Ten simple rules for building and maintaining a responsible data science workflow

Sara Stoudt; Yacine Jernite; Brandeis Marshall; Ben Marwick; Malvika Sharan; Kirstie Whitaker; Valentin Danchev

doi:10.1371/journal.pcbi.1012232

Ten simple rules for building and maintaining a responsible data science workflow

PLoS Comput Biol. 2024 Jul 18;20(7):e1012232. doi: 10.1371/journal.pcbi.1012232. eCollection 2024 Jul.

Authors

Sara Stoudt¹, Yacine Jernite², Brandeis Marshall³, Ben Marwick⁴, Malvika Sharan⁵, Kirstie Whitaker⁵, Valentin Danchev⁶

Affiliations

¹ Department of Mathematics, Bucknell University, Lewisburg, Pennsylvania, United States of America.
² Hugging Face, Inc., New York, New York, United States of America.
³ DataedX Group, Atlanta, Georgia, United States of America.
⁴ Department of Anthropology, University of Washington, Seattle, Washington, United States of America.
⁵ The Alan Turing Institute, London, United Kingdom.
⁶ School of Business and Management, Queen Mary University of London, London, United Kingdom.

No abstract available

Publication types

Editorial

MeSH terms

Computational Biology
Data Science* / methods
Humans
Workflow*

Grants and funding

MS and KJW were supported by Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1 and EPSRC Grant EP/W006022/1, particularly the “Tools, Practices & Systems” theme within those grants, and by the Ecosystem Leadership Award under the EPSRC Grant EP/X03870X/1 & The Alan Turing Institute. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.