Journal:Development and governance of FAIR thresholds for a data federation
| Full article title | Development and governance of FAIR thresholds for a data federation |
|---|---|
| Journal | Data Science Journal |
| Author(s) | Wong, Megan; Levett, Kerry; Lee, Ashlin; Box, Paul; Simons, Bruce; David, Rakesh; MacLeod, Andrew; Taylor, Nicolas; Schneider, Derek; Thompson, Helen |
| Author affiliation(s) | Federation University, Australian Research Data Commons, Commonwealth Scientific and Industrial Research Organisation, University of Adelaide, The University of Western Australia, University of New England |
| Primary contact | Email: mr dot wong at federation dot edu dot au |
| Year published | 2022 |
| Volume and issue | 21(1) |
| Article # | 13 |
| DOI | 10.5334/dsj-2022-013 |
| ISSN | 1683-1470 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://datascience.codata.org/articles/10.5334/dsj-2022-013/ |
| Download | https://datascience.codata.org/articles/10.5334/dsj-2022-013/galley/1138/download/ (PDF) |
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This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Abstract
The FAIR (findable, accessible, interoperable, and re-usable) principles and practice recommendations provide high level guidance and recommendations that are not research-domain specific in nature. There remains a gap in practice at the data provider and domain scientist level, demonstrating how the FAIR principles can be applied beyond a set of generalist guidelines to meet the needs of a specific domain community.
We present our insights developing FAIR thresholds in a domain-specific context for self-governance by a community (in this case, agricultural research). "Minimum thresholds" for FAIR data are required to align expectations for data delivered from providers’ distributed data stores through a community-governed federation (the Agricultural Research Federation, AgReFed).
Data providers were supported to make data holdings more FAIR. There was a range of different FAIR starting points, organizational goals, and end user needs, solutions, and capabilities. This informed the distilling of a set of FAIR criteria ranging from "Minimum thresholds" to "Stretch targets." These were operationalized through consensus into a framework for governance and implementation by the agricultural research domain community.
Improving the FAIR maturity of data took resourcing and incentive to do so, highlighting the challenge for data federations to generate value whilst reducing costs of participation. Our experience showed a role for supporting collective advocacy, relationship brokering, tailored support, and low-bar tooling access, particularly across the areas of data structure, access, and semantics that were challenging to domain researchers. Active democratic participation supported by a governance framework like AgReFed’s will ensure participants have a say in how federations can deliver individual and collective benefits for members.
Keywords: agriculture, AgReFed, FAIR data, community, governance, RM-ODP, data federation
Context and contribution
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Notes
This presentation is faithful to the original, with only a few minor changes to presentation, grammar, and punctuation. In some cases important information was missing from the references, and that information was added.
