Assessing data management and sharing plans: The “state of play” at Duke and opportunities for cross-campus collaborations
DOI:
https://doi.org/10.29173/iq1168Keywords:
data management plan, data sharing, collaboration, data repositoriesAbstract
Over the past few years, the United States has implemented a second round of data management policies, exemplified by the 2023 NIH Data Management and Sharing Policy and 2022 “Nelson Memo.” Effectively supporting public access to data and a data sharing culture at an academic research institution requires collaboration across various research support staff and central offices as well as knowledge of the current practices of researchers. Two research support groups at Duke University, the University Libraries (DUL) and the Office of Scientific Integrity (DOSI), have forged a strong working relationship for supporting data management and sharing practices, including an active Teams channel for communication, developing tools collaboratively, delivering trainings, and providing co-consults for data management. To more effectively understand “the state of play” at our institution, DUL and DOSI analyzed data management and sharing plans (DMSPs) submitted to the National Science Foundation (NSF) in 2021. The project team used a modified version of the DART rubric (https://osf.io/qh6ad/) to score DMSPs against required elements in key areas, including types of data; standards for data and metadata; access, sharing, and preservation; limitations on access, distribution, and reuse; and roles and responsibilities. In this paper we will present the key findings from the DMSP assessment project and discuss how, as data management specialists, we can use this information to plan for ongoing education, training, and resource development using a cross-campus collaboration model.
References
Bell, L. C., & Shimron, E. (2024). Sharing data is essential for the future of AI in medical imaging. Radiology: Artificial Intelligence, 6(1), e230337. https://doi.org/10.1148/ryai.230337
Fleiss, J. L. (1971). Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5), 378–382. https://doi.org/10.1037/h0031619
Green, P., Cairns, A., & White, H. (2019). Evaluating data management plans—are they good and are they effective? Proceedings of the IATUL Conferences. https://docs.lib.purdue.edu/iatul/2019/fair/3
Lafferty-Hess, S., Krenzer, W., Ariansen, J., Darragh, J. (2025). Data and code from: Assessing data management and sharing plans: The "state of play" at Duke and opportunities for cross-campus collaborations. Duke Research Data Repository. https://doi.org/10.7924/r4668q866
Landis, J. R. & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310
Moorthy, V., Henao Restrepo, A. M., Preziosi, M. P., & Swaminathan, S. (2020). Data sharing for novel coronavirus (COVID-19). Bulletin of the World Health Organization, 98(3), 150. https://doi.org/10.2471/BLT.20.251561
National Institutes of Health (NIH). (2020). Final NIH policy for data management and sharing. https://grants.nih.gov/grants/guide/notice-files/NOT-OD-21-013.html
National Science and Technology Council. (2022). Desirable characteristics of data repositories for federally funded research. https://bidenwhitehouse.archives.gov/wp-content/uploads/2022/05/05-2022-Desirable-Characteristics-of-Data-Repositories.pdf
National Science Foundation (2023). Public access plan 2.0. https://nsf-gov-resources.nsf.gov/pubs/2023/nsf23104/nsf23104.pdf
Office of Science and Technology Policy. (2022). Ensuring free, immediate, and equitable access to federally funded research [Memo]. https://bidenwhitehouse.archives.gov/wp-content/uploads/2022/08/08-2022-OSTP-Public-Access-Memo.pdf
Naughton, L., & Kernohan, D. (2016). Making sense of journal research data policies. Insights 29 (1): 84–89. http://doi.org/10.1629/uksg.284
Parham, S. W., Carlson, J., Hswe, P., Westra, B., & Whitmire, A. (2016). Using data management plans to explore variability in research data management practices across domains. International Journal of Digital Curation, 11(1), 53–67. https://doi.org/10.2218/ijdc.v11i1.423
Pasek, J. E. (2017). Historical development and key issues of data management plan requirements for national science foundation grants: a review. Issues in Science and Technology Librarianship, 87. https://doi.org/10.29173/istl1709
PLOS One. (2014). Data availability policy. https://journals.plos.org/plosone/s/data-availability
Praetzellis, M., Grady, B., & Taylor, S. (2025). Piloting maDMPs for streamlined research data management workflows. International Digital Curation Conference, The Hague, Netherlands. https://doi.org/10.5281/ZENODO.14969693
Rinehart, A. (2016). Finding the connection: Research data management and the office of research. Bulletin of the Association for Information Science and Technology, 43(1), 28–30. https://doi.org/10.1002/bul2.2016.1720430107
Tenopir, C., Sandusky, R. J., Allard, S., & Birch, B. (2014). Research data management services in academic research libraries and perceptions of librarians. Library & Information Science Research, 36(2), 84–90. https://doi.org/10.1016/j.lisr.2013.11.003
Van Loon, J. E., Akers, K. G., Hudson, C., & Sarkozy, A. (2017). Quality evaluation of data management plans at a research university. IFLA Journal, 43(1), 98–104. https://doi.org/10.1177/0340035216682041
Whitmire, A. L., Carlson, J., Westra, B., Hswe, P., & Parham, S. (2016). The DART Project: Using data management plans as a research tool. Open Science Framework. http://osf.io/kh2y6
Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18
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Copyright (c) 2025 Sophia Lafferty-Hess, William Krenzer, Jenny Ariansen, Jennifer Darragh

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