Abstract: What are the challenges and best practices for doing data-intensive research in teams, labs, and other groups? This paper reports from a discussion in which researchers from many different disciplines and departments shared their experiences on doing data science in their domains. The issues we discuss range from the technical to the social, including issues with getting on the same computational stack, workflow and pipeline management, handoffs, composing a well-balanced team, dealing with fluid membership, fostering coordination and communication, and not abandoning best practices when deadlines loom. We conclude by reflecting about the extent to which there are universal best practices for all teams, as well as how these kinds of informal discussions around the challenges of doing research can help combat impostor syndrome.
Recommended citation: R. Stuart Geiger, Dan Sholler, Aaron Culich, Ciera Martinez, Fernando Hoces de la Guardia, François Lanusse, Kellie Ottoboni, Marla Stuart, Maryam Vareth, Nelle Varoquaux, Sara Stoudt, and Stéfan van der Walt. “Challenges of Doing Data-Intensive Research in Teams, Labs, and Groups.” BIDS Best Practices in Data Science Series. Berkeley Institute for Data Science: Berkeley, California. 2018. doi:10.31235/osf.io/a7b3m