Purpose When faced with events, such as the global pandemic of coronavirus disease 2019 (COVID-19), libraries have a unique opportunity to develop a community facing response through born-digital collections. These collections provide challenges for metadata creation, collection development policies, workflows, and digital preservation. This paper aims to provide an overview of the Utah COVID-19 digital collection, with a discussion of impact and lessons learned. Design/methodology/approach This paper provides a case study of a born-digital collection initiative undertaken at the University of Utah in response to the COVID-19 pandemic. The project prompted engagement with the University of Utah communities and people across the state. Workflows, metadata management and partnerships are discussed, to provide a model for institutions developing similar projects during a time of crisis. Findings While the project was launched with open-ended and flexible goals, the response from the community has been both surprising and gratifying. Statistics and examples demonstrating reuse of collection materials are provided to highlight the impact and potential of community engagement. Originality/value Digital collecting projects during a historical event are not new, however the restrictions placed upon people worldwide during the COVID-19 pandemic created interesting circumstances for building this collection. Several lessons were learned throughout the project which will be useful for other institutions embarking upon related projects.

This article discusses the burgeoning “collections as data” movement within the fields of digital libraries and digital humanities. Faculty at the University of Utah’s Marriott Library are developing a collections as data strategy by leveraging existing Digital Library and Digital Matters programs. By selecting various digital collections, small- and large-scale approaches to developing open datasets are explored. Five case studies chronicling this strategy are reviewed, along with testing the datasets using various digital humanities methods, such as text mining, topic modeling, and GIS (geographic information system).

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