Publication Date
2-28-2017
Article Type
EScience in Action
Abstract
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.
Keywords
digital repositories, research data management (RDM) services, institutional repositories, academic libraries, scholarly communications, data curation
Acknowledgments
The Data Curation Network is funded by the Alfred P. Sloan Foundation.
Recommended Citation
Johnston, Lisa R., Jake R. Carlson, Patricia Hswe, Cynthia Hudson-Vitale, Heidi Imker, Wendy Kozlowski, Robert K. Olendorf, and Claire Stewart. 2017. "Data Curation Network: How Do We Compare? A Snapshot of Six Academic Library Institutions’ Data Repository and Curation Services." Journal of eScience Librarianship 6(1): e1102. https://doi.org/10.7191/jeslib.2017.1102
Rights and Permissions
Copyright © 2017 Johnston et al.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.
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.
Figure 2_1102.png (372 kB)
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).
Figure 3_1102.png (244 kB)
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.
Figure 4_1102.png (462 kB)
Figure 4: University of Minnesota Data Curation Workflow Diagram
Figure 5_1102.jpg (303 kB)
Figure 5: Cornell University Data Curation Workflow Diagram
Figure 6_1102.png (289 kB)
Figure 6: University of Illinois Data Curation Workflow Diagram
Figure 7_1102.png (55 kB)
Figure 7: Washington University in St. Louis Data Curation Workflow Diagram
