Identifying new roles for academic libraries in supporting data-intensive research
https://doi.org/10.20913/1815-3186-2019-4-97-102
Abstract
Reacting to the appearance of data-intensive research prompts academic libraries to become service providers for scholars, who work with research data. Although this is an imperative for libraries worldwide, due to the differences between countries and institutions, the level of readiness to engage in related activities differs from country to country. While some of the related tasks are fairly novel, others heavily build on librarians’ traditional, well-known skills. To identify these tasks, as well as making an inventory of the required skills and abilities, this paper, based on a non-exhaustive review of the recent literature, presents both theoretical and practical issues. It is demonstrated that the most obvious directions of the service development in academic libraries to support data-intensive science are research data management, data curation, data literacy education for users, and data literacy education for librarians.
About the Author
T. KoltayHungary
Koltay Tibor, Dr. habil., PhD, Professor, Institute of Learning Technologies
Jászberény
References
1. Pinfield S., Cox A. M., Rutter S. Mapping the future of academic libraries: a report for SCONUL. London, 2017. 67 p.
2. Boyce G., Greenwood A., Haworth A., Hodgson J., Jones C., Marsh G., Sadler R. Visions of value: leading the development of a view of the university library in the 21st century. The Journal of Academic Librarianship, 2019, 45 (5), 1–8. DOI: https://doi.org/10.1016/j.acalib.2019.102046.
3. Pasquetto I. V., Sands A. E., Borgman C. L. Exploring openness in data and science: what is “open”, to whom, when, and why? Proceedings of the 78th American Society for Information Science and Technology annual meeting. St. Louis, 2015, 141–144. DOI: https://doi.org/10.1002/pra2.2015.1450520100141.
4. Borgman Ch. L. Big data, little data, no data: scholarship in the networked world. Cambridge, MIT Press, 2015. 416 p.
5. Kirkwood R. J. Collection development or data-driven content curation? Library Management, 2016, 37 (4/5), 275– 284. DOI: https://doi.org/10.1108/LM-05-2016-0044.
6. Schmidt B., Shearer K. Librarians’ competencies profile for research data management. Joint Task Force on librarians’ competencies in support of e-research and scholarly communication. Gottingen, June 7–9, 2016. URL: https://www.coar-repositories.org/files/Competencies-for-RDM_June-2016.pdf (accessed 10.09.2019).
7. Tenopir C., Sandusky R. J., Allard S., Birch B. Research data management services in academic research libraries and perceptions of librarians. Library & Information Science Research, 2014, 36(2), 84–90. DOI: https://doi.org/10.1016/j.lisr.2013.11.003.6а.
8. Robinson L, Bawden D. “The story of data”: a socio-technical approach to education for the data librarian role in the CityLIS library school at City, University of London. Library Management, 2018, 38 (6/7), 312–322. DOI: https://doi.org/10.1108/LM-01-2017-0009.
9. Burton M., Lyon L. Data science in libraries. Bulletin of the Association for Information Science and Technology, 2017, 43 (4), 33–35. DOI: https://doi.org/10.1080/01930826.2019.1583015.
10. LERU roadmap for research data. Leuven, 2013. 36 p. URL: https://www.leru.org/publications/leruroadmap-for-research-data (accessed 11.09.2019).
11. Semeler A. R., Pinto A. L., Rozados H. B. F. Data science in data librarianship: core competencies of a data librarian. Journal of Librarianship and Information Science, 2019, 51(3), 771–780. DOI: http://journals.sagepub.com/doi/10.1177/0961000617742465.
12. Tenopir C., Talja S., Horstmann W., Late E. Hughes D., Pollock D. Schmidt B., Baird L., Sandusky R. J., Allard S. Research data services in European academic research libraries. LIBER Quarterly. 2016, 27 (1), 23–44. DOI: http://doi.org/10.18352/lq.10180.
13. Rice R, Southall J. The data librarian’s handbook. London, Facet Publ., 2016. 192 p.
14. Huling N, Dallas L. J., Kinder J. B., Whitlatch J. B., Woodard B. Professional competencies for reference and user services librarians. RUSA. 2017. URL: http://www.ala.org/rusa/resources/guidelines/professional (accessed 12.09.2019).
15. Whitlatch J. B., Bodner N. E., Diefenthal M. Z., Huling N, Kluegel K. M. Professional competencies for reference and user services librarians. Reference User Services Quarterly, 2003, 42 (4), 290–295.
16. Cox A. M., Kennan M. A., Lyon L., Pinfield S. Developments in research data management in academic libraries: towards an understanding of research data service maturity. Journal of the Association for Information Science and Technology, 2017, 68 (9), 2182– 2200. DOI: https://doi.org/10.1002/asi.23781.
17. Silvello G. Theory and practice of data citation. Journal of the Association for Information Science and Technology, 2018, 69 (1), 6–20. DOI: https://doi.org/10.1002/asi.23917.
18. Sayre F., Riegelman A. Replicable Services for Reproducible Research: A Model for Academic Libraries. College & Research Libraries, 2019, 80 (2), 260–272. DOI: https://doi.org/10.5860/crl.80.2.260.
19. Molloy L., Hodson S., Goldstein S., Davidson J. Addressing data management training needs: a practice based approach from the UK. iPres2012: proc. of the 9th Intern. conf. on preservation of digital objects (Toronto, 1–5 Oct. 2012). Toronto, 2012, 249–256.
20. Koltay T. Data literacy for researchers and data librarians. Journal of Librarianship and Information Science, 2017, 49 (1), 3–14. URL: http://journals.sagepub.com/doi/10.1177/0961000615616450.
21. Bugaje M., Chowdhury G. Is data retrieval different from text retrieval? An exploratory study. International Conference on Asian Digital Libraries. Cham, 2017, 97–103. DOI: https://doi.org/10.1007/978-3-319-70232-2_8.
22. Faniel I. M, Connaway L. S. Librarians’ perspectives on the factors influencing research data management programs. College & Research Libraries, 2018, 79(1), 100–119. DOI: https://doi.org/10.5860/crl.79.1.100.
23. Bryant R., Lavoie B. F., Malpas C. The realities of research data management. Pt. 1: A tour of the Research Data Management (RDM) service space. Dublin, 2017. 20 p. URL: https://www.oclc.org/research/publications/2017/oclcresearch-rdm-part-one-servicespace-tour.html (accessed 13.09.2019).
24. Cox A. M. Academic librarianship as a data profession: the familiar and unfamiliar in the data role spectrum. eLucidate, 2018, 15 (1/2), 7–10.
25. Ray J. The rise of digital curation and cyberinfrastructure: From experimentation to implementation and maybe integration. Library Hi Tech, 2013, 30 (4), 604–622. DOI: https://doi.org/10.1108/07378831211285086.
26. Bracke M. S. Emerging data curation roles for librarians: a case study of agricultural data. Journal of Agricultural Food Information, 2013, 12(1), 65–74. DOI: https://doi.org/10.1080/10496505.2011.539158.
27. Sposito F. A. What do data curators care about? Data quality, user trust, and the data reuse plan. IFLA WLIC2017 satellite meeting. URL: http://library.ifla.org/1797 (accessed 14.09.2019).
28. Thomas C. V., Urban R. J. What do data librarians think of the MLIS? Professionals’ perceptions of knowledge transfer, trends, and challenges. College & Research Libraries, 2018, 79 (3), 401–423. DOI: https://doi.org/10.5860/crl.79.3.401.
29. Johnston L. R., Carlson J., HudsonVitale C., Imker H., Kozlowski W., Olendorf R., Stewart C. How important are data curation activities to researchers? Gaps and opportunities for academic libraries. Journal of Librarianship and Scholarly Communication, 2018, 6, eP2198, 1–24. DOI: https://doi.org/10.7710/2162-3309.2198.
30. Poole A. H. The conceptual landscape of digital curation. Journal of Documentation, 2016, 72(5), 961–986. DOI: https://doi.org/10.1108/JD-10-2015-0123.
31. Lucky S., Harkema C. Back to basics: supporting digital humanities and community collaboration using the core strength of the academic library. Digital Library Perspectives, 2018, 34 (3), 188–199. DOI: https://doi.org/10.1108/DLP-03-2018-0009.
32. Koltay T. Data literacy: in search of a name and identity. Journal of Documentation, 2015, 71(2), 401–415. DOI: https://doi.org/10.1108/JD-02-2014-0026.
33. Corrall S. Repositioning data literacy as a missioncritical competence. ACRL 2019: Recasting the Narrative (Cleaveland, 10–13 Apr. 2019). Cleaveland, 2019. 7 p. URL: http://d-scholarship.pitt.edu/36975/ (accessed 16.09.2019).
34. Morrison L., Weech T. Reading data: the missing literacy from LIS education. The power of reading : proc. of the XXVI Bobcatsss symp. Riga, 2018, 75–80.
35. Mason J., Khan K., Smith S. Literate, numerate, discriminate: realigning 21st century skills”. Proceedings of the 24th International Conference on Computers in Education (Bombay, Nov. 28 – Dec. 2, 2016). Bombay, 2016, 609–614.
36. Daraio C., Lenzerini M., Leporelli C., Naggar P., Bonaccorsi A., Bartolucci A. The advantages of an ontology-based data management approach: openness, interoperability and data quality. Scientometrics, 2016, 108 (1), 441–455. DOI: https://doi.org/10.1007/s11192-016-1913-6.
37. Koltay T. Data governance, data literacy and the management of data quality. IFLA Journal, 2016, 42 (4), 303–312. DOI: http://journals.sagepub.com/doi/10.1177/0340035216672238.
38. Mattern E., Brenner A., Lyon L. Learning by teaching about RDM: an active learning model for internal library education. International Journal of Digital Curation, 2016, 11 (2), 27–38. DOI: http://dx.doi.org/10.2218/ijdc.v11i2.414.
39. Federer L. Defining data librarianship: a survey of competencies, skills, and training. Journal of the Medical Library Association, 2018, 106 (3), 294–303. DOI: https://doi.org/10.5195/jmla.2018.306.
40. Maxwell D., Norton H., Wu J. The data science opportunity: crafting a holistic strategy. Journal of Library Administration, 2018, 58 (2), 111–127. DOI: https://www.tandfonline.com/doi/full/10.1080/01930826.2017.1412704.
41. Ogier A. L., Stamper M. J. Data visualization as a library service: embedding visualization services in the library research lifecycle. Journal of eScience Librarianship, 2018, 7 (1), e1126, 1–11. DOI: https://doi.org/10.7191/jeslib.2018.1126.
42. Cao L. Data science: nature and pitfalls. IEEE Intelligent Systems, 2016, 31 (5), 66–75. DOI: https://doi.org/10.1109/MIS.2016.86.
43. Robinson L . Informationscience : communication chain and domain analysis. Journal of Documentation, 2009, 65 (4), 578–591. DOI: https://doi.org/10.1108/00220410910970267.
44. Koltay T. Accepted and emerging roles of academic libraries in supporting research 2.0. The Journal of Academic Librarianship, 2019, 45 (2), 75–80. DOI: https://doi.org/10.1016/j.acalib.2019.01.001.
45. Robinson L. Between the deluge and the dark age: perspectives on data curation. Alexandria, 2016, 26 (6), 73–76. DOI: https://doi.org/10.1177/0955749016661067.
46. Scaramozzino J. M., Ramírez M. L., McGaughey K. J. A study of faculty data curation behaviors and attitudes at a teaching-centered university. College & Research Libraries, 2012, 73 (4), 349–365. DOI: https://doi.org/10.5860/crl-255.
Review
For citations:
Koltay T. Identifying new roles for academic libraries in supporting data-intensive research. Bibliosphere. 2019;(4):97-102. https://doi.org/10.20913/1815-3186-2019-4-97-102