FOREIGN AND UKRAINIAN EXPERIENCE IN THE FORMATION AND USE OF FAIR DATA IN EDUCATIONAL RESEARCH
DOI:
https://doi.org/10.28925/2412-0774.2026.2.15Keywords:
educational sciences research, FAIR data, FAIR data taxonomy, metadata standardisation, Open Science, research data management.Abstract
This article highlights the issue of responsible experimental data management within the context of scientific digitalisation and the transition towards the Open Science paradigm. The research aims to generalise contemporary foreign and domestic experience in generating and using FAIR data within the social sciences and humanities, with a specific focus on the field of education. Based on the analysis of article metadata from the Scopus and Web of Science databases, key trends in FAIR data implementation have been identified. These include the development of institutional data management policies, the provision of semantic interoperability through predefined metadata profiles or knowledge graphs, and the targeted development of researchers’ FAIR data management competencies. Despite significant experience in researching the implementation of institutional repositories and electronic libraries, an analysis of article data indexed by the OUCI (Open Ukrainian Scientific Content Initiative) service revealed that the study of FAIR data in the domestic scientific and pedagogical discourse is still in its initial stages. The authors demonstrate that applying FAIR principles within the educational sciences has specific characteristics. These are driven by the multidisciplinary nature of the field, the combination of qualitative and quantitative methods, and the legal requirements for protecting the personal data of both educational process participants and research subjects. As a result of the study, the authors have developed an original taxonomy of FAIR data for educational research. This taxonomy classifies data according to various criteria, including the intended purpose, level of education, research methods, data sources, and formats. This taxonomy establishes a foundation for designing a FAIR data standardisation model and developing metrics to assess compliance with FAIR principles. Furthermore, recommendations for this model have been formulated. Among other things, these suggest providing institutional support for Open Science, specifying metadata formats for scientific and pedagogical research, configuring institutional repositories accordingly, developing additional digital tools for workflow automation, and training academic and teaching staff.
References
Marienko, M. V., Shyshkina, M. P., & Konoval, O. A. (2022). Metodologichni zasadi formuvannya hmaro oriyentovanih sistem vidkritoyi nauki u zakladah vishoyi pedagogichnoyi osviti [Methodological principles of formation of cloud-oriented systems of open science in institutions of higher pedagogical education]. Information Technologies and Learning Tools, 89 (3), 209–232. https://doi.org/10.33407/itlt.v89i3.4981
Mintii, I. S., Vakaliuk, T. A., & Ivanova, S. M. (2026). Cifrova kompetentnist naukovih i naukovo-pedagogichnih pracivnikiv u sferi fair danih: rezultati konstatuvalnogo eksperimentu [Digital competence of researchers and academic staff in fair data management: results of a statement experiment]. Innovate Pedagogy, 2 (92), 304–307. https://doi.org/10.32782/ip/92.2.56
Oleksyuk, V. P., & Oleksyuk, O. R. (2012). Institucijnij repozitarij: mozhlivosti zastosuvannya u navchalnomu procesi [Institutional repository: employment in education]. Information Technologies and Learning Tools, 32 (6). https://doi.org/10.33407/itlt.v32i6.755
Pavlyk, N., Seiko, N., & Kotlovuy, S. (2025). Etika roboti z personalnimi danimi u doslidzhennyah iz socialnoyi roboti: vrazlivist, dovira i vidpovidalnist [Ethics of working with personal data in social work research: vulnerability, trust and responsibility]. Scientific Bulletin of Uzhhorod University. Series: «Pedagogy. Social Work», 2 (57), 128–133. https://doi.org/10.24144/2524-0609.2025.57.128-133
Sych, O. (2020). Licenzuvannya osvitnoyi diyalnosti yak instrument zabezpechennya yakosti vishoyi osviti [Licensing of educational activities as a mechanism for quality assurance of higher education]. Cherkasy University Bulletin: Pedagogical Sciences, 4, 137–152. https://doi.org/10.31651/2524-2660-2020-4-137-152
Spirin, O. M., Ivanova, S. M., Novytskyi, O. V., & Shynenko, M. A. (2010). Proekt koncepciyi elektronnoyi biblioteki Nacionalnoyi akademiyi pedagogichnih nauk Ukrayini [A project of concepttion of elektronic library of National academy of pedagogical sciences of Ukraine]. Information Technologies and Learning Tools, 20 (6). https://doi.org/10.33407/itlt.v20i6.396
Tiutiunnyk, A. (2022). Vidslidkovuvannya dinamiki rejtingovih pokaznikiv vikladacha dlya zabezpechennya yakosti vishoyi osviti [Tracking the dynamics of lecturer rating indicators to ensure the quality of higher education]. Electronic Scientific Professional Journal “Open educational E-environment of modern university”, 13, 141–152. https://doi.org/10.28925/2414-0325.2022.1312
Bellgard, M. I. (2020). ERDMAS: An exemplar-driven institutional research data management and analysis strategy. International Journal of Information Management, 50, 337–340. https://doi.org/10.1016/j.ijinfomgt.2019.08.009
Bongiovani, P. C., Díaz Pacífico, F., & Freán, P. (2025). Datos FAIR en Argentina. Desarrollo y desafíos del Repositorio de Datos Académicos RDA-UNR. Información, cultura y sociedad, 52, 175–202. https://doi.org/10.34096/ics.i52.16501
Crystal-Ornelas, R., Varadharajan, C., O’Ryan, D., Beilsmith, K., Bond-Lamberty, B., Boye, K., Burrus, M., Cholia, S., Christianson, D. S., Crow, M., Damerow, J., Ely, K. S., Goldman, A. E., Heinz, S. L., Hendrix, V. C., Kakalia, Z., Mathes, K., O’Brien, F., Pennington, S. C., ... Agarwal, D. A. (2022). Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats. Scientific Data, 9 (1). https://doi.org/10.1038/s41597-022-01606-w
Ćwiek-Kupczyńska, H., Filipiak, K., Markiewicz, A., Rocca-Serra, P., Gonzalez-Beltran, A. N., Sansone, S.-A., Millet, E. J., van Eeuwijk, F., Ławrynowicz, A., & Krajewski, P. (2020). Semantic concept schema of the linear mixed model of experimental observations. Scientific Data, 7 (1). https://doi.org/10.1038/s41597-020-0409-7
Garske, B., Holz, W., & Ekardt, F. (2024). Digital twins in sustainable transition: exploring the role of EU data governance. Frontiers in Research Metrics and Analytics, 9. https://doi.org/10.3389/frma.2024.1303024
Gualandi, B., Pareschi, L., & Peroni, S. (2022). What do we mean by “data”? A proposed classification of data types in the arts and humanities. Journal of Documentation, 79 (7), 51–71. https://doi.org/10.1108/jd-07-2022-0146
Hahnel, M., & Valen, D. (2020). How to (Easily) Extend the FAIRness of Existing Repositories. Data Intelligence, 2 (1–2), 192–198. https://doi.org/10.1162/dint_a_00041
Hofman, J. M., Watts, D. J., Athey, S., Garip, F., Griffiths, T. L., Kleinberg, J., Margetts, H., Mullainathan, S., Salganik, M. J., Vazire, S., Vespignani, A., & Yarkoni, T. (2021). Integrating explanation and prediction in computational social science. Nature, 595, 181–188. https://doi.org/10.1038/s41586-021-03659-0
Mustillo, T. (2025). Building a FAIR, linked, and open data ecosystem: innovation cascades in political science. European Political Science, 24 (4), 810-825. https://doi.org/10.1057/s41304-025-00520-0
Oladipo, F., Folorunso, S., Ogundepo, E., Osigwe, O., & Akindele, A. (2022). Curriculum Development for FAIR Data Stewardship. Data Intelligence, 4 (4), 991–1012. https://doi.org/10.1162/dint_a_00183
Penev, L., Dimitrova, M., Senderov, V., Zhelezov, G., Georgiev, T., Stoev, P., & Simov, K. (2019). OpenBiodiv: A Knowledge Graph for Literature-Extracted Linked Open Data in Biodiversity Science. Publications, 7 (2), 38. https://doi.org/10.3390/publications7020038
Rousi, A. M. (2023). Using current research information systems to investigate data acquisition and data sharing practices of computer scientists. Journal of Librarianship and Information Science, 55 (3), 596–608. https://doi.org/10.1177/09610006221093049
Skluzacek, T. J., Chard, K., & Foster, I. (2022). Automated metadata extraction: challenges and opportunities. 2022 IEEE 18th International Conference on e-Science (e-Science), 495–500. https://doi.org/10.1109/escience55777.2022.00088
Thalhath, N., Nagamori, M., & Sakaguchi, T. (2025). Metadata application profile as a mechanism for semantic interoperability in FAIR and open data publishing. Data and Information Management, 9 (1), 100068. https://doi.org/10.1016/j.dim.2024.100068
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