Best Practices for Managing Turnover in Data Science Groups, Teams, and Labs

Dan Sholler, Diya Das, Fernando Hoces de la Guardia, Chris Hoffman, Francois Lanusse, Nelle Varoquaux, Rolando Garcia, R. Stuart Geiger, Shana McDevitt, Scott Peterson, Sara Stoudt

SocArXiv
March 5, 2019

Abstract: 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.

Recommended citation: Recommended citation: Dan Sholler, Diya Das, Fernando Hoces de la Guardia, Chris Hoffmann, François Lanusse, Nelle Varoquaux, Rolando Garcia, R. Stuart Geiger, Shana McDevitt, Scott Peterson, Sara Stoudt. “Best Practices for Managing Turnover in Data Science Groups, Teams, and Labs.” BIDS Best Practices in Data Science Series. Berkeley, CA: Berkeley Institute for Data Science. 2019. doi:10.31235/osf.io/wsxru



Featured Fellows

Diya Das

Molecular & Cell Biology
Alumni - DATA SCIENCE FELLOW

François Lanusse

Berkeley Center for Cosmological Physics, FODA Institute

Nelle Varoquaux

Statistics
Alumni - DATA SCIENCE FELLOW

R. Stuart Geiger

Ethnographer

Sara Stoudt

Statistics