Libraries Facilitating

The University of Notre Dame was awarded a one-year Institute of Museum and Library Services (IMLS) planning grant to assess the need for library-based machine learning and natural language processing tools to facilitate automated metadata creation and classification in support of cross-disciplinary discovery and research.

Grant Overview and Outcomes Summary

The grant coordination team from Hesburgh Libraries conducted one-day workshops between March 2019 and October 2019 in South Bend, IN, Palo Alto, CA, New York, NY, and Washington, D.C. The workshops were attended by computer scientists, librarians, and disciplinary scholars who engaged in cross-community conversations about the values, challenges, tools, and other topics related to machine learning and cross-disciplinary discovery.

Interactive workshops featured peer presentations about work happening in higher education and cultural heritage organizations, brainstorms about critical topics, and breakout group discussions in more narrow areas of focus. Inspired by the creative ideas and diversity of perspectives shared during the workshops, the grant team decided to coordinate an edited collection of essays authored by workshop participants in order to document this evolving conversation.

As a result of the grant work, the team identified nearly 100 people who are heavily engaged in the use of machine learning in higher education and cultural heritage organizations. We determined the following:

  • The implementation of machine learning in higher education is still relatively nascent.
  • The issue of how to facilitate cross-disciplinary research using machine learning will be an issue worth greater exploration in the future as the use of machine learning matures.
  • While there is great interest in this area, the more critical, immediate need is developing greater knowledge and experience in machine learning throughout academia.

As part of the grant deliverables, the team generated survey data, workshop and discussion records, observations and analysis, and final recommendations. In addition, as previously mentioned, the team produced an open access edited collection of essays available online.

See Full Proposal

Reflections from the Principal Investigator

Final White Paper

White Paper Appendices

Edited Collection of Essays