Poster Session

Start Date

6-4-2016 12:30 PM

Description

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.

Keywords

curation, data repository, research data, datasets

Comments

This poster was awarded "Best Poster Overall" at the 2016 e-Science Symposium.

Creative Commons License

Creative Commons License
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Apr 6th, 12:30 PM

Curating Research Data in DRUM: A workflow and distributed staffing model for institutional data repositories

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.

 

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