A Data-Driven Approach to Reparative Description at the University of Chicago

Reparative description of collections is a growing element of diversity, equity, and inclusion efforts at cultural heritage institutions. However, the scale and complexity of the work can be overwhelming in practice. I demonstrate that computational methodologies and data analytics can be used to kickstart the planning stage for reparative description of archival finding aids. I discuss auditing and analyzing finding aids at the University of Chicago Library’s Hanna Holborn Gray Special Collections Research Center for potentially problematic language utilizing Python, Trifacta, Tableau, and Neo4j. I describe insights gained by treating finding aids as data, and I share recommendations for structuring reparative description work in a logical and attainable way.