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.

Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist

Nature Medicine(2020)10.1038/s41591-020-1041-ySource: DataRank Database

Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist is a research paper published in Nature Medicine (2020). On theSindex it has a DataRank of 0.947. It has been cited 551 times.

N/A

0.947DataRank · unranked

0.947

Open Access551 citations · base score 6.3

Cite:

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

This FAIRsharing record describes: The MI-CLAIM checklist is a guideline intended to improve transparent reporting of AI algorithms in medicine. MI-CLAIM was created to enable a direct assessment of clinical impact (including FAIRness), and to allow rapid replication of the technical design process of any legitimate clinical AI study.

›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.

Run a calibrated FAIR evaluation for this paper →

DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

0.947

From this paper's citation signal

Citation Network Contribution

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(C_q) ÷ max(outdegree_q, 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 →