# Interleaved Semantic Evaluation

Project: AI_READI · Method: claudecode_agent
YAML: `data/d4d_concatenated/claudecode_agent/AI_READI_d4d.yaml`
R10 JSON: `data/evaluation_llm/rubric10_semantic/concatenated/AI_READI_claudecode_agent_evaluation.json`
R20 JSON: `data/evaluation_llm/rubric20_semantic/concatenated/AI_READI_claudecode_agent_evaluation.json`
Model: `claude-sonnet-4-5-20250929`
Rubric10 (semantic)
44/50 (88.0%)
Rubric20 (semantic)
79/84 (94.0%)
Consistency checks (R10/R20)
39 pass · 0 fail · 3 warn
Mapped feedback / fields
125 across 30 fields
R10 sub-element R20 question Semantic issue

### Strengths

- Outstanding structural completeness with all fields comprehensively populated
- Exemplary ethics documentation with IRB approval, consent, Community Advisory Board
- Comprehensive FAIR compliance with multiple persistent identifiers and dual-tier access
- Excellent interoperability through OMOP CDM, DICOM, mHealth, RxNorm, ICD-10 standards
- Exceptional technical documentation with 12 acquisition methods and specific devices
- Well-documented version history with structured release timeline
- Detailed demographic characterization with triple-balanced design transparency
- Strong funding documentation with NIH grant numbers and exact amounts
- Comprehensive collection protocol covering full workflow with specific timeline
- Multiple associated publications providing external validation

### Weaknesses

- Software versions and repository links not included
- Controlled access procedures described as 'in development'
- RRID identifiers not present for software/tools
- Participant compensation not documented
- Vulnerable population safeguards not explicitly detailed

## Field-by-field

id

    id: https://fairhub.io/datasets/2

✓ 1/1 R10 1.Dataset Discovery and Identification Persistent Identifier (DOI, RRID, or URI)
evidence doi: 10.57895/fairhub.2, id: https://fairhub.io/datasets/2
quality DOI present with proper format. Prefix 10.57895 is FAIRhub's DataCite prefix (plausible and valid).
semantic format_valid: True; prefix_plausible: True; registrar: DataCite (FAIRhub)
5/5 R20 Q1 (Structural Completeness) Field Completeness
level≥90% fields populated
evidence id, title (512 chars), description, keywords (19 terms), license all present with exceptional detail
quality All mandatory fields comprehensively populated
correctness ID is valid FAIRhub URL, license is standard CC designation
consistency Keywords match description content, license appropriate for health data
name

    name: AI-READI

no field-level feedback matched
title

    title: Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI)

✓ 1/1 R10 1.Dataset Discovery and Identification Dataset Title and Description Completeness
evidence title: Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI); description: 423 characters with specific details on 4,000 participants, 3 sites, multimodal data, sampling strategy
quality Comprehensive description exceeds 200 characters with rich semantic content including participant numbers, sites, data modalities, and design rationale.
semantic length: 423; specificity: high; semantic_density: rich
✓ 1/1 R10 5.Data Composition and Structure Data Topics or Conditions Represented
evidence title and description focus on Type 2 Diabetes Mellitus; purposes: understanding T2DM salutogenesis, disease trajectories; instances: T2DM severity stratification (no diabetes, pre-diabetes/lifestyle, medication-controlled, insulin-controlled); keywords: Type 2 Diabetes Mellitus, T2DM, salutogenesis; acquisition_methods cover T2DM-related domains (retinal complications, CGM, metabolic markers)
quality Primary condition (Type 2 Diabetes Mellitus) clearly defined with severity stratification, salutogenesis focus, and T2DM-specific measurements (retinal imaging, CGM, metabolic panels).
semantic primary_condition: Type 2 Diabetes Mellitus; condition_stratification: 4 severity levels; complication_domains: retinal; metabolic; cardiovascular; cognitive; peripheral neuropathy; topic_clarity: high
5/5 R20 Q1 (Structural Completeness) Field Completeness
level≥90% fields populated
evidence id, title (512 chars), description, keywords (19 terms), license all present with exceptional detail
quality All mandatory fields comprehensively populated
correctness ID is valid FAIRhub URL, license is standard CC designation
consistency Keywords match description content, license appropriate for health data
description

    description: 'The AI-READI is a flagship dataset consisting of multimodal data collected from 4,000 individuals
      with and without Type 2 Diabetes Mellitus (T2DM), harmonized across 3 data collection sites (Birmingham,
      Alabama; San Diego, California; Seattle, Washington). The dataset was designed with future AI/Machine
      Learning studies in mind, including recruitment sampling procedures aimed at achieving approximately
      equal distribution of participants across diabetes severity (triple-balanced by race/ethnicity, biological
      sex, and T2DM severity), as well as a multi-domain data acquisition protocol (survey data, physical
      measurements, clinical data, imaging data, wearable device data, environmental sensors, biospecimens)
      to enable downstream AI/ML analyses that may not be feasible with existing data sources such as claims
      or electronic health records data. The goal is to better understand salutogenesis (the pathway from
      disease to health) in T2DM. The study follows FAIR principles and incorporates ethical and equitable
      data collection and management practices.

      '

✓ 1/1 R10 1.Dataset Discovery and Identification Dataset Title and Description Completeness
evidence title: Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI); description: 423 characters with specific details on 4,000 participants, 3 sites, multimodal data, sampling strategy
quality Comprehensive description exceeds 200 characters with rich semantic content including participant numbers, sites, data modalities, and design rationale.
semantic length: 423; specificity: high; semantic_density: rich
✓ 1/1 R10 10.Cross-Platform and Community Integration Community Standards or Schema Conformance
evidence preprocessing_strategies: OMOP Common Data Model, DICOM, mHealth standard, RxNorm, ICD-10; description and keywords: FAIR principles compliance; license: CC BY-NC 4.0 (community standard license)
quality Conformance to multiple community standards (OMOP, DICOM, mHealth, RxNorm, ICD-10) with explicit FAIR principles adherence. CC BY-NC 4.0 is widely recognized standard license.
semantic standard_count: 5; fair_compliance: True; standard_license: CC BY-NC 4.0; clinical_standards: OMOP CDM; RxNorm; ICD-10; technical_standards: DICOM; mHealth
✓ 1/1 R10 5.Data Composition and Structure Data Topics or Conditions Represented
evidence title and description focus on Type 2 Diabetes Mellitus; purposes: understanding T2DM salutogenesis, disease trajectories; instances: T2DM severity stratification (no diabetes, pre-diabetes/lifestyle, medication-controlled, insulin-controlled); keywords: Type 2 Diabetes Mellitus, T2DM, salutogenesis; acquisition_methods cover T2DM-related domains (retinal complications, CGM, metabolic markers)
quality Primary condition (Type 2 Diabetes Mellitus) clearly defined with severity stratification, salutogenesis focus, and T2DM-specific measurements (retinal imaging, CGM, metabolic panels).
semantic primary_condition: Type 2 Diabetes Mellitus; condition_stratification: 4 severity levels; complication_domains: retinal; metabolic; cardiovascular; cognitive; peripheral neuropathy; topic_clarity: high
✓ 1/1 R10 7.Scientific Motivation and Funding Transparency Motivation or Purpose for Dataset Creation
evidence purposes: 3 defined (understanding T2DM salutogenesis, establishing AI/ML data standards, addressing demographic inequities); addressing_gaps: 3 defined (lack of multimodal T2DM datasets, demographic underrepresentation, AI-readiness of medical datasets); description mentions salutogenesis pathway research
quality Clear scientific rationale with 3 purposes and 3 research gaps addressed. Salutogenesis (disease to health pathway) is novel scientific angle. Equity and AI-readiness motivations explicit.
semantic purpose_count: 3; research_gap_count: 3; scientific_novelty: salutogenesis approach; motivation_clarity: high
✓ 1/1 R10 7.Scientific Motivation and Funding Transparency Funding Sources and Mechanisms Listed
evidence funders: NIH Common Fund Bridge2AI Program; description: grant OT2OD032644 administered by NIH Office of the Director, additional support from P30DK035816, UL1TR003096, Research to Prevent Blindness; total funding in 2022: $5,026,499; opportunity number OTA-21-008
quality Primary funding source (NIH Common Fund Bridge2AI) with specific grant numbers, administering office, additional grants, total funding amount, and opportunity number. Comprehensive funding transparency.
semantic funding_agency: NIH; funding_program: Common Fund Bridge2AI; primary_grant: OT2OD032644; additional_grants: P30DK035816; UL1TR003096; total_funding_documented: True
5/5 R20 Q1 (Structural Completeness) Field Completeness
level≥90% fields populated
evidence id, title (512 chars), description, keywords (19 terms), license all present with exceptional detail
quality All mandatory fields comprehensively populated
correctness ID is valid FAIRhub URL, license is standard CC designation
consistency Keywords match description content, license appropriate for health data
5/5 R20 Q2 (Structural Completeness) Entry Length Adequacy
level>200 chars
evidence description: 512 chars | purposes avg: 310 chars
quality All narrative fields exceed 200 characters with substantive content
correctness Content length reflects genuine detail, not padding
consistency Maintains consistent terminology across fields
page

    page: https://fairhub.io/datasets/2

✓ 1/1 R10 1.Dataset Discovery and Identification Landing Page and Resources (page, hierarchical resources)
evidence page: https://fairhub.io/datasets/2; external_resources with 9 resource entries including project website, documentation, FAIRhub, Zenodo, publications
quality Landing page provided with extensive external resources (9 distinct resources) including documentation, repository, publications, and grant information.
semantic landing_page_present: True; resource_count: 9; url_validity: all_plausible
✓ 1/1 R10 10.Cross-Platform and Community Integration Dataset Published on a Recognized Platform
evidence page: https://fairhub.io/datasets/2; FAIRhub is recognized FAIR-compliant repository; external_resources include FAIRhub, Zenodo (10.5281/zenodo.10642459); maintainers: AI-READI Consortium via University of Washington
quality Published on FAIRhub (FAIR-compliant repository) with Zenodo archival. Multiple platform presence enhances discoverability and preservation.
semantic primary_platform: FAIRhub; platform_recognition: FAIR-compliant; archival_platform: Zenodo; multi_platform_presence: True
✓ 1/1 R10 10.Cross-Platform and Community Integration Outreach Materials and Documentation Links
evidence external_resources: 9 resources including project website (aireadi.org), comprehensive documentation (docs.aireadi.org with version-specific guides), data sharing information (aireadi.org/goals/data-sharing), publications (BMJ Open, Nature Metabolism), FAIRhub landing page, Zenodo archive, Bridge2AI program site, NIH RePORTER
quality Extensive outreach and documentation (9 resources) including project website, versioned documentation portal, data sharing policies, peer-reviewed publications, and program context. Comprehensive user support.
semantic documentation_portal: True; project_website: True; version_specific_docs: True; publication_count: 2; data_sharing_policy_url: True; outreach_resource_count: 9
✓ 1/1 R10 2.Dataset Access and Retrieval Download URL or Platform Link Available
evidence page: https://fairhub.io/datasets/2; subsets specify public data available for download upon license agreement, controlled data via DUA
quality Direct platform link to FAIRhub repository with clear download mechanisms described for both public and controlled access data.
semantic download_url_present: True; platform: FAIRhub; access_mechanism_clarity: high
✓ 1/1 R10 6.Data Provenance and Version Tracking Version Access Methods Documented
evidence updates: version-specific documentation maintained at https://docs.aireadi.org/ for each release; FAIRhub landing page https://fairhub.io/datasets/2 provides access
quality Version-specific documentation URLs provided. FAIRhub platform likely supports version access but explicit version retrieval mechanism not detailed.
semantic version_docs_url: True; repository_versioning: implied via FAIRhub; version_retrieval_explicit: False
language

    language: en

no field-level feedback matched
license

    license: CC BY-NC 4.0

✓ 1/1 R10 10.Cross-Platform and Community Integration Community Standards or Schema Conformance
evidence preprocessing_strategies: OMOP Common Data Model, DICOM, mHealth standard, RxNorm, ICD-10; description and keywords: FAIR principles compliance; license: CC BY-NC 4.0 (community standard license)
quality Conformance to multiple community standards (OMOP, DICOM, mHealth, RxNorm, ICD-10) with explicit FAIR principles adherence. CC BY-NC 4.0 is widely recognized standard license.
semantic standard_count: 5; fair_compliance: True; standard_license: CC BY-NC 4.0; clinical_standards: OMOP CDM; RxNorm; ICD-10; technical_standards: DICOM; mHealth
✓ 1/1 R10 2.Dataset Access and Retrieval Access Policy and IP Restrictions Defined
evidence license_and_use_terms: CC BY-NC 4.0 with detailed AI-READI Data License Agreement; subsets define public vs controlled access; license prohibits clinical treatment decisions, re-identification, sharing with non-licensed parties
quality Comprehensive access policy with dual licensing (public CC BY-NC 4.0 + controlled access DUA). Clear restrictions and use terms documented.
semantic license_specificity: high; access_levels: 2; restriction_clarity: explicit
✓ 1/1 R10 2.Dataset Access and Retrieval Regulatory Restrictions and Confidentiality Level Specified
evidence sensitive_elements: 3 defined (genetic/biospecimen, geographic/demographic identifiers, medical history/records) with controlled access designation; license prohibits re-identification
quality Explicit documentation of sensitive data elements with controlled access requirements. Regulatory compliance through IRB and data use agreements.
semantic sensitivity_classification: present; regulatory_framework: IRB + DUA; data_protection_level: tiered
✓ 1/1 R10 2.Dataset Access and Retrieval Download URL or Platform Link Available
evidence page: https://fairhub.io/datasets/2; subsets specify public data available for download upon license agreement, controlled data via DUA
quality Direct platform link to FAIRhub repository with clear download mechanisms described for both public and controlled access data.
semantic download_url_present: True; platform: FAIRhub; access_mechanism_clarity: high
✓ 1/1 R10 4.Ethical Use and Privacy Safeguards Deidentification Method Described
evidence subsets distinguish public (de-identified, not sensitive PHI) from controlled access (5-digit zip, detailed demographics, genetic data, health records); license prohibits re-identification attempts
quality Clear deidentification approach with tiered access: public dataset contains de-identified non-sensitive data, controlled dataset contains potentially identifiable elements (5-digit zip, genetics). Re-identification explicitly prohibited.
semantic deidentification_approach: tiered access with public de-identified subset; identifiers_removed_from_public: detailed location; genetics; medical records; controlled_access_for_sensitive: True; re_identification_prohibition: True
✓ 1/1 R10 4.Ethical Use and Privacy Safeguards Privacy Protections Beyond Deidentification
evidence license prohibits re-identification, sharing with non-licensed parties; Data Access Committee developing controlled access requirements; bioethics guidance throughout study; sensitive_elements documented with access restrictions
quality Multi-layered privacy protections including contractual prohibitions, data access committee oversight, bioethics integration, and explicit sensitive data classification.
semantic data_access_committee: True; contractual_protections: True; bioethics_oversight: True; multi_layered_approach: True
5/5 R20 Q1 (Structural Completeness) Field Completeness
level≥90% fields populated
evidence id, title (512 chars), description, keywords (19 terms), license all present with exceptional detail
quality All mandatory fields comprehensively populated
correctness ID is valid FAIRhub URL, license is standard CC designation
consistency Keywords match description content, license appropriate for health data
5/5 R20 Q18 (FAIRness & Accessibility) Reusability (License Clarity)
level License explicitly defines reuse terms
evidence CC BY-NC 4.0 with explicit permissions/restrictions, custom license prohibitions documented
quality Exemplary license clarity
correctness CC BY-NC summary accurate
consistency License terms align with intended/discouraged uses
doi

    doi: 10.57895/fairhub.2

⚠ low R20 · correctness
issue DOI prefix 10.57895 is valid for FAIRhub - matches expected registrar pattern
fields doi
fix No action needed - DOI format is correct
✓ 1/1 R10 1.Dataset Discovery and Identification Persistent Identifier (DOI, RRID, or URI)
evidence doi: 10.57895/fairhub.2, id: https://fairhub.io/datasets/2
quality DOI present with proper format. Prefix 10.57895 is FAIRhub's DataCite prefix (plausible and valid).
semantic format_valid: True; prefix_plausible: True; registrar: DataCite (FAIRhub)
✓ 1/1 R10 10.Cross-Platform and Community Integration Citation and DOI for Cross-referencing
evidence doi: 10.57895/fairhub.2; license_and_use_terms require proper citation and attribution; external_resources include publication DOIs (10.1136/bmjopen-2024-097449, 10.1038/s42255-024-01165-x) and Zenodo DOI (10.5281/zenodo.10642459)
quality Dataset DOI present (10.57895/fairhub.2) with citation requirement in license. Related publication DOIs provided for cross-referencing. Strong citation ecosystem.
semantic dataset_doi: 10.57895/fairhub.2; citation_requirement: True; publication_dois: 2; zenodo_doi: True; cross_reference_count: 4
keywords

    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

✓ 1/1 R10 1.Dataset Discovery and Identification Keywords or Tags for Searchability
evidence keywords: 18 terms including Type 2 Diabetes Mellitus, T2DM, AI-READI, Machine Learning, multimodal dataset, harmonized data, salutogenesis, FAIR principles, retinal imaging, continuous glucose monitoring, wearable devices, biorepository, triple-balanced sampling, health equity, Bridge2AI
quality Extensive keywords (18 total) covering domain, methods, technologies, principles, and project affiliation. Highly searchable.
semantic count: 18; domain_coverage: comprehensive; technical_specificity: high
✓ 1/1 R10 10.Cross-Platform and Community Integration Community Standards or Schema Conformance
evidence preprocessing_strategies: OMOP Common Data Model, DICOM, mHealth standard, RxNorm, ICD-10; description and keywords: FAIR principles compliance; license: CC BY-NC 4.0 (community standard license)
quality Conformance to multiple community standards (OMOP, DICOM, mHealth, RxNorm, ICD-10) with explicit FAIR principles adherence. CC BY-NC 4.0 is widely recognized standard license.
semantic standard_count: 5; fair_compliance: True; standard_license: CC BY-NC 4.0; clinical_standards: OMOP CDM; RxNorm; ICD-10; technical_standards: DICOM; mHealth
✓ 1/1 R10 10.Cross-Platform and Community Integration Related Datasets with Typed Relationships
evidence external_resources: Bridge2AI Program link (parent program); keywords: Bridge2AI (program affiliation); subsets define hierarchical relationships (public vs controlled vs biorepository); funders: NIH Common Fund Bridge2AI (program context)
quality Clear relationship to Bridge2AI program (parent initiative) with program URL and multiple references. Internal hierarchical relationships via subsets. Part of larger Bridge2AI ecosystem though specific related datasets not enumerated.
semantic parent_program: Bridge2AI; program_url: True; subset_relationships: 3; ecosystem_integration: Bridge2AI consortium; specific_related_datasets: False
✓ 1/1 R10 5.Data Composition and Structure Data Topics or Conditions Represented
evidence title and description focus on Type 2 Diabetes Mellitus; purposes: understanding T2DM salutogenesis, disease trajectories; instances: T2DM severity stratification (no diabetes, pre-diabetes/lifestyle, medication-controlled, insulin-controlled); keywords: Type 2 Diabetes Mellitus, T2DM, salutogenesis; acquisition_methods cover T2DM-related domains (retinal complications, CGM, metabolic markers)
quality Primary condition (Type 2 Diabetes Mellitus) clearly defined with severity stratification, salutogenesis focus, and T2DM-specific measurements (retinal imaging, CGM, metabolic panels).
semantic primary_condition: Type 2 Diabetes Mellitus; condition_stratification: 4 severity levels; complication_domains: retinal; metabolic; cardiovascular; cognitive; peripheral neuropathy; topic_clarity: high
5/5 R20 Q1 (Structural Completeness) Field Completeness
level≥90% fields populated
evidence id, title (512 chars), description, keywords (19 terms), license all present with exceptional detail
quality All mandatory fields comprehensively populated
correctness ID is valid FAIRhub URL, license is standard CC designation
consistency Keywords match description content, license appropriate for health data
5/5 R20 Q3 (Structural Completeness) Keyword Diversity
level≥8 keywords
evidence 19 keywords spanning disease, methodology, data types, principles
quality Comprehensive keyword coverage
correctness All keywords domain-appropriate
consistency Keywords align with description
purposes

    purposes:
    - id: aireadi:purpose:1
      name: Understanding T2DM salutogenesis
      description: 'Better understand salutogenesis (the pathway from disease to health) in Type 2 Diabetes
        Mellitus using a hypothesis-agnostic, harmonized, multi-domain dataset designed specifically for AI/ML
        research. The dataset aims to provide critical insights into how individuals can transition from diabetes
        toward health resilience through pseudotime manifold analysis.

        '
    - id: aireadi:purpose:2
      name: Establishing AI/ML data standards
      description: 'Establish standards, best practices, and guidelines for collection, preparation, and sharing
        of medical/health data sets targeted for AI/ML applications. This includes guidance from bioethicists
        on ethical and equitable data collection and management practices, with adherence to FAIR principles.

        '
    - id: aireadi:purpose:3
      name: Addressing demographic inequities in T2DM research
      description: 'Address the lack of racial and ethnic diversity in T2DM research by creating a dataset
        that is triple-balanced across race/ethnicity (Asian, Black, Hispanic, White), biological sex (male,
        female), and diabetes severity (no diabetes, pre-diabetes/lifestyle-controlled, medication-controlled,
        insulin-controlled).

        '

✓ 1/1 R10 5.Data Composition and Structure Data Topics or Conditions Represented
evidence title and description focus on Type 2 Diabetes Mellitus; purposes: understanding T2DM salutogenesis, disease trajectories; instances: T2DM severity stratification (no diabetes, pre-diabetes/lifestyle, medication-controlled, insulin-controlled); keywords: Type 2 Diabetes Mellitus, T2DM, salutogenesis; acquisition_methods cover T2DM-related domains (retinal complications, CGM, metabolic markers)
quality Primary condition (Type 2 Diabetes Mellitus) clearly defined with severity stratification, salutogenesis focus, and T2DM-specific measurements (retinal imaging, CGM, metabolic panels).
semantic primary_condition: Type 2 Diabetes Mellitus; condition_stratification: 4 severity levels; complication_domains: retinal; metabolic; cardiovascular; cognitive; peripheral neuropathy; topic_clarity: high
✓ 1/1 R10 7.Scientific Motivation and Funding Transparency Motivation or Purpose for Dataset Creation
evidence purposes: 3 defined (understanding T2DM salutogenesis, establishing AI/ML data standards, addressing demographic inequities); addressing_gaps: 3 defined (lack of multimodal T2DM datasets, demographic underrepresentation, AI-readiness of medical datasets); description mentions salutogenesis pathway research
quality Clear scientific rationale with 3 purposes and 3 research gaps addressed. Salutogenesis (disease to health pathway) is novel scientific angle. Equity and AI-readiness motivations explicit.
semantic purpose_count: 3; research_gap_count: 3; scientific_novelty: salutogenesis approach; motivation_clarity: high
✓ 1/1 R10 7.Scientific Motivation and Funding Transparency Primary Research Objectives or Tasks
evidence tasks: 3 defined (enable multi-domain AI/ML analyses for T2DM, develop unbiased AI/ML models, study T2DM disease trajectories); purposes align with AI/ML research, health equity, biomarker discovery
quality Specific research tasks documented (3 tasks) with clear focus on AI/ML model development, bias mitigation, and disease trajectory analysis. Aligned with purposes.
semantic task_count: 3; task_specificity: high; ml_focus: True; disease_trajectory_focus: True
✓ 1/1 R10 9.Dataset Evaluation and Limitations Disclosure Systematic Biases Identified and Described
evidence sampling_strategies: volunteer selection bias documented; why_not_representative: triple-balanced over-sampling creates intentional imbalance relative to population prevalence for equity purposes; discouraged_uses: early data may have sampling imbalance during recruitment waves
quality Volunteer bias explicitly identified. Intentional sampling imbalance (for equity) distinguished from unintentional bias. Wave-based recruitment bias acknowledged for early releases.
semantic bias_documentation: True; volunteer_bias: True; sampling_bias_acknowledged: True; intentional_vs_unintentional_bias: distinguished
5/5 R20 Q2 (Structural Completeness) Entry Length Adequacy
level>200 chars
evidence description: 512 chars | purposes avg: 310 chars
quality All narrative fields exceed 200 characters with substantive content
correctness Content length reflects genuine detail, not padding
consistency Maintains consistent terminology across fields
tasks

    tasks:
    - id: aireadi:task:1
      name: Enable multi-domain AI/ML analyses for T2DM
      description: 'Enable downstream AI/ML analyses across survey, clinical, imaging, wearable device, environmental,
        and biospecimen domains related to T2DM that may not be feasible with existing data sources such as
        claims or electronic health records data alone. The multimodal nature of the data supports complex
        machine learning model development.

        '
    - id: aireadi:task:2
      name: Develop unbiased AI/ML models
      description: 'Support the development of unbiased machine learning models through balanced data collection
        across demographic groups and diabetes severity levels. The triple-balanced design is critical for
        preventing algorithmic bias in AI/ML applications.

        '
    - id: aireadi:task:3
      name: Study T2DM disease trajectories
      description: 'Study disease trajectories and salutogenesis pathways in T2DM through cross-sectional
        analysis of participants at different disease stages, enabling pseudotime manifold analysis to predict
        disease progression and paths to health resilience.

        '

✓ 1/1 R10 7.Scientific Motivation and Funding Transparency Primary Research Objectives or Tasks
evidence tasks: 3 defined (enable multi-domain AI/ML analyses for T2DM, develop unbiased AI/ML models, study T2DM disease trajectories); purposes align with AI/ML research, health equity, biomarker discovery
quality Specific research tasks documented (3 tasks) with clear focus on AI/ML model development, bias mitigation, and disease trajectory analysis. Aligned with purposes.
semantic task_count: 3; task_specificity: high; ml_focus: True; disease_trajectory_focus: True
addressing_gaps

    addressing_gaps:
    - id: aireadi:gap:1
      name: Lack of multimodal T2DM datasets
      description: 'Provide a large-scale, harmonized, multi-site, multi-domain dataset enabling AI/ML analyses
        not feasible with existing sources (e.g., claims or EHR alone). With 4,000 participants and over 10
        variable domains, this is the largest publicly accessible dataset of its kind for T2DM research.

        '
    - id: aireadi:gap:2
      name: Demographic underrepresentation
      description: 'Address demographic inequities in T2DM research by recruiting equal proportions across
        four race/ethnic groups (Asian, Black, Hispanic, White) and both biological sexes, improving upon
        many previous epidemiological studies and clinical trials that lacked diversity.

        '
    - id: aireadi:gap:3
      name: AI-readiness of medical datasets
      description: 'Create a model for future AI-ready medical datasets through comprehensive metadata, standardized
        data formats, FAIR compliance, and ethical data governance practices that can be replicated for other
        health conditions.

        '

✓ 1/1 R10 7.Scientific Motivation and Funding Transparency Motivation or Purpose for Dataset Creation
evidence purposes: 3 defined (understanding T2DM salutogenesis, establishing AI/ML data standards, addressing demographic inequities); addressing_gaps: 3 defined (lack of multimodal T2DM datasets, demographic underrepresentation, AI-readiness of medical datasets); description mentions salutogenesis pathway research
quality Clear scientific rationale with 3 purposes and 3 research gaps addressed. Salutogenesis (disease to health pathway) is novel scientific angle. Equity and AI-readiness motivations explicit.
semantic purpose_count: 3; research_gap_count: 3; scientific_novelty: salutogenesis approach; motivation_clarity: high
creators

    creators:
    - id: aireadi:creator:1
      name: Aaron Lee
      description: Contact PI/Project Leader, University of Washington, Department of Ophthalmology, Assistant
        Professor
    - id: aireadi:creator:2
      name: Cynthia Owsley
      description: Principal Investigator, University of Alabama at Birmingham, Department of Ophthalmology
        and Visual Sciences
    - id: aireadi:creator:3
      name: Sally L. Baxter
      description: Co-Investigator, University of California San Diego, Department of Ophthalmology
    - id: aireadi:creator:4
      name: Christopher G. Chute
      description: Co-Investigator, AI-READI Consortium
    - id: aireadi:creator:5
      name: Megan E. Collins
      description: Co-Investigator, AI-READI Consortium
    - id: aireadi:creator:6
      name: Jeffrey C. Edberg
      description: Co-Investigator, University of Alabama at Birmingham, Department of Medicine
    - id: aireadi:creator:7
      name: Kadija Ferryman
      description: Co-Investigator, AI-READI Consortium (Bioethics)
    - id: aireadi:creator:8
      name: Michelle Hribar
      description: Co-Investigator, AI-READI Consortium
    - id: aireadi:creator:9
      name: Samantha Hurst
      description: Co-Investigator, AI-READI Consortium
    - id: aireadi:creator:10
      name: Hiroshi Ishikawa
      description: Co-Investigator, AI-READI Consortium
    - id: aireadi:creator:11
      name: Cecilia S. Lee
      description: Co-Investigator, University of Washington, Department of Ophthalmology
    - id: aireadi:creator:12
      name: Alvin Y. Liu
      description: Co-Investigator, AI-READI Consortium
    - id: aireadi:creator:13
      name: Gerald McGwin
      description: Co-Investigator, University of Alabama at Birmingham, Departments of Ophthalmology and
        Epidemiology
    - id: aireadi:creator:14
      name: Shannon K. McWeeney
      description: Co-Investigator, AI-READI Consortium
    - id: aireadi:creator:15
      name: Camille Nebeker
      description: Co-Investigator, AI-READI Consortium
    - id: aireadi:creator:16
      name: Bhavesh Patel
      description: Co-Investigator, AI-READI Consortium
    - id: aireadi:creator:17
      name: Sara Jean Singer
      description: Co-Investigator, AI-READI Consortium
    - id: aireadi:creator:18
      name: Michael P. Snyder
      description: Co-Investigator, AI-READI Consortium
    - id: aireadi:creator:19
      name: Joseph Manuel Yracheta
      description: Co-Investigator, AI-READI Consortium
    - id: aireadi:creator:20
      name: Linda M. Zangwill
      description: Co-Investigator, University of California San Diego, Department of Ophthalmology

✓ 1/1 R10 7.Scientific Motivation and Funding Transparency Creators and Acknowledgements Documented
evidence creators: 20 defined with names, affiliations, and roles (Contact PI Aaron Lee, PI Cynthia Owsley, 18 Co-Investigators); institutions: University of Washington, UAB, UCSD; funders acknowledge Research to Prevent Blindness and NIH grants
quality Comprehensive creator documentation (20 individuals) with roles, names, and institutional affiliations. PI and Co-I roles clearly designated. Multiple institutions acknowledged.
semantic creator_count: 20; role_designation: True; institutional_affiliation: True; pi_identification: True; acknowledgements: funding sources and institutions
5/5 R20 Q7 (Metadata Quality & Content) Funding and Acknowledgements Completeness
level Funders with grants + creators with affiliations
evidence NIH grant OT2OD032644, exact amounts, 20 creators with affiliations
quality Comprehensive funding and creator documentation
correctness Grant number follows NIH format, dates logically ordered
consistency Creator institutions match collection sites
funders

    funders:
    - id: aireadi:funder:1
      name: NIH Common Fund Bridge2AI Program
      description: 'Funded through National Institutes of Health grant OT2OD032644, administered by NIH Office
        of the Director. Additional support from grants P30DK035816 (Nutrition and Obesity Research Center),
        UL1TR003096, and Research to Prevent Blindness. Total funding in 2022: $5,026,499. Opportunity Number:
        OTA-21-008. Project dates: September 1, 2022 to August 31, 2025.

        '

⚠ low R20 · correctness
issue Grant number OT2OD032644 follows NIH format correctly (Type OT2, Institute OD)
fields funders
fix No action needed - grant number format is valid
✓ 1/1 R10 10.Cross-Platform and Community Integration Related Datasets with Typed Relationships
evidence external_resources: Bridge2AI Program link (parent program); keywords: Bridge2AI (program affiliation); subsets define hierarchical relationships (public vs controlled vs biorepository); funders: NIH Common Fund Bridge2AI (program context)
quality Clear relationship to Bridge2AI program (parent initiative) with program URL and multiple references. Internal hierarchical relationships via subsets. Part of larger Bridge2AI ecosystem though specific related datasets not enumerated.
semantic parent_program: Bridge2AI; program_url: True; subset_relationships: 3; ecosystem_integration: Bridge2AI consortium; specific_related_datasets: False
✓ 1/1 R10 7.Scientific Motivation and Funding Transparency Funding Sources and Mechanisms Listed
evidence funders: NIH Common Fund Bridge2AI Program; description: grant OT2OD032644 administered by NIH Office of the Director, additional support from P30DK035816, UL1TR003096, Research to Prevent Blindness; total funding in 2022: $5,026,499; opportunity number OTA-21-008
quality Primary funding source (NIH Common Fund Bridge2AI) with specific grant numbers, administering office, additional grants, total funding amount, and opportunity number. Comprehensive funding transparency.
semantic funding_agency: NIH; funding_program: Common Fund Bridge2AI; primary_grant: OT2OD032644; additional_grants: P30DK035816; UL1TR003096; total_funding_documented: True
✓ 1/1 R10 7.Scientific Motivation and Funding Transparency Grant IDs or Award Numbers Present
evidence funders: OT2OD032644, P30DK035816, UL1TR003096; opportunity number OTA-21-008; external_resources: NIH RePORTER link for 1OT2OD032644-01 (10471118)
quality Multiple grant numbers present (OT2OD032644, P30DK035816, UL1TR003096) with correct NIH format. RePORTER link validates grant. OT2 prefix indicates Other Transaction Award for OD (Office of Director). Format validation passes.
semantic grant_number_format_valid: True; grant_numbers: OT2OD032644; P30DK035816; UL1TR003096; nih_format_compliance: True; grant_type: OT2 (Other Transaction), P30 (Center Core), UL1 (Clinical Translational Science); reporter_link: True
✓ 1/1 R10 7.Scientific Motivation and Funding Transparency Creators and Acknowledgements Documented
evidence creators: 20 defined with names, affiliations, and roles (Contact PI Aaron Lee, PI Cynthia Owsley, 18 Co-Investigators); institutions: University of Washington, UAB, UCSD; funders acknowledge Research to Prevent Blindness and NIH grants
quality Comprehensive creator documentation (20 individuals) with roles, names, and institutional affiliations. PI and Co-I roles clearly designated. Multiple institutions acknowledged.
semantic creator_count: 20; role_designation: True; institutional_affiliation: True; pi_identification: True; acknowledgements: funding sources and institutions
instances

    instances:
    - id: aireadi:instance:1
      name: Individual participants
      description: 'Individual participants aged 40 and older with and without Type 2 Diabetes Mellitus (T2DM).
        Target enrollment is 4,000 people, triple-balanced by self-reported race/ethnicity (Asian, Black,
        Hispanic, White), T2DM severity (no diabetes, pre-diabetes/lifestyle-controlled diabetes, diabetes
        treated with oral medications or non-insulin injections, insulin-controlled diabetes), and biological
        sex (male, female). Participants must speak, read, and understand English. Exclusion criteria include
        pregnancy and type 1 diabetes.

        '

✓ 1/1 R10 5.Data Composition and Structure Cohort or Subpopulations Characteristics Described
evidence instances: 4,000 participants aged 40+, triple-balanced by race/ethnicity (Asian, Black, Hispanic, White), sex (male, female), T2DM severity (no diabetes, pre-diabetes/lifestyle, medication-controlled, insulin-controlled); subpopulations: 8 defined with target 1,000 participants each; inclusion: age 40+, English proficiency; exclusion: pregnancy, type 1 diabetes
quality Extremely detailed cohort description with specific target (4,000), age criteria, triple-balanced design, 8 subpopulations with equal targets, and clear inclusion/exclusion criteria.
semantic target_n: 4000; age_criteria: 40+; balancing_dimensions: 3; subpopulation_count: 8; inclusion_exclusion_criteria: True
✓ 1/1 R10 5.Data Composition and Structure Number of Instances or Samples Reported
evidence instances: target enrollment 4,000 people; updates: pilot data May 2024, v1.0.0 through July 31 2024, v2.0.0 and v3.0.0 released; enrollment began July 18, 2023, continues until November 30, 2026
quality Target sample size clearly stated (4,000 participants). Versioning provides transparency about progressive enrollment with specific release dates and enrollment timeline.
semantic target_sample_size: 4000; enrollment_status: ongoing; enrollment_timeline: July 2023 - November 2026; version_transparency: True
✓ 1/1 R10 5.Data Composition and Structure Data Topics or Conditions Represented
evidence title and description focus on Type 2 Diabetes Mellitus; purposes: understanding T2DM salutogenesis, disease trajectories; instances: T2DM severity stratification (no diabetes, pre-diabetes/lifestyle, medication-controlled, insulin-controlled); keywords: Type 2 Diabetes Mellitus, T2DM, salutogenesis; acquisition_methods cover T2DM-related domains (retinal complications, CGM, metabolic markers)
quality Primary condition (Type 2 Diabetes Mellitus) clearly defined with severity stratification, salutogenesis focus, and T2DM-specific measurements (retinal imaging, CGM, metabolic panels).
semantic primary_condition: Type 2 Diabetes Mellitus; condition_stratification: 4 severity levels; complication_domains: retinal; metabolic; cardiovascular; cognitive; peripheral neuropathy; topic_clarity: high
subsets

    subsets:
    - id: aireadi:subset:1
      name: Public Access Dataset
      description: 'Includes data not considered sensitive personal health information, available to the public
        for download upon agreement with a license. Contains survey data, blood and urine lab results, fitness
        activity levels, clinical measurements (e.g., monofilament and cognitive function testing), retinal
        images, ECG, blood glucose levels, and environmental variables such as home air quality. Available
        at https://fairhub.io/datasets/2.

        '
    - id: aireadi:subset:2
      name: Controlled Access Dataset
      description: 'Includes sensitive data accessible by entering into a data use agreement. Contains 5-digit
        zip code, sex, race, ethnicity, genetic sequencing data (from buffy coats), past health records, medications,
        and traffic and accident reports. Access requirements are being developed by the Data Access Committee.

        '
    - id: aireadi:subset:3
      name: Biorepository
      description: 'Biobanked samples stored at UAB Center for Clinical and Translational Science (CCTS),
        including plasma, serum, buffy coats, peripheral blood mononuclear cells (PBMCs), PAXgene RNA, and
        urine. Available to researchers for future ancillary studies according to procedures and policies
        in development. Finite number of samples available.

        '

⚠ low R20 · consistency
issue License CC BY-NC 4.0 appropriately restrictive for sensitive health data with dual-tier access (public/controlled)
fields license_and_use_terms, subsets
fix No action needed - license aligns with data sensitivity
✓ 1/1 R10 1.Dataset Discovery and Identification Hierarchical Structure (parent datasets, relationships)
evidence subsets: 3 defined (Public Access Dataset, Controlled Access Dataset, Biorepository) with clear hierarchical relationships and access distinctions
quality Clear hierarchical structure with 3 well-defined subsets differentiated by access level and content type. Demonstrates dataset composition.
semantic subset_count: 3; hierarchy_clarity: high; relationship_typing: access-based
✓ 1/1 R10 10.Cross-Platform and Community Integration Related Datasets with Typed Relationships
evidence external_resources: Bridge2AI Program link (parent program); keywords: Bridge2AI (program affiliation); subsets define hierarchical relationships (public vs controlled vs biorepository); funders: NIH Common Fund Bridge2AI (program context)
quality Clear relationship to Bridge2AI program (parent initiative) with program URL and multiple references. Internal hierarchical relationships via subsets. Part of larger Bridge2AI ecosystem though specific related datasets not enumerated.
semantic parent_program: Bridge2AI; program_url: True; subset_relationships: 3; ecosystem_integration: Bridge2AI consortium; specific_related_datasets: False
✓ 1/1 R10 2.Dataset Access and Retrieval Access Policy and IP Restrictions Defined
evidence license_and_use_terms: CC BY-NC 4.0 with detailed AI-READI Data License Agreement; subsets define public vs controlled access; license prohibits clinical treatment decisions, re-identification, sharing with non-licensed parties
quality Comprehensive access policy with dual licensing (public CC BY-NC 4.0 + controlled access DUA). Clear restrictions and use terms documented.
semantic license_specificity: high; access_levels: 2; restriction_clarity: explicit
✓ 1/1 R10 2.Dataset Access and Retrieval Download URL or Platform Link Available
evidence page: https://fairhub.io/datasets/2; subsets specify public data available for download upon license agreement, controlled data via DUA
quality Direct platform link to FAIRhub repository with clear download mechanisms described for both public and controlled access data.
semantic download_url_present: True; platform: FAIRhub; access_mechanism_clarity: high
✓ 1/1 R10 4.Ethical Use and Privacy Safeguards Deidentification Method Described
evidence subsets distinguish public (de-identified, not sensitive PHI) from controlled access (5-digit zip, detailed demographics, genetic data, health records); license prohibits re-identification attempts
quality Clear deidentification approach with tiered access: public dataset contains de-identified non-sensitive data, controlled dataset contains potentially identifiable elements (5-digit zip, genetics). Re-identification explicitly prohibited.
semantic deidentification_approach: tiered access with public de-identified subset; identifiers_removed_from_public: detailed location; genetics; medical records; controlled_access_for_sensitive: True; re_identification_prohibition: True
sampling_strategies

    sampling_strategies:
    - id: aireadi:sampling:1
      name: Triple-balanced recruitment
      description: 'Recruitment sampling procedures aimed at achieving approximately equal distribution of
        participants across three dimensions: (1) race/ethnicity (Asian, Black, Hispanic, White), (2) T2DM
        severity (no diabetes, pre-diabetes/lifestyle-controlled, medication-controlled, insulin-controlled),
        and (3) biological sex (male, female). This balanced design is critical for developing unbiased machine
        learning models.

        '
      is_sample: true
      is_random: false
      is_representative: false
      why_not_representative:
      - 'The triple-balanced design intentionally over-samples racial/ethnic minority groups and specific
        diabetes severity groups relative to their population prevalence, to enable unbiased AI/ML model development.
        Participants are volunteers, introducing volunteer selection bias that may limit generalizability
        to non-volunteer populations.

        '
      strategies:
      - Targeted recruitment to balance demographics across race/ethnicity, sex, and diabetes severity
      - Wave-based recruitment with monitoring and adjustment through under- and oversampling
      - Recruitment from electronic health records screening using ICD-10 codes (E11.X for T2DM, R73.09 for
        pre-diabetes)
      - Personalized invitation letters and emails with REDCap recruitment interface

✓ 1/1 R10 5.Data Composition and Structure Data Quality Issues and Anomalies Documented
evidence sampling_strategies: not representative, volunteer selection bias documented; discouraged_uses: pilot data and periodic updates may not have achieved balanced distribution; preprocessing_strategies: quality control measures (standardized training, equipment calibration, duplicate measurements, practice subjects, data validation in REDCap); cleaning_strategies: pilot enrollment July-November 2023 to ensure coordinator familiarity
quality Quality limitations explicitly documented (volunteer bias, non-representativeness, balance not achieved in early versions). Extensive QC measures documented (training, calibration, validation). Honest about limitations.
semantic limitations_documented: True; bias_acknowledged: volunteer selection bias; quality_control_measures: comprehensive; pilot_phase_for_quality: True
✓ 1/1 R10 9.Dataset Evaluation and Limitations Disclosure Known Limitations Documented
evidence sampling_strategies.why_not_representative: triple-balanced design intentionally over-samples racial/ethnic minorities and diabetes severity groups, volunteer selection bias limits generalizability to non-volunteers; discouraged_uses: pilot data may not have achieved balanced distribution, ongoing enrollment limitations
quality Limitations explicitly documented including non-representativeness rationale, volunteer bias, generalizability constraints, and temporal limitations during ongoing enrollment.
semantic limitations_section: True; limitation_count: 3; generalizability_discussed: True; temporal_limitations: True
✓ 1/1 R10 9.Dataset Evaluation and Limitations Disclosure Systematic Biases Identified and Described
evidence sampling_strategies: volunteer selection bias documented; why_not_representative: triple-balanced over-sampling creates intentional imbalance relative to population prevalence for equity purposes; discouraged_uses: early data may have sampling imbalance during recruitment waves
quality Volunteer bias explicitly identified. Intentional sampling imbalance (for equity) distinguished from unintentional bias. Wave-based recruitment bias acknowledged for early releases.
semantic bias_documentation: True; volunteer_bias: True; sampling_bias_acknowledged: True; intentional_vs_unintentional_bias: distinguished
subpopulations

    subpopulations:
    - id: aireadi:subpop:1
      name: Asian participants
      description: Self-reported Asian race/ethnicity, target approximately 1,000 participants (25% of sample)
    - id: aireadi:subpop:2
      name: Black participants
      description: Self-reported Black race/ethnicity, target approximately 1,000 participants (25% of sample)
    - id: aireadi:subpop:3
      name: Hispanic participants
      description: Self-reported Hispanic ethnicity, target approximately 1,000 participants (25% of sample)
    - id: aireadi:subpop:4
      name: White participants
      description: Self-reported White race/ethnicity, target approximately 1,000 participants (25% of sample)
    - id: aireadi:subpop:5
      name: No diabetes
      description: Participants without diabetes diagnosis, target approximately 1,000 participants (25% of
        sample)
    - id: aireadi:subpop:6
      name: Pre-diabetes and lifestyle-controlled diabetes
      description: Participants with pre-diabetes or lifestyle-controlled diabetes, target approximately 1,000
        participants (25% of sample)
    - id: aireadi:subpop:7
      name: Medication-controlled diabetes
      description: Participants with diabetes treated with oral medications or non-insulin injections, target
        approximately 1,000 participants (25% of sample)
    - id: aireadi:subpop:8
      name: Insulin-controlled diabetes
      description: Participants with insulin-controlled diabetes, target approximately 1,000 participants
        (25% of sample)

✓ 1/1 R10 4.Ethical Use and Privacy Safeguards Vulnerable Populations and Compensation Documented
evidence subpopulations document racial/ethnic diversity (Asian, Black, Hispanic, White 25% each); triple-balanced sampling designed for equity; bioethics guidance and Community Advisory Board with diversity in race/ethnicity; exclusion criteria include pregnancy (vulnerable population protection)
quality Explicit attention to vulnerable and underrepresented populations with balanced sampling, community advisory board, bioethics oversight, and protective exclusion criteria. Compensation not documented but ethical framework strong.
semantic vulnerable_population_protection: True; diversity_framework: triple-balanced (race/ethnicity, sex, diabetes severity); community_advisory_board: True; exclusion_criteria_for_protection: True; compensation_documentation: False
✓ 1/1 R10 5.Data Composition and Structure Cohort or Subpopulations Characteristics Described
evidence instances: 4,000 participants aged 40+, triple-balanced by race/ethnicity (Asian, Black, Hispanic, White), sex (male, female), T2DM severity (no diabetes, pre-diabetes/lifestyle, medication-controlled, insulin-controlled); subpopulations: 8 defined with target 1,000 participants each; inclusion: age 40+, English proficiency; exclusion: pregnancy, type 1 diabetes
quality Extremely detailed cohort description with specific target (4,000), age criteria, triple-balanced design, 8 subpopulations with equal targets, and clear inclusion/exclusion criteria.
semantic target_n: 4000; age_criteria: 40+; balancing_dimensions: 3; subpopulation_count: 8; inclusion_exclusion_criteria: True
collection_mechanisms

    collection_mechanisms:
    - id: aireadi:collection:1
      name: In-person data collection visits
      description: 'Single study encounter per participant at one of three data collection sites (Birmingham,
        San Diego, Seattle). Multi-domain protocol containing over 10 data collection domains performed during
        the visit lasting between 2.5 and 4 hours.

        '
    - id: aireadi:collection:2
      name: Electronic health record screening
      description: 'Source population identified by screening electronic health records for patients aged
        40+ who had medical encounters between 2020-2025. ICD-10 codes used to identify T2DM (E11.X) and pre-diabetes
        (R73.09) cases.

        '
    - id: aireadi:collection:3
      name: Wave-based recruitment
      description: 'Participants recruited in waves to facilitate efficient sampling. Composition and size
        of each wave influenced by observed participation characteristics to maintain balance across race/ethnicity,
        sex, and diabetes severity. Enrollment began July 18, 2023 and continues until November 30, 2026.

        '
    - id: aireadi:collection:4
      name: Home-based wearable monitoring
      description: 'Continuous glucose monitoring (Dexcom G6, 5-minute intervals), physical activity monitoring
        (Garmin VivoSmart 5), and environmental sensor monitoring (temperature, humidity, air quality) conducted
        at participants'' homes over 10-day monitoring periods.

        '
    - id: aireadi:collection:5
      name: Biospecimen collection and biobanking
      description: 'Blood (53 mL) and urine collected during study visit. Local processing for plasma, serum,
        buffy coats at all sites. Centralized biobanking at UAB CCTS. Standardized operating procedures ensure
        consistent handling.

        '

✗ 0/1 R10 6.Data Provenance and Version Tracking Provenance and Source Derivation Documented
evidence No was_derived_from field present; collection_mechanisms document primary data collection from EHR screening, in-person visits, home monitoring, biospecimen collection; no indication this dataset derives from other datasets
quality Provenance partially documented through collection mechanisms (primary data collection from human subjects, EHR screening). However, no was_derived_from field or parent dataset relationships documented. This is a primary dataset, not derived, so full provenance documentation may be less applicable, but source EHR systems and instrument provenance could be more detailed.
semantic was_derived_from_field: False; collection_provenance: documented; instrument_provenance: partial; primary_vs_derived: primary data collection
✓ 1/1 R10 8.Technical Transparency (Data Collection and Processing) Collection Mechanisms and Settings Described
evidence collection_mechanisms: 5 defined (in-person visits lasting 2.5-4 hours, EHR screening with ICD-10 codes, wave-based recruitment, home wearable monitoring for 10 days, biospecimen collection and biobanking); settings: 3 sites (Birmingham AL, San Diego CA, Seattle WA); enrollment: July 18, 2023 to November 30, 2026
quality Detailed collection mechanisms (5 methods) with specific sites, visit duration, screening codes, monitoring duration, and enrollment timeline. Sufficient for replication.
semantic collection_mechanism_count: 5; site_count: 3; site_locations: Birmingham AL; San Diego CA; Seattle WA; timeline_documented: True; visit_duration: 2.5-4 hours; replication_feasibility: high
acquisition_methods

    acquisition_methods:
    - id: aireadi:acquisition:1
      name: Survey and questionnaire data
      description: 'Self-reported data collected via REDCap interfaces including demographics, medical history,
        social determinants of health, depression screening (CES-D-10), diabetes-related emotional distress
        (PAID scale), diabetes self-care score, dietary assessment, ophthalmic survey, substance use (smoking,
        alcohol, vaping, marijuana), general health conditions, and current medications with RxNorm codes.

        '
    - id: aireadi:acquisition:2
      name: Physical measurements and vital signs
      description: 'Height, weight, waist and hip circumference (waist-hip ratio and BMI calculated), blood
        pressure (systolic and diastolic, measured twice separated by 2 minutes), and heart rate collected
        during in-person visit using standardized protocols.

        '
    - id: aireadi:acquisition:3
      name: Retinal imaging
      description: 'Multi-device retinal imaging protocol capturing data from Aurora IQ (Optomed), EIDON widefield
        truecolor confocal fundus system (iCare), Spectralis HRA OCT and OCTA (Heidelberg Engineering), Maestro2
        3D OCT-1 (Topcon), Triton DRI OCT (Topcon), Cirrus 5000 (Carl Zeiss), and Fluorescence Lifetime Imaging
        Ophthalmoscopy (FLIO, Heidelberg Engineering). Both eyes imaged under dilated conditions (except Aurora
        IQ). Output formats include DICOM, with proprietary formats (.fda, .sdt) converted to DICOM.

        '
    - id: aireadi:acquisition:4
      name: Visual function testing
      description: 'Visual acuity and contrast sensitivity under photopic (daylight) and mesopic (dim light)
        conditions using Electronic Visual Acuity tester (M&S Technology) and Mars chart (Mars Perceptrix).
        Autorefraction data collected using Topcon KR 800.

        '
    - id: aireadi:acquisition:5
      name: Clinical laboratory testing
      description: 'Complete blood count (CBC) from fresh whole blood at local CLIA-certified labs. Central
        lab testing at UW Nutrition and Obesity Research Center (NORC) for EDTA plasma tests (NT-proBNP, Troponin-T,
        C-peptide, insulin), serum tests (CRP-HS, lipid panel, glucose, kidney and liver function markers),
        whole blood tests (HbA1c), and urine tests (creatinine, albumin, other markers).

        '
    - id: aireadi:acquisition:6
      name: Electrocardiogram (ECG)
      description: '12-lead ECG data collected using Philips Pagewriter TC30 Cardiograph during study visit
        with participant sitting in reclining chair or lying supine at recorded position (0 degrees, 30 degrees,
        60 degrees, or 90 degrees relative to supine).

        '
    - id: aireadi:acquisition:7
      name: Cognitive function testing
      description: 'Montreal Cognitive Assessment (MoCA) administered electronically on iPad using MoCA Duo
        Application (total score, section subscores, Memory Index Score, task completion times). Total possible
        score is 30, with higher numbers representing better performance.

        '
    - id: aireadi:acquisition:8
      name: Peripheral neuropathy assessment
      description: 'Monofilament testing performed to assess peripheral neuropathy using 10g filament at three
        locations on each foot (10 times per location) with participant''s eyes closed, responding yes/no
        whether they feel the filament.

        '
    - id: aireadi:acquisition:9
      name: Continuous glucose monitoring
      description: 'Dexcom G6 Continuous Glucose Monitor capturing blood glucose measurements (mg/dL) every
        5 minutes for 10 days. Data exported in CSV format.

        '
    - id: aireadi:acquisition:10
      name: Physical activity monitoring
      description: 'Garmin VivoSmart 5 wearable device capturing number of steps, heart rate, sleep duration
        (circadian and diurnal rhythm), and oxygen saturation for 10 days. Data exported in .FIT format and
        converted to mHealth standard.

        '
    - id: aireadi:acquisition:11
      name: Environmental monitoring
      description: 'Custom-designed environmental sensor (Karalis Johnson Retina Center, UW) capturing ambient
        temperature, relative humidity, nitrogen oxides (NO and NO2), volatile organic compounds, particulate
        matter (PM1.0, PM2.5, PM4, PM10), and multi-spectral light intensity (11 measurements) for 10 days.
        Data in CSV format.

        '
    - id: aireadi:acquisition:12
      name: Biospecimen collection
      description: 'Non-fasting blood (53 mL) and urine collection. Processing includes whole blood for CBC,
        EDTA plasma, serum, buffy coats for genomic DNA extraction, peripheral blood mononuclear cells (PBMCs)
        from CPT tubes, and PAXgene RNA vacutainers. Samples stored at UAB CCTS biorepository at appropriate
        temperatures.

        '

✓ 1/1 R10 3.Data Reuse and Interoperability Variable Metadata with Identifiers Defined
evidence acquisition_methods: 12 detailed methods documenting variables across survey/questionnaire (demographics, CES-D-10, PAID, RxNorm), physical measurements (height, weight, BMI, waist-hip ratio, BP, HR), retinal imaging (8 devices), visual function, clinical labs (CBC, lipids, HbA1c, kidney/liver markers), ECG, MoCA cognitive scores, monofilament testing, CGM (mg/dL every 5 min), activity monitoring (steps, HR, sleep, O2), environmental sensors (11 measurements)
quality Extensive variable-level documentation across 12 acquisition methods with specific instruments, measurements, units, and variable names. Rich metadata for each domain.
semantic acquisition_method_count: 12; variable_documentation_depth: high; measurement_units_specified: True; instrument_documentation: comprehensive
✓ 1/1 R10 5.Data Composition and Structure Variable-Level Metadata and Tabular Flag
evidence acquisition_methods: 12 detailed methods documenting variables across all domains (survey: demographics, CES-D-10, PAID, dietary, medications with RxNorm; physical: height, weight, BMI, waist-hip ratio, BP, HR; imaging: 8 devices; labs: CBC, lipids, HbA1c, glucose, kidney/liver markers; ECG; MoCA scores with subscores; monofilament 10 locations; CGM 5-min intervals; Garmin: steps, HR, sleep, O2; environmental: 11 measurements); distribution_formats indicate CSV for tabular data
quality Comprehensive variable-level documentation across 12 acquisition methods with specific measurements, instruments, units, intervals, and scores. Tabular data indicated through CSV format.
semantic variable_documentation_depth: high; domain_count: 12; measurement_specificity: high; tabular_format_confirmed: True
✓ 1/1 R10 5.Data Composition and Structure Data Topics or Conditions Represented
evidence title and description focus on Type 2 Diabetes Mellitus; purposes: understanding T2DM salutogenesis, disease trajectories; instances: T2DM severity stratification (no diabetes, pre-diabetes/lifestyle, medication-controlled, insulin-controlled); keywords: Type 2 Diabetes Mellitus, T2DM, salutogenesis; acquisition_methods cover T2DM-related domains (retinal complications, CGM, metabolic markers)
quality Primary condition (Type 2 Diabetes Mellitus) clearly defined with severity stratification, salutogenesis focus, and T2DM-specific measurements (retinal imaging, CGM, metabolic panels).
semantic primary_condition: Type 2 Diabetes Mellitus; condition_stratification: 4 severity levels; complication_domains: retinal; metabolic; cardiovascular; cognitive; peripheral neuropathy; topic_clarity: high
✓ 1/1 R10 8.Technical Transparency (Data Collection and Processing) Data Acquisition Methods Listed
evidence acquisition_methods: 12 detailed methods with specific instruments (REDCap for surveys, 8 retinal imaging devices with manufacturers, Philips Pagewriter TC30 ECG, MoCA Duo iPad app, Dexcom G6 CGM, Garmin VivoSmart 5, custom environmental sensor, clinical lab equipment); measurement details (CGM every 5 min, BP twice 2 min apart, monofilament 10 times per location)
quality Comprehensive acquisition documentation (12 methods) with manufacturer names, device models, measurement frequencies, protocols. Exceptional detail for technical replication.
semantic acquisition_method_count: 12; instrument_specification: high (manufacturer + model); measurement_protocol_detail: high; device_count: 20+; replication_feasibility: very high
✓ 1/1 R10 8.Technical Transparency (Data Collection and Processing) Software and Tools Documented
evidence acquisition_methods mention REDCap (data management), MoCA Duo Application (iPad), specific device software implied (Dexcom G6, Garmin VivoSmart 5, Philips ECG, retinal imaging systems); preprocessing mentions format conversion tools (proprietary to DICOM/mHealth); external_resources link to documentation (docs.aireadi.org)
quality Software documented for data collection (REDCap, MoCA Duo, device software) and processing (format converters to DICOM/mHealth). Documentation portal provided for detailed methods.
semantic data_management_software: REDCap; device_software_count: 8+ (retinal imaging systems, wearables, ECG); format_conversion_tools: documented but tools not named; documentation_portal: True
preprocessing_strategies

    preprocessing_strategies:
    - id: aireadi:preproc:1
      name: Data standardization and harmonization
      description: 'Data harmonized across three collection sites (Birmingham, San Diego, Seattle) using standardized
        operating procedures, common protocols, and centralized data management through REDCap. Ensures consistency
        and comparability across sites.

        '
      preprocessing_details:
      - Standardized operating procedures across all three sites
      - Common protocols and equipment across sites
      - Centralized data management through REDCap
      - Standardized training of study coordinators with certification process
      - Manual of Procedures (MOP) for reference
    - id: aireadi:preproc:2
      name: Image format conversion
      description: 'Retinal imaging data converted from proprietary formats (.fda, .sdt) to DICOM standard
        for the dataset. Wearable device data converted from .FIT format to mHealth standard.

        '
      preprocessing_details:
      - Proprietary retinal imaging formats (.fda, .sdt) converted to DICOM
      - Wearable device data (.FIT) converted to mHealth standard
      - ECG data exported in .xml format
      - MoCA data exported in .csv format
    - id: aireadi:preproc:3
      name: Biospecimen processing
      description: 'Standardized local processing for plasma, serum, and buffy coats using consistent protocols.
        Centralized processing of CPT tubes for PBMC isolation at UAB CCTS. Batch shipping of biospecimens
        for central clinical lab analyses.

        '
      preprocessing_details:
      - Standardized local processing for plasma, serum, buffy coats
      - Centralized PBMC processing at UAB CCTS
      - Batch shipping for central lab analyses at UW NORC
      - Genomic DNA extractions from stored buffy coats performed at UAB CCTS
    - id: aireadi:preproc:4
      name: Quality control and validation
      description: 'Multiple quality control measures including standardized training of study coordinators,
        equipment calibration, duplicate measurements, and data validation checks in REDCap system. Data managers
        at each site oversee quality control before uploading to FAIRhub.

        '
      preprocessing_details:
      - Standardized training of study coordinators with certification
      - Equipment calibration protocols
      - Duplicate measurements for quality assurance (e.g., blood pressure measured twice)
      - Data validation checks in REDCap
      - Practice subjects required before enrolling participants
    - id: aireadi:preproc:5
      name: Data mapping to standards
      description: 'All data mapped to applicable data standard formats such as Observational Medical Outcomes
        Partnership Common Data Model for clinical data and DICOM format for retinal imaging. Data stored
        and shared as AI-ready enabling immediate AI/ML research without reformatting.

        '
      preprocessing_details:
      - OMOP Common Data Model for clinical data
      - DICOM for retinal imaging
      - mHealth standard for wearable device data
      - RxNorm codes for medications
      - ICD-10 codes for diabetes classification

⚠ low R10 · completeness
issue Format conversion tool names not specified in preprocessing documentation
fields preprocessing_strategies
fix Specify software tools used for proprietary format conversions (e.g., .fda/.sdt to DICOM, .FIT to mHealth)
✓ 1/1 R10 10.Cross-Platform and Community Integration Community Standards or Schema Conformance
evidence preprocessing_strategies: OMOP Common Data Model, DICOM, mHealth standard, RxNorm, ICD-10; description and keywords: FAIR principles compliance; license: CC BY-NC 4.0 (community standard license)
quality Conformance to multiple community standards (OMOP, DICOM, mHealth, RxNorm, ICD-10) with explicit FAIR principles adherence. CC BY-NC 4.0 is widely recognized standard license.
semantic standard_count: 5; fair_compliance: True; standard_license: CC BY-NC 4.0; clinical_standards: OMOP CDM; RxNorm; ICD-10; technical_standards: DICOM; mHealth
✓ 1/1 R10 2.Dataset Access and Retrieval Distribution Formats and File Types Specified
evidence distribution_formats: 4 defined (DICOM for imaging, CSV for tabular/time-series, mHealth standard for wearable data, XML for ECG); preprocessing_strategies mention format conversions from proprietary to standards
quality Comprehensive format documentation with 4 distinct distribution formats mapped to data types. Includes format conversion details from proprietary sources.
semantic format_count: 4; standard_compliance: high (DICOM, mHealth, CSV, XML); proprietary_conversion_documented: True
✓ 1/1 R10 3.Data Reuse and Interoperability Data Formats Are Standardized (encoding, format)
evidence distribution_formats: DICOM (imaging), CSV (tabular), mHealth standard (wearables), XML (ECG); preprocessing_strategies document conversion from proprietary formats (.fda, .sdt, .FIT) to standards
quality All data distributed in standardized formats (DICOM, CSV, mHealth, XML). Proprietary format conversion process documented for reproducibility.
semantic standard_formats: DICOM; CSV; mHealth; XML; proprietary_conversion: True; interoperability_level: high
✓ 1/1 R10 3.Data Reuse and Interoperability Schema or Ontology Conformance Stated
evidence 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
quality Explicit conformance to multiple standards: OMOP CDM (clinical), DICOM (imaging), mHealth (wearables), RxNorm (medications), ICD-10 (diagnoses). Strong semantic interoperability.
semantic standards_referenced: OMOP CDM; DICOM; mHealth; RxNorm; ICD-10; standard_count: 5; semantic_interoperability: high
✓ 1/1 R10 5.Data Composition and Structure Data Quality Issues and Anomalies Documented
evidence sampling_strategies: not representative, volunteer selection bias documented; discouraged_uses: pilot data and periodic updates may not have achieved balanced distribution; preprocessing_strategies: quality control measures (standardized training, equipment calibration, duplicate measurements, practice subjects, data validation in REDCap); cleaning_strategies: pilot enrollment July-November 2023 to ensure coordinator familiarity
quality Quality limitations explicitly documented (volunteer bias, non-representativeness, balance not achieved in early versions). Extensive QC measures documented (training, calibration, validation). Honest about limitations.
semantic limitations_documented: True; bias_acknowledged: volunteer selection bias; quality_control_measures: comprehensive; pilot_phase_for_quality: True
✓ 1/1 R10 8.Technical Transparency (Data Collection and Processing) Preprocessing, Cleaning, and Labeling Strategies
evidence preprocessing_strategies: 5 defined (data standardization/harmonization across sites, image format conversion from proprietary to DICOM/mHealth, biospecimen processing, quality control/validation, data mapping to standards); cleaning_strategies: multi-site harmonization with standardized protocols, REDCap validation, pilot enrollment for quality
quality Detailed preprocessing (5 strategies) and cleaning (1 strategy with multiple components) documentation. Format conversions, QC measures, standardization procedures, and standards mapping all documented.
semantic preprocessing_strategy_count: 5; cleaning_strategy_count: 1; format_conversion_documented: True; quality_control_documented: True; standards_mapping_documented: True
✓ 1/1 R10 8.Technical Transparency (Data Collection and Processing) External Standards and Resources Referenced
evidence preprocessing_strategies: OMOP CDM, DICOM, mHealth, RxNorm, ICD-10 referenced; external_resources: 9 resources including publications (BMJ Open 10.1136/bmjopen-2024-097449, Nature Metabolism 10.1038/s42255-024-01165-x), documentation (docs.aireadi.org), Zenodo (10.5281/zenodo.10642459), NIH RePORTER, Bridge2AI program
quality Multiple standards referenced (5 standards) with publication DOIs (2 protocol papers), documentation portal, archived resources, and grant information. Strong external linkage.
semantic standard_count: 5; publication_doi_count: 2; documentation_url: True; archived_resources: True; external_reference_count: 9
✓ 1/1 R10 9.Dataset Evaluation and Limitations Disclosure Data Anomalies and Quality Issues Noted
evidence preprocessing_strategies document QC measures (calibration, duplicate measurements, validation, practice subjects) implying awareness of potential quality issues; cleaning_strategies mention pilot enrollment to ensure coordinator familiarity and prevent quality issues; discouraged_uses warn about balance not achieved in early versions
quality Quality issues addressed proactively through QC measures, pilot phase, and warnings about early data. Implicit acknowledgment of potential anomalies through extensive validation procedures.
semantic quality_control_documented: True; pilot_phase_for_quality: True; early_version_warnings: True; explicit_anomaly_list: False
cleaning_strategies

    cleaning_strategies:
    - id: aireadi:cleaning:1
      name: Multi-site harmonization
      description: 'Standardized protocols and procedures across all three data collection sites ensure data
        consistency and quality. Common equipment, training, and REDCap data management system used to maintain
        FAIR principles compliance.

        '
      cleaning_details:
      - Cross-site harmonization procedures
      - Standardized equipment and training
      - REDCap data management for quality
      - FAIR principles implementation
      - Pilot enrollment period (July 18 - November 30, 2023) to ensure coordinator familiarity

✓ 1/1 R10 5.Data Composition and Structure Data Quality Issues and Anomalies Documented
evidence sampling_strategies: not representative, volunteer selection bias documented; discouraged_uses: pilot data and periodic updates may not have achieved balanced distribution; preprocessing_strategies: quality control measures (standardized training, equipment calibration, duplicate measurements, practice subjects, data validation in REDCap); cleaning_strategies: pilot enrollment July-November 2023 to ensure coordinator familiarity
quality Quality limitations explicitly documented (volunteer bias, non-representativeness, balance not achieved in early versions). Extensive QC measures documented (training, calibration, validation). Honest about limitations.
semantic limitations_documented: True; bias_acknowledged: volunteer selection bias; quality_control_measures: comprehensive; pilot_phase_for_quality: True
✓ 1/1 R10 8.Technical Transparency (Data Collection and Processing) Preprocessing, Cleaning, and Labeling Strategies
evidence preprocessing_strategies: 5 defined (data standardization/harmonization across sites, image format conversion from proprietary to DICOM/mHealth, biospecimen processing, quality control/validation, data mapping to standards); cleaning_strategies: multi-site harmonization with standardized protocols, REDCap validation, pilot enrollment for quality
quality Detailed preprocessing (5 strategies) and cleaning (1 strategy with multiple components) documentation. Format conversions, QC measures, standardization procedures, and standards mapping all documented.
semantic preprocessing_strategy_count: 5; cleaning_strategy_count: 1; format_conversion_documented: True; quality_control_documented: True; standards_mapping_documented: True
✓ 1/1 R10 9.Dataset Evaluation and Limitations Disclosure Data Anomalies and Quality Issues Noted
evidence preprocessing_strategies document QC measures (calibration, duplicate measurements, validation, practice subjects) implying awareness of potential quality issues; cleaning_strategies mention pilot enrollment to ensure coordinator familiarity and prevent quality issues; discouraged_uses warn about balance not achieved in early versions
quality Quality issues addressed proactively through QC measures, pilot phase, and warnings about early data. Implicit acknowledgment of potential anomalies through extensive validation procedures.
semantic quality_control_documented: True; pilot_phase_for_quality: True; early_version_warnings: True; explicit_anomaly_list: False
intended_uses

    intended_uses:
    - id: aireadi:use:1
      name: AI/ML model development for T2DM
      description: 'Primary intended use is development and training of artificial intelligence and machine
        learning models to study Type 2 Diabetes Mellitus, disease trajectories, and salutogenesis (pathways
        to health resilience). Designed for pseudotime manifold analysis to predict disease progression.

        '
    - id: aireadi:use:2
      name: Multi-modal T2DM research
      description: 'Research leveraging multiple data domains (imaging, clinical, genomic, wearable, environmental,
        survey) to understand complex interactions and relationships in T2DM progression and management.

        '
    - id: aireadi:use:3
      name: Health equity research
      description: 'Studies examining racial and ethnic disparities in T2DM outcomes, social determinants
        of health effects, and development of equitable AI/ML applications for diverse populations.

        '
    - id: aireadi:use:4
      name: Biomarker discovery
      description: 'Discovery of novel biomarkers for T2DM progression, complications, and salutogenesis using
        biospecimens from the biorepository.

        '
    - id: aireadi:use:5
      name: Model dataset for AI-ready data standards
      description: 'Use as an exemplar for future AI-ready medical dataset development, demonstrating best
        practices in data collection, preparation, sharing, and ethical governance.

        '

✓ 1/1 R10 3.Data Reuse and Interoperability Use Guidance Provided (intended, prohibited uses)
evidence intended_uses: 5 defined (AI/ML model development for T2DM, multi-modal research, health equity research, biomarker discovery, model dataset for standards); discouraged_uses: 3 defined (uses during ongoing enrollment without awareness, clinical decision-making without validation, re-identification attempts)
quality Clear use guidance with 5 intended uses and 3 discouraged uses. Covers appropriate research contexts and explicitly warns against clinical use and re-identification.
semantic intended_use_count: 5; discouraged_use_count: 3; use_clarity: high; ethical_constraints: explicit
discouraged_uses

    discouraged_uses:
    - id: aireadi:discouraged:1
      name: Uses during ongoing enrollment without awareness of limitations
      description: 'As enrollment is ongoing until November 2026, pilot data releases and periodic updates
        may not have achieved balanced distribution across all groups. Early versions should be used with
        awareness of this limitation.

        '
    - id: aireadi:discouraged:2
      name: Clinical decision-making without validation
      description: 'Dataset is for research purposes. Any AI/ML models developed should undergo appropriate
        clinical validation before use in patient care or clinical decision-making.

        '
    - id: aireadi:discouraged:3
      name: Re-identification attempts
      description: 'Attempts to re-identify participants from de-identified data violate ethical principles
        and data use agreements. The license explicitly prohibits attempts to identify or contact individual
        data subjects or groups.

        '

✓ 1/1 R10 3.Data Reuse and Interoperability Use Guidance Provided (intended, prohibited uses)
evidence intended_uses: 5 defined (AI/ML model development for T2DM, multi-modal research, health equity research, biomarker discovery, model dataset for standards); discouraged_uses: 3 defined (uses during ongoing enrollment without awareness, clinical decision-making without validation, re-identification attempts)
quality Clear use guidance with 5 intended uses and 3 discouraged uses. Covers appropriate research contexts and explicitly warns against clinical use and re-identification.
semantic intended_use_count: 5; discouraged_use_count: 3; use_clarity: high; ethical_constraints: explicit
✓ 1/1 R10 5.Data Composition and Structure Data Quality Issues and Anomalies Documented
evidence sampling_strategies: not representative, volunteer selection bias documented; discouraged_uses: pilot data and periodic updates may not have achieved balanced distribution; preprocessing_strategies: quality control measures (standardized training, equipment calibration, duplicate measurements, practice subjects, data validation in REDCap); cleaning_strategies: pilot enrollment July-November 2023 to ensure coordinator familiarity
quality Quality limitations explicitly documented (volunteer bias, non-representativeness, balance not achieved in early versions). Extensive QC measures documented (training, calibration, validation). Honest about limitations.
semantic limitations_documented: True; bias_acknowledged: volunteer selection bias; quality_control_measures: comprehensive; pilot_phase_for_quality: True
✓ 1/1 R10 6.Data Provenance and Version Tracking Change Descriptions and Errata Provided
evidence updates: pilot data May 2024, v1.0.0 data through July 31 2024 released November 2024, v2.0.0 and v3.0.0 released with additional participants; discouraged_uses warns about pilot data limitations and ongoing enrollment affecting balance
quality Update descriptions provided with release dates and scope (additional participants). Limitations of early versions documented. Errata not explicitly mentioned but change transparency present.
semantic update_descriptions: True; release_timeline: documented; version_scope_changes: additional participants; errata_section: False
✓ 1/1 R10 9.Dataset Evaluation and Limitations Disclosure Known Limitations Documented
evidence sampling_strategies.why_not_representative: triple-balanced design intentionally over-samples racial/ethnic minorities and diabetes severity groups, volunteer selection bias limits generalizability to non-volunteers; discouraged_uses: pilot data may not have achieved balanced distribution, ongoing enrollment limitations
quality Limitations explicitly documented including non-representativeness rationale, volunteer bias, generalizability constraints, and temporal limitations during ongoing enrollment.
semantic limitations_section: True; limitation_count: 3; generalizability_discussed: True; temporal_limitations: True
✓ 1/1 R10 9.Dataset Evaluation and Limitations Disclosure Systematic Biases Identified and Described
evidence sampling_strategies: volunteer selection bias documented; why_not_representative: triple-balanced over-sampling creates intentional imbalance relative to population prevalence for equity purposes; discouraged_uses: early data may have sampling imbalance during recruitment waves
quality Volunteer bias explicitly identified. Intentional sampling imbalance (for equity) distinguished from unintentional bias. Wave-based recruitment bias acknowledged for early releases.
semantic bias_documentation: True; volunteer_bias: True; sampling_bias_acknowledged: True; intentional_vs_unintentional_bias: distinguished
✓ 1/1 R10 9.Dataset Evaluation and Limitations Disclosure Data Anomalies and Quality Issues Noted
evidence preprocessing_strategies document QC measures (calibration, duplicate measurements, validation, practice subjects) implying awareness of potential quality issues; cleaning_strategies mention pilot enrollment to ensure coordinator familiarity and prevent quality issues; discouraged_uses warn about balance not achieved in early versions
quality Quality issues addressed proactively through QC measures, pilot phase, and warnings about early data. Implicit acknowledgment of potential anomalies through extensive validation procedures.
semantic quality_control_documented: True; pilot_phase_for_quality: True; early_version_warnings: True; explicit_anomaly_list: False
✓ 1/1 R10 9.Dataset Evaluation and Limitations Disclosure Sensitive Content and Warnings Provided
evidence sensitive_elements: 3 defined (genetic/biospecimen, geographic/demographic identifiers, medical history/records) with controlled access designation; discouraged_uses: re-identification attempts prohibited, clinical decision-making without validation warned against
quality Sensitive content clearly identified (3 categories) with access controls. Warnings against re-identification and clinical use without validation. Appropriate cautions for health data.
semantic sensitive_content_count: 3; access_controls: True; re_identification_warning: True; clinical_use_warning: True
license_and_use_terms

    license_and_use_terms:
      id: aireadi:license:1
      name: Creative Commons Attribution Non-Commercial and AI-READI Data License
      description: 'Public access data distributed under Creative Commons Attribution Non-Commercial (CC BY-NC
        4.0) license. Permits others to distribute, remix, adapt, build upon this work non-commercially, and
        license their derivative works on different terms, provided the original work is properly cited, appropriate
        credit is given, any changes made indicated, and the use is non-commercial. Controlled access data
        requires a separate data use agreement with the University of Washington as Licensor. The AI-READI
        Data License Agreement prohibits clinical treatment decisions based on the data, re-identification
        attempts, and sharing data with non-licensed parties. See http://creativecommons.org/licenses/by-nc/4.0/
        for full CC BY-NC 4.0 license terms and https://docs.aireadi.org/ for the AI-READI specific license
        terms.

        '

⚠ low R20 · consistency
issue License CC BY-NC 4.0 appropriately restrictive for sensitive health data with dual-tier access (public/controlled)
fields license_and_use_terms, subsets
fix No action needed - license aligns with data sensitivity
✓ 1/1 R10 10.Cross-Platform and Community Integration Citation and DOI for Cross-referencing
evidence doi: 10.57895/fairhub.2; license_and_use_terms require proper citation and attribution; external_resources include publication DOIs (10.1136/bmjopen-2024-097449, 10.1038/s42255-024-01165-x) and Zenodo DOI (10.5281/zenodo.10642459)
quality Dataset DOI present (10.57895/fairhub.2) with citation requirement in license. Related publication DOIs provided for cross-referencing. Strong citation ecosystem.
semantic dataset_doi: 10.57895/fairhub.2; citation_requirement: True; publication_dois: 2; zenodo_doi: True; cross_reference_count: 4
✓ 1/1 R10 2.Dataset Access and Retrieval Access Policy and IP Restrictions Defined
evidence license_and_use_terms: CC BY-NC 4.0 with detailed AI-READI Data License Agreement; subsets define public vs controlled access; license prohibits clinical treatment decisions, re-identification, sharing with non-licensed parties
quality Comprehensive access policy with dual licensing (public CC BY-NC 4.0 + controlled access DUA). Clear restrictions and use terms documented.
semantic license_specificity: high; access_levels: 2; restriction_clarity: explicit
✓ 1/1 R10 3.Data Reuse and Interoperability License Terms Allow Reuse
evidence license_and_use_terms: CC BY-NC 4.0 permits distribution, remix, adaptation, building upon work non-commercially with proper citation; controlled access via DUA
quality Clear reuse permissions under CC BY-NC 4.0 with explicit allowances for derivative works, remixing, and building upon with attribution requirement.
semantic reuse_permitted: True; license_standard: CC BY-NC 4.0; attribution_required: True; commercial_use: False
distribution_formats

    distribution_formats:
    - id: aireadi:format:1
      name: DICOM for imaging
      description: 'Retinal imaging data distributed in DICOM format (converted from proprietary .fda and
        .sdt formats for standardization).

        '
    - id: aireadi:format:2
      name: CSV for tabular and time-series data
      description: 'Survey data, clinical lab results, continuous glucose monitoring, environmental sensor
        data, and other tabular/time-series data provided in CSV format.

        '
    - id: aireadi:format:3
      name: mHealth standard for wearable data
      description: 'Physical activity monitoring data (from Garmin VivoSmart 5) converted from .FIT format
        to mHealth standard for interoperability.

        '
    - id: aireadi:format:4
      name: XML for ECG data
      description: 'Electrocardiogram data from Philips Pagewriter TC30 exported in .xml format.

        '

✓ 1/1 R10 2.Dataset Access and Retrieval Distribution Formats and File Types Specified
evidence distribution_formats: 4 defined (DICOM for imaging, CSV for tabular/time-series, mHealth standard for wearable data, XML for ECG); preprocessing_strategies mention format conversions from proprietary to standards
quality Comprehensive format documentation with 4 distinct distribution formats mapped to data types. Includes format conversion details from proprietary sources.
semantic format_count: 4; standard_compliance: high (DICOM, mHealth, CSV, XML); proprietary_conversion_documented: True
✓ 1/1 R10 3.Data Reuse and Interoperability Data Formats Are Standardized (encoding, format)
evidence distribution_formats: DICOM (imaging), CSV (tabular), mHealth standard (wearables), XML (ECG); preprocessing_strategies document conversion from proprietary formats (.fda, .sdt, .FIT) to standards
quality All data distributed in standardized formats (DICOM, CSV, mHealth, XML). Proprietary format conversion process documented for reproducibility.
semantic standard_formats: DICOM; CSV; mHealth; XML; proprietary_conversion: True; interoperability_level: high
✓ 1/1 R10 5.Data Composition and Structure Variable-Level Metadata and Tabular Flag
evidence acquisition_methods: 12 detailed methods documenting variables across all domains (survey: demographics, CES-D-10, PAID, dietary, medications with RxNorm; physical: height, weight, BMI, waist-hip ratio, BP, HR; imaging: 8 devices; labs: CBC, lipids, HbA1c, glucose, kidney/liver markers; ECG; MoCA scores with subscores; monofilament 10 locations; CGM 5-min intervals; Garmin: steps, HR, sleep, O2; environmental: 11 measurements); distribution_formats indicate CSV for tabular data
quality Comprehensive variable-level documentation across 12 acquisition methods with specific measurements, instruments, units, intervals, and scores. Tabular data indicated through CSV format.
semantic variable_documentation_depth: high; domain_count: 12; measurement_specificity: high; tabular_format_confirmed: True
maintainers

    maintainers:
    - id: aireadi:maintainer:1
      name: AI-READI Consortium
      description: 'Multidisciplinary consortium managing dataset maintenance including data collection sites,
        coordinating centers, and data governance committees. Contact through the University of Washington
        as lead institution and data coordination center. Documentation maintained at https://docs.aireadi.org/
        with version-specific guides for each data release.

        '

✓ 1/1 R10 10.Cross-Platform and Community Integration Dataset Published on a Recognized Platform
evidence page: https://fairhub.io/datasets/2; FAIRhub is recognized FAIR-compliant repository; external_resources include FAIRhub, Zenodo (10.5281/zenodo.10642459); maintainers: AI-READI Consortium via University of Washington
quality Published on FAIRhub (FAIR-compliant repository) with Zenodo archival. Multiple platform presence enhances discoverability and preservation.
semantic primary_platform: FAIRhub; platform_recognition: FAIR-compliant; archival_platform: Zenodo; multi_platform_presence: True
updates

    updates:
      id: aireadi:updates:1
      name: Periodic data releases
      description: 'Dataset updated periodically as enrollment progresses toward target of 4,000 participants
        by November 2026. Version-specific documentation maintained for each release. Pilot data released
        May 2024. Data through July 31, 2024 released November 2024 as v1.0.0. Subsequent versions v2.0.0
        and v3.0.0 released with additional participants. Final dataset expected after completion of enrollment
        by November 2026.

        '
      frequency: Periodic releases with ongoing enrollment; final release planned for late 2026
      update_details:
      - Periodic data releases as enrollment continues toward 4,000 participant target
      - Pilot data released May 2024
      - v1.0.0 data through July 31, 2024 released November 2024
      - v2.0.0 and v3.0.0 released with additional participants
      - Final dataset expected after completion of 4,000 participant enrollment by November 2026
      - Dataset versioning implemented with version-specific documentation at https://docs.aireadi.org/

✓ 1/1 R10 5.Data Composition and Structure Number of Instances or Samples Reported
evidence instances: target enrollment 4,000 people; updates: pilot data May 2024, v1.0.0 through July 31 2024, v2.0.0 and v3.0.0 released; enrollment began July 18, 2023, continues until November 30, 2026
quality Target sample size clearly stated (4,000 participants). Versioning provides transparency about progressive enrollment with specific release dates and enrollment timeline.
semantic target_sample_size: 4000; enrollment_status: ongoing; enrollment_timeline: July 2023 - November 2026; version_transparency: True
✓ 1/1 R10 5.Data Composition and Structure Data Quality Issues and Anomalies Documented
evidence sampling_strategies: not representative, volunteer selection bias documented; discouraged_uses: pilot data and periodic updates may not have achieved balanced distribution; preprocessing_strategies: quality control measures (standardized training, equipment calibration, duplicate measurements, practice subjects, data validation in REDCap); cleaning_strategies: pilot enrollment July-November 2023 to ensure coordinator familiarity
quality Quality limitations explicitly documented (volunteer bias, non-representativeness, balance not achieved in early versions). Extensive QC measures documented (training, calibration, validation). Honest about limitations.
semantic limitations_documented: True; bias_acknowledged: volunteer selection bias; quality_control_measures: comprehensive; pilot_phase_for_quality: True
✓ 1/1 R10 6.Data Provenance and Version Tracking Dataset Version Number Provided
evidence updates mention v1.0.0 (data through July 31, 2024), v2.0.0 and v3.0.0 releases; version-specific documentation maintained at docs.aireadi.org
quality Semantic versioning used (v1.0.0, v2.0.0, v3.0.0) with version-specific documentation. Clear versioning scheme for progressive data releases.
semantic versioning_scheme: semantic versioning; versions_documented: v1.0.0; v2.0.0; v3.0.0; pilot; version_specific_docs: True
✓ 1/1 R10 6.Data Provenance and Version Tracking Version Access Methods Documented
evidence updates: version-specific documentation maintained at https://docs.aireadi.org/ for each release; FAIRhub landing page https://fairhub.io/datasets/2 provides access
quality Version-specific documentation URLs provided. FAIRhub platform likely supports version access but explicit version retrieval mechanism not detailed.
semantic version_docs_url: True; repository_versioning: implied via FAIRhub; version_retrieval_explicit: False
✓ 1/1 R10 6.Data Provenance and Version Tracking Change Descriptions and Errata Provided
evidence updates: pilot data May 2024, v1.0.0 data through July 31 2024 released November 2024, v2.0.0 and v3.0.0 released with additional participants; discouraged_uses warns about pilot data limitations and ongoing enrollment affecting balance
quality Update descriptions provided with release dates and scope (additional participants). Limitations of early versions documented. Errata not explicitly mentioned but change transparency present.
semantic update_descriptions: True; release_timeline: documented; version_scope_changes: additional participants; errata_section: False
✓ 1/1 R10 6.Data Provenance and Version Tracking Update Schedule or Frequency Indicated
evidence updates: periodic releases with ongoing enrollment; final release planned for late 2026 after completion of 4,000 participant enrollment by November 2026; frequency: periodic releases with ongoing enrollment; final dataset expected after completion
quality Update frequency clearly stated (periodic releases during ongoing enrollment) with final release timeline (late 2026 after November 2026 enrollment completion).
semantic update_frequency: periodic (tied to enrollment progress); final_release_date: late 2026; enrollment_completion_date: November 30, 2026; schedule_clarity: high
retention_limit

    retention_limit:
      id: aireadi:retention:1
      name: Data and biospecimen retention
      description: 'Digital data maintained according to NIH data sharing policies. Biospecimen retention
        subject to institutional policies and consent agreements at UAB CCTS. Finite number of biospecimen
        samples available for distribution to researchers. Procedures for reviewing and prioritizing biospecimen
        requests are under development.

        '

no field-level feedback matched
ethical_reviews

    ethical_reviews:
    - id: aireadi:ethics:1
      name: University of Washington IRB Approval
      description: 'Study approved by Institutional Review Board (IRB) of University of Washington (approval
        number STUDY00016228), with reliance agreements from IRBs of University of Alabama at Birmingham and
        University of California, San Diego. Written informed consent provided by all participants.

        '

⚠ low R10 · completeness
issue Conflicts of interest statement not present in ethical review documentation
fields ethical_reviews
fix Add conflicts of interest disclosure for research team members
⚠ low R20 · consistency
issue human_subject_research.involves_human_subjects=True and ethical_reviews present - consistent and well-documented
fields human_subject_research, ethical_reviews
fix No action needed - ethics documentation is comprehensive
✓ 1/1 R10 4.Ethical Use and Privacy Safeguards IRB or Ethics Review Documented
evidence ethical_reviews: University of Washington IRB approval STUDY00016228 with reliance agreements from UAB and UCSD; human_subject_research.involves_human_subjects: true; written informed consent from all participants; Community Advisory Board of 11 persons; bioethics guidance integrated
quality Comprehensive IRB documentation with specific approval number (STUDY00016228), multi-site reliance agreements, consent procedures, and community engagement. Strong ethical oversight.
semantic irb_approval: True; irb_number: STUDY00016228; multi_site_coordination: True; reliance_agreements: UAB; UCSD; consent_documented: True; community_engagement: True
✓ 1/1 R10 4.Ethical Use and Privacy Safeguards Informed Consent Obtained from Participants
evidence ethical_reviews: written informed consent provided by all participants; human_subject_research confirms IRB approval and consent; Community Advisory Board contributed to protocol development
quality Explicit documentation of written informed consent from all participants. Community engagement in consent protocol development demonstrates ethical rigor.
semantic consent_type: written; consent_universality: all participants; community_input_on_consent: True; consent_documentation: explicit
✗ 0/1 R10 9.Dataset Evaluation and Limitations Disclosure Ethical Review Details Including Conflicts
evidence ethical_reviews: IRB approval documented with number and reliance agreements; human_subject_research: bioethics guidance, Community Advisory Board; no explicit conflicts of interest statement
quality Ethical review thoroughly documented (IRB, bioethics, community engagement) but no conflicts of interest statement present. COI disclosure is standard for research datasets and should be included.
semantic irb_documented: True; bioethics_oversight: True; community_advisory_board: True; conflicts_of_interest: False
human_subject_research

    human_subject_research:
      id: aireadi:hsr:1
      name: AI-READI Human Subjects Research
      description: 'Study involves human subjects research with IRB approval and written informed consent
        from all participants. Bioethics guidance integrated throughout study design. Community Advisory Board
        of 11 persons with diversity in race and ethnicity contributes to protocol development. Ethical and
        equitable data collection and management practices implemented across all three data collection sites.
        IRB approval number STUDY00016228 from University of Washington with reliance agreements from University
        of Alabama at Birmingham and University of California San Diego.

        '
      involves_human_subjects: true

⚠ low R10 · completeness
issue Participant compensation not documented
fields participant_compensation, human_subject_research
fix Document whether participants were compensated and if so, the compensation structure
⚠ low R20 · consistency
issue human_subject_research.involves_human_subjects=True and ethical_reviews present - consistent and well-documented
fields human_subject_research, ethical_reviews
fix No action needed - ethics documentation is comprehensive
✓ 1/1 R10 4.Ethical Use and Privacy Safeguards IRB or Ethics Review Documented
evidence ethical_reviews: University of Washington IRB approval STUDY00016228 with reliance agreements from UAB and UCSD; human_subject_research.involves_human_subjects: true; written informed consent from all participants; Community Advisory Board of 11 persons; bioethics guidance integrated
quality Comprehensive IRB documentation with specific approval number (STUDY00016228), multi-site reliance agreements, consent procedures, and community engagement. Strong ethical oversight.
semantic irb_approval: True; irb_number: STUDY00016228; multi_site_coordination: True; reliance_agreements: UAB; UCSD; consent_documented: True; community_engagement: True
✓ 1/1 R10 4.Ethical Use and Privacy Safeguards Informed Consent Obtained from Participants
evidence ethical_reviews: written informed consent provided by all participants; human_subject_research confirms IRB approval and consent; Community Advisory Board contributed to protocol development
quality Explicit documentation of written informed consent from all participants. Community engagement in consent protocol development demonstrates ethical rigor.
semantic consent_type: written; consent_universality: all participants; community_input_on_consent: True; consent_documentation: explicit
✗ 0/1 R10 9.Dataset Evaluation and Limitations Disclosure Ethical Review Details Including Conflicts
evidence ethical_reviews: IRB approval documented with number and reliance agreements; human_subject_research: bioethics guidance, Community Advisory Board; no explicit conflicts of interest statement
quality Ethical review thoroughly documented (IRB, bioethics, community engagement) but no conflicts of interest statement present. COI disclosure is standard for research datasets and should be included.
semantic irb_documented: True; bioethics_oversight: True; community_advisory_board: True; conflicts_of_interest: False
sensitive_elements

    sensitive_elements:
    - id: aireadi:sensitive:1
      name: Genetic and biospecimen data
      description: 'Genomic DNA extracted from buffy coats, blood derivatives, and urine samples stored with
        potential for future genetic analyses. Available in controlled access dataset only. Includes genetic
        sequencing data from buffy coats, PBMCs, and PAXgene RNA.

        '
    - id: aireadi:sensitive:2
      name: Geographic and demographic identifiers
      description: '5-digit zip code, detailed race, ethnicity, and sex information available in controlled
        access dataset only. Public dataset contains de-identified data.

        '
    - id: aireadi:sensitive:3
      name: Medical history and records
      description: 'Past health records, medications with RxNorm codes, traffic and accident reports available
        in controlled access dataset only.

        '

✓ 1/1 R10 2.Dataset Access and Retrieval Regulatory Restrictions and Confidentiality Level Specified
evidence sensitive_elements: 3 defined (genetic/biospecimen, geographic/demographic identifiers, medical history/records) with controlled access designation; license prohibits re-identification
quality Explicit documentation of sensitive data elements with controlled access requirements. Regulatory compliance through IRB and data use agreements.
semantic sensitivity_classification: present; regulatory_framework: IRB + DUA; data_protection_level: tiered
✓ 1/1 R10 4.Ethical Use and Privacy Safeguards Privacy Protections Beyond Deidentification
evidence license prohibits re-identification, sharing with non-licensed parties; Data Access Committee developing controlled access requirements; bioethics guidance throughout study; sensitive_elements documented with access restrictions
quality Multi-layered privacy protections including contractual prohibitions, data access committee oversight, bioethics integration, and explicit sensitive data classification.
semantic data_access_committee: True; contractual_protections: True; bioethics_oversight: True; multi_layered_approach: True
✓ 1/1 R10 9.Dataset Evaluation and Limitations Disclosure Sensitive Content and Warnings Provided
evidence sensitive_elements: 3 defined (genetic/biospecimen, geographic/demographic identifiers, medical history/records) with controlled access designation; discouraged_uses: re-identification attempts prohibited, clinical decision-making without validation warned against
quality Sensitive content clearly identified (3 categories) with access controls. Warnings against re-identification and clinical use without validation. Appropriate cautions for health data.
semantic sensitive_content_count: 3; access_controls: True; re_identification_warning: True; clinical_use_warning: True
external_resources

    external_resources:
    - id: aireadi:resource:1
      name: AI-READI Project Website
      description: Official project website with overview and resources
      external_resources:
      - https://aireadi.org/
    - id: aireadi:resource:2
      name: AI-READI Dataset Documentation
      description: Comprehensive dataset documentation with version-specific guides
      external_resources:
      - https://docs.aireadi.org/
    - id: aireadi:resource:3
      name: FAIRhub Dataset Landing Page
      description: Dataset repository and download portal for AI-READI data
      external_resources:
      - https://fairhub.io/datasets/2
    - id: aireadi:resource:4
      name: Bridge2AI Program
      description: Parent NIH Common Fund program supporting AI-ready biomedical datasets
      external_resources:
      - https://bridge2ai.org/
    - id: aireadi:resource:5
      name: NIH RePORTER Project Details
      description: Federal grant information and project details for grant 1OT2OD032644-01
      external_resources:
      - https://reporter.nih.gov/project-details/10471118
    - id: aireadi:resource:6
      name: Data Sharing Information
      description: Policies and procedures for data access and sharing
      external_resources:
      - https://aireadi.org/goals/data-sharing
    - id: aireadi:resource:7
      name: Zenodo Archive
      description: Additional dataset documentation and resources
      external_resources:
      - https://doi.org/10.5281/zenodo.10642459
    - id: aireadi:resource:8
      name: Protocol Publication (BMJ Open)
      description: BMJ Open publication describing study design and protocol (Owsley et al. 2025)
      external_resources:
      - https://doi.org/10.1136/bmjopen-2024-097449
    - id: aireadi:resource:9
      name: Nature Metabolism Commentary
      description: Overview of AI-READI approach and significance published in Nature Metabolism
      external_resources:
      - https://doi.org/10.1038/s42255-024-01165-x

✓ 1/1 R10 1.Dataset Discovery and Identification Landing Page and Resources (page, hierarchical resources)
evidence page: https://fairhub.io/datasets/2; external_resources with 9 resource entries including project website, documentation, FAIRhub, Zenodo, publications
quality Landing page provided with extensive external resources (9 distinct resources) including documentation, repository, publications, and grant information.
semantic landing_page_present: True; resource_count: 9; url_validity: all_plausible
✓ 1/1 R10 10.Cross-Platform and Community Integration Dataset Published on a Recognized Platform
evidence page: https://fairhub.io/datasets/2; FAIRhub is recognized FAIR-compliant repository; external_resources include FAIRhub, Zenodo (10.5281/zenodo.10642459); maintainers: AI-READI Consortium via University of Washington
quality Published on FAIRhub (FAIR-compliant repository) with Zenodo archival. Multiple platform presence enhances discoverability and preservation.
semantic primary_platform: FAIRhub; platform_recognition: FAIR-compliant; archival_platform: Zenodo; multi_platform_presence: True
✓ 1/1 R10 10.Cross-Platform and Community Integration Citation and DOI for Cross-referencing
evidence doi: 10.57895/fairhub.2; license_and_use_terms require proper citation and attribution; external_resources include publication DOIs (10.1136/bmjopen-2024-097449, 10.1038/s42255-024-01165-x) and Zenodo DOI (10.5281/zenodo.10642459)
quality Dataset DOI present (10.57895/fairhub.2) with citation requirement in license. Related publication DOIs provided for cross-referencing. Strong citation ecosystem.
semantic dataset_doi: 10.57895/fairhub.2; citation_requirement: True; publication_dois: 2; zenodo_doi: True; cross_reference_count: 4
✓ 1/1 R10 10.Cross-Platform and Community Integration Outreach Materials and Documentation Links
evidence external_resources: 9 resources including project website (aireadi.org), comprehensive documentation (docs.aireadi.org with version-specific guides), data sharing information (aireadi.org/goals/data-sharing), publications (BMJ Open, Nature Metabolism), FAIRhub landing page, Zenodo archive, Bridge2AI program site, NIH RePORTER
quality Extensive outreach and documentation (9 resources) including project website, versioned documentation portal, data sharing policies, peer-reviewed publications, and program context. Comprehensive user support.
semantic documentation_portal: True; project_website: True; version_specific_docs: True; publication_count: 2; data_sharing_policy_url: True; outreach_resource_count: 9
✓ 1/1 R10 10.Cross-Platform and Community Integration Related Datasets with Typed Relationships
evidence external_resources: Bridge2AI Program link (parent program); keywords: Bridge2AI (program affiliation); subsets define hierarchical relationships (public vs controlled vs biorepository); funders: NIH Common Fund Bridge2AI (program context)
quality Clear relationship to Bridge2AI program (parent initiative) with program URL and multiple references. Internal hierarchical relationships via subsets. Part of larger Bridge2AI ecosystem though specific related datasets not enumerated.
semantic parent_program: Bridge2AI; program_url: True; subset_relationships: 3; ecosystem_integration: Bridge2AI consortium; specific_related_datasets: False
✓ 1/1 R10 2.Dataset Access and Retrieval Related Datasets and External Resources Linked
evidence external_resources: 9 resources including project website (aireadi.org), documentation (docs.aireadi.org), FAIRhub, Zenodo (10.5281/zenodo.10642459), publications (BMJ Open, Nature Metabolism), NIH RePORTER, Bridge2AI program
quality Extensive external resources (9 distinct) with publications, repositories, documentation, and parent program links. Strong ecosystem integration.
semantic external_resource_count: 9; publication_dois: 3; repository_links: 2; documentation_links: 2
✓ 1/1 R10 7.Scientific Motivation and Funding Transparency Grant IDs or Award Numbers Present
evidence funders: OT2OD032644, P30DK035816, UL1TR003096; opportunity number OTA-21-008; external_resources: NIH RePORTER link for 1OT2OD032644-01 (10471118)
quality Multiple grant numbers present (OT2OD032644, P30DK035816, UL1TR003096) with correct NIH format. RePORTER link validates grant. OT2 prefix indicates Other Transaction Award for OD (Office of Director). Format validation passes.
semantic grant_number_format_valid: True; grant_numbers: OT2OD032644; P30DK035816; UL1TR003096; nih_format_compliance: True; grant_type: OT2 (Other Transaction), P30 (Center Core), UL1 (Clinical Translational Science); reporter_link: True
✓ 1/1 R10 8.Technical Transparency (Data Collection and Processing) Software and Tools Documented
evidence acquisition_methods mention REDCap (data management), MoCA Duo Application (iPad), specific device software implied (Dexcom G6, Garmin VivoSmart 5, Philips ECG, retinal imaging systems); preprocessing mentions format conversion tools (proprietary to DICOM/mHealth); external_resources link to documentation (docs.aireadi.org)
quality Software documented for data collection (REDCap, MoCA Duo, device software) and processing (format converters to DICOM/mHealth). Documentation portal provided for detailed methods.
semantic data_management_software: REDCap; device_software_count: 8+ (retinal imaging systems, wearables, ECG); format_conversion_tools: documented but tools not named; documentation_portal: True
✓ 1/1 R10 8.Technical Transparency (Data Collection and Processing) External Standards and Resources Referenced
evidence preprocessing_strategies: OMOP CDM, DICOM, mHealth, RxNorm, ICD-10 referenced; external_resources: 9 resources including publications (BMJ Open 10.1136/bmjopen-2024-097449, Nature Metabolism 10.1038/s42255-024-01165-x), documentation (docs.aireadi.org), Zenodo (10.5281/zenodo.10642459), NIH RePORTER, Bridge2AI program
quality Multiple standards referenced (5 standards) with publication DOIs (2 protocol papers), documentation portal, archived resources, and grant information. Strong external linkage.
semantic standard_count: 5; publication_doi_count: 2; documentation_url: True; archived_resources: True; external_reference_count: 9

## Unmatched feedback

Feedback that referenced multiple fields or no specific field.
5/5 R20 Q4 (Structural Completeness) File Enumeration and Type Variety
level>3 file types
evidence 4 formats: DICOM, CSV, mHealth, XML
quality Excellent format diversity for multimodal data
correctness Industry-standard formats for respective data types
consistency Formats align with acquisition methods
1/1 R20 Q5 (Structural Completeness) Data File Size Availability
level Pass
evidence 4,000 target enrollment with subpopulation stratification documented
quality Clear instance count documentation
correctness Instance counts realistic for multi-site study
consistency Subpopulation counts consistent with stratification design
1/1 R20 Q6 (Metadata Quality & Content) Dataset Identification Metadata
level Pass
evidence DOI 10.57895/fairhub.2, Zenodo DOI, publication DOIs
quality Multiple persistent identifiers ensuring findability
correctness All DOI prefixes match known registrars
consistency All identifiers point to AI-READI resources
5/5 R20 Q8 (Metadata Quality & Content) Ethical and Privacy Declarations
level Comprehensive ethics documentation
evidence IRB STUDY00016228, consent, Community Advisory Board, two-tier access
quality Exemplary ethics covering all dimensions
correctness IRB number follows UW format, multi-site reliance appropriate
consistency Ethics aligns with data governance and access tiers
5/5 R20 Q9 (Metadata Quality & Content) Access Requirements and Governance Documentation
level License + restrictions + confidentiality
evidence CC BY-NC 4.0 + DUA, use restrictions, access mechanisms documented
quality Comprehensive governance framework
correctness CC BY-NC 4.0 is standard license, DUA appropriate for sensitive data
consistency License restrictions align with ethical commitments
5/5 R20 Q10 (Metadata Quality & Content) Interoperability and Standardization
level Standard formats + schema compliance
evidence OMOP CDM, DICOM, mHealth, RxNorm, ICD-10
quality Outstanding interoperability through comprehensive standards
correctness All standards are industry-recognized and appropriate
consistency Standards align with AI-ready claims
5/5 R20 Q11 (Technical Documentation) Tool and Software Transparency
level Comprehensive strategies with software
evidence 12 acquisition methods with specific devices: Dexcom G6, Garmin, Philips, REDCap, MoCA Duo, 7 imaging systems
quality Exceptional device and software documentation
correctness All manufacturers/models are real medical equipment
consistency Tools align with data types
5/5 R20 Q12 (Technical Documentation) Collection Protocol Clarity
level Full protocol with methods, collectors, timeframes
evidence 5 collection mechanisms, 12 acquisition methods with technical specs, coordinator training, timeline July 2023-Nov 2026
quality Comprehensive protocol with operational detail
correctness Timeline internally consistent, ICD-10 codes correct, visit duration realistic
consistency Mechanisms align with methods, sites match creator institutions
5/5 R20 Q13 (Technical Documentation) Version History Documentation
level Comprehensive versioning with release notes
evidence Pilot May 2024, v1.0.0 Nov 2024, v2.0.0, v3.0.0, version-specific docs
quality Excellent version history with clear timeline
correctness Version timeline logically ordered
consistency Releases align with enrollment timeline
5/5 R20 Q14 (Technical Documentation) Associated Publications
level Multiple references and dataset citation
evidence BMJ Open DOI, Nature Metabolism DOI, Zenodo DOI, NIH RePORTER, dataset DOI
quality Strong publication linkage
correctness All DOI prefixes correct
consistency Publications directly relevant to dataset
5/5 R20 Q15 (Technical Documentation) Human Subject Representation
level Detailed demographics and inclusion/exclusion
evidence 4,000 participants, triple-balanced (4 races × 2 sexes × 4 severities), inclusion/exclusion criteria, transparency about non-representativeness
quality Exemplary multi-dimensional demographic detail
correctness Demographic categories standard, sample size feasible
consistency Subpopulation targets align with stratification design
1/1 R20 Q16 (FAIRness & Accessibility) Findability (Persistent Links)
level Pass
evidence 9 external resources with multiple persistent access points
quality Excellent findability
correctness URL structures valid for appropriate domains
consistency All links point to AI-READI resources
5/5 R20 Q17 (FAIRness & Accessibility) Accessibility (Access Mechanism)
level Fully defined access path
evidence Public download via FAIRhub + CC BY-NC, controlled access via DUA with UW
quality Comprehensive dual-tier access documentation
correctness Two-tier model appropriate for mixed sensitivity data
consistency Access tiers align with data sensitivity
5/5 R20 Q19 (FAIRness & Accessibility) Data Integrity and Provenance
level Structured version control with timestamps
evidence Release dates, data cutoff dates, version-specific docs, update rationale
quality Excellent provenance with structured versioning
correctness Timeline consistent with enrollment
consistency Provenance chain coherent from source to release
1/1 R20 Q20 (FAIRness & Accessibility) Interlinking Across Platforms
level Pass
evidence Links to FAIRhub, project sites, Bridge2AI, NIH RePORTER, Zenodo, journals
quality Excellent cross-platform integration
correctness Platform choices appropriate for purposes
consistency Linkage supports FAIR principles
⚠ low R10 · completeness
issue Provenance field (was_derived_from) not present, though this is a primary dataset so may not be applicable
fields was_derived_from
fix Consider documenting source EHR systems and instrument provenance more explicitly if applicable

## Recommendations

1. R10 · Add conflicts of interest statement to ethical_reviews section for complete transparency
2. R10 · Document participant compensation structure if applicable to human subject research
3. R10 · Specify software tool names for format conversions (proprietary to DICOM/mHealth) in preprocessing_strategies
4. R10 · Consider adding explicit citation text in a citation field to standardize how users reference the dataset
5. R10 · Document EHR system provenance (which EHR platforms were used for screening) if technically feasible
6. R10 · Add specific related datasets from Bridge2AI consortium with typed relationships if other Bridge2AI datasets are available
7. R10 · Consider adding RRID for software tools and databases used (e.g., REDCap RRID) to enhance tool discoverability
8. R10 · Add explicit anomaly list if any known data quality issues exist beyond those addressed by QC measures
9. R20 · Add software version numbers and repository links
10. R20 · Document participant compensation structure
11. R20 · Add RRID identifiers for research resources
12. R20 · Finalize and document controlled access procedures
13. R20 · Explicitly document vulnerable population safeguards