Mondo: Unifying diseases for the world, by the world
Mondo: Unifying diseases for the world, by the world is a dataset (2022). On theSindex it has a DataRank of 2.6, placing it in the top 33.7% of the data-sharing corpus. It has been cited 100 times, with 72 citing works in its 1-hop citation network. Its calibrated FAIR score is 56/100.
Abstract
There are thousands of distinct disease entities and concepts, each of which are known by different and sometimes contradictory names. The lack of a unified system for managing these entities poses a major challenge for both machines and humans that need to harmonize information to better predict causes and treatments for disease. The Mondo Disease Ontology is an open, community-driven ontology that integrates key medical and biomedical terminologies, supporting disease data integration to improve diagnosis, treatment, and translational research. Mondo records the sources of all data and is continually updated, making it suitable for research and clinical applications that require up-to-date disease knowledge.
›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
0.686
From this paper's citation signal
Citation Network Contribution
1.9
From 49 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.
- Considerations for building and using integrated single-cell atlasesNature Methods202432 citationsDataRank 0.524
- The Single-cell Pediatric Cancer Atlas: Data portal and open-source tools for single-cell transcriptomics of pediatric tumors202411 citationsDataRank 0.424Top 50%
- Exploratory electronic health record analysis with ehrapy20232 citationsDataRank 0.165
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 27% comes from its base citations and 73% from the citation network (49 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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