Preparing clinical research data for artificial intelligence readiness: insights from the National Institute of Diabetes and Digestive and Kidney Diseases data centric challenge is a dataset published in Journal of the American Medical Informatics Association (2025). On theSindex it has a DataRank of 0.232, placing it in the top 61.7% of the data-sharing corpus. It has been cited 3 times, with 3 citing works in its 1-hop citation network. Its calibrated FAIR score is 21/100.
Ranks in the top 62% for downstream scientific impact
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Full FAIR picture · advisory
The headline score is computed from the scored criteria — the fact-shaped checks (a repository, an accession, a licence) that two independent models agree on. The advisory criteria below are real FAIR guidance but rest on judgment calls that models read differently, so they inform without moving the number.
No persistent identifier is given for the paper's own AI-ready dataset; the GitHub URL is for scripts, not the data, and the TEDDY DOI belongs to the source data. [majority verdict 'no' (3/5 passes agreed)]
RDA-F1-01D — FAIR Data Maturity Model: 'Data is identified by a persistent identifier' (priorit · RDA-F1-02D — FAIR Data Maturity Model: 'Data is identified by a globally unique identifier' · FsF-F1-02D — F-UJI/FAIRsFAIR: 'Data is assigned a persistent identifier'
No repository is named as the holder of the paper's own AI-ready dataset; the GitHub repository holds scripts, not the data itself. [majority verdict 'no' (4/5 passes agreed)]
RDA-F4-01M — FAIR Data Maturity Model: metadata is offered so it can be harvested and indexed ( · NIH DMS Policy Element 4 (NOT-OD-21-014) — name the repository where data will be archived · NSTC Desirable Characteristics of Data Repositories (2022) — 'Long-Term Sustainability', 'Reten
The paper's own AI-ready dataset is not cited in the reference list; the TEDDY source dataset is cited but that is not the paper's own data. [majority verdict 'no' (3/5 passes agreed)]
FORCE11 Joint Declaration of Data Citation Principles (2014) — data should be cited as a first- · RDA-F3-01M — metadata clearly and explicitly includes the identifier of the data it describes · FsF-F3-01M — F-UJI: 'Metadata includes the identifier of the data it describes'
Advisory · not in the published score
The data availability statement points to the original TEDDY data (with a DOI) and the GitHub repository for scripts, but does not provide a repository record for the paper's own AI-ready dataset. [majority verdict 'no' (3/5 passes agreed)]
Colavizza, Hrynaszkiewicz, Staden, Whitaker & McGillivray (2020), 'The citation advantage of li · Springer Nature research data policy — Data Availability Statements: standard statement templat · RDA-F3-01M — metadata clearly and explicitly includes the identifier of the data it describes
Table 1 provides an itemised inventory of dataset dimensions at each processing step, serving as a structured description of the dataset. [downgraded to 'partial' — no verifiable quote from the paper] [majority verdict 'partial' (2/5 passes agreed)]
RDA-F2-01M — 'Rich metadata is provided to allow discovery' (priority Essential) · FsF-F2-01M — F-UJI: 'Metadata includes descriptive core elements to support data findability' · FsF-R1-01MD — F-UJI: 'Metadata specifies the content of the data'
The paper does not provide any route to the AI-ready dataset itself; only the scripts are available, and the source data is available on request, which is not the paper's own data.
RDA-A1.1-01D — 'Data is accessible through a free access protocol' · FsF-A1-01M — F-UJI: 'Metadata contains access level and access conditions of the data' · NSTC Desirable Characteristics of Data Repositories (2022) — 'Free and Easy Access'
Advisory · not in the published score
No access-level label is applied to the paper's own AI-ready dataset; the data availability statement refers to the original TEDDY data, not the derived dataset. [majority verdict 'no' (4/5 passes agreed)]
FsF-A1-01M — F-UJI: 'Metadata contains access level and access conditions of the data' · RDA-A1-01M — metadata contains information to enable the user to get access to the data · COAR Controlled Vocabularies — Access Rights v1.0 (open / embargoed / restricted / metadata-onl
No gatekeeper is named for the paper's own AI-ready dataset; the original TEDDY data is available on request, but that is not the paper's own data.
NIH Genomic Data Sharing Policy (NOT-OD-14-124) — controlled-access via a Data Access Committee · RDA-A1.2-01D — 'Data is accessible through an access protocol that supports authentication and · NIH DMS Policy Element 5 (NOT-OD-21-014) — Access, Distribution, or Reuse Considerations (conse
No statement about the persistence or availability timing of the paper's own AI-ready dataset is given.
NIH DMS Plan Element 4 (NOT-OD-21-014) — Data Preservation, Access, and Associated Timelines · NSTC Desirable Characteristics (2022), Organizational Infrastructure: 'Retention Policy' · RDA-A2-01M — 'Metadata is guaranteed to remain available after data is no longer available'
“the submitted AI-ready file was read-in using the read.csv function in R”
The AI-ready dataset is in CSV format, an open, community-standard format. [majority verdict 'yes' (3/5 passes agreed)]
FsF-R1.3-02D — F-UJI: 'Data is available in a file format recommended by the target research co · RDA-R1.3-02D — data is expressed in a machine-understandable community standard · RDA-I1-01D — data uses a knowledge representation expressed in a standardised format
Advisory · not in the published score
“standardizing the use of internal and International Organization for Standardization (ISO) codes for attributes with categorized values”
The paper explicitly applies ISO standards (ISO 639, ISO 3166) to the data, which are community standards. [majority verdict 'yes' (4/5 passes agreed)]
RDA-R1.3-01M — 'Metadata complies with a community standard' (priority Essential) · RDA-R1.3-01D — 'Data complies with a community standard' · RDA-I2-01M — '(Meta)data use vocabularies that follow FAIR principles'
“https://doi.org/10.58020/y3jk-x087”
The paper provides a DOI for the TEDDY source dataset, which is a resource other than the paper's own dataset. [majority verdict 'yes' (4/5 passes agreed)]
RDA-I3-01M — '(meta)data include references to other (meta)data' · RDA-I3-03M — 'metadata includes qualified references to other metadata' · FsF-I3-01M — F-UJI: 'Metadata includes links between the data and its related entities'
No license is stated for the paper's own AI-ready dataset; the paper's CC BY-NC-ND license applies to the article, not the data.
RDA-R1.1-01M — 'Metadata includes information about the licence under which the data can be reu · RDA-R1.1-02M — 'Metadata refers to a standard reuse licence' · RDA-R1.1-03M — 'Metadata refers to a machine-understandable reuse licence'
No version token or date is provided for the paper's own AI-ready dataset; the TEDDY source data version is given but that is not the paper's own.
DataCite Metadata Schema 4.6 — the 'Version' property · RDA-R1.2-01M — provenance information (which version was used is provenance) · NSTC Desirable Characteristics of Data Repositories (2022) — 'Provenance', 'Retention Policy'
“Scripts developed to generate the AI-ready dataset described in this paper and associated documentation prepared for this challenge are available in the public NIDDK GitHub repository: https://github.com/niddk-data-challenge/Beginner-Level-Challenge-AI-Ready-TEDDY-Dataset .”
The paper provides a machine-resolvable GitHub URL for the study's own code. [majority verdict 'yes' (4/5 passes agreed)]
NIH DMS Policy Element 2 (NOT-OD-21-014) — 'Related Tools, Software and/or Code' · FAIR4RS Principles v1.0 (Chue Hong et al., 2022; RDA/FORCE11/ReSA) — FAIR Principles for Resear · FORCE11 Software Citation Principles (Smith, Katz & Niemeyer, 2016, PeerJ CS 2:e86)
“federal contract number 75N94021D00001/75N94021DF00001”
The paper includes an award number for the funding supporting the work. [majority verdict 'yes' (4/5 passes agreed)]
DataCite Metadata Schema 4.6 — 'FundingReference' property (funderName, funderIdentifier, award · Crossref Funder Registry — canonical funder identifiers for funding metadata · RDA-F2-01M — rich metadata provided to allow discovery (funding is part of the descriptive reco
Advisory · not in the published score
The paper names specific tools and libraries (e.g., pandas, fancyimpute, MICE) used in data processing, providing provenance. [downgraded to 'partial' — no verifiable quote from the paper] [majority verdict 'partial' (4/5 passes agreed)]
RDA-R1.2-01M — 'Metadata includes provenance information according to community- specific standa · FsF-R1.2-01M — F-UJI: 'Metadata includes provenance information about data creation or generati · W3C PROV-O (W3C Recommendation, 2013) — the entity/activity/agent model of provenance
“We created a comprehensive data dictionary/codebook from the AI-ready datasets to provide clear and detailed descriptions of each variable.”
A data dictionary is named as a documentation object that accompanies the data (available in the GitHub repository). [majority verdict 'yes' (3/5 passes agreed)]
RDA-R1-01M — '(Meta)data are richly described with a plurality of accurate and relevant attribu · FsF-R1-01MD — F-UJI: 'Metadata specifies the content of the data' · NIH DMS Policy Element 3 (NOT-OD-21-014) — Standards (documentation and metadata to accompany t
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
Base Score Contribution
0.208
From this paper's citation signal
Citation Network Contribution
0.0240
From 1 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 3 citers.
Office of Data Science Strategy
Grant: 75N94021D00001/75N94021DF00001
FWCI
1.10
Citation Percentile
0.8%
Citation Trend
Fields of Study
MeSH Terms
Keywords