Assessing data deposits in an institutional repository (U of T Dataverse in Borealis)

U of T Dataverse, the University of Toronto's data repository, is one of the largest institutional dataverse collections in Borealis with over 1000 published datasets. It currently follows a self-deposit model that allows U of T researchers to deposit and publish their data without intervention unless requested. Considering increased usage of the repository and new and developing data deposit policies, we conducted an assessment of U of T Dataverse to (1) review the quality of published datasets, and (2) understand who is using the repository. This was accomplished by analyzing the monthly Borealis metrics and conducting a quality assessment of select datasets’ structure and associated metadata. Ultimately, this assessment will be used to help conceptualize curation services and identify resources to develop that would enable high-quality data deposits. In this presentation we will discuss our approach to this assessment, preliminary findings, and how this project will shape our approach to service and resource development. Overall, this project will allow us to better understand disciplinary trends relating to who is (and isn’t) using U of T Dataverse, help develop clear processes and guidelines, and inform training and departmental outreach. Longer-term, it will help us estimate the effort and time required to provide curation services, inform priorities for repository development, and help us anticipate the impact any change in national policy may have in demand for institutional services.