Historic Black Lives Matter: Recovering Hidden Knowledge in Archives through Interactive Data Visualization

This paper presents the Historical Black Lives Matter (HBLM) case study, an exploratory application of interactive data visualization to a collection of manumissions documents in the Legacy of Slavery (LoS) project at the Maryland State Archives, with the goal of enhancing discovery and recovering hidden knowledge. The case study extends prior interdisciplinary research on applying computational treatments to LoS collections and contributes to research in Computational Archival Science (CAS), computational thinking, and data visualization to enhance access to archival collections. Three design objectives are addressed: representation of people, user experience, and facilitation of knowledge discovery. The paper is organized to demonstrate a customizable workflow for the process of formulating design based on data visualization principles, implementing designs with open-source tools, and incorporating user evaluation in service to successfully fulfilling the design objectives and related functionality in the final implemented design. Examples of hidden knowledge recovered using the visualizations are presented, providing new insights into Maryland’s antebellum Black population. The data visualization design methods and practices permitted investigation at a more granular level, and enabled communication of a richer narrative. Use of open-source software makes these methods accessible to archivists, information professionals, and researchers, and supports creation of artifacts for research, teaching and learning. Future extensions could incorporate advanced computational techniques to enable map features, network and textual analysis, and dynamic query-based composition of visualizations.