Bridging Data Communities: Interoperability through inclusive, cross-institutional collaboration
Objectives: To demonstrate how librarians can use engagement strategies to foster the exchange of knowledge and skills for data analysis and to build bridges between data communities. A second objective is to help student instructors to develop effective live-coding pedagogical practices and to gain...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
UMass Chan Medical School, Lamar Soutter Library
2024-12-01
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| Series: | Journal of eScience Librarianship |
| Subjects: | |
| Online Access: | https://publishing.escholarship.umassmed.edu/jeslib/article/id/970/ |
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| Summary: | Objectives: To demonstrate how librarians can use engagement strategies to foster the exchange of knowledge and skills for data analysis and to build bridges between data communities. A second objective is to help student instructors to develop effective live-coding pedagogical practices and to gain practical experience in leading participatory workshop sessions.
Methods: Librarians developed a low-barrier introductory peer-to-peer data science workshop series to support students seeking to develop coding, data analysis, and visualization skills, with a focus on Python and SQL. We guided undergraduate peer instructors in participatory live-coding pedagogy, organized practice sessions for instructors, and managed the scheduling, logistics, outreach, and hosting of the workshops.
Results: In Fall 2023 sessions in the workshop series were delivered synchronously to over 100 participants, including students from our home institution and more than a dozen community colleges; one workshop was delivered twice—once in English, once in Spanish. Workshop recordings posted online have been viewed over 1000 times.
Conclusions: We successfully identified strategies for building upon existing relationships and strengthening connections among diverse data communities; designing programs and outreach efforts to lower barriers to participation in data science; and fostering a culture of diversity, equity, and inclusion in data science knowledge sharing. |
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| ISSN: | 2161-3974 |