Archival Science
Advancing how we build and curate archives through co-design with communities, computational science, and other emerging methodology.
Research Projects
Opera on the Nation’s Stage
Principal Investigator(s): Jessica Grimmer
Funder: Music Library Association (MLA) Other Non-Federal
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Digital Humanities
This project examines the history of opera in Washington, D.C., using digital humanities methods and a new “narrative bibliography” to map its artistic and cultural networks.
Principal Investigator(s): Jessica Grimmer
Funder: Music Library Association (MLA) Other Non-Federal
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Digital Humanities
This project examines the history of opera in Washington, D.C., using digital humanities methods and a new “narrative bibliography” to map its artistic and cultural networks.
Transforming Indigenous Archival Search: Evaluating Reparative Aggregation, Linked Data, and Cultural-Technical Infrastructures in the SNAC Platform
Principal Investigator(s): Diana E. Marsh
Funder: Andrew W. Mellon Foundation Other Non-Federal
Research Areas: Archival Science
Improving search and access of archival records for Indigenous communities through the web platform SNAC.
Principal Investigator(s): Diana E. Marsh
Funder: Andrew W. Mellon Foundation Other Non-Federal
Research Areas: Archival Science
Improving search and access of archival records for Indigenous communities through the web platform SNAC.
Harnessing Generative AI to Support Exploration and Discovery in Library and Archival Collection
Principal Investigator(s): Richard Marciano
Funder: 8/1/2024 - 4/9/2025 Institute of Museum and Library Services
Research Areas: Archival Science > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Harnessing generative AI to support exploration and discovery in library and archival collections.
Principal Investigator(s): Richard Marciano
Funder: 8/1/2024 - 4/9/2025 Institute of Museum and Library Services
Research Areas: Archival Science > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Harnessing generative AI to support exploration and discovery in library and archival collections.
Faculty
Staff
Recent News
Credit: AIP Foundation / Niels Bohr Library & Archives
(Video) AIP Foundation News: Guarding the History of Science: Jamila Hinds at the Niels Bohr Library & Archives
MLIS student Jamila Hinds protects rare AIP archives and enhances public access in an effort to preserve scientific history
Lauren Schirle, MIM '19
Alumni Profile: From Archives to Analytics: How a MIM Alum’s Unconventional Path Shapes Fintech Innovation
A profile of MIM alum Lauren Schirle
Douglas Oard, a noted expert in information retrieval, has been at the University of Maryland since 1996 and has been an active member of UMIACS since 1998.