Panel Discussion: Building Inclusive Data Communities
summary
Panel covers concrete programs and tactics rather than theory: the D‑Lab and Data Science Education program describe an NSF $3M IUSE grant, student advisory board placements and a Discovery/URAP pipeline for matching community projects to undergraduates, labs describe practical trainings like Steve Robbins videos and upstander training, and researchers push standards like FAIR and the Manifest‑No principles. Expect candid examples from Quizlet and NERSCC about biased data and hiring/ranking pitfalls and discussions of when to retire datasets. Watch if practical building blocks and governance matter.
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chapters (6)
- 0:00Introduction and panel setup
- 1:02Panelist introductions
- 13:03Building inclusive data
- 21:23Creating inclusive communities
- 37:29Engaging beyond campus
- 49:23Ideal future for data science
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