EScience in Action
Objective: Many academic and research institutions are exploring opportunities to better support researchers in sharing their data. As partners in the Data Curation Network project, our six institutions developed a comparison of the current levels of support provided for researchers to meet their data sharing goals through library-based data repository and curation services.
Methods: Each institutional lead provided a written summary of their services based on a previously developed structure, followed by group discussion and refinement of descriptions. Service areas assessed include the repository services for data, technologies used, policies, and staffing in place.
Conclusions: Through this process we aim to better define the current levels of support offered by our institutions as a first step toward meeting our project's overarching goal to develop a shared staffing model for data curation across multiple institutions.
digital repositories, research data management (RDM) services, institutional repositories, academic libraries, scholarly communications, data curation
The Data Curation Network is funded by the Alfred P. Sloan Foundation.
Johnston LR, Carlson JR, Hswe P, Hudson-Vitale C, Imker H, Kozlowski W, Olendorf RK, Stewart C. Data Curation Network: How Do We Compare? A Snapshot of Six Academic Library Institutions’ Data Repository and Curation Services. Journal of eScience Librarianship 2017;6(1): e1102. https://doi.org/10.7191/jeslib.2017.1102. Retrieved from https://escholarship.umassmed.edu/jeslib/vol6/iss1/3
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Figure 1: Example dataset in DRUM (left, http://dx.doi.org/10.13020/D6PK5C) and in Cornell eCommons (right, http://hdl.handle.net/1813/43783) which both use the DSpace software.
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Figure 2: Example dataset in ScholarSphere (left, https://scholarsphere.psu.edu/files/m900nt50p) and Deep Blue Data (right, https://deepblue.lib.umich.edu/data/concern/generic_works/rf55z7781) both using Hydra (https://projecthydra.org) with Fedora (http://fedorarepository.org).
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Figure 3: Example dataset in the Illinois Data Bank (left, https://doi.org/10.13012/J8PN93H8) using a custom-build Ruby on Rails application and the Digital Research Materials Repository (right, https://doi.org/10.7936/K7J67F60) using Digital Commons by BePress.
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Figure 4: University of Minnesota Data Curation Workflow Diagram
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Figure 5: Cornell University Data Curation Workflow Diagram
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Figure 6: University of Illinois Data Curation Workflow Diagram
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Figure 7: Washington University in St. Louis Data Curation Workflow Diagram