- We live in a time where the scientific community is generating information at an unprecedented rate. In the ecological community alone, we now generate a high volume of sensor-derived data in addition to experimental and observational data collected over many decades. While such datasets (and other products of scientific endavours) could be leveraged to generated new synthesis and breakthroughs, social (such as outdated publishing practices) and techological barriers have made it difficult to access such data. We need to make science more open, transparent and reproducible. (10.1126/science.1197962). To that end, I am working on a handful of projects.
In collaboration with folks from DataONE, I am trying to understand how scientists do their research. In particular I spent the summer working with Bertram Ludäscher & Bill Michener to understand more about workflows used in science and how they are used and reused by the community. I mentored Richard Littaeur on a DataONE internship Understanding workflows.
R for Open Science
In July 2011 I began working with Carl Boettiger and Scott Chamberlain on developing a series of tools to facilitate open science in the R statistical environment. Over the course of the next several months we plan to release a set of packages that can interact with journals and data repositories to bring a open access data directly into the R environment. Although our motivation is to leverage openly available data to answer ecological questions, many of our tools will be useful to scientists across disciplines. Read more about the project at ropensci.org