machine-learning-libraries-and-cross-disciplinary-research.pdf (6.76 MB)
Machine Learning, Libraries, and Cross-Disciplinary Research: Possibilities and Provocations
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posted on 2020-12-08, 00:00 authored by Not AssignedThis collection of essays is the unexpected culmination of a 2018–2020 grant from the Institute of Museum and Library Services to the Hesburgh Libraries at the University of Notre Dame. The plan called for a survey and a series of workshops hosted across the country to explore, originally, “the national need for library based topic modeling tools in support of cross-disciplinary discovery systems.” As the project developed, however, it became apparent that the scope of re- search should expand beyond topic modeling and that the scope of output might expand beyond a white paper. The end of the 2010s, we found, was swelling with library-centered investigations of broader machine learning applications across the disciplines, and our workshops demonstrated such a compelling mixture of perspectives on this development that we felt an edited collection of essays from our participants would be an essential witness to the moment in history.
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Date Modified
2021-03-29Language
- English
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9780578881539Relations
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Hesburgh Libraries, University of Notre DameContributor
Arend Hintze|Jorden Schossau|Charlie Harper|Marisa Plumb|Andrew Janco|Sue Wiegand|Meng Jiang|Bohyun Kim|Audrey Altman|Michael Lesk|Eric Lease Morgan|Max Prud’homme|Jason Cohen|Mario Nakazawa|Ana Lucic|John Shanahan|Samuel Hansen|Daniel JohnsonSource
https://curate.nd.edu/show/7d278s48s11|https://osf.io/tmxqf/|https://curate.nd.edu/show/br86b28087gUsage metrics
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