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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.

Turnover is a fact of life for any project, and academic research teams can face particularly high levels of people who come and go through the duration of a project. In this article, we discuss the challenges of turnover and some potential practices for helping manage it, particularly for computational- and data-intensive research teams and projects. The topics we discuss include establishing and implementing data management plans, file and format standardization, workflow and process documentation, clear team roles, and check-in and check-out procedures.

For preprint version, please see: https://doi.org/10.31222/osf.io/b4v8p This version contains all indexed files in all formats as part of the second release for the Strategy. All content is openly licensed for re-use, including the website.