Authors:
Otmane Azeroual
1
;
Joachim Schöpfel
2
;
Janne Pölönen
3
and
Anastasija Nikiforova
4
;
5
Affiliations:
1
German Centre for Higher Education Research and Science Studies (DZHW), Schützenstraße 6A, 10117 Berlin, Germany
;
2
GERiiCO Laboratory, University of Lille, 59653 Villeneuve-d’Ascq, France
;
3
Federation of Finnish Learned Societies, Snellmaninkatu 13, 00170 Helsinki, Finland
;
4
Institute of Computer Science, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia
;
5
European Open Science Cloud (EOSC) Task Force “FAIR metrics and data quality”, 1050 Brussels, Belgium
Keyword(s):
CRIS, FAIR, Findability, Data Management, Knowledge, Information Management, Interoperability, Research Data Management, Research Information System, Open Science.
Abstract:
Digitization in the research domain refers to the increasing integration and analysis of research information in the process of research data management. However, it is not clear whether it is used and, more importantly, whether the data are of sufficient quality, and value and knowledge could be extracted from them. FAIR principles (Findability, Accessibility, Interoperability, Reusability) represent a promising asset to achieve this. Since their publication, they have rapidly proliferated and have become part of (inter-)national research funding programs. A special feature of the FAIR principles is the emphasis on the legibility, readability, and understandability of data. At the same time, they pose a prerequisite for data for their reliability, trustworthiness, and quality. In this sense, the importance of applying FAIR principles to research information and respective systems such as Current Research Information Systems (CRIS), which is an underrepresented subject for research,
is the subject of the paper. Supporting the call for the need for a ”one-stop-shop and register-once-use-many approach”, we argue that CRIS is a key component of the research infrastructure landscape, directly targeted and enabled by operational application and the promotion of FAIR principles. We hypothesize that the improvement of FAIRness is a bidirectional process, where CRIS promotes FAIRness of data and infrastructures, and FAIR principles push further improvements to the underlying CRIS.
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