinfo-smdu/M3DC geosampa@prefeitura.sp.gov.br [GeoSampa - o mapa digital da cidade de São Paulo](http://geosampa.prefeitura.sp Local survey executed by demand generates new data as local point clouds. [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.html) cities, land, lidar, urban, geospatial, elevation, mapping, aws-pds Prefeitura Municipal de São Paulo (PMSP) LiDAR Point Cloud São Paulo city's 3D LiDAR - LAZ Files arn:aws:s3:::laz-m3dc-pmsp sa-east-1 S3 Bucket https://github.com/geoinfo-smdu/M3DC geosampa@prefeitura.sp.gov.br [GeoSampa - o mapa digital da cidade de São Paulo](http://geosampa.prefeitura.sp Local survey executed by demand generates new data as local point clouds. [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.html) cities, land, lidar, urban, geospatial, elevation, mapping, aws-pds +Prefeitura Municipal de São Paulo (PMSP) LiDAR Point Cloud São Paulo city's 3D LiDAR - Entwine Point Tiles arn:aws:s3:::ept-m3dc-pmsp sa-east-1 S3 Bucket https://github.com/geoinfo-smdu/M3DC geosampa@prefeitura.sp.gov.br [GeoSampa - o mapa digital da cidade de São Paulo](http://geosampa.prefeitura.sp Local survey executed by demand generates new data as local point clouds. [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.html) cities, land, lidar, urban, geospatial, elevation, mapping, aws-pds Product Comparison Dataset for Online Shopping Product Comparison Dataset for Online Shopping arn:aws:s3:::prod-comp-shopping-dataset us-west-2 S3 Bucket https://prod-comp-shopping-dataset.s3.us-west-2.amazonaws.com/README.md Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 [Amazon](https://www.amazon.com/) None [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) product comparison, online shopping, amazon.science, natural language processing, machine learning ['[final_prodcomp_dataset_cleaned.tsv](https://prod-comp-shopping-dataset.s3.us-west-2.amazonaws.com/final_prodcomp_dataset_cleaned.tsv)'] Protein Data Bank 3D Structural Biology Data Historical snapshots of archival datasets from 2005 onwards Snapshots are gener arn:aws:s3:::pdbsnapshots us-west-2 S3 Bucket https://www.wwpdb.org/documentation/file-format https://www.wwpdb.org/about/contact [Worldwide Protein Data Bank Partnership](wwpdb.org) New and updated data files are published weekly and released on Wednesdays 0:00 https://creativecommons.org/publicdomain/zero/1.0/ aws-pds, amino acid, archives, bioinformatics, biomolecular modeling, cell biology, chemical biology, COVID-19, electron microscopy, electron tomography, enzyme, life sciences, molecule, nuclear magnetic resonance, pharmaceutical, protein, protein template, SARS-CoV-2, structural biology, x-ray crystallography ['[Browse Bucket](https://pdbsnapshots.s3.us-west-2.amazonaws.com/index.html)'] Protein Data Bank 3D Structural Biology Data Globally cached distribution of the dataset Web frontend also available to brow us-west-2 CloudFront Distribution https://www.wwpdb.org/documentation/file-format https://www.wwpdb.org/about/contact [Worldwide Protein Data Bank Partnership](wwpdb.org) New and updated data files are published weekly and released on Wednesdays 0:00 https://creativecommons.org/publicdomain/zero/1.0/ aws-pds, amino acid, archives, bioinformatics, biomolecular modeling, cell biology, chemical biology, COVID-19, electron microscopy, electron tomography, enzyme, life sciences, molecule, nuclear magnetic resonance, pharmaceutical, protein, protein template, SARS-CoV-2, structural biology, x-ray crystallography ['[Browse Dataset](https://s3.rcsb.org)'] @@ -946,8 +946,8 @@ Provision of Web-Scale Parallel Corpora for Official European Languages (ParaCra PubSeq - Public Sequence Resource Pubseq output data (Arvados Keep) arn:aws:s3:::pubseq-output-data us-east-2 S3 Bucket https://covid19.genenetwork.org/about https://covid19.genenetwork.org/contact [UTHSC GeneNetwork](https://covid19.genenetwork.org/) Rolling dataset. Creative Commons Attribution 4.0 International (CC BY 4.0) unless otherwise spec aws-pds, bam, bioinformatics, biology, coronavirus, COVID-19, fasta, fastq, fast5, genetic, genomic, health, json, life sciences, long read sequencing, open source software, MERS, metadata, medicine, RDF, SARS, SARS-CoV-2, SPARQL ['[Arvados download](https://covid19.genenetwork.org/download)'] PubSeq - Public Sequence Resource PubSeq submitted datasets (FASTA and JSON metadata) arn:aws:s3:::pubseq-datasets us-east-2 S3 Bucket https://covid19.genenetwork.org/about https://covid19.genenetwork.org/contact [UTHSC GeneNetwork](https://covid19.genenetwork.org/) Rolling dataset. Creative Commons Attribution 4.0 International (CC BY 4.0) unless otherwise spec aws-pds, bam, bioinformatics, biology, coronavirus, COVID-19, fasta, fastq, fast5, genetic, genomic, health, json, life sciences, long read sequencing, open source software, MERS, metadata, medicine, RDF, SARS, SARS-CoV-2, SPARQL ['[Browse Bucket](https://pubseq-datasets.s3.amazonaws.com/)'] Public Utility Data Liberation Project All PUDL data outputs arn:aws:s3:::pudl.catalyst.coop us-west-2 S3 Bucket You can download the [data directly](https://catalystcoop-pudl.readthedocs.io/en For general questions or feedback about the data, create an GitHub issue or disc [Catalyst Cooperative](https://catalyst.coop/) The federal agencies that publish the raw data PUDL processes release new data, The PUDL data and documentation are published under the [Creative Commons Attrib aws-pds, climate, climate model, energy, environmental, government records, infrastructure, open source software, electricity, energy modeling, utilities -PyEnvs and CallArgs PyEnvs arn:aws:s3:::pyenvs-and-callargs/pyenvs/ us-west-2 S3 Bucket https://github.com/amazon-research/function-call-argument-completion Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 Amazon None Planned Please note that while we are providing this aggregation of code snippets unlice machine learning, code completion PyEnvs and CallArgs CallArgs arn:aws:s3:::pyenvs-and-callargs/callargs/ us-west-2 S3 Bucket https://github.com/amazon-research/function-call-argument-completion Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 Amazon None Planned Please note that while we are providing this aggregation of code snippets unlice machine learning, code completion +PyEnvs and CallArgs PyEnvs arn:aws:s3:::pyenvs-and-callargs/pyenvs/ us-west-2 S3 Bucket https://github.com/amazon-research/function-call-argument-completion Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 Amazon None Planned Please note that while we are providing this aggregation of code snippets unlice machine learning, code completion QIIME 2 User Tutorial Datasets Rendered docs and tutorial data, as generated by https://githubcom/qiime2/docs arn:aws:s3:::qiime2-data us-west-2 S3 Bucket https://docs.qiime2.org https://forum.qiime2.org The QIIME 2 Development Team Quarterly BSD 3-Clause License aws-pds, bioinformatics, biology, denoising, ecosystems, environmental, genetic, genomic, health, microbiome, statistics Quoref Project data files in a public bucket arn:aws:s3:::ai2-public-datasets us-west-2 S3 Bucket https://allenai.org/data/quoref info@allenai.org [Allen Institute for AI](https://allenai.org) Not updated [CC BY](https://creativecommons.org/licenses/by/4.0) aws-pds, machine learning, natural language processing RACECAR Dataset The RACECAR dataset is the first open dataset for full-scale and high-speed auto arn:aws:s3:::racecar-dataset us-west-2 S3 Bucket https://github.com/linklab-uva/RACECAR_DATA Prof. Madhur Behl (madhur.behl@viginia.edu) Amar Kulkarni (ark8su@virginia.edu) This dataset was constructed during a single racing season (2021-22). Future sea Creative Commons Attribution-NonCommercial 4.0 International Public License [(CC aws-pds, autonomous vehicles, autonomous racing, robotics, computer vision, perception, lidar, radar, GNSS, image processing, localization, object detection, object tracking @@ -969,12 +969,12 @@ Reference Elevation Model of Antarctica (REMA) REMA DEM Mosaics arn:aws:s3:::pgc Reference Elevation Model of Antarctica (REMA) REMA DEM Strips arn:aws:s3:::pgc-opendata-dems/rema/strips/ us-west-2 S3 Bucket https://www.pgc.umn.edu/data/rema/ pgc-support@umn.edu [Polar Geospatial Center](https://www.pgc.umn.edu/) New DEM strips are added twice yearly. Mosaic products are added as soon as the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecom aws-pds, elevation, earth observation, geospatial, mapping, open source software, satellite imagery, cog, stac ['[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/strips.json)'] Reference data for HiFi human WGS HiFi Human WGS Reference data arn:aws:s3:::pacbio-hifi-human-wgs-reference us-west-2 S3 Bucket https://zenodo.org/records/8415406 dl_it-awsopendata@pacificbiosciences.com [Pacific Biosciences of California, Inc](https://www.pacb.com/) Files are updated to reflect the support for the lastest version of[PacBio WGS V [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) aws-pds, health, life sciences, Homo sapiens, long read sequencing, genetic, mapping, whole genome sequencing, vcf, variant annotation Refgenie reference genome assets Refgenie S3 Bucket arn:aws:s3:::awspds.refgenie.databio.org us-east-1 S3 Bucket http://refgenie.databio.org https://github.com/databio/refgenie/issues Sheffield lab at the University of Virginia As new data becomes available (roughly quarterly) Public domain aws-pds, biology, bioinformatics, genetic, genomic, infrastructure, life sciences, single-cell transcriptomics, transcriptomics, whole genome sequencing -Registry of Open Data on AWS Registry of Open Data on AWS arn:aws:s3:::registry.opendata.aws/roda/ndjson/ us-east-1 S3 Bucket https://github.com/awslabs/open-data-registry#how-are-datasets-added-to-the-regi opendata@amazon.com [Amazon Web Services](https://aws.amazon.com/) Automatically when new datasets are added [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) amazon.science, json, metadata Registry of Open Data on AWS SNS topic for object create events arn:aws:sns:us-east-1:652627389412:roda-object_created us-east-1 SNS Topic https://github.com/awslabs/open-data-registry#how-are-datasets-added-to-the-regi opendata@amazon.com [Amazon Web Services](https://aws.amazon.com/) Automatically when new datasets are added [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) amazon.science, json, metadata +Registry of Open Data on AWS Registry of Open Data on AWS arn:aws:s3:::registry.opendata.aws/roda/ndjson/ us-east-1 S3 Bucket https://github.com/awslabs/open-data-registry#how-are-datasets-added-to-the-regi opendata@amazon.com [Amazon Web Services](https://aws.amazon.com/) Automatically when new datasets are added [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) amazon.science, json, metadata SILAM Air Quality Surface NetCDF files arn:aws:s3:::fmi-opendata-silam-surface-netcdf eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological ['[Browse Bucket](https://fmi-opendata-silam-surface-netcdf.s3.amazonaws.com/index.html)'] -SILAM Air Quality Surface Zarr files arn:aws:s3:::fmi-opendata-silam-surface-zarr eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological ['[Browse Bucket](https://fmi-opendata-silam-surface-zarr.s3.amazonaws.com/index.html)'] -SILAM Air Quality Notifications for new netcdf surface data arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-silam-surface-netcdf eu-west-1 SNS Topic http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological SILAM Air Quality Notifications for new zarr surface data arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-silam-surface-zarr eu-west-1 SNS Topic http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological +SILAM Air Quality Notifications for new netcdf surface data arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-silam-surface-netcdf eu-west-1 SNS Topic http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological +SILAM Air Quality Surface Zarr files arn:aws:s3:::fmi-opendata-silam-surface-zarr eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological ['[Browse Bucket](https://fmi-opendata-silam-surface-zarr.s3.amazonaws.com/index.html)'] SILO climate data on AWS SILO open data arn:aws:s3:::silo-open-data ap-southeast-2 S3 Bucket https://www.longpaddock.qld.gov.au/silo/gridded-data https://www.longpaddock.qld.gov.au/silo/contact-us Queensland Government Daily SILO datasets are constructed by the [Queensland Government](http://www.qld.gov. aws-pds, agriculture, climate, earth observation, environmental, meteorological, model, sustainability, water, weather SISSA daily forecast retrospective database CRC-SAS/SISSA Retrospective Daily forecast database arn:aws:s3:::sissa-forecast-database us-west-2 S3 Bucket General information, tutorials and examples, contact us atl sissa-aws@smn.gob.ar For any questions regarding the data set or any general questions, you can conta [SISSA](https://sissa.crc-sas.org/) Static database from 2000-2019 without correction and 2010-2019 with correction. [Creative Commons Attribution 2.5 Argentina License](https://creativecommons.org aws-pds, earth observation, natural resource, weather, forecast, meteorological, agriculture, hydrology ['[Browse Bucket](https://s3-us-west-2.amazonaws.com/sissa-forecast-database/index.html)'] SMN Hi-Res Weather Forecast over Argentina WRF SMN data arn:aws:s3:::smn-ar-wrf us-west-2 S3 Bucket General information, tutorials and examples:[https://odp-aws-smn.github.io/docum For any questions regarding the data set or any general questions, you can conta [SMN](http://www.smn.gov.ar/) New data is added as soon as it's available. Two forecast cycles a day initializ [Creative Commons Attribution 2.5 Argentina License](https://creativecommons.org aws-pds, earth observation, natural resource, weather, meteorological ['[Browse Bucket](https://smn-ar-wrf.s3.amazonaws.com/index.html)'] @@ -982,43 +982,43 @@ SPARTAN Data All data products (PM25, aerosol chemical components, scattering) p SPaRCNet data:Seizures, Rhythmic and Periodic Patterns in ICU Electroencephalography ICU EEG Dataset arn:aws:s3:us-east-1:184438910517:accesspoint/bdsp-sparcnet-access-point us-east-1 S3 Bucket More documentation can be found [here](https://doi.org/10.60508/cw6j-s785) admin@bdsp.io [Brain Data Science Platform](https://bdsp.io/) New data is added as soon as it is available. "BDSP Restricted Health Data License 1.0.0 ""[BDSP Licence](https://bdsp.io/conten" aws-pds, neurophysiology, medicine, machine learning, neuroscience, deep learning, life sciences, bioinformatics https://doi.org/10.60508/cw6j-s785 STOIC2021 Training The data set contains 2000 CT scans stored as compressed mha files Each file c arn:aws:s3:::stoic2021-training us-west-2 S3 Bucket https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384 support@grand-challenge.org Radboud University Medical Center The full training set was published at the release. CC-BY-NC 4.0 aws-pds, life sciences, computed tomography, computer vision, coronavirus, COVID-19, grand-challenge.org, imaging, SARS-CoV-2 SUCHO Ukrainian Cultural Heritage Web Archives WACZ archives arn:aws:s3:::sucho-opendata eu-central-1 S3 Bucket https://www.sucho.org/tutorials info@sucho.org Saving Ukrainian Cultural Heritage Online (SUCHO) Periodically Public Domain (CC0) ukraine, internet, cultural preservation, aws-pds -Safecast New air and radiation measurement payloads arn:aws:sns:us-west-2:985752656544:ingest-measurements-prd us-west-2 SNS Topic https://github.com/Safecast/safecastapi/wiki/Data-Sets https://groups.google.com/forum/#!forum/safecast-devices [Safecast](https://safecast.org/) Continuous Safecast data is published under a [CC0 designation](https://creativecommons.org air quality, aws-pds, climate, environmental, geospatial, radiation Safecast Bulk exports of air and radiation measurements arn:aws:s3:::safecast-opendata-public-us-east-1 us-east-1 S3 Bucket https://github.com/Safecast/safecastapi/wiki/Data-Sets https://groups.google.com/forum/#!forum/safecast-devices [Safecast](https://safecast.org/) Continuous Safecast data is published under a [CC0 designation](https://creativecommons.org air quality, aws-pds, climate, environmental, geospatial, radiation ['[Browse Bucket](https://safecast-opendata-public-us-east-1.s3.amazonaws.com/index.html)'] +Safecast New air and radiation measurement payloads arn:aws:sns:us-west-2:985752656544:ingest-measurements-prd us-west-2 SNS Topic https://github.com/Safecast/safecastapi/wiki/Data-Sets https://groups.google.com/forum/#!forum/safecast-devices [Safecast](https://safecast.org/) Continuous Safecast data is published under a [CC0 designation](https://creativecommons.org air quality, aws-pds, climate, environmental, geospatial, radiation Satellite - Sea surface temperature - Level 3 - Single sensor - 1 day - Day and night time Cloud Optimised AODN dataset of IMOS - SRS - SST - L3S - Single Sensor - 1 day - arn:aws:s3:::aodn-cloud-optimised/satellite_ghrsst_l3s_1day_daynighttime_single_sensor_australia.zarr ap-southeast-2 S3 Bucket https://catalogue-imos.org.au/geonetwork/srv/eng/catalog.search#/metadata/a136ee info@aodn.org.au AODN As Needed http://creativecommons.org/licenses/by/4.0/ oceans, satellite imagery Scottish Public Sector LiDAR Dataset LiDAR data (DSM, DTM and Laz) arn:aws:s3:::srsp-open-data eu-west-2 S3 Bucket https://remotesensingdata.gov.scot/data#/list https://remotesensingdata.gov.scot/feedback or email Scottish Government on gi-s [Joint Nature Conservation Committee](https://jncc.gov.uk/) New datasets have historically been added every 2-3 years but there is no guaran All data is made available under the [Open Government Licence v3](http://www.nat lidar, cities, coastal, environmental, urban, elevation, cog, aws-pds Sea Around Us Global Fisheries Catch Data Global Fisheries Catch Data arn:aws:s3:::fisheries-catch-data us-west-2 S3 Bucket https://www.seaaroundus.org/ubc-cic-sea-around-us-project-collaboration/ https://www.seaaroundus.org/feedback/ [Sea Around Us](https://www.seaaroundus.org/) The full dataset is computed only once or twice a year or when there is a signif This data is available for anyone to use under the [Sea Around Us Terms of Use]( aws-pds, fisheries, ecosystems, biodiversity, marine Sea Surface Temperature Daily Analysis: European Space Agency Climate Change Initiative product version 2.1 Global daily-mean sea surface temperatures from 1981 onwards, in Zarr format Th arn:aws:s3:::surftemp-sst us-west-2 S3 Bucket https://surftemp.github.io/sst-data-tutorials/ https://www.reading.ac.uk/met/ [University of Reading, Department of Meteorology](https://www.reading.ac.uk/met yearly Creative Commons Licence by attribution (https://creativecommons.org/licenses/by aws-pds, earth observation, oceans, climate, environmental, global, geospatial -Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) Spatial transcriptomics data files in a public bucket arn:aws:s3:::sea-ad-spatial-transcriptomics us-west-2 S3 Bucket https://portal.brain-map.org/explore/seattle-alzheimers-disease/seattle-alzheime awspublicdataset@alleninstitute.org [Allen Institute](http://www.alleninstitute.org/) Annually https://alleninstitute.org/legal/terms-use/ aws-pds, biology, cell biology, cell imaging, epigenomics, gene expression, histopathology, Homo sapiens, imaging, medicine, microscopy, neurobiology, neuroscience, single-cell transcriptomics, transcriptomics ['[Browse Bucket](https://sea-ad-spatial-transcriptomics.s3.amazonaws.com/index.html)'] Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) Single cell profiling (transcriptomics and epigenomics) data files in a public b arn:aws:s3:::sea-ad-single-cell-profiling us-west-2 S3 Bucket https://portal.brain-map.org/explore/seattle-alzheimers-disease/seattle-alzheime awspublicdataset@alleninstitute.org [Allen Institute](http://www.alleninstitute.org/) Annually https://alleninstitute.org/legal/terms-use/ aws-pds, biology, cell biology, cell imaging, epigenomics, gene expression, histopathology, Homo sapiens, imaging, medicine, microscopy, neurobiology, neuroscience, single-cell transcriptomics, transcriptomics ['[Browse Bucket](https://sea-ad-single-cell-profiling.s3.amazonaws.com/index.html)'] +Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) Spatial transcriptomics data files in a public bucket arn:aws:s3:::sea-ad-spatial-transcriptomics us-west-2 S3 Bucket https://portal.brain-map.org/explore/seattle-alzheimers-disease/seattle-alzheime awspublicdataset@alleninstitute.org [Allen Institute](http://www.alleninstitute.org/) Annually https://alleninstitute.org/legal/terms-use/ aws-pds, biology, cell biology, cell imaging, epigenomics, gene expression, histopathology, Homo sapiens, imaging, medicine, microscopy, neurobiology, neuroscience, single-cell transcriptomics, transcriptomics ['[Browse Bucket](https://sea-ad-spatial-transcriptomics.s3.amazonaws.com/index.html)'] Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) Quantitative neuropathology (full resolution images, processed images, and quant arn:aws:s3:::sea-ad-quantitative-neuropathology us-west-2 S3 Bucket https://portal.brain-map.org/explore/seattle-alzheimers-disease/seattle-alzheime awspublicdataset@alleninstitute.org [Allen Institute](http://www.alleninstitute.org/) Annually https://alleninstitute.org/legal/terms-use/ aws-pds, biology, cell biology, cell imaging, epigenomics, gene expression, histopathology, Homo sapiens, imaging, medicine, microscopy, neurobiology, neuroscience, single-cell transcriptomics, transcriptomics ['[Browse Bucket](https://sea-ad-quantitative-neuropathology.s3.amazonaws.com/index.html)'] Sentinel Near Real-time Canada Mirror | Miroir Sentinel temps quasi réel du Canada Sentinel data over Canada | Données sentinelles au Canada arn:aws:s3:::sentinel-products-ca-mirror ca-central-1 S3 Bucket https://sentinel.esa.int/web/sentinel/home eodms-sgdot@nrcan-rncan.gc.ca [Natural Resources Canada](https://www.nrcan.gc.ca/) Sentinel-1 is an NRT dataset retrieved from ESA within 90 minutes of satellite d The access and use of Copernicus Sentinel data is available on a free, full and aws-pds, agriculture, earth observation, satellite imagery, geospatial, sustainability, disaster response, synthetic aperture radar, stac ['[EODMS STAC for Sentinel products](https://www.eodms-sgdot.nrcan-rncan.gc.ca/stac/)'] -Sentinel-1 GRD in a Requester Pays S3 bucket arn:aws:s3:::sentinel-s1-l1c eu-central-1 S3 Bucket https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, disaster response, cog, synthetic aperture radar ['[STAC V1.0.0 endpoint](https://sentinel-s1-l1c-stac.s3.amazonaws.com/)'] True Sentinel-1 SNS topic for notification of new scenes, can subscribe with Lambda arn:aws:sns:eu-central-1:214830741341:SentinelS1L1C eu-central-1 SNS Topic https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, disaster response, cog, synthetic aperture radar Sentinel-1 S3 Inventory files for L1C and CSV arn:aws:s3:::sentinel-inventory/ eu-central-1 S3 Bucket https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, disaster response, cog, synthetic aperture radar +Sentinel-1 GRD in a Requester Pays S3 bucket arn:aws:s3:::sentinel-s1-l1c eu-central-1 S3 Bucket https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, disaster response, cog, synthetic aperture radar ['[STAC V1.0.0 endpoint](https://sentinel-s1-l1c-stac.s3.amazonaws.com/)'] True Sentinel-1 Precise Orbit Determination (POD) Products Sentinel-1 Orbits bucket arn:aws:s3:::s1-orbits us-west-2 S3 Bucket https://s1-orbits.s3.us-west-2.amazonaws.com/README.html https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) Updated as new data becomes available on the [Copernicus Data Space Ecosystem](h Access to Sentinel data is free, full and open for the broad Regional, National, auxiliary data, disaster response, earth observation, earthquakes, floods, geophysics, sentinel-1, synthetic aperture radar ['[AWS S3 Explorer](https://s1-orbits.s3.amazonaws.com/index.html)'] Sentinel-1 Precise Orbit Determination (POD) Products Notifications for new data arn:aws:sns:us-west-2:211125554030:s1-orbits-object_created us-west-2 SNS Topic https://s1-orbits.s3.us-west-2.amazonaws.com/README.html https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) Updated as new data becomes available on the [Copernicus Data Space Ecosystem](h Access to Sentinel data is free, full and open for the broad Regional, National, auxiliary data, disaster response, earth observation, earthquakes, floods, geophysics, sentinel-1, synthetic aperture radar Sentinel-1 SLC dataset for Germany Public access to Sentinel-1 SLC IW scenes over Germany arn:aws:s3:::sentinel1-slc eu-west-1 S3 Bucket https://github.com/live-eo/sentinel1-slc/ For any enquires regarding the dataset, please email OpenData at Live-EO opendat [LiveEO](https://live-eo.com/) New Sentinel1-SLC IW data are updated regularly in an interval of 6 days, after The data usage will inherit and fully comply with the free and open data policy aws-pds, disaster response, satellite imagery, geospatial, sustainability, earth observation, environmental, synthetic aperture radar Sentinel-1 SLC dataset for South and Southeast Asia, Taiwan, Korea and Japan Public access to Sentinel-1 SLC IW scenes over South and Southeast Asia, Taiwan arn:aws:s3:::sentinel1-slc-seasia-pds ap-southeast-1 S3 Bucket https://github.com/earthobservatory/sentinel1-opds/ For any enquires regarding data delivery, please email ehill@ntu.edu.sg and stch [Earth Observatory of Singapore, Nanyang Technological University](https://earth S1 SLC data for the region of interest will be updated regularly, as it becomes The data usage will inherit and fully comply with the free and open data policy aws-pds, disaster response, satellite imagery, geospatial, earth observation, environmental, synthetic aperture radar -Sentinel-2 Zipped archives for each L1C product with 3 day retention period, in Requester P arn:aws:s3:::sentinel-s2-l1c-zips eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac True -Sentinel-2 Zipped archives for each L2A product with 3 day retention period, in Requester P arn:aws:s3:::sentinel-s2-l2a-zips eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac True +Sentinel-2 S3 Inventory files for L2A and CSV arn:aws:s3:::sentinel-inventory/sentinel-s2-l2a eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac Sentinel-2 Level 2A scenes and metadata, in Requester Pays S3 bucket arn:aws:s3:::sentinel-s2-l2a eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac ['[STAC V1.0.0 endpoint](https://sentinel-s2-l2a-stac.s3.amazonaws.com/)'] True Sentinel-2 S3 Inventory files for L1C and CSV arn:aws:s3:::sentinel-inventory/sentinel-s2-l1c eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac Sentinel-2 Level 1C scenes and metadata, in Requester Pays S3 bucket arn:aws:s3:::sentinel-s2-l1c eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac ['[Earth Search STAC L1C Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l1c)', '[Earth Search STAC Browser L1C Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l1c)', '[STAC V1.0.0 endpoint](https://sentinel-s2-l1c-stac.s3.amazonaws.com/)', '[Earth Viewer by Element 84](https://viewer.aws.element84.com/)'] True +Sentinel-2 Zipped archives for each L2A product with 3 day retention period, in Requester P arn:aws:s3:::sentinel-s2-l2a-zips eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac True +Sentinel-2 Zipped archives for each L1C product with 3 day retention period, in Requester P arn:aws:s3:::sentinel-s2-l1c-zips eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac True Sentinel-2 New scene notifications for L1C, can subscribe with Lambda arn:aws:sns:eu-west-1:214830741341:NewSentinel2Product eu-west-1 SNS Topic Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac Sentinel-2 New scene notifications for L2A, can subscribe with Lambda arn:aws:sns:eu-central-1:214830741341:SentinelS2L2A eu-central-1 SNS Topic Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac -Sentinel-2 S3 Inventory files for L2A and CSV arn:aws:s3:::sentinel-inventory/sentinel-s2-l2a eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac -Sentinel-2 Cloud-Optimized GeoTIFFs S3 Inventory files for L1C and CSV arn:aws:s3:::sentinel-cogs-inventory us-west-2 S3 Bucket https://github.com/cirrus-geo/cirrus-earth-search opendata@element84.com [Element 84](https://www.element84.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, cog, stac Sentinel-2 Cloud-Optimized GeoTIFFs Level 2A scenes and metadata arn:aws:s3:::sentinel-cogs us-west-2 S3 Bucket https://github.com/cirrus-geo/cirrus-earth-search opendata@element84.com [Element 84](https://www.element84.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, cog, stac ['[Earth Search STAC L2A Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)', '[STAC Browser L2A Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)'] False +Sentinel-2 Cloud-Optimized GeoTIFFs S3 Inventory files for L1C and CSV arn:aws:s3:::sentinel-cogs-inventory us-west-2 S3 Bucket https://github.com/cirrus-geo/cirrus-earth-search opendata@element84.com [Element 84](https://www.element84.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, cog, stac Sentinel-2 Cloud-Optimized GeoTIFFs New scene notifications, can subscribe with Lambda arn:aws:sns:us-west-2:608149789419:cirrus-v0-publish us-west-2 SNS Topic https://github.com/cirrus-geo/cirrus-earth-search opendata@element84.com [Element 84](https://www.element84.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, cog, stac Sentinel-2 L2A 120m Mosaic Sentinel-2 L2A 120m mosaics data in a S3 bucket arn:aws:s3:::sentinel-s2-l2a-mosaic-120 eu-central-1 S3 Bucket Documentation is available [here](https://sentinel-s2-l2a-mosaic-120.s3.amazonaw https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New data will be added annually. CC-BY 4.0, Credit: Contains modified Copernicus data [year] processed by Sentine aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, machine learning, cog False -Sentinel-3 Sentinel-3 Near Real Time Data (NRT) format arn:aws:s3:::meeo-s3/NRT/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac -Sentinel-3 Sentinel-3 Not Time Critical (NTC) format arn:aws:s3:::meeo-s3/NTC/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac -Sentinel-3 Sentinel-3 Short Time Critical (STC) format arn:aws:s3:::meeo-s3/STC/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac Sentinel-3 Sentinel-3 Cloud Optimized GeoTIFF (COG) format arn:aws:s3:::meeo-s3-cog/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac ['[STAC V1.0.0 endpoint](https://meeo-s3.s3.amazonaws.com/)'] +Sentinel-3 Sentinel-3 Short Time Critical (STC) format arn:aws:s3:::meeo-s3/STC/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac +Sentinel-3 Sentinel-3 Not Time Critical (NTC) format arn:aws:s3:::meeo-s3/NTC/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac +Sentinel-3 Sentinel-3 Near Real Time Data (NRT) format arn:aws:s3:::meeo-s3/NRT/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac +Sentinel-5P Level 2 Sentinel-5p Off Line Data (OFFL) NetCDF format arn:aws:s3:::meeo-s5p/OFFL/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Desc sentinel5p@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, air quality, atmosphere, earth observation, environmental, geospatial, satellite imagery, cog, stac Sentinel-5P Level 2 Sentinel-5p Near Real Time Data (NRTI) NetCDF format arn:aws:s3:::meeo-s5p/NRTI/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Desc sentinel5p@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, air quality, atmosphere, earth observation, environmental, geospatial, satellite imagery, cog, stac Sentinel-5P Level 2 Sentinel-5p Cloud Optimised GeoTIFF (COGT) TIFF format arn:aws:s3:::meeo-s5p/COGT/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Desc sentinel5p@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, air quality, atmosphere, earth observation, environmental, geospatial, satellite imagery, cog, stac ['[STAC V1.0.0 endpoint](https://meeo-s5p.s3.amazonaws.com/index.html?t=catalogs)'] Sentinel-5P Level 2 Sentinel-5p Reprocessed Data (RPRO) NetCDF format arn:aws:s3:::meeo-s5p/RPRO/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Desc sentinel5p@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, air quality, atmosphere, earth observation, environmental, geospatial, satellite imagery, cog, stac -Sentinel-5P Level 2 Sentinel-5p Off Line Data (OFFL) NetCDF format arn:aws:s3:::meeo-s5p/OFFL/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Desc sentinel5p@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, air quality, atmosphere, earth observation, environmental, geospatial, satellite imagery, cog, stac Serratus: Ultra-deep Search for Novel Viruses - Versioned Data Release Versioned and structured data releases from the Serratus project Current versio arn:aws:s3:::lovelywater2 us-east-1 S3 Bucket https://github.com/ababaian/serratus/wiki/Access-Data-Release https://github.com/ababaian/serratus/issues Serratus / UBC Cloud Innovation Centre Quarterly [CC-0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, life sciences, genetic, genomic, bam, virus, COVID-19, SARS, SARS-CoV-2, MERS Ships of Opportunity - Biogeochemical sensors - Delayed mode Cloud Optimised AODN dataset of IMOS - SOOP Underway CO2 Measurements Research G arn:aws:s3:::aodn-cloud-optimised/vessel_co2_delayed_qc.parquet ap-southeast-2 S3 Bucket https://catalogue-imos.org.au/geonetwork/srv/eng/catalog.search#/metadata/63db58 info@aodn.org.au AODN As Needed http://creativecommons.org/licenses/by/4.0/ oceans, chemistry Ships of Opportunity - Expendable bathythermographs - Real time Cloud Optimised AODN dataset of IMOS - SOOP Expendable Bathythermographs (XBT) R arn:aws:s3:::aodn-cloud-optimised/vessel_xbt_realtime_nonqc.parquet ap-southeast-2 S3 Bucket https://catalogue-imos.org.au/geonetwork/srv/eng/catalog.search#/metadata/352349 info@aodn.org.au AODN As Needed http://creativecommons.org/licenses/by/4.0/ oceans @@ -1030,23 +1030,24 @@ SiPeCaM (Sitios Permanentes de la Calibración y Monitoreo de la Biodiversidad) Single-Cell Atlas of Human Blood During Healthy Aging Raw sequencing data (fastqgz), TCR/BCR clonotype tables (csv), normalized coun arn:aws:s3:::single.cell.human.blood.atlas.opendata.sagebase.org us-east-1 S3 Bucket https://www.synapse.org/#!Synapse:syn49637038/ martyomov@wustl.edu Sage Bionetworks Never [CC BY] aws-pds, protein, single-cell transcriptomics https://www.synapse.org/#!Synapse:syn49637038/ False Smithsonian Open Access Smithsonian Open Access Media and Metadata arn:aws:s3:::smithsonian-open-access us-west-2 S3 Bucket http://edan.si.edu/openaccess/docs/ openaccess@si.edu [SI](http://www.si.edu/) New / updated metadata and image files will be pushed weekly. CC0 aws-pds, art, history, culture, museum, encyclopedic Sofar Spotter Archive Hourly position, wave spectra and bulk wave parameters from global free drifting arn:aws:s3:::sofar-spotter-archive us-west-2 S3 Bucket [Spotter Technical Reference Manual](https://content.sofarocean.com/hubfs/Spotte opendata@sofarocean.com [Sofar Ocean](https://www.sofarocean.com/company/contact-us) As available [Sofar Data Access Agreement](https://sofarocean.notion.site/sofarocean/Sofar-Da aws-pds, climate, meteorological, sustainability, weather, oceans, environmental, oceans ['[Browse Bucket](https://sofar-spotter-archive.s3.amazonaws.com/index.html)'] -Software Heritage Graph Dataset Software Heritage Graph Dataset arn:aws:s3:::softwareheritage us-east-1 S3 Bucket https://docs.softwareheritage.org/devel/swh-dataset/graph/athena.html aws@softwareheritage.org Software Heritage Data is updated yearly Creative Commons Attribution 4.0 International.By accessing the dataset, you agr aws-pds, source code, open source software, free software, digital preservation Software Heritage Graph Dataset S3 Inventory files arn:aws:s3:::softwareheritage-inventory us-east-1 S3 Bucket https://docs.softwareheritage.org/devel/swh-dataset/graph/athena.html aws@softwareheritage.org Software Heritage Data is updated yearly Creative Commons Attribution 4.0 International.By accessing the dataset, you agr aws-pds, source code, open source software, free software, digital preservation +Software Heritage Graph Dataset Software Heritage Graph Dataset arn:aws:s3:::softwareheritage us-east-1 S3 Bucket https://docs.softwareheritage.org/devel/swh-dataset/graph/athena.html aws@softwareheritage.org Software Heritage Data is updated yearly Creative Commons Attribution 4.0 International.By accessing the dataset, you agr aws-pds, source code, open source software, free software, digital preservation Solar Dynamics Observatory (SDO) Machine Learning Dataset The v1 dataset includes AIA observations 2010-2018 and v2 includes AIA observati arn:aws:s3:::gov-nasa-hdrl-data1/contrib/fdl-sdoml/ us-west-2 S3 Bucket https://github.com/SDOML/sdoml.github.io Meng Jin (jinmeng@lmsal.com) and Paul Wright (paul@pauljwright.co.uk) [NASA](http://www.nasa.gov/) N/A (The IDL/Python scripts for generating the datasets are published online, wh There are no restrictions on the use of this data. aws-pds, machine learning, NASA SMD AI SondeHub Radiosonde Telemetry Radiosonde Telemetry as JSON blobs of Universal Telemetry format arn:aws:s3:::sondehub-history us-east-1 S3 Bucket https://github.com/projecthorus/sondehub-infra/wiki/Amazon-Open-Data Michaela Wheeler [SondeHub](https://sondehub.org/) Data is updated as we receive it Creative Commons BY-SA 2.0 aws-pds, climate, environmental, weather, GPS ['[Browse Bucket by serial number](http://sondehub-history.s3-website-us-east-1.amazonaws.com/#serial/)', '[Browse Bucket by date/time](http://sondehub-history.s3-website-us-east-1.amazonaws.com/#date/)'] Sophos/ReversingLabs 20 Million malware detection dataset Sophos/ReversingLabs 20 million sample dataset arn:aws:s3:::sorel-20m/ us-west-2 S3 Bucket https://github.com/sophos-ai/SOREL-20M/blob/master/README.md sorel-dataset@sophos.com Sophos AI At most annually See the [Terms of Use](https://github.com/sophos-ai/SOREL-20M/blob/master/Terms% aws-pds, cyber security, deep learning, labeled, machine learning Sounds of Central African landscapes Nouabale-Ndoki landscape sound data arn:aws:s3:::congo8khz-pnnn us-west-2 S3 Bucket https://elephantlisteningproject.org/congo-soundscapes-public-database/ elephant-lp@cornell.edu Center for Conservation Bioacoustics, Cornell University (https://elephantlisten New sound data spanning 4-month time periods added as soon as possible These sound files are freely available for scientific study andexploration, incl aws-pds, biodiversity, ecosystems, biology, land, life sciences, natural resource, survey, geospatial Southern California Earthquake Data Seismic waveform data (miniSEED format) and earthquake catalog (ascii) arn:aws:s3:::scedc-pds us-west-2 S3 Bucket https://scedc.caltech.edu/data/cloud.html scedc@gps.caltech.edu [Southern California Earthquake Data Center](https://scedc.caltech.edu) Daily SCEDC herby grants the non-exclusive, royalty free, non-transferable, worldwide aws-pds, earth observation, earthquakes, seismology SpaceNet Imagery and metadata in a S3 bucket arn:aws:s3:::spacenet-dataset us-east-1 S3 Bucket https://spacenet.ai/ https://spacenet.ai/contact-us/ [SpaceNet](https://spacenet.ai/) New imagery and features are added quarterly Various (See [here](https://spacenet.ai/datasets/) for more details) aws-pds, geospatial, computer vision, machine learning, earth observation, disaster response, satellite imagery +Spatiam Corporation National Lab Research Announcement International Space Station Technology Demonstration Spatiam Corporation NLRA ISS Technology Demonstration archive data arn:aws:s3:::spatiam-nlra-iss-experiment-open-data us-east-1 S3 Bucket https://spatiam-nlra-iss-experiment-open-data.s3.amazonaws.com/README.md info@spatiam.com [Spatiam Corporation](https://www.spatiam.com/) No updates envisioned after the conclusion of the experiment. There are no restrictions on the use of this data. network traffic, telecommunications ['[Browse Bucket](https://spatiam-nlra-iss-experiment-open-data.s3.amazonaws.com/index.html)'] Speedtest by Ookla Global Fixed and Mobile Network Performance Maps Parquet and Shapefiles arn:aws:s3:::ookla-open-data us-west-2 S3 Bucket [Performance Maps Overview](https://github.com/teamookla/ookla-open-data) opendata@ookla.com [Ookla](https://www.ookla.com/ookla-for-good) Quarterly """[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)""" analytics, aws-pds, broadband, cities, civic, disaster response, geospatial, global, government spending, infrastructure, internet, mapping, parquet, network traffic, regulatory, telecommunications, tiles Spitzer Enhanced Imaging Products (SEIP) Super Mosaics SEIP Super Mosaics: 36, 45, 58, 8, and 24 micron mean and median mosaics with arn:aws:s3:::nasa-irsa-spitzer/spitzer/seip us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/data/SPITZER/Enhanced/SEIP/overview.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca This data set may be updated once or twice in the future. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, satellite imagery, survey False False Storm EVent ImageRy (SEVIR) Dataset of storm imagery arn:aws:s3:::sevir us-west-2 S3 Bucket https://nbviewer.jupyter.org/github/MIT-AI-Accelerator/eie-sevir/blob/master/exa mark.veillette@mit.edu Mark S. Veillette New events will be added to SEVIR yearly There are no restrictions on the use of this data. satellite imagery, weather, meteorological, aws-pds Sub-Meter Canopy Tree Height of California in 2020 by CTrees.org Cloud-optimized GeoTIFF files with names corresponding to image of California fo arn:aws:s3:::ctrees-tree-height-ca-2020/ us-west-2 S3 Bucket [Project overview](https://ctrees.org/products/tree-level) info@ctrees.org [CTrees](https://ctrees.org/) TBD https://creativecommons.org/licenses/by/4.0/ aws-pds, cog, earth observation, land cover, deep learning, aerial imagery, image processing, environmental, conservation, geospatial Sudachi Language Resources Cloudfront CDN mirror ap-northeast-1 CloudFront Distribution https://worksapplications.github.io/Sudachi/ sudachi@worksap.co.jp [Works Applications](https://www.worksap.co.jp/about/csr/nlp/) The dictionaries are updated every few months to include neologism and fixes for Apache-2.0 aws-pds, natural language processing d2ej7fkh96fzlu.cloudfront.net Sudachi Language Resources SudachiDict: Binary format of the mophological analysis dictionarieschiVe: Pret arn:aws:s3:::sudachi ap-northeast-1 S3 Bucket https://worksapplications.github.io/Sudachi/ sudachi@worksap.co.jp [Works Applications](https://www.worksap.co.jp/about/csr/nlp/) The dictionaries are updated every few months to include neologism and fixes for Apache-2.0 aws-pds, natural language processing -Sup3rCC Sup3rCC - CONUS - MRI ESM 20 - SSP585 - r1i1p1f1 arn:aws:s3:::nrel-pds-sup3rcc/conus_mriesm20_ssp585_r1i1p1f1/ us-west-2 S3 Bucket https://github.com/NREL/sup3r Grant Buster (grant.buster@nrel.gov) [National Renewable Energy Laboratory](https://www.nrel.gov/) Annual Creative Commons Attribution 4.0 United States License aws-pds, energy, solar, air temperature, climate model ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_mriesm20_ssp585_r1i1p1f1%2F)'] Sup3rCC Sup3rCC arn:aws:s3:::nrel-pds-sup3rcc/ us-west-2 S3 Bucket https://github.com/NREL/sup3r Grant Buster (grant.buster@nrel.gov) [National Renewable Energy Laboratory](https://www.nrel.gov/) Annual Creative Commons Attribution 4.0 United States License aws-pds, energy, solar, air temperature, climate model ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc)'] Sup3rCC Sup3rCC Generative Models arn:aws:s3:::nrel-pds-sup3rcc/models/ us-west-2 S3 Bucket https://github.com/NREL/sup3r Grant Buster (grant.buster@nrel.gov) [National Renewable Energy Laboratory](https://www.nrel.gov/) Annual Creative Commons Attribution 4.0 United States License aws-pds, energy, solar, air temperature, climate model ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=models%2F)'] +Sup3rCC Sup3rCC - CONUS - MRI ESM 20 - SSP585 - r1i1p1f1 arn:aws:s3:::nrel-pds-sup3rcc/conus_mriesm20_ssp585_r1i1p1f1/ us-west-2 S3 Bucket https://github.com/NREL/sup3r Grant Buster (grant.buster@nrel.gov) [National Renewable Energy Laboratory](https://www.nrel.gov/) Annual Creative Commons Attribution 4.0 United States License aws-pds, energy, solar, air temperature, climate model ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_mriesm20_ssp585_r1i1p1f1%2F)'] Swiss Public Transport Stops data files ESRI FGDB, CSV , MapInfo, Interlis arn:aws:s3:::data.geo.admin.ch/ch.bav.haltestellen-oev/data.zip eu-west-1 S3 Bucket https://www.bav.admin.ch/bav/de/home/allgemeine-themen/fachthemen/geoinformation fredi.daellenbach@bav.admin.ch Swiss Geoportal annually You may use this dataset for non-commercial purposes. You may use this dataset f aws-pds, cities, geospatial, infrastructure, mapping, traffic, transportation ['[Browse Bucket](https://data.geo.admin.ch/index.html)'] Synthea Coherent Data Set Synthetic data set that includes FHIR resources, DICOM images, genomic data, phy arn:aws:s3:::synthea-open-data/coherent/ us-east-1 S3 Bucket https://doi.org/10.3390/electronics11081199 synthea-list@groups.mitre.org [The MITRE Corporation](https://www.mitre.org) Rarely [Creative Commons Attribution 4.0 International License](https://creativecommons aws-pds, health, bioinformatics, life sciences, medicine, csv, dicom, genomic, imaging Synthea synthetic patient generator data in OMOP Common Data Model Project data files arn:aws:s3:::synthea-omop us-east-1 S3 Bucket https://github.com/synthetichealth/synthea/wiki Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 [Amazon Web Sevices](https://aws.amazon.com/) Not updated https://github.com/synthetichealth/synthea/blob/master/LICENSE aws-pds, bioinformatics, health, life sciences, natural language processing, us @@ -1056,11 +1057,11 @@ Tabula Muris https://githubcom/czbiohub/tabula-muris arn:aws:s3:::czb-tabula-mur Tabula Muris Senis https://githubcom/czbiohub/tabula-muris-senis arn:aws:s3:::czb-tabula-muris-senis us-west-2 S3 Bucket https://github.com/czbiohub/tabula-muris-senis/blob/master/tabula-muris-senis-on If you have questions about the data, you can create an Issue at https://github. [Chan Zuckerberg Biohub](https://www.czbiohub.org/) This is the first version of the dataset and it will be updated after the manusc https://github.com/czbiohub/tabula-muris-senis/blob/master/LICENSE aws-pds, biology, encyclopedic, genomic, health, life sciences, medicine, single-cell transcriptomics Tabula Sapiens http://tabula-sapiens-portaldsczbiohuborg arn:aws:s3:::czb-tabula-sapiens us-west-2 S3 Bucket http://tabula-sapiens-portal.ds.czbiohub.org/ https://github.com/czbiohub/tabula-muris-senis/issues [Chan Zuckerberg Biohub](https://www.czbiohub.org/) This is the first version of the dataset and it will be updated once per month u http://tabula-sapiens-portal.ds.czbiohub.org/whereisthedata aws-pds, biology, encyclopedic, genetic, genomic, health, life sciences, medicine, single-cell transcriptomics https://docs.google.com/forms/d/e/1FAIpQLSeeB0N7TrklXbCbpc6nDi5e77uad3uZDZ4WCMV77jwhVzxUtQ/viewform Terra Fusion Data Sampler AWS S3 Public Bucket Containing Terra Basic Fusion Hierarchical Data Format 5 (H arn:aws:s3:::terrafusiondatasampler us-west-2 S3 Bucket https://go.illinois.edu/terra-fusion-doc gdi@illinois.edu University of Illinois Static, with a planned update for years 2016-2020 in the future. Creative Commons Level 0 aws-pds, geospatial, satellite imagery -Terrain Tiles Gridded elevation tiles - replication in EU region arn:aws:s3:::elevation-tiles-prod-eu eu-central-1 S3 Bucket https://github.com/tilezen/joerd/tree/master/docs https://github.com/tilezen/joerd/issues Mapzen, a Linux Foundation project New data is added based on community feedback https://github.com/tilezen/joerd/blob/master/docs/attribution.md aws-pds, agriculture, elevation, earth observation, geospatial, disaster response Terrain Tiles Gridded elevation tiles arn:aws:s3:::elevation-tiles-prod us-east-1 S3 Bucket https://github.com/tilezen/joerd/tree/master/docs https://github.com/tilezen/joerd/issues Mapzen, a Linux Foundation project New data is added based on community feedback https://github.com/tilezen/joerd/blob/master/docs/attribution.md aws-pds, agriculture, elevation, earth observation, geospatial, disaster response ['[Browse Map](https://elevation-tiles-prod.s3.amazonaws.com/index.html)'] +Terrain Tiles Gridded elevation tiles - replication in EU region arn:aws:s3:::elevation-tiles-prod-eu eu-central-1 S3 Bucket https://github.com/tilezen/joerd/tree/master/docs https://github.com/tilezen/joerd/issues Mapzen, a Linux Foundation project New data is added based on community feedback https://github.com/tilezen/joerd/blob/master/docs/attribution.md aws-pds, agriculture, elevation, earth observation, geospatial, disaster response Textbook Question Answering (TQA) Project data files in a public bucket arn:aws:s3:::ai2-public-datasets us-west-2 S3 Bucket https://allenai.org/data/tqa info@allenai.org [Allen Institute for AI](https://allenai.org) Not updated [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/) aws-pds, machine learning -The Cancer Genome Atlas WXS/RNA-Seq/miRNA-Seq/ATAC-Seq Aligned Reads, WXS Annotated Somatic Mutation, WX arn:aws:s3:::tcga-2-controlled us-east-1 S3 Bucket https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/t dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, life sciences, cancer, genomic, whole genome sequencing, STRIDES https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000178.v1.p1 The Cancer Genome Atlas Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantificat arn:aws:s3:::tcga-2-open us-east-1 S3 Bucket https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/t dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, life sciences, cancer, genomic, whole genome sequencing, STRIDES +The Cancer Genome Atlas WXS/RNA-Seq/miRNA-Seq/ATAC-Seq Aligned Reads, WXS Annotated Somatic Mutation, WX arn:aws:s3:::tcga-2-controlled us-east-1 S3 Bucket https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/t dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, life sciences, cancer, genomic, whole genome sequencing, STRIDES https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000178.v1.p1 The Genome Modeling System https://gmsdatas3amazonawscom/indexhtml arn:aws:s3:::gmsdata us-west-2 S3 Bucket https://github.com/genome/gms/wiki https://github.com/genome/gms/issues Genome Institute at the Washington University School of Medicine in St. Louis Not updated [GNU Lesser General Public License v3.0](https://github.com/genome/gms/blob/ubun aws-pds, genetic, genomic, life sciences ['[Browse Bucket](https://gmsdata.s3.amazonaws.com/index.html)'] The Human Connectome Project https://wwwhumanconnectomeorg/study/hcp-young-adult/overview arn:aws:s3:::hcp-openaccess us-east-1 S3 Bucket http://www.humanconnectome.org/study/hcp-young-adult/document/1200-subjects-data hcp-users@humanconnectome.org [Connectome Coordination Facility](https://www.humanconnectome.org/ccf-staff) Uncertain [HCP Data Use Agreement](https://www.humanconnectome.org/storage/app/media/data_ aws-pds, biology, imaging, neurobiology, neuroimaging, neuroscience, life sciences https://wiki.humanconnectome.org/docs/How%20To%20Connect%20to%20Connectome%20Data%20via%20AWS.html The Human Microbiome Project https://awsamazoncom/datasets/human-microbiome-project/ arn:aws:s3:::human-microbiome-project us-west-2 S3 Bucket https://commonfund.nih.gov/hmp https://commonfund.nih.gov/hmp/related_activities [The National Institutes of Health Office of Strategic Coordination - The Common Uncertain The data is publicly available to the community free of charge. aws-pds, life sciences, genetic, genomic, metagenomics, microbiome, fasta, amino acid, fastq @@ -1082,39 +1083,39 @@ UK Biobank Linkage Disequilibrium Matrices Linkage disequilibrium (LD) matrices UK Biobank Pan-Ancestry Summary Statistics Summary statistics from Genome Wide Association Studies (GWASes) of multiple anc arn:aws:s3:::pan-ukb-us-east-1 us-east-1 S3 Bucket https://pan.ukbb.broadinstitute.org ukb.diverse.gwas@gmail.com Analytic and Translational Genetics Unit, Massachusetts General Hospital and the Occasional "CC BY-4.0 (usage may be restricted by UK Biobank, more details on the ""[Download" aws-pds, genetic, genome wide association study, genomic, life sciences, population genetics UK Biobank Pharma Proteomics Project (UKB-PPP) Population-specific GWAS summary statistics per protein measurement, as well as arn:aws:s3:::ukbiobank.opendata.sagebase.org us-east-1 S3 Bucket https://www.synapse.org/#!Synapse:syn51364943/ matthias.arnold@helmholtz-munich.de Sage Bionetworks Never [CC BY] aws-pds, genome wide association study, population genetics https://doi.org/10.7303/syn51364943 False UK Earth System Model (UKESM1) ARISE-SAI geoengineering experiment data CMIP6 standards-compliant netCDF data arn:aws:s3:::met-office-ukesm1-arise eu-west-2 S3 Bucket (https://github.com/MetOffice/arise-cmor-tables) https://github.com/MetOffice/arise-cmor-tables/issues [Met Office](https://www.metoffice.gov.uk) Rare once complete CMIP6 data included is licensed under CC-BY 4.0 (see [here](https://wcrp-cmip.gi climate, model, climate model, atmosphere, oceans, land, ice, geospatial, aws-pds, sustainability, CMIP6 ['[Browse Bucket](https://met-office-ukesm1-arise.s3.amazonaws.com/index.html)'] -USGS 3DEP LiDAR Point Clouds A Requester Pays Bucket of Raw LAZ 14 3DEP data Data in this bucket is more co arn:aws:s3:::usgs-lidar us-west-2 S3 Bucket https://github.com/hobu/usgs-lidar/ https://github.com/hobu/usgs-lidar [Hobu, Inc.](https://hobu.co) Periodically US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensin aws-pds, agriculture, elevation, disaster response, geospatial, lidar, stac True USGS 3DEP LiDAR Point Clouds Public access Entwine Point Tiles of most resources from the ``arn:aws:s3:::usgs arn:aws:s3:::usgs-lidar-public us-west-2 S3 Bucket https://github.com/hobu/usgs-lidar/ https://github.com/hobu/usgs-lidar [Hobu, Inc.](https://hobu.co) Periodically US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensin aws-pds, agriculture, elevation, disaster response, geospatial, lidar, stac ['[STAC Catalog](https://usgs-lidar-stac.s3-us-west-2.amazonaws.com/ept/catalog.json)'] +USGS 3DEP LiDAR Point Clouds A Requester Pays Bucket of Raw LAZ 14 3DEP data Data in this bucket is more co arn:aws:s3:::usgs-lidar us-west-2 S3 Bucket https://github.com/hobu/usgs-lidar/ https://github.com/hobu/usgs-lidar [Hobu, Inc.](https://hobu.co) Periodically US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensin aws-pds, agriculture, elevation, disaster response, geospatial, lidar, stac True USGS COAWST (Coupled Ocean Atmosphere Wave and Sediment Transport) Forecast Model Archive, US East and Gulf Coasts A collection of NetCDF4 files, kerchunk generated JSON files, and an Intake cata arn:aws:s3:::usgs-coawst us-west-2 S3 Bucket https://www.sciencebase.gov/catalog/item/610acd4fd34ef8d7056893da jbzambon@fathomscience.com Fathom Science None CC0 aws-pds, oceans USGS Landsat New scene notifications, Level 3 Science Products arn:aws:sns:us-west-2:673253540267:public-c2-level-3-tile-notify-v2 us-west-2 SNS Topic https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-d https://answers.usgs.gov/ [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac, cog -USGS Landsat New scene notifications, Level-1 and Level-2 Scenes arn:aws:sns:us-west-2:673253540267:public-c2-notify-v2 us-west-2 SNS Topic https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-d https://answers.usgs.gov/ [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac, cog USGS Landsat Scenes and metadata arn:aws:s3:::usgs-landsat/collection02/ us-west-2 S3 Bucket https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-d https://answers.usgs.gov/ [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac, cog ['[STAC Catalog](https://landsatlook.usgs.gov/stac-server/collections)'] True +USGS Landsat New scene notifications, Level-1 and Level-2 Scenes arn:aws:sns:us-west-2:673253540267:public-c2-notify-v2 us-west-2 SNS Topic https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-d https://answers.usgs.gov/ [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac, cog USGS Landsat New scene notifications, US ARD Tiles arn:aws:sns:us-west-2:673253540267:public-c2-ard-tile-notify-v2 us-west-2 SNS Topic https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-d https://answers.usgs.gov/ [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac, cog USearch Molecules Project data files in a public bucket arn:aws:s3:::usearch-molecules us-west-2 S3 Bucket https://github.com/ashvardanian/usearch-molecules ash.vardanian@unum.cloud [Ash Vardanian](https://ashvardanian.com) Not updated [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) aws-pds, life sciences, biology, chemical biology, pharmaceutical Umbra Synthetic Aperture Radar (SAR) Open Data Umbra Spotlight collects including GEC, SICD, SIDD, CPHD data and metadata arn:aws:s3:::umbra-open-data-catalog us-west-2 S3 Bucket https://help.umbra.space/product-guide help@umbra.space [Umbra](http://umbra.space/) New data is added frequently. The frequent updates enable users to analyze the t All data is provided with a Creative Commons License ([CC by 4.0](https://umbra. aws-pds, synthetic aperture radar, stac, satellite imagery, earth observation, image processing, geospatial ['[Browse Bucket](http://umbra-open-data-catalog.s3-website.us-west-2.amazonaws.com/)', '[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/s3.us-west-2.amazonaws.com/umbra-open-data-catalog/stac/catalog.json)'] False UniProt UniProt 2022_03 arn:aws:s3:::aws-open-data-uniprot-rdf/2022-03/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL +UniProt UniProt 2021_01 arn:aws:s3:::aws-open-data-uniprot-rdf/2021-01/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2021_02 arn:aws:s3:::aws-open-data-uniprot-rdf/2021-02/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2021_03 arn:aws:s3:::aws-open-data-uniprot-rdf/2021-03/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2021_04 arn:aws:s3:::aws-open-data-uniprot-rdf/2021-04/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2022_01 arn:aws:s3:::aws-open-data-uniprot-rdf/2022-01/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2022_02 arn:aws:s3:::aws-open-data-uniprot-rdf/2022-02/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2022_04 arn:aws:s3:::aws-open-data-uniprot-rdf/2022-04/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL -UniProt UniProt 2021_01 arn:aws:s3:::aws-open-data-uniprot-rdf/2021-01/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL +UniProt UniProt 2024_02 arn:aws:s3:::aws-open-data-uniprot-rdf/2024-02/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2023_01 arn:aws:s3:::aws-open-data-uniprot-rdf/2023-01/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2023_02 arn:aws:s3:::aws-open-data-uniprot-rdf/2023-02/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2023_03 arn:aws:s3:::aws-open-data-uniprot-rdf/2023-03/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2023_04 arn:aws:s3:::aws-open-data-uniprot-rdf/2023-04/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2023_05 arn:aws:s3:::aws-open-data-uniprot-rdf/2023-05/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2024_01 arn:aws:s3:::aws-open-data-uniprot-rdf/2024-01/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL -UniProt UniProt 2024_02 arn:aws:s3:::aws-open-data-uniprot-rdf/2024-02/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL -UniProt UniProt 2022_05 arn:aws:s3:::aws-open-data-uniprot-rdf/2022-05/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2024_03 arn:aws:s3:::aws-open-data-uniprot-rdf/2024-03/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL +UniProt UniProt 2022_05 arn:aws:s3:::aws-open-data-uniprot-rdf/2022-05/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL University of British Columbia Sunflower Genome Dataset UBC Sunflower Genome Data 1 arn:aws:s3:::ubc-sunflower-genome us-west-2 S3 Bucket https://rieseberglab.github.io/ubc-sunflower-genome/ UBC Botany Sunflower The Rieseberg Lab at the University of British Columbia Twice per year. Public Domain aws-pds, agriculture, biodiversity, bioinformatics, biology, food security, genetic, genomic, life sciences, whole genome sequencing -VENUS L2A Cloud-Optimized GeoTIFFs Venus L2A dataset (COG) and metadata (STAC) arn:aws:s3:::venus-l2a-cogs us-east-1 S3 Bucket https://github.com/earthdaily/venus-on-aws/ Klaus Bachhuber - klaus.bachhuber@earthdaily.com [EarthDaily Analytics](https://earthdaily.com/) New Venus data are added regularly https://creativecommons.org/licenses/by-nc/4.0/ aws-pds, agriculture, earth observation, satellite imagery, geospatial, image processing, natural resource, disaster response, cog, stac, activity detection, environmental, land cover ['[STAC Browser Venus L2A (COG) Catalog](https://radiantearth.github.io/stac-browser/#/external/venus-l2a-cogs.s3.us-east-1.amazonaws.com/catalog.json)'] False VENUS L2A Cloud-Optimized GeoTIFFs New Venus L2A dataset notifications, can subscribe with Lambda arn:aws:sns:us-east-1:794383284256:venus-l2a-cogs-object_created us-east-1 SNS Topic https://github.com/earthdaily/venus-on-aws/ Klaus Bachhuber - klaus.bachhuber@earthdaily.com [EarthDaily Analytics](https://earthdaily.com/) New Venus data are added regularly https://creativecommons.org/licenses/by-nc/4.0/ aws-pds, agriculture, earth observation, satellite imagery, geospatial, image processing, natural resource, disaster response, cog, stac, activity detection, environmental, land cover +VENUS L2A Cloud-Optimized GeoTIFFs Venus L2A dataset (COG) and metadata (STAC) arn:aws:s3:::venus-l2a-cogs us-east-1 S3 Bucket https://github.com/earthdaily/venus-on-aws/ Klaus Bachhuber - klaus.bachhuber@earthdaily.com [EarthDaily Analytics](https://earthdaily.com/) New Venus data are added regularly https://creativecommons.org/licenses/by-nc/4.0/ aws-pds, agriculture, earth observation, satellite imagery, geospatial, image processing, natural resource, disaster response, cog, stac, activity detection, environmental, land cover ['[STAC Browser Venus L2A (COG) Catalog](https://radiantearth.github.io/stac-browser/#/external/venus-l2a-cogs.s3.us-east-1.amazonaws.com/catalog.json)'] False Variant Effect Predictor (VEP) and the Loss-Of-Function Transcript Effect Estimator (LOFTEE) Plugin VEP and LOFTEE data arn:aws:s3:::hail-vep-pipeline us-east-1 S3 Bucket https://hail-vep-pipeline.public.tennex.io/ https://www.tennex.io/contact [Tennex](https://www.tennex.io/) New packages are added as soon as they are available and confirmed to work with [VEP](https://uswest.ensembl.org/info/about/publications.html) use is governed b aws-pds, genome wide association study, genomic, life sciences, vep, loftee +Vermont Open Geospatial on AWS Elevation datsets (primarily lidar based) are organized in this bucket as statew arn:aws:s3:::vtopendata-prd/Elevation us-east-2 S3 Bucket https://vcgi.vermont.gov/data-and-programs/ If you have specific questions please contact - vcgi@vermont.gov [Vermont Center for Geographic Information](https://vcgi.vermont.gov) Vermont acquires statewide imagery approximately once every other year. Lidar is Public Domain with Attribution earth observation, aerial imagery, geospatial, lidar, elevation, land cover False Vermont Open Geospatial on AWS Imagery datsets are organized in this bucket as statewide file mosaics and by ac arn:aws:s3:::vtopendata-prd/Imagery us-east-2 S3 Bucket https://vcgi.vermont.gov/data-and-programs/ If you have specific questions please contact - vcgi@vermont.gov [Vermont Center for Geographic Information](https://vcgi.vermont.gov) Vermont acquires statewide imagery approximately once every other year. Lidar is Public Domain with Attribution earth observation, aerial imagery, geospatial, lidar, elevation, land cover False Vermont Open Geospatial on AWS Landcover datsets are organized in this bucket as statewide file mosaics These arn:aws:s3:::vtopendata-prd/Landcover us-east-2 S3 Bucket https://vcgi.vermont.gov/data-and-programs/ If you have specific questions please contact - vcgi@vermont.gov [Vermont Center for Geographic Information](https://vcgi.vermont.gov) Vermont acquires statewide imagery approximately once every other year. Lidar is Public Domain with Attribution earth observation, aerial imagery, geospatial, lidar, elevation, land cover False -Vermont Open Geospatial on AWS Elevation datsets (primarily lidar based) are organized in this bucket as statew arn:aws:s3:::vtopendata-prd/Elevation us-east-2 S3 Bucket https://vcgi.vermont.gov/data-and-programs/ If you have specific questions please contact - vcgi@vermont.gov [Vermont Center for Geographic Information](https://vcgi.vermont.gov) Vermont acquires statewide imagery approximately once every other year. Lidar is Public Domain with Attribution earth observation, aerial imagery, geospatial, lidar, elevation, land cover False Virginia Coastal Resilience Master Plan, Phase 1 - December 2021 Data Product List See readmetxt file for more information on the folder struc arn:aws:s3:::vadcr-frp us-east-1 S3 Bucket https://www.dcr.virginia.gov/crmp/ flood.resilience@dcr.virginia.gov [Virginia Department of Conservation and Recreation](https://www.dcr.virginia.go Every 5 years or as data becomes available Conditions of Release - Data is available by permission of the Virginia Departme aws-pds, coastal, floods ['[Browse Data](https://vadcr-frp.s3.us-east-1.amazonaws.com/index.html)'] VirtualFlow Ligand Libraries VirtualFlow Version of the Enamine REAL Space Molecule Library (version 2022q12) arn:aws:s3:::vf-libraries us-east-2 S3 Bucket https://virtual-flow.org/real-space-2022q12 c.gorgulla@gmail.com [VirtualFlow Project](https://virtual-flow.org/) Every ~3 years There are no restrictions on the use of this data. Redistribution is not allowed aws-pds, structural biology, pharmaceutical, medicine, bioinformatics, life sciences ['[Browse Bucket](https://vf-libraries.s3.amazonaws.com/index.html)'] Visual Anomaly (VisA) The tar file for the Visual Anomaly (VisA) dataset arn:aws:s3:::amazon-visual-anomaly/VisA_20220922.tar us-west-2 S3 Bucket https://github.com/amazon-research/spot-diff Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 [Amazon Web Services](https://aws.amazon.com/) Not updated https://creativecommons.org/licenses/by/4.0/ amazon.science, anomaly detection, classification, segmentation, industrial, fewshot @@ -1124,16 +1125,16 @@ Voices Obscured in Complex Environmental Settings (VOiCES) wav audio files, orth Wave buoys observations - Real time Cloud Optimised AODN dataset of Wave buoys Observations - Australia - near real- arn:aws:s3:::aodn-cloud-optimised/wave_buoy_realtime_nonqc.parquet ap-southeast-2 S3 Bucket https://catalogue-imos.org.au/geonetwork/srv/eng/catalog.search#/metadata/b299cd info@aodn.org.au AODN As Needed http://creativecommons.org/licenses/by/4.0/ oceans Whiffle WINS50 Open Data on AWS Whiffle WINS50 LES Data arn:aws:s3:::whiffle-wins50-data eu-central-1 S3 Bucket https://gitlab.com/whiffle-public/whiffle-open-data support@whiffle.nl [Whiffle](http://www.whiffle.nl/) No updates planned. CC BY-SA 4.0 aws-pds, weather, sustainability, atmosphere, electricity, meteorological, model, zarr, turbulence ['[Browse Bucket](https://whiffle-wins50-data.s3.amazonaws.com/index.html)'] WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation WikiSum Dataset arn:aws:s3:::wikisum us-east-1 S3 Bucket https://wikisum.s3.amazonaws.com/README.txt nachshon@amazon.com, orenk@amazon.com [Amazon](https://www.amazon.com/) Not currently being updated Dataset is published under [CC-NC-SA-3.0](https://creativecommons.org/licenses/b amazon.science, natural language processing, machine learning ['[wikisum.zip](https://wikisum.s3.amazonaws.com/WikiSumDataset.zip)', '[wikisum-human-eval.zip](https://wikisum.s3.amazonaws.com/HumanEvaluation.zip)'] -Wind AI Bench Wind AI Bench Airfoil Computational Fluid Dynamics (CFD) - 2k Shapes Data Sets arn:aws:s3:::nrel-pds-windai/aerodynamic_shapes/2D/2K_airfoils/ us-west-2 S3 Bucket https://github.com/NREL/windAI_bench Ryan King (ryan.king@nrel.gov) [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 4.0 United States License aws-pds, energy, benchmark, machine learning ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=aerodynamic_shapes%2F2D%2F2k_airfoils%2F)'] -Wind AI Bench Wind AI Bench Airfoil Computational Fluid Dynamics (CFD) - 9k Shapes Data Sets arn:aws:s3:::nrel-pds-windai/aerodynamic_shapes/2D/9K_airfoils/ us-west-2 S3 Bucket https://github.com/NREL/windAI_bench Ryan King (ryan.king@nrel.gov) [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 4.0 United States License aws-pds, energy, benchmark, machine learning ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=aerodynamic_shapes%2F2D%2F9k_airfoils%2F)'] Wind AI Bench Wind AI Bench Flow Redirection and Induction in Steady State (FLORIS) Wind Plant arn:aws:s3:::nrel-pds-windai/wind_plant_power/floris/ us-west-2 S3 Bucket https://github.com/NREL/windAI_bench Ryan King (ryan.king@nrel.gov) [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 4.0 United States License aws-pds, energy, benchmark, machine learning ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=wind_plant_power%2Ffloris%2F)'] +Wind AI Bench Wind AI Bench Airfoil Computational Fluid Dynamics (CFD) - 9k Shapes Data Sets arn:aws:s3:::nrel-pds-windai/aerodynamic_shapes/2D/9K_airfoils/ us-west-2 S3 Bucket https://github.com/NREL/windAI_bench Ryan King (ryan.king@nrel.gov) [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 4.0 United States License aws-pds, energy, benchmark, machine learning ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=aerodynamic_shapes%2F2D%2F9k_airfoils%2F)'] +Wind AI Bench Wind AI Bench Airfoil Computational Fluid Dynamics (CFD) - 2k Shapes Data Sets arn:aws:s3:::nrel-pds-windai/aerodynamic_shapes/2D/2K_airfoils/ us-west-2 S3 Bucket https://github.com/NREL/windAI_bench Ryan King (ryan.king@nrel.gov) [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 4.0 United States License aws-pds, energy, benchmark, machine learning ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=aerodynamic_shapes%2F2D%2F2k_airfoils%2F)'] Wizard of Tasks Wizard of Tasks Dataset arn:aws:s3:::wizard-of-tasks us-west-2 S3 Bucket https://wizard-of-tasks.s3.us-west-2.amazonaws.com/README.md giusecas@amazon.com [Amazon](https://www.amazon.com/) Not currently being updated [cc-by-sa 4.0](https://creativecommons.org/licenses/by-sa/4.0/) conversation data, dialog, amazon.science, natural language processing, machine learning ['[wizard_of_tasks_cooking_v1.0.json](https://wizard-of-tasks.s3.us-west-2.amazonaws.com/wizard_of_tasks_cooking_v1.0.json)', '[wizard_of_tasks_diy_v1.0.json](https://wizard-of-tasks.s3.us-west-2.amazonaws.com/wizard_of_tasks_diy_v1.0.json)'] World Bank - Light Every Night Light Every Night dataset of all VIIRS DNB and DMSP-OLS nighttime satellite data arn:aws:s3:::globalnightlight us-east-1 S3 Bucket https://worldbank.github.io/OpenNightLights/wb-light-every-night-readme.html Trevor Monroe tmonroe@worldbank.org; Benjamin P. Stewart bstewart@worldbankgroup [World Bank Group](https://www.worldbank.org/en/home) Quarterly [World Bank Open Database License (ODbL)](https://creativecommons.org/licenses/b disaster response, earth observation, satellite imagery, aws-pds, stac, cog ['[STAC 1.0.0-beta.2 endpoint](https://stacindex.org/catalogs/world-bank-light-every-night#/)'] World Bank Climate Change Knowledge Portal (CCKP) World Bank Climate Change Knowledge Portal observed and projected climate datase arn:aws:s3:::wbg-cckp us-west-2 S3 Bucket https://worldbank.github.io/climateknowledgeportal C. MacKenzie Dove cdove@worldbank.org; askclimate@worldbank.org [World Bank Group](https://www.worldbank.org/en/home) Semi-annually [World Bank Open Database License (ODbL)](https://creativecommons.org/licenses/b aws-pds, climate, climate model, earth observation, climate projections, CMIP6, netcdf Xiph.Org Test Media Video and imagery data arn:aws:s3:::xiph-media us-east-1 S3 Bucket https://media.xiph.org/aws.html Thomas Daede tdaede@xiph.org [Xiph.org](https://xiph.org/) New videos are added when contributors submit them. Various. Most are under the CC-BY license. License text accompanies each sequenc aws-pds, computer vision, image processing, imaging, media, movies, multimedia, video Yale-CMU-Berkeley (YCB) Object and Model Set Project data files arn:aws:s3:::ycb-benchmarks us-east-1 S3 Bucket http://www.ycbbenchmarks.com/ bcalli@wpi.edu Yale University and Berkeley Yearly Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, robotics ['[Browse Bucket](https://ycb-benchmarks.s3.amazonaws.com/index.html)'] -YouTube 8 Million - Data Lakehouse Ready Original YT8M *tfrecords File structure info can be found here arn:aws:s3:::aws-roda-ml-datalake/yt8m/ us-west-2 S3 Bucket https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_install. https://github.com/aws-samples/data-lake-as-code/issues [Amazon Web Services](https://aws.amazon.com/) Google Research has not updated the dataset since 2019. https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_attribut amazon.science, computer vision, machine learning, labeled, parquet, video YouTube 8 Million - Data Lakehouse Ready Lakehouse ready YT8M as Glue Parquet files Install instructions here arn:aws:s3:::aws-roda-ml-datalake/yt8m_ods/ us-west-2 S3 Bucket https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_install. https://github.com/aws-samples/data-lake-as-code/issues [Amazon Web Services](https://aws.amazon.com/) Google Research has not updated the dataset since 2019. https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_attribut amazon.science, computer vision, machine learning, labeled, parquet, video +YouTube 8 Million - Data Lakehouse Ready Original YT8M *tfrecords File structure info can be found here arn:aws:s3:::aws-roda-ml-datalake/yt8m/ us-west-2 S3 Bucket https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_install. https://github.com/aws-samples/data-lake-as-code/issues [Amazon Web Services](https://aws.amazon.com/) Google Research has not updated the dataset since 2019. https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_attribut amazon.science, computer vision, machine learning, labeled, parquet, video YouTube 8 Million - Data Lakehouse Ready Replica of the two locations above in us-east-1 arn:aws:s3:::aws-roda-ml-datalake-us-east-1/ us-east-1 S3 Bucket https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_install. https://github.com/aws-samples/data-lake-as-code/issues [Amazon Web Services](https://aws.amazon.com/) Google Research has not updated the dataset since 2019. https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_attribut amazon.science, computer vision, machine learning, labeled, parquet, video ZEST: ZEroShot learning from Task descriptions Project data files in a public bucket arn:aws:s3:::ai2-public-datasets/zest/ us-west-2 S3 Bucket https://allenai.org/data/zest info@allenai.org [Allen Institute for AI](https://allenai.org) Not updated [CC BY](https://creativecommons.org/licenses/by/4.0) aws-pds, machine learning, natural language processing ZINC Database 3D molecular docking structure files in db2gz, sdf and mol2 formats arn:aws:s3:::zinc3d us-east-1 S3 Bucket http://wiki.docking.org/index.php/ZINC15:Resources [John Irwin](chemistry4biology@gmail.com) [John Irwin](chemistry4biology@gmail.com) Monthly ZINC is free as in beer. You may not redistribute without the written permission aws-pds, life sciences, biology, chemical biology, pharmaceutical, molecular docking, protein @@ -1146,9 +1147,9 @@ iNaturalist Licensed Observation Images Image files (eg JPEG) associated with me iSDAsoil iSDAsoil data and covariates arn:aws:s3:::isdasoil us-west-2 S3 Bucket https://www.isda-africa.com/isdasoil/isdasoil-on-aws/ info@isda-africa.com [Innovative Solutions for Decision Agriculture (iSDA)](https://www.isda-africa.c Based upon the availability of new data CC-BY 4.0 agriculture, analytics, aws-pds, biodiversity, conservation, deep learning, food security, geospatial, machine learning, satellite imagery ['[Browse data using the STAC viewer](https://isdasoil.s3.amazonaws.com/index.html)'] nuPlan Globally cached distribution of the nuPlan Dataset Web frontend is available to ap-northeast-1 CloudFront Distribution https://nuplan.org https://nuplan.org [Motional, Inc.](https://motional.com) Finalized [Commercial](https://www.nuscenes.org/terms-of-use) aws-pds, autonomous vehicles, lidar, robotics, transportation, urban https://d1qinkmu0ju04f.cloudfront.net nuPlan nuPlan Dataset arn:aws:s3:::motional-nuplan ap-northeast-1 S3 Bucket https://nuplan.org https://nuplan.org [Motional, Inc.](https://motional.com) Finalized [Commercial](https://www.nuscenes.org/terms-of-use) aws-pds, autonomous vehicles, lidar, robotics, transportation, urban ['[Browse Bucket](https://motional-nuplan.s3.ap-northeast-1.amazonaws.com/index.html)'] -nuScenes Globally cached distribution of the nuScenes Dataset Web frontend is available ap-northeast-1 CloudFront Distribution https://www.nuscenes.org https://www.nuscenes.org [Motional, Inc.](https://motional.com) Finalized [Commercial](https://www.nuscenes.org/terms-of-use) aws-pds, autonomous vehicles, computer vision, lidar, robotics, transportation, urban https://d36yt3mvayqw5m.cloudfront.net nuScenes nuScenes Dataset arn:aws:s3:::motional-nuscenes ap-northeast-1 S3 Bucket https://www.nuscenes.org https://www.nuscenes.org [Motional, Inc.](https://motional.com) Finalized [Commercial](https://www.nuscenes.org/terms-of-use) aws-pds, autonomous vehicles, computer vision, lidar, robotics, transportation, urban ['[Browse Bucket](https://motional-nuscenes.s3.ap-northeast-1.amazonaws.com/index.html)'] -real-changesets real-changesets arn:aws:s3:::real-changesets us-west-2 S3 Bucket https://github.com/osmus/osmcha-charter-project/blob/main/real-changesets-docs.m team@openstreetmap.us OpenStreetMap US Minutely [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/) geospatial, osm, mapping, disaster response, aws-pds +nuScenes Globally cached distribution of the nuScenes Dataset Web frontend is available ap-northeast-1 CloudFront Distribution https://www.nuscenes.org https://www.nuscenes.org [Motional, Inc.](https://motional.com) Finalized [Commercial](https://www.nuscenes.org/terms-of-use) aws-pds, autonomous vehicles, computer vision, lidar, robotics, transportation, urban https://d36yt3mvayqw5m.cloudfront.net real-changesets New File Notification arn:aws:sns:us-west-2:877446169145:real-changesets-object_created us-west-2 SNS Topic https://github.com/osmus/osmcha-charter-project/blob/main/real-changesets-docs.m team@openstreetmap.us OpenStreetMap US Minutely [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/) geospatial, osm, mapping, disaster response, aws-pds +real-changesets real-changesets arn:aws:s3:::real-changesets us-west-2 S3 Bucket https://github.com/osmus/osmcha-charter-project/blob/main/real-changesets-docs.m team@openstreetmap.us OpenStreetMap US Minutely [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/) geospatial, osm, mapping, disaster response, aws-pds recount3 https://rnarecountbio/docs/ arn:aws:s3:::recount-opendata us-east-1 S3 Bucket https://rna.recount.bio/ https://rna.recount.bio/docs/how-to-ask-for-help.html Johns Hopkins University Not currently updated [recount3 is Public Domain, recount2 is CC0](https://creativecommons.org/share-y aws-pds, bioinformatics, biology, cancer, csv, gene expression, genetic, genomic, Homo sapiens, life sciences, Mus musculus, neuroscience, transcriptomics stdpopsim species resources https://stdpopsimreadthedocsio/en/latest/ arn:aws:s3:::stdpopsim us-west-2 S3 Bucket https://stdpopsim.readthedocs.io/en/latest/catalog.html https://github.com/popsim-consortium/stdpopsim/issues Andrew Kern & Jerome Kelleher Data will be added as new species, genome assemblies, and genetic map data for a Please see the individual datasets compiled here for licensing details and make aws-pds, genetic maps, life sciences, population genetics, recombination maps, simulations diff --git a/gee_catalog.json b/gee_catalog.json index bc1ab8b..37fd8d8 100644 --- a/gee_catalog.json +++ b/gee_catalog.json @@ -114,7 +114,7 @@ "snippet": "ee.ImageCollection('ASTER/AST_L1T_003')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-03-04", - "end_date": "2024-08-14", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aster, eos, imagery, nasa, nir, radiance, swir, terra, thermal, tir, toa, usgs, vnir", @@ -708,7 +708,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S1_GRD')", "provider": "European Union/ESA/Copernicus", "state_date": "2014-10-03", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "backscatter, copernicus, esa, eu, polarization, radar, sar, sentinel", @@ -726,7 +726,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S2')", "provider": "European Union/ESA/Copernicus", "state_date": "2015-06-27", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -56, 180, 83", "deprecated": true, "keywords": "copernicus, esa, eu, msi, radiance, sentinel", @@ -744,7 +744,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY')", "provider": "European Union/ESA/Copernicus/SentinelHub", "state_date": "2015-06-27", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -56, 180, 83", "deprecated": false, "keywords": "cloud, copernicus, esa, eu, msi, radiance, sentinel, sentinelhub", @@ -762,7 +762,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S2_HARMONIZED')", "provider": "European Union/ESA/Copernicus", "state_date": "2015-06-27", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -56, 180, 83", "deprecated": false, "keywords": "copernicus, esa, eu, msi, radiance, sentinel", @@ -780,7 +780,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S2_SR')", "provider": "European Union/ESA/Copernicus", "state_date": "2017-03-28", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -56, 180, 83", "deprecated": true, "keywords": "copernicus, esa, eu, msi, reflectance, sentinel, sr", @@ -798,7 +798,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')", "provider": "European Union/ESA/Copernicus", "state_date": "2017-03-28", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -56, 180, 83", "deprecated": false, "keywords": "copernicus, esa, eu, msi, reflectance, sentinel, sr", @@ -816,7 +816,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S3/OLCI')", "provider": "European Union/ESA/Copernicus", "state_date": "2016-10-18", - "end_date": "2024-08-15", + "end_date": "2024-08-16", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "copernicus, esa, eu, olci, radiance, sentinel, toa", @@ -834,7 +834,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_AI')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-10", - "end_date": "2024-08-15", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai", @@ -852,7 +852,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_LH')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-10", - "end_date": "2024-08-15", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai", @@ -870,7 +870,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CLOUD')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-05", - "end_date": "2024-08-15", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi", @@ -888,7 +888,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-11-22", - "end_date": "2024-08-15", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi", @@ -906,7 +906,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_HCHO')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-10-02", - "end_date": "2024-08-15", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi", @@ -924,7 +924,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-10", - "end_date": "2024-08-15", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi", @@ -942,7 +942,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_O3')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-10", - "end_date": "2024-08-15", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi", @@ -960,7 +960,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_SO2')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-10", - "end_date": "2024-08-15", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi", @@ -978,7 +978,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_AI')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-04", - "end_date": "2024-08-12", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai", @@ -996,7 +996,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_LH')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-04", - "end_date": "2024-08-12", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai", @@ -1014,7 +1014,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CH4')", "provider": "European Union/ESA/Copernicus", "state_date": "2019-02-08", - "end_date": "2024-08-12", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, copernicus, esa, eu, knmi, methane, s5p, sentinel, sron, tropomi", @@ -1032,7 +1032,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CLOUD')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-07-04", - "end_date": "2024-08-12", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi", @@ -1050,7 +1050,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CO')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-06-28", - "end_date": "2024-08-12", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi", @@ -1068,7 +1068,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_HCHO')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-12-05", - "end_date": "2024-08-12", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi", @@ -1086,7 +1086,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-06-28", - "end_date": "2024-08-06", + "end_date": "2024-08-08", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi", @@ -1104,7 +1104,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-09-08", - "end_date": "2024-08-12", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi", @@ -1122,7 +1122,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3_TCL')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-04-30", - "end_date": "2024-07-31", + "end_date": "2024-08-01", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi", @@ -1140,7 +1140,7 @@ "snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_SO2')", "provider": "European Union/ESA/Copernicus", "state_date": "2018-12-05", - "end_date": "2024-08-12", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi", @@ -1554,7 +1554,7 @@ "snippet": "ee.ImageCollection('ECMWF/CAMS/NRT')", "provider": "European Centre for Medium-Range Weather Forecasts (ECMWF)", "state_date": "2016-06-22", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aerosol, atmosphere, climate, copernicus, ecmwf, forecast, particulate_matter", @@ -2274,7 +2274,7 @@ "snippet": "ee.ImageCollection('FIRMS')", "provider": "NASA / LANCE / EOSDIS", "state_date": "2000-11-01", - "end_date": "2024-08-14", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal", @@ -2562,7 +2562,7 @@ "snippet": "ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED')", "provider": "Google Earth Engine", "state_date": "2015-06-27", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "google, cloud, sentinel2_derived", @@ -2580,7 +2580,7 @@ "snippet": "ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1')", "provider": "World Resources Institute", "state_date": "2015-06-27", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "global, google, landcover, landuse, nrt, sentinel2_derived", @@ -2796,7 +2796,7 @@ "snippet": "ee.ImageCollection('HYCOM/sea_surface_elevation')", "provider": "NOPP", "state_date": "1992-10-02", - "end_date": "2024-08-15", + "end_date": "2024-08-16", "bbox": "-180, -80.48, 180, 80.48", "deprecated": false, "keywords": "elevation, hycom, nopp, ocean, ssh, water", @@ -2814,7 +2814,7 @@ "snippet": "ee.ImageCollection('HYCOM/sea_temp_salinity')", "provider": "NOPP", "state_date": "1992-10-02", - "end_date": "2024-08-15", + "end_date": "2024-08-16", "bbox": "-180, -80.48, 180, 80.48", "deprecated": false, "keywords": "hycom, nopp, ocean, salinity, sst, water, water_temp", @@ -2832,7 +2832,7 @@ "snippet": "ee.ImageCollection('HYCOM/sea_water_velocity')", "provider": "NOPP", "state_date": "1992-10-02", - "end_date": "2024-08-15", + "end_date": "2024-08-16", "bbox": "-180, -80.48, 180, 80.48", "deprecated": false, "keywords": "hycom, nopp, ocean, velocity, water", @@ -2850,7 +2850,7 @@ "snippet": "ee.ImageCollection('IDAHO_EPSCOR/GRIDMET')", "provider": "University of California Merced", "state_date": "1979-01-01", - "end_date": "2024-08-13", + "end_date": "2024-08-14", "bbox": "-124.9, 24.9, -66.8, 49.6", "deprecated": false, "keywords": "climate, fireburning, gridmet, humidity, merced, metdata, nfdrs, precipitation, radiation, temperature, wind", @@ -3768,7 +3768,7 @@ "snippet": "ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/operational')", "provider": "JAXA Earth Observation Research Center", "state_date": "2014-03-01", - "end_date": "2024-08-15", + "end_date": "2024-08-16", "bbox": "-180, -60, 180, 60", "deprecated": false, "keywords": "climate, geophysical, gpm, hourly, jaxa, precipitation, weather", @@ -3804,7 +3804,7 @@ "snippet": "ee.ImageCollection('JAXA/GPM_L3/GSMaP/v7/operational')", "provider": "JAXA Earth Observation Research Center", "state_date": "2014-03-01", - "end_date": "2024-08-15", + "end_date": "2024-08-16", "bbox": "-180, -60, 180, 60", "deprecated": false, "keywords": "climate, geophysical, gpm, hourly, jaxa, precipitation, weather", @@ -3822,7 +3822,7 @@ "snippet": "ee.ImageCollection('JAXA/GPM_L3/GSMaP/v8/operational')", "provider": "JAXA Earth Observation Research Center", "state_date": "1998-01-01", - "end_date": "2024-08-15", + "end_date": "2024-08-16", "bbox": "-180, -60, 180, 60", "deprecated": false, "keywords": "climate, geophysical, gpm, hourly, jaxa, precipitation, weather", @@ -5280,7 +5280,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC08/C02/T1_RT')", "provider": "USGS", "state_date": "2013-03-18", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, l8, landsat, lc8, nrt, oli_tirs, radiance, rt, t1, tier1, usgs", @@ -5298,7 +5298,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC08/C02/T1_RT_TOA')", "provider": "USGS/Google", "state_date": "2013-03-18", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, l8, landsat, lc8, toa, usgs", @@ -5388,7 +5388,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T1')", "provider": "USGS", "state_date": "2021-10-31", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, l9, landsat, lc9, oli_tirs, radiance, t1, tier1, usgs", @@ -5406,7 +5406,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T1_L2')", "provider": "USGS", "state_date": "2021-10-31", - "end_date": "2024-08-14", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs", @@ -5424,7 +5424,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T1_TOA')", "provider": "USGS/Google", "state_date": "2021-10-31", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, landsat, toa, usgs", @@ -5442,7 +5442,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T2')", "provider": "USGS", "state_date": "2021-11-02", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, l9, landsat, lc9, oli_tirs, radiance, t2, tier2, usgs", @@ -5460,7 +5460,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T2_L2')", "provider": "USGS", "state_date": "2021-10-31", - "end_date": "2024-08-14", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs", @@ -5478,7 +5478,7 @@ "snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T2_TOA')", "provider": "USGS/Google", "state_date": "2021-11-02", - "end_date": "2024-08-15", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "c2, global, l9, landsat, lc9, toa, usgs", @@ -7332,7 +7332,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MCD19A1_GRANULES')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-12-21", - "end_date": "2024-08-06", + "end_date": "2024-08-13", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aerosol, aod, aqua, daily, global, maiac, modis, nasa, terra, usgs", @@ -7350,7 +7350,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MCD19A2_GRANULES')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-08-06", + "end_date": "2024-08-13", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aerosol, aod, aqua, daily, global, maiac, mcd19a2, modis, nasa, terra, usgs", @@ -7584,7 +7584,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MOD10A1')", "provider": "NASA NSIDC DAAC at CIRES", "state_date": "2000-02-24", - "end_date": "2024-08-13", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "albedo, daily, geophysical, global, mod10a1, modis, nasa, nsidc, snow, terra", @@ -7890,7 +7890,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MOD21A1N')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-08-13", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs", @@ -7908,7 +7908,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MOD21C1')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-08-13", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs", @@ -8070,7 +8070,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MYD10A1')", "provider": "NASA NSIDC DAAC at CIRES", "state_date": "2002-07-04", - "end_date": "2024-08-13", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "albedo, aqua, daily, geophysical, global, modis, myd10a1, nasa, nsidc, snow", @@ -8322,7 +8322,7 @@ "snippet": "ee.ImageCollection('MODIS/061/MYD21A1N')", "provider": "NASA LP DAAC at the USGS EROS Center", "state_date": "2000-02-24", - "end_date": "2024-08-13", + "end_date": "2024-08-14", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs", @@ -9366,7 +9366,7 @@ "snippet": "ee.ImageCollection('NASA/EMIT/L1B/RAD')", "provider": "NASA Jet Propulsion Laboratory", "state_date": "2022-08-09", - "end_date": "2024-08-15", + "end_date": "2024-08-16", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, emit, nasa, radiance", @@ -9456,7 +9456,7 @@ "snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/fcst/htf')", "provider": "NASA / GMAO", "state_date": "2022-10-01", - "end_date": "2024-08-14", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "composition, forecast, geos, gmao, nasa", @@ -9474,7 +9474,7 @@ "snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/fcst/tavg1hr')", "provider": "NASA / GMAO", "state_date": "2022-10-01", - "end_date": "2024-08-14", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "composition, forecast, geos, gmao, nasa", @@ -9492,7 +9492,7 @@ "snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/rpl/htf')", "provider": "NASA / GMAO", "state_date": "2018-01-01", - "end_date": "2024-08-14", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "composition, forecast, geos, gmao, nasa", @@ -9510,7 +9510,7 @@ "snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/rpl/tavg1hr')", "provider": "NASA / GMAO", "state_date": "2018-01-01", - "end_date": "2024-08-14", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "composition, forecast, geos, gmao, nasa", @@ -9942,7 +9942,7 @@ "snippet": "ee.ImageCollection('NASA/HLS/HLSL30/v002')", "provider": "NASA LP DAAC", "state_date": "2013-04-11", - "end_date": "2024-08-13", + "end_date": "2024-08-14", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "landsat, nasa, sentinel, usgs", @@ -9978,7 +9978,7 @@ "snippet": "ee.ImageCollection('NASA/LANCE/NOAA20_VIIRS/C2')", "provider": "NASA / LANCE / NOAA20_VIIRS", "state_date": "2023-10-08", - "end_date": "2024-08-14", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs", @@ -9996,7 +9996,7 @@ "snippet": "ee.ImageCollection('NASA/LANCE/SNPP_VIIRS/C2')", "provider": "NASA / LANCE / SNPP_VIIRS", "state_date": "2023-09-03", - "end_date": "2024-08-14", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs", @@ -10266,7 +10266,7 @@ "snippet": "ee.ImageCollection('NASA/SMAP/SPL4SMGP/007')", "provider": "Google and NSIDC", "state_date": "2015-03-31", - "end_date": "2024-08-13", + "end_date": "2024-08-14", "bbox": "-180, -84, 180, 84", "deprecated": false, "keywords": "drought, nasa, smap, soil_moisture, surface, weather", @@ -10284,7 +10284,7 @@ "snippet": "ee.ImageCollection('NASA/VIIRS/002/VNP09GA')", "provider": "NASA Land SIPS", "state_date": "2012-01-19", - "end_date": "2024-08-14", + "end_date": "2024-08-15", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, nasa, noaa, npp, reflectance, sr, viirs, vnp09ga", @@ -10392,7 +10392,7 @@ "snippet": "ee.ImageCollection('NCEP_RE/sea_level_pressure')", "provider": "NCEP", "state_date": "1948-01-01", - "end_date": "2024-08-12", + "end_date": "2024-08-13", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "atmosphere, climate, geophysical, ncep, noaa, pressure, reanalysis", @@ -10410,7 +10410,7 @@ "snippet": "ee.ImageCollection('NCEP_RE/surface_temp')", "provider": "NCEP", "state_date": "1948-01-01", - "end_date": "2024-08-12", + "end_date": "2024-08-13", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "atmosphere, climate, geophysical, ncep, noaa, reanalysis, temperature", @@ -10428,7 +10428,7 @@ "snippet": "ee.ImageCollection('NCEP_RE/surface_wv')", "provider": "NCEP", "state_date": "1948-01-01", - "end_date": "2024-08-12", + "end_date": "2024-08-13", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "atmosphere, climate, geophysical, ncep, noaa, precipitable, reanalysis, vapor", @@ -10662,7 +10662,7 @@ "snippet": "ee.ImageCollection('NOAA/CDR/OISST/V2_1')", "provider": "NOAA", "state_date": "1981-09-01", - "end_date": "2024-08-11", + "end_date": "2024-08-13", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "avhrr, cdr, daily, ice, noaa, ocean, oisst, real_time, sst, temperature", @@ -10734,7 +10734,7 @@ "snippet": "ee.ImageCollection('NOAA/CFSR')", "provider": "NOAA NWS National Centers for Environmental Prediction (NCEP)", "state_date": "2018-12-13", - "end_date": "2024-08-15", + "end_date": "2024-08-16", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather", @@ -10752,7 +10752,7 @@ "snippet": "ee.ImageCollection('NOAA/CFSV2/FOR6H')", "provider": "NOAA NWS National Centers for Environmental Prediction (NCEP)", "state_date": "1979-01-01", - "end_date": "2024-08-15", + "end_date": "2024-08-16", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather", @@ -10806,7 +10806,7 @@ "snippet": "ee.ImageCollection('NOAA/GFS0P25')", "provider": "NOAA/NCEP/EMC", "state_date": "2015-07-01", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "climate, cloud, emc, flux, forecast, geophysical, gfs, humidity, ncep, noaa, precipitation, radiation, temperature, vapor, weather, wind", @@ -10824,7 +10824,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/16/FDCC')", "provider": "NOAA", "state_date": "2017-05-24", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-152.11, 14, -49.18, 56.77", "deprecated": false, "keywords": "abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire", @@ -10842,7 +10842,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/16/FDCF')", "provider": "NOAA", "state_date": "2017-05-24", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire", @@ -10860,7 +10860,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/16/MCMIPC')", "provider": "NOAA", "state_date": "2017-07-10", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-152.11, 14, -49.18, 56.77", "deprecated": false, "keywords": "abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather", @@ -10878,7 +10878,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/16/MCMIPF')", "provider": "NOAA", "state_date": "2017-07-10", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather", @@ -10896,7 +10896,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/16/MCMIPM')", "provider": "NOAA", "state_date": "2017-07-10", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather", @@ -11004,7 +11004,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/18/FDCC')", "provider": "NOAA", "state_date": "2022-10-13", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, 14.57, 180, 53.51", "deprecated": false, "keywords": "abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire", @@ -11022,7 +11022,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/18/FDCF')", "provider": "NOAA", "state_date": "2022-10-13", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire", @@ -11040,7 +11040,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/18/MCMIPC')", "provider": "NOAA", "state_date": "2018-12-04", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, 14.57, 180, 53.51", "deprecated": false, "keywords": "abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather", @@ -11058,7 +11058,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/18/MCMIPF')", "provider": "NOAA", "state_date": "2018-12-04", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather", @@ -11076,7 +11076,7 @@ "snippet": "ee.ImageCollection('NOAA/GOES/18/MCMIPM')", "provider": "NOAA", "state_date": "2018-12-04", - "end_date": "2024-08-16", + "end_date": "2024-08-17", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather", @@ -11184,7 +11184,7 @@ "snippet": "ee.ImageCollection('NOAA/NWS/RTMA')", "provider": "NOAA/NWS", "state_date": "2011-01-01", - "end_date": "2024-08-15", + "end_date": "2024-08-16", "bbox": "-130.17, 20.15, -60.81, 52.91", "deprecated": false, "keywords": "climate, cloud, geophysical, humidity, noaa, nws, precipitation, pressure, rtma, surface, temperature, visibility, weather, wind", @@ -11400,7 +11400,7 @@ "snippet": "ee.ImageCollection('NOAA/VIIRS/001/VNP46A1')", "provider": "NASA LAADS DAAC", "state_date": "2012-01-19", - "end_date": "2024-08-15", + "end_date": "2024-08-16", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "daily, dnb, nasa, noaa, viirs", @@ -11562,7 +11562,7 @@ "snippet": "ee.ImageCollection('OREGONSTATE/PRISM/AN81d')", "provider": "PRISM / OREGONSTATE", "state_date": "1981-01-01", - "end_date": "2024-08-12", + "end_date": "2024-08-13", "bbox": "-125, 24, -66, 50", "deprecated": false, "keywords": "climate, daily, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather", @@ -12696,7 +12696,7 @@ "snippet": "ee.ImageCollection('TOMS/MERGED')", "provider": "NASA / GES DISC", "state_date": "1978-11-01", - "end_date": "2024-08-13", + "end_date": "2024-08-14", "bbox": "-180, -90, 180, 90", "deprecated": false, "keywords": "atmosphere, aura, climate, geophysical, ges_disc, goddard, nasa, omi, ozone, toms", @@ -12804,7 +12804,7 @@ "snippet": "ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY')", "provider": "UCSB/CHG", "state_date": "1981-01-01", - "end_date": "2024-06-30", + "end_date": "2024-07-31", "bbox": "-180, -50, 180, 50", "deprecated": false, "keywords": "chg, climate, geophysical, precipitation, ucsb, weather", @@ -14334,7 +14334,7 @@ "snippet": "ee.ImageCollection('UTOKYO/WTLAB/KBDI/v1')", "provider": "Institute of Industrial Science, The University of Tokyo, Japan", "state_date": "2007-01-01", - "end_date": "2024-08-14", + "end_date": "2024-08-15", "bbox": "60, -60, 180, 60", "deprecated": false, "keywords": "drought, kbdi, lst_derived, rainfall, utokyo, wtlab", diff --git a/gee_catalog.tsv b/gee_catalog.tsv index eae95b9..4d60612 100644 --- a/gee_catalog.tsv +++ b/gee_catalog.tsv @@ -5,7 +5,7 @@ ACA/reef_habitat/v2_0 Allen Coral Atlas (ACA) - Geomorphic Zonation and Benthic AHN/AHN2_05M_INT AHN Netherlands 0.5m DEM, Interpolated image ee.Image('AHN/AHN2_05M_INT') AHN 2012-01-01 2012-01-01 3.35, 50.74, 7.24, 53.55 False ahn, dem, elevation, geophysical, lidar, netherlands https://storage.googleapis.com/earthengine-stac/catalog/AHN/AHN_AHN2_05M_INT.json https://developers.google.com/earth-engine/datasets/catalog/AHN_AHN2_05M_INT CC0-1.0 AHN/AHN2_05M_NON AHN Netherlands 0.5m DEM, Non-Interpolated image ee.Image('AHN/AHN2_05M_NON') AHN 2012-01-01 2012-01-01 3.35, 50.74, 7.24, 53.55 False ahn, dem, elevation, geophysical, lidar, netherlands https://storage.googleapis.com/earthengine-stac/catalog/AHN/AHN_AHN2_05M_NON.json https://developers.google.com/earth-engine/datasets/catalog/AHN_AHN2_05M_NON CC0-1.0 AHN/AHN2_05M_RUW AHN Netherlands 0.5m DEM, Raw Samples image ee.Image('AHN/AHN2_05M_RUW') AHN 2012-01-01 2012-01-01 3.35, 50.74, 7.24, 53.55 False ahn, dem, elevation, geophysical, lidar, netherlands https://storage.googleapis.com/earthengine-stac/catalog/AHN/AHN_AHN2_05M_RUW.json https://developers.google.com/earth-engine/datasets/catalog/AHN_AHN2_05M_RUW CC0-1.0 -ASTER/AST_L1T_003 ASTER L1T Radiance image_collection ee.ImageCollection('ASTER/AST_L1T_003') NASA LP DAAC at the USGS EROS Center 2000-03-04 2024-08-14 -180, -90, 180, 90 False aster, eos, imagery, nasa, nir, radiance, swir, terra, thermal, tir, toa, usgs, vnir https://storage.googleapis.com/earthengine-stac/catalog/ASTER/ASTER_AST_L1T_003.json https://developers.google.com/earth-engine/datasets/catalog/ASTER_AST_L1T_003 proprietary +ASTER/AST_L1T_003 ASTER L1T Radiance image_collection ee.ImageCollection('ASTER/AST_L1T_003') NASA LP DAAC at the USGS EROS Center 2000-03-04 2024-08-15 -180, -90, 180, 90 False aster, eos, imagery, nasa, nir, radiance, swir, terra, thermal, tir, toa, usgs, vnir https://storage.googleapis.com/earthengine-stac/catalog/ASTER/ASTER_AST_L1T_003.json https://developers.google.com/earth-engine/datasets/catalog/ASTER_AST_L1T_003 proprietary AU/GA/AUSTRALIA_5M_DEM Australian 5M DEM image_collection ee.ImageCollection('AU/GA/AUSTRALIA_5M_DEM') Geoscience Australia 2015-12-01 2015-12-01 114.09, -43.45, 153.64, -9.88 False australia, dem, elevation, ga, geophysical, geoscience_australia, lidar https://storage.googleapis.com/earthengine-stac/catalog/AU/AU_GA_AUSTRALIA_5M_DEM.json https://developers.google.com/earth-engine/datasets/catalog/AU_GA_AUSTRALIA_5M_DEM CC-BY-4.0 AU/GA/DEM_1SEC/v10/DEM-H DEM-H: Australian SRTM Hydrologically Enforced Digital Elevation Model image ee.Image('AU/GA/DEM_1SEC/v10/DEM-H') Geoscience Australia 2010-02-01 2010-02-01 112.99, -44.06, 154, -9.99 False australia, dem, elevation, ga, geophysical, geoscience_australia, smoothed, srtm https://storage.googleapis.com/earthengine-stac/catalog/AU/AU_GA_DEM_1SEC_v10_DEM-H.json https://developers.google.com/earth-engine/datasets/catalog/AU_GA_DEM_1SEC_v10_DEM-H CC-BY-4.0 AU/GA/DEM_1SEC/v10/DEM-S DEM-S: Australian Smoothed Digital Elevation Model image ee.Image('AU/GA/DEM_1SEC/v10/DEM-S') Geoscience Australia 2010-02-01 2010-02-01 112.99, -44.06, 154, -9.99 False australia, dem, elevation, ga, geophysical, geoscience_australia, smoothed, srtm https://storage.googleapis.com/earthengine-stac/catalog/AU/AU_GA_DEM_1SEC_v10_DEM-S.json https://developers.google.com/earth-engine/datasets/catalog/AU_GA_DEM_1SEC_v10_DEM-S CC-BY-4.0 @@ -38,31 +38,31 @@ COPERNICUS/CORINE/V20/100m Copernicus CORINE Land Cover image_collection ee.Imag COPERNICUS/DEM/GLO30 Copernicus DEM GLO-30: Global 30m Digital Elevation Model image_collection ee.ImageCollection('COPERNICUS/DEM/GLO30') Copernicus 2010-12-01 2015-01-31 -180, -90, 180, 90 False copernicus, dem, elevation, geophysical https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_DEM_GLO30.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_DEM_GLO30 proprietary COPERNICUS/Landcover/100m/Proba-V-C3/Global Copernicus Global Land Cover Layers: CGLS-LC100 Collection 3 image_collection ee.ImageCollection('COPERNICUS/Landcover/100m/Proba-V-C3/Global') Copernicus 2015-01-01 2019-12-31 -180, -90, 180, 90 False copernicus, eea, esa, eu, landcover, proba, probav, vito https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_Landcover_100m_Proba-V-C3_Global.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global proprietary COPERNICUS/Landcover/100m/Proba-V/Global Copernicus Global Land Cover Layers: CGLS-LC100 Collection 2 [deprecated] image_collection ee.ImageCollection('COPERNICUS/Landcover/100m/Proba-V/Global') Copernicus 2015-01-01 2015-01-01 -180, -90, 180, 90 True copernicus, eea, esa, eu, landcover, proba, probav, vito https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_Landcover_100m_Proba-V_Global.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V_Global proprietary -COPERNICUS/S1_GRD Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected, log scaling image_collection ee.ImageCollection('COPERNICUS/S1_GRD') European Union/ESA/Copernicus 2014-10-03 2024-08-16 -180, -90, 180, 90 False backscatter, copernicus, esa, eu, polarization, radar, sar, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S1_GRD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD proprietary -COPERNICUS/S2 Sentinel-2 MSI: MultiSpectral Instrument, Level-1C [deprecated] image_collection ee.ImageCollection('COPERNICUS/S2') European Union/ESA/Copernicus 2015-06-27 2024-08-16 -180, -56, 180, 83 True copernicus, esa, eu, msi, radiance, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 proprietary -COPERNICUS/S2_CLOUD_PROBABILITY Sentinel-2: Cloud Probability image_collection ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY') European Union/ESA/Copernicus/SentinelHub 2015-06-27 2024-08-16 -180, -56, 180, 83 False cloud, copernicus, esa, eu, msi, radiance, sentinel, sentinelhub https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_CLOUD_PROBABILITY.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_CLOUD_PROBABILITY proprietary -COPERNICUS/S2_HARMONIZED Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-1C image_collection ee.ImageCollection('COPERNICUS/S2_HARMONIZED') European Union/ESA/Copernicus 2015-06-27 2024-08-16 -180, -56, 180, 83 False copernicus, esa, eu, msi, radiance, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED proprietary -COPERNICUS/S2_SR Sentinel-2 MSI: MultiSpectral Instrument, Level-2A [deprecated] image_collection ee.ImageCollection('COPERNICUS/S2_SR') European Union/ESA/Copernicus 2017-03-28 2024-08-16 -180, -56, 180, 83 True copernicus, esa, eu, msi, reflectance, sentinel, sr https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR proprietary -COPERNICUS/S2_SR_HARMONIZED Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-2A image_collection ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED') European Union/ESA/Copernicus 2017-03-28 2024-08-16 -180, -56, 180, 83 False copernicus, esa, eu, msi, reflectance, sentinel, sr https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED proprietary -COPERNICUS/S3/OLCI Sentinel-3 OLCI EFR: Ocean and Land Color Instrument Earth Observation Full Resolution image_collection ee.ImageCollection('COPERNICUS/S3/OLCI') European Union/ESA/Copernicus 2016-10-18 2024-08-15 -180, -90, 180, 90 False copernicus, esa, eu, olci, radiance, sentinel, toa https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S3_OLCI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S3_OLCI proprietary -COPERNICUS/S5P/NRTI/L3_AER_AI Sentinel-5P NRTI AER AI: Near Real-Time UV Aerosol Index image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_AI') European Union/ESA/Copernicus 2018-07-10 2024-08-15 -180, -90, 180, 90 False aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_AI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_AI proprietary -COPERNICUS/S5P/NRTI/L3_AER_LH Sentinel-5P NRTI AER LH: Near Real-Time UV Aerosol Layer Height image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_LH') European Union/ESA/Copernicus 2018-07-10 2024-08-15 -180, -90, 180, 90 False aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_LH.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_LH proprietary -COPERNICUS/S5P/NRTI/L3_CLOUD Sentinel-5P NRTI CLOUD: Near Real-Time Cloud image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CLOUD') European Union/ESA/Copernicus 2018-07-05 2024-08-15 -180, -90, 180, 90 False climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CLOUD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CLOUD proprietary -COPERNICUS/S5P/NRTI/L3_CO Sentinel-5P NRTI CO: Near Real-Time Carbon Monoxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO') European Union/ESA/Copernicus 2018-11-22 2024-08-15 -180, -90, 180, 90 False air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CO proprietary -COPERNICUS/S5P/NRTI/L3_HCHO Sentinel-5P NRTI HCHO: Near Real-Time Formaldehyde image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_HCHO') European Union/ESA/Copernicus 2018-10-02 2024-08-15 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_HCHO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_HCHO proprietary -COPERNICUS/S5P/NRTI/L3_NO2 Sentinel-5P NRTI NO2: Near Real-Time Nitrogen Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2') European Union/ESA/Copernicus 2018-07-10 2024-08-15 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_NO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_NO2 proprietary -COPERNICUS/S5P/NRTI/L3_O3 Sentinel-5P NRTI O3: Near Real-Time Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_O3') European Union/ESA/Copernicus 2018-07-10 2024-08-15 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_O3.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_O3 proprietary -COPERNICUS/S5P/NRTI/L3_SO2 Sentinel-5P NRTI SO2: Near Real-Time Sulfur Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_SO2') European Union/ESA/Copernicus 2018-07-10 2024-08-15 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_SO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_SO2 proprietary -COPERNICUS/S5P/OFFL/L3_AER_AI Sentinel-5P OFFL AER AI: Offline UV Aerosol Index image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_AI') European Union/ESA/Copernicus 2018-07-04 2024-08-12 -180, -90, 180, 90 False aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_AI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_AI proprietary -COPERNICUS/S5P/OFFL/L3_AER_LH Sentinel-5P OFFL AER LH: Offline UV Aerosol Layer Height image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_LH') European Union/ESA/Copernicus 2018-07-04 2024-08-12 -180, -90, 180, 90 False aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_LH.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_LH proprietary -COPERNICUS/S5P/OFFL/L3_CH4 Sentinel-5P OFFL CH4: Offline Methane image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CH4') European Union/ESA/Copernicus 2019-02-08 2024-08-12 -180, -90, 180, 90 False climate, copernicus, esa, eu, knmi, methane, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CH4.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CH4 proprietary -COPERNICUS/S5P/OFFL/L3_CLOUD Sentinel-5P OFFL CLOUD: Near Real-Time Cloud image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CLOUD') European Union/ESA/Copernicus 2018-07-04 2024-08-12 -180, -90, 180, 90 False climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CLOUD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CLOUD proprietary -COPERNICUS/S5P/OFFL/L3_CO Sentinel-5P OFFL CO: Offline Carbon Monoxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CO') European Union/ESA/Copernicus 2018-06-28 2024-08-12 -180, -90, 180, 90 False air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CO proprietary -COPERNICUS/S5P/OFFL/L3_HCHO Sentinel-5P OFFL HCHO: Offline Formaldehyde image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_HCHO') European Union/ESA/Copernicus 2018-12-05 2024-08-12 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_HCHO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO proprietary -COPERNICUS/S5P/OFFL/L3_NO2 Sentinel-5P OFFL NO2: Offline Nitrogen Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2') European Union/ESA/Copernicus 2018-06-28 2024-08-06 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_NO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2 proprietary -COPERNICUS/S5P/OFFL/L3_O3 Sentinel-5P OFFL O3: Offline Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3') European Union/ESA/Copernicus 2018-09-08 2024-08-12 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3 proprietary -COPERNICUS/S5P/OFFL/L3_O3_TCL Sentinel-5P OFFL O3 TCL: Offline Tropospheric Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3_TCL') European Union/ESA/Copernicus 2018-04-30 2024-07-31 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3_TCL.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3_TCL proprietary -COPERNICUS/S5P/OFFL/L3_SO2 Sentinel-5P OFFL SO2: Offline Sulfur Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_SO2') European Union/ESA/Copernicus 2018-12-05 2024-08-12 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_SO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2 proprietary +COPERNICUS/S1_GRD Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected, log scaling image_collection ee.ImageCollection('COPERNICUS/S1_GRD') European Union/ESA/Copernicus 2014-10-03 2024-08-17 -180, -90, 180, 90 False backscatter, copernicus, esa, eu, polarization, radar, sar, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S1_GRD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD proprietary +COPERNICUS/S2 Sentinel-2 MSI: MultiSpectral Instrument, Level-1C [deprecated] image_collection ee.ImageCollection('COPERNICUS/S2') European Union/ESA/Copernicus 2015-06-27 2024-08-17 -180, -56, 180, 83 True copernicus, esa, eu, msi, radiance, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 proprietary +COPERNICUS/S2_CLOUD_PROBABILITY Sentinel-2: Cloud Probability image_collection ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY') European Union/ESA/Copernicus/SentinelHub 2015-06-27 2024-08-17 -180, -56, 180, 83 False cloud, copernicus, esa, eu, msi, radiance, sentinel, sentinelhub https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_CLOUD_PROBABILITY.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_CLOUD_PROBABILITY proprietary +COPERNICUS/S2_HARMONIZED Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-1C image_collection ee.ImageCollection('COPERNICUS/S2_HARMONIZED') European Union/ESA/Copernicus 2015-06-27 2024-08-17 -180, -56, 180, 83 False copernicus, esa, eu, msi, radiance, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED proprietary +COPERNICUS/S2_SR Sentinel-2 MSI: MultiSpectral Instrument, Level-2A [deprecated] image_collection ee.ImageCollection('COPERNICUS/S2_SR') European Union/ESA/Copernicus 2017-03-28 2024-08-17 -180, -56, 180, 83 True copernicus, esa, eu, msi, reflectance, sentinel, sr https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR proprietary +COPERNICUS/S2_SR_HARMONIZED Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-2A image_collection ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED') European Union/ESA/Copernicus 2017-03-28 2024-08-17 -180, -56, 180, 83 False copernicus, esa, eu, msi, reflectance, sentinel, sr https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED proprietary +COPERNICUS/S3/OLCI Sentinel-3 OLCI EFR: Ocean and Land Color Instrument Earth Observation Full Resolution image_collection ee.ImageCollection('COPERNICUS/S3/OLCI') European Union/ESA/Copernicus 2016-10-18 2024-08-16 -180, -90, 180, 90 False copernicus, esa, eu, olci, radiance, sentinel, toa https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S3_OLCI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S3_OLCI proprietary +COPERNICUS/S5P/NRTI/L3_AER_AI Sentinel-5P NRTI AER AI: Near Real-Time UV Aerosol Index image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_AI') European Union/ESA/Copernicus 2018-07-10 2024-08-17 -180, -90, 180, 90 False aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_AI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_AI proprietary +COPERNICUS/S5P/NRTI/L3_AER_LH Sentinel-5P NRTI AER LH: Near Real-Time UV Aerosol Layer Height image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_LH') European Union/ESA/Copernicus 2018-07-10 2024-08-17 -180, -90, 180, 90 False aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_LH.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_LH proprietary +COPERNICUS/S5P/NRTI/L3_CLOUD Sentinel-5P NRTI CLOUD: Near Real-Time Cloud image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CLOUD') European Union/ESA/Copernicus 2018-07-05 2024-08-17 -180, -90, 180, 90 False climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CLOUD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CLOUD proprietary +COPERNICUS/S5P/NRTI/L3_CO Sentinel-5P NRTI CO: Near Real-Time Carbon Monoxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO') European Union/ESA/Copernicus 2018-11-22 2024-08-17 -180, -90, 180, 90 False air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CO proprietary +COPERNICUS/S5P/NRTI/L3_HCHO Sentinel-5P NRTI HCHO: Near Real-Time Formaldehyde image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_HCHO') European Union/ESA/Copernicus 2018-10-02 2024-08-17 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_HCHO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_HCHO proprietary +COPERNICUS/S5P/NRTI/L3_NO2 Sentinel-5P NRTI NO2: Near Real-Time Nitrogen Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2') European Union/ESA/Copernicus 2018-07-10 2024-08-17 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_NO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_NO2 proprietary +COPERNICUS/S5P/NRTI/L3_O3 Sentinel-5P NRTI O3: Near Real-Time Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_O3') European Union/ESA/Copernicus 2018-07-10 2024-08-17 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_O3.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_O3 proprietary +COPERNICUS/S5P/NRTI/L3_SO2 Sentinel-5P NRTI SO2: Near Real-Time Sulfur Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_SO2') European Union/ESA/Copernicus 2018-07-10 2024-08-17 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_SO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_SO2 proprietary +COPERNICUS/S5P/OFFL/L3_AER_AI Sentinel-5P OFFL AER AI: Offline UV Aerosol Index image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_AI') European Union/ESA/Copernicus 2018-07-04 2024-08-15 -180, -90, 180, 90 False aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_AI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_AI proprietary +COPERNICUS/S5P/OFFL/L3_AER_LH Sentinel-5P OFFL AER LH: Offline UV Aerosol Layer Height image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_LH') European Union/ESA/Copernicus 2018-07-04 2024-08-15 -180, -90, 180, 90 False aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_LH.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_LH proprietary +COPERNICUS/S5P/OFFL/L3_CH4 Sentinel-5P OFFL CH4: Offline Methane image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CH4') European Union/ESA/Copernicus 2019-02-08 2024-08-15 -180, -90, 180, 90 False climate, copernicus, esa, eu, knmi, methane, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CH4.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CH4 proprietary +COPERNICUS/S5P/OFFL/L3_CLOUD Sentinel-5P OFFL CLOUD: Near Real-Time Cloud image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CLOUD') European Union/ESA/Copernicus 2018-07-04 2024-08-15 -180, -90, 180, 90 False climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CLOUD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CLOUD proprietary +COPERNICUS/S5P/OFFL/L3_CO Sentinel-5P OFFL CO: Offline Carbon Monoxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CO') European Union/ESA/Copernicus 2018-06-28 2024-08-15 -180, -90, 180, 90 False air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CO proprietary +COPERNICUS/S5P/OFFL/L3_HCHO Sentinel-5P OFFL HCHO: Offline Formaldehyde image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_HCHO') European Union/ESA/Copernicus 2018-12-05 2024-08-15 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_HCHO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO proprietary +COPERNICUS/S5P/OFFL/L3_NO2 Sentinel-5P OFFL NO2: Offline Nitrogen Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2') European Union/ESA/Copernicus 2018-06-28 2024-08-08 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_NO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2 proprietary +COPERNICUS/S5P/OFFL/L3_O3 Sentinel-5P OFFL O3: Offline Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3') European Union/ESA/Copernicus 2018-09-08 2024-08-15 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3 proprietary +COPERNICUS/S5P/OFFL/L3_O3_TCL Sentinel-5P OFFL O3 TCL: Offline Tropospheric Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3_TCL') European Union/ESA/Copernicus 2018-04-30 2024-08-01 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3_TCL.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3_TCL proprietary +COPERNICUS/S5P/OFFL/L3_SO2 Sentinel-5P OFFL SO2: Offline Sulfur Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_SO2') European Union/ESA/Copernicus 2018-12-05 2024-08-15 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_SO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2 proprietary CPOM/CryoSat2/ANTARCTICA_DEM CryoSat-2 Antarctica 1km DEM image ee.Image('CPOM/CryoSat2/ANTARCTICA_DEM') CPOM 2010-07-01 2016-07-01 -180, -88, 180, -60 False antarctica, cpom, cryosat_2, dem, elevation, polar https://storage.googleapis.com/earthengine-stac/catalog/CPOM/CPOM_CryoSat2_ANTARCTICA_DEM.json https://developers.google.com/earth-engine/datasets/catalog/CPOM_CryoSat2_ANTARCTICA_DEM proprietary CSIC/SPEI/2_8 SPEIbase: Standardised Precipitation-Evapotranspiration Index database, Version 2.8 [deprecated] image_collection ee.ImageCollection('CSIC/SPEI/2_8') Spanish National Research Council (CSIC) 1901-01-01 2021-01-01 -180, -90, 180, 90 True climate, climate_change, drought, evapotranspiration, global, monthly, palmer, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/CSIC/CSIC_SPEI_2_8.json https://developers.google.com/earth-engine/datasets/catalog/CSIC_SPEI_2_8 CC-BY-4.0 CSIC/SPEI/2_9 SPEIbase: Standardised Precipitation-Evapotranspiration Index database, Version 2.9 image_collection ee.ImageCollection('CSIC/SPEI/2_9') Spanish National Research Council (CSIC) 1901-01-01 2023-01-01 -180, -90, 180, 90 False climate, climate_change, drought, evapotranspiration, global, monthly, palmer, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/CSIC/CSIC_SPEI_2_9.json https://developers.google.com/earth-engine/datasets/catalog/CSIC_SPEI_2_9 CC-BY-4.0 @@ -85,7 +85,7 @@ CSP/ERGo/1_0/US/topoDiversity US NED Topographic Diversity image ee.Image('CSP/E CSP/HM/GlobalHumanModification CSP gHM: Global Human Modification image_collection ee.ImageCollection('CSP/HM/GlobalHumanModification') Conservation Science Partners 2016-01-01 2016-12-31 -180, -90, 180, 90 False csp, fragmentation, human_modification, landcover, landscape_gradient, stressors, tnc https://storage.googleapis.com/earthengine-stac/catalog/CSP/CSP_HM_GlobalHumanModification.json https://developers.google.com/earth-engine/datasets/catalog/CSP_HM_GlobalHumanModification CC-BY-NC-SA-4.0 DLR/WSF/WSF2015/v1 World Settlement Footprint 2015 image ee.Image('DLR/WSF/WSF2015/v1') Deutsches Zentrum für Luft- und Raumfahrt (DLR) 2015-01-01 2016-01-01 -180, -90, 180, 90 False landcover, landsat_derived, sentinel1_derived, settlement, urban https://storage.googleapis.com/earthengine-stac/catalog/DLR/DLR_WSF_WSF2015_v1.json https://developers.google.com/earth-engine/datasets/catalog/DLR_WSF_WSF2015_v1 CC0-1.0 DOE/ORNL/LandScan_HD/Ukraine_202201 LandScan High Definition Data for Ukraine, January 2022 image ee.Image('DOE/ORNL/LandScan_HD/Ukraine_202201') Oak Ridge National Laboratory 2022-01-01 2022-02-01 22.125, 44.175, 40.225, 52.4 False landscan, population, ukraine https://storage.googleapis.com/earthengine-stac/catalog/DOE/DOE_ORNL_LandScan_HD_Ukraine_202201.json https://developers.google.com/earth-engine/datasets/catalog/DOE_ORNL_LandScan_HD_Ukraine_202201 CC-BY-4.0 -ECMWF/CAMS/NRT Copernicus Atmosphere Monitoring Service (CAMS) Global Near-Real-Time image_collection ee.ImageCollection('ECMWF/CAMS/NRT') European Centre for Medium-Range Weather Forecasts (ECMWF) 2016-06-22 2024-08-16 -180, -90, 180, 90 False aerosol, atmosphere, climate, copernicus, ecmwf, forecast, particulate_matter https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_CAMS_NRT.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_CAMS_NRT proprietary +ECMWF/CAMS/NRT Copernicus Atmosphere Monitoring Service (CAMS) Global Near-Real-Time image_collection ee.ImageCollection('ECMWF/CAMS/NRT') European Centre for Medium-Range Weather Forecasts (ECMWF) 2016-06-22 2024-08-17 -180, -90, 180, 90 False aerosol, atmosphere, climate, copernicus, ecmwf, forecast, particulate_matter https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_CAMS_NRT.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_CAMS_NRT proprietary ECMWF/ERA5/DAILY ERA5 Daily Aggregates - Latest Climate Reanalysis Produced by ECMWF / Copernicus Climate Change Service image_collection ee.ImageCollection('ECMWF/ERA5/DAILY') ECMWF / Copernicus Climate Change Service 1979-01-02 2020-07-09 -180, -90, 180, 90 False climate, copernicus, dewpoint, ecmwf, era5, precipitation, pressure, reanalysis, surface, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_DAILY.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_DAILY proprietary ECMWF/ERA5/MONTHLY ERA5 Monthly Aggregates - Latest Climate Reanalysis Produced by ECMWF / Copernicus Climate Change Service image_collection ee.ImageCollection('ECMWF/ERA5/MONTHLY') ECMWF / Copernicus Climate Change Service 1979-01-01 2020-06-01 -180, -90, 180, 90 False climate, copernicus, dewpoint, ecmwf, era5, precipitation, pressure, reanalysis, surface, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_MONTHLY.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_MONTHLY proprietary ECMWF/ERA5_LAND/DAILY_AGGR ERA5-Land Daily Aggregated - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/DAILY_AGGR') Daily Aggregates: Google and Copernicus Climate Data Store 1950-01-02 2024-08-08 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_DAILY_AGGR.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_DAILY_AGGR proprietary @@ -125,7 +125,7 @@ FAO/WAPOR/2/L1_NPP_D WAPOR Dekadal Net Primary Production 2.0 image_collection e FAO/WAPOR/2/L1_RET_D WAPOR Dekadal Reference Evapotranspiration 2.0 image_collection ee.ImageCollection('FAO/WAPOR/2/L1_RET_D') FAO UN 2009-01-01 2023-03-11 -30.15, -39.9953437, 65.13, 40.0044643 False agriculture, fao, wapor, water https://storage.googleapis.com/earthengine-stac/catalog/FAO/FAO_WAPOR_2_L1_RET_D.json https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_RET_D proprietary FAO/WAPOR/2/L1_RET_E WAPOR Daily Reference Evapotranspiration 2.0 image_collection ee.ImageCollection('FAO/WAPOR/2/L1_RET_E') FAO UN 2009-01-01 2023-03-20 -30.15, -39.9953437, 65.13, 40.0044643 False agriculture, fao, wapor, water https://storage.googleapis.com/earthengine-stac/catalog/FAO/FAO_WAPOR_2_L1_RET_E.json https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_RET_E proprietary FAO/WAPOR/2/L1_T_D WAPOR Dekadal Transpiration 2.0 image_collection ee.ImageCollection('FAO/WAPOR/2/L1_T_D') FAO UN 2009-01-01 2023-03-01 -30.0044643, -40.0044644, 65.0044644, 40.0044643 False agriculture, fao, wapor, water https://storage.googleapis.com/earthengine-stac/catalog/FAO/FAO_WAPOR_2_L1_T_D.json https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_T_D proprietary -FIRMS FIRMS: Fire Information for Resource Management System image_collection ee.ImageCollection('FIRMS') NASA / LANCE / EOSDIS 2000-11-01 2024-08-14 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal https://storage.googleapis.com/earthengine-stac/catalog/FIRMS/FIRMS.json https://developers.google.com/earth-engine/datasets/catalog/FIRMS proprietary +FIRMS FIRMS: Fire Information for Resource Management System image_collection ee.ImageCollection('FIRMS') NASA / LANCE / EOSDIS 2000-11-01 2024-08-15 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal https://storage.googleapis.com/earthengine-stac/catalog/FIRMS/FIRMS.json https://developers.google.com/earth-engine/datasets/catalog/FIRMS proprietary FORMA/FORMA_500m FORMA Global Forest Watch Deforestation Alerts, 500m [deprecated] image ee.Image('FORMA/FORMA_500m') Global Forest Watch, World Resources Institute 2006-01-01 2015-06-10 -180, -90, 180, 90 True alerts, deforestation, forest, forma, geophysical, gfw, modis, nasa, wri https://storage.googleapis.com/earthengine-stac/catalog/FORMA/FORMA_FORMA_500m.json https://developers.google.com/earth-engine/datasets/catalog/FORMA_FORMA_500m proprietary Finland/MAVI/VV/50cm Finland NRG NLS orthophotos 50 cm by Mavi image_collection ee.ImageCollection('Finland/MAVI/VV/50cm') NLS orthophotos 2015-01-01 2018-01-01 59, 18, 69.4, 29.2 False falsecolor, finland, mavi, nrg, orthophoto https://storage.googleapis.com/earthengine-stac/catalog/Finland/Finland_MAVI_VV_50cm.json https://developers.google.com/earth-engine/datasets/catalog/Finland_MAVI_VV_50cm CC-BY-4.0 Finland/SMK/V/50cm Finland RGB NLS orthophotos 50 cm by SMK image_collection ee.ImageCollection('Finland/SMK/V/50cm') NLS orthophotos 2015-01-01 2023-01-01 59, 18, 69.4, 29.2 False finland, orthophoto, rgb, smk https://storage.googleapis.com/earthengine-stac/catalog/Finland/Finland_SMK_V_50cm.json https://developers.google.com/earth-engine/datasets/catalog/Finland_SMK_V_50cm proprietary @@ -141,8 +141,8 @@ GLIMS/20230607 GLIMS 2023: Global Land Ice Measurements From Space table ee.Feat GLIMS/current GLIMS Current: Global Land Ice Measurements From Space table ee.FeatureCollection('GLIMS/current') National Snow and Ice Data Center (NSDIC) 1750-01-01 2023-06-07 -180, -90, 180, 90 False glacier, glims, ice, landcover, nasa, nsidc, snow https://storage.googleapis.com/earthengine-stac/catalog/GLIMS/GLIMS_current.json https://developers.google.com/earth-engine/datasets/catalog/GLIMS_current proprietary GLOBAL_FLOOD_DB/MODIS_EVENTS/V1 Global Flood Database v1 (2000-2018) image_collection ee.ImageCollection('GLOBAL_FLOOD_DB/MODIS_EVENTS/V1') Cloud to Street (C2S) / Dartmouth Flood Observatory (DFO) 2000-02-17 2018-12-10 -180, -90, 180, 90 False c2s, cloudtostreet, dartmouth, dfo, flood, gfd, inundation, surface, water https://storage.googleapis.com/earthengine-stac/catalog/GLOBAL_FLOOD_DB/GLOBAL_FLOOD_DB_MODIS_EVENTS_V1.json https://developers.google.com/earth-engine/datasets/catalog/GLOBAL_FLOOD_DB_MODIS_EVENTS_V1 CC-BY-NC-4.0 GOOGLE/AirView/California_Unified_2015_2019 Google Street View Air Quality: High Resolution Air Pollution Mapping in California table ee.FeatureCollection('GOOGLE/AirView/California_Unified_2015_2019') Google / Aclima 2015-05-28 2019-06-07 -180, -90, 180, 90 False air_quality, nitrogen_dioxide, pollution https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_AirView_California_Unified_2015_2019.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_AirView_California_Unified_2015_2019 CC-BY-NC-4.0 -GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED Cloud Score+ S2_HARMONIZED V1 image_collection ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED') Google Earth Engine 2015-06-27 2024-08-16 -180, -90, 180, 90 False google, cloud, sentinel2_derived https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED CC-BY-4.0 -GOOGLE/DYNAMICWORLD/V1 Dynamic World V1 image_collection ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1') World Resources Institute 2015-06-27 2024-08-16 -180, -90, 180, 90 False global, google, landcover, landuse, nrt, sentinel2_derived https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_DYNAMICWORLD_V1.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1 CC-BY-4.0 +GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED Cloud Score+ S2_HARMONIZED V1 image_collection ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED') Google Earth Engine 2015-06-27 2024-08-17 -180, -90, 180, 90 False google, cloud, sentinel2_derived https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED CC-BY-4.0 +GOOGLE/DYNAMICWORLD/V1 Dynamic World V1 image_collection ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1') World Resources Institute 2015-06-27 2024-08-17 -180, -90, 180, 90 False global, google, landcover, landuse, nrt, sentinel2_derived https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_DYNAMICWORLD_V1.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1 CC-BY-4.0 GOOGLE/GLOBAL_CCDC/V1 Google Global Landsat-based CCDC Segments (1999-2019) image_collection ee.ImageCollection('GOOGLE/GLOBAL_CCDC/V1') Google 1999-01-01 2020-01-01 -180, -60, 180, 72 False change_detection, google, landcover, landsat_derived, landuse https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_GLOBAL_CCDC_V1.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_GLOBAL_CCDC_V1 CC-BY-4.0 GOOGLE/Research/open-buildings/v1/polygons Open Buildings V1 Polygons [deprecated] table ee.FeatureCollection('GOOGLE/Research/open-buildings/v1/polygons') Google Research - Open Buildings 2021-04-30 2021-04-30 -180, -90, 180, 90 True africa, building, built_up, open_buildings, structure https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_Research_open-buildings_v1_polygons.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_Research_open-buildings_v1_polygons CC-BY-4.0 GOOGLE/Research/open-buildings/v2/polygons Open Buildings V2 Polygons [deprecated] table ee.FeatureCollection('GOOGLE/Research/open-buildings/v2/polygons') Google Research - Open Buildings 2022-08-30 2022-08-30 -180, -90, 180, 90 True africa, asia, building, built_up, open_buildings, south_asia, southeast_asia, structure https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_Research_open-buildings_v2_polygons.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_Research_open-buildings_v2_polygons CC-BY-4.0 @@ -154,10 +154,10 @@ HU_BERLIN/EPFD/V2/polygons European Primary Forest Dataset - Polygons table ee.F HYCOM/GLBu0_08/sea_surface_elevation HYCOM: Hybrid Coordinate Ocean Model, Sea Surface Elevation [deprecated] image_collection ee.ImageCollection('HYCOM/GLBu0_08/sea_surface_elevation') NOPP 1992-10-02 2018-12-09 -180, -80.48, 180, 80.48 True elevation, hycom, nopp, ocean, ssh, water https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_GLBu0_08_sea_surface_elevation.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_GLBu0_08_sea_surface_elevation proprietary HYCOM/GLBu0_08/sea_temp_salinity HYCOM: Hybrid Coordinate Ocean Model, Water Temperature and Salinity [deprecated] image_collection ee.ImageCollection('HYCOM/GLBu0_08/sea_temp_salinity') NOPP 1992-10-02 2018-12-09 -180, -80.48, 180, 80.48 True hycom, nopp, ocean, salinity, sst, water, water_temp https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_GLBu0_08_sea_temp_salinity.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_GLBu0_08_sea_temp_salinity proprietary HYCOM/GLBu0_08/sea_water_velocity HYCOM: Hybrid Coordinate Ocean Model, Water Velocity [deprecated] image_collection ee.ImageCollection('HYCOM/GLBu0_08/sea_water_velocity') NOPP 1992-10-02 2018-12-09 -180, -80.48, 180, 80.48 True hycom, nopp, ocean, velocity, water https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_GLBu0_08_sea_water_velocity.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_GLBu0_08_sea_water_velocity proprietary -HYCOM/sea_surface_elevation HYCOM: Hybrid Coordinate Ocean Model, Sea Surface Elevation image_collection ee.ImageCollection('HYCOM/sea_surface_elevation') NOPP 1992-10-02 2024-08-15 -180, -80.48, 180, 80.48 False elevation, hycom, nopp, ocean, ssh, water https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_surface_elevation.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_surface_elevation proprietary -HYCOM/sea_temp_salinity HYCOM: Hybrid Coordinate Ocean Model, Water Temperature and Salinity image_collection ee.ImageCollection('HYCOM/sea_temp_salinity') NOPP 1992-10-02 2024-08-15 -180, -80.48, 180, 80.48 False hycom, nopp, ocean, salinity, sst, water, water_temp https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_temp_salinity.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_temp_salinity proprietary -HYCOM/sea_water_velocity HYCOM: Hybrid Coordinate Ocean Model, Water Velocity image_collection ee.ImageCollection('HYCOM/sea_water_velocity') NOPP 1992-10-02 2024-08-15 -180, -80.48, 180, 80.48 False hycom, nopp, ocean, velocity, water https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_water_velocity.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_water_velocity proprietary -IDAHO_EPSCOR/GRIDMET GRIDMET: University of Idaho Gridded Surface Meteorological Dataset image_collection ee.ImageCollection('IDAHO_EPSCOR/GRIDMET') University of California Merced 1979-01-01 2024-08-13 -124.9, 24.9, -66.8, 49.6 False climate, fireburning, gridmet, humidity, merced, metdata, nfdrs, precipitation, radiation, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_GRIDMET.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_GRIDMET proprietary +HYCOM/sea_surface_elevation HYCOM: Hybrid Coordinate Ocean Model, Sea Surface Elevation image_collection ee.ImageCollection('HYCOM/sea_surface_elevation') NOPP 1992-10-02 2024-08-16 -180, -80.48, 180, 80.48 False elevation, hycom, nopp, ocean, ssh, water https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_surface_elevation.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_surface_elevation proprietary +HYCOM/sea_temp_salinity HYCOM: Hybrid Coordinate Ocean Model, Water Temperature and Salinity image_collection ee.ImageCollection('HYCOM/sea_temp_salinity') NOPP 1992-10-02 2024-08-16 -180, -80.48, 180, 80.48 False hycom, nopp, ocean, salinity, sst, water, water_temp https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_temp_salinity.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_temp_salinity proprietary +HYCOM/sea_water_velocity HYCOM: Hybrid Coordinate Ocean Model, Water Velocity image_collection ee.ImageCollection('HYCOM/sea_water_velocity') NOPP 1992-10-02 2024-08-16 -180, -80.48, 180, 80.48 False hycom, nopp, ocean, velocity, water https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_water_velocity.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_water_velocity proprietary +IDAHO_EPSCOR/GRIDMET GRIDMET: University of Idaho Gridded Surface Meteorological Dataset image_collection ee.ImageCollection('IDAHO_EPSCOR/GRIDMET') University of California Merced 1979-01-01 2024-08-14 -124.9, 24.9, -66.8, 49.6 False climate, fireburning, gridmet, humidity, merced, metdata, nfdrs, precipitation, radiation, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_GRIDMET.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_GRIDMET proprietary IDAHO_EPSCOR/MACAv2_METDATA MACAv2-METDATA: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global Climate Models image_collection ee.ImageCollection('IDAHO_EPSCOR/MACAv2_METDATA') University of California Merced 1900-01-01 2100-12-31 -124.9, 24.9, -67, 49.6 False climate, conus, geophysical, idaho, maca, monthly https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_MACAv2_METDATA.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_MACAv2_METDATA CC0-1.0 IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY MACAv2-METDATA Monthly Summaries: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global Climate Models image_collection ee.ImageCollection('IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY') University of California Merced 1900-01-01 2099-12-31 -124.9, 24.9, -67, 49.6 False climate, conus, geophysical, idaho, maca, monthly https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_MACAv2_METDATA_MONTHLY.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_MACAv2_METDATA_MONTHLY CC0-1.0 IDAHO_EPSCOR/PDSI PDSI: University of Idaho Palmer Drought Severity Index [deprecated] image_collection ee.ImageCollection('IDAHO_EPSCOR/PDSI') University of California Merced 1979-03-01 2020-06-20 -124.9, 24.9, -66.8, 49.6 True climate, conus, crop, drought, geophysical, merced, palmer, pdsi https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_PDSI.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_PDSI proprietary @@ -208,10 +208,10 @@ JAXA/GCOM-C/L3/OCEAN/CHLA/V3 GCOM-C/SGLI L3 Chlorophyll-a Concentration (V3) ima JAXA/GCOM-C/L3/OCEAN/SST/V1 GCOM-C/SGLI L3 Sea Surface Temperature (V1) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V1') Global Change Observation Mission (GCOM) 2018-01-01 2020-06-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V1.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V1 proprietary JAXA/GCOM-C/L3/OCEAN/SST/V2 GCOM-C/SGLI L3 Sea Surface Temperature (V2) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V2') Global Change Observation Mission (GCOM) 2018-01-01 2021-11-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V2.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V2 proprietary JAXA/GCOM-C/L3/OCEAN/SST/V3 GCOM-C/SGLI L3 Sea Surface Temperature (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V3') Global Change Observation Mission (GCOM) 2021-11-29 2024-08-12 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V3 proprietary -JAXA/GPM_L3/GSMaP/v6/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V6 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/operational') JAXA Earth Observation Research Center 2014-03-01 2024-08-15 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v6_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_operational proprietary +JAXA/GPM_L3/GSMaP/v6/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V6 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/operational') JAXA Earth Observation Research Center 2014-03-01 2024-08-16 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v6_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_operational proprietary JAXA/GPM_L3/GSMaP/v6/reanalysis GSMaP Reanalysis: Global Satellite Mapping of Precipitation image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/reanalysis') JAXA Earth Observation Research Center 2000-03-01 2014-03-12 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v6_reanalysis.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_reanalysis proprietary -JAXA/GPM_L3/GSMaP/v7/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V7 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v7/operational') JAXA Earth Observation Research Center 2014-03-01 2024-08-15 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v7_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v7_operational proprietary -JAXA/GPM_L3/GSMaP/v8/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V8 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v8/operational') JAXA Earth Observation Research Center 1998-01-01 2024-08-15 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v8_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v8_operational proprietary +JAXA/GPM_L3/GSMaP/v7/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V7 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v7/operational') JAXA Earth Observation Research Center 2014-03-01 2024-08-16 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v7_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v7_operational proprietary +JAXA/GPM_L3/GSMaP/v8/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V8 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v8/operational') JAXA Earth Observation Research Center 1998-01-01 2024-08-16 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v8_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v8_operational proprietary JCU/Murray/GIC/global_tidal_wetland_change/2019 Murray Global Tidal Wetland Change v1.0 (1999-2019) image ee.Image('JCU/Murray/GIC/global_tidal_wetland_change/2019') Murray/JCU 1999-01-01 2019-12-31 -180, -90, 180, 90 False coastal, ecosystem, intertidal, landsat_derived, mangrove, murray, saltmarsh, tidal_flat, tidal_marsh https://storage.googleapis.com/earthengine-stac/catalog/JCU/JCU_Murray_GIC_global_tidal_wetland_change_2019.json https://developers.google.com/earth-engine/datasets/catalog/JCU_Murray_GIC_global_tidal_wetland_change_2019 CC-BY-4.0 JRC/D5/EUCROPMAP/V1 EUCROPMAP image_collection ee.ImageCollection('JRC/D5/EUCROPMAP/V1') Joint Research Center (JRC) 2018-01-01 2022-01-01 -16.171875, 34.313433, 36.386719, 72.182526 False crop, eu, jrc, lucas, sentinel1_derived https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_D5_EUCROPMAP_V1.json https://developers.google.com/earth-engine/datasets/catalog/JRC_D5_EUCROPMAP_V1 CC-BY-4.0 JRC/GFC2020/V1 EC JRC global map of forest cover 2020, V1 image_collection ee.ImageCollection('JRC/GFC2020/V1') Joint Research Centre, European Commission 2020-12-31 2020-12-31 -180, -90, 180, 90 False eudr, forest, jrc https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GFC2020_V1.json https://developers.google.com/earth-engine/datasets/catalog/JRC_GFC2020_V1 proprietary @@ -292,18 +292,18 @@ LANDSAT/GLS2005_L5 Landsat Global Land Survey 2005, Landsat 5 scenes image_colle LANDSAT/GLS2005_L7 Landsat Global Land Survey 2005, Landsat 7 scenes image_collection ee.ImageCollection('LANDSAT/GLS2005_L7') USGS 2003-07-29 2008-07-29 -180, -90, 180, 90 False etm, gls, l7, landsat, radiance, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_GLS2005_L7.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS2005_L7 PDDL-1.0 LANDSAT/LC08/C02/T1 USGS Landsat 8 Collection 2 Tier 1 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1') USGS 2013-03-18 2024-08-12 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, oli_tirs, radiance, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1 PDDL-1.0 LANDSAT/LC08/C02/T1_L2 USGS Landsat 8 Level 2, Collection 2, Tier 1 image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') USGS 2013-03-18 2024-08-12 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l8sr, landsat, lasrc, lc08, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2 proprietary -LANDSAT/LC08/C02/T1_RT USGS Landsat 8 Collection 2 Tier 1 and Real-Time data Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_RT') USGS 2013-03-18 2024-08-16 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, nrt, oli_tirs, radiance, rt, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_RT.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT PDDL-1.0 -LANDSAT/LC08/C02/T1_RT_TOA USGS Landsat 8 Collection 2 Tier 1 and Real-Time data TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_RT_TOA') USGS/Google 2013-03-18 2024-08-16 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_RT_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT_TOA PDDL-1.0 +LANDSAT/LC08/C02/T1_RT USGS Landsat 8 Collection 2 Tier 1 and Real-Time data Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_RT') USGS 2013-03-18 2024-08-17 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, nrt, oli_tirs, radiance, rt, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_RT.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT PDDL-1.0 +LANDSAT/LC08/C02/T1_RT_TOA USGS Landsat 8 Collection 2 Tier 1 and Real-Time data TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_RT_TOA') USGS/Google 2013-03-18 2024-08-17 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_RT_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT_TOA PDDL-1.0 LANDSAT/LC08/C02/T1_TOA USGS Landsat 8 Collection 2 Tier 1 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA') USGS/Google 2013-03-18 2024-08-12 -180, -90, 180, 90 False c2, global, landsat, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_TOA PDDL-1.0 LANDSAT/LC08/C02/T2 USGS Landsat 8 Collection 2 Tier 2 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC08/C02/T2') USGS 2021-10-28 2024-08-12 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, oli_tirs, radiance, t2, tier2, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T2 PDDL-1.0 LANDSAT/LC08/C02/T2_L2 USGS Landsat 8 Level 2, Collection 2, Tier 2 image_collection ee.ImageCollection('LANDSAT/LC08/C02/T2_L2') USGS 2013-03-18 2024-08-12 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l8sr, landsat, lasrc, lc08, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T2_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T2_L2 proprietary LANDSAT/LC08/C02/T2_TOA USGS Landsat 8 Collection 2 Tier 2 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC08/C02/T2_TOA') USGS/Google 2021-10-28 2024-08-12 -180, -90, 180, 90 False c2, global, landsat, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T2_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T2_TOA PDDL-1.0 -LANDSAT/LC09/C02/T1 USGS Landsat 9 Collection 2 Tier 1 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1') USGS 2021-10-31 2024-08-16 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, oli_tirs, radiance, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1 PDDL-1.0 -LANDSAT/LC09/C02/T1_L2 USGS Landsat 9 Level 2, Collection 2, Tier 1 image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1_L2') USGS 2021-10-31 2024-08-14 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_L2 proprietary -LANDSAT/LC09/C02/T1_TOA USGS Landsat 9 Collection 2 Tier 1 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1_TOA') USGS/Google 2021-10-31 2024-08-16 -180, -90, 180, 90 False c2, global, landsat, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_TOA PDDL-1.0 -LANDSAT/LC09/C02/T2 USGS Landsat 9 Collection 2 Tier 2 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2') USGS 2021-11-02 2024-08-16 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, oli_tirs, radiance, t2, tier2, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2 PDDL-1.0 -LANDSAT/LC09/C02/T2_L2 USGS Landsat 9 Level 2, Collection 2, Tier 2 image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2_L2') USGS 2021-10-31 2024-08-14 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2_L2 proprietary -LANDSAT/LC09/C02/T2_TOA USGS Landsat 9 Collection 2 Tier 2 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2_TOA') USGS/Google 2021-11-02 2024-08-15 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2_TOA PDDL-1.0 +LANDSAT/LC09/C02/T1 USGS Landsat 9 Collection 2 Tier 1 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1') USGS 2021-10-31 2024-08-17 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, oli_tirs, radiance, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1 PDDL-1.0 +LANDSAT/LC09/C02/T1_L2 USGS Landsat 9 Level 2, Collection 2, Tier 1 image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1_L2') USGS 2021-10-31 2024-08-15 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_L2 proprietary +LANDSAT/LC09/C02/T1_TOA USGS Landsat 9 Collection 2 Tier 1 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1_TOA') USGS/Google 2021-10-31 2024-08-17 -180, -90, 180, 90 False c2, global, landsat, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_TOA PDDL-1.0 +LANDSAT/LC09/C02/T2 USGS Landsat 9 Collection 2 Tier 2 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2') USGS 2021-11-02 2024-08-17 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, oli_tirs, radiance, t2, tier2, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2 PDDL-1.0 +LANDSAT/LC09/C02/T2_L2 USGS Landsat 9 Level 2, Collection 2, Tier 2 image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2_L2') USGS 2021-10-31 2024-08-15 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2_L2 proprietary +LANDSAT/LC09/C02/T2_TOA USGS Landsat 9 Collection 2 Tier 2 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2_TOA') USGS/Google 2021-11-02 2024-08-17 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2_TOA PDDL-1.0 LANDSAT/LE07/C02/T1 USGS Landsat 7 Collection 2 Tier 1 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LE07/C02/T1') USGS 1999-05-28 2024-01-19 -180, -90, 180, 90 False c2, etm, global, l7, landsat, le7, radiance, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LE07_C02_T1.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1 PDDL-1.0 LANDSAT/LE07/C02/T1_L2 USGS Landsat 7 Level 2, Collection 2, Tier 1 image_collection ee.ImageCollection('LANDSAT/LE07/C02/T1_L2') USGS 1999-05-28 2024-01-19 -180, -90, 180, 90 False cfmask, cloud, etm, fmask, global, landsat, lasrc, le07, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LE07_C02_T1_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_L2 proprietary LANDSAT/LE07/C02/T1_RT USGS Landsat 7 Collection 2 Tier 1 and Real-Time data Raw Scenes image_collection ee.ImageCollection('LANDSAT/LE07/C02/T1_RT') USGS 1999-05-28 2024-01-19 -180, -90, 180, 90 False c2, etm, global, l7, landsat, le7, nrt, radiance, rt, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LE07_C02_T1_RT.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_RT PDDL-1.0 @@ -406,8 +406,8 @@ MODIS/061/MCD12Q2 MCD12Q2.006 Land Cover Dynamics Yearly Global 500m image_colle MODIS/061/MCD15A3H MCD15A3H.061 MODIS Leaf Area Index/FPAR 4-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MCD15A3H') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-08-08 -180, -90, 180, 90 False 4_day, fpar, global, lai, mcd15a3h, modis, nasa, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD15A3H.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD15A3H proprietary MODIS/061/MCD18A1 MCD18A1.061 Surface Radiation Daily/3-Hour image_collection ee.ImageCollection('MODIS/061/MCD18A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-06-01 -180, -90, 180, 90 False par, radiation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD18A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD18A1 proprietary MODIS/061/MCD18C2 MCD18C2.061 Photosynthetically Active Radiation Daily 3-Hour image_collection ee.ImageCollection('MODIS/061/MCD18C2') NASA LP DAAC at the USGS EROS Center 2002-02-24 2024-06-01 -180, -90, 180, 90 False par, radiation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD18C2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD18C2 proprietary -MODIS/061/MCD19A1_GRANULES MCD19A1.061: Land Surface BRF Daily L2G Global 500m and 1km image_collection ee.ImageCollection('MODIS/061/MCD19A1_GRANULES') NASA LP DAAC at the USGS EROS Center 2000-12-21 2024-08-06 -180, -90, 180, 90 False aerosol, aod, aqua, daily, global, maiac, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A1_GRANULES.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A1_GRANULES proprietary -MODIS/061/MCD19A2_GRANULES MCD19A2.061: Terra & Aqua MAIAC Land Aerosol Optical Depth Daily 1km image_collection ee.ImageCollection('MODIS/061/MCD19A2_GRANULES') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-06 -180, -90, 180, 90 False aerosol, aod, aqua, daily, global, maiac, mcd19a2, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A2_GRANULES.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A2_GRANULES proprietary +MODIS/061/MCD19A1_GRANULES MCD19A1.061: Land Surface BRF Daily L2G Global 500m and 1km image_collection ee.ImageCollection('MODIS/061/MCD19A1_GRANULES') NASA LP DAAC at the USGS EROS Center 2000-12-21 2024-08-13 -180, -90, 180, 90 False aerosol, aod, aqua, daily, global, maiac, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A1_GRANULES.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A1_GRANULES proprietary +MODIS/061/MCD19A2_GRANULES MCD19A2.061: Terra & Aqua MAIAC Land Aerosol Optical Depth Daily 1km image_collection ee.ImageCollection('MODIS/061/MCD19A2_GRANULES') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-13 -180, -90, 180, 90 False aerosol, aod, aqua, daily, global, maiac, mcd19a2, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A2_GRANULES.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A2_GRANULES proprietary MODIS/061/MCD43A1 MCD43A1.061 MODIS BRDF-Albedo Model Parameters Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-06 -180, -90, 180, 90 False albedo, brdf, daily, global, mcd43a1, modis, nasa, reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A1 proprietary MODIS/061/MCD43A2 MCD43A2.061 MODIS BRDF-Albedo Quality Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A2') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-06 -180, -90, 180, 90 False albedo, brdf, daily, global, modis, nasa, quality, reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A2 proprietary MODIS/061/MCD43A3 MCD43A3.061 MODIS Albedo Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A3') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-06 -180, -90, 180, 90 False albedo, black_sky, daily, global, modis, nasa, usgs, white_sky https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A3 proprietary @@ -420,7 +420,7 @@ MODIS/061/MOD09CMG MOD09CMG.061 Terra Surface Reflectance Daily L3 Global 0.05 D MODIS/061/MOD09GA MOD09GA.061 Terra Surface Reflectance Daily Global 1km and 500m image_collection ee.ImageCollection('MODIS/061/MOD09GA') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-13 -180, -90, 180, 90 False daily, global, mod09ga, modis, nasa, sr, surface_reflectance, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09GA.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09GA proprietary MODIS/061/MOD09GQ MOD09GQ.061 Terra Surface Reflectance Daily Global 250m image_collection ee.ImageCollection('MODIS/061/MOD09GQ') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-13 -180, -90, 180, 90 False daily, global, mod09gq, modis, nasa, sr, surface_reflectance, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09GQ.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09GQ proprietary MODIS/061/MOD09Q1 MOD09Q1.061 Terra Surface Reflectance 8-Day Global 250m image_collection ee.ImageCollection('MODIS/061/MOD09Q1') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-08-04 -180, -90, 180, 90 False 8_day, global, mod09q1, modis, nasa, sr, surface_reflectance, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09Q1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09Q1 proprietary -MODIS/061/MOD10A1 MOD10A1.061 Terra Snow Cover Daily Global 500m image_collection ee.ImageCollection('MODIS/061/MOD10A1') NASA NSIDC DAAC at CIRES 2000-02-24 2024-08-13 -180, -90, 180, 90 False albedo, daily, geophysical, global, mod10a1, modis, nasa, nsidc, snow, terra https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD10A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD10A1 proprietary +MODIS/061/MOD10A1 MOD10A1.061 Terra Snow Cover Daily Global 500m image_collection ee.ImageCollection('MODIS/061/MOD10A1') NASA NSIDC DAAC at CIRES 2000-02-24 2024-08-15 -180, -90, 180, 90 False albedo, daily, geophysical, global, mod10a1, modis, nasa, nsidc, snow, terra https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD10A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD10A1 proprietary MODIS/061/MOD11A1 MOD11A1.061 Terra Land Surface Temperature and Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD11A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-13 -180, -90, 180, 90 False daily, emissivity, global, lst, mod11a1, modis, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A1 proprietary MODIS/061/MOD11A2 MOD11A2.061 Terra Land Surface Temperature and Emissivity 8-Day Global 1km image_collection ee.ImageCollection('MODIS/061/MOD11A2') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-08-04 -180, -90, 180, 90 False 8_day, emissivity, global, lst, mod11a2, modis, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A2 proprietary MODIS/061/MOD13A1 MOD13A1.061 Terra Vegetation Indices 16-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD13A1') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-07-27 -180, -90, 180, 90 False 16_day, evi, global, mod13a1, modis, nasa, ndvi, terra, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13A1 proprietary @@ -437,8 +437,8 @@ MODIS/061/MOD17A2H MOD17A2H.061: Terra Gross Primary Productivity 8-Day Global 5 MODIS/061/MOD17A2HGF MOD17A2HGF.061: Terra Gross Primary Productivity 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD17A2HGF') NASA LP DAAC at the USGS EROS Center 2021-01-01 2023-12-27 -180, -90, 180, 90 False 8_day, global, gpp, modis, nasa, photosynthesis, productivity, psn, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD17A2HGF.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD17A2HGF proprietary MODIS/061/MOD17A3HGF MOD17A3HGF.061: Terra Net Primary Production Gap-Filled Yearly Global 500m image_collection ee.ImageCollection('MODIS/061/MOD17A3HGF') NASA LP DAAC at the USGS EROS Center 2001-01-01 2023-01-01 -180, -90, 180, 90 False global, gpp, nasa, npp, photosynthesis, productivity, psn, terra, usgs, yearly https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD17A3HGF.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD17A3HGF proprietary MODIS/061/MOD21A1D MOD21A1D.061 Terra Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD21A1D') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-13 -180, -90, 180, 90 False daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21A1D.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21A1D proprietary -MODIS/061/MOD21A1N MOD21A1N.061 Terra Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD21A1N') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-13 -180, -90, 180, 90 False daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21A1N.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21A1N proprietary -MODIS/061/MOD21C1 MOD21C1.061 Terra Land Surface Temperature and 3-Band Emissivity Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MOD21C1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-13 -180, -90, 180, 90 False daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21C1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21C1 proprietary +MODIS/061/MOD21A1N MOD21A1N.061 Terra Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD21A1N') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-15 -180, -90, 180, 90 False daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21A1N.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21A1N proprietary +MODIS/061/MOD21C1 MOD21C1.061 Terra Land Surface Temperature and 3-Band Emissivity Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MOD21C1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-15 -180, -90, 180, 90 False daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21C1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21C1 proprietary MODIS/061/MOD21C2 MOD21C2.061 Terra Land Surface Temperature and 3-Band Emissivity 8-Day L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MOD21C2') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-04 -180, -90, 180, 90 False emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21C2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21C2 proprietary MODIS/061/MOD21C3 MOD21C3.061 Terra Land Surface Temperature and 3-Band Emissivity Monthly L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MOD21C3') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-06-01 -180, -90, 180, 90 False emissivity, global, lst, monthly, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21C3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21C3 proprietary MODIS/061/MYD08_M3 MYD08_M3.061 Aqua Atmosphere Monthly Global Product image_collection ee.ImageCollection('MODIS/061/MYD08_M3') NASA LAADS DAAC at NASA Goddard Space Flight Center 2002-07-01 2024-07-01 -180, -90, 180, 90 False aqua, atmosphere, geophysical, global, modis, monthly, myd08, myd08_m3, nasa, temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD08_M3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD08_M3 proprietary @@ -447,7 +447,7 @@ MODIS/061/MYD09CMG MYD09CMG.061 Aqua Surface Reflectance Daily L3 Global 0.05 De MODIS/061/MYD09GA MYD09GA.061 Aqua Surface Reflectance Daily Global 1km and 500m image_collection ee.ImageCollection('MODIS/061/MYD09GA') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-08-13 -180, -90, 180, 90 False aqua, daily, global, modis, myd09ga, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GA.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09GA proprietary MODIS/061/MYD09GQ MYD09GQ.061 Aqua Surface Reflectance Daily Global 250m image_collection ee.ImageCollection('MODIS/061/MYD09GQ') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-08-13 -180, -90, 180, 90 False aqua, daily, global, modis, myd09gq, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GQ.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09GQ proprietary MODIS/061/MYD09Q1 MYD09Q1.061 Aqua Surface Reflectance 8-Day Global 250m image_collection ee.ImageCollection('MODIS/061/MYD09Q1') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-08-04 -180, -90, 180, 90 False 8_day, aqua, global, modis, myd09q1, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09Q1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09Q1 proprietary -MODIS/061/MYD10A1 MYD10A1.061 Aqua Snow Cover Daily Global 500m image_collection ee.ImageCollection('MODIS/061/MYD10A1') NASA NSIDC DAAC at CIRES 2002-07-04 2024-08-13 -180, -90, 180, 90 False albedo, aqua, daily, geophysical, global, modis, myd10a1, nasa, nsidc, snow https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD10A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD10A1 proprietary +MODIS/061/MYD10A1 MYD10A1.061 Aqua Snow Cover Daily Global 500m image_collection ee.ImageCollection('MODIS/061/MYD10A1') NASA NSIDC DAAC at CIRES 2002-07-04 2024-08-15 -180, -90, 180, 90 False albedo, aqua, daily, geophysical, global, modis, myd10a1, nasa, nsidc, snow https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD10A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD10A1 proprietary MODIS/061/MYD11A1 MYD11A1.061 Aqua Land Surface Temperature and Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MYD11A1') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-08-13 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, modis, myd11a1, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD11A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD11A1 proprietary MODIS/061/MYD11A2 MYD11A2.061 Aqua Land Surface Temperature and Emissivity 8-Day Global 1km image_collection ee.ImageCollection('MODIS/061/MYD11A2') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-08-04 -180, -90, 180, 90 False 8_day, aqua, emissivity, global, lst, modis, myd11a2, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD11A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD11A2 proprietary MODIS/061/MYD13A1 MYD13A1.061 Aqua Vegetation Indices 16-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MYD13A1') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-07-19 -180, -90, 180, 90 False 16_day, aqua, evi, global, modis, myd13a1, nasa, ndvi, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD13A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD13A1 proprietary @@ -461,7 +461,7 @@ MODIS/061/MYD15A2H MYD15A2H.061: Aqua Leaf Area Index/FPAR 8-Day Global 500m ima MODIS/061/MYD17A2H MYD17A2H.061: Aqua Gross Primary Productivity 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MYD17A2H') NASA LP DAAC at the USGS EROS Center 2021-01-01 2024-08-04 -180, -90, 180, 90 False 8_day, aqua, global, gpp, modis, myd17a2, nasa, photosynthesis, productivity, psn, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD17A2H.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD17A2H proprietary MODIS/061/MYD17A3HGF MYD17A3HGF.061: Aqua Net Primary Production Gap-Filled Yearly Global 500m image_collection ee.ImageCollection('MODIS/061/MYD17A3HGF') NASA LP DAAC at the USGS EROS Center 2001-01-01 2023-01-01 -180, -90, 180, 90 False aqua, global, gpp, nasa, npp, photosynthesis, productivity, psn, usgs, yearly https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD17A3HGF.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD17A3HGF proprietary MODIS/061/MYD21A1D MYD21A1D.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MYD21A1D') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-13 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21A1D.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21A1D proprietary -MODIS/061/MYD21A1N MYD21A1N.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MYD21A1N') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-13 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21A1N.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21A1N proprietary +MODIS/061/MYD21A1N MYD21A1N.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MYD21A1N') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-14 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21A1N.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21A1N proprietary MODIS/061/MYD21C1 MYD21C1.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MYD21C1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-13 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21C1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21C1 proprietary MODIS/061/MYD21C2 MYD21C2.061 Aqua Land Surface Temperature and 3-Band Emissivity 8-Day L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MYD21C2') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-08-04 -180, -90, 180, 90 False aqua, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21C2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21C2 proprietary MODIS/061/MYD21C3 MYD21C3.061 Aqua Land Surface Temperature and 3-Band Emissivity Monthly L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MYD21C3') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-06-01 -180, -90, 180, 90 False aqua, emissivity, global, lst, monthly, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21C3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21C3 proprietary @@ -519,15 +519,15 @@ MODIS/MYD13A1 MYD13A1.005 Vegetation Indices 16-Day L3 Global 500m [deprecated] MODIS/MYD13Q1 MYD13Q1.005 Vegetation Indices 16-Day Global 250m [deprecated] image_collection ee.ImageCollection('MODIS/MYD13Q1') NASA LP DAAC at the USGS EROS Center 2002-07-04 2017-03-14 -180, -90, 180, 90 True 16_day, aqua, evi, global, modis, myd13q1, ndvi, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MYD13Q1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD13Q1 proprietary MODIS/NTSG/MOD16A2/105 MOD16A2: MODIS Global Terrestrial Evapotranspiration 8-Day Global 1km image_collection ee.ImageCollection('MODIS/NTSG/MOD16A2/105') Numerical Terradynamic Simulation Group, The University of Montana 2000-01-01 2014-12-27 -180, -90, 180, 90 False 8_day, evapotranspiration, global, mod16a2, modis https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_NTSG_MOD16A2_105.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_NTSG_MOD16A2_105 proprietary NASA/ASTER_GED/AG100_003 AG100: ASTER Global Emissivity Dataset 100-meter V003 image ee.Image('NASA/ASTER_GED/AG100_003') NASA LP DAAC at the USGS EROS Center 2000-01-01 2008-12-31 -180, -59, 180, 80 False aster, caltech, elevation, emissivity, ged, geophysical, infrared, jpl, lst, nasa, ndvi, temperature, thermal https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_ASTER_GED_AG100_003.json https://developers.google.com/earth-engine/datasets/catalog/NASA_ASTER_GED_AG100_003 proprietary -NASA/EMIT/L1B/RAD EMIT L1B At-Sensor Calibrated Radiance and Geolocation Data 60 m image_collection ee.ImageCollection('NASA/EMIT/L1B/RAD') NASA Jet Propulsion Laboratory 2022-08-09 2024-08-15 -180, -90, 180, 90 False daily, emit, nasa, radiance https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_EMIT_L1B_RAD.json https://developers.google.com/earth-engine/datasets/catalog/NASA_EMIT_L1B_RAD proprietary +NASA/EMIT/L1B/RAD EMIT L1B At-Sensor Calibrated Radiance and Geolocation Data 60 m image_collection ee.ImageCollection('NASA/EMIT/L1B/RAD') NASA Jet Propulsion Laboratory 2022-08-09 2024-08-16 -180, -90, 180, 90 False daily, emit, nasa, radiance https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_EMIT_L1B_RAD.json https://developers.google.com/earth-engine/datasets/catalog/NASA_EMIT_L1B_RAD proprietary NASA/EMIT/L2B/CH4ENH Earth Surface Mineral Dust Source Investigation- Methane Enhancement image_collection ee.ImageCollection('NASA/EMIT/L2B/CH4ENH') NASA Jet Propulsion Laboratory 2022-08-10 2024-07-20 -180, -90, 180, 90 False daily, emit, nasa, methane https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_EMIT_L2B_CH4ENH.json https://developers.google.com/earth-engine/datasets/catalog/NASA_EMIT_L2B_CH4ENH proprietary NASA/EMIT/L2B/CH4PLM Earth Surface Mineral Dust Source Investigation- Methane Plume Complexes image_collection ee.ImageCollection('NASA/EMIT/L2B/CH4PLM') NASA Jet Propulsion Laboratory 2022-08-10 2024-07-03 -180, -90, 180, 90 False daily, emit, nasa, methane https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_EMIT_L2B_CH4PLM.json https://developers.google.com/earth-engine/datasets/catalog/NASA_EMIT_L2B_CH4PLM proprietary NASA/FLDAS/NOAH01/C/GL/M/V001 FLDAS: Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System image_collection ee.ImageCollection('NASA/FLDAS/NOAH01/C/GL/M/V001') NASA GES DISC at NASA Goddard Space Flight Center 1982-01-01 2024-06-01 -180, -60, 180, 90 False climate, evapotranspiration, famine, fldas, humidity, ldas, monthly, nasa, runoff, snow, soil_moisture, soil_temperature, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_FLDAS_NOAH01_C_GL_M_V001.json https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001 proprietary NASA/GDDP-CMIP6 NEX-GDDP-CMIP6: NASA Earth Exchange Global Daily Downscaled Climate Projections image_collection ee.ImageCollection('NASA/GDDP-CMIP6') NASA / Climate Analytics Group 1950-01-01 2100-12-31 -180, -90, 180, 90 False cag, climate, gddp, geophysical, nasa, nex, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GDDP-CMIP6.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GDDP-CMIP6 various -NASA/GEOS-CF/v1/fcst/htf GEOS-CF fcst htf v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/fcst/htf') NASA / GMAO 2022-10-01 2024-08-14 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_fcst_htf.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_htf proprietary -NASA/GEOS-CF/v1/fcst/tavg1hr GEOS-CF fcst tavg1hr v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/fcst/tavg1hr') NASA / GMAO 2022-10-01 2024-08-14 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_fcst_tavg1hr.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_tavg1hr proprietary -NASA/GEOS-CF/v1/rpl/htf GEOS-CF rpl htf v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/rpl/htf') NASA / GMAO 2018-01-01 2024-08-14 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_rpl_htf.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_htf proprietary -NASA/GEOS-CF/v1/rpl/tavg1hr GEOS-CF rpl tavg1hr v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/rpl/tavg1hr') NASA / GMAO 2018-01-01 2024-08-14 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_rpl_tavg1hr.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_tavg1hr proprietary +NASA/GEOS-CF/v1/fcst/htf GEOS-CF fcst htf v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/fcst/htf') NASA / GMAO 2022-10-01 2024-08-15 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_fcst_htf.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_htf proprietary +NASA/GEOS-CF/v1/fcst/tavg1hr GEOS-CF fcst tavg1hr v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/fcst/tavg1hr') NASA / GMAO 2022-10-01 2024-08-15 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_fcst_tavg1hr.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_tavg1hr proprietary +NASA/GEOS-CF/v1/rpl/htf GEOS-CF rpl htf v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/rpl/htf') NASA / GMAO 2018-01-01 2024-08-15 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_rpl_htf.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_htf proprietary +NASA/GEOS-CF/v1/rpl/tavg1hr GEOS-CF rpl tavg1hr v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/rpl/tavg1hr') NASA / GMAO 2018-01-01 2024-08-15 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_rpl_tavg1hr.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_tavg1hr proprietary NASA/GIMMS/3GV0 GIMMS NDVI From AVHRR Sensors (3rd Generation) image_collection ee.ImageCollection('NASA/GIMMS/3GV0') NASA/NOAA 1981-07-01 2013-12-16 -180, -90, 180, 90 False avhrr, gimms, nasa, ndvi, noaa, vegetation https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GIMMS_3GV0.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GIMMS_3GV0 proprietary NASA/GLDAS/V021/NOAH/G025/T3H GLDAS-2.1: Global Land Data Assimilation System image_collection ee.ImageCollection('NASA/GLDAS/V021/NOAH/G025/T3H') NASA GES DISC at NASA Goddard Space Flight Center 2000-01-01 2024-07-16 -180, -90, 180, 90 False 3_hourly, climate, evaporation, forcing, geophysical, gldas, humidity, ldas, nasa, precipitation, pressure, radiation, soil, soil_moisture, surface, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GLDAS_V021_NOAH_G025_T3H.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V021_NOAH_G025_T3H proprietary NASA/GLDAS/V022/CLSM/G025/DA1D GLDAS-2.2: Global Land Data Assimilation System image_collection ee.ImageCollection('NASA/GLDAS/V022/CLSM/G025/DA1D') NASA GES DISC at NASA Goddard Earth Sciences Data and Information Services Center 2003-01-01 2024-03-31 -180, -90, 180, 90 False 3_hourly, climate, evaporation, forcing, geophysical, gldas, humidity, ldas, nasa, precipitation, pressure, radiation, soil, soil_moisture, surface, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GLDAS_V022_CLSM_G025_DA1D.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V022_CLSM_G025_DA1D proprietary @@ -551,10 +551,10 @@ NASA/GSFC/MERRA/flx/2 MERRA-2 M2T1NXFLX: Surface Flux Diagnostics V5.12.4 image_ NASA/GSFC/MERRA/lnd/2 MERRA-2 M2T1NXLND: Land Surface Diagnostics V5.12.4 image_collection ee.ImageCollection('NASA/GSFC/MERRA/lnd/2') NASA/MERRA 1980-01-01 2024-07-01 -180, -90, 180, 90 False evaporation, ice, merra, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GSFC_MERRA_lnd_2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GSFC_MERRA_lnd_2 proprietary NASA/GSFC/MERRA/rad/2 MERRA-2 M2T1NXRAD: Radiation Diagnostics V5.12.4 image_collection ee.ImageCollection('NASA/GSFC/MERRA/rad/2') NASA/MERRA 1980-01-01 2024-07-01 -180, -90, 180, 90 False albedo, emissivity, merra, shortwave, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GSFC_MERRA_rad_2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GSFC_MERRA_rad_2 proprietary NASA/GSFC/MERRA/slv/2 MERRA-2 M2T1NXSLV: Single-Level Diagnostics V5.12.4 image_collection ee.ImageCollection('NASA/GSFC/MERRA/slv/2') NASA/MERRA 1980-01-01 2024-07-01 -180, -90, 180, 90 False condensation, humidity, merra, nasa, omega, pressure, slv, temperature, vapor, water, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GSFC_MERRA_slv_2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GSFC_MERRA_slv_2 proprietary -NASA/HLS/HLSL30/v002 HLSL30: HLS-2 Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m image_collection ee.ImageCollection('NASA/HLS/HLSL30/v002') NASA LP DAAC 2013-04-11 2024-08-13 -180, -90, 180, 90 False landsat, nasa, sentinel, usgs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_HLS_HLSL30_v002.json https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSL30_v002 proprietary +NASA/HLS/HLSL30/v002 HLSL30: HLS-2 Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m image_collection ee.ImageCollection('NASA/HLS/HLSL30/v002') NASA LP DAAC 2013-04-11 2024-08-14 -180, -90, 180, 90 False landsat, nasa, sentinel, usgs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_HLS_HLSL30_v002.json https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSL30_v002 proprietary NASA/JPL/global_forest_canopy_height_2005 Global Forest Canopy Height, 2005 image ee.Image('NASA/JPL/global_forest_canopy_height_2005') NASA/JPL 2005-05-20 2005-06-23 -180, -90, 180, 90 False canopy, forest, geophysical, glas, jpl, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_JPL_global_forest_canopy_height_2005.json https://developers.google.com/earth-engine/datasets/catalog/NASA_JPL_global_forest_canopy_height_2005 proprietary -NASA/LANCE/NOAA20_VIIRS/C2 VJ114IMGTDL_NRT Daily Raster: VIIRS (NOAA-20) Band 375m Active Fire image_collection ee.ImageCollection('NASA/LANCE/NOAA20_VIIRS/C2') NASA / LANCE / NOAA20_VIIRS 2023-10-08 2024-08-14 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_LANCE_NOAA20_VIIRS_C2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_LANCE_NOAA20_VIIRS_C2 proprietary -NASA/LANCE/SNPP_VIIRS/C2 VNP14IMGTDL_NRT Daily Raster: VIIRS (S-NPP) Band 375m Active Fire image_collection ee.ImageCollection('NASA/LANCE/SNPP_VIIRS/C2') NASA / LANCE / SNPP_VIIRS 2023-09-03 2024-08-14 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_LANCE_SNPP_VIIRS_C2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_LANCE_SNPP_VIIRS_C2 proprietary +NASA/LANCE/NOAA20_VIIRS/C2 VJ114IMGTDL_NRT Daily Raster: VIIRS (NOAA-20) Band 375m Active Fire image_collection ee.ImageCollection('NASA/LANCE/NOAA20_VIIRS/C2') NASA / LANCE / NOAA20_VIIRS 2023-10-08 2024-08-15 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_LANCE_NOAA20_VIIRS_C2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_LANCE_NOAA20_VIIRS_C2 proprietary +NASA/LANCE/SNPP_VIIRS/C2 VNP14IMGTDL_NRT Daily Raster: VIIRS (S-NPP) Band 375m Active Fire image_collection ee.ImageCollection('NASA/LANCE/SNPP_VIIRS/C2') NASA / LANCE / SNPP_VIIRS 2023-09-03 2024-08-15 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_LANCE_SNPP_VIIRS_C2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_LANCE_SNPP_VIIRS_C2 proprietary NASA/MEASURES/GFCC/TC/v3 Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30m image_collection ee.ImageCollection('NASA/MEASURES/GFCC/TC/v3') NASA LP DAAC at the USGS EROS Center 2000-01-01 2015-01-01 -180, -90, 180, 90 False forest, glcf, landsat_derived, nasa, umd https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_MEASURES_GFCC_TC_v3.json https://developers.google.com/earth-engine/datasets/catalog/NASA_MEASURES_GFCC_TC_v3 proprietary NASA/NASADEM_HGT/001 NASADEM: NASA NASADEM Digital Elevation 30m image ee.Image('NASA/NASADEM_HGT/001') NASA / USGS / JPL-Caltech 2000-02-11 2000-02-22 -180, -56, 180, 60 False dem, elevation, geophysical, nasa, nasadem, srtm, topography, usgs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NASADEM_HGT_001.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NASADEM_HGT_001 proprietary NASA/NEX-DCP30 NEX-DCP30: NASA Earth Exchange Downscaled Climate Projections image_collection ee.ImageCollection('NASA/NEX-DCP30') NASA / Climate Analytics Group 1950-01-01 2099-12-01 -125.03, 24.07, -66.47, 53.74 False cag, climate, cmip5, geophysical, nasa, nex, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NEX-DCP30.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-DCP30 proprietary @@ -569,16 +569,16 @@ NASA/ORNL/DAYMET_V4 Daymet V4: Daily Surface Weather and Climatological Summarie NASA/ORNL/biomass_carbon_density/v1 Global Aboveground and Belowground Biomass Carbon Density Maps image_collection ee.ImageCollection('NASA/ORNL/biomass_carbon_density/v1') NASA ORNL DAAC at Oak Ridge National Laboratory 2010-01-01 2010-12-31 -180, -61.1, 180, 84 False aboveground, belowground, biomass, carbon, density, forest, nasa, ornl, vegetation https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_ORNL_biomass_carbon_density_v1.json https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_biomass_carbon_density_v1 proprietary NASA/SMAP/SPL3SMP_E/005 SPL3SMP_E.005 SMAP L3 Radiometer Global Daily 9 km Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL3SMP_E/005') Google and NSIDC 2015-03-31 2023-12-03 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL3SMP_E_005.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL3SMP_E_005 proprietary NASA/SMAP/SPL3SMP_E/006 SPL3SMP_E.006 SMAP L3 Radiometer Global Daily 9 km Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL3SMP_E/006') Google and NSIDC 2023-12-04 2024-08-14 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL3SMP_E_006.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL3SMP_E_006 proprietary -NASA/SMAP/SPL4SMGP/007 SPL4SMGP.007 SMAP L4 Global 3-hourly 9-km Surface and Root Zone Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL4SMGP/007') Google and NSIDC 2015-03-31 2024-08-13 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL4SMGP_007.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL4SMGP_007 proprietary -NASA/VIIRS/002/VNP09GA VNP09GA: VIIRS Surface Reflectance Daily 500m and 1km image_collection ee.ImageCollection('NASA/VIIRS/002/VNP09GA') NASA Land SIPS 2012-01-19 2024-08-14 -180, -90, 180, 90 False daily, nasa, noaa, npp, reflectance, sr, viirs, vnp09ga https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP09GA.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP09GA proprietary +NASA/SMAP/SPL4SMGP/007 SPL4SMGP.007 SMAP L4 Global 3-hourly 9-km Surface and Root Zone Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL4SMGP/007') Google and NSIDC 2015-03-31 2024-08-14 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL4SMGP_007.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL4SMGP_007 proprietary +NASA/VIIRS/002/VNP09GA VNP09GA: VIIRS Surface Reflectance Daily 500m and 1km image_collection ee.ImageCollection('NASA/VIIRS/002/VNP09GA') NASA Land SIPS 2012-01-19 2024-08-15 -180, -90, 180, 90 False daily, nasa, noaa, npp, reflectance, sr, viirs, vnp09ga https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP09GA.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP09GA proprietary NASA/VIIRS/002/VNP09H1 VNP09H1: VIIRS Surface Reflectance 8-Day L3 Global 500m image_collection ee.ImageCollection('NASA/VIIRS/002/VNP09H1') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-08-04 -180, -90, 180, 90 False daily, nasa, noaa, npp, reflectance, sr, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP09H1.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP09H1 proprietary NASA/VIIRS/002/VNP15A2H VNP15A2H: LAI/FPAR 8-Day L4 Global 500m SIN Grid image_collection ee.ImageCollection('NASA/VIIRS/002/VNP15A2H') NASA LP DAAC at the USGS EROS Center 2012-01-17 2024-08-04 -180, -90, 180, 90 False land, nasa, noaa, surface, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP15A2H.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP15A2H proprietary NASA_USDA/HSL/SMAP10KM_soil_moisture NASA-USDA Enhanced SMAP Global Soil Moisture Data [deprecated] image_collection ee.ImageCollection('NASA_USDA/HSL/SMAP10KM_soil_moisture') NASA GSFC 2015-04-02 2022-08-02 -180, -60, 180, 90 True geophysical, hsl, nasa, smap, soil, soil_moisture, usda https://storage.googleapis.com/earthengine-stac/catalog/NASA_USDA/NASA_USDA_HSL_SMAP10KM_soil_moisture.json https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_SMAP10KM_soil_moisture proprietary NASA_USDA/HSL/SMAP_soil_moisture NASA-USDA SMAP Global Soil Moisture Data [deprecated] image_collection ee.ImageCollection('NASA_USDA/HSL/SMAP_soil_moisture') NASA GSFC 2015-04-02 2020-12-31 -180, -60, 180, 90 True geophysical, hsl, nasa, smap, soil, soil_moisture, usda https://storage.googleapis.com/earthengine-stac/catalog/NASA_USDA/NASA_USDA_HSL_SMAP_soil_moisture.json https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_SMAP_soil_moisture proprietary NASA_USDA/HSL/soil_moisture NASA-USDA Global Soil Moisture Data [deprecated] image_collection ee.ImageCollection('NASA_USDA/HSL/soil_moisture') NASA GSFC 2010-01-13 2020-12-31 -180, -60, 180, 90 True geophysical, hsl, nasa, smos, soil, soil_moisture, usda https://storage.googleapis.com/earthengine-stac/catalog/NASA_USDA/NASA_USDA_HSL_soil_moisture.json https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_soil_moisture proprietary -NCEP_RE/sea_level_pressure NCEP/NCAR Reanalysis Data, Sea-Level Pressure image_collection ee.ImageCollection('NCEP_RE/sea_level_pressure') NCEP 1948-01-01 2024-08-12 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, pressure, reanalysis https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_sea_level_pressure.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_sea_level_pressure proprietary -NCEP_RE/surface_temp NCEP/NCAR Reanalysis Data, Surface Temperature image_collection ee.ImageCollection('NCEP_RE/surface_temp') NCEP 1948-01-01 2024-08-12 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, reanalysis, temperature https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_surface_temp.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_temp proprietary -NCEP_RE/surface_wv NCEP/NCAR Reanalysis Data, Water Vapor image_collection ee.ImageCollection('NCEP_RE/surface_wv') NCEP 1948-01-01 2024-08-12 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, precipitable, reanalysis, vapor https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_surface_wv.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_wv proprietary +NCEP_RE/sea_level_pressure NCEP/NCAR Reanalysis Data, Sea-Level Pressure image_collection ee.ImageCollection('NCEP_RE/sea_level_pressure') NCEP 1948-01-01 2024-08-13 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, pressure, reanalysis https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_sea_level_pressure.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_sea_level_pressure proprietary +NCEP_RE/surface_temp NCEP/NCAR Reanalysis Data, Surface Temperature image_collection ee.ImageCollection('NCEP_RE/surface_temp') NCEP 1948-01-01 2024-08-13 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, reanalysis, temperature https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_surface_temp.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_temp proprietary +NCEP_RE/surface_wv NCEP/NCAR Reanalysis Data, Water Vapor image_collection ee.ImageCollection('NCEP_RE/surface_wv') NCEP 1948-01-01 2024-08-13 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, precipitable, reanalysis, vapor https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_surface_wv.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_wv proprietary NOAA/CDR/ATMOS_NEAR_SURFACE/V2 NOAA CDR: Ocean Near-Surface Atmospheric Properties, Version 2 image_collection ee.ImageCollection('NOAA/CDR/ATMOS_NEAR_SURFACE/V2') NOAA 1988-01-01 2021-08-31 -180, -90, 180, 90 False air_temperature, atmospheric, cdr, hourly, humidity, noaa, ocean, osb, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_ATMOS_NEAR_SURFACE_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_ATMOS_NEAR_SURFACE_V2 proprietary NOAA/CDR/AVHRR/AOT/V3 NOAA CDR AVHRR AOT: Daily Aerosol Optical Thickness Over Global Oceans, v03 [deprecated] image_collection ee.ImageCollection('NOAA/CDR/AVHRR/AOT/V3') NOAA 1981-01-01 2022-03-31 -180, -90, 180, 90 True aerosol, aot, atmospheric, avhrr, cdr, daily, noaa, optical, pollution https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_AOT_V3.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_AOT_V3 proprietary NOAA/CDR/AVHRR/AOT/V4 NOAA CDR AVHRR AOT: Daily Aerosol Optical Thickness Over Global Oceans, v04 image_collection ee.ImageCollection('NOAA/CDR/AVHRR/AOT/V4') NOAA 1981-01-01 2024-06-30 -180, -90, 180, 90 False aerosol, aot, atmospheric, avhrr, cdr, daily, noaa, optical, pollution https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_AOT_V4.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_AOT_V4 proprietary @@ -591,36 +591,36 @@ NOAA/CDR/AVHRR/SR/V5 NOAA CDR AVHRR: Surface Reflectance, Version 5 image_collec NOAA/CDR/GRIDSAT-B1/V2 NOAA CDR GRIDSAT-B1: Geostationary IR Channel Brightness Temperature image_collection ee.ImageCollection('NOAA/CDR/GRIDSAT-B1/V2') NOAA 1980-01-01 2024-03-31 -180, -90, 180, 90 False brightness, cdr, fundamental, geostationary, infrared, isccp, noaa, reflectance, sr https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_GRIDSAT-B1_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_GRIDSAT-B1_V2 proprietary NOAA/CDR/HEAT_FLUXES/V2 NOAA CDR: Ocean Heat Fluxes, Version 2 image_collection ee.ImageCollection('NOAA/CDR/HEAT_FLUXES/V2') NOAA 1988-01-01 2021-08-31 -180, -90, 180, 90 False atmospheric, cdr, flux, heat, hourly, noaa, ocean, osb https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_HEAT_FLUXES_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_HEAT_FLUXES_V2 proprietary NOAA/CDR/OISST/V2 NOAA CDR OISST v2: Optimum Interpolation Sea Surface Temperature [deprecated] image_collection ee.ImageCollection('NOAA/CDR/OISST/V2') NOAA 1981-09-01 2020-04-26 -180, -90, 180, 90 True avhrr, cdr, daily, ice, noaa, ocean, oisst, real_time, sst, temperature https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_OISST_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_OISST_V2 proprietary -NOAA/CDR/OISST/V2_1 NOAA CDR OISST v02r01: Optimum Interpolation Sea Surface Temperature image_collection ee.ImageCollection('NOAA/CDR/OISST/V2_1') NOAA 1981-09-01 2024-08-11 -180, -90, 180, 90 False avhrr, cdr, daily, ice, noaa, ocean, oisst, real_time, sst, temperature https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_OISST_V2_1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_OISST_V2_1 proprietary +NOAA/CDR/OISST/V2_1 NOAA CDR OISST v02r01: Optimum Interpolation Sea Surface Temperature image_collection ee.ImageCollection('NOAA/CDR/OISST/V2_1') NOAA 1981-09-01 2024-08-13 -180, -90, 180, 90 False avhrr, cdr, daily, ice, noaa, ocean, oisst, real_time, sst, temperature https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_OISST_V2_1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_OISST_V2_1 proprietary NOAA/CDR/PATMOSX/V53 NOAA CDR PATMOSX: Cloud Properties, Reflectance, and Brightness Temperatures, Version 5.3 image_collection ee.ImageCollection('NOAA/CDR/PATMOSX/V53') NOAA 1979-01-01 2022-01-01 -180, -90, 180, 90 False atmospheric, avhrr, brightness, cdr, cloud, metop, noaa, optical, poes, reflectance, temperature https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_PATMOSX_V53.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_PATMOSX_V53 proprietary NOAA/CDR/SST_PATHFINDER/V53 NOAA AVHRR Pathfinder Version 5.3 Collated Global 4km Sea Surface Temperature image_collection ee.ImageCollection('NOAA/CDR/SST_PATHFINDER/V53') NOAA 1981-08-24 2023-12-30 -180, -90, 180, 90 False avhrr, noaa, pathfinder, sea_ice, sst, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_SST_PATHFINDER_V53.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_SST_PATHFINDER_V53 proprietary NOAA/CDR/SST_WHOI/V2 NOAA CDR WHOI: Sea Surface Temperature, Version 2 image_collection ee.ImageCollection('NOAA/CDR/SST_WHOI/V2') NOAA 1988-01-01 2021-08-31 -180, -90, 180, 90 False atmospheric, cdr, hourly, noaa, ocean, oisst, osb, sst, whoi https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_SST_WHOI_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_SST_WHOI_V2 proprietary -NOAA/CFSR CFSR: Climate Forecast System Reanalysis image_collection ee.ImageCollection('NOAA/CFSR') NOAA NWS National Centers for Environmental Prediction (NCEP) 2018-12-13 2024-08-15 -180, -90, 180, 90 False climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSR.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSR proprietary -NOAA/CFSV2/FOR6H CFSV2: NCEP Climate Forecast System Version 2, 6-Hourly Products image_collection ee.ImageCollection('NOAA/CFSV2/FOR6H') NOAA NWS National Centers for Environmental Prediction (NCEP) 1979-01-01 2024-08-15 -180, -90, 180, 90 False climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSV2_FOR6H.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSV2_FOR6H proprietary +NOAA/CFSR CFSR: Climate Forecast System Reanalysis image_collection ee.ImageCollection('NOAA/CFSR') NOAA NWS National Centers for Environmental Prediction (NCEP) 2018-12-13 2024-08-16 -180, -90, 180, 90 False climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSR.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSR proprietary +NOAA/CFSV2/FOR6H CFSV2: NCEP Climate Forecast System Version 2, 6-Hourly Products image_collection ee.ImageCollection('NOAA/CFSV2/FOR6H') NOAA NWS National Centers for Environmental Prediction (NCEP) 1979-01-01 2024-08-16 -180, -90, 180, 90 False climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSV2_FOR6H.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSV2_FOR6H proprietary NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4 DMSP OLS: Global Radiance-Calibrated Nighttime Lights Version 4, Defense Meteorological Program Operational Linescan System image_collection ee.ImageCollection('NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4') Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines 1996-03-16 2011-07-31 -180, -65, 180, 75 False calibrated, dmsp, eog, imagery, lights, nighttime, ols, radiance, visible, yearly https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_DMSP-OLS_CALIBRATED_LIGHTS_V4.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_DMSP-OLS_CALIBRATED_LIGHTS_V4 proprietary NOAA/DMSP-OLS/NIGHTTIME_LIGHTS DMSP OLS: Nighttime Lights Time Series Version 4, Defense Meteorological Program Operational Linescan System image_collection ee.ImageCollection('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS') Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines 1992-01-01 2014-01-01 -180, -65, 180, 75 False dmsp, eog, imagery, lights, nighttime, ols, visible, yearly https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS proprietary -NOAA/GFS0P25 GFS: Global Forecast System 384-Hour Predicted Atmosphere Data image_collection ee.ImageCollection('NOAA/GFS0P25') NOAA/NCEP/EMC 2015-07-01 2024-08-16 -180, -90, 180, 90 False climate, cloud, emc, flux, forecast, geophysical, gfs, humidity, ncep, noaa, precipitation, radiation, temperature, vapor, weather, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GFS0P25.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GFS0P25 proprietary -NOAA/GOES/16/FDCC GOES-16 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS image_collection ee.ImageCollection('NOAA/GOES/16/FDCC') NOAA 2017-05-24 2024-08-16 -152.11, 14, -49.18, 56.77 False abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_FDCC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCC proprietary -NOAA/GOES/16/FDCF GOES-16 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk image_collection ee.ImageCollection('NOAA/GOES/16/FDCF') NOAA 2017-05-24 2024-08-16 -180, -90, 180, 90 False abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_FDCF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCF proprietary -NOAA/GOES/16/MCMIPC GOES-16 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPC') NOAA 2017-07-10 2024-08-16 -152.11, 14, -49.18, 56.77 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPC proprietary -NOAA/GOES/16/MCMIPF GOES-16 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPF') NOAA 2017-07-10 2024-08-16 -180, -90, 180, 90 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPF proprietary -NOAA/GOES/16/MCMIPM GOES-16 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Mesoscale image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPM') NOAA 2017-07-10 2024-08-16 -180, -90, 180, 90 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPM.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPM proprietary +NOAA/GFS0P25 GFS: Global Forecast System 384-Hour Predicted Atmosphere Data image_collection ee.ImageCollection('NOAA/GFS0P25') NOAA/NCEP/EMC 2015-07-01 2024-08-17 -180, -90, 180, 90 False climate, cloud, emc, flux, forecast, geophysical, gfs, humidity, ncep, noaa, precipitation, radiation, temperature, vapor, weather, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GFS0P25.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GFS0P25 proprietary +NOAA/GOES/16/FDCC GOES-16 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS image_collection ee.ImageCollection('NOAA/GOES/16/FDCC') NOAA 2017-05-24 2024-08-17 -152.11, 14, -49.18, 56.77 False abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_FDCC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCC proprietary +NOAA/GOES/16/FDCF GOES-16 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk image_collection ee.ImageCollection('NOAA/GOES/16/FDCF') NOAA 2017-05-24 2024-08-17 -180, -90, 180, 90 False abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_FDCF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCF proprietary +NOAA/GOES/16/MCMIPC GOES-16 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPC') NOAA 2017-07-10 2024-08-17 -152.11, 14, -49.18, 56.77 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPC proprietary +NOAA/GOES/16/MCMIPF GOES-16 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPF') NOAA 2017-07-10 2024-08-17 -180, -90, 180, 90 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPF proprietary +NOAA/GOES/16/MCMIPM GOES-16 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Mesoscale image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPM') NOAA 2017-07-10 2024-08-17 -180, -90, 180, 90 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPM.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPM proprietary NOAA/GOES/17/FDCC GOES-17 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS image_collection ee.ImageCollection('NOAA/GOES/17/FDCC') NOAA 2018-08-27 2023-01-10 -180, 14.57, 180, 53.51 False abi, climate, fdc, fire, goes, goes_17, goes_s, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_17_FDCC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_FDCC proprietary NOAA/GOES/17/FDCF GOES-17 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk image_collection ee.ImageCollection('NOAA/GOES/17/FDCF') NOAA 2018-08-27 2023-01-10 -180, -90, 180, 90 False abi, climate, fdc, fire, goes, goes_17, goes_s, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_17_FDCF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_FDCF proprietary NOAA/GOES/17/MCMIPC GOES-17 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS image_collection ee.ImageCollection('NOAA/GOES/17/MCMIPC') NOAA 2018-12-04 2023-01-10 -180, 14.57, 180, 53.51 False abi, climate, goes, goes_17, goes_s, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_17_MCMIPC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPC proprietary NOAA/GOES/17/MCMIPF GOES-17 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/17/MCMIPF') NOAA 2018-12-04 2023-01-10 -180, -90, 180, 90 False abi, climate, goes, goes_17, goes_s, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_17_MCMIPF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPF proprietary NOAA/GOES/17/MCMIPM GOES-17 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/17/MCMIPM') NOAA 2018-12-04 2023-01-10 -180, -90, 180, 90 False abi, climate, goes, goes_17, goes_s, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_17_MCMIPM.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPM proprietary -NOAA/GOES/18/FDCC GOES-18 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS image_collection ee.ImageCollection('NOAA/GOES/18/FDCC') NOAA 2022-10-13 2024-08-16 -180, 14.57, 180, 53.51 False abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_FDCC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_FDCC proprietary -NOAA/GOES/18/FDCF GOES-18 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/FDCF') NOAA 2022-10-13 2024-08-16 -180, -90, 180, 90 False abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_FDCF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_FDCF proprietary -NOAA/GOES/18/MCMIPC GOES-18 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPC') NOAA 2018-12-04 2024-08-16 -180, 14.57, 180, 53.51 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPC proprietary -NOAA/GOES/18/MCMIPF GOES-18 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPF') NOAA 2018-12-04 2024-08-16 -180, -90, 180, 90 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPF proprietary -NOAA/GOES/18/MCMIPM GOES-18 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPM') NOAA 2018-12-04 2024-08-16 -180, -90, 180, 90 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPM.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPM proprietary +NOAA/GOES/18/FDCC GOES-18 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS image_collection ee.ImageCollection('NOAA/GOES/18/FDCC') NOAA 2022-10-13 2024-08-17 -180, 14.57, 180, 53.51 False abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_FDCC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_FDCC proprietary +NOAA/GOES/18/FDCF GOES-18 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/FDCF') NOAA 2022-10-13 2024-08-17 -180, -90, 180, 90 False abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_FDCF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_FDCF proprietary +NOAA/GOES/18/MCMIPC GOES-18 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPC') NOAA 2018-12-04 2024-08-17 -180, 14.57, 180, 53.51 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPC proprietary +NOAA/GOES/18/MCMIPF GOES-18 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPF') NOAA 2018-12-04 2024-08-17 -180, -90, 180, 90 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPF proprietary +NOAA/GOES/18/MCMIPM GOES-18 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPM') NOAA 2018-12-04 2024-08-17 -180, -90, 180, 90 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPM.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPM proprietary NOAA/IBTrACS/v4 International Best Track Archive for Climate Stewardship Project table ee.FeatureCollection('NOAA/IBTrACS/v4') NOAA NCEI 1842-10-25 2024-05-19 -180, 0.4, 180, 63.1 False hurricane, noaa, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_IBTrACS_v4.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_IBTrACS_v4 proprietary NOAA/NCEP_DOE_RE2/total_cloud_coverage NCEP-DOE Reanalysis 2 (Gaussian Grid), Total Cloud Coverage image_collection ee.ImageCollection('NOAA/NCEP_DOE_RE2/total_cloud_coverage') NOAA 1979-01-01 2024-07-31 -180, -90, 180, 90 False atmosphere, climate, cloud, geophysical, ncep, noaa, reanalysis https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NCEP_DOE_RE2_total_cloud_coverage.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NCEP_DOE_RE2_total_cloud_coverage proprietary NOAA/NGDC/ETOPO1 ETOPO1: Global 1 Arc-Minute Elevation image ee.Image('NOAA/NGDC/ETOPO1') NOAA 2008-08-01 2008-08-01 -180, -90, 180, 90 False bedrock, dem, elevation, geophysical, ice, noaa, topography https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NGDC_ETOPO1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NGDC_ETOPO1 proprietary NOAA/NHC/HURDAT2/atlantic NOAA NHC HURDAT2 Atlantic Hurricane Catalog table ee.FeatureCollection('NOAA/NHC/HURDAT2/atlantic') NOAA NHC 1851-06-25 2018-11-04 -109.5, 7.2, 63, 81 False hurricane, nhc, noaa, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NHC_HURDAT2_atlantic.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NHC_HURDAT2_atlantic proprietary NOAA/NHC/HURDAT2/pacific NOAA NHC HURDAT2 Pacific Hurricane Catalog table ee.FeatureCollection('NOAA/NHC/HURDAT2/pacific') NOAA NHC 1949-06-11 2018-11-09 -180, 0.4, 180, 63.1 False hurricane, nhc, noaa, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NHC_HURDAT2_pacific.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NHC_HURDAT2_pacific proprietary -NOAA/NWS/RTMA RTMA: Real-Time Mesoscale Analysis image_collection ee.ImageCollection('NOAA/NWS/RTMA') NOAA/NWS 2011-01-01 2024-08-15 -130.17, 20.15, -60.81, 52.91 False climate, cloud, geophysical, humidity, noaa, nws, precipitation, pressure, rtma, surface, temperature, visibility, weather, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NWS_RTMA.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NWS_RTMA proprietary +NOAA/NWS/RTMA RTMA: Real-Time Mesoscale Analysis image_collection ee.ImageCollection('NOAA/NWS/RTMA') NOAA/NWS 2011-01-01 2024-08-16 -130.17, 20.15, -60.81, 52.91 False climate, cloud, geophysical, humidity, noaa, nws, precipitation, pressure, rtma, surface, temperature, visibility, weather, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NWS_RTMA.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NWS_RTMA proprietary NOAA/PERSIANN-CDR PERSIANN-CDR: Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record image_collection ee.ImageCollection('NOAA/PERSIANN-CDR') NOAA NCDC 1983-01-01 2023-12-31 -180, -60, 180, 60 False cdr, climate, geophysical, ncdc, noaa, persiann, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_PERSIANN-CDR.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_PERSIANN-CDR proprietary NOAA/VIIRS/001/VNP09GA VNP09GA: VIIRS Surface Reflectance Daily 500m and 1km [deprecated] image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP09GA') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-06-16 -180, -90, 180, 90 True daily, nasa, noaa, npp, reflectance, sr, viirs, vnp09ga https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP09GA.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP09GA proprietary NOAA/VIIRS/001/VNP09H1 VNP09H1: VIIRS Surface Reflectance 8-Day L3 Global 500m [deprecated] image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP09H1') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-06-09 -180, -90, 180, 90 True daily, nasa, noaa, npp, reflectance, sr, viirs https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP09H1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP09H1 proprietary @@ -632,7 +632,7 @@ NOAA/VIIRS/001/VNP21A1N VNP21A1N: Night Land Surface Temperature and Emissivity NOAA/VIIRS/001/VNP22Q2 VNP22Q2: Land Surface Phenology Yearly L3 Global 500m SIN Grid image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP22Q2') NASA LP DAAC at the USGS EROS Center 2013-01-01 2022-01-01 -180, -90, 180, 90 False land, nasa, ndvi, noaa, npp, onset_greenness, phenology, surface, vegetation, viirs https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP22Q2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP22Q2 proprietary NOAA/VIIRS/001/VNP43IA1 VNP43IA1: BRDF/Albedo Model Parameters Daily L3 Global 500m SIN Grid image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP43IA1') NASA LP DAAC at the USGS EROS Center 2012-01-17 2024-06-09 -180, -90, 180, 90 False land, nasa, noaa, surface, viirs https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP43IA1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP43IA1 proprietary NOAA/VIIRS/001/VNP43IA2 VNP43IA2: BRDF/Albedo Quality Daily L3 Global 500m SIN Grid image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP43IA2') NASA LP DAAC at the USGS EROS Center 2012-01-17 2024-06-09 -180, -90, 180, 90 False land, nasa, noaa, surface, viirs https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP43IA2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP43IA2 proprietary -NOAA/VIIRS/001/VNP46A1 VNP46A1: VIIRS Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP46A1') NASA LAADS DAAC 2012-01-19 2024-08-15 -180, -90, 180, 90 False daily, dnb, nasa, noaa, viirs https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP46A1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP46A1 proprietary +NOAA/VIIRS/001/VNP46A1 VNP46A1: VIIRS Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP46A1') NASA LAADS DAAC 2012-01-19 2024-08-16 -180, -90, 180, 90 False daily, dnb, nasa, noaa, viirs https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP46A1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP46A1 proprietary NOAA/VIIRS/001/VNP46A2 VNP46A2: VIIRS Lunar Gap-Filled BRDF Nighttime Lights Daily L3 Global 500m image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP46A2') NASA LAADS DAAC 2012-01-19 2024-08-08 -180, -90, 180, 90 False brdf, daily, nasa, noaa, viirs https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP46A2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP46A2 proprietary NOAA/VIIRS/001/VNP64A1 VNP64A1: Burned Area Monthly L4 Global 500m SIN Grid image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP64A1') NASA LP DAAC at the USGS EROS Center 2014-01-01 2019-01-01 -180, -90, 180, 90 False burn, change_detection, land, nasa, noaa, surface, viirs https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP64A1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP64A1 proprietary NOAA/VIIRS/DNB/ANNUAL_V21 VIIRS Nighttime Day/Night Annual Band Composites V2.1 image_collection ee.ImageCollection('NOAA/VIIRS/DNB/ANNUAL_V21') Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines 2012-04-01 2021-01-01 -180, -65, 180, 75 False annual, dnb, eog, lights, nighttime, noaa, viirs, visible https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_DNB_ANNUAL_V21.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_ANNUAL_V21 proprietary @@ -641,7 +641,7 @@ NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG VIIRS Nighttime Day/Night Band Composites Versi NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG VIIRS Stray Light Corrected Nighttime Day/Night Band Composites Version 1 image_collection ee.ImageCollection('NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG') Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines 2014-01-01 2024-04-01 -180, -65, 180, 75 False dnb, eog, lights, monthly, nighttime, noaa, stray_light, viirs, visible https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG proprietary NRCan/CDEM Canadian Digital Elevation Model image_collection ee.ImageCollection('NRCan/CDEM') NRCan 1945-01-01 2011-01-01 -142, 41, -52, 84 False canada, cdem, dem, elevation, geophysical, nrcan, topography https://storage.googleapis.com/earthengine-stac/catalog/NRCan/NRCan_CDEM.json https://developers.google.com/earth-engine/datasets/catalog/NRCan_CDEM OGL-Canada-2.0 Netherlands/Beeldmateriaal/LUCHTFOTO_RGB Netherlands orthophotos image_collection ee.ImageCollection('Netherlands/Beeldmateriaal/LUCHTFOTO_RGB') Beeldmateriaal Nederland 2021-01-01 2022-12-31 50.75, 3.2, 53.7, 7.22 False orthophoto, rgb, netherlands https://storage.googleapis.com/earthengine-stac/catalog/Netherlands/Netherlands_Beeldmateriaal_LUCHTFOTO_RGB.json https://developers.google.com/earth-engine/datasets/catalog/Netherlands_Beeldmateriaal_LUCHTFOTO_RGB CC-BY-4.0 -OREGONSTATE/PRISM/AN81d PRISM Daily Spatial Climate Dataset AN81d image_collection ee.ImageCollection('OREGONSTATE/PRISM/AN81d') PRISM / OREGONSTATE 1981-01-01 2024-08-12 -125, 24, -66, 50 False climate, daily, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_AN81d.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81d proprietary +OREGONSTATE/PRISM/AN81d PRISM Daily Spatial Climate Dataset AN81d image_collection ee.ImageCollection('OREGONSTATE/PRISM/AN81d') PRISM / OREGONSTATE 1981-01-01 2024-08-13 -125, 24, -66, 50 False climate, daily, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_AN81d.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81d proprietary OREGONSTATE/PRISM/AN81m PRISM Monthly Spatial Climate Dataset AN81m image_collection ee.ImageCollection('OREGONSTATE/PRISM/AN81m') PRISM / OREGONSTATE 1895-01-01 2024-07-01 -125, 24, -66, 50 False climate, geophysical, monthly, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_AN81m.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81m proprietary OREGONSTATE/PRISM/Norm81m PRISM Long-Term Average Climate Dataset Norm81m [deprecated] image_collection ee.ImageCollection('OREGONSTATE/PRISM/Norm81m') PRISM / OREGONSTATE 1981-01-01 2010-12-31 -125, 24, -66, 50 True 30_year, climate, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_Norm81m.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_Norm81m proprietary OREGONSTATE/PRISM/Norm91m PRISM Long-Term Average Climate Dataset Norm91m image_collection ee.ImageCollection('OREGONSTATE/PRISM/Norm91m') PRISM / OREGONSTATE 1991-01-01 2020-12-31 -125, 24, -66, 50 False 30_year, climate, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_Norm91m.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_Norm91m proprietary @@ -704,13 +704,13 @@ TIGER/2018/States TIGER: US Census States 2018 table ee.FeatureCollection('TIGER TIGER/2020/BG TIGER: US Census Block Groups (BG) 2020 table ee.FeatureCollection('TIGER/2020/BG') United States Census Bureau 2020-01-01 2020-01-02 -180, -14.69, -64.435, 71.567 False census, city, neighborhood, tiger, urban, us https://storage.googleapis.com/earthengine-stac/catalog/TIGER/TIGER_2020_BG.json https://developers.google.com/earth-engine/datasets/catalog/TIGER_2020_BG proprietary TIGER/2020/TABBLOCK20 TIGER: 2020 Tabulation (Census) Block table ee.FeatureCollection('TIGER/2020/TABBLOCK20') United States Census Bureau 2020-01-01 2020-01-02 -180, -14.69, -64.435, 71.567 False census, city, neighborhood, tiger, urban, us https://storage.googleapis.com/earthengine-stac/catalog/TIGER/TIGER_2020_TABBLOCK20.json https://developers.google.com/earth-engine/datasets/catalog/TIGER_2020_TABBLOCK20 proprietary TIGER/2020/TRACT TIGER: US Census Tracts table ee.FeatureCollection('TIGER/2020/TRACT') United States Census Bureau 2020-01-01 2020-01-02 -180, -14.69, -64.435, 71.567 False census, city, neighborhood, tiger, urban, us https://storage.googleapis.com/earthengine-stac/catalog/TIGER/TIGER_2020_TRACT.json https://developers.google.com/earth-engine/datasets/catalog/TIGER_2020_TRACT proprietary -TOMS/MERGED TOMS and OMI Merged Ozone Data image_collection ee.ImageCollection('TOMS/MERGED') NASA / GES DISC 1978-11-01 2024-08-13 -180, -90, 180, 90 False atmosphere, aura, climate, geophysical, ges_disc, goddard, nasa, omi, ozone, toms https://storage.googleapis.com/earthengine-stac/catalog/TOMS/TOMS_MERGED.json https://developers.google.com/earth-engine/datasets/catalog/TOMS_MERGED proprietary +TOMS/MERGED TOMS and OMI Merged Ozone Data image_collection ee.ImageCollection('TOMS/MERGED') NASA / GES DISC 1978-11-01 2024-08-14 -180, -90, 180, 90 False atmosphere, aura, climate, geophysical, ges_disc, goddard, nasa, omi, ozone, toms https://storage.googleapis.com/earthengine-stac/catalog/TOMS/TOMS_MERGED.json https://developers.google.com/earth-engine/datasets/catalog/TOMS_MERGED proprietary TRMM/3B42 TRMM 3B42: 3-Hourly Precipitation Estimates image_collection ee.ImageCollection('TRMM/3B42') NASA GES DISC at NASA Goddard Space Flight Center 1998-01-01 2019-12-31 -180, -50, 180, 50 False 3_hourly, climate, geophysical, jaxa, nasa, precipitation, rainfall, trmm, weather https://storage.googleapis.com/earthengine-stac/catalog/TRMM/TRMM_3B42.json https://developers.google.com/earth-engine/datasets/catalog/TRMM_3B42 proprietary TRMM/3B43V7 TRMM 3B43: Monthly Precipitation Estimates image_collection ee.ImageCollection('TRMM/3B43V7') NASA GES DISC at NASA Goddard Space Flight Center 1998-01-01 2019-12-01 -180, -50, 180, 50 False climate, geophysical, jaxa, nasa, precipitation, rainfall, trmm, weather https://storage.googleapis.com/earthengine-stac/catalog/TRMM/TRMM_3B43V7.json https://developers.google.com/earth-engine/datasets/catalog/TRMM_3B43V7 proprietary TUBerlin/BigEarthNet/v1 TUBerlin/BigEarthNet/v1 image_collection ee.ImageCollection('TUBerlin/BigEarthNet/v1') BigEarthNet 2017-06-01 2018-05-31 -9, 36.9, 31.6, 68.1 False chip, copernicus, corine_derived, label, ml, sentinel, tile https://storage.googleapis.com/earthengine-stac/catalog/TUBerlin/TUBerlin_BigEarthNet_v1.json https://developers.google.com/earth-engine/datasets/catalog/TUBerlin_BigEarthNet_v1 proprietary Tsinghua/DESS/ChinaTerraceMap/v1 DESS China Terrace Map v1 image ee.Image('Tsinghua/DESS/ChinaTerraceMap/v1') Department of Earth System Science, Tsinghua University (DESS, THU) 2018-01-01 2019-01-01 70, 0, 140, 60 False agriculture, china, dess, landcover, landuse, terrace, tsinghua https://storage.googleapis.com/earthengine-stac/catalog/Tsinghua/Tsinghua_DESS_ChinaTerraceMap_v1.json https://developers.google.com/earth-engine/datasets/catalog/Tsinghua_DESS_ChinaTerraceMap_v1 CC-BY-4.0 Tsinghua/FROM-GLC/GAIA/v10 Tsinghua FROM-GLC Year of Change to Impervious Surface image ee.Image('Tsinghua/FROM-GLC/GAIA/v10') Tsinghua University 1985-01-01 2018-12-31 -180, -90, 180, 90 False built, development, impervious, tsinghua, urban https://storage.googleapis.com/earthengine-stac/catalog/Tsinghua/Tsinghua_FROM-GLC_GAIA_v10.json https://developers.google.com/earth-engine/datasets/catalog/Tsinghua_FROM-GLC_GAIA_v10 CC-BY-4.0 -UCSB-CHG/CHIRPS/DAILY CHIRPS Daily: Climate Hazards Center InfraRed Precipitation With Station Data (Version 2.0 Final) image_collection ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY') UCSB/CHG 1981-01-01 2024-06-30 -180, -50, 180, 50 False chg, climate, geophysical, precipitation, ucsb, weather https://storage.googleapis.com/earthengine-stac/catalog/UCSB-CHG/UCSB-CHG_CHIRPS_DAILY.json https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY proprietary +UCSB-CHG/CHIRPS/DAILY CHIRPS Daily: Climate Hazards Center InfraRed Precipitation With Station Data (Version 2.0 Final) image_collection ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY') UCSB/CHG 1981-01-01 2024-07-31 -180, -50, 180, 50 False chg, climate, geophysical, precipitation, ucsb, weather https://storage.googleapis.com/earthengine-stac/catalog/UCSB-CHG/UCSB-CHG_CHIRPS_DAILY.json https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY proprietary UCSB-CHG/CHIRPS/PENTAD CHIRPS Pentad: Climate Hazards Center InfraRed Precipitation With Station Data (Version 2.0 Final) image_collection ee.ImageCollection('UCSB-CHG/CHIRPS/PENTAD') UCSB/CHG 1981-01-01 2024-05-26 -180, -50, 180, 50 False chg, climate, geophysical, precipitation, ucsb, weather https://storage.googleapis.com/earthengine-stac/catalog/UCSB-CHG/UCSB-CHG_CHIRPS_PENTAD.json https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD proprietary UK/EA/ENGLAND_1M_TERRAIN/2022 England 1m Composite DTM/DSM (Environment Agency) image ee.Image('UK/EA/ENGLAND_1M_TERRAIN/2022') UK Environment Agency 2000-06-06 2022-04-02 -180, -90, 180, 90 False lidar, elevation, dem https://storage.googleapis.com/earthengine-stac/catalog/UK/UK_EA_ENGLAND_1M_TERRAIN_2022.json https://developers.google.com/earth-engine/datasets/catalog/UK_EA_ENGLAND_1M_TERRAIN_2022 proprietary UMD/GLAD/PRIMARY_HUMID_TROPICAL_FORESTS/v1 Primary Humid Tropical Forests image_collection ee.ImageCollection('UMD/GLAD/PRIMARY_HUMID_TROPICAL_FORESTS/v1') UMD/GLAD 2001-01-01 2002-01-01 -180, -90, 180, 90 False forest, glad, global, landsat_derived, umd https://storage.googleapis.com/earthengine-stac/catalog/UMD/UMD_GLAD_PRIMARY_HUMID_TROPICAL_FORESTS_v1.json https://developers.google.com/earth-engine/datasets/catalog/UMD_GLAD_PRIMARY_HUMID_TROPICAL_FORESTS_v1 proprietary @@ -795,7 +795,7 @@ USGS/WBD/2017/HUC06 HUC06: USGS Watershed Boundary Dataset of Basins table ee.Fe USGS/WBD/2017/HUC08 HUC08: USGS Watershed Boundary Dataset of Subbasins table ee.FeatureCollection('USGS/WBD/2017/HUC08') United States Geological Survey 2017-04-22 2017-04-23 -180, -14.69, 180, 71.567 False hydrology, usgs, water, watershed, wbd https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_WBD_2017_HUC08.json https://developers.google.com/earth-engine/datasets/catalog/USGS_WBD_2017_HUC08 proprietary USGS/WBD/2017/HUC10 HUC10: USGS Watershed Boundary Dataset of Watersheds table ee.FeatureCollection('USGS/WBD/2017/HUC10') United States Geological Survey 2017-04-22 2017-04-23 -180, -14.69, 180, 71.567 False hydrology, usgs, water, watershed, wbd https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_WBD_2017_HUC10.json https://developers.google.com/earth-engine/datasets/catalog/USGS_WBD_2017_HUC10 proprietary USGS/WBD/2017/HUC12 HUC12: USGS Watershed Boundary Dataset of Subwatersheds table ee.FeatureCollection('USGS/WBD/2017/HUC12') United States Geological Survey 2017-04-22 2017-04-23 -180, -14.69, 180, 71.567 False hydrology, usgs, water, watershed, wbd https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_WBD_2017_HUC12.json https://developers.google.com/earth-engine/datasets/catalog/USGS_WBD_2017_HUC12 proprietary -UTOKYO/WTLAB/KBDI/v1 KBDI: Keetch-Byram Drought Index image_collection ee.ImageCollection('UTOKYO/WTLAB/KBDI/v1') Institute of Industrial Science, The University of Tokyo, Japan 2007-01-01 2024-08-14 60, -60, 180, 60 False drought, kbdi, lst_derived, rainfall, utokyo, wtlab https://storage.googleapis.com/earthengine-stac/catalog/UTOKYO/UTOKYO_WTLAB_KBDI_v1.json https://developers.google.com/earth-engine/datasets/catalog/UTOKYO_WTLAB_KBDI_v1 CC-BY-4.0 +UTOKYO/WTLAB/KBDI/v1 KBDI: Keetch-Byram Drought Index image_collection ee.ImageCollection('UTOKYO/WTLAB/KBDI/v1') Institute of Industrial Science, The University of Tokyo, Japan 2007-01-01 2024-08-15 60, -60, 180, 60 False drought, kbdi, lst_derived, rainfall, utokyo, wtlab https://storage.googleapis.com/earthengine-stac/catalog/UTOKYO/UTOKYO_WTLAB_KBDI_v1.json https://developers.google.com/earth-engine/datasets/catalog/UTOKYO_WTLAB_KBDI_v1 CC-BY-4.0 VITO/PROBAV/C1/S1_TOC_100M PROBA-V C1 Top Of Canopy Daily Synthesis 100m image_collection ee.ImageCollection('VITO/PROBAV/C1/S1_TOC_100M') Vito / ESA 2013-10-17 2021-10-31 -180, -90, 180, 90 False esa, multispectral, nir, proba, probav, swir, vito https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_C1_S1_TOC_100M.json https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_C1_S1_TOC_100M proprietary VITO/PROBAV/C1/S1_TOC_333M PROBA-V C1 Top Of Canopy Daily Synthesis 333m image_collection ee.ImageCollection('VITO/PROBAV/C1/S1_TOC_333M') Vito / ESA 2013-10-17 2021-10-31 -180, -90, 180, 90 False esa, multispectral, nir, proba, probav, swir, vito https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_C1_S1_TOC_333M.json https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_C1_S1_TOC_333M proprietary VITO/PROBAV/S1_TOC_100M PROBA-V C0 Top Of Canopy Daily Synthesis 100m [deprecated] image_collection ee.ImageCollection('VITO/PROBAV/S1_TOC_100M') Vito / ESA 2013-10-17 2016-12-14 -180, -90, 180, 90 True esa, multispectral, nir, proba, probav, swir, vito https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_S1_TOC_100M.json https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_S1_TOC_100M proprietary diff --git a/nasa_cmr_catalog.json b/nasa_cmr_catalog.json index f7b03b5..8f7fe58 100644 --- a/nasa_cmr_catalog.json +++ b/nasa_cmr_catalog.json @@ -1273,19 +1273,6 @@ "description": "AM1EPHNE is the Terra Near Real Time (NRT) 2-hour spacecraft Extrapolated ephemeris data file in native format. The file name format is the following: AM1EPHNE.Ayyyyddd.hhmm.vvv.yyyydddhhmmss where from left to right: E = Extrapolated; N = Native format; A = AM1 (Terra); yyyy = data year, ddd = Julian data day, hh = data hour, mm = data minute; vvv = Version ID; yyyy = production year, ddd = Julian production day, hh = production hour, mm = production minute, and ss = production second. Data set information: http://modis.gsfc.nasa.gov/sci_team/", "license": "not-provided" }, - { - "id": "AMZ1-WFI-L4-SR-1", - "title": "AMAZONIA-1/WFI - Level-4-SR - Cloud Optimized GeoTIFF", - "catalog": "INPE", - "state_date": "2024-01-01", - "end_date": "2024-06-09", - "bbox": "-135.151782, -45.613218, 106.18473, 63.78312", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204639-INPE.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204639-INPE.html", - "href": "https://cmr.earthdata.nasa.gov/stac/INPE/collections/AMZ1-WFI-L4-SR-1", - "description": "AMAZONIA-1/WFI Surface Reflectance product over Brazil. L4 SR product provides orthorectified surface reflectance images. This dataset is provided as Cloud Optimized GeoTIFF (COG).", - "license": "not-provided" - }, { "id": "APSF", "title": "Aerial Photo Single Frames", @@ -2001,97 +1988,6 @@ "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty, comprising 3 independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf). Corresponding emissivity values can be found in the CAM5K30EM data product. Provided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information. ", "license": "not-provided" }, - { - "id": "CB4-MUX-L4-SR-1", - "title": "CBERS-4/MUX - Level-4-SR - Cloud Optimized GeoTIFF", - "catalog": "INPE", - "state_date": "2016-01-01", - "end_date": "2024-06-09", - "bbox": "-79.392902, -40.793661, 49.863137, 28.185483", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204643-INPE.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204643-INPE.html", - "href": "https://cmr.earthdata.nasa.gov/stac/INPE/collections/CB4-MUX-L4-SR-1", - "description": "CBERS-4/MUX Surface Reflectance product over Brazil. L4 SR product provides orthorectified surface reflectance images. This dataset is provided as Cloud Optimized GeoTIFF (COG).", - "license": "not-provided" - }, - { - "id": "CB4-WFI-L4-SR-1", - "title": "CBERS-4/WFI - Level-4-SR - Cloud Optimized GeoTIFF", - "catalog": "INPE", - "state_date": "2016-01-01", - "end_date": "2024-06-09", - "bbox": "-84.468045, -46.643149, 57.533125, 42.454712", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204413-INPE.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204413-INPE.html", - "href": "https://cmr.earthdata.nasa.gov/stac/INPE/collections/CB4-WFI-L4-SR-1", - "description": "CBERS-4/WFI Surface Reflectance product over Brazil. L4 SR product provides orthorectified surface reflectance images. This dataset is provided as Cloud Optimized GeoTIFF (COG).", - "license": "not-provided" - }, - { - "id": "CB4A-WFI-L4-SR-1", - "title": "CBERS-4A/WFI - Level-4-SR - Cloud Optimized GeoTIFF", - "catalog": "INPE", - "state_date": "2020-01-01", - "end_date": "2024-06-09", - "bbox": "-81.507167, -38.111915, 51.482289, 39.663251", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204696-INPE.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204696-INPE.html", - "href": "https://cmr.earthdata.nasa.gov/stac/INPE/collections/CB4A-WFI-L4-SR-1", - "description": "CBERS-4A/WFI Surface Reflectance product over Brazil. L4 SR product provides orthorectified surface reflectance images. This dataset is provided as Cloud Optimized GeoTIFF (COG).", - "license": "not-provided" - }, - { - "id": "CB4A-WPM-PCA-FUSED-1", - "title": "CBERS-4A/WPM - Multispectral and Panchromatic Bands Fusioned", - "catalog": "INPE", - "state_date": "2023-03-02", - "end_date": "2024-06-16", - "bbox": "-74.678026, -34.572211, -34.099281, 6.02882", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204169-INPE.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204169-INPE.html", - "href": "https://cmr.earthdata.nasa.gov/stac/INPE/collections/CB4A-WPM-PCA-FUSED-1", - "description": "This collection contains 2 meter high-resolution, RGB products, generated using the Principal Components Fusion (PCA) method, with values coded between 1 and 255, with 0 being reserved for 'No Data'. This product is derived from the original CBERS-4A WPM Level-4 Digital Number with 10 bit of quantization.", - "license": "not-provided" - }, - { - "id": "CBERS-WFI-8D-1", - "title": "CBERS/WFI - Level-4-SR - Data Cube - LCF 8 days", - "catalog": "INPE", - "state_date": "2020-01-01", - "end_date": "2024-05-31", - "bbox": "-76.2226604, -36.733211, -32.761135, 6.013904", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204417-INPE.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204417-INPE.html", - "href": "https://cmr.earthdata.nasa.gov/stac/INPE/collections/CBERS-WFI-8D-1", - "description": "Earth Observation Data Cube generated from CBERS-4/WFI and CBERS-4A/WFI Level-4 SR products over Brazil extension. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 64 meters of spatial resolution, reprojected and cropped to BDC_LG grid Version 2 (BDC_LG V2), considering a temporal compositing function of 8 days using the Least Cloud Cover First (LCF) best pixel approach.", - "license": "not-provided" - }, - { - "id": "CBERS4-MUX-2M-1", - "title": "CBERS-4/MUX - Level-4-SR - Data Cube - LCF 2 months", - "catalog": "INPE", - "state_date": "2016-01-01", - "end_date": "2024-04-30", - "bbox": "-75.9138367, -34.6755646, -27.9114219, 5.9260044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204197-INPE.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204197-INPE.html", - "href": "https://cmr.earthdata.nasa.gov/stac/INPE/collections/CBERS4-MUX-2M-1", - "description": "Earth Observation Data Cube generated from CBERS-4/MUX Level-4 SR product over Brazil extension. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 20 meters of spatial resolution, reprojected and cropped to BDC_MD grid Version 2 (BDC_MD V2), considering a temporal compositing function of 2 months using the Least Cloud Cover First (LCF) best pixel approach.", - "license": "not-provided" - }, - { - "id": "CBERS4-WFI-16D-2", - "title": "CBERS-4/WFI - Level-4-SR - Data Cube - LCF 16 days", - "catalog": "INPE", - "state_date": "2016-01-01", - "end_date": "2024-05-23", - "bbox": "-76.2226604, -36.7332109, -32.7611351, 6.0139036", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204143-INPE.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204143-INPE.html", - "href": "https://cmr.earthdata.nasa.gov/stac/INPE/collections/CBERS4-WFI-16D-2", - "description": "Earth Observation Data Cube generated from CBERS-4/WFI Level-4 SR product over Brazil extension. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 64 meters of spatial resolution, reprojected and cropped to BDC_LG grid Version 2 (BDC_LG V2), considering a temporal compositing function of 16 days using the Least Cloud Cover First (LCF) best pixel approach.", - "license": "not-provided" - }, { "id": "CDDIS MEASURES products strain rate grids.v1", "title": "CDDIS SESES MEaSUREs products strain rate grids", @@ -3080,6 +2976,136 @@ "description": "Abstract: The ISS SERVIR Environmental Research and Visualization System (ISERV) acquired images of the Earth's surface from the International Space Station (ISS). The goal was to improve automatic image capturing and data transfer. ISERV's main component was the optical assembly which consisted of a 9.25 inch Schmidt-Cassegrain telescope, a focal reducer (field of view enlarger), a digital single lens reflex camera, and a high precision focusing mechanism. A motorized 2-axis pointing mount allowed pointing at targets approximately 23 degrees from nadir in both along- and across-track directions.", "license": "not-provided" }, + { + "id": "KOPRI-KPDC-00000008.v1", + "title": "1998 Seismic Data, Antarctica", + "catalog": "AMD_KOPRI", + "state_date": "1998-12-07", + "end_date": "1998-12-11", + "bbox": "-66.266667, -64.616667, -64.416667, -62.995", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2244292774-AMD_KOPRI.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2244292774-AMD_KOPRI.html", + "href": "https://cmr.earthdata.nasa.gov/stac/AMD_KOPRI/collections/KOPRI-KPDC-00000008.v1", + "description": "Korean Antarctic survey carried out as part of step 2 project in year 2 of 'the Antarctic Undersea Geological Survey' was conducted in the \u2161 region around the northwestern continent of the Antarctic Peninsula. This area is northwest of Anvers Island, including areas around the pericontinent from the continental shelf to the continental rise zone. The investigation period for this project took a total of 8 days for moving navigation, the survey of the side lines and drilling investigation. After seismic investigation, a surface drilling investigation was conducted in coring point was decided from the reference seismic section. 10 researcher from \u2018Korea Ocean Research and Development Institute\u2019 participated in the field survey. We took on lease Russian icebreaker \"Yuzhmorgeologiya\".", + "license": "not-provided" + }, + { + "id": "KOPRI-KPDC-00000009.v1", + "title": "1997 Seismic Data, Antarctica", + "catalog": "AMD_KOPRI", + "state_date": "1997-12-23", + "end_date": "1997-12-28", + "bbox": "-64.699722, -63.525, -62.157778, -62.041389", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2244293126-AMD_KOPRI.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2244293126-AMD_KOPRI.html", + "href": "https://cmr.earthdata.nasa.gov/stac/AMD_KOPRI/collections/KOPRI-KPDC-00000009.v1", + "description": "Korean Antarctic survey carried out as part of step 2 project in year 1 of \u2018The Antarctic Undersea Geological Survey\u2019 in 1997 was conducted in a continental shelf in the northwestern part of the Antarctic Peninsula. The research period took a total of 8 days, including 6 days for the seismic survey and 2 days for the drilling investigation. We took on lease Norway R/V 'Polar Duke' and 10 researchers from \u2018Korea Ocean Research and Development Institute\u2019 participated as field investigation personnel. The Teac single-channel recorder, EPC Recorder, Q/C MicroMax system etc. was used mainly by Sleeve gun used as a sound source, compressor for creating compressed air, DFS-V Recorder for multi-channel Seismic record, 12 \u2013channel geophone of seismic streamers. Additional Gravity Core was used for sediment research through drilling.", + "license": "not-provided" + }, + { + "id": "KOPRI-KPDC-00000011.v1", + "title": "1996 Seismic Data, Antarctica", + "catalog": "AMD_KOPRI", + "state_date": "1996-12-17", + "end_date": "1996-12-26", + "bbox": "-62.766667, -63.583333, -60.233333, -62.733333", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2244293499-AMD_KOPRI.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2244293499-AMD_KOPRI.html", + "href": "https://cmr.earthdata.nasa.gov/stac/AMD_KOPRI/collections/KOPRI-KPDC-00000011.v1", + "description": "Korean Antarctic survey carried out as in year 3 project of 'the Antarctic Undersea Geological Survey' was conducted in the basin region of western part of the Bransfeed Strait between the Antarctic Peninsula and the South Shetland Islands . During the field investigation, the seismic investigation and the drilling investigation was conducted at the same time. The investigation period took 9 days. 10 researchers from \u2018Korea Ocean Research and Development Institute\u2019 and 3 academic personnel participated in the cruise as field investigation personnel. We took on lease Russian R/V \"Yuzhmorgeologiya\" which is marine geology, geophysical survey vessel and Icebreaker.", + "license": "not-provided" + }, + { + "id": "KOPRI-KPDC-00000012.v1", + "title": "1995 Seismic Data, Antarctica", + "catalog": "AMD_KOPRI", + "state_date": "1995-12-13", + "end_date": "1995-12-18", + "bbox": "-58.335, -62.984444, -54.101944, -61.301111", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2244291641-AMD_KOPRI.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2244291641-AMD_KOPRI.html", + "href": "https://cmr.earthdata.nasa.gov/stac/AMD_KOPRI/collections/KOPRI-KPDC-00000012.v1", + "description": "Korean Antarctic survey carried out as in year 2 project of \"Antarctic submarine topography and sediment investigation\", The Field Survey of Antarctica was conducted at the end of 1995 was conducted the multi-channel Seismic Investigation and the drilling Investigation in the eastern part of the Bransfield Strait between the Antarctic Peninsula and the South Shetland Islands and near Sejong Station. We took on lease Russian R/V \"Yuzhmorgeologiya\" which is marine geology, geophysical survey vessel and Icebreaker for field investigation.", + "license": "not-provided" + }, + { + "id": "KOPRI-KPDC-00000014.v1", + "title": "1994 Seismic Data, Antarctica", + "catalog": "AMD_KOPRI", + "state_date": "1994-12-19", + "end_date": "1994-12-27", + "bbox": "-59.352778, -63.060278, -56.167778, -62.030833", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2244291414-AMD_KOPRI.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2244291414-AMD_KOPRI.html", + "href": "https://cmr.earthdata.nasa.gov/stac/AMD_KOPRI/collections/KOPRI-KPDC-00000014.v1", + "description": "Korean Antarctic survey carried out as in year 1 of 'the Antarctic Undersea Geological Survey' was conducted at the end of 1994 was conducted Multi-channel Seismic Investgation and Drilling investigation in the central basin of the Bransfield Strait was located in between the Antarctic Peninsula and the South Shetland Islands and the Maxwell Bay area near Sejong Station. The field research was conducted wih other research at the same time. The research period was from 11 Dec. in 1994 to 23 Jan. in 1995 (13 days). - Korean Antarctic survey carried out as part of step 1 project in year 1 to investigate the possibility of oil resources in the Bransfield Strait of Antarctica. - Securing data for tectonic settings research in the same region. - Obtaining basic data for understanding marine geology and sedimentary layers in the same region.", + "license": "not-provided" + }, + { + "id": "KOPRI-KPDC-00000051.v1", + "title": "1994 Sediment Core, Antarctica", + "catalog": "AMD_KOPRI", + "state_date": "1994-12-31", + "end_date": "1995-01-02", + "bbox": "-58.026667, -62.42, -57.739722, -62.32", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2244291543-AMD_KOPRI.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2244291543-AMD_KOPRI.html", + "href": "https://cmr.earthdata.nasa.gov/stac/AMD_KOPRI/collections/KOPRI-KPDC-00000051.v1", + "description": "For the first year of study \"The Antarctic Undersea Geological Survey\", The Field Survey of Antarctica was conducted at the end of 1994 was conducted multi-channel seismic Investigation and drilling Investigation in the central basin of the Bransfield Strait was located in between the south Shetland Islands and the Antarctic peninsula and Maxwell bay area near Sejong Station. The field investigation was conducted research projects at the same time took 13 days from 11 Dec. in 1994 to 23 Jan. in 1995. - Korean Antarctic survey carried out as part of step 1 project in year 1 to investigate the possibility of oil resources in the Bransfield Strait of Antarctica. - Securing data for tectonic settings research in the same region. - Obtaining basic data for understanding marine geology and sedimentary layers in the same region.", + "license": "not-provided" + }, + { + "id": "KOPRI-KPDC-00000052.v1", + "title": "1995 Sediment Core, Antarctica", + "catalog": "AMD_KOPRI", + "state_date": "1995-12-19", + "end_date": "1995-12-23", + "bbox": "-55.951111, -61.969167, -55.051111, -61.951111", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2244291581-AMD_KOPRI.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2244291581-AMD_KOPRI.html", + "href": "https://cmr.earthdata.nasa.gov/stac/AMD_KOPRI/collections/KOPRI-KPDC-00000052.v1", + "description": "Korean Antarctic survey was conducted in the east basin of the Bransfield Strait between the Antarctic peninsula and south Shetland Islands and Maxwell Bay located at Sejong Station was conducted multi-channel seismic investigation and drilling investigation. We took on lease Russian \"Yuzhmorgeologiya\"(5500 ton, ice strengthed vessel) which is marine geology, geophysical survey vessel and Icebreaker for field investigation.", + "license": "not-provided" + }, + { + "id": "KOPRI-KPDC-00000053.v1", + "title": "1996 Sediment Core, Antarctica", + "catalog": "AMD_KOPRI", + "state_date": "1996-12-16", + "end_date": "1996-12-16", + "bbox": "-60.151944, -62.100278, -59.717778, -62.051389", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2244291950-AMD_KOPRI.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2244291950-AMD_KOPRI.html", + "href": "https://cmr.earthdata.nasa.gov/stac/AMD_KOPRI/collections/KOPRI-KPDC-00000053.v1", + "description": "Korean Antarctic survey was conducted in west of the Bransfeed Strait, a basin between the Antarctic Peninsula and the south Shetland Islands. It tooks 9 days. seismic investigation and drilling investigation were conducted at the same time during the field survey. We took on lease Russian R/V \"Yuzhmorgeologiya\" which is marine geology, geophysical survey vessel and Icebreaker and 10 researchers from \u2018Korea Ocean Research and Development Institute\u2019 and 3 academic personnel participated in the cruise as field investigation personnel.", + "license": "not-provided" + }, + { + "id": "KOPRI-KPDC-00000054.v1", + "title": "1997 Sediment Core, Antarctica", + "catalog": "AMD_KOPRI", + "state_date": "1997-12-28", + "end_date": "1997-12-29", + "bbox": "-63.396667, -63.886111, -62.700833, -62.536389", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2244292254-AMD_KOPRI.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2244292254-AMD_KOPRI.html", + "href": "https://cmr.earthdata.nasa.gov/stac/AMD_KOPRI/collections/KOPRI-KPDC-00000054.v1", + "description": "Korean Antarctic survey was conducted in 1997 carried out in a continental shelf in the northwestern part of the Antarctic Peninsula. It took 2 days. We took on lease Norway R/V 'Polar Duke' and 11 researchers from \u2018Korea Ocean Research and Development Institute\u2019 participated as field investigation personnel. The Teac single-channel recorder, EPC Recorder, Q/C MicroMax system etc. was used mainly by Sleeve gun used as a sound source, compressor for creating compressed air, DFS-V Recorder for multi-channel Seismic record, 12-channel geophone of seismic streamers. Additional Gravity Core was used for sediment research through drilling.", + "license": "not-provided" + }, + { + "id": "KOPRI-KPDC-00000055.v1", + "title": "1998 Sediment Core, Antarctica", + "catalog": "AMD_KOPRI", + "state_date": "1998-12-11", + "end_date": "1998-12-12", + "bbox": "-66.32, -63.95, -63.47, -62.943333", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2244294165-AMD_KOPRI.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2244294165-AMD_KOPRI.html", + "href": "https://cmr.earthdata.nasa.gov/stac/AMD_KOPRI/collections/KOPRI-KPDC-00000055.v1", + "description": "Korean Antarctic survey was conducted in the continental margin (II region) of the northwestern Antarctic Peninsula. We took on lease Russian R/V \"Yuzhmorgeologiya\" (5500 ton, ice strengthed vessel) and 10 researchers participated in the cruise, including acquisition of multichannel seismic, gravity, and magnetometer as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches", + "license": "not-provided" + }, { "id": "L1B_Wind_Products", "title": "Aeolus preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers", @@ -3210,19 +3236,6 @@ "description": "The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B. Currently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product. The METOP-B AVHRR Atmospherically Corrected Normalized Difference Vegetation Index (NDVI) Daily L3 Global 0.05 Deg CMG, short-name M1_AVH13C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (M1_AVH01C1). The M1_AVH13C1 product is available in HDF4 file format. ", "license": "not-provided" }, - { - "id": "MCD14DL_C5_NRT.v005", - "title": "MODIS/Aqua+Terra Thermal Anomalies/Fire locations 1km FIRMS V005 NRT", - "catalog": "LM_FIRMS", - "state_date": "2014-01-28", - "end_date": "", - "bbox": "-180, -80, 180, 80", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1219768065-LM_FIRMS.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1219768065-LM_FIRMS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/LM_FIRMS/collections/MCD14DL_C5_NRT.v005", - "description": "Near Real-Time (NRT) MODIS Thermal Anomalies / Fire locations processed by FIRMS (Fire Information for Resource Management System) - Land Atmosphere Near real time Capability for EOS (LANCE), using swath products (MOD14/MYD14) rather than the tiled MOD14A1 and MYD14A1 products. The thermal anomalies / active fire represent the center of a 1km pixel that is flagged by the MODIS MOD14/MYD14 Fire and Thermal Anomalies algorithm (Giglio 2003) as containing one or more fires within the pixel. This is the most basic fire product in which active fires and other thermal anomalies, such as volcanoes, are identified.MCD14DL are available in the following formats: TXT, SHP, KML, WMS. These data are also provided through the FIRMS Fire Email Alerts. Please note only the TXT and SHP files contain all the attributes.", - "license": "not-provided" - }, { "id": "MIANACP.v1", "title": "MISR Aerosol Climatology Product V001", @@ -3535,6 +3548,136 @@ "description": "The OMPS-NPP L2 NP Ozone (O3) Total Column swath orbital product provides ozone profile retrievals from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Profiler (NP) instrument on the Suomi-NPP satellite in Near Real Time. The V8 ozone profile algorithm relies on nadir profiler measurements made in the 250 to 310 nm range, as well as from measurements from the nadir mapper in the 300 to 380 nm range. Ozone mixing ratios are reported at 15 pressure levels between 50 and 0.5 hPa. Additionally, this data product contains measurements of total ozone, UV aerosol index and reflectivities at 331 and 380 nm. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 80 profiles. The NP footprint size is 250 km x 250 km. The L2 NP Ozone data are written using the Hierarchical Data Format Version 5 or HDF5.", "license": "not-provided" }, + { + "id": "NRSCC_GLASS_ FAPAR_MODIS_0.05D.v11", + "title": "NRSCC_GLASS_ FAPAR_MODIS_0.05D", + "catalog": "NRSCC", + "state_date": "2010-02-18", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351149-NRSCC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351149-NRSCC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NRSCC/collections/NRSCC_GLASS_ FAPAR_MODIS_0.05D.v11", + "description": "This Global LAnd Surface Satellite (GLASS) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product was generated using MODIS products.", + "license": "not-provided" + }, + { + "id": "NRSCC_GLASS_ FAPAR_MODIS_1KM.v11", + "title": "NRSCC_GLASS_ FAPAR_MODIS_1KM", + "catalog": "NRSCC", + "state_date": "2000-02-18", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351155-NRSCC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351155-NRSCC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NRSCC/collections/NRSCC_GLASS_ FAPAR_MODIS_1KM.v11", + "description": "This Global LAnd Surface Satellite (GLASS) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product was developed using MODIS datasets.", + "license": "not-provided" + }, + { + "id": "NRSCC_GLASS_ LAI_AVHRR_0.05D.v11", + "title": "NRSCC_GLASS_ LAI_AVHRR_0.05D", + "catalog": "NRSCC", + "state_date": "1981-01-01", + "end_date": "2018-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351175-NRSCC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351175-NRSCC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NRSCC/collections/NRSCC_GLASS_ LAI_AVHRR_0.05D.v11", + "description": "This Global LAnd Surface Satellite (GLASS) Leaf Area Index (LAI) product was developed using AVHRR datasets.", + "license": "not-provided" + }, + { + "id": "NRSCC_GLASS_ LAI_MODIS_0.05D.v11", + "title": "NRSCC_GLASS_ LAI_MODIS_0.05D", + "catalog": "NRSCC", + "state_date": "2000-02-18", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351151-NRSCC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351151-NRSCC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NRSCC/collections/NRSCC_GLASS_ LAI_MODIS_0.05D.v11", + "description": "This Global LAnd Surface Satellite (GLASS) Leaf Area Index (LAI) product was developed using MODIS datasets.", + "license": "not-provided" + }, + { + "id": "NRSCC_GLASS_Albedo_AVHRR.v11", + "title": "NRSCC_GLASS_Albedo_AVHRR", + "catalog": "NRSCC", + "state_date": "2002-01-01", + "end_date": "2015-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351177-NRSCC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351177-NRSCC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NRSCC/collections/NRSCC_GLASS_Albedo_AVHRR.v11", + "description": "Global high-resolution land surface albedo data from NOAA/AVHRR, generated by Global LAnd Surface Satellite (GLASS) Dataset production team.", + "license": "not-provided" + }, + { + "id": "NRSCC_GLASS_Albedo_MODIS_0.05D.v11", + "title": "NRSCC_GLASS_Albedo_MODIS_0.05D", + "catalog": "NRSCC", + "state_date": "2000-01-01", + "end_date": "2018-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351167-NRSCC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351167-NRSCC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NRSCC/collections/NRSCC_GLASS_Albedo_MODIS_0.05D.v11", + "description": "The Global LAnd Surface Satellite (GLASS) Albedo product derived from MODIS. The horizontal resolution is 0.05 Degree.", + "license": "not-provided" + }, + { + "id": "NRSCC_GLASS_Albedo_MODIS_1KM.v11", + "title": "NRSCC_GLASS_Albedo_MODIS_1KM", + "catalog": "NRSCC", + "state_date": "2000-01-01", + "end_date": "2018-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351152-NRSCC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351152-NRSCC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NRSCC/collections/NRSCC_GLASS_Albedo_MODIS_1KM.v11", + "description": "The Global LAnd Surface Satellite (GLASS) Albedo product derived from MODIS. The horizontal resolution is 1KM.", + "license": "not-provided" + }, + { + "id": "NRSCC_GLASS_BBE_AVHRR.v11", + "title": "NRSCC_GLASS_BBE_AVHRR", + "catalog": "NRSCC", + "state_date": "1982-01-01", + "end_date": "2017-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351148-NRSCC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351148-NRSCC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NRSCC/collections/NRSCC_GLASS_BBE_AVHRR.v11", + "description": "The Global LAnd Surface Satellite (GLASS) broadband emissivity (BBE) product derived from AVHRR.", + "license": "not-provided" + }, + { + "id": "NRSCC_GLASS_BBE_MODIS_0.05D.v11", + "title": "NRSCC_GLASS_BBE_MODIS_0.05D", + "catalog": "NRSCC", + "state_date": "2000-02-18", + "end_date": "2018-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351185-NRSCC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351185-NRSCC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NRSCC/collections/NRSCC_GLASS_BBE_MODIS_0.05D.v11", + "description": "The Global LAnd Surface Satellite (GLASS) broadband emissivity (BBE) product derived from MODIS. The horizontal resolution is 0.05 Degree.", + "license": "not-provided" + }, + { + "id": "NRSCC_GLASS_BBE_MODIS_1KM.v11", + "title": "NRSCC_GLASS_BBE_MODIS_1KM", + "catalog": "NRSCC", + "state_date": "2000-02-18", + "end_date": "2018-12-31", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351153-NRSCC.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2205351153-NRSCC.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NRSCC/collections/NRSCC_GLASS_BBE_MODIS_1KM.v11", + "description": "NRSCC_GLASS_BBE_MODIS_1KM", + "license": "not-provided" + }, { "id": "NSF-ANT05-37371", "title": "A Broadband Seismic Experiment to Image the Lithosphere Beneath the Gamburtsev Mountains and Surrounding Areas, East Antarctica", @@ -4120,6 +4263,136 @@ "description": "Current networking capacity at McMurdo Station is insufficient to even be considered broadband, with a summer population of up to 1000 people sharing what is equivalent to the connection enjoyed by a typical family of three in the United States. The changing Antarctic ice sheets and Southern Ocean are large, complex systems that require cutting edge technology to do cutting edge research, with remote technology becoming increasingly useful and even necessary to monitor changes at sufficient spatial and temporal scales. Antarctic science also often involves large data-transfer needs not currently met by existing satellite communication infrastructure. This workshop will gather representatives from across Antarctic science disciplinesfrom astronomy to zoologyas well as research and education networking experts to explore the scientific advances that would be enabled through dramatically increased real-time network connectivity, and also consider opportunities for subsea cable instrumentation. This workshop will assess the importance of a subsea fiber optic cable for high-capacity real-time connectivity in the US Antarctic Program, which is at the forefront of some of the greatest climate-related challenges facing our planet. The workshop will: (1) document unmet or poorly met current scientific and logistic needs for connectivity; (2) explore connectivity needs for planned future research and note the scientific advances that would be possible if the full value of modern cyberinfrastructure-empowered research could be brought to the Antarctic research community; and (3) identify scientific opportunities in planning a fully instrumented communication cable as a scientific observatory. Due to the ongoing COVID-19 pandemic, the workshop will be hosted and streamed online. While the workshop will be limited to invited personnel in order to facilitate a collaborative working environment, broad community input will be sought via survey and via comment on draft outputs. A workshop summary document and report will be delivered to NSF. Increasing US Antarctic connectivity by orders of magnitude will be transformative for science and logistics, and it may well usher in a new era of Antarctic science that is more accessible, efficient and sustainable. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "license": "not-provided" }, + { + "id": "USGS_DDS_P14_cells", + "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Los Angeles Basin Province", + "catalog": "CEOS_EXTRA", + "state_date": "1990-12-01", + "end_date": "1990-12-01", + "bbox": "-119.63631, 32.7535, -117.52315, 34.17464", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552049-CEOS_EXTRA.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552049-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DDS_P14_cells", + "description": "The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 14 (Los Angeles Basin) are listed here by play number, type, and name: Number Type Name 1401 conventional Santa Monica Fault System and Las Cienegas Fault and Block 1402 conventional Southwestern Shelf and Adjacent Offshore State Lands 1403 conventional Newport-Inglewood Deformation Zone and Southwestern Flank of Central Syncline 1404 conventional Whittier Fault Zone and Fullerton Embayment 1405 conventional Northern Shelf and Northern Flank of Central Syncline 1406 conventional Anaheim Nose 1407 conventional Chino Marginal Basin, Puente and San Jose Hills, and San Gabriel Valley Marginal Basin", + "license": "not-provided" + }, + { + "id": "USGS_DDS_P16_cells", + "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Salton Trough Province", + "catalog": "CEOS_EXTRA", + "state_date": "1990-12-01", + "end_date": "1990-12-01", + "bbox": "-116.66911, 32.634293, -114.74501, 34.02059", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548651-CEOS_EXTRA.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548651-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DDS_P16_cells", + "description": "The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 16 (Salton Trough) are listed here by play number, type, and name.", + "license": "not-provided" + }, + { + "id": "USGS_DDS_P17_cells", + "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Idaho - Snake River Downwarp Province", + "catalog": "CEOS_EXTRA", + "state_date": "1990-12-01", + "end_date": "1990-12-01", + "bbox": "-117.24303, 41.99332, -111.04548, 49.00115", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550494-CEOS_EXTRA.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550494-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DDS_P17_cells", + "description": "The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 17 (Idaho - Snake River Downwarp) are listed here by play number, type, and name: Number Type Name 1701 conventional Miocene Lacustrine (Lake Bruneau) 1702 conventional Pliocene Lacustrine (Lake Idaho) 1703 conventional Pre-Miocene 1704 conventional Older Tertiary", + "license": "not-provided" + }, + { + "id": "USGS_DDS_P19_cells", + "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Eastern Great Basin Province", + "catalog": "CEOS_EXTRA", + "state_date": "1990-12-01", + "end_date": "1990-12-01", + "bbox": "-117.02622, 35.002083, -111.170425, 43.022377", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552402-CEOS_EXTRA.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552402-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DDS_P19_cells", + "description": "The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number, type, and name: Number Type Name 1901 conventional Unconformity \"A\" 1902 conventional Late Paleozoic 1903 conventional Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 conventional Younger Tertiary Basins 1906 conventional Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 conventional Sevier Frontal Zone", + "license": "not-provided" + }, + { + "id": "USGS_DDS_P2_cells", + "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Alaska Province", + "catalog": "CEOS_EXTRA", + "state_date": "1990-12-01", + "end_date": "1990-12-01", + "bbox": "-173.22636, 58.49761, -140.99017, 68.01999", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550471-CEOS_EXTRA.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550471-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_DDS_P2_cells", + "description": "The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 2 (Central Alaska) are listed here by play number, type, and name: Number Type Name 201 conventional Central Alaska Cenozoic Gas 202 conventional Central Alaska Mesozoic Gas 203 conventional Central Alaska Paleozoic Oil 204 conventional Kandik Pre-Mid-Cretaceous Strata 205 conventional Kandik Upper Cretaceous and Tertiary Non-Marine Stata", + "license": "not-provided" + }, + { + "id": "USGS_P-11_cells", + "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Coastal Province", + "catalog": "CEOS_EXTRA", + "state_date": "1990-12-01", + "end_date": "1990-12-01", + "bbox": "-123.80987, 34.66294, -118.997696, 39.082233", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552077-CEOS_EXTRA.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231552077-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_P-11_cells", + "description": "The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 11 (Central Coastal) are listed here by play number, type, and name: Number Type Name 1101 conventional Point Arena Oil 1102 conventional Point Reyes Oil 1103 conventional Pescadero Oil 1104 conventional La Honda Oil 1105 conventional Bitterwater Oil 1106 conventional Salinas Oil 1107 conventional Western Cuyama Basin 1109 conventional Cox Graben", + "license": "not-provided" + }, + { + "id": "USGS_SOFIA_eco_hist_db1995-2007.vversion 7", + "title": "1995 - 2007 Ecosystem History of South Florida's Estuaries Database version 7", + "catalog": "CEOS_EXTRA", + "state_date": "1994-09-27", + "end_date": "2007-04-03", + "bbox": "-81.83, 24.75, -80, 26.5", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554288-CEOS_EXTRA.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554288-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_SOFIA_eco_hist_db1995-2007.vversion 7", + "description": "The 1995 - 2007 Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), modern monitoring site survey information (water chemistry, floral and faunal data, etc.), and published core data. Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - primarily faunal assemblages. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain either percent abundance data or actual counts of the distribution of mollusks, ostracodes, forams, and pollen within the cores collected in the estuaries. For some cores dinocyst or diatom data may be available.", + "license": "not-provided" + }, + { + "id": "USGS_cont1992", + "title": "1992 Water-Table Contours of the Mojave River Ground-Water Basin, San Bernardino County, California", + "catalog": "CEOS_EXTRA", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-117.652695, 34.364513, -116.55357, 35.081955", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553864-CEOS_EXTRA.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231553864-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_cont1992", + "description": "This data set consists of digital water-table contours for the Mojave River Basin. The U.S. Geological Survey, in cooperation with the Mojave Water Agency, constructed a water-table map of the Mojave River ground-water basin for ground-water levels measured in November 1992. Water-level data were collected from approximately 300 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,200 to 1,600 feet above sea level. [Summary provided by the USGS.]", + "license": "not-provided" + }, + { + "id": "USGS_cont1994", + "title": "1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California", + "catalog": "CEOS_EXTRA", + "state_date": "1970-01-01", + "end_date": "", + "bbox": "-117.07194, 34.095333, -115.98976, 34.64026", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554677-CEOS_EXTRA.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231554677-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/USGS_cont1994", + "description": "This data set consists of digital water-table contours for the Morongo Basin. The U.S. Geological Survey constructed a water-table map of the Morongo ground-water basin for ground-water levels measured during the period January-October 1994. Water-level data were collected from 248 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,400 to 1,500 feet above sea level. [Summary provided by the USGS.]", + "license": "not-provided" + }, + { + "id": "UTC_1990countyboundaries", + "title": "1990 County Boundaries of the United States", + "catalog": "CEOS_EXTRA", + "state_date": "1972-01-01", + "end_date": "1990-12-31", + "bbox": "-177.1, 13.71, -61.48, 76.63", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550562-CEOS_EXTRA.json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231550562-CEOS_EXTRA.html", + "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/UTC_1990countyboundaries", + "description": "This data set portrays the 1990 State and county boundaries of the United States, Puerto Rico, and the U.S. Virgin Islands. The data set was created by extracting county polygon features from the individual 1:2,000,000-scale State boundary Digital Line Graph (DLG) files produced by the U.S. Geological Survey. These files were then merged into a single file and the boundaries were modified to what they were in 1990. This is a revised version of the March 2000 data set.", + "license": "not-provided" + }, { "id": "WV01_Pan_L1B.v1", "title": "WorldView-1 Level 1B Panchromatic Satellite Imagery", @@ -4224,19 +4497,6 @@ "description": "The AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H09 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-9 platform currently serves in the operational Himawari position (near 140.7\u00b0E) since it was launched November 2, 2016, and replaces Himawari-8. The Himawari-9/AHI collection record spans from 13th December 2022 through 31st December 2022. The XAERDT_L2_AHI_H09 product is a part of the Geostationary Earth Orbit (GEO)\u2013Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA\u2019s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H09 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H09 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/", "license": "not-provided" }, - { - "id": "a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics.v1.0", - "title": "A numerical solver for heat and mass transport in snow based on FEniCS", - "catalog": "ENVIDAT", - "state_date": "2022-01-01", - "end_date": "2022-01-01", - "bbox": "9.8472494, 46.812044, 9.8472494, 46.812044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814662-ENVIDAT.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814662-ENVIDAT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics.v1.0", - "description": "This python code uses the Finite Element library FEniCS (via docker) to solve the one dimensional partial differential equations for heat and mass transfer in snow. The results are written in vtk format. The dataset contains the code and the output data to reproduce the key Figure 5 from the related publication: _Sch\u00fcrholt, K., Kowalski, J., L\u00f6we, H.; Elements of future snowpack modeling - Part 1: A physical instability arising from the non-linear coupling of transport and phase changes, The Cryosphere, 2022_ The code and potential updates can be accessed directly through git via: https://gitlabext.wsl.ch/snow-physics/snowmodel_fenics", - "license": "not-provided" - }, { "id": "a6efcb0868664248b9cb212aba44313d", "title": "ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 2 aerosol products from MERIS (ALAMO algorithm), Version 2.2", @@ -4263,19 +4523,6 @@ "description": "The AAMH CPEX dataset contains products obtained from the MetOp-A, MetOp-B, NOAA-18, and NOAA-19 satellites. These data were collected in support of the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May to 25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May to 24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 26, 2017, through July 15, 2017, and are available in netCDF-4 format.", "license": "not-provided" }, - { - "id": "above-and-below-ground-herbivore-communities-along-elevation.v1.0", - "title": "Above- and below-ground herbivore communities along elevation", - "catalog": "ENVIDAT", - "state_date": "2020-01-01", - "end_date": "2020-01-01", - "bbox": "5.95587, 45.81802, 10.49203, 47.80838", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814648-ENVIDAT.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814648-ENVIDAT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/above-and-below-ground-herbivore-communities-along-elevation.v1.0", - "description": "Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017. ", - "license": "not-provided" - }, { "id": "aces1am.v1", "title": "ACES Aircraft and Mechanical Data", @@ -4354,58 +4601,6 @@ "description": "The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from the following instruments: Slow/Fast antenna, Electric Field Mill, Optical Pulse Sensors, Searchcoil Magnetometer, Accelerometer, and Gerdien Conductivity Probe. These data were collected at 200KHz from the first 16 telemetry items collected on the aircraft, were initiated by an operator selected trigger (e.g. DOPS), and continued collecting for as long as the trigger continued.", "license": "not-provided" }, - { - "id": "aerosol-data-davos-wolfgang.v1.0", - "title": "Aerosol Data Davos Wolfgang", - "catalog": "ENVIDAT", - "state_date": "2020-01-01", - "end_date": "2020-01-01", - "bbox": "9.853594, 46.835577, 9.853594, 46.835577", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/aerosol-data-davos-wolfgang.v1.0", - "description": "Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 \u2013 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle\u2019s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. ", - "license": "not-provided" - }, - { - "id": "aerosol-data-weissfluhjoch.v1.0", - "title": "Aerosol Data Weissfluhjoch", - "catalog": "ENVIDAT", - "state_date": "2020-01-01", - "end_date": "2020-01-01", - "bbox": "9.806475, 46.832964, 9.806475, 46.832964", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/aerosol-data-weissfluhjoch.v1.0", - "description": "Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 \u2013 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle\u2019s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. ", - "license": "not-provided" - }, - { - "id": "alnus-glutinosa-orientus-ishidae-flavescence-doree.v1.0", - "title": "Alnus glutinosa (L.) Gaertn. and Orientus ishidae (Matsumura, 1902) share phytoplasma genotypes linked to the \u201cFlavescence dor\u00e9e\u201d epidemics", - "catalog": "ENVIDAT", - "state_date": "2021-01-01", - "end_date": "2021-01-01", - "bbox": "8.4484863, 45.8115721, 9.4372559, 46.4586735", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814963-ENVIDAT.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814963-ENVIDAT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/alnus-glutinosa-orientus-ishidae-flavescence-doree.v1.0", - "description": "Flavescence dor\u00e9e (FD) is a grapevine disease caused by associated phytoplasmas (FDp), which are epidemically spread by their main vector Scaphoideus titanus. The possible roles of alternative and secondary FDp plant hosts and vectors have gained interest to better understand the FDp ecology and epidemiology. A survey conducted in the surroundings of a vineyard in the Swiss Southern Alps aimed at studying the possible epidemiological role of the FDp secondary vector Orientus ishidae and the FDp host plant Alnus glutinosa is reported. Data used for the publication. Insects were captured by using a sweeping net (on common alder trees) and yellow sticky traps (Rebell Giallo, Andermatt Biocontrol AG, Switzerland) placed in the vineyard canopy. Insects were later determined and selected for molecular analyses. Grapevines and common alder samples were collected using the standard techniques. The molecular analyses were conducted in order to identify samples infected by the Flavescence dor\u00e9e phytoplasma (16SrV-p) and the Bois Noir phytoplasma (16SrXII-p). A selection of the infected sampled were further characterized by map genotype and sequenced in order to compare the genotypes in insects, grapevines and common alder trees. ", - "license": "not-provided" - }, - { - "id": "alpine3d-simulations-of-future-climate-scenarios-for-graubunden.v1.0", - "title": "Alpine3D simulations of future climate scenarios for Graubunden", - "catalog": "ENVIDAT", - "state_date": "2019-01-01", - "end_date": "2019-01-01", - "bbox": "8.6737061, 46.2216525, 10.6347656, 47.1075228", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814545-ENVIDAT.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814545-ENVIDAT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/alpine3d-simulations-of-future-climate-scenarios-for-graubunden.v1.0", - "description": "This is the simulation dataset from _\"Response of snow cover and runoff to climate change in high Alpine catchments of Eastern Switzerland\"_, M. Bavay, T. Gr\u00fcnewald, M. Lehning, Advances in Water Resources __55__, 4-16, 2013 A model study on the impact of climate change on snow cover and runoff has been conducted for the Swiss Canton of Graub\u00fcnden. The model Alpine3D has been forced with the data from 35 Automatic Weather Stations in order to investigate snow and runoff dynamics for the current climate. The data set has then been modified to reflect climate change as predicted for the 2021-2050 and 2070-2095 periods from an ensemble of regional climate models. The predicted changes in snow cover will be moderate for 2021-2050 and become drastic in the second half of the century. Towards the end of the century the snow cover changes will roughly be equivalent to an elevation shift of 800 m. Seasonal snow water equivalents will decrease by one to two thirds and snow seasons will be shortened by five to nine weeks in 2095. Small, higher elevation catchments will show more winter runoff, earlier spring melt peaks and reduced summer runoff. Where glacierized areas exist, the transitional increase in glacier melt will initially offset losses from snow melt. Larger catchments, which reach lower elevations will show much smaller changes since they are already dominated by summer precipitation.", - "license": "not-provided" - }, { "id": "amprimpacts.v1", "title": "Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS", @@ -4497,19 +4692,6 @@ "description": "Horizontal blue ice core collected from the surface of a blue ice area in Scharffenbergbotnen, Heimefrontfjella, DML. Samples were collected in austral summer 2003/2004 and transported to Finland for chemical analyses. The blue ice core is estimated to represent a 1000-year period of climate history 20 - 40 kyr B.P.. The results of the analyses will be available in 2005.", "license": "not-provided" }, - { - "id": "ch2014.v1", - "title": "Alpine3D simulations of future climate scenarios CH2014", - "catalog": "ENVIDAT", - "state_date": "2014-01-01", - "end_date": "2014-01-01", - "bbox": "8.227, 46.79959, 8.227, 46.79959", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814657-ENVIDAT.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814657-ENVIDAT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/ch2014.v1", - "description": "# Overview The CH2014-Impacts initiative is a concerted national effort to describe impacts of climate change in Switzerland quantitatively, drawing on the scientific resources available in Switzerland today. The initiative links the recently developed Swiss Climate Change Scenarios CH2011 with an evolving base of quantitative impact models. The use of a common climate data set across disciplines and research groups sets a high standard of consistency and comparability of results. Impact studies explore the wide range of climatic changes in temperature and precipitation projected in CH2011 for the 21st century, which vary with the assumed global level of greenhouse gases, the time horizon, the underlying climate model, and the geographical region within Switzerland. The differences among climate projections are considered using three greenhouse gas scenarios, three future time periods in the 21st century, and three climate uncertainty levels (Figure 1). Impacts are shown with respect to the reference period 1980-2009 of CH2011, and add to any impacts that have already emerged as a result of earlier climate change. # Experimental Setup Future snow cover changes are simulated with the physics-based model Alpine3D (Lehning et al., 2006). It is applied to two regions: The canton of Graub\u00fcnden and the Aare catchment. These domains are modeled with a Digital Elevation Model (DEM) with a resolution of 200 m \u00d7 200 m. This defines the simulation grid that has to be filled with land cover data and downscaled meteorological input data for each cell for the time period of interest at hourly resolution. The reference data set consists of automatic weather station data. All meteorological input parameters are spatially interpolated to the simulation grid. The reference period comprises only thirteen years (1999\u20132012), because the number of available high elevation weather stations for earlier times is not sufficient to achieve unbiased distribution of the observations with elevation. The model uses projected temperature and precipitation changes for all greenhouse gas scenarios (A1B, A2, and RCP3PD) and CH2011 time periods (2035, 2060, and 2085). # Data Snow cover changes are projected to be relatively small in the near term (2035) (Figure 5.1 top), in particular at higher elevations above 2000 m asl. As shown by Bavay et al. (2013) the spread in projected snow cover for this period is greater between different climate model chains (Chapter 3) than between the reference period and the model chain exhibiting the most moderate change. In the 2085 period much larger changes with the potential to fundamentally transform the snow dominated alpine area become apparent (Figure 5.1 bottom). These changes include a shortening of the snow season by 5\u20139 weeks for the A1B scenario. This is roughly equivalent to an elevation shift of 400\u2013800 m. The slight increase of winter precipitation and therefore snow fall projected in the CH2011 scenarios (with high associated uncertainty) can no longer compensate for the effect of increasing winter temperatures even at high elevations. In terms of Snow Water Equivalents (SWE), the projected reduction is up to two thirds toward the end of the century (2085). A continuous snow cover will be restricted to a shorter time period and/or to regions at increasingly high elevation. In Bern, for example, the number of days per year with at least 5 cm snow depth will decrease by 90% from now 20 days to only 2 days on average. ", - "license": "not-provided" - }, { "id": "chesapeake_val_2013.v0", "title": "2013 Chesapeake Bay measurements", @@ -4575,19 +4757,6 @@ "description": "Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. The satellite has two panchromatic cameras that were especially designed for in flight stereo viewing. However, this collection contains the monoscopic data.", "license": "not-provided" }, - { - "id": "envidat-lwf-34.v2019-03-06", - "title": "10-HS Pfynwald", - "catalog": "ENVIDAT", - "state_date": "2019-01-01", - "end_date": "2019-01-01", - "bbox": "7.61211, 46.30279, 7.61211, 46.30279", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815241-ENVIDAT.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815241-ENVIDAT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/envidat-lwf-34.v2019-03-06", - "description": "Continuous measurement of soil water content at 10 and 80 cm depth (3 replications) with 10-HS soil moisture probes (Decagon Incorporation, Pullman, WA, USA). ### Purpose: ### Monitoring of the soil water matrix potential ### Paper Citation: ### * Dobbertin, M.; Eilmann, B.; Bleuler, P.; Giuggiola, A.; Graf Pannatier, E.; Landolt, W.; Schleppi, P.; Rigling, A., 2010: Effect of irrigation on needle morphology, shoot and stem growth in a drought-exposed Pinus sylvestris forest. Tree Physiology, 30, 3: 346-360. [doi: 10.1093/treephys/tpp123](http://doi.org/10.1093/treephys/tpp123) ", - "license": "not-provided" - }, { "id": "fife_hydrology_strm_15m_1.v1", "title": "15 Minute Stream Flow Data: USGS (FIFE)", @@ -4796,19 +4965,6 @@ "description": "2014 Lake Erie measurements.", "license": "not-provided" }, - { - "id": "latent-reserves-in-the-swiss-nfi.v1.0", - "title": "'Latent reserves' within the Swiss NFI", - "catalog": "ENVIDAT", - "state_date": "2020-01-01", - "end_date": "2020-01-01", - "bbox": "5.95587, 45.81802, 10.49203, 47.80838", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815280-ENVIDAT.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815280-ENVIDAT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/latent-reserves-in-the-swiss-nfi.v1.0", - "description": "The files refer to the data used in Portier et al. \"\u2018Latent reserves\u2019: a hidden treasure in National Forest Inventories\" (2020) *Journal of Ecology*. **'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for >40 to >70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered \u2018latent reserves\u2019, criteria were defined based on the information available in the Swiss NFI database: * Shrub forests were excluded. * Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested. * To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Kl\u00f6tzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994). * Plots had to originate from natural regeneration. * Intensive livestock grazing must never have occurred on the plots. The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). **** The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper. The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement. ", - "license": "not-provided" - }, { "id": "mbs_wilhelm_msa_hooh.v1", "title": "15 year Wilhelm II Land MSA and HOOH shallow ice core record from Mount Brown South (MBS)", @@ -4822,45 +4978,6 @@ "description": "This work presents results from a short firn core spanning 15 years collected from near Mount Brown, Wilhelm II Land, East Antarctica. Variations of methanesulphonic acid (MSA) at Mount Brown were positively correlated with sea-ice extent from the coastal region surrounding Mount Brown (60-1208 E) and from around the entire Antarctic coast (0-3608 E). Previous results from Law Dome identified this MSA-sea-ice relationship and proposed it as an Antarctic sea-ice proxy (Curran and others, 2003), with the strongest results found for the local Law Dome region. Our data provide supporting evidence for the Law Dome proxy (at another site in East Antarctica), but a deeper Mount Brown ice core is required to confirm the sea-ice decline suggested by Curran and others (2003). Results also indicate that this deeper record may also provide a more circum-Antarctic sea-ice proxy. This work was completed as part of ASAC project 757 (ASAC_757).", "license": "not-provided" }, - { - "id": "mosaic-cbers4-brazil-3m-1", - "title": "CBERS-4/WFI Image Mosaic of Brazil - 3 Months", - "catalog": "INPE", - "state_date": "2020-04-01", - "end_date": "2020-06-30", - "bbox": "-76.6054059, -33.7511817, -27.7877802, 6.3052432", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204634-INPE.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204634-INPE.html", - "href": "https://cmr.earthdata.nasa.gov/stac/INPE/collections/mosaic-cbers4-brazil-3m-1", - "description": "CBERS-4/WFI image mosaic of Brazil with 64m of spatial resolution. The mosaic was prepared in order to demonstrate the technological capabilities of the Brazil Data Cube project tools. The false color composition is based on the WFI bands 15, 16 and 13 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in April 2020 and ending in June 2020, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 1200 CBERS-4 scenes and was generated based on an existing CBERS-4/WFI image collection.", - "license": "not-provided" - }, - { - "id": "mosaic-cbers4a-paraiba-3m-1", - "title": "CBERS-4A/WFI Image Mosaic of Brazil Para\u00edba State - 3 Months", - "catalog": "INPE", - "state_date": "2020-07-01", - "end_date": "2020-09-30", - "bbox": "-38.8134896, -8.3976443, -34.7223714, -5.87659", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204719-INPE.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204719-INPE.html", - "href": "https://cmr.earthdata.nasa.gov/stac/INPE/collections/mosaic-cbers4a-paraiba-3m-1", - "description": "CBERS-4A/WFI image mosaic of Brazil Para\u00edba State with 55m of spatial resolution. The mosaic was prepared in order to demonstrate the technological capabilities of the Brazil Data Cube project tools. The false color composition is based on the WFI bands 16, 15 and 14 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in July 2020 and ending in September 2020, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 50 CBERS-4A scenes and was generated based on an existing CBERS-4A/WFI image collection.", - "license": "not-provided" - }, - { - "id": "pfynwaldgasexchange.v1.0", - "title": "2013-2020 gas exchange at Pfynwald", - "catalog": "ENVIDAT", - "state_date": "2021-01-01", - "end_date": "2021-01-01", - "bbox": "7.6105556, 46.3001905, 7.6163921, 46.3047564", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816347-ENVIDAT.json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816347-ENVIDAT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/pfynwaldgasexchange.v1.0", - "description": "Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds. All metadata can be found in the attached documents.", - "license": "not-provided" - }, { "id": "urn:ogc:def:EOP:VITO:VGT_S10.v1", "title": "10 Days Synthesis of SPOT VEGETATION Images (VGT-S10)", diff --git a/nasa_cmr_catalog.tsv b/nasa_cmr_catalog.tsv index 45c5a77..42f2fa8 100644 --- a/nasa_cmr_catalog.tsv +++ b/nasa_cmr_catalog.tsv @@ -97,7 +97,6 @@ ALOS.PALSAR.FBS.FBD.PLR.products ALOS PALSAR products ESA 2006-05-02 2011-04-14 ALOSIPY ALOS PALSAR International Polar Year Antarctica ESA 2008-07-25 2010-03-31 -180, -90, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1965336817-ESA.json International Polar Year (IPY), focusing on the north and south polar regions, aimed to investigate the impact of how changes to the ice sheets affect ocean and climate change to the habitats in these regions. IPY was a collaborative project involving over sixty countries for two years from March 2007 to March 2009. To meet the project goal, world space agencies observed these regions intensively using their own Earth observation satellites. One of these satellites, ALOS - with the PALSAR (Phased Array type L-band Synthetic Aperture Radar) sensor - observed these regions independently from day-night conditions or weather conditions. Carrying on this initiative, ESA is providing the ALOS PALSAR IPY Antarctica dataset, which consists of full resolution ALOS PALSAR ScanSAR WB1 products (100m spatial resolution) over Antarctica from July 2008 (cycle 21) to December 2008 (Cycle 24) and from May 2009 (cycle 27) to March 2010 (cycle 31). Missing products between the two periods above is due to L0 data over Antarctica not being available in ADEN archives and not processed to L1. Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/ALOSIPY/ available on the Third Party Missions Dissemination Service. not-provided ALOS_PRISM_L1B Alos PRISM L1B ESA 2006-07-09 2011-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689640-ESA.json This collection provides access to the ALOS-1 PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) L1B data acquired by ESA stations in the ADEN zone plus some data requested by European scientists over their areas of interest around the world. The ADEN zone (https://earth.esa.int/eogateway/documents/20142/37627/ALOS-ADEN-Zone.pdf) was the area belonging to the European Data node and covered both the European and African continents, a large part of Greenland and the Middle East. The full mission is covered, though with gaps outside of the ADEN zone: Time window: from 2006-07-09 to 2011-03-31 Orbits: from 2425 to 24189 Path (corresponds to JAXA track number): from 1 to 668 Row (corresponds to JAXA scene centre frame number): from 55 to 7185. Two different Level 1B product types (Panchromatic images in VIS-NIR bands, 2.5 m resolution at nadir) are offered, one for each available sensor mode: PSM_OB1_11 -> composed of up to three views; Nadir, Forward and Backward at 35 km swath PSM_OB2_11 -> composed of up to two views; Nadir view at 70 km width and Backward view at 35 km width. All ALOS PRISM EO-SIP products have, at least, the Nadir view which is used for the frame number identification. All views are packaged together; each view, in CEOS format, is stored in a directory named according to the view ID according to the JAXA naming convention. not-provided AM1EPHNE.v6.1NRT Files containing only extrapolated orbital metadata, to be read via SDP Toolkit, Binary Format LANCEMODIS 2016-01-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1426293893-LANCEMODIS.json AM1EPHNE is the Terra Near Real Time (NRT) 2-hour spacecraft Extrapolated ephemeris data file in native format. The file name format is the following: AM1EPHNE.Ayyyyddd.hhmm.vvv.yyyydddhhmmss where from left to right: E = Extrapolated; N = Native format; A = AM1 (Terra); yyyy = data year, ddd = Julian data day, hh = data hour, mm = data minute; vvv = Version ID; yyyy = production year, ddd = Julian production day, hh = production hour, mm = production minute, and ss = production second. Data set information: http://modis.gsfc.nasa.gov/sci_team/ not-provided -AMZ1-WFI-L4-SR-1 AMAZONIA-1/WFI - Level-4-SR - Cloud Optimized GeoTIFF INPE 2024-01-01 2024-06-09 -135.151782, -45.613218, 106.18473, 63.78312 https://cmr.earthdata.nasa.gov/search/concepts/C3108204639-INPE.json AMAZONIA-1/WFI Surface Reflectance product over Brazil. L4 SR product provides orthorectified surface reflectance images. This dataset is provided as Cloud Optimized GeoTIFF (COG). not-provided APSF Aerial Photo Single Frames USGS_LTA 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220567654-USGS_LTA.json The Aerial Photography Single Frame Records collection is a large and diverse group of imagery acquired by Federal organizations from 1937 to the present. Over 6.4 million frames of photographic images are available for download as medium and high resolution digital products. The high resolution data provide access to photogrammetric quality scans of aerial photographs with sufficient resolution to reveal landscape detail and to facilitate the interpretability of landscape features. Coverage is predominantly over the United States and includes portions of Central America and Puerto Rico. Individual photographs vary in scale, size, film type, quality, and coverage. not-provided AQUARIUS_ANCILLARY_CELESTIALSKY_V1.v1 Aquarius Celestial Sky Microwave Emission Map Ancillary Dataset V1.0 POCLOUD 2011-09-01 2015-06-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2617176761-POCLOUD.json "This datasets contains three maps of L-band (wavelength = 21 cm) brightness temperature of the celestial sky (""Galaxy"") used in the processing of the NASA Aquarius instrument data. The maps report Sky brightness temperatures in Kelvin gridded on the Earth Centered Inertial (ECI) reference frame epoch J2000. They are sampled over 721 Declinations between -90 degrees and +90 degrees and 1441 Right Ascensions between 0 degrees and 360 degrees, all evenly spaced at 0.25 degrees intervals. The brightness temperatures are assumed temporally invariant and polarization has been neglected. They include microwave continuum and atomic hydrogen line (HI) emissions. The maps differ only in how the strong radio source Cassiopeia A has been included into the whole sky background surveys: 1/ TB_no_Cas_A does not include Cassiopeia A and reports only the whole Sky surveys. 2/ TB_Cas_A_1cell spread Cas A total flux homogeneously over 1 map grid cell (i.e. 9.8572E-6 sr). 3/ TB_Cas_A_beam spreads Cas A over surrounding grid cells using a convolution by a Gaussian beam with HPBW of 35 arcmin (equivalent to the instrument used for the Sky surveys). Cassiopeia A is a supernova remnant (SNR) in the constellation Cassiopeia and the brightest extra-solar radio source in the sky at frequencies above 1." not-provided AQUARIUS_L2_SSS_CAP_V5.v5.0 Aquarius CAP Level 2 Sea Surface Salinity, Wind Speed & Direction Data V5.0 POCLOUD 2011-08-26 2015-06-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2205121315-POCLOUD.json The version 5.0 Aquarius CAP Level 2 product contains the fourth release of the AQUARIUS/SAC-D orbital/swath data based on the Combined Active Passive (CAP) algorithm. CAP is a P.I. produced dataset developed and provided by JPL. This Level 2 dataset contains sea surface salinity (SSS), wind speed and wind direction data derived from 3 different radiometers and the onboard scatterometer. The CAP algorithm simultaneously retrieves the salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. Each L2 data file covers one 98 minute orbit. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath. not-provided @@ -153,13 +152,6 @@ C1_PANF_STUC00GTD.v1 Cartosat-1 PANF Standard Products ISRO 2005-08-05 -180, -9 CAM5K30CF.v002 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Coefficient Monthly Global 0.05Deg V002 LPCLOUD 2000-04-01 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763266335-LPCLOUD.json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly coefficients at 0.05 degree (~5 kilometer) resolution (CAM5K30CF). The CAMEL Principal Components Analysis (PCA) input coefficients utilized in the CAMEL high spectral resolution (HSR) algorithm are provided in the CAM5K30CF data product and are congruent to the temporally equivalent CAM5K30EM emissivity data product. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf). Provided in the CAM5K30CF product are layers for PCA coefficients, number of PCA coefficients, laboratory version, snow fraction derived from MODIS Snow Cover data (MOD10), latitude, longitude, and the CAMEL quality information. PCA coefficients are dependent on the version of lab PC data and number of PCs used. not-provided CAM5K30EM.v002 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Emissivity Monthly Global 0.05Deg V002 LPCLOUD 2000-04-01 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763266338-LPCLOUD.json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Infrared Emissivity dataset (UWIREMIS) and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4 (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics. The integration steps include: adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) 5 kilometer resolution, merging of the 5 ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf). Provided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and Best Fit Emissivity (BFE) quality information. not-provided CAM5K30UC.v002 Combined ASTER and MODIS Emissivity database over Land (CAMEL) Uncertainty Monthly Global 0.05Deg V002 LPCLOUD 2000-04-01 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763266343-LPCLOUD.json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty, comprising 3 independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/219/cam5k30_v2_user_guide_atbd.pdf). Corresponding emissivity values can be found in the CAM5K30EM data product. Provided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information. not-provided -CB4-MUX-L4-SR-1 CBERS-4/MUX - Level-4-SR - Cloud Optimized GeoTIFF INPE 2016-01-01 2024-06-09 -79.392902, -40.793661, 49.863137, 28.185483 https://cmr.earthdata.nasa.gov/search/concepts/C3108204643-INPE.json CBERS-4/MUX Surface Reflectance product over Brazil. L4 SR product provides orthorectified surface reflectance images. This dataset is provided as Cloud Optimized GeoTIFF (COG). not-provided -CB4-WFI-L4-SR-1 CBERS-4/WFI - Level-4-SR - Cloud Optimized GeoTIFF INPE 2016-01-01 2024-06-09 -84.468045, -46.643149, 57.533125, 42.454712 https://cmr.earthdata.nasa.gov/search/concepts/C3108204413-INPE.json CBERS-4/WFI Surface Reflectance product over Brazil. L4 SR product provides orthorectified surface reflectance images. This dataset is provided as Cloud Optimized GeoTIFF (COG). not-provided -CB4A-WFI-L4-SR-1 CBERS-4A/WFI - Level-4-SR - Cloud Optimized GeoTIFF INPE 2020-01-01 2024-06-09 -81.507167, -38.111915, 51.482289, 39.663251 https://cmr.earthdata.nasa.gov/search/concepts/C3108204696-INPE.json CBERS-4A/WFI Surface Reflectance product over Brazil. L4 SR product provides orthorectified surface reflectance images. This dataset is provided as Cloud Optimized GeoTIFF (COG). not-provided -CB4A-WPM-PCA-FUSED-1 CBERS-4A/WPM - Multispectral and Panchromatic Bands Fusioned INPE 2023-03-02 2024-06-16 -74.678026, -34.572211, -34.099281, 6.02882 https://cmr.earthdata.nasa.gov/search/concepts/C3108204169-INPE.json This collection contains 2 meter high-resolution, RGB products, generated using the Principal Components Fusion (PCA) method, with values coded between 1 and 255, with 0 being reserved for 'No Data'. This product is derived from the original CBERS-4A WPM Level-4 Digital Number with 10 bit of quantization. not-provided -CBERS-WFI-8D-1 CBERS/WFI - Level-4-SR - Data Cube - LCF 8 days INPE 2020-01-01 2024-05-31 -76.2226604, -36.733211, -32.761135, 6.013904 https://cmr.earthdata.nasa.gov/search/concepts/C3108204417-INPE.json Earth Observation Data Cube generated from CBERS-4/WFI and CBERS-4A/WFI Level-4 SR products over Brazil extension. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 64 meters of spatial resolution, reprojected and cropped to BDC_LG grid Version 2 (BDC_LG V2), considering a temporal compositing function of 8 days using the Least Cloud Cover First (LCF) best pixel approach. not-provided -CBERS4-MUX-2M-1 CBERS-4/MUX - Level-4-SR - Data Cube - LCF 2 months INPE 2016-01-01 2024-04-30 -75.9138367, -34.6755646, -27.9114219, 5.9260044 https://cmr.earthdata.nasa.gov/search/concepts/C3108204197-INPE.json Earth Observation Data Cube generated from CBERS-4/MUX Level-4 SR product over Brazil extension. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 20 meters of spatial resolution, reprojected and cropped to BDC_MD grid Version 2 (BDC_MD V2), considering a temporal compositing function of 2 months using the Least Cloud Cover First (LCF) best pixel approach. not-provided -CBERS4-WFI-16D-2 CBERS-4/WFI - Level-4-SR - Data Cube - LCF 16 days INPE 2016-01-01 2024-05-23 -76.2226604, -36.7332109, -32.7611351, 6.0139036 https://cmr.earthdata.nasa.gov/search/concepts/C3108204143-INPE.json Earth Observation Data Cube generated from CBERS-4/WFI Level-4 SR product over Brazil extension. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 64 meters of spatial resolution, reprojected and cropped to BDC_LG grid Version 2 (BDC_LG V2), considering a temporal compositing function of 16 days using the Least Cloud Cover First (LCF) best pixel approach. not-provided CDDIS MEASURES products strain rate grids.v1 CDDIS SESES MEaSUREs products strain rate grids CDDIS 1992-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2978524117-CDDIS.json Making Earth System Data Records for Use in Research Environments (MEaSUREs) empowers the research community to participate in developing and generating data products that complement and augment NASA produced and distributed Earth science data products. NASA’s Enhanced Solid Earth Science Earth Science Data Record (ESDR) System (ESESES) continues and extends mature geodetic data product generation and archival as part of the MEaSUREs SESES project providing new, multi-decade, calibrated and validated geodetic-derived ESDRs obtained by the Scripps Institution of Oceanography (SIO) and NASA's Jet Propulsion Laboratory (JPL). These data-derived products include continuous multi-year high-rate GNSS, seismogeodetic, and meteorological time series, a catalog of transient deformation in tectonically active areas known for aseismic motion such as ETS with focus in Cascadia, and continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S. not-provided CDDIS MEaSURES products velocities.v1 CDDIS SESES MEaSUREs products velocities CDDIS 1992-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2978562718-CDDIS.json Making Earth System Data Records for Use in Research Environments (MEaSUREs) empowers the research community to participate in developing and generating data products that complement and augment NASA produced and distributed Earth science data products. NASA’s Enhanced Solid Earth Science Earth Science Data Record (ESDR) System (ESESES) continues and extends mature geodetic data product generation and archival as part of the MEaSUREs SESES project providing new, multi-decade, calibrated and validated geodetic-derived ESDRs obtained by the Scripps Institution of Oceanography (SIO) and NASA's Jet Propulsion Laboratory (JPL). These data-derived products include continuous multi-year high-rate GNSS, seismogeodetic, and meteorological time series, a catalog of transient deformation in tectonically active areas known for aseismic motion such as ETS with focus in Cascadia, and continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S. not-provided CDDIS_GNSS_products_IGS20.v1 CDDIS GNSS ITRF2020 IGS products (IGS20) CDDIS 1983-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2433571719-CDDIS.json These data-derived products are the International GNSS Service (IGS) Analysis Centers' (AC) contribution to the International Terrestrial Reference Frame (ITRF) 2020. not-provided @@ -236,6 +228,16 @@ IKONOS_MSI_L1B.v1 IKONOS Level 1B Multispectral 4-Band Satellite Imagery CSDA 19 IKONOS_Pan_L1B.v1 IKONOS Level 1B Panchromatic Satellite Imagery CSDA 1999-10-24 2015-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2497468825-CSDA.json The IKONOS Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the IKONOS satellite using the Optical Sensor Assembly instrument across the global land surface from October 1999 to March 2015. This data product includes panchromatic imagery with a spatial resolution of 0.82m at nadir and a temporal resolution of approximately 3 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program. not-provided IMS1_HYSI_GEO.v1.0 IMS-1 HYSI TOA Radiance and Reflectance Product ISRO 2008-06-22 2012-09-10 -6.0364, -78.8236, 152.6286, 78.6815 https://cmr.earthdata.nasa.gov/search/concepts/C1214622602-ISRO.json The data received from IMS1, HySI which operates in 64 spectral bands in VNIR bands(400-900nm) with 500 meter spatial resolution and swath of 128 kms. not-provided ISERV.v1 International Space Station SERVIR Environmental Research and Visualization System V1 USGS_EROS 2013-03-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1379906336-USGS_EROS.json Abstract: The ISS SERVIR Environmental Research and Visualization System (ISERV) acquired images of the Earth's surface from the International Space Station (ISS). The goal was to improve automatic image capturing and data transfer. ISERV's main component was the optical assembly which consisted of a 9.25 inch Schmidt-Cassegrain telescope, a focal reducer (field of view enlarger), a digital single lens reflex camera, and a high precision focusing mechanism. A motorized 2-axis pointing mount allowed pointing at targets approximately 23 degrees from nadir in both along- and across-track directions. not-provided +KOPRI-KPDC-00000008.v1 1998 Seismic Data, Antarctica AMD_KOPRI 1998-12-07 1998-12-11 -66.266667, -64.616667, -64.416667, -62.995 https://cmr.earthdata.nasa.gov/search/concepts/C2244292774-AMD_KOPRI.json "Korean Antarctic survey carried out as part of step 2 project in year 2 of 'the Antarctic Undersea Geological Survey' was conducted in the Ⅱ region around the northwestern continent of the Antarctic Peninsula. This area is northwest of Anvers Island, including areas around the pericontinent from the continental shelf to the continental rise zone. The investigation period for this project took a total of 8 days for moving navigation, the survey of the side lines and drilling investigation. After seismic investigation, a surface drilling investigation was conducted in coring point was decided from the reference seismic section. 10 researcher from ‘Korea Ocean Research and Development Institute’ participated in the field survey. We took on lease Russian icebreaker ""Yuzhmorgeologiya""." not-provided +KOPRI-KPDC-00000009.v1 1997 Seismic Data, Antarctica AMD_KOPRI 1997-12-23 1997-12-28 -64.699722, -63.525, -62.157778, -62.041389 https://cmr.earthdata.nasa.gov/search/concepts/C2244293126-AMD_KOPRI.json Korean Antarctic survey carried out as part of step 2 project in year 1 of ‘The Antarctic Undersea Geological Survey’ in 1997 was conducted in a continental shelf in the northwestern part of the Antarctic Peninsula. The research period took a total of 8 days, including 6 days for the seismic survey and 2 days for the drilling investigation. We took on lease Norway R/V 'Polar Duke' and 10 researchers from ‘Korea Ocean Research and Development Institute’ participated as field investigation personnel. The Teac single-channel recorder, EPC Recorder, Q/C MicroMax system etc. was used mainly by Sleeve gun used as a sound source, compressor for creating compressed air, DFS-V Recorder for multi-channel Seismic record, 12 –channel geophone of seismic streamers. Additional Gravity Core was used for sediment research through drilling. not-provided +KOPRI-KPDC-00000011.v1 1996 Seismic Data, Antarctica AMD_KOPRI 1996-12-17 1996-12-26 -62.766667, -63.583333, -60.233333, -62.733333 https://cmr.earthdata.nasa.gov/search/concepts/C2244293499-AMD_KOPRI.json "Korean Antarctic survey carried out as in year 3 project of 'the Antarctic Undersea Geological Survey' was conducted in the basin region of western part of the Bransfeed Strait between the Antarctic Peninsula and the South Shetland Islands . During the field investigation, the seismic investigation and the drilling investigation was conducted at the same time. The investigation period took 9 days. 10 researchers from ‘Korea Ocean Research and Development Institute’ and 3 academic personnel participated in the cruise as field investigation personnel. We took on lease Russian R/V ""Yuzhmorgeologiya"" which is marine geology, geophysical survey vessel and Icebreaker." not-provided +KOPRI-KPDC-00000012.v1 1995 Seismic Data, Antarctica AMD_KOPRI 1995-12-13 1995-12-18 -58.335, -62.984444, -54.101944, -61.301111 https://cmr.earthdata.nasa.gov/search/concepts/C2244291641-AMD_KOPRI.json "Korean Antarctic survey carried out as in year 2 project of ""Antarctic submarine topography and sediment investigation"", The Field Survey of Antarctica was conducted at the end of 1995 was conducted the multi-channel Seismic Investigation and the drilling Investigation in the eastern part of the Bransfield Strait between the Antarctic Peninsula and the South Shetland Islands and near Sejong Station. We took on lease Russian R/V ""Yuzhmorgeologiya"" which is marine geology, geophysical survey vessel and Icebreaker for field investigation." not-provided +KOPRI-KPDC-00000014.v1 1994 Seismic Data, Antarctica AMD_KOPRI 1994-12-19 1994-12-27 -59.352778, -63.060278, -56.167778, -62.030833 https://cmr.earthdata.nasa.gov/search/concepts/C2244291414-AMD_KOPRI.json Korean Antarctic survey carried out as in year 1 of 'the Antarctic Undersea Geological Survey' was conducted at the end of 1994 was conducted Multi-channel Seismic Investgation and Drilling investigation in the central basin of the Bransfield Strait was located in between the Antarctic Peninsula and the South Shetland Islands and the Maxwell Bay area near Sejong Station. The field research was conducted wih other research at the same time. The research period was from 11 Dec. in 1994 to 23 Jan. in 1995 (13 days). - Korean Antarctic survey carried out as part of step 1 project in year 1 to investigate the possibility of oil resources in the Bransfield Strait of Antarctica. - Securing data for tectonic settings research in the same region. - Obtaining basic data for understanding marine geology and sedimentary layers in the same region. not-provided +KOPRI-KPDC-00000051.v1 1994 Sediment Core, Antarctica AMD_KOPRI 1994-12-31 1995-01-02 -58.026667, -62.42, -57.739722, -62.32 https://cmr.earthdata.nasa.gov/search/concepts/C2244291543-AMD_KOPRI.json "For the first year of study ""The Antarctic Undersea Geological Survey"", The Field Survey of Antarctica was conducted at the end of 1994 was conducted multi-channel seismic Investigation and drilling Investigation in the central basin of the Bransfield Strait was located in between the south Shetland Islands and the Antarctic peninsula and Maxwell bay area near Sejong Station. The field investigation was conducted research projects at the same time took 13 days from 11 Dec. in 1994 to 23 Jan. in 1995. - Korean Antarctic survey carried out as part of step 1 project in year 1 to investigate the possibility of oil resources in the Bransfield Strait of Antarctica. - Securing data for tectonic settings research in the same region. - Obtaining basic data for understanding marine geology and sedimentary layers in the same region." not-provided +KOPRI-KPDC-00000052.v1 1995 Sediment Core, Antarctica AMD_KOPRI 1995-12-19 1995-12-23 -55.951111, -61.969167, -55.051111, -61.951111 https://cmr.earthdata.nasa.gov/search/concepts/C2244291581-AMD_KOPRI.json "Korean Antarctic survey was conducted in the east basin of the Bransfield Strait between the Antarctic peninsula and south Shetland Islands and Maxwell Bay located at Sejong Station was conducted multi-channel seismic investigation and drilling investigation. We took on lease Russian ""Yuzhmorgeologiya""(5500 ton, ice strengthed vessel) which is marine geology, geophysical survey vessel and Icebreaker for field investigation." not-provided +KOPRI-KPDC-00000053.v1 1996 Sediment Core, Antarctica AMD_KOPRI 1996-12-16 1996-12-16 -60.151944, -62.100278, -59.717778, -62.051389 https://cmr.earthdata.nasa.gov/search/concepts/C2244291950-AMD_KOPRI.json "Korean Antarctic survey was conducted in west of the Bransfeed Strait, a basin between the Antarctic Peninsula and the south Shetland Islands. It tooks 9 days. seismic investigation and drilling investigation were conducted at the same time during the field survey. We took on lease Russian R/V ""Yuzhmorgeologiya"" which is marine geology, geophysical survey vessel and Icebreaker and 10 researchers from ‘Korea Ocean Research and Development Institute’ and 3 academic personnel participated in the cruise as field investigation personnel." not-provided +KOPRI-KPDC-00000054.v1 1997 Sediment Core, Antarctica AMD_KOPRI 1997-12-28 1997-12-29 -63.396667, -63.886111, -62.700833, -62.536389 https://cmr.earthdata.nasa.gov/search/concepts/C2244292254-AMD_KOPRI.json Korean Antarctic survey was conducted in 1997 carried out in a continental shelf in the northwestern part of the Antarctic Peninsula. It took 2 days. We took on lease Norway R/V 'Polar Duke' and 11 researchers from ‘Korea Ocean Research and Development Institute’ participated as field investigation personnel. The Teac single-channel recorder, EPC Recorder, Q/C MicroMax system etc. was used mainly by Sleeve gun used as a sound source, compressor for creating compressed air, DFS-V Recorder for multi-channel Seismic record, 12-channel geophone of seismic streamers. Additional Gravity Core was used for sediment research through drilling. not-provided +KOPRI-KPDC-00000055.v1 1998 Sediment Core, Antarctica AMD_KOPRI 1998-12-11 1998-12-12 -66.32, -63.95, -63.47, -62.943333 https://cmr.earthdata.nasa.gov/search/concepts/C2244294165-AMD_KOPRI.json "Korean Antarctic survey was conducted in the continental margin (II region) of the northwestern Antarctic Peninsula. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 10 researchers participated in the cruise, including acquisition of multichannel seismic, gravity, and magnetometer as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" not-provided L1B_Wind_Products Aeolus preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers ESA 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689596-ESA.json The Level 1B wind product of the Aeolus mission contains the preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers, which are generated in Near Real Time. Standard atmospheric correction (Rayleigh channel), receiver response and bias correction is applied. The product is generated within 3 hours after data acquisition. not-provided L2B_Wind_Products Aeolus Scientific L2B Rayleigh/Mie wind product ESA 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689544-ESA.json The Level 2B wind product of the Aeolus mission is a geo-located consolidated HLOS (horizontal line-of-sight) wind observation with actual atmospheric correction applied to Rayleigh channel. The product is generated by within 3 hours after data acquisition. not-provided L2C_Wind_products Aeolus Level 2C assisted wind fields resulting from NWP Numerical Weather Prediction assimilation processing ESA 2020-07-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2619280864-ESA.json The Level 2C wind product of the Aeolus mission provides ECMWF analysis horizontal wind vectors at the geolocations of assimilated L2B HLOS wind components. The L2C can therefore be described as an Aeolus-assisted horizontal wind vector product. The L2C is a distinct product, however the L2C and L2B share a common Earth Explorer file template, with the L2C being a superset of the L2B. The L2C consists of extra datasets appended to the L2B product with information which are relevant to the data assimilation of the L2B winds. not-provided @@ -246,7 +248,6 @@ Leaf_Photosynthesis_Traits_1224.v1 A Global Data Set of Leaf Photosynthetic Rate Level_2A_aerosol_cloud_optical_products Aeolus L2A Aerosol/Cloud optical product ESA 2021-05-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207498185-ESA.json "The Level 2A aerosol/cloud optical products of the Aeolus mission include geo-located consolidated backscatter and extinction profiles, backscatter-to-extinction coefficient, LIDAR ratio, scene classification, heterogeneity index and attenuated backscatter signals. Resolution - Horizontal resolution of L2A optical properties at observation scale (~87 km); Exceptions are group properties (horizontal accumulation of measurements from ~3 km to ~87 km) and attenuated backscatters (~3 km); Note: the resolution of ""groups"" in the L2A can only go down to 5 measurements at the moment, i.e. ~15 km horizontal resolution. This could be configured to go to 1 measurement - Vertical resolution 250-2000 m (Defined by Range Bin Settings https://earth.esa.int/eogateway/instruments/aladin/overview-of-the-main-wind-rbs-changes)." not-provided M1_AVH09C1.v6 METOP-B AVHRR Atmospherically Corrected Surface Reflectance Daily L3 Global 0.05 Deg CMG LAADS 2013-01-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2187507677-LAADS.json The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B. Currently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product. The METOP-B AVHRR Atmospherically Corrected Surface Reflectance Daily L3 Global 0.05 Deg CMG, short-name M1_ AVH09C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (AVH01C1). The M1_ AVH09C1 consist of BRDF-corrected surface reflectance for bands 1, 2, and 3, data Quality flags, angles (solar zenith, view zenith, and relative azimuth), and thermal data (thermal bands 3, 4, and 5). The AVH09C1 product is available in HDF4 file format. not-provided M1_AVH13C1.v6 METOP-B AVHRR Atmospherically Corrected Normalized Difference Vegetation Index Daily L3 Global 0.05 Deg. CMG LAADS 2013-01-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2751635237-LAADS.json The Long-Term Data Record (LTDR) produces, validates, and distributes a global land surface climate data record (CDR) that uses both mature and well-tested algorithms in concert with the best-available polar-orbiting satellite data from past to the present. The CDR is critically important to studying global climate change. The LTDR project is unique in that it serves as a bridge that connects data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR), the EOS Moderate resolution Imaging Spectroradiometer (MODIS), the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Joint Polar Satellite System (JPSS) VIIRS missions. The LTDR draws from the following eight AVHRR missions: NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19, and MetOp-B. Currently, the project generates a daily surface reflectance product as the fundamental climate data record (FCDR) and derives daily Normalized Differential Vegetation Index (NDVI) and Leaf-Area Index/fraction of absorbed Photosynthetically Active Radiation (LAI/fPAR) as two thematic CDRs (TCDR). LAI/fPAR was developed as an experimental product. The METOP-B AVHRR Atmospherically Corrected Normalized Difference Vegetation Index (NDVI) Daily L3 Global 0.05 Deg CMG, short-name M1_AVH13C1 is generated from GIMMS Advanced Processing System (GAPS) BRDF-corrected Surface Reflectance product (M1_AVH01C1). The M1_AVH13C1 product is available in HDF4 file format. not-provided -MCD14DL_C5_NRT.v005 MODIS/Aqua+Terra Thermal Anomalies/Fire locations 1km FIRMS V005 NRT LM_FIRMS 2014-01-28 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C1219768065-LM_FIRMS.json Near Real-Time (NRT) MODIS Thermal Anomalies / Fire locations processed by FIRMS (Fire Information for Resource Management System) - Land Atmosphere Near real time Capability for EOS (LANCE), using swath products (MOD14/MYD14) rather than the tiled MOD14A1 and MYD14A1 products. The thermal anomalies / active fire represent the center of a 1km pixel that is flagged by the MODIS MOD14/MYD14 Fire and Thermal Anomalies algorithm (Giglio 2003) as containing one or more fires within the pixel. This is the most basic fire product in which active fires and other thermal anomalies, such as volcanoes, are identified.MCD14DL are available in the following formats: TXT, SHP, KML, WMS. These data are also provided through the FIRMS Fire Email Alerts. Please note only the TXT and SHP files contain all the attributes. not-provided MIANACP.v1 MISR Aerosol Climatology Product V001 LARC 1999-11-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C185127378-LARC.json MIANACP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Aerosol Climatology Product version 1. It is 1) the microphysical and scattering characteristics of pure aerosol upon which routine retrievals are based; 2) mixtures of pure aerosol to be compared with MISR observations; and 3) likelihood value assigned to each mode geographically. The ACP describes mixtures of up to three component aerosol types from a list of eight components, in varying proportions. ACP component aerosol particle data quality depends on the ACP input data, which are based on aerosol particles described in the literature, and consider MISR-specific sensitivity to particle size, single-scattering albedo, and shape, and shape - roughly: small, medium and large; dirty and clean; spherical and nonspherical [Kahn et al. , 1998; 2001]. Also reported in the ACP are the mixtures of these components used by the retrieval algorithm. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. not-provided MIANCAGP.v1 MISR Ancillary Geographic Product V001 LARC 1999-11-07 2005-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C183897339-LARC.json MIANCAGP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Ancillary Geographic Product version 1. It is a set of 233 pre-computed files. Each AGP file pertains to a single Terra orbital path. MISR production software relies on information in the AGP, such as digital terrain elevation, as input to the algorithms which generate MISR products. The AGP contains eleven fields of geographical data. This product consists primarily of geolocation data on a Space Oblique Mercator (SOM) Grid. It has 233 parts, corresponding to the 233 repeat orbits of the EOS-AM1 Spacecraft. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. not-provided MIANCARP.v2 MISR Ancillary Radiometric Product V002 LARC 1999-12-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179031521-LARC.json MIANCARP_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Ancillary Radiometric Product version 2. It is composed of 4 files covering instrument characterization data, pre-flight calibration data, in-flight calibration data, and configuration parameters. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. not-provided @@ -271,6 +272,16 @@ NIPR_UAP_ELF_SYO 1-100Hz ULF/ELF Electromagnetic Wave Observation at Syowa Stati NMMIEAI-L2-NRT.v2 OMPS-NPP L2 NM Aerosol Index swath orbital NRT OMINRT 2011-11-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1657477341-OMINRT.json The OMPS-NPP L2 NM Aerosol Index swath orbital V2 for Near Real Time. For the standard product see the OMPS_NPP_NMMIEAI_L2 product in CMR .The aerosol index is derived from normalized radiances using 2 wavelength pairs at 340 and 378.5 nm. Additionally, this data product contains measurements of normalized radiances, reflectivity, cloud fraction, reflectivity, and other ancillary variables. not-provided NMTO3NRT.v2 OMPS-NPP L2 NM Ozone (O3) Total Column swath orbital NRT OMINRT 2011-10-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1439272084-OMINRT.json The OMPS-NPP L2 NM Ozone (O3) Total Column swath orbital product provides total ozone measurements from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi-NPP satellite.The total column ozone amount is derived from normalized radiances using 2 wavelength pairs 317.5 and 331.2 nm under most conditions, and 331.2 and 360 nm for high ozone and high solar zenith angle conditions. Additionally, this data product contains measurements of UV aerosol index and reflectivity at 331 nm.Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-90 to 90 degrees latitude), and there are about 14.5 orbits per day, each has typically 400 swaths. The swath width of the NM is about 2800 km with 36 scenes, or pixels, with a footprint size of 50 km x 50 km at nadir. The L2 NM Ozone data are written using the Hierarchical Data Format Version 5 or HDF5. not-provided NPBUVO3-L2-NRT.v2 OMPS-NPP L2 NP Ozone (O3) Vertical Profile swath orbital NRT OMINRT 2011-10-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1439296101-OMINRT.json The OMPS-NPP L2 NP Ozone (O3) Total Column swath orbital product provides ozone profile retrievals from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Profiler (NP) instrument on the Suomi-NPP satellite in Near Real Time. The V8 ozone profile algorithm relies on nadir profiler measurements made in the 250 to 310 nm range, as well as from measurements from the nadir mapper in the 300 to 380 nm range. Ozone mixing ratios are reported at 15 pressure levels between 50 and 0.5 hPa. Additionally, this data product contains measurements of total ozone, UV aerosol index and reflectivities at 331 and 380 nm. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 80 profiles. The NP footprint size is 250 km x 250 km. The L2 NP Ozone data are written using the Hierarchical Data Format Version 5 or HDF5. not-provided +NRSCC_GLASS_ FAPAR_MODIS_0.05D.v11 NRSCC_GLASS_ FAPAR_MODIS_0.05D NRSCC 2010-02-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2205351149-NRSCC.json This Global LAnd Surface Satellite (GLASS) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product was generated using MODIS products. not-provided +NRSCC_GLASS_ FAPAR_MODIS_1KM.v11 NRSCC_GLASS_ FAPAR_MODIS_1KM NRSCC 2000-02-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2205351155-NRSCC.json This Global LAnd Surface Satellite (GLASS) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product was developed using MODIS datasets. not-provided +NRSCC_GLASS_ LAI_AVHRR_0.05D.v11 NRSCC_GLASS_ LAI_AVHRR_0.05D NRSCC 1981-01-01 2018-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2205351175-NRSCC.json This Global LAnd Surface Satellite (GLASS) Leaf Area Index (LAI) product was developed using AVHRR datasets. not-provided +NRSCC_GLASS_ LAI_MODIS_0.05D.v11 NRSCC_GLASS_ LAI_MODIS_0.05D NRSCC 2000-02-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2205351151-NRSCC.json This Global LAnd Surface Satellite (GLASS) Leaf Area Index (LAI) product was developed using MODIS datasets. not-provided +NRSCC_GLASS_Albedo_AVHRR.v11 NRSCC_GLASS_Albedo_AVHRR NRSCC 2002-01-01 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2205351177-NRSCC.json Global high-resolution land surface albedo data from NOAA/AVHRR, generated by Global LAnd Surface Satellite (GLASS) Dataset production team. not-provided +NRSCC_GLASS_Albedo_MODIS_0.05D.v11 NRSCC_GLASS_Albedo_MODIS_0.05D NRSCC 2000-01-01 2018-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2205351167-NRSCC.json The Global LAnd Surface Satellite (GLASS) Albedo product derived from MODIS. The horizontal resolution is 0.05 Degree. not-provided +NRSCC_GLASS_Albedo_MODIS_1KM.v11 NRSCC_GLASS_Albedo_MODIS_1KM NRSCC 2000-01-01 2018-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2205351152-NRSCC.json The Global LAnd Surface Satellite (GLASS) Albedo product derived from MODIS. The horizontal resolution is 1KM. not-provided +NRSCC_GLASS_BBE_AVHRR.v11 NRSCC_GLASS_BBE_AVHRR NRSCC 1982-01-01 2017-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2205351148-NRSCC.json The Global LAnd Surface Satellite (GLASS) broadband emissivity (BBE) product derived from AVHRR. not-provided +NRSCC_GLASS_BBE_MODIS_0.05D.v11 NRSCC_GLASS_BBE_MODIS_0.05D NRSCC 2000-02-18 2018-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2205351185-NRSCC.json The Global LAnd Surface Satellite (GLASS) broadband emissivity (BBE) product derived from MODIS. The horizontal resolution is 0.05 Degree. not-provided +NRSCC_GLASS_BBE_MODIS_1KM.v11 NRSCC_GLASS_BBE_MODIS_1KM NRSCC 2000-02-18 2018-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2205351153-NRSCC.json NRSCC_GLASS_BBE_MODIS_1KM not-provided NSF-ANT05-37371 A Broadband Seismic Experiment to Image the Lithosphere Beneath the Gamburtsev Mountains and Surrounding Areas, East Antarctica AMD_USAPDC 2007-10-01 2013-09-30 40, -84, 140, -76 https://cmr.earthdata.nasa.gov/search/concepts/C2532069799-AMD_USAPDC.json This award supports a seismological study of the Gamburtsev Subglacial Mountains (GSM), a Texas-sized mountain range buried beneath the ice sheets of East Antarctica. The project will perform a passive seismic experiment deploying twenty-three seismic stations over the GSM to characterize the structure of the crust and upper mantle, and determine the processes driving uplift. The outcomes will also offer constraints on the terrestrial heat flux, a key variable in modeling ice sheet formation and behavior. Virtually unexplored, the GSM represents the largest unstudied area of crustal uplift on earth. As well, the region is the starting point for growth of the Antarctic ice sheets. Because of these outstanding questions, the GSM has been identified by the international Antarctic science community as a research focus for the International Polar Year (2007-2009). In addition to this seismic experiment, NSF is also supporting an aerogeophysical survey of the GSM under award number 0632292. Major international partners in the project include Germany, China, Australia, and the United Kingdom. For more information see IPY Project #67 at IPY.org. In terms of broader impacts, this project also supports postdoctoral and graduate student research, and various forms of outreach. not-provided NSF-ANT10-43485.v1 A New Reconstruction of the Last West Antarctic Ice Sheet Deglaciation in the Ross Sea AMD_USAPDC 2011-07-01 2015-06-30 -160, -78, -150, -68 https://cmr.earthdata.nasa.gov/search/concepts/C2532069944-AMD_USAPDC.json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. not-provided NSF-ANT10-43517 A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea AMD_USAPDC 2011-07-01 2015-06-30 163.5, -78.32, 165.35, -77.57 https://cmr.earthdata.nasa.gov/search/concepts/C2532070432-AMD_USAPDC.json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. not-provided @@ -316,6 +327,16 @@ USAP-1643722.v1 A High Resolution Atmospheric Methane Record from the South Pole USAP-1744755.v1 A mechanistic study of bio-physical interaction and air-sea carbon transfer in the Southern Ocean AMD_USAPDC 2018-05-01 2022-04-30 -80, -70, -30, -45 https://cmr.earthdata.nasa.gov/search/concepts/C2545372297-AMD_USAPDC.json Current generation of coupled climate models, that are used to make climate projections, lack the resolution to adequately resolve ocean mesoscale (10 - 100km) processes, exhibiting significant biases in the ocean carbon uptake. Mesoscale processes include many features including jets, fronts and eddies that are crucial for bio-physical interactions, air-sea CO2 exchange and the supply of iron to the surface ocean. This modeling project will support the eddy resolving regional simulations to understand the mechanisms that drives bio-physical interaction and air-sea exchange of carbon dioxide. not-provided USAP-1744989.v1 A Multi-scale Approach to Understanding Spatial and Population Variability in Emperor Penguins AMD_USAPDC 2018-07-15 2022-06-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2705787178-AMD_USAPDC.json This project on emperor penguin populations will quantify penguin presence/absence, and colony size and trajectory, across the entire Antarctic continent using high-resolution satellite imagery. For a subset of the colonies, population estimates derived from high-resolution satellite images will be compared with those determined by aerial surveys - these results have been uploaded to MAPPPD (penguinmap.com) and are freely available for use. This validated information will be used to determine population estimates for all emperor penguin colonies through iterations of supervised classification and maximum likelihood calculations on the high-resolution imagery. The effect of spatial, geophysical, and environmental variables on population size and decadal-scale trends will be assessed using generalized linear models. This research will result in a first ever empirical result for emperor penguin population trends and habitat suitability, and will leverage currently-funded NSF infrastructure and hosting sites to publish results in near-real time to the public. not-provided USAP-2130663.v1 2021 Antarctic Subsea Cable Workshop: High-Speed Connectivity Needs to Advance US Antarctic Science AMD_USAPDC 2021-06-01 2023-05-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2556670196-AMD_USAPDC.json Current networking capacity at McMurdo Station is insufficient to even be considered broadband, with a summer population of up to 1000 people sharing what is equivalent to the connection enjoyed by a typical family of three in the United States. The changing Antarctic ice sheets and Southern Ocean are large, complex systems that require cutting edge technology to do cutting edge research, with remote technology becoming increasingly useful and even necessary to monitor changes at sufficient spatial and temporal scales. Antarctic science also often involves large data-transfer needs not currently met by existing satellite communication infrastructure. This workshop will gather representatives from across Antarctic science disciplinesfrom astronomy to zoologyas well as research and education networking experts to explore the scientific advances that would be enabled through dramatically increased real-time network connectivity, and also consider opportunities for subsea cable instrumentation. This workshop will assess the importance of a subsea fiber optic cable for high-capacity real-time connectivity in the US Antarctic Program, which is at the forefront of some of the greatest climate-related challenges facing our planet. The workshop will: (1) document unmet or poorly met current scientific and logistic needs for connectivity; (2) explore connectivity needs for planned future research and note the scientific advances that would be possible if the full value of modern cyberinfrastructure-empowered research could be brought to the Antarctic research community; and (3) identify scientific opportunities in planning a fully instrumented communication cable as a scientific observatory. Due to the ongoing COVID-19 pandemic, the workshop will be hosted and streamed online. While the workshop will be limited to invited personnel in order to facilitate a collaborative working environment, broad community input will be sought via survey and via comment on draft outputs. A workshop summary document and report will be delivered to NSF. Increasing US Antarctic connectivity by orders of magnitude will be transformative for science and logistics, and it may well usher in a new era of Antarctic science that is more accessible, efficient and sustainable. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. not-provided +USGS_DDS_P14_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Los Angeles Basin Province CEOS_EXTRA 1990-12-01 1990-12-01 -119.63631, 32.7535, -117.52315, 34.17464 https://cmr.earthdata.nasa.gov/search/concepts/C2231552049-CEOS_EXTRA.json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 14 (Los Angeles Basin) are listed here by play number, type, and name: Number Type Name 1401 conventional Santa Monica Fault System and Las Cienegas Fault and Block 1402 conventional Southwestern Shelf and Adjacent Offshore State Lands 1403 conventional Newport-Inglewood Deformation Zone and Southwestern Flank of Central Syncline 1404 conventional Whittier Fault Zone and Fullerton Embayment 1405 conventional Northern Shelf and Northern Flank of Central Syncline 1406 conventional Anaheim Nose 1407 conventional Chino Marginal Basin, Puente and San Jose Hills, and San Gabriel Valley Marginal Basin not-provided +USGS_DDS_P16_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Salton Trough Province CEOS_EXTRA 1990-12-01 1990-12-01 -116.66911, 32.634293, -114.74501, 34.02059 https://cmr.earthdata.nasa.gov/search/concepts/C2231548651-CEOS_EXTRA.json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 16 (Salton Trough) are listed here by play number, type, and name. not-provided +USGS_DDS_P17_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Idaho - Snake River Downwarp Province CEOS_EXTRA 1990-12-01 1990-12-01 -117.24303, 41.99332, -111.04548, 49.00115 https://cmr.earthdata.nasa.gov/search/concepts/C2231550494-CEOS_EXTRA.json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 17 (Idaho - Snake River Downwarp) are listed here by play number, type, and name: Number Type Name 1701 conventional Miocene Lacustrine (Lake Bruneau) 1702 conventional Pliocene Lacustrine (Lake Idaho) 1703 conventional Pre-Miocene 1704 conventional Older Tertiary not-provided +USGS_DDS_P19_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Eastern Great Basin Province CEOS_EXTRA 1990-12-01 1990-12-01 -117.02622, 35.002083, -111.170425, 43.022377 https://cmr.earthdata.nasa.gov/search/concepts/C2231552402-CEOS_EXTRA.json "The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number, type, and name: Number Type Name 1901 conventional Unconformity ""A"" 1902 conventional Late Paleozoic 1903 conventional Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 conventional Younger Tertiary Basins 1906 conventional Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 conventional Sevier Frontal Zone" not-provided +USGS_DDS_P2_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Alaska Province CEOS_EXTRA 1990-12-01 1990-12-01 -173.22636, 58.49761, -140.99017, 68.01999 https://cmr.earthdata.nasa.gov/search/concepts/C2231550471-CEOS_EXTRA.json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 2 (Central Alaska) are listed here by play number, type, and name: Number Type Name 201 conventional Central Alaska Cenozoic Gas 202 conventional Central Alaska Mesozoic Gas 203 conventional Central Alaska Paleozoic Oil 204 conventional Kandik Pre-Mid-Cretaceous Strata 205 conventional Kandik Upper Cretaceous and Tertiary Non-Marine Stata not-provided +USGS_P-11_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Coastal Province CEOS_EXTRA 1990-12-01 1990-12-01 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231552077-CEOS_EXTRA.json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 11 (Central Coastal) are listed here by play number, type, and name: Number Type Name 1101 conventional Point Arena Oil 1102 conventional Point Reyes Oil 1103 conventional Pescadero Oil 1104 conventional La Honda Oil 1105 conventional Bitterwater Oil 1106 conventional Salinas Oil 1107 conventional Western Cuyama Basin 1109 conventional Cox Graben not-provided +USGS_SOFIA_eco_hist_db1995-2007.vversion 7 1995 - 2007 Ecosystem History of South Florida's Estuaries Database version 7 CEOS_EXTRA 1994-09-27 2007-04-03 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231554288-CEOS_EXTRA.json The 1995 - 2007 Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), modern monitoring site survey information (water chemistry, floral and faunal data, etc.), and published core data. Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - primarily faunal assemblages. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain either percent abundance data or actual counts of the distribution of mollusks, ostracodes, forams, and pollen within the cores collected in the estuaries. For some cores dinocyst or diatom data may be available. not-provided +USGS_cont1992 1992 Water-Table Contours of the Mojave River Ground-Water Basin, San Bernardino County, California CEOS_EXTRA 1970-01-01 -117.652695, 34.364513, -116.55357, 35.081955 https://cmr.earthdata.nasa.gov/search/concepts/C2231553864-CEOS_EXTRA.json This data set consists of digital water-table contours for the Mojave River Basin. The U.S. Geological Survey, in cooperation with the Mojave Water Agency, constructed a water-table map of the Mojave River ground-water basin for ground-water levels measured in November 1992. Water-level data were collected from approximately 300 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,200 to 1,600 feet above sea level. [Summary provided by the USGS.] not-provided +USGS_cont1994 1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California CEOS_EXTRA 1970-01-01 -117.07194, 34.095333, -115.98976, 34.64026 https://cmr.earthdata.nasa.gov/search/concepts/C2231554677-CEOS_EXTRA.json This data set consists of digital water-table contours for the Morongo Basin. The U.S. Geological Survey constructed a water-table map of the Morongo ground-water basin for ground-water levels measured during the period January-October 1994. Water-level data were collected from 248 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,400 to 1,500 feet above sea level. [Summary provided by the USGS.] not-provided +UTC_1990countyboundaries 1990 County Boundaries of the United States CEOS_EXTRA 1972-01-01 1990-12-31 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231550562-CEOS_EXTRA.json This data set portrays the 1990 State and county boundaries of the United States, Puerto Rico, and the U.S. Virgin Islands. The data set was created by extracting county polygon features from the individual 1:2,000,000-scale State boundary Digital Line Graph (DLG) files produced by the U.S. Geological Survey. These files were then merged into a single file and the boundaries were modified to what they were in 1990. This is a revised version of the March 2000 data set. not-provided WV01_Pan_L1B.v1 WorldView-1 Level 1B Panchromatic Satellite Imagery CSDA 2007-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2497387766-CSDA.json The WorldView-1 Level 1B Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Panchromatic imagery is collected by the DigitalGlobe WorldView-1 satellite using the WorldView-60 camera across the global land surface from September 2007 to the present. Data have a spatial resolution of 0.5 meters at nadir and a temporal resolution of approximately 1.7 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program. not-provided WV02_MSI_L1B.v1 WorldView-2 Level 1B Multispectral 8-Band Satellite Imagery CSDA 2009-10-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2497404794-CSDA.json The WorldView-2 Level 1B Multispectral 8-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-2 satellite using the WorldView-110 camera across the global land surface from October 2009 to the present. This satellite imagery is in the visible and near-infrared waveband range with data in the coastal, blue, green, yellow, red, red edge, and near-infrared (2 bands) wavelengths. It has a spatial resolution of 1.85m at nadir and a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program. not-provided WV02_Pan_L1B.v1 WorldView-2 Level 1B Panchromatic Satellite Imagery CSDA 2009-10-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2497398128-CSDA.json The WorldView-2 Level 1B Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery is collected by the DigitalGlobe WorldView-2 satellite using the WorldView-110 camera across the global land surface from October 2009 to the present. This data product includes panchromatic imagery with a spatial resolution of 0.46m and a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program. not-provided @@ -324,20 +345,14 @@ XAERDT_L2_ABI_G16.v1 ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km L XAERDT_L2_ABI_G17.v1 ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km LAADS 2019-01-01 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859265967-LAADS.json The ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G17 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite – GOES-17 served in the operational GOES-West position (near -137°W), from February 12, 2019, through January 4, 2023. The GOES-16/ABI collection record spans from January 2019 through December 2022. The XAERDT_L2_ABI_G17 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_ABI_G17 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G17 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ not-provided XAERDT_L2_AHI_H08.v1 AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km LAADS 2019-01-01 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859255251-LAADS.json The AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H08 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-8 platform served in the operational Himawari position (near 140.7°E) between October 2014 and 13 December 2022. Himawari-9 replaced Himawari-8 and is currently operational. The Himawari-8/AHI collection record spans from January 2019 through 12th December 2022. The final 19 days of 2022 (December 13 through 31) are served by L2 products derived from the Himawari-9/AHI instrument. The XAERDT_L2_AHI_H08 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H08 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H08 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ not-provided XAERDT_L2_AHI_H09.v1 AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km LAADS 2022-12-13 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859261579-LAADS.json The AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H09 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-9 platform currently serves in the operational Himawari position (near 140.7°E) since it was launched November 2, 2016, and replaces Himawari-8. The Himawari-9/AHI collection record spans from 13th December 2022 through 31st December 2022. The XAERDT_L2_AHI_H09 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H09 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H09 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ not-provided -a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics.v1.0 A numerical solver for heat and mass transport in snow based on FEniCS ENVIDAT 2022-01-01 2022-01-01 9.8472494, 46.812044, 9.8472494, 46.812044 https://cmr.earthdata.nasa.gov/search/concepts/C2789814662-ENVIDAT.json This python code uses the Finite Element library FEniCS (via docker) to solve the one dimensional partial differential equations for heat and mass transfer in snow. The results are written in vtk format. The dataset contains the code and the output data to reproduce the key Figure 5 from the related publication: _Schürholt, K., Kowalski, J., Löwe, H.; Elements of future snowpack modeling - Part 1: A physical instability arising from the non-linear coupling of transport and phase changes, The Cryosphere, 2022_ The code and potential updates can be accessed directly through git via: https://gitlabext.wsl.ch/snow-physics/snowmodel_fenics not-provided a6efcb0868664248b9cb212aba44313d ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 2 aerosol products from MERIS (ALAMO algorithm), Version 2.2 FEDEO 2008-01-01 2008-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142742-FEDEO.json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises the Level 2 aerosol products from MERIS for 2008, using the ALAMO algorithm, version 2.2. The data have been provided by Hygeos.For further details about these data products please see the linked documentation. not-provided aamhcpex.v1 AAMH CPEX GHRC_DAAC 2017-05-26 2017-07-16 154.716, 0.6408, -19.5629, 44.9689 https://cmr.earthdata.nasa.gov/search/concepts/C2645106424-GHRC_DAAC.json The AAMH CPEX dataset contains products obtained from the MetOp-A, MetOp-B, NOAA-18, and NOAA-19 satellites. These data were collected in support of the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May to 25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May to 24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 26, 2017, through July 15, 2017, and are available in netCDF-4 format. not-provided -above-and-below-ground-herbivore-communities-along-elevation.v1.0 Above- and below-ground herbivore communities along elevation ENVIDAT 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814648-ENVIDAT.json Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017. not-provided aces1am.v1 ACES Aircraft and Mechanical Data GHRC_DAAC 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977826980-GHRC_DAAC.json The ACES Aircraft and Mechanical Data consist of aircraft (e.g. pitch, roll, yaw) and mechanical (e.g. aircraft engine speed, tail commands, fuel levels) data recorded by the Altus II Unmanned Aerial Vehicle (Altus II UAV) system during the Altus Cumulus Electrification Study (ACES) based at the Naval Air Facility Key West in Florida. ACES aimed to provide extensive observations of the cloud electrification process and its effects by using the Altus II UAV to collect cloud top observations of thunderstorms. The campaign also worked to validate satellite lightning measurements. The Altus II aircraft and mechanical data files are available from July 10 through August 30, 2002 in MATLAB data format (.mat). not-provided aces1cont.v1 ACES CONTINUOUS DATA V1 GHRC_DAAC 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847043-GHRC_DAAC.json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August, 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloudelectrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, Dual Optical Pulse Sensor, Searchcoil Magnetometer, Accelerometers, Gerdien Conductivity Probe, and the Fluxgate Magnetometer. Data consists of sensor reads at 50HZ throughout the flight from all 64 channels. not-provided aces1efm.v1 ACES ELECTRIC FIELD MILL V1 GHRC_DAAC 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847178-GHRC_DAAC.json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data from Electric Field Mills, which yield information about the atmospheric electrical fields above the instruments. not-provided aces1log.v1 ACES LOG DATA GHRC_DAAC 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977853903-GHRC_DAAC.json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of log data from each flight, and yields instrument and aircraft status throughout the flight. not-provided aces1time.v1 ACES TIMING DATA GHRC_DAAC 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977855412-GHRC_DAAC.json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August or 2002, ACES researchers overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of timing data used for the experiment. When used it provides: syncclock_time = time found at the syncclock (VSI-SYnCCLOCK-32) in seconds from first file name, syncclock_m_time = time found at the syncclock (VSI-SYnCCLOCK-32) in Matlab dateform format, system_time = system time in seconds from first file name, system_m_time = system time in dateform format, gps_time = time found at the GPS unit in seconds from first file name, gps_m_time = time found at GPS unit in dateform, cmos_time = time found at the computer CMOS in seconds from first file name, cmos_m_time = time found at the computer CMOS in dateform. not-provided aces1trig.v1 ACES TRIGGERED DATA GHRC_DAAC 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977858342-GHRC_DAAC.json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from the following instruments: Slow/Fast antenna, Electric Field Mill, Optical Pulse Sensors, Searchcoil Magnetometer, Accelerometer, and Gerdien Conductivity Probe. These data were collected at 200KHz from the first 16 telemetry items collected on the aircraft, were initiated by an operator selected trigger (e.g. DOPS), and continued collecting for as long as the trigger continued. not-provided -aerosol-data-davos-wolfgang.v1.0 Aerosol Data Davos Wolfgang ENVIDAT 2020-01-01 2020-01-01 9.853594, 46.835577, 9.853594, 46.835577 https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. not-provided -aerosol-data-weissfluhjoch.v1.0 Aerosol Data Weissfluhjoch ENVIDAT 2020-01-01 2020-01-01 9.806475, 46.832964, 9.806475, 46.832964 https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. not-provided -alnus-glutinosa-orientus-ishidae-flavescence-doree.v1.0 Alnus glutinosa (L.) Gaertn. and Orientus ishidae (Matsumura, 1902) share phytoplasma genotypes linked to the “Flavescence dorée” epidemics ENVIDAT 2021-01-01 2021-01-01 8.4484863, 45.8115721, 9.4372559, 46.4586735 https://cmr.earthdata.nasa.gov/search/concepts/C2789814963-ENVIDAT.json Flavescence dorée (FD) is a grapevine disease caused by associated phytoplasmas (FDp), which are epidemically spread by their main vector Scaphoideus titanus. The possible roles of alternative and secondary FDp plant hosts and vectors have gained interest to better understand the FDp ecology and epidemiology. A survey conducted in the surroundings of a vineyard in the Swiss Southern Alps aimed at studying the possible epidemiological role of the FDp secondary vector Orientus ishidae and the FDp host plant Alnus glutinosa is reported. Data used for the publication. Insects were captured by using a sweeping net (on common alder trees) and yellow sticky traps (Rebell Giallo, Andermatt Biocontrol AG, Switzerland) placed in the vineyard canopy. Insects were later determined and selected for molecular analyses. Grapevines and common alder samples were collected using the standard techniques. The molecular analyses were conducted in order to identify samples infected by the Flavescence dorée phytoplasma (16SrV-p) and the Bois Noir phytoplasma (16SrXII-p). A selection of the infected sampled were further characterized by map genotype and sequenced in order to compare the genotypes in insects, grapevines and common alder trees. not-provided -alpine3d-simulations-of-future-climate-scenarios-for-graubunden.v1.0 Alpine3D simulations of future climate scenarios for Graubunden ENVIDAT 2019-01-01 2019-01-01 8.6737061, 46.2216525, 10.6347656, 47.1075228 https://cmr.earthdata.nasa.gov/search/concepts/C2789814545-ENVIDAT.json "This is the simulation dataset from _""Response of snow cover and runoff to climate change in high Alpine catchments of Eastern Switzerland""_, M. Bavay, T. Grünewald, M. Lehning, Advances in Water Resources __55__, 4-16, 2013 A model study on the impact of climate change on snow cover and runoff has been conducted for the Swiss Canton of Graubünden. The model Alpine3D has been forced with the data from 35 Automatic Weather Stations in order to investigate snow and runoff dynamics for the current climate. The data set has then been modified to reflect climate change as predicted for the 2021-2050 and 2070-2095 periods from an ensemble of regional climate models. The predicted changes in snow cover will be moderate for 2021-2050 and become drastic in the second half of the century. Towards the end of the century the snow cover changes will roughly be equivalent to an elevation shift of 800 m. Seasonal snow water equivalents will decrease by one to two thirds and snow seasons will be shortened by five to nine weeks in 2095. Small, higher elevation catchments will show more winter runoff, earlier spring melt peaks and reduced summer runoff. Where glacierized areas exist, the transitional increase in glacier melt will initially offset losses from snow melt. Larger catchments, which reach lower elevations will show much smaller changes since they are already dominated by summer precipitation." not-provided amprimpacts.v1 Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS GHRC_DAAC 2020-01-18 2023-03-02 -124.153, 26.507, -64.366, 49.31 https://cmr.earthdata.nasa.gov/search/concepts/C2004708841-GHRC_DAAC.json The Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS dataset consists of brightness temperature measurements collected by the Advanced Microwave Precipitation Radiometer (AMPR) onboard the NASA ER-2 high-altitude research aircraft. AMPR provides multi-frequency microwave imagery, with high spatial and temporal resolution for deriving cloud, precipitation, water vapor, and surface properties. These measurements were taken during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Data files are available from January 18, 2020, through March 2, 2023, in netCDF-4 format. not-provided amsua15sp.v1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15 GHRC_DAAC 1998-08-03 -180, -90, 180, 89.756 https://cmr.earthdata.nasa.gov/search/concepts/C1996541017-GHRC_DAAC.json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). NOAA-15 was the first spacecraft to fly AMSU. Launched on 13 May 1998, NOAA-15 is in a sun synchronous near polar orbit. not-provided amsua16sp.v1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-16 GHRC_DAAC 2001-05-27 2009-07-30 -180, -89.91, 180, 89.73 https://cmr.earthdata.nasa.gov/search/concepts/C1979956366-GHRC_DAAC.json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). Launched on 21 September 2000, NOAA-16 is in a sun synchronous near polar orbit. not-provided @@ -345,13 +360,11 @@ asas Advanced Solid-state Array Spectroradiometer (ASAS) USGS_LTA 1988-06-26 -1 aster_global_dem ASTER Global DEM USGS_LTA 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220567908-USGS_LTA.json ASTER is capable of collecting in-track stereo using nadir- and aft-looking near infrared cameras. Since 2001, these stereo pairs have been used to produce single-scene (60- x 60-kilomenter (km)) digital elevation models (DEM) having vertical (root-mean-squared-error) accuracies generally between 10- and 25-meters (m). The methodology used by Japan's Sensor Information Laboratory Corporation (SILC) to produce the ASTER GDEM involves automated processing of the entire ASTER Level-1A archive. Stereo-correlation is used to produce over one million individual scene-based ASTER DEMs, to which cloud masking is applied to remove cloudy pixels. All cloud-screened DEMS are stacked and residual bad values and outliers are removed. Selected data are averaged to create final pixel values, and residual anomalies are corrected before partitioning the data into 1 degree (°) x 1° tiles. The ASTER GDEM covers land surfaces between 83°N and 83°S and is comprised of 22,702 tiles. Tiles that contain at least 0.01% land area are included. The ASTER GDEM is distributed as Geographic Tagged Image File Format (GeoTIFF) files with geographic coordinates (latitude, longitude). The data are posted on a 1 arc-second (approximately 30–m at the equator) grid and referenced to the 1984 World Geodetic System (WGS84)/ 1996 Earth Gravitational Model (EGM96) geoid. not-provided b673f41b-d934-49e4-af6b-44bbdf164367 AVHRR - Land Surface Temperature (LST) - Europe, Daytime FEDEO 1998-02-23 -24, 28, 57, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2207458008-FEDEO.json "The ""Land Surface Temperature derived from NOAA-AVHRR data (LST_AVHRR)"" is a fixed grid map (in stereographic projection ) with a spatial resolution of 1.1 km. The total size covering Europe is 4100 samples by 4300 lines. Within 24 hours of acquiring data from the satellite, day-time and night-time LSTs are calculated. In general, the products utilise data from all six of the passes that the satellite makes over Europe in each 24 hour period. For the daily day-time LST maps, the compositing criterion for the three day-time passes is maximum NDVI value and for daily night-time LST maps, the criterion is the maximum night-time LST value of the three night-time passes. Weekly and monthly day-time or night-time LST composite products are also produced by averaging daily day-time or daily night-time LST values, respectively. The range of LST values is scaled between –39.5°C and +87°C with a radiometric resolution of 0.5°C. A value of –40°C is used for water. Clouds are masked out as bad values. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/" not-provided blue_ice_core_DML2004_AS 101.1 m long horizontal blue ice core collected from Scharffenbergbotnen, DML, Antarctica, in 2003/2004 SCIOPS 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C1214614210-SCIOPS.json Horizontal blue ice core collected from the surface of a blue ice area in Scharffenbergbotnen, Heimefrontfjella, DML. Samples were collected in austral summer 2003/2004 and transported to Finland for chemical analyses. The blue ice core is estimated to represent a 1000-year period of climate history 20 - 40 kyr B.P.. The results of the analyses will be available in 2005. not-provided -ch2014.v1 Alpine3D simulations of future climate scenarios CH2014 ENVIDAT 2014-01-01 2014-01-01 8.227, 46.79959, 8.227, 46.79959 https://cmr.earthdata.nasa.gov/search/concepts/C2789814657-ENVIDAT.json # Overview The CH2014-Impacts initiative is a concerted national effort to describe impacts of climate change in Switzerland quantitatively, drawing on the scientific resources available in Switzerland today. The initiative links the recently developed Swiss Climate Change Scenarios CH2011 with an evolving base of quantitative impact models. The use of a common climate data set across disciplines and research groups sets a high standard of consistency and comparability of results. Impact studies explore the wide range of climatic changes in temperature and precipitation projected in CH2011 for the 21st century, which vary with the assumed global level of greenhouse gases, the time horizon, the underlying climate model, and the geographical region within Switzerland. The differences among climate projections are considered using three greenhouse gas scenarios, three future time periods in the 21st century, and three climate uncertainty levels (Figure 1). Impacts are shown with respect to the reference period 1980-2009 of CH2011, and add to any impacts that have already emerged as a result of earlier climate change. # Experimental Setup Future snow cover changes are simulated with the physics-based model Alpine3D (Lehning et al., 2006). It is applied to two regions: The canton of Graubünden and the Aare catchment. These domains are modeled with a Digital Elevation Model (DEM) with a resolution of 200 m × 200 m. This defines the simulation grid that has to be filled with land cover data and downscaled meteorological input data for each cell for the time period of interest at hourly resolution. The reference data set consists of automatic weather station data. All meteorological input parameters are spatially interpolated to the simulation grid. The reference period comprises only thirteen years (1999–2012), because the number of available high elevation weather stations for earlier times is not sufficient to achieve unbiased distribution of the observations with elevation. The model uses projected temperature and precipitation changes for all greenhouse gas scenarios (A1B, A2, and RCP3PD) and CH2011 time periods (2035, 2060, and 2085). # Data Snow cover changes are projected to be relatively small in the near term (2035) (Figure 5.1 top), in particular at higher elevations above 2000 m asl. As shown by Bavay et al. (2013) the spread in projected snow cover for this period is greater between different climate model chains (Chapter 3) than between the reference period and the model chain exhibiting the most moderate change. In the 2085 period much larger changes with the potential to fundamentally transform the snow dominated alpine area become apparent (Figure 5.1 bottom). These changes include a shortening of the snow season by 5–9 weeks for the A1B scenario. This is roughly equivalent to an elevation shift of 400–800 m. The slight increase of winter precipitation and therefore snow fall projected in the CH2011 scenarios (with high associated uncertainty) can no longer compensate for the effect of increasing winter temperatures even at high elevations. In terms of Snow Water Equivalents (SWE), the projected reduction is up to two thirds toward the end of the century (2085). A continuous snow cover will be restricted to a shorter time period and/or to regions at increasingly high elevation. In Bern, for example, the number of days per year with at least 5 cm snow depth will decrease by 90% from now 20 days to only 2 days on average. not-provided chesapeake_val_2013.v0 2013 Chesapeake Bay measurements OB_DAAC 2013-04-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360188-OB_DAAC.json 2013 Chesapeake Bay measurements. not-provided darling_sst_82-93 1982-1989 and 1993 Seawater Temperatures at the Darling Marine Center SCIOPS 1982-03-01 1993-12-31 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214621676-SCIOPS.json Seawater Surface Temperature Data Collected between the years 1982-1989 and 1993 off the dock at the Darling Marine Center, Walpole, Maine not-provided eMASL1B.v1 Enhanced MODIS Airborne Simulator (eMAS) Calibrated, Geolocated Radiances L1B 50m Data LAADS 2013-08-01 2019-08-22 -180, -35, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2801308027-LAADS.json The Enhanced Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (eMAS)instrument is maintained and operated by the Airborne Sensor Facility at NASA Ames Research Center in Mountain View, California, under the oversight of the EOS Project Science Office at NASA Goddard. Prior to 1995, the MAS was deployed on the NASA's ER-2 and C-130 aircraft platforms using a 12-channel, 8-bit data system that somewhat constrained the full benefit of having a 50-channel scanning spectrometer. Beginning in January 1995, a 50-channel, 16-bit digitizer was used on the ER-2 platform, which greatly enhanced the capability of MAS to simulate MODIS data over a wide range of environmental conditions. Recently, it has undergone extensive upgrades to the optics and other components. New detectors have been installed and the spectral bands have been streamlined. The eMAS instrument is now a 38-channel instrument, sensing in the range from 0.445 to 13.844 um. For more information and for a list of MAS campaign flights visit ladsweb at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/mas/ or, visit the eMAS Homepage at: https://asapdata.arc.nasa.gov/emas/ not-provided eMASL2CLD.v1 Enhanced MODIS Airborne Simulator (eMAS) L2 Cloud Data LAADS 2013-08-01 2016-09-28 -180, -35, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2801723593-LAADS.json The Enhanced Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (eMAS)instrument is maintained and operated by the Airborne Sensor Facility at NASA Ames Research Center in Mountain View, California, under the oversight of the EOS Project Science Office at NASA Goddard. The eMAS instrument is now a 38-channel instrument, sensing in the range from 0.445 to 13.844 um. The Enhanced MODIS Airborne Simulator (eMAS) L2 Cloud Data product (eMASL2CLD) consists of cloud optical and physical parameters. These parameters are derived using remotely sensed infrared and near infrared solar reflected radiances. Multispectral images of the reflectance and brightness temperature at 10 wavelengths between 0.66 and 13.98nm were used to derive the probability of clear sky (or cloud), cloud thermodynamic phase, and the optical thickness and effective radius of liquid water and ice clouds. The eMASL2CLD product files are stored in Hierarchical Data Format (HDF-EOS). All gridded cloud parameters are stored as Scientific Data Sets (SDS) within the file. For more information and for a list of MAS campaign flights visit ladsweb at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/mas/ or, visit the eMAS Homepage at: https://asapdata.arc.nasa.gov/emas/ not-provided ef6a9266-a210-4431-a4af-06cec4274726 Cartosat-1 (IRS-P5) - Panchromatic Images (PAN) - Europe, Monographic FEDEO 2015-02-10 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207457985-FEDEO.json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. The satellite has two panchromatic cameras that were especially designed for in flight stereo viewing. However, this collection contains the monoscopic data. not-provided -envidat-lwf-34.v2019-03-06 10-HS Pfynwald ENVIDAT 2019-01-01 2019-01-01 7.61211, 46.30279, 7.61211, 46.30279 https://cmr.earthdata.nasa.gov/search/concepts/C2789815241-ENVIDAT.json Continuous measurement of soil water content at 10 and 80 cm depth (3 replications) with 10-HS soil moisture probes (Decagon Incorporation, Pullman, WA, USA). ### Purpose: ### Monitoring of the soil water matrix potential ### Paper Citation: ### * Dobbertin, M.; Eilmann, B.; Bleuler, P.; Giuggiola, A.; Graf Pannatier, E.; Landolt, W.; Schleppi, P.; Rigling, A., 2010: Effect of irrigation on needle morphology, shoot and stem growth in a drought-exposed Pinus sylvestris forest. Tree Physiology, 30, 3: 346-360. [doi: 10.1093/treephys/tpp123](http://doi.org/10.1093/treephys/tpp123) not-provided fife_hydrology_strm_15m_1.v1 15 Minute Stream Flow Data: USGS (FIFE) ORNL_CLOUD 1984-12-25 1988-03-04 -96.6, 39.1, -96.6, 39.1 https://cmr.earthdata.nasa.gov/search/concepts/C2977827088-ORNL_CLOUD.json USGS 15 minute stream flow data for Kings Creek on the Konza Prairie not-provided fife_sur_met_rain_30m_2.v1 30 Minute Rainfall Data (FIFE) ORNL_CLOUD 1987-05-29 1987-10-26 -96.6, 39.08, -96.55, 39.11 https://cmr.earthdata.nasa.gov/search/concepts/C2977893818-ORNL_CLOUD.json 30 minute rainfall data for the Konza Prairie not-provided gov.noaa.nodc:0000029 1990, 1991, 1992 and 1995 CRETM/LMER Zooplankton Data Sets (NCEI Accession 0000029) NOAA_NCEI 1990-09-26 1995-05-26 -124.041667, 0.766667, -16.25, 46.263167 https://cmr.earthdata.nasa.gov/search/concepts/C2089372282-NOAA_NCEI.json Not provided not-provided @@ -368,9 +381,5 @@ gov.noaa.nodc:GHRSST-OISST_UHR_NRT-GOS-L4-BLK.v2.0 Black Sea Ultra High Resoluti gov.noaa.nodc:GHRSST-REMSS-L2P_GRIDDED_25-TMI.v4.0 GHRSST L2P Gridded Global Subskin Sea Surface Temperature from the Tropical Rainfall Mapping Mission (TRMM) Microwave Imager (TMI) (GDS version 1) GHRSSTCWIC 1998-01-01 2015-04-06 -180, -40, 180, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2213645156-GHRSSTCWIC.json "The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is a well calibrated passive microwave radiometer, similar to SSM/I, that contains lower frequency channels required for sea surface temperature (SST) retrievals. The TRMM is a joint venture between NASA and the Japan Aerospace Exploration Agency (JAXA) to measure precipitation, water vapor, SST and wind in the global tropical regions and was launched in November 1997. The TRMM satellite travels west to east in a 402 km altitude semi-equatorial precessing orbit that results in day-to-day changes in the observation time of any given earth location between 38S and 38N. In contrast to infrared SST observations, microwave retrievals can be measured through most clouds, and are also insensitive to water vapor and aerosols. Remote Sensing Systems is the producer of these gridded TMI SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project. Although the product designation is ""L2P_GRIDDED"" it is in actuality a Level 3 Collated (L3C) product as defined in the GHRSST Data Processing Specification (GDS) version 2.0. Its ""L2P_GRIDDED"" name derives from a deprecated specification in the early Pilot Project phase of GHRSST (pre 2008) and has remained for file naming continuity. In this dataset, both ascending (daytime) and descending (daytime) gridded orbital passes on packaged into the same daily file." not-provided gov.noaa.nodc:GHRSST-VIIRS_NPP-NAVO-L2P.v3.0 GHRSST Level 2P 1 m Depth Global Sea Surface Temperature version 3.0 from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi NPP satellite (GDS version 2) GHRSSTCWIC 2013-06-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2213644303-GHRSSTCWIC.json A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). This sensor resides on the Suomi National Polar-orbiting Partnership (Suomi_NPP) satellite launched on 28 October 2011. VIIRS is a whiskbroom scanning radiometer which takes measurements in the cross-track direction within a field of regard of 112.56 degrees using 16 detectors and a double-sided mirror assembly. At a nominal altitude of 829 km, the swath width is 3060 km, providing full daily coverage both on the day and night side of the Earth. The VIIRS instrument is a 22-band, multi-spectral scanning radiometer that builds on the heritage of the MODIS, AVHRR and SeaWiFS sensors for sea surface temperature (SST) and ocean color. For the infrared bands for SST the effective pixel size is 750 meters at nadir and the pixel size variation across the swath is constrained to no more than 1600 meters at the edge of the swath. This L2P SST v3.0 is upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades. It contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates. Each netCDF file has 768 x 3200 pixels in size, in compliance with the GHRSST Data Processing Specification (GDS) version 2 format specifications. not-provided lake_erie_aug_2014.v0 2014 Lake Erie measurements OB_DAAC 2014-08-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360418-OB_DAAC.json 2014 Lake Erie measurements. not-provided -latent-reserves-in-the-swiss-nfi.v1.0 'Latent reserves' within the Swiss NFI ENVIDAT 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815280-ENVIDAT.json "The files refer to the data used in Portier et al. ""‘Latent reserves’: a hidden treasure in National Forest Inventories"" (2020) *Journal of Ecology*. **'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for >40 to >70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered ‘latent reserves’, criteria were defined based on the information available in the Swiss NFI database: * Shrub forests were excluded. * Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested. * To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Klötzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994). * Plots had to originate from natural regeneration. * Intensive livestock grazing must never have occurred on the plots. The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). **** The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper. The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement. " not-provided mbs_wilhelm_msa_hooh.v1 15 year Wilhelm II Land MSA and HOOH shallow ice core record from Mount Brown South (MBS) AU_AADC 1984-01-01 1998-12-31 86.082, -69.13, 86.084, -69.12 https://cmr.earthdata.nasa.gov/search/concepts/C1214313640-AU_AADC.json This work presents results from a short firn core spanning 15 years collected from near Mount Brown, Wilhelm II Land, East Antarctica. Variations of methanesulphonic acid (MSA) at Mount Brown were positively correlated with sea-ice extent from the coastal region surrounding Mount Brown (60-1208 E) and from around the entire Antarctic coast (0-3608 E). Previous results from Law Dome identified this MSA-sea-ice relationship and proposed it as an Antarctic sea-ice proxy (Curran and others, 2003), with the strongest results found for the local Law Dome region. Our data provide supporting evidence for the Law Dome proxy (at another site in East Antarctica), but a deeper Mount Brown ice core is required to confirm the sea-ice decline suggested by Curran and others (2003). Results also indicate that this deeper record may also provide a more circum-Antarctic sea-ice proxy. This work was completed as part of ASAC project 757 (ASAC_757). not-provided -mosaic-cbers4-brazil-3m-1 CBERS-4/WFI Image Mosaic of Brazil - 3 Months INPE 2020-04-01 2020-06-30 -76.6054059, -33.7511817, -27.7877802, 6.3052432 https://cmr.earthdata.nasa.gov/search/concepts/C3108204634-INPE.json CBERS-4/WFI image mosaic of Brazil with 64m of spatial resolution. The mosaic was prepared in order to demonstrate the technological capabilities of the Brazil Data Cube project tools. The false color composition is based on the WFI bands 15, 16 and 13 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in April 2020 and ending in June 2020, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 1200 CBERS-4 scenes and was generated based on an existing CBERS-4/WFI image collection. not-provided -mosaic-cbers4a-paraiba-3m-1 CBERS-4A/WFI Image Mosaic of Brazil Paraíba State - 3 Months INPE 2020-07-01 2020-09-30 -38.8134896, -8.3976443, -34.7223714, -5.87659 https://cmr.earthdata.nasa.gov/search/concepts/C3108204719-INPE.json CBERS-4A/WFI image mosaic of Brazil Paraíba State with 55m of spatial resolution. The mosaic was prepared in order to demonstrate the technological capabilities of the Brazil Data Cube project tools. The false color composition is based on the WFI bands 16, 15 and 14 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in July 2020 and ending in September 2020, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 50 CBERS-4A scenes and was generated based on an existing CBERS-4A/WFI image collection. not-provided -pfynwaldgasexchange.v1.0 2013-2020 gas exchange at Pfynwald ENVIDAT 2021-01-01 2021-01-01 7.6105556, 46.3001905, 7.6163921, 46.3047564 https://cmr.earthdata.nasa.gov/search/concepts/C2789816347-ENVIDAT.json Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds. All metadata can be found in the attached documents. not-provided urn:ogc:def:EOP:VITO:VGT_S10.v1 10 Days Synthesis of SPOT VEGETATION Images (VGT-S10) FEDEO 1998-04-01 2014-05-31 -180, -56, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2207472890-FEDEO.json The VGT-S10 are near-global or continental, 10-daily composite images which are synthesised from the 'best available' observations registered in the course of every 'dekad' by the orbiting earth observation system SPOT-VEGETATION. The products provide data from all spectral bands (SWIR, NIR, RED, BLUE), the NDVI and auxiliary data on image acquisition parameters. The VEGETATION system allows operational and near real-time applications, at global, continental and regional scales, in very broad environmentally and socio-economically critical fields. The VEGETATION instrument is operational since April 1998, first with VGT1, from March 2003 onwards, with VGT2. More information is available on: https://docs.terrascope.be/#/DataProducts/SPOT-VGT/Level3/Level3 not-provided