EURO-NMD registry: federated FAIR infrastructure, innovative technologies and concepts of a patient-centred registry for rare neuromuscular disorders
EURO-NMD registry: federated FAIR infrastructure, innovative technologies and concepts of a patient-centred registry for rare neuromuscular disorders is a dataset published in Orphanet Journal of Rare Diseases (2024). On theSindex it has a DataRank of 1.1, placing it in the top 40.8% of the data-sharing corpus. It has been cited 19 times, with 15 citing works in its 1-hop citation network. Its calibrated FAIR score is 58/100.
Abstract
BackgroundThe EURO-NMD Registry collects data from all neuromuscular patients seen at EURO-NMD's expert centres. In-kind contributions from three patient organisations have ensured that the registry is patient-centred, meaningful, and impactful. The consenting process covers other uses, such as research, cohort finding and trial readiness.ResultsThe registry has three-layered datasets, with European Commission-mandated data elements (EU-CDEs), a set of cross-neuromuscular data elements (NMD-CDEs) and a dataset of disease-specific data elements that function modularly (DS-DEs). The registry captures clinical, neuromuscular imaging, neuromuscular histopathology, biological and genetic data and patient-reported outcomes in a computer-interpretable format using selected ontologies and classifications. The EURO-NMD registry is connected to the EURO-NMD Registry Hub through an interoperability layer. The Hub provides an entry point to other neuromuscular registries that follow the FAIR data stewardship principles and enable GDPR-compliant information exchange. Four national or disease-specific patient registries are interoperable with the EURO-NMD Registry, allowing for federated analysis across these different resources.ConclusionsCollectively, the Registry Hub brings together data that are currently siloed and fragmented to improve healthcare and advance research for neuromuscular diseases.
›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
0.442
From this paper's citation signal
Citation Network Contribution
0.678
From 11 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics supportJournal of Biomedical Informatics200950,615 citationsDataRank 1.6
- The REDCap consortium: Building an international community of software platform partnersJournal of Biomedical Informatics201923,252 citationsDataRank 1.5
- The FAIR Guiding Principles for scientific data management and stewardshipScientific Data201617,221 citationsDataRank 1.5
- SIMPATHIC: Accelerating drug repurposing for rare diseases by exploiting SIMilarities in clinical and molecular PATHologyMolecular Genetics and Metabolism20252 citationsDataRank 0.165
- The FAIR journey of a patient-driven registry: Reflections and practical solutions from the Duchenne Data Platform FAIRification experienceJournal of Neuromuscular Diseases20250 citationsDataRank 0
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 39% comes from its base citations and 61% from the citation network (11 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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