Machine Learning, Libraries, and Cross-Disciplinary Research: Possibilities and Provocations



This 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.


Attribute NameValues
Document Type
  • Book

  • Daniel Johnson

Contributing Editor
  • Donald Brower

  • Alex Papson

  • Mark Dehmlow

  • Eric Lease Morgan

  • John (Zheng) Wang

  • 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

Copyright Date
  • 9780578881539

  • Hesburgh Libraries, University of Notre Dame

Publication Date
  • 2020

  • Machine learning

  • Topic modelling

Related Resource(s)
  • English

Record Visibility Public
Content License
  • All rights reserved

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