New Perspectives in Science Education

Edition 13

Accepted Abstracts

On FAIR Data Principles of Institutional Data And Information of Universities

Masao Mori, Tokyo Institute of Technology (Japan)

Tetsuya Oishi, Tokyo Institute of Technology (Japan)

Eiichi Takata, Kobe University (Japan)

Kahori Ogashiwa, Teikyo University (Japan)

Naruhiko Shiratori, Kaetsu University (Japan)

Shintaro Tajiri, Yokohama College of Commerce (Japan)


In scientific research, it cost a lot to reproduce experiments succeeded in the past. For efficiency of research, scientific researchers come to reuse research data of the past research. To do so, it is necessary not only to archive research data, but research data should be arranged confirming to FAIR data principles. FAIR is acronym of “findability”, “accessibility”, “interoperability” and “re-usability”.  To realize FAIR data principle, fulfillment of metadata and persistent identifier on data is significant. On the other hands, institutional data on education and institutional management are coming to be recognized significant for university management to secure fundamental information of universities. In that context the FAIR data principle is effective to facilitate collecting institutional data as well. However, generally speaking, as the technical situations of institutional information are different from university to university, it is difficult to acquire a general resolution. We study the problem to make institutional data and information to be abstract using ontology engineering, and consider the design of institutional data and information that meets FAIR data principles.

Keywords: FAIR data principle, open science, institutional research, ontology;

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