Evidence map›Paper›PMID 38298923›Full record

ArticleOpen research Europe2022

Community-driven governance of FAIRness assessment: an open issue, an open discussion.

Mark D Wilkinson, Susanna-Assunta Sansone, Eva Méndez, Romain David, Richard Dennis, David Hecker, Mari Kleemola, Carlo Lacagnina, Anastasija Nikiforova, Leyla Jael Castro

Abstract read
In one paragraph

Article in Open research Europe, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Mark D WilkinsonEOSC Task Force on FAIR Metrics and Data Quality, EOSC, Brussels, Belgium.ORCID https://orcid.org/0000-0001-6960-357X
Susanna-Assunta SansoneEOSC Task Force on FAIR Metrics and Data Quality, EOSC, Brussels, Belgium.ORCID https://orcid.org/0000-0001-5306-5690
Eva MéndezLibrary and Information Science Department, Universidad Carlos III de Madrid, Madrid, Spain.ORCID https://orcid.org/0000-0002-5337-4722
Romain DavidEOSC Task Force on FAIR Metrics and Data Quality, EOSC, Brussels, Belgium.ORCID https://orcid.org/0000-0003-4073-7456
Richard DennisEOSC Task Force on FAIR Metrics and Data Quality, EOSC, Brussels, Belgium.ORCID https://orcid.org/0000-0002-4472-7194
David HeckerEOSC Task Force on FAIR Metrics and Data Quality, EOSC, Brussels, Belgium.ORCID https://orcid.org/0000-0003-3836-2800
Mari KleemolaEOSC Task Force on FAIR Metrics and Data Quality, EOSC, Brussels, Belgium.ORCID https://orcid.org/0000-0001-8855-5075
Carlo LacagninaEOSC Task Force on FAIR Metrics and Data Quality, EOSC, Brussels, Belgium.ORCID https://orcid.org/0000-0001-9434-9809
Anastasija NikiforovaEOSC Task Force on FAIR Metrics and Data Quality, EOSC, Brussels, Belgium.ORCID https://orcid.org/0000-0002-0532-3488
Leyla Jael CastroSemantic Technologies team, ZB MED Information Centre for Life Sciences, Cologne, Germany.ORCID https://orcid.org/0000-0003-3986-0510

Funding

Biotechnology and Biological Sciences Research Council BB/H024921/1Wellcome Trust
6 · The paper itself

Abstract

Although FAIR Research Data Principles are targeted at and implemented by different communities, research disciplines, and research stakeholders (data stewards, curators, etc.), there is no conclusive way to determine the level of FAIRness intended or required to make research artefacts (including, but not limited to, research data) Findable, Accessible, Interoperable, and Reusable. The FAIR Principles cover all types of digital objects, metadata, and infrastructures. However, they focus their narrative on data features that support their reusability. FAIR defines principles, not standards, and therefore they do not propose a mechanism to achieve the behaviours they describe in an attempt to be technology/implementation neutral. Various FAIR assessment metrics and tools have been designed to measure FAIRness. Unfortunately, the same digital objects assessed by different tools often exhibit widely different outcomes because of these independent interpretations of FAIR. This results in confusion among the publishers, the funders, and the users of digital research objects. Moreover, in the absence of a standard and transparent definition of what constitutes FAIR behaviours, there is a temptation to define existing approaches as being FAIR-compliant rather than having FAIR define the expected behaviours. This whitepaper identifies three high-level stakeholder categories -FAIR decision and policymakers, FAIR custodians, and FAIR practitioners - and provides examples outlining specific stakeholders' (hypothetical but anticipated) needs. It also examines possible models for governance based on the existing peer efforts, standardisation bodies, and other ways to acknowledge specifications and potential benefits. This whitepaper can serve as a starting point to foster an open discussion around FAIRness governance and the mechanism(s) that could be used to implement it, to be trusted, broadly representative, appropriately scoped, and sustainable. We invite engagement in this conversation in an open Google Group fair-assessment-governance@googlegroups.com.

Indexed as

FAIRFAIR evaluatorsFAIRness assessmentGovernance

Identifiers

PMID38298923
PMCPMC10828551

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.