- Олексюк, Василь Петрович (orcid.org/0000-0003-2206-8447), Спірін, О.М. (orcid.org/0000-0002-9594-6602), Осадча, К. П. (orcid.org/0000-0003-0653-6423) and Шиненко, М.А. (orcid.org/0000-0001-6697-747X) (2026) Foreign and Ukrainian experience in the formation and use of FAIR data in educational research Continuing Professional Education: Theory and Practice, 2 (87). pp. 207-221. ISSN 2412-0774
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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.
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