🏆 Finalist — NIH Data Sharing Index (“S-Index”) Challenge
Demo corpus. Scores are computed on a select set of biomedical paper/datasets and may be inaccurate for papers outside this corpus — DataRank relies on network effects that improve with scale. We aim to expand this into a fully open resource pending additional funding.

The FAIR Cookbook - the essential resource for and by FAIR doers

Scientific Data(2023)10.1038/s41597-023-02166-3Source: DataRank Database

The FAIR Cookbook - the essential resource for and by FAIR doers is a research paper published in Scientific Data (2023). On theSindex it has a DataRank of 0.609. It has been cited 57 times.

N/A
0.609DataRank · unranked
0.609
Open Access57 citations · base score 4.1
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

The notion that data should be Findable, Accessible, Interoperable and Reusable, according to the FAIR Principles, has become a global norm for good data stewardship and a prerequisite for reproducibility. Nowadays, FAIR guides data policy actions and professional practices in the public and private sectors. Despite such global endorsements, however, the FAIR Principles are aspirational, remaining elusive at best, and intimidating at worst. To address the lack of practical guidance, and help with capability gaps, we developed the FAIR Cookbook, an open, online resource of hands-on recipes for "FAIR doers" in the Life Sciences. Created by researchers and data managers professionals in academia, (bio)pharmaceutical companies and information service industries, the FAIR Cookbook covers the key steps in a FAIRification journey, the levels and indicators of FAIRness, the maturity model, the technologies, the tools and the standards available, as well as the skills required, and the challenges to achieve and improve data FAIRness. Part of the ELIXIR ecosystem, and recommended by funders, the FAIR Cookbook is open to contributions of new recipes.

Data sources & pipeline
Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring
Enrichment:Pending

FAIR Checklist

Context only (not used in score)
Findable (1/2)
  • Has DOI
Accessible (1/2)
  • Open Access
Interoperable (0/2)
    Reusable (0/3)

      FAIR checklist signals are shown for context only and do not affect DataRank scoring.

      DataRank Breakdown

      Base Score 100%Citation Network 0%

      Base Score Contribution

      0.609

      From this paper's citation signal

      Citation Network Contribution

      0

      Citation network not refreshed for this result

      This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

      Learn more about DataRank methodology →
      Why this DataRank?

      DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.

      Base score B(p)
      log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
      Network N(p)
      Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
      Damping factor d = 0.85
      DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
      Self-citations excluded
      Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.

      Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.

      Read the full methodology →

      Related Papers (10)

      Scientific Data(2016)
      co-citedsame journal
      10.1038/sdata.2016.18
      Scientific Data(2023)
      co-citedsame journal
      10.1038/s41597-023-02167-2
      Machine actionable metadata models
      N/A
      0.917DataRank · unranked
      Scientific Data(2022)
      co-citedsame journal
      10.1038/s41597-022-01707-6
      Scientific Data(2017)
      co-citedsame journal
      10.1038/sdata.2017.59
      Data Science Journal(2020)
      co-cited
      10.5334/dsj-2020-032
      D2.1 FAIR Cookbook
      N/A
      0.312DataRank · unranked
      Zenodo (CERN European Organization for Nuclear Research)(2022)
      co-cited
      10.5281/zenodo.6783564
      Zenodo (CERN European Organization for Nuclear Research)(2022)
      co-cited
      10.5281/zenodo.7157285