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

SocArXiv, 2019

Recommended citation: Dan Sholler*, Diya Das*, Fernando Hoces de la Guardia*, Chris Hoffman, Francois Lanusse, Nelle Varoquaux, Rolando Garcia, R. Stuart Geiger, Shana McDevitt, Scott Peterson and Sara Stoudt. (2019). Best Practices for Managing Turnover in Data Science Groups, Teams, and Labs. SocArXiv. osf.io/preprints/socarxiv/xe823

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.