Archival Science
Advancing how we build and curate archives through co-design with communities, computational science, and other emerging methodology.
Research Projects
Mapping Inequality — Redlining in New Deal America
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science
Providing online access to the totality of the maps and neighborhood descriptions for the national redlining collection.
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science
Providing online access to the totality of the maps and neighborhood descriptions for the national redlining collection.
Testbed for the Redlining Archives of California’s Exclusionary Spaces (T-RACES)
Principal Investigator(s): Richard Marciano
Funder: Unfunded Other Non-Federal
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Library and Information Science > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Making publicly accessible online documents relating to the practice of “redlining” neighborhoods in the 1930s and 1940s in eight California cities. “Redlining” refers to the practice of flagging minority neighborhoods as undesirable for home loans. The project creates a searchable database and interactive map interface.
Principal Investigator(s): Richard Marciano
Funder: Unfunded Other Non-Federal
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Library and Information Science > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Making publicly accessible online documents relating to the practice of “redlining” neighborhoods in the 1930s and 1940s in eight California cities. “Redlining” refers to the practice of flagging minority neighborhoods as undesirable for home loans. The project creates a searchable database and interactive map interface.
Computational Treatments to re-member the Legacy of Slavery (CT-LoS)
Principal Investigator(s): Richard Marciano
Funder: Unfunded Other Non-Federal
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics
Using Computational Archival Science to unlock records related to the Legacy of Slavery and provide new point of interaction and analysis.
Principal Investigator(s): Richard Marciano
Funder: Unfunded Other Non-Federal
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics
Using Computational Archival Science to unlock records related to the Legacy of Slavery and provide new point of interaction and analysis.
Faculty
Staff
Recent News
Knowledge graph for Southside owners harmed by urban renewal, by Nick de Raet
UMD Students Studying Archival Technology & AI Make Real-World Impact
19 UMD students participating in AICollaboratory summer courses aid Ashville Reparations Commission
“It’s My Turn”: From School Teacher to Director of Education at the National Cryptologic Museum
A profile of MLIS alum Jennifer Robertson