PL EN


2017 | 55 | 1(109) | 7-18
Article title

Beyond Literacies: The Evolving Landscape of Library Support to Research 2.0

Authors
Title variants
PL
Poza edukacją kompetencji: rozwój sektora bibliotecznego wsparcia dla badań 2.0
Languages of publication
EN PL
Abstracts
EN
PURPOSE/THESIS: This paper identifies some of the tasks and roles that academic libraries have to perform in order to react to the emergence of Research 2.0. APPROACH/METHODS: The argument is based on a non-exhaustive review of the recent literature. RESULTS AND CONCLUSIONS: Academic libraries should respond to the emergence of Research 2.0 by filling niches in services provided by other academic units. RESEARCH LIMITATIONS: As a rule, only the literature of the second half of the 2010s was taken into consideration. PRACTICAL IMPLICATIONS: The tasks identified in this paper may not seem urgent today, but the likelihood that they will become an imperative in the future is high. ORIGINALITY/VALUE: The issues identified in this paper are already a part of everyday best practices in several countries.
PL
CEL/TEZA: W artykule wskazano niektóre zadania i role, które biblioteki akademickie muszą spełnić w odpowiedzi na pojawienie się tzw. badań 2.0. KONCEPCJA/METODY BADAŃ: Argumentacja oparta jest na niewyczerpującym przeglądzie najnowszej literatury. WYNIKI I WNIOSKI: Biblioteki akademickie powinny reagować na pojawienie się badań 2.0, wypełniając nisze w usługach, które temu typowi badań zapewniają inne jednostki akademickie. OGRANICZENIA BADAŃ: Zasadniczo w przedstawionych rozważaniach wykorzystano jedynie piśmiennictwo, które ukazało się po 2015 r. ZASTOSOWANIA PRAKTYCZNE: Zadania wskazane w niniejszym artykule nie muszą wydawać się pilne dzisiaj, ale prawdopodobieństwo, że w przyszłości staną się imperatywem jest wysokie. ORYGINALNOŚĆ?WARTOŚĆ POZNAWCZA: Zagadnienia wskazane w niniejszym artykule już dzisiaj są częścią codziennych dobrych praktyk w wielu krajach.
Year
Volume
55
Issue
Pages
7-18
Physical description
Dates
received
2017-04-27
revised
2017-07-19
accepted
2017-08-30
Contributors
author
  • Institute of Learning Technologies, Eszterházy Károly University in Jászberény, Hungary
References
  • ACRL (2000). Information Literacy Competency Standards for Higher Education. Chicago, IL.: Association of College and Research Libraries.
  • ACRL (2015). Framework for Information Literacy for Higher Education. Chicago, IL.: Association of College and Research Libraries.
  • ANDS (2017). Data citation [online]. Australian National Data Service, [09.04.2017], http://www.ands.org.au/working-with-data/citation-and-identifiers/data-citation
  • Boyd, D.; Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological,and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679.
  • Briney, K. (2016). Strategic planning for research data services. Bulletin of the Association for InformationScience and Technology, 42(4), 39–41.
  • Bruce, C. S. (2008). Informed learning. Chicago, IL: American Library Association.
  • Brydges, B.; Clarke, K. (2015). Is it time to re-envision the role of academic librarians in facultyresearch? [online] Library Connect, 13(7), [09.04.2017] http://libraryconnect.elsevier.com/articles/2015–07/it-time-re-envision-role-academic-librarians-faculty-research
  • Calzada Prado, J.; Marzal, M. Á. (2013). Incorporating Data Literacy into Information LiteracyPrograms: Core Competencies and Contents. Libri, 63(2), 123–134.
  • Cao, L. (2016). Data science: nature and pitfalls. IEEE Intelligent Systems, 31(5), 66–75.
  • Chao, T. (2015). Mapping methods metadata for research data. International Journal of Digital Curation 10(1), 82–94.
  • Carter, D.; Sholler, D. (2016). Data science on the ground: Hype, criticism, and everyday work. Journal of the Association for Information Science and Technology 7(10), 2309–2319.
  • Christensen-Dalsgaard, B. et al (2012), Ten recommendations for libraries to get started with research data management. [online], LIBER, [09.04.2017] http://www.libereurope.eu/news/ten-recommendations-for-libraries-to-get-started-with-research-data-management
  • CIBER (2016). Early career researchers: the harbingers of change? Final report from CIBER. Year 1[online], CIBER-Research.UK, [09.04.2017], http://ciber-research.eu/download/20161120-ECR_Year_1_final_report_071116.pdf
  • Crusoe, D. (2016). Data Literacy defined pro populo: To read this article, please provide a little information.The Journal of Community Informatics, 12(3), 27–46.
  • DATACITE (2016a). Why is it so important to cite data? [online]. DataCite, [09.04.2017], https://www.datacite.org/cite-your-data.html
  • DATACITE, (2016b). Metadata Schema 4.0 [online]. DataCite, [09.04.2017], https://schema.datacite.org/
  • DCC (2015). What is digital curation? [online]. London: Digital Curation Centre [09.04.2017] http://www.dcc.ac.uk/digital-curation/what-digital-curation
  • DGI (2017). The DGI Data Governance Framework. [online]. London: Data Governance Institute,[09.04.2017], http://www.datagovernance.com/dgi-data-governance-framework/
  • ECAR (2015). The Compelling Case for Data Governance. [online]. EDUCAUSE ECAR Working Group [09.04.2017], http://www.educause.edu/library/resources/compelling-case-data-governance
  • Eaker, C. (2014). Educating researchers for effective data management. Bulletin of the American Society for Information Science and Technology, 40(3), 45–46.
  • Erway, R; Horton, L.; Nurnberger, A.; Otsuji, R.; Rushing, A. (2015). Building Blocks: Laying the Foundation for a Research Data Management Program. Dublin, Ohio: OCLC Research [online], OCLC [09.04.2017], http://www.oclc.org/content/dam/research/publications/2016/oclcresearch-datamanagement-building-blocks-2016.pdf
  • Giarlo, M. (2013). Academic Libraries as Quality Hubs. Journal of Librarianship and Scholarly Communication, 1(3), 1–10.
  • Goben, A.; Raszewski, R. (2015). Research data management self-education for librarians: a webliography [online]. Issues in Science and Technology Librarianship 82, [09.04.2017], http://www.istl.org/15-fall/internet2.html
  • Higman, R.; Teperek, M.; Kingsley, D. (2017). Creating a Community of Data Champions. bioRxiv, 104661, DOI 10.1101/104661
  • IBM (2012). Successful information governance through high-quality data. Somers, NY: IBM Corporation.
  • Jackman, L. W.; Weiner, S. A. (2017). The rescinding of the ACRL 2000 Information Literacy Competency Standards for Higher Education–Really? College & Undergraduate Libraries, 24(1), 117–119.
  • Jahnke, L.; Asher, A.; Keralis, S. D. (2012). The problem of data. Washington, DC: Council on Library and Information Resources.
  • Khatri, V.; Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148–152.
  • Koltay, T. (2015a). Data literacy: In search of a name and identity. Journal of Documentation, 71(2), 401–415.
  • Koltay, T. (2015b). Research 2.0, research data services and academic libraries. Praktyka i Teoria Informacji Naukowej i Technicznej, 23(4), 3–12.
  • Koltay, T. (2016a). Are you ready? Tasks and roles for academic libraries in supporting Research 2.0. New Library World, 117(1–2), 94–104.
  • Koltay, T. (2016b). Data governance, data literacy and the management of data quality, IFLA Journal, 42(4), 303–312.
  • Koltay, T.; Špiranec, S.; Z. Karvalics, L. (2015). The shift of information literacy towards research 2.0. The Journal of Academic Librarianship, 41(1), 87–93.
  • Kouper, I. (2016). Professional participation in digital curation. Library & Information Science Research, 38(3), 212–223.
  • LERU (2013). LERU Roadmap for Research Data. Leuven: League of European Research Universities Research Data Working Group [online]. LERU [29.08.2017], http://www.leru.org/index.php/public/publications/year/2013/
  • Lyon, L.; Mattern, E. (2016). Education for real-world data science roles (Part 2): A translational approach to curriculum development. International Journal of Digital Curation, 11(2), 13–26.
  • Lyon, L.; Mattern, E.; Acker, A.; Langmead, A. (2015). Applying translational principles to data science curriculum development [online]. In: iPres Conference Proceedings, Chapel Hill, North Carolina, 2–6 November 2015, D-Scholarship, Institutional Repository of the University of Pittsburgh [29.08.2017], http://d-scholarship.pitt.edu/27159/
  • Maybee, C.; Zilinski, L. (2015). Data informed learning: A next phase data literacy framework for higher education. Proceedings of the Association for Information Science and Technology, 52(1), 1–4.
  • McLure, M.; Level, A.V.; Cranston, C.L.; Oehlerts, B.; Culbertson, M. (2014). Data curation: A study of researcher practices and needs. portal: Libraries and the Academy, 14(2), 139–164.
  • Mooney, H.; Newton, M. P. (2012). The anatomy of a data citation: Discovery, reuse, and credit. Journal of Librarianship and Scholarly Communication, 1(1), 1–14.
  • Nelson, N.; Huffman, J. (2015). Predatory journals in library databases: How much should we worry? The Serials Librarian 69(2), 169–192.
  • Nicholas, D.; Clark, D.; Herman, E. (2016). ResearchGate: Reputation uncovered. Learned Publishing, 29(3), 173–182.
  • Nielsen, H. J.; Hjørland, B. (2014). Curating research data: the potential roles of libraries and information professionals. Journal of Documentation, 70(2), 221–240.
  • Partlo, K., Symons, D., & Carlson, J. D. (2015). Revolutionary or evolutionary? Making research data management manageable. In: B. L. Eden (ed.) Creating Research Infrastructures in the 21st--Century Academic Library: Conceiving, Funding, and Building New Facilities and Staff. Lanham (MD): Rowman & Littlefield. 175–201.
  • Pasquetto, I. V.; Sands, A. E.; Borgman, C. L. (2015). Exploring openness in data and science: What is “open,” to whom, when, and why? Proceedings of the Association for Information Science and Technology, 52(1), 1–2.
  • Patel, D. (2016). Research data management: a conceptual framework. Library Review, 65(4–5), 226–241.
  • Peters, I.; Bar-Ilan, J. (2014). Informetrics, bibliometrics, altmetrics: What is it all about? Proceedings of the American Society for Information Science and Technology, 51(1), 1–4.
  • Poole, A. H. (2017). The conceptual ecology of digital humanities. Journal of Documentation, 73(1), 91–122.
  • Rice, R.; Southall, J. (2016). The data librarian’s handbook. London: Facet Publishing.
  • Ridsdale, Ch.; Rothwell, J.; Smit, M., Ali-Hassan, H.; Bliemel, M.; Dean, I.; Kelley, D.; Matwin, S.; Wuetherick, B. (2015). Strategies and best practices for data literacy education: Knowledge synthesis report. Halifax, NS: Dalhousie University [online], Mike Smit, [09.04.2017], http://www.mikesmit.com/wp-content/papercite-data/pdf/data_literacy.pdf
  • Robinson, L. (2016). Editorial. Alexandria, 26(2), 73–76.
  • Sharma, S.; Qin, J. (2014). Data management: Graduate student’s awareness of practices and policies. Proceedings of the Association for Information Science and Technology, 51(1), 1–3.
  • Tenopir, C.; Allard, S.; Sinha, P.; Pollock, D.; Newman, J.; Dalton, E.; Frame, M.; Baird, L. (2016). Data management education from the perspective of science educators. International Journal of Digital Curation, 11(1), 232–251.
  • Tenopir, C.; Pollock, D.; Allard, S.; Hughes, D. (2016). Research data services in European and north American libraries: Current offerings and plans for the future. Proceedings of the Association for Information Science and Technology, 53(1), 1–6.
  • University of Edinburgh (2015). Why is data management important? Edinburgh: University of Edinburgh [online], [29.08.2017], http://www.ed.ac.uk/schools-departments/information-services/research-support/data-management/why-manage-data
  • Watkinson, A.; Nicholas, D.; Thornley, C.; Herman, E.; Jamali, H. R.; Volentine, R.; Allard, S.; Levine, K.; Tenopir, C. (2016). Changes in the digital scholarly environment and issues of trust: An exploratory, qualitative analysis. Information Processing & Management, 52(3), 446–458.
  • York, J.; Gutmann, M.; Berman, F. (2016), What Do We Know About The Stewardship Gap? [online]. Ann Arbor, MI: University of Michigan, [29.08.2017] http://hdl.handle.net/2027.42/122726
  • Zilinski, L. D.; Nelson, M. S. (2014). Thinking critically about data consumption: Creating the data credibility checklist. Proceedings of the American Society for Information Science and Technology, 51(1), 1–4.
Document Type
Publication order reference
Identifiers
ISSN
0324-8194
e-ISSN
2392-2648
YADDA identifier
bwmeta1.element.desklight-1c2ab1c5-6fa8-4f7f-b187-984ac100b8f2
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.