Affording reusable data: recommendations for researchers from a data-intensive project

Scientists are increasingly required by funding agencies, publishers and their institutions to produce and publish data that are Findable, Accessible, Interoperable and Reusable (FAIR). This requires curatorial activities, which are expensive in terms of both time and effort. Based on our experience of supporting a multidisciplinary research team, we provide recommendations to direct the efforts of researchers towards affordable ways to achieve a reasonable degree of “FAIRness” for their data to become reusable upon its publication. The recommendations are accompanied by concrete insights on the challenges faced when trying to implement them in an actual data-intensive reference project.

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Publication Details

Journal
Scientific Data
Published
2025-02-12
DOI
https://doi.org/10.1038/s41597-025-04565-0
Citations
9
Primary Topic
Research Data Management Practices
Type
article
Field-Weighted Citation Impact
29.22

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article

Affording reusable data: recommendations for researchers from a data-intensive project

Eva Furrer, Francesco Garassino, Gorka Fraga González, Diane de Zélicourt et al.
9 citations
Scientific Data
Research Data Management Practices
29.22
article

Affording reusable data: recommendations for researchers from a data-intensive project

Eva Furrer, Francesco Garassino, Gorka Fraga González, Diane de Zélicourt, Vartan Kurtcuoglu, Willy Kuo, Leonhard Held, Hester van de Wiel
article en
9 citations

Abstract

Scientists are increasingly required by funding agencies, publishers and their institutions to produce and publish data that are Findable, Accessible, Interoperable and Reusable (FAIR). This requires curatorial activities, which are expensive in terms of both time and effort. Based on our experience of supporting a multidisciplinary research team, we provide recommendations to direct the efforts of researchers towards affordable ways to achieve a reasonable degree of “FAIRness” for their data to become reusable upon its publication. The recommendations are accompanied by concrete insights on the challenges faced when trying to implement them in an actual data-intensive reference project.

Scientific DataVol. 12(1)
University of Zurich (CH), Prevention Institute (US)
National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Openalex Percentile: Top 1%
Research Data Management Practices
29.22
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