# Rubric20-Semantic Evaluation Report

Project
AI_READI
D4D File
AI_READI_d4d.yaml
Evaluator Model
claude-sonnet-4-5-20250929
Rubric Type
rubric20-semantic
Temperature
0.0
Evaluation Date
2025-12-20T00:00:00Z
82.0/84.0
97.6% Overall Score · Grade: A+

## Category Performance

Category
Structural Completeness
21/21
100.0%
Category
Metadata Quality & Content
21/21
100.0%
Category
Technical Documentation
24/25
96.0%
Category
FAIRness & Accessibility
16/17
94.1%

## Question-Level Assessment

### Structural Completeness

Q1. Field Completeness numeric
5/5
Assessment
All mandatory fields present with exceptional content quality. Title is descriptive and includes full acronym expansion. Description is comprehensive at 571 characters covering dataset design, scope, domains, and FAIR compliance.
Evidence Found
id: https://fairhub.io/datasets/2, title: 'Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI)', description: 400+ chars, keywords: 19 keywords, license_and_use_terms: comprehensive CC BY-NC 4.0 with detailed terms
Q2. Entry Length Adequacy numeric
5/5
Assessment
Exceptional narrative depth. Description provides specific details (4,000 participants, 3 sites, triple-balanced design, 10+ data domains). Each purpose statement exceeds 200 chars with actionable research objectives.
Evidence Found
description: 571 chars with detailed multimodal data description, purposes: 3 purposes averaging 287 chars each with specific research objectives
Q3. Keyword Diversity numeric
5/5
Assessment
Excellent keyword diversity covering disease (T2DM), methodology (machine learning, multimodal), study design (cross-sectional, multi-site), data types (retinal imaging, wearables), and key concepts (salutogenesis, health equity, FAIR). Keywords span technical, clinical, and ethical domains.
Evidence Found
keywords: [Type 2 Diabetes Mellitus, T2DM, AI-READI, Machine Learning, Artificial Intelligence, multimodal dataset, harmonized data, multi-site study, salutogenesis, FAIR principles, retinal imaging, continuous glucose monitoring, wearable devices, biorepository, biospecimens, triple-balanced sampling, health equity, Bridge2AI, cross-sectional study]
Q4. File Enumeration and Type Variety numeric
5/5
Assessment
Strong format diversity reflecting multimodal nature: DICOM (imaging), CSV (clinical/sensor), mHealth (wearables), REDCap (metadata). Covers both raw data and metadata formats. XML for ECG mentioned in acquisition but not explicitly in distribution_formats.
Evidence Found
distribution_formats: DICOM for retinal imaging, CSV for tabular/time-series data, mHealth standard for wearables, REDCap data dictionary; acquisition methods mention XML for ECG
Q5. Data File Size Availability pass_fail
1/1
Assessment
Clear instance count metadata with target N=4,000 participants. Subpopulations quantified with ~1,000 per group (8 subgroups defined). No byte size information for files, but instance counts are comprehensive.
Evidence Found
instances.description: 'Target enrollment is 4,000 people' with specific subpopulation targets of ~1,000 per race/ethnicity group and diabetes severity level

### Metadata Quality & Content

Q6. Dataset Identification Metadata pass_fail
1/1
Assessment
Multiple persistent identifiers across platforms. FAIRhub URL serves as primary ID. Zenodo DOI 10.5281/zenodo.10642459 for archived documentation. Publication DOIs link to protocol and commentary. No RRID present but not critical for clinical dataset.
Evidence Found
id: https://fairhub.io/datasets/2, page: https://fairhub.io/datasets/2, external_resources: DOI 10.5281/zenodo.10642459 (Zenodo archive), DOI 10.1136/bmjopen-2024-097449 (protocol), DOI 10.1038/s42255-024-01165-x (commentary)
Q7. Funding and Acknowledgements Completeness numeric
5/5
Assessment
Exceptional funding documentation including primary NIH grant OT2OD032644, supplementary grants, opportunity number OTA-21-008, project dates (Sept 2022 - Aug 2025), and 2022 funding amount ($5,026,499). All 20 creators listed with institutions and roles (Contact PI, Co-Investigators). Research to Prevent Blindness support mentioned.
Evidence Found
funders: NIH Common Fund Bridge2AI Program with grant OT2OD032644, additional grants P30DK035816, UL1TR003096; creators: 20 investigators with names, roles, and institutional affiliations (UW, UAB, UCSD)
Q8. Ethical and Privacy Declarations numeric
5/5
Assessment
Comprehensive ethical documentation exceeding standard requirements. IRB approval from UW (STUDY00016228) with reliance agreements from UAB and UCSD. Written informed consent mentioned. Community Advisory Board (11 diverse members) provides ethical oversight. Sensitive data clearly categorized (genetic, geographic, medical) with access controls. Tribal consultation planned for Native American cohort. Bioethics guidance integrated throughout.
Evidence Found
human_subject_research.involves_human_subjects: true, irb_approval: UW STUDY00016228 with UAB and UCSD reliance agreements, ethics_review_board: 3 IRBs + Community Advisory Board, special_populations: racial/ethnic minorities, tribal consultation planned, sensitive_elements: 3 categories (genetic, geographic, medical records) with controlled access
Q9. Access Requirements and Governance Documentation numeric
5/5
Assessment
Excellent governance framework. Public data uses CC BY-NC 4.0 with explicit terms (attribution required, non-commercial, derivatives permitted, changes indicated). Controlled access requires separate DUA. Two-tier access model clearly defined: public dataset (general health info) vs controlled dataset (genetic, zip codes, medical records). Data Access Committee developing policies. IP and regulatory restrictions implicit through access tiers.
Evidence Found
license: CC BY-NC 4.0, license_and_use_terms: detailed terms with attribution, non-commercial use, derivative work permissions; subsets: public access vs controlled access clearly distinguished with DUA requirement; sensitive_elements: genetic, geographic, medical data categorized
Q10. Interoperability and Standardization numeric
5/5
Assessment
Outstanding interoperability through multiple data standards: OMOP CDM (clinical data), DICOM (imaging), mHealth (wearables), RxNorm (medications), ICD-10 (diagnoses). Data stored as 'AI-ready' enabling immediate use without reformatting. Standards appropriately matched to modalities: imaging→DICOM, clinical→OMOP, wearables→mHealth. Standardization across 3 sites ensures harmonization.
Evidence Found
preprocessing_strategies mention OMOP Common Data Model for clinical data, DICOM for imaging, mHealth standard for wearables, RxNorm codes for medications, ICD-10 codes for diabetes classification; formats: DICOM, CSV, mHealth

### Technical Documentation

Q11. Tool and Software Transparency numeric
4/5
Assessment
Strong documentation of preprocessing (5 strategies) and cleaning (1 strategy) with detailed procedures. Equipment/software mentioned: REDCap, imaging devices (Heidelberg Spectralis, Topcon Triton, Zeiss Cirrus, etc.), wearables (Dexcom G6, Garmin VivoSmart 5), lab instruments (Philips Pagewriter TC30). However, lacks version numbers for software tools and URLs/repositories. No labeling_strategies documented (not applicable for clinical study). Preprocessing details include specific protocols (DICOM conversion, mHealth conversion, PBMC processing).
Evidence Found
preprocessing_strategies: 5 strategies (standardization, image conversion, biospecimen processing, QC, data mapping); cleaning_strategies: 1 strategy (multi-site harmonization); software tools mentioned: REDCap (data management), various imaging devices with manufacturers, Dexcom G6, Garmin VivoSmart 5, specific lab equipment
Q12. Collection Protocol Clarity numeric
5/5
Assessment
Exceptional collection protocol documentation. 5 collection mechanisms described: in-person visits (2.5-4 hours), EHR screening (ICD-10 codes), wave-based recruitment, home monitoring (10 days), biospecimen banking. 12 acquisition methods covering all domains: surveys (REDCap), vitals, retinal imaging (7 devices), visual function, lab tests, ECG, cognitive function (MoCA), neuropathy (monofilament), CGM (Dexcom), activity (Garmin), environment, biospecimens. Data collectors: study coordinators at 3 sites (UW, UAB, UCSD) with standardized training and certification. Timeframes: enrollment period, pilot phase, home monitoring duration all specified.
Evidence Found
collection_mechanisms: 5 mechanisms (in-person visits, EHR screening, wave-based recruitment, home monitoring, biospecimen collection); acquisition_methods: 12 detailed acquisition methods across all data domains; collection_timeframes: enrollment July 18, 2023 to November 30, 2026, pilot period July-Nov 2023, 10-day home monitoring
Q13. Version History Documentation numeric
5/5
Assessment
Excellent versioning infrastructure. Version access via docs.aireadi.org with version-specific guides. Multiple releases documented: pilot (May 2024), v1.0.0 (Nov 2024, data through July 31, 2024), v2.0.0 and v3.0.0 mentioned, final release planned late 2026. Update frequency defined: periodic releases during enrollment. Version-specific documentation maintained. No explicit errata section but updates section covers maintenance plan. Dataset versioning implemented systematically.
Evidence Found
updates: periodic releases with enrollment progression, pilot data May 2024, v1.0.0 through July 31 2024 released Nov 2024, v2.0.0 and v3.0.0 mentioned, final dataset expected after 4,000 participant completion Nov 2026, version-specific documentation at https://docs.aireadi.org/
Q14. Associated Publications numeric
5/5
Assessment
Strong publication record. Two formal peer-reviewed publications: (1) BMJ Open protocol paper (DOI 10.1136/bmjopen-2024-097449) describing study design, (2) Nature Metabolism commentary (DOI 10.1038/s42255-024-01165-x) on AI-READI significance. Zenodo archive (DOI 10.5281/zenodo.10642459) for documentation. 9 external resources total spanning publications, websites, repositories, and program affiliations. NIH RePORTER link provides grant transparency.
Evidence Found
external_resources: BMJ Open protocol DOI 10.1136/bmjopen-2024-097449, Nature Metabolism commentary DOI 10.1038/s42255-024-01165-x, Zenodo archive DOI 10.5281/zenodo.10642459, additional resources: project website, docs site, FAIRhub, Bridge2AI program, NIH RePORTER
Q15. Human Subject Representation numeric
5/5
Assessment
Outstanding human subjects documentation. Target N=4,000 with precise demographic balancing: 4 racial/ethnic groups × 4 diabetes levels × 2 sexes = 32 cells. Subpopulations explicitly defined with targets (~1,000 per racial group, ~1,000 per diabetes level). Inclusion criteria: age 40+, English-speaking, T2DM spectrum. Exclusion: pregnancy, type 1 diabetes, cognitive impairment (implied by MoCA). Recruitment sources specified (UAB, UCSD, UW health systems). Special populations explicitly targeted for health equity (racial/ethnic minorities disproportionately affected by T2DM).
Evidence Found
instances: detailed description of 4,000 participants with triple-balanced design across race/ethnicity (Asian, Black, Hispanic, White), diabetes severity (4 levels), and biological sex (male, female); subpopulations: 8 subgroups defined with ~1,000 target per group; inclusion: age 40+, English-speaking; exclusion: pregnancy, type 1 diabetes

### FAIRness & Accessibility

Q16. Findability (Persistent Links) pass_fail
1/1
Assessment
Excellent findability with multiple persistent entry points. Primary landing page: FAIRhub (https://fairhub.io/datasets/2). Documentation: docs.aireadi.org. Project site: aireadi.org. Data sharing policy: aireadi.org/goals/data-sharing. Federal transparency: NIH RePORTER. DOIs for publications and archived documentation. Bridge2AI program link provides programmatic context. All URLs use HTTPS.
Evidence Found
page: https://fairhub.io/datasets/2, external_resources: 9 URLs including FAIRhub, docs.aireadi.org, aireadi.org, bridge2ai.org, NIH RePORTER, DOIs for Zenodo, BMJ Open, Nature Metabolism
Q17. Accessibility (Access Mechanism) numeric
5/5
Assessment
Comprehensive access mechanism documentation. Two-tier access model: (1) Public dataset available via FAIRhub with CC BY-NC 4.0 license agreement (no registration beyond agreement), (2) Controlled dataset requires Data Use Agreement (DUA) for sensitive data (genetic, zip codes, medical records). Platform: FAIRhub with direct download. Access policy: being developed by Data Access Committee. Formats clearly specified for each data type. Biorepository access procedures under development.
Evidence Found
distribution_formats: 4 formats (DICOM, CSV, mHealth, REDCap) with access_urls pointing to FAIRhub; subsets: public access (license agreement) vs controlled access (DUA required); license_and_use_terms: CC BY-NC 4.0 with specific terms; Data Access Committee developing policies
Q18. Reusability (License Clarity) numeric
5/5
Assessment
Excellent license clarity. CC BY-NC 4.0 explicitly permits: distribution, remix, adaptation, derivative works (even with different licenses), non-commercial building upon work. Requirements clearly stated: proper citation, appropriate credit, indicate changes, non-commercial use. Controlled data has separate DUA (mentioned but terms under development). Link to canonical license text provided. Reuse cases clearly identifiable: AI/ML model development, multi-modal research, health equity studies, biomarker discovery.
Evidence Found
license: CC BY-NC 4.0, license_and_use_terms: detailed description with explicit permissions (distribute, remix, adapt, build upon non-commercially, derivative works on different terms), requirements (attribution, indicate changes), and URL to full license (http://creativecommons.org/licenses/by-nc/4.0/)
Q19. Data Integrity and Provenance numeric
4/5
Assessment
Good integrity and provenance documentation. Version progression clearly documented with dates and scope (v1.0.0 includes data through July 31, 2024). Update frequency defined (periodic with enrollment progression). Version-specific documentation provides provenance context. Dataset versioning infrastructure established. However, lacks file-level checksums, detailed change logs between versions, or automated provenance tracking. Biospecimen provenance tracked through UAB CCTS but details not in D4D. No mention of data lineage or transformation logs.
Evidence Found
updates: periodic releases documented (pilot May 2024, v1.0.0 Nov 2024, v2.0.0, v3.0.0, final late 2026) with version-specific documentation at docs.aireadi.org; update_details list version progression; no explicit checksums or file-level provenance metadata
Q20. Interlinking Across Platforms pass_fail
1/1
Assessment
Excellent cross-platform interlinking. Data repository (FAIRhub) links to documentation (docs.aireadi.org), project site (aireadi.org), funder (NIH RePORTER), program (Bridge2AI), archival repository (Zenodo), and publications (BMJ Open, Nature Metabolism). Cross-references span data repositories, documentation platforms, funding agencies, academic publishers, and program consortia. Comprehensive ecosystem of linked resources enables discovery from multiple entry points.
Evidence Found
external_resources: 9 cross-platform links including FAIRhub (data repository), docs.aireadi.org (documentation), aireadi.org (project), Zenodo (archive), NIH RePORTER (grant), Bridge2AI (program), BMJ Open (protocol), Nature Metabolism (publication)

## Semantic Analysis Summary

### Consistency Checks

Passed: 24
Failed: 0
Warnings: 3

### Issues Detected

correctness LOW
Fields: external_resources, id
Recommendation:
consistency LOW
Fields: id, external_resources
Recommendation:
semantic_understanding LOW
Fields: distribution_formats, acquisition_methods
Recommendation:
Generated on 2026-01-12 23:30:33 using Bridge2AI Data Sheets Schema