Community Data Repositories Working With The Library & University: A Harvard Dataverse Use Case

Abstract

Purpose: This poster examines the collaboration between an academic library, various university departments, and an open source data repository to help faculty and affiliated researchers curate, share and archive research data. Brief Description: The Harvard Dataverse (https://dataverse.harvard.edu)--powered by the Dataverse Project, open source data publishing software, developed at Harvard’s Institute for Quantitative Social Science (IQSS) for nearly a decade--has recently been collaborating with Harvard Library, Harvard Medical School, Harvard-Smithsonian Center for Astrophysics (CfA), and other groups from the university to provide a data repository solution for sharing, publishing and archiving research data for Harvard faculty and affiliated researchers. This collaboration has expanded the scope of the Dataverse Project, data repository open source software, to better support research data beyond just the social sciences. The Harvard Dataverse team has also extended its services to provide user support, training, and targeted data curation services to the Harvard community. Results/Outcome: Current and upcoming collaborative projects include: connecting faculty publications with their underlying research data by integrating Dataverse with Harvard’s institutional repository Digital Access to Scholarship at Harvard (DASH); extending metadata support for astronomy with the Center for Astrophysics (CfA) and biomedical datasets with the Harvard Medical School (HMS); providing university-wide open data awareness and support via the Harvard Open Data Assistance Program (ODAP); make licensed datasets available to the Harvard community (Harvard Subscription Data Dataverse); helping researchers meet the requirements of funder mandated data management plans through customized DMPTool services; and making faculty datasets more widely discoverable by exporting metadata (MARC) into the Harvard Library Catalog, HOLLIS. Evaluation Method: Site metrics to measure if there is an increase in usage, which includes number of new datasets, and dataset downloads and views.