Challenges of Doing Data-Intensive Research in Teams, Labs, and Groups: Report from the BIDS Best Practices in Data Science Series
| Autor: | Geiger, R. Stuart, Sholler, Dan, Culich, Aaron, Martinez, Ciera, Hoces de la Guardia, Fernando, Lanusse, Francois, Ottoboni, Kellie, Stuart, Marla, Vareth, Maryam, Varoquaux, Nelle, Stoudt, Sara, van der Walt, Stefan |
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| Rok vydání: | 2022 |
| Předmět: |
data science studies
SocArXiv|Social and Behavioral Sciences|Library and Information Science bepress|Social and Behavioral Sciences best practices SocArXiv|Social and Behavioral Sciences data science research data management bepress|Social and Behavioral Sciences|Science and Technology Studies SocArXiv|Social and Behavioral Sciences|Science and Technology Studies bepress|Social and Behavioral Sciences|Library and Information Science teams collaboration management |
| DOI: | 10.17605/osf.io/uv6fy |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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