Semantic modelling of common data elements for rare disease registries, and a prototype workflow for their deployment over registry data
Semantic modelling of common data elements for rare disease registries, and a prototype workflow for their deployment over registry data is a dataset published in Journal of Biomedical Semantics (2022). On theSindex it has a DataRank of 1.5, placing it in the top 38% of the data-sharing corpus. It has been cited 53 times, with 29 citing works in its 1-hop citation network. Its calibrated FAIR score is 63/100.
Abstract
BackgroundThe European Platform on Rare Disease Registration (EU RD Platform) aims to address the fragmentation of European rare disease (RD) patient data, scattered among hundreds of independent and non-coordinating registries, by establishing standards for integration and interoperability. The first practical output of this effort was a set of 16 Common Data Elements (CDEs) that should be implemented by all RD registries. Interoperability, however, requires decisions beyond data elements - including data models, formats, and semantics. Within the European Joint Programme on Rare Diseases (EJP RD), we aim to further the goals of the EU RD Platform by generating reusable RD semantic model templates that follow the FAIR Data Principles.ResultsThrough a team-based iterative approach, we created semantically grounded models to represent each of the CDEs, using the SemanticScience Integrated Ontology as the core framework for representing the entities and their relationships. Within that framework, we mapped the concepts represented in the CDEs, and their possible values, into domain ontologies such as the Orphanet Rare Disease Ontology, Human Phenotype Ontology and National Cancer Institute Thesaurus. Finally, we created an exemplar, reusable ETL pipeline that we will be deploying over these non-coordinating data repositories to assist them in creating model-compliant FAIR data without requiring site-specific coding nor expertise in Linked Data or FAIR.ConclusionsWithin the EJP RD project, we determined that creating reusable, expert-designed templates reduced or eliminated the requirement for our participating biomedical domain experts and rare disease data hosts to understand OWL semantics. This enabled them to publish highly expressive FAIR data using tools and approaches that were already familiar to them.
›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
0.596
From this paper's citation signal
Citation Network Contribution
0.884
From 22 citing papers with measurable signal
Top 3 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- The FAIR Guiding Principles for scientific data management and stewardshipScientific Data201617,221 citationsDataRank 1.5
- The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discoveryJournal of Biomedical Semantics2014272 citationsDataRank 9.9
- Building Expertise on FAIR Through Evolving Bring Your Own Data (BYOD) Workshops: Describing the Data, Software, and Management-focused Approaches and Their EvolutionData Intelligence20240 citationsDataRank 0
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 40% comes from its base citations and 60% from the citation network (22 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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