Challenges of Doing Data-Intensive Research in Teams, Labs, and Groups
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: Report from the BIDS Best Practices in Data Science Series.” BIDS Best Practices in Data Science Series. Berkeley Institute for Data Science: Berkeley, California. 2018. doi:10.31235/osf.io/a7b3m
Best Practices for Fostering Diversity and Inclusion in Data Science
Abstract: What actions can we take to foster diverse and inclusive workplaces in the broad fields around data science? This paper reports from a discussion in which researchers from many different disciplines and departments raised questions and shared their experiences with various aspects around diversity, inclusion, and equity. The issues we discuss include fostering inclusive interpersonal and small group dynamics, rules and codes of conduct, increasing diversity in less-representative groups and disciplines, organizing events for diversity and inclusion, and long-term efforts to champion change.
Recommended citation: R. Stuart Geiger, Orianna DeMasi, Aaron Culich, Andreas Zoglauer, Diya Das, Fernando Hoces de la Guardia, Kellie Ottoboni, Marsha Fenner, Nelle Varoquaux, Rebecca Barter, Richard Barnes, Sara Stoudt, Stacey Dorton, Stéfan van der Walt. “Best Practices for Fostering Diversity and Inclusion in Data Science: Report from the BIDS Best Practices in Data Science Series.” BIDS Best Practices in Data Science Series. Berkeley, CA: Berkeley Institute for Data Science. 2019. doi:10.31235/osf.io/8gsjz