Objective: The University of Minnesota Libraries launched a data repository in March 2015 called the Data Repository for the University of Minnesota (DRUM). Based on past pilots of data curation services it was clear that we required a distributed data curation approach to handling the wide variety of data sets that our large diverse academic community will produce. Methods: Our procedure and staffing model are currently operational. We review all incoming data sets for their subject area and data type; we assign the new curation task to one of 6 appropriate data curators based in five subjects: scientific, social sciences, GIS/spatial, digital humanities, and health sciences. Curators review the data for usability and quality issues and work directly with the data authors to enrich the submission. Curation approaches include generating custom metadata, arrangement and description of the objects, and file transformation for preservation needs. Metrics such as time taken to curate the data and interactions with data authors are tracked. Results: The data curation procedures have been tested and refined based on one year of implementation. This poster will visualize the current approach taken, the skills of needed curator staff, and share summary statistics of the data curated to date. Conclusions: An institutional data repository has the burden of collecting a diverse array of digital data, but, with appropriate staffing and careful procedures, each dataset regardless of its' distinctiveness can be enriched for dissemination and reuse.

Data repositories: the answer that actually came with a question. Funders, journal publishers, and disciplinary societies recognize the benefits of long-term access to valuable data that could validate results, increase scholarly democracy, or possibly lead to future discoveries. With this in mind, a majority of research now being done in academia is subject to data sharing requirements that the underlying data be publicly accessible, citable, and persevered. As many subject-based data repositories help make this happen, particularly for computing-intensive disciplines with shared infrastructure, such as high-energy physics or real-time climate monitoring, who will manage the "long-tail" of smaller or multi-disciplinary research data? Our institutional repositories (IR) could be the answer. With a few key policy decisions, and robust review and curation procedures, libraries are well-positioned to help researchers comply with mandates to share and archive their data. Whether you use Hydra, DSpace, Fedora, E-prints, or Digital Commons, this talk will outline important issues to consider as you build new capacity with existing IR infrastructure or a custom data repository, including staffing, curation procedures, and metadata and documentation requirements. Finally it will explore the results and faculty response to launching the Data Repository for the University of Minnesota in 2015, which is based on the Libraries’ existing IR service. Our data submission process, curation procedures, faculty usage, and lessons learned will be placed in context of our broader data management and curation program. Lisa Johnston is Research Data Management/Curation Lead and Co-Director of the University Digital Conservancy, University of Minnesota.