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Building a Literacy Pedagogy Framework for Approaching Visualization of Archival Data (SIG-VIS)

Various tools have emerged in the past decade to facilitate the visualization of computer- and human-generated data in the Big Data environment with the help of algorithms and data architectures. Tools such as Tableau, ArchiveGrid, ArchivesHub, ArchivesSpace, and OpenRefine, have significantly accelerated the discovery of archival collections across computer networks in combination with archival description standards (e.g., Description of Archival Content Standard, or DACS), metadata schemata (e.g., Encoded Archival Description, or EAD), and data types (e.g., XML, JSON, and others) for visualization. Yet, there remains a vast body of data in legacy (including early electronic files but mostly those in print, film-based and analog) formats that may require manual interventions for Big Data applications—a hybrid (both human and artificial) intelligence approach may well serve archivists in this emerging environment. Archival collections are unique with respect to structure, content, provenance, and—particularly in the case of multicultural community archives—ontologies, which can lead to “cultural infodiversity” (to use Tony Gill’s term) because of disparate data. Knowledge organization and description practices in the archival field differ from those in the libraries and databases: they represents provenance, original order, and other emerging cultural practices. Visualizing data in archival finding aids and metadata records will include utilizing legacy finding aids, metadata records, and ontologies, and the mapping of data structures to emerging data models used for born digital collections. While archival skills training is inevitable most digital specialists can increase their literacies through workshops, self-didactic methods, or formal certification and degree programs. Because archives are so unique, literacy pedagogy frameworks may vary from one institution to the next but critical literacies such as archival (and digital archival), data, visual, and computer literacies will lend consistency to such frameworks and methods. This webinar addresses two joint concerns: among visualization experts, the need to understand archival description practices; and among archivists, to prepare metadata for visualization. While it focuses on the visualization of archival data, it also aims to inform methods already in use by digital archivists, data scientists, and others interested in visualizing archival collections.


Arjun Sabharwal joined the University of Toledo Libraries faculty as Digital Initiatives Librarian in January 2009. His areas of responsibilities have included digitizing manuscript collections and archival records and developing virtual exhibitions at the Ward M. Canaday Center for Special Collections, managing the University of Toledo Digital Repository, and curating the Toledo's Attic Virtual Museum focusing on the local history and cultural heritage of Northwest Ohio. Since 2017, he has also been the lead technical contact for the Open Journal Systems, which houses several Open Access journals at the University of Toledo. His research interests include Digital Humanities, interdisciplinary approaches to digital curation, digital preservation, archival science, and information architecture. He has been a member of ASIS&T since 2002 and particularly Arts & Humanities, Visualization, and Digital Library SIGs. He is the author of Digital Curation in the Digital Humanities: Preserving and Promoting Archival and Special Collections (Oxford, UK: Chandos Publishing, 2015). His sabbatical research focused on institutional repository engagement frameworks. Additional responsibilities include serving as the University Libraries' Diversity and Inclusion Officer (2021-2026) and as subject liaison to the Geography & Planning and Political Science/Public Administration departments.