Information and cultural heritage professionals have become increasingly interested in using data science, machine learning, and AI in the typical data and information management operations of libraries, archives and museums. Given the importance of our cultural heritage, the positive effects these data-oriented practices may have for the provision of information services is significant. Implementing machine learning and AI methods, however, has proven difficult for museums because of the technical, organizational, and social barriers. Simply training staff to learn new methods is not sufficient. Workforce development and interprofessional and interdisciplinary collaboration are a necessary path forward. This project will introduce pedagogy, training, and research in library and archival collections when data science, machine learning, and AI practices are not currently part of or supported by that culture. The aim is to train graduate students to use these new methods before they assume operations of museums in their future careers.
Project team: Tanya Elizabeth Clement (Department of English), Amelia Acker (School of Information), Aaron Choate (University of Texas Libraries), Andrea Gustavson (Harry Ransom Center) and Lauren Walker (Harry Ransom Center)