re3data – Indexing the Global Research Data Repository Landscape Since 2012
re3data – Indexing the Global Research Data Repository Landscape Since 2012 is a dataset published in Scientific Data (2023). On theSindex it has a DataRank of 1.1, placing it in the top 41.3% of the data-sharing corpus. It has been cited 20 times, with 19 citing works in its 1-hop citation network. Its calibrated FAIR score is 58/100.
Abstract
For more than ten years, re3data, a global registry of research data repositories (RDRs), has been helping scientists, funding agencies, libraries, and data centers with finding, identifying, and referencing RDRs. As the world's largest directory of RDRs, re3data currently describes over 3,000 RDRs on the basis of a comprehensive metadata schema. The service allows searching for RDRs of any type and from all disciplines, and users can filter results based on a wide range of characteristics. The re3data RDR descriptions are available as Open Data accessible through an API and are utilized by numerous Open Science services. re3data is engaged in various initiatives and projects concerning data management and is mentioned in the policies of many scientific institutions, funding organizations, and publishers. This article reflects on the ten-year experience of running re3data and discusses ten key issues related to the management of an Open Science service that caters to RDRs worldwide.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
0.449
From this paper's citation signal
Citation Network Contribution
0.613
From 14 citing papers with measurable signal
Top 4 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Big Data, Little Data, No DataThe MIT Press eBooks2015549 citationsDataRank 0.946
- Open research data repositories: Practices, norms, and metadata for sharing imagesJournal of the Association for Information Science and Technology202127 citationsDataRank 0.500
- Understanding Research Data Repositories as InfrastructuresProceedings of the Association for Information Science and Technology20218 citationsDataRank 0.330
- Lessons learned on stakeholder engagement in radiation protection, and communication and dissemination of results from the radonorm projectRadiation and Environmental Biophysics20260 citationsDataRank 0
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 42% comes from its base citations and 58% from the citation network (14 citing papers contributed measurable signal).
- 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.
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