- Осадча, К. П. (orcid.org/0000-0003-0653-6423), Спірін, О.М. (orcid.org/0000-0002-9594-6602) and Олексюк, Василь Петрович (orcid.org/0000-0003-2206-8447) (2026) FAIR data taxonomy for scientific research in education in the context of digital transformation Bulletin of Oleksandr Dovzhenko Hlukhiv National Pedagogical University. Series: Pedagogical Sciences, 2 (61). pp. 10-22. ISSN 2410-0897
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Abstract
Introduction. The problem of data accumulation, including in the field of education, necessitates their systematisation to ensure consistency, transparency, compatibility, and the possibility of reuse. Different institutions and researchers use various classifications determined by specific goals – that is, for what purpose these data are needed or collected. As a result, it is difficult to build educational analytics models without a clear data structure, especially in the context of Ukraine's integration into the European and broader international educational space. An analysis of Internet resources showed that in the Ukrainian segment of the Internet, the concept of «data taxonomy» aroused the greatest interest in 2004, and since 2022, the concept of «data classification» began to play a role. In the English-speaking segment of the Internet, moderate interest in the concept of «data classification» has been demonstrated since 2004, which significantly increased in 2025. An analysis of scientific works in Scopus and Web of Science showed that over the past 5 years, interest in solving this problem has not been a priority in scientific research. Therefore, it is insufficiently studied, which prompted the authors to solve the problem of creating a scientifically sound, multidimensional, and FAIR-oriented data taxonomy for scientific research in the field of education in the context of the modern development of digital technologies. Purpose. Taking into account all of the above, the purpose of the article is to develop a scientifically sound, multidimensional, and FAIR-oriented data taxonomy for scientific research in the field of education in the context of the modern development of digital technologies. Methods. The study used a set of theoretical research methods, including analysis and generalisation of scientific literature to study taxonomies and classifications that demonstrate important approaches to data structuring, in particular for scientific research in the field of education, as well as an inductive approach to the formation of categories. Results. As a result, a FAIR data taxonomy was proposed for scientific research in the field of education, which classifies educational data by the purpose of scientific research (focus, nature of the expected result), by specialties/areas in the field of educational/pedagogical sciences, by the subject of data submission and execution (number of performers/form of organization of research activities), by types and methods of research, by types (formal/non-formal), formal/informal), levels and forms of education, by data sources, data format, level of aggregation, access mode, degree of identification, and life cycle. Thus, the developed taxonomy has 11 dimensions and 2 categories at the 1st and 2nd levels, which reveal each dimension in more detail. Originality: The scientific novelty of the research results lies in the fact that for the first time, a comprehensive taxonomy of FAIR data for scientific research in the field of education has been proposed, which allows systematising educational data according to 11 dimensions. Conclusion. The proposed taxonomy of FAIR data for scientific research in the field of education forms a new systematic approach to the classification and organisation of educational research data in accordance with the FAIR principles, which has not previously been comprehensively implemented in the context of educational sciences.
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