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
A mural in Asheville, North Carolina, depicts the history of the city's Southside neighborhood and the impact of urban renewal. Students in INFO's Computational Archival Science courses analyze archival records on that history.
INFO Students Learn to Use AI With Records They Know Best
UMD iSchool graduate student Connor Mullane assisted in digitizing and processing images.
What Information Science Can Do for Opera History
UMD iSchool graduate student Connor Mullane assisted in digitizing and processing images.
2026 Kaleidoscope Program Scholars Jazmyn Gray (left) and Sofia Isabel Peña Wallace (right).