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Keynote Panel: Data Driven DEI Perspectives: Know Where You Are, So You Know Where You Need To Go

by | Nov 3, 2022 | 0 comments


Robin Pryce, Bibliometrics Manager, Imperial College London said that there used to be almost a sense of: “We are scientists and deal in objective facts; we’re not biased, we deal in merit.” We may think that high-quality research comes from a scientific institution and may decide whether to read an articlebased on the affiliation or geographic location of the author.  Limitations of this approach are both technical and conceptual. Articles must have a DOI and be a article. Country affiliation is a narrow indicator of author identity and experience. Is there a place for quantitative data? The aim is to make the lack of other voices visible.

Beth Blanton from the University of Virginia Library discussed the use of circulation data for inclusive collections. How does the print collection contribute to the success of students and faculty? Why should we print circulation user data? You are what you measure. We are very good at collecting data, but it is often in disparate systems. To understand users and collections we must respect user privacy, support academic freedom, and shield the community from online surveillance. The goals of this are to reduce risk to user privacy, manage data from the systems, and provide access to anonymized data. Data are everywhere! We want to create a data set allowing searching, mining, and extraction. Desired outcomes are access to living data aggregated across systems, using actual usage patterns vs. anecdotes. and identifying areas for instruction, collection areas for outreach, and informing DEI efforts.

Gwen Evans, VP of Global Library Relations at Elsevier noted that Elsevier’s efforts in equity in research include why it matters and use of publication data. They are expanding efforts on self-reported gender, race, and ethnicity data. Major considerations are the data schemas, platform, communication, and reporting, for example on peer reviewers. In a large-scale test survey there was a high rate of self-identification and largely positive feedback with some concerns about why they need the data on ethnic origin and race.

Don Hawkins


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