Computational Treatments to
re-member
the Legacy of Slavery
(CT-LoS)
— “Reasserting Erased Memory” —
![]() Measuring the Impact of Urban Renewal Computational Archival Science (CAS) supports the Racial Reparations Commission work in Asheville, NC. |
![]() Exploring AI and ML to Analyze Chattel Slavery Advertisements in the State of Maryland between 1824 and 1864 A partnership with the Maryland State Archives to computationally explore the nature of the trading of enslaved people. Paper accepted as a book chapter in Artificial Intelligence for Cultural Heritage Organisations. |
![]() Building a Digital Asset Management System to Preserve and Better Access the Current and Future Assets of the Mary McLeod Bethune Council House National Historic Site Researchers partner with the National Park Service. The archival holdings of the site represent the institutional holdings of the National Council for Negro Women and their efforts to create a National Archive for Black Women’s History. |
![]() African American Soldiers: Visualizing Their Role During World War I Researchers develop an interactive visualization interface to highlight the role African American soldiers played during World War I through photographs. |
![]() A Data-Driven Approach to Reparative Description at the University of Chicago Researchers use computational methodologies and data analytics to automate reparative description of archival finding aids. |
![]() Testbed for the Redlining Archives of California’s Exclusionary Spaces (T-RACES) T-RACES is a data visualization design that makes the history and effects of redlining in CA and NC newly tangible. Researchers map racial discrimination in housing using collections from the National Archives. For a link to a national database of over 200 cities see: MappingInequality, co-created by UMD (Richard Marciano), U. Richmond (Robert K. Nelson), Virginia Tech (laDale Winling), and Johns Hopkins (N.D.B. Connolly). |
![]() Using Transfer Learning to Improve Text Extraction of Runaway Slave Ads |
![]() An AI-Assisted Framework for Rapid Conversion of Descriptive Photo Metadata into Linked Data |
![]() Erasure and Representation in Archives at SAA 2020 |
![]() Student-Led “Datathon” Working in small interdisciplinary student teams from the University of Maryland iSchool Programs (Undergraduate InfoSci, MLIS – Master of Library and Information Science, MIM – Master of Information Management, HCIM – Master of Science in Human-Computer Interaction, and Doctoral program), and from the Drexel LEADS Initiative (Library & Information Science Education and Data Science), the project explored selected datasets from the Maryland State Archives Legacy of Slavery Project under the leadership of domain experts. The “Datathon” was the culmination of 8 weeks of data exploration in the classroom in September and October, with 17 students (undergraduate, master’s, and doctoral). Partners included the Maryland State Archives (MSA) in the US, together with King’s College London’s Department of Digital Humanities and The National Archives (TNA), in the UK. With Richard Marciano at the U. Maryland, they were awarded a one-year Arts and Humanities Research Council (AHRC) International Research Networking grant for UK-US Collaborations in Digital Scholarship in Cultural Institutions, running from 1 February 2019 to 31 January 2020. |