For the last 5 years I’ve been actively involved in training efforts through Software Carpentry and Data Carpentry to train researchers in best practices for software development and data analysis. These are concepts that are fundamental to the research we do in my gropu and my commitment to open and reproducible research.
As one of the founding members of the Data Carpentry Steering Committee, I am excited to announce that Data Carpentry has received a grant from the Gordon and Betty Moore Foundation that will help support our work over the next two years.
For those of you who aren’t familiar with Data Carpentry, we are a non-profit organization whose goal is to help teach scientists the skills they need to manage and analyze the increasingly large amounts of data that are being generated across the sciences. We do this through a combination of 2 day workshops at universities (if you’re interested in a workshop at your university request one here), and online resources including lesson material and forums. Data Carpentry is both similar to, and associated with, Software Carpentry, but with an emphasis on teaching material that is specific to particular scientific disciplines and focused on data management and analysis. We currently deliver courses for ecology/organismal biology and are in the process of developing material on genomics and geospatial data. The later in collaboration with awesome training group at NEON.
The support from the Moore Foundation will help us expand our efforts to cover new scientific domains, run far more workshops than we could have otherwise, and develop strategies for delivering this material in online workshops. I will also be leading the development of a semester long Data Carpentry course designed to make it easy to integrate these crucial skills into university classrooms. Check out the full proposal for more details.
I look forward to continuing my work with Data Carpentry and am excited about the opportunity for us to continue to enable data-intensive science by providing scientists the computational and data-oriented training they need to work with the large quantities of data we now have access to.