Practical considerations for a library’s research data management services: the case of the National Institutes of Health Library
DOI:
https://doi.org/10.5195/jmla.2021.995Keywords:
research data management, case study, library servicesAbstract
Objective: This study investigates research data management (RDM) services using a crosstab framework with the National Institutes of Health (NIH) Library as a case study to provide practical considerations for libraries seeking to improve their RDM services.
Methods: We conducted semistructured interviews with four librarians who provide data services at the NIH Library regarding library user characteristics, RDM services provided, RDM infrastructure, and collaboration experiences. Through the analysis of interview transcripts, we identified and analyzed the NIH Library’s RDM services according to Online Computer Library Center (OCLC)'s three categories of RDM services and the six stages of the data lifecycle.
Results: The findings show that the two models’ crosstab framework can provide an overview of an institution’s current RDM services and identify service gaps. The NIH Library tends to take more responsibility in providing education and expertise services while relying more on information technology departments for curation services. The library provides significant support for data creation, analysis, and sharing stages to meet biomedical researchers’ needs, suggesting areas for potential expansion of RDM services in the less supported stages of data description, storage, and preservation. Based on these findings, we recommend three key considerations for libraries: identify gaps in current services, identify services that can be supported via partnerships, and get regular feedback from users.
Conclusion: These findings provide a deeper understanding of RDM support on the basis of RDM service categories and the data lifecycle and promote discussion of issues to be considered for future improvements in RDM services.
References
Diekema AR, Wesolek A, Walters C. The NSF/NIH effect: surveying the effect of data management requirements on faculty, sponsored programs, and institutional repositories. J Acad Libr. 2014 May;40(3–4): 322–31. DOI: https:// 10.1016/j.acalib.2014.04.010.
Wiley CA, Burnette MH. Assessing data management support needs of bioengineering and biomedical research faculty. J eScience Librariansh. 2019 March;8(1):e1132. DOI: https://doi.org/10.7191/jeslib.2019.1132.
Latham B. Research data management: defining roles, prioritizing services, and enumerating challenges. J Acad Libr. 2017 May;43(3):263–5. DOI: https://doi.org/10.1016/j.acalib.2017.04.004.
Flores JR, Brodeur JJ, Daniels MG, Nichools N, Turnator E. Libraries and the research data management landscape. In: Maclachlan JC, Waraksa EA, Williford C, ed. The process of discovery: The CLIR Postdoctoral Fellowship Program and the future of the academy. Washington, DC: Council on Library and Information Resources; 2015. p. 82–102. [cited 18 Feb 2020]. <http://www.clir.org/pubs/reports/pub167/pub167.pdf >
Creamer AT, Martin ER, Kafel, D. Research data management and the health sciences librarian [Internet]. Library Publications and Presentations. [cited 20 Feb 2020]. <https://escholarship.umassmed.edu/lib_articles/147>.
Tang R, Hu Z. Providing research data management (RDM) services in libraries: preparedness, roles, challenges, and training for RDM practice. Data Inf Mgmt. 2019 Jun;3(2):84–101. DOI: https://doi.org/10.2478/dim-2019-0009.
Johnson LM, Butler JT, Johnston LR. Developing e-science and research services and support at the University of Minnesota Health Sciences Libraries. J Libr Adm. 2012 Dec;52(8):754–69. DOI: https://doi.org/10.1080/01930826.2012.751291.
Li M, Chen YB, Clintworth WA. Expanding roles in a library-based bioinformatics service program: a case study. J Med Libr Assoc. 2013 Oct;101(4):303–9. DOI: https://doi.org/10.3163/1536-5050.101.4.012.
Surkis A, LaPolla, FWZ, Contaxis N, Read KB. Data day to day: building a community of expertise to address data skills gaps in an academic medical center. J Med Libr Assoc. 2017 Apr;105(2):185–91. DOI: https://doi.org/10.5195/jmla.2017.35.
Read KB. Adapting data management education to support clinical research projects in an academic medical center. J Med Libr Assoc. 2019 Jan;107(1):89–97. DOI: https://doi.org/10.5195/jmla.2019.580.
Gore SA. A librarian by any other name: The role of the informationist on a clinical research team. J eScience Librariansh. 2013 May;2(1):20–4. DOI: https://doi.org/10.7191/jeslib.2013.1041.
Hasman L, Berryman D, Mcintosh S. NLM informationist grant–web assisted tobacco intervention for community college students. J eScience Librariansh. 2013 May;2(1):30–4. DOI: https://doi.org/10.7191/jeslib.2013.1034.
Delserone LM. At the watershed: Preparing for research data management and stewardship at the University of Minnesota Libraries. Lib Trends. 2008 Fall;57(2):202–10. DOI: https://doi.org/10.1353/lib.0.0032.
Pinfield S, Cox AM, Smith J. Research data management and libraries: relationships, activities, drivers and influences. PLoS ONE. 2014 Dec;9(12):e114734. DOI: https://doi.org/10.1371/journal.pone.0114734.
Bryant R, Lavoie B, Malpas C. A tour of the research data management (RDM) service space. The realities of research data management, Part 1. Dublin, OH: OCLC Research. 2017. DOI: https://doi.org/10.25333/C3PG8J.
Matusiak KK, Sposito FA. Types of research data management services: an international perspective. Proceedings ASIST. 2017 Oct;54(1):754–6. DOI: https://doi.org/10.1002/pra2.2017.14505401144.
Federer LM, Lu YL, Joubert DJ. Data literacy training needs of biomedical researchers. J Med Libr Assoc. 2016 Jan;104(1):52–7. DOI: https://doi.org/10.3163/1536-5050.104.1.008.
Federer LM, Lu YL, Joubert DJ, Welsh J, Brandys B. Biomedical data sharing and reuse: attitudes and practices of clinical and scientific research staff. PLoS ONE. 2015 Jun;10(6):e0129506. DOI: https://doi.org/10.1371/journal.pone.0129506.
DataONE. Data life cycle [Internet]. DataONE [cited 25 July 2020]. <https://old.dataone.org/data-life-cycle>.
The UK Data Service. Research data lifecycle [Internet]. University of Essex, University of Manchester and Jisc. [cited 25 July 2020]. <https://www.ukdataservice.ac.uk/manage-data/lifecycle>.
The Digital Curation Centre (DCC). Curation lifecycle model [Internet]. DCC [cited 25 July 2020]. <https://www.dcc.ac.uk/guidance/curation-lifecycle-model>.
U.S. Geological Survey (USGS). Data Lifecycle [Internet]. USGS. [cited 21 September 2020]. <https://www.usgs.gov/products/data- institutes and-tools/data-management/data-lifecycle>.
National Institutes of Health. List of NIH, centers and offices [Internet]. The Institutes [cited 6 Feb 2021]. < https://www.nih.gov/institutes-nih/list-nih-institutes-centers-offices>.
National Institutes of Health. NIH strategic plan for data science [Internet]. The Institutes [cited 6 Feb 2021]. <https://datascience.nih.gov/nih-strategic-plan-data-science>.
National Institutes of Health, National Library of Medicine. Glossary [Internet]. The Institutes [cited 6 Feb 2021]. <https://wayback.archive-it.org/org-350/20200602040927/https://www.nlm.nih.gov/cde/glossary.html#cdedefinition>.
National Institutes of Health, National Library of Medicine. NIH CDE Repository [Internet]. The Institutes [cited 6 Feb 2021]. <https://cde.nlm.nih.gov/home>.
Cox AM, Kennan MA, Lyon L, Pinfield S. Developments in research data management in academic libraries: towards an understanding of research data service maturity. J Assoc Inf Sci Tech. 2017 Mar;68(9):2182–200. DOI: https://doi.org/10.1002/asi.23781.
Thompson H. Managing a biomedical libraries’ instruction program: redefining scope. Med Ref Serv Q. 2018 Jan–Mar;37(1):97–104. DOI: https://doi.org/10.1080/02763869.2017.1404402.
The Carpentries. About us [Internet]. The Carpentries [cited 6 Feb 2021]. <https://carpentries.org/about/>.
National Institutes of Health. Center for Information Technology (CIT) [Internet]. The Institutes [cited 6 Feb 2021]. <https://www.nih.gov/about-nih/what-we-do/nih-almanac/center-information-technology-cit>.
Federer L. Research data management in the age of big data: roles and opportunities for librarians. Inf Serv Use. 2016 Sept;36(1–2):35–43. DOI: http://10.3233/ISU-160797.
Lyon L. The information transform: re-engineering libraries for the data decade. Int J Digit Curation. 2012 Mar;7(1):126–138. DOI: https://10.2218/ijdc.v7i1.220.
Patel D. Research data management: a conceptual framework. Lib Rev. 2016 July;65(4–5):226–41. DOI: https://doi.org/10.1108/LR-01-2016-0001.
Van Tuyl S, Whitmire AL. Water, water, everywhere: defining and assessing data sharing in academia. PLoS ONE. 2016 Feb;11(2):e0147942. DOI: https://doi.org/10.1371/journal.pone.0147942.
