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Participant Intros
Please list:
- Name
- Github account
- Brief introduction. Include relevant interests, projects (if any), Jupyter/Python expertise (if any) & experience teaching Reproducible Research practices (if any).
In my day job I am Director of Informatics at Duke's Center for Genomic and Computational Biology (GCB). I am interested in how informatics tools, resources, and approaches can be used to enable more and better science, in particular for the long tail. Much of my work centers around making scientific data, software, and knowledge more reusable and more interoperable, which I belief especially for computational research goes hand in hand with reproducible science. I have co-taught in all 3 of the Reproducible Science Curriculum teaching workshops held so far. I have very little experience using Python or Jupyter, and abhor the idea of using whitespace indentation for language syntax 😏
I'm a PhD student in the Statistics Department at Berkeley and a Fellow at BIDS. My thesis work is about pseudorandom number generators, random sampling, and permutation testing. More broadly, I'm interested in how these statistical issues relate to reproducible science and teaching Statistics with attention to algorithms, good coding practices, and clear communication through figures and narrative. I use Jupyter to report the results of simulations in my research, and have worked on several open source Python packages for Statistics.
I'm a PhD student in the Cell & Molecular Biology program at the University of Texas. My thesis research is in the realm of behavioral genomics. In particular, I'm investigating the molecular and physiological changes that happen in the brain of a mouse as it learns and forms memories. I am an an active member of the Software Carpentry and Data Carpentry communities. I'm most excited about my new(ish) role as a Data Carpentry instructor trainer, I'm looking forward to empowering our instructors and community with a new curricula for reproducible research. I'm a novice python user.
I'm a PhD candidate (<6 months away!) in Bioinformatics and Systems Biology at UC San Diego. For my dissertation, I develop machine-learning algorithms to quantify and classify single-cell alternative splicing in neurodevelopment. I love teaching programming and machine learning, especially to beginners, because (1) they get the most out of it and (2) it makes me so happy to show that programming isn't scary. I taught ~20 hours of bioinformatics at a 2-week course in Long Island (notebooks) and three weeks of a graduate-level quantitative genomics course (notebooks). I'm active in the scientific Python community - I developed prettyplotlib, contribute to seaborn and write piles and piles and piles of code for machine learning and RNA-seq data.
Clara Sorensen, @clarasorensen
I'm an undergraduate double majoring in Biology and Computer Science at Wellesley College. As a student researcher in the Data, Analytics, and Visualization lab, I've been helping to develop software applications on multi-touch displays that abstractly teach data science skills. For fun, I use Jupyter notebooks to analyze both personal (ex: browser history) and public (ex: CDC diabetes rates) datasets. With my background in Python and some of the open source data analysis libraries, I'm looking forward to contributing as best I can with coding knowledge. I also look forward to learning all about reproducible science.
Elizabeth Wickes, @elliewix
I am a data curation specialist with the University Library at the University of Illinois at Urbana-Champaign. My work with the Research Data Service unit is focused on outreach, data management training, and the promotion of data and code sharing practices. All these intersect with reproducible research. I conduct all our internal analytics to measure the usage and engagement with our workshops, social media, and data repository services. I'm also an adjunct instructor with the School of Information at Illinois, where I teach a grad level introduction to programming with Python. I also co-organize the Champaign-Urbana Python User Group (PyCU). I use Jupyter Notebooks and R as my primary vehicles for data work, and I'm eager to learn more about Jupyter. I am also involved with teaching Software and Data Carpentry here at UIUC.
Bridget Hass, @bridgethass
I work with aerial remote sensing data (primarily LiDAR and hyperspectral) at the National Ecological Observatory Network Airborne Observation Platform (NEON AOP). Prior to starting at NEON, I worked in marine geophysics as a shipboard technician at Scripps Institution of Oceanography and as a graduate researcher at Oregon State University. While most of my programming experience has been in Matlab, I have been learning Python and am currently helping develop a Remote Sensing Data Institute in Python Jupyter Notebooks that is designed to help NEON AOP data users learn how to access, manipulate, and work with NEON's hyperspectral data products. Throughout my scientific career, I have been continually trying to improve reproducibility of my workflows, and I am excited to contribute to this workshop as I think it has great potential to enhance best practices in the scientific research community. I wish I had been able to attend a workshop on reproducible science as an undergraduate or Master's student!
I'm a (hopefully soon to finish) graduate student in neuroscience here at UC Berkeley, as well as a fellow at the Berkeley Institute for Data Science. I spend my science time trying to fit models that predict brain activity using features of the world in order to study auditory perception. I've been an editor and the web director with the Berkeley Science Review for about 7 years now, and I've got a general passion for making scientific research and tools more understandable and useful for people. To that effect I've also been working with Berkeley's Data 8 course for undergraduates, which utilizes the jupyter stack heavily in order to make it easier for students to learn how to analyze data. I think this hackathon and the products from it have the potential to save many researchers a lot of time, and I'm excited to see what comes out of it!
I'm a Postdoc at Berkeley in the Molecular and Cellular Biology department. I research enhancer function and divergence using comparative genomic approaches in Drosophila. I code in both R and Python, but am more familiar with R. I became interested in reproducibility while learning to code and found it frustrating that more information was not available on how to properly execute reproducible research. I came to learn that there are no set standards, which is why I love being involved in workshops like these where scientists get together and have dialog about what works and what does’t. I have been involved in the Ropensci and the Carpentry communities and have helped create and teach the Reproducible Research Curriculum previously. I look forward to expanding the curriculum for Python and Jupyter notebooks.
I am a PhD student in the Division of Biostatistics here at UC Berkeley. My research combines causal inference, machine learning, and nonparametric statistics, focusing on the development of robust methods for addressing inference problems from precision medicine, computational biology, and clinical trials. My engagement in reproducible research includes promoting open source software, designing reproducible workflows for data analysis, and the development of statistical software (mostly R, but some Python). I often use the Jupyter software stack and related tools (e.g., R notebooks) for communicating scientific findings. I am excited to participate in the development of a curriculum for better promoting these tools in the scientific community.
Erin Becker, @ErinBecker
I'm the Associate Director with Data Carpentry. We develop and teach workshops to give researchers the fundamental data skills they need to conduct their research. My work focuses on supporting the Data Carpentry volunteer instructor community. I have a background in education research, specifically in curriculum development, assessment design, and effective methods for training instructors in evidence-based teaching practices. I have used Jupyter notebooks in my own research, but have not interacted with them in several years. I'm excited to be joining the hackathon and helping offer perspective on best practices for curriculum development and assessment.
Leah Wasser, @lwasser I am the director of the Earth Analytics Education Initiative at Earth Lab, CU Boulder. Here, I am building a program in Earth Analytics which teaches skills that facilitate the integration of heterogeneous data using computationally intensive approaches. Previously, I was at NEON and built the Data Skills program, which hosted workshops and a suite of online tutorials related to R, remote sensing and spatial data. I have been actively involved with Data Carpentry - building lessons and teaching workshops. I have been using components of the existing reproducible science curriculum in many of my workshops and presentations. I think it's terrific and I look forward to being a part of this new development effort! Our lab is very focused on teaching open science skills and will definitely use / teach materials developed through this event. Excited to meet everyone!
Brian Avery, @nerdcommander
I've been teaching genetics, neuroscience, development, and cell biology full time for 15 years at Westminster College in Salt Lake City, UT. I'm a more recent convert from "old school" science to data science but have been messing around with computers and programming since the '80s. I've been learning R and Rmarkdown for about a year and have recently launched headlong into Python and Jupyter. I often teach quantitative skills in my science classes and research methods classes, and this coming semester I'm co-teaching scientific computing in Python using Jupyter notebooks and am looking forward to using the materials developed during the hackathon. (a side note, this is my first hackathon and I'm pretty excited about it!)
Tony Hirst, @psychemedia
I'm a Senior Lecturer from the UK's Open University, most recently working as part of a team producing an undergrad computing course on Data Analysis and Management, which used Jupyter notebooks alongside postgres and mongo databases in a custom virtual machine; we also used Jupyter notebooks for an entry level "Learn to Code for Data Analysis" MOOC (notebooks, also here (no registration required). I'm interested in lowering barriers to entry/widening engagement and participation around the use of open data sets and open data analysis tools, eg in education, journalism, public and third sector, without the need to be skilled in sys admin.
Analise Hofmann, @ahofmann4
I am a PhD candidate at The University of British Columbia in the Genome Science & Technology Program. I have a background in biochemistry, cell biology, and a little programming. I dabble in automating my analysis with R and Matlab, and I have started learning a little Python. I have a passion for teaching, and have been learning teaching pedagogy in the Certificate in Advanced Teaching and Learning program at UBC. Furthermore, I have some teaching experience in a variety of levels mainly of biological topics, but I would love to move into the realm between biology and programming. I look forward to learning more about reproducible data analysis, and participating in my first hackathon.
I'm an Assistant Professor of Environmental Studies and Planning at Sonoma State University. My teaching focuses on energy and the environment and my research on the barriers to energy access in underdeveloped areas. In my teaching and mentoring I use the Jupyter Notebook to empower students to create and share the results of their computations and estimations in a way that can be replicated. I've started developing notes that treat the Jupyter Notebook as a natural extension of the calculator for undergraduates with no programming experience for work that is quantitative but not computationally intensive. I'm looking forward to brainstorming about the core concepts, competencies, and tools to get folks using computation more reproducibly.
François Michonneau, @fmichonneau
I'm a postdoctoral researcher at the Whitney Lab for Marine Bioscience at the University of Florida. My research interests are motivated by biodiversity, especially among marine invertebrates. I received my PhD studying species limits and systematics of sea cucumbers. My research involves dealing with messy, heterogeneous data (DNA sequences, collection information, locality data, etc.), and I use R to assemble all these data sources for analysis. I think reproducibility is necessary to speed up science, and that teaching basic computing skills to scientists early in their careers could be transformative. For these reasons, I have been actively involved in Data Carpentry and was an organizing member of the Reproducible Science Curriculum. I have almost no experience with Python and notebooks, and I'm curious to see how they can be used to adapt the material initially developed.
I'm a postdoctoral research at the University of California, Davis where I work on the Data Carpentry Reproducible Research with R Curriculum. I received my PhD from Michigan State University where I worked on species delimitation in butterflies. My research combines field work and large data (genomic sequencing, ecological data, building databases) to answer fundamental questions about species diversity. I am an instructor for both Software Carpentry and Data Carpentry and am actively involved with curriculum development. I work primarily in 'R' but have been working regularly with 'Python' and Jupyter Notebook.
R. Burke Squires, @burkesquires I am a contract Computational Biologist at National Institutes of Allergy and Infectious Disease (NIAID) at the National Institutes of Health (NIH) in Bethesda, Maryland in the Bioinformatics and Computational Biosciences Branch (BCBB). I have been programming in python for about 7 or 8 years. I teach a python programming as a series of 3 hour informal seminars on the NIH campus, as well as a more traditional course semester long course at the Foundation for Advanced Education in the Sciences (FAES) also on the NIH campus. I converted all of my training to Jupyter Notebooks 2 - 3 years ago. I am planning on teaching a reproducible science seminar at the NIH this spring.
I'm a new Assistant Professor of Biology (teaching Bioinformatics and Ecology) at the University of San Francisco. My research focuses on the microbial ecology of plant-fungal interactions. I primarily do my own computational work in R, but occasionally dip into Python and Jupyter notebooks to do sequence processing or image analysis. I'm a cofounder of the International Network of Next-Generation Ecologists. I have also been teaching for Software Carpentry and Data Carpentry for several years now and am a maintainer for lesson repositories of R materials for those workshops. I became particularly interested in reproducible research after discovering how challenging it is to keep track of complex analyses in my own dissertation and postdoctoral work. I am also very interested in thinking about how to improve undergraduate and graduate education in Ecology, particularly with regard to the development of computational and statistical skills. I've contributed to or organized a number of workshops at conferences (ESA, INTECOL) focused on software skill development (ggplot, git, vegan), all of which were written and developed collaboratively using github and either beamer or md slides.