International Journal of Knowledge Content Development & Technology

Research Institute for Knowledge Content Development & Technology (건국대학교 지식콘텐츠연구소)
권호 표지
DOI QR코드

DOI QR Code

Research data repository requirements: A case study from universities in North Macedonia

  • Fidan Limani (ZBW Leibniz Information Center for Economics) ;
  • Arben Hajra (ZBW Leibniz Information Center for Economics) ;
  • Mexhid Ferati (Department of Informatics, Faculty of Technology at Linnaeus University) ;
  • Vladimir Radevski (South East European University)
  • Received : 2022.01.14
  • Accepted : 2022.02.26
  • Published : 2023.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

With research data generation on the rise, Institutional Repositories (IR) are one of the tools to manage it. However, the variety of data practices across institutions, domains, communities, etc., often requires dedicated studies in order to identify the research data management (RDM) require- ments and mapping them to IR features to support them. In this study, we investigated the data practices for a few national universities in North Macedonia, including 110 participants from different departments. The methodology we adopted to this end enabled us to derive some of the key RDM requirements for a variety of data-related activities. Finally, we mapped these requirements to 6 features that our participants asked for in an IR solution: (1) create (meta)data and documentation, (2) distribute, share, and promote data, (3) provide access control, (4) store, (5) backup, and (6) archive. This list of IR features could prove useful for any university that has not yet established an IR solution.

Keywords

References

  1. Akers, K. G. & Doty, J. (2013). Disciplinary differences in faculty research data management practices and perspectives. International Journal of Digital Curation, 8(2), 5-26. https://doi.org/10.2218/ijdc.v8i2.263
  2. Akers, K.G. & Green, J.A. (2014). Towards a Symbiotic Relationship Between Academic Libraries and Disciplinary Data Repositories: A Dryad and University of Michigan Case Study. Int. J. Digit. Curation, 9, 119-131. https://doi.org/10.2218/ijdc.v9i1.306
  3. Alcala Ponce de Leon, M. & Anglada i de Ferrer, L. M., (2019). FAIR x FAIR. Feasible, Affordable and Implementable Requirements for a FAIR research data repository. Report, p. 44.
  4. Artini, M., Candela, L., Manghi, P. & Giannini, S., 2020, January. RepOSGate: Open Science Gateways for Institutional Repositories. In Italian Research Conference on Digital Libraries (pp. 151-162). Springer, Cham.
  5. Asadi, S., Abdullah, R., Yah, Y. & Nazir, S. (2019). Understanding Institutional Repository in Higher Learning Institutions: A Systematic Literature Review and Directions for Future Research, in IEEE Access, vol. 7, pp. 35242-35263. COUNTER: Consistent, Credible, Comparable. (n.d.). Retrieved from https://www.projectcounter.org/. https://doi.org/10.1109/ACCESS.2019.2897729
  6. Burns, C. Sean, Amy Lana, & John M. Budd. "Institutional repositories: Exploration of costs and value." D-Lib Magazine 19, no. 1/2 (2013).
  7. Confederation of Open Access Repositories. (2020, October 8). COAR Community Framework for Best Practices in Repositories. (Version 1). Zenodo. http://doi.org/10.5281/zenodo.4110829
  8. FAIR Principles. (n.d.). Retrieved from https://www.go-fair.org/fair-principles/.
  9. Franke, T., Grutz, R., & Dickmann, F. (2013). Functional Requirements for a Central Research Imaging Data Repository. Studies in health technology and informatics, 192, 298-302.
  10. Gordon, A. S., Millman, D. S., Steiger, L., Adolph, K. E., & Gilmore, R. O. (2015). Researcher-Library Collaborations: Data Repositories as a Service for Researchers. J. of Librarianship and Scholarly Communication, 3(2), eP1238.
  11. Gray, A. (2009). Institutional Repositories for Creative and Applied Arts Research: The Kultur Project.
  12. Guindon, A., & Dennie, D. (2016). Concordia University Research Data Management Survey 2015-16. Retrieved from https://spectrum.library.concordia.ca/982722/2/RDM-Concordia-Questionnaire-Final.pdf
  13. Guindon, A., & Dennie, D. (2017, April 28). Research Data Management Survey, Concordia University [PowerPoint slides]. Concordia University Library's 15th Annual Research Forum. Retrieved from https://spectrum.library.concordia.ca/982529/1/Guidon-Dennie-library-forum-2017.pdf.
  14. H2020 Programme. (2017). Guidelines to the Rules on Open Access to Scientific Publications and Open Access to Research Data in Horizon 2020 [online] p. 8. Retrieved from https://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-pilot-guide_en.pdf [Accessed: 2 Dec. 2019].
  15. Hahnel, M., McIntosh Borrelli, L., Hyndman, A., Baynes, G., Crosas, M., ... Research, N.. (2020). The State of Open Data 2020. doi: 10.6084/m9.figshare.13227875.v2.
  16. Hey, T. (2016). The Fourth paradigm - data-intensive scientific discovery and open science. Book of abstracts, Barcelona: Barcelona Supercomputing Center, p. 34-36.
  17. Hsu, L. (2016). RDA: Functional Requirements for Research Data Repository Platforms. Retrieved from https://my.usgs.gov/confluence/display/cdi/RDA%3A+Functional+Requirements+for+Research+Data+Repository+Platforms.
  18. Kim, S. (2018). Functional requirements for research data repositories. International Journal of Knowledg e Content Development & Technology. 8(1), 25.
  19. Kim, S. T. (2020). Functional requirements of data repository for DMP support and CoreTrustSeal authenticati on. International Journal of Knowledge Content Development & Technology, 10(1), 7-20.
  20. Kipnis, D. G., & Palmer, L. A. (2018). Medical institutional repositories in a changing scholarly communication landscape. Against the Grain, 30(4), 56.
  21. Limani, F., Hajra, A., Ferati, M. and Radevski, V. (2020). Requirements and Recommendations for University Research Data Repository: A Case Study. In 18th International Conference e-Society, 2-4 April, 2020 (pp. 51-58). IADIS Press.
  22. Lin J, & Strasser C. (2014). Recommendations for the Role of Publishers in Access to Data. PLoS Biol 12(10): e1001975.
  23. Luther, J. (2018).The Evolving Institutional Repository Landscape. ACRL/Choice publisher.
  24. Lynch, C. A. (2003). Institutional repositories: essential infrastructure for scholarship in the digital age. portal: Libraries and the Academy, 3(2), 327-336. https://doi.org/10.1353/pla.2003.0039
  25. MacIntyre, R., & Jones, H. (2016). IRUS-UK: Improving understanding of the value and impact of institutional repositories. The Serials Librarian, 70(1-4), 100-105. https://doi.org/10.1080/0361526X.2016.1148423
  26. McQuilton, P., Sansone, S. A., Cousijn, H., Cannon, M., Chan, W., Carnevale, I., & Cranston, I. (2020). FAIRsharing Collaboration with DataCite and Publishers: Data Repository Selection, Criteria That Matter. Open Science Framework.
  27. Needham, P., & Lambert, J. (2019). Institutional repositories and the item and research data metrics landscape. Insights, 32(1).
  28. Pampel, H., Vierkant, P., Scholze, F., Bertelmann, R., Kindling, M., Klump, J., ... & Dierolf, U. (2013). Making research data repositories visible: the re3data. org registry. PloS one, 8(11), e78080.
  29. Petritsch, B. (2017). Implementing the institutional data repository IST DataRep.
  30. Registry of Research Data Repositories. (n.d.). Retrieved from https://www.re3data.org/.
  31. Repository Platforms for Research Data IG. (n.d.). Retrieved from https://www.rd-alliance.org/groups/re pository-platforms-research-data.html.
  32. Research360 Project (2013). Institutional Data Repository User Stories. University of Bath.
  33. Royster, P. (2019). IRs in America: "Land of the Free" or "Free Online Access". Retrieved from https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1164&context=library_talks.
  34. Scherer, D., & Valen, D. (2019). Balancing Multiple Roles of Repositories: Developing a Comprehensive Repository at Carnegie Mellon University. Publications, 7(2), 30.
  35. Uzwyshyn, R. (2018) Research Data Repositories: Developing and Implementing Infrastructures for Institutional and Consortial Environments. Coalition for Networked Information. San Diego, CA. April 12-13 2018. Retrieved from https://www.cni.org/wp-content/uploads/2018/04/cni_researchdata_uzwyshyn.pdf.
  36. Vicente-Saez, R., & Martinez-Fuentes, C. (2018). Open Science now: A systematic literature review for an integrated definition. Journal of Business Research, 88, 428-436. https://doi.org/10.1016/j.jbusres.2017.12.043
  37. Welcome to DataCite. (n.d). Retrieved from https://datacite.org
  38. Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., ... & Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3(1), 1-9. https://doi.org/10.1038/sdata.2016.18
  39. Wu, M., Psomopoulos, F., Khalsa, S. J., & de Waard, A. (2019). Data discovery paradigms: User requirements and recommendations for data repositories. Data Science Journal, 18(1).
  40. Yin, S., Zhang, J., Jia, M., & Hu, J. (2020). How to Evaluate and Select a Data Repository for Humanities and Social Science: A Case Study of Fudan University Data Repository for Humanities and Social Science. Library Trends 69(1), 125-137. doi:10.1353/lib.2020.0024.