# AI READI all combined d4d

Datasheet for Dataset - Human Readable Format

🎯

## Motivation

Why was the dataset created?

Purposes

- Name
    primary-purpose
  Response
    Create a harmonized, multi-modal flagship dataset to enable AI/ML research on Type 2 Diabetes Mellitus (T2DM), with a focus on salutogenesis (the pathway from disease to health).

📊

## Composition

What do the instances represent?

Instances

1. Name
     participant-level-multimodal-records
   Representation
     Participant-centric records with linked multi-domain measurements and observations
   Instance Type
     Participants and their associated multi-modal data (surveys, clinical assessments, labs, imaging, wearables, environmental)
   Data Type
     Mixed data modalities including tabular variables (surveys, labs, clinical), imaging (retinal), physiological signals (ECG), continuous glucose monitor streams, and derived features; harmonized across sites.
   Label
     No universal target; labels/variables vary by domain and analysis
   Sampling Strategies
     1. Name
          recruitment-and-balance
        Is Sample
          - True
        Is Random
          - False
        Source Data
          - Prospective recruitment across three clinical sites
        Is Representative
          - Partially; enrollment ongoing
        Representative Verification
          - Recruitment targeted approximate balance by diabetes severity; ongoing monitoring via study dashboard
        Why Not Representative
          - Pilot and early releases may not fully achieve balance across groups as enrollment continues
        Strategies
          - Targeted stratified recruitment by diabetes severity across sites

Subpopulations

- Name
    diabetes-status-and-severity
  Identification
    - Participants with and without T2DM; severity strata targeted during recruitment.
  Distribution
    - Approximate balance targeted; may vary in pilot/early releases due to ongoing enrollment.

Distribution Formats

- Name
    distribution
  Description
    - Distributed via the FAIRhub data portal; gated controlled-access workflow; mixed modalities and file types across imaging, signals, and tabular data.

Distribution Dates

Description | Name
--- | ---
{'2024-05-03 (DOI': '10.60775/fairhub.1)'} | version-1.0.0
{'2024-11-08 (DOI': '10.60775/fairhub.2)'} | version-2.0.0

🔍

## Collection Process

How was the data acquired?

ID *
doi:10.60775/fairhub.2
Name
ai-readi-flagship-dataset-t2dm
Title
Flagship Dataset of Type 2 Diabetes from the AI-READI Project
Description
The AI-READI Flagship Dataset is a harmonized, multi-modal human health dataset collected across three data collection sites from individuals with and without Type 2 Diabetes Mellitus (T2DM). The dataset was designed with future AI/ML applications in mind, including a recruitment and sampling approach intended to approximate equal distribution across diabetes severity levels and a standardized data acquisition protocol spanning multiple domains: survey data, physical measurements, clinical assessments, imaging, wearable device data, and more. The overarching scientific goal is to support research into salutogenesis (the pathway from disease to health) in T2DM. The dataset is distributed via the FAIRhub data portal and has both a public component (de-identified and non-sensitive health data, available under a license) and a controlled-access component (additional sensitive elements, accessible via a data use agreement and an approval workflow). As enrollment is ongoing, early/pilot releases and periodic updates may not fully achieve the target balanced distribution across groups. Documentation for each dataset version is maintained and includes clinical context, acquisition methods, variable summaries, domain-specific processing and formats, and additional resources for AI-readiness, FAIR principles, and ethical considerations.
Language
English
Page
https://docs.aireadi.org/docs/2
Issued
2024-11-08
Created On
2024-05-03
Created By

- AI-READI Consortium

Keywords

- Diabetes mellitus
- Machine Learning
- Artificial Intelligence
- Electrocardiography
- Continuous Glucose Monitoring
- Retinal imaging
- Eye exam

Is Tabular
mixed (tabular, imaging, waveform, sensor-derived)
Creators

- Name
    AI-READI Consortium

Addressing Gaps

- Name
    addressing-data-gaps
  Response
    Provide integrated, multi-domain data not feasible to obtain from single sources such as claims or EHR alone; harmonize data across multiple collection sites to support robust AI/ML development and evaluation.

Subsets

Description | Is Data Split | Is Subpopulation | Name
--- | --- | --- | ---
De-identified, non-sensitive health data available for download upon agreement to the dataset licens... | False | False | Public subset
Additional sensitive elements available under a data use agreement and approval process. Includes 5-... | False | False | Controlled-access subset

Anomalies

- Name
    distribution-notes
  Description
    - Early releases and periodic updates may not fully achieve balanced distribution across groups as enrollment is ongoing.

External Resources

1. Name
     linked-external-context
   External Resources
     - Environmental variables (e.g., home air quality) and traffic/accident reports may be derived from or linked to external sources.
   Future Guarantees
     - Not specified; see dataset documentation and FAIRhub records for archival details.
   Archival
     - Versioned releases with documentation are maintained; prior versions retain DOIs.
   Restrictions
     - External datasets, if used, may carry their own licenses/terms; users must review terms where applicable.

Confidential Elements

- Name
    controlled-elements
  Description
    - Controlled-access data include quasi-identifiers and sensitive health/genetic data (e.g., 5-digit ZIP, sex, race, ethnicity, genetic sequencing, past health records, medications, traffic/accident reports).

Content Warnings

- Name
    potential-sensitive-health-content
  Warnings
    - Contains medical and genetic information; exposure to sensitive health topics.

Sensitive Elements

- Name
    sensitive-health-and-demographic-elements
  Description
    - Health data (labs, clinical assessments), genetic data, demographic attributes (sex, race, ethnicity), location-derived data (5-digit ZIP).

Is Deidentified

Name
  deidentification-summary
Description
  - Public subset is de-identified and excludes sensitive personal health data.
  - Controlled-access subset includes additional sensitive/quasi-identifying elements and is governed by a DUA and access controls.

Acquisition Methods

1. Name
     mixed-acquisition
   Description
     - Multi-domain acquisition across surveys, clinical measurements, labs, imaging devices, ECG, CGM, wearables, and linked environmental/contextual sources.
   Was Directly Observed
     yes (e.g., imaging, ECG, CGM, physical measurements)
   Was Reported By Subjects
     yes (e.g., survey responses)
   Was Inferred Derived
     yes (e.g., derived features, harmonized variables)
   Was Validated Verified
     Not specified in provided sources

Collection Mechanisms

- Name
    instruments-and-procedures
  Description
    - Standardized data acquisition protocols across three sites, including clinical instruments, imaging hardware, physiological sensors, wearables, survey tools, and EHR extraction; harmonization applied across sites.

Data Collectors

- Name
    collection-sites-and-study-staff
  Description
    - Data collected at three clinical sites by study staff under a common protocol.

Collection Timeframes

- Name
    enrollment-and-releases
  Description
    - Pilot phase data comprise v1.0.0 (2024-05-03). Enrollment continued with v2.0.0 (2024-11-08). Ongoing releases are planned as data collection proceeds.

Ethical Reviews

- Name
    ethical-sourcing-statement
  Description
    - Described as an ethically-sourced dataset; consult site-specific documentation for IRB and ethics review details.

Data Protection Impacts

- Name
    data-protection-and-access-controls
  Description
    - Public data under license; controlled-access data require a data use agreement and justification of research purpose; multi-step access workflow includes login, training, purpose statement, license acceptance, and data selection.

Preprocessing Strategies

- Name
    harmonization
  Description
    - Harmonized across three collection sites; domain-specific processing described in versioned documentation.

Cleaning Strategies

- Name
    quality-and-standardization
  Description
    - Standardization and quality checks applied per domain; refer to domain-specific docs for details.

Raw Sources

- Name
    primary-data-sources
  Description
    - Surveys, clinical measurements and tests, laboratory results (blood, urine), imaging (retinal), ECG, CGM and wearable device data, EHR-derived variables, environmental/traffic context.

Ip Restrictions

Name
  third-party-ip
Description
  - Users should review terms for any linked external datasets (e.g., environmental, traffic) that may impose additional restrictions.

Maintainers

- Name
    dataset-maintenance
  Description
    - AI-READI Consortium and FAIRhub platform maintain the dataset and documentation; "Changelog" and versioned docs are provided.

Errata

- Name
    changelog
  Description
    - A versioned changelog is maintained in the documentation site.

Extension Mechanism

Name
  community-and-contact
Description
  - Contribution and inquiry via the project documentation site and "Contact Us"; project GitHub links are provided in the docs.

🚀

## Uses

What (other) tasks could the dataset be used for?

Tasks

- Name
    intended-ai-ml-uses
  Response
    Broad AI/ML analyses across multiple domains (survey, clinical, lab, imaging, wearable/device, environmental) to support prediction, discovery, and integrative modeling in T2DM.

Existing Uses

- Name
    usage-and-stats
  Description
    - FAIRhub provides usage statistics and a "Dataset Uses" section; see portal for details.

Use Repository

- Name
    citation-and-docs
  Description
    - Citation Instructions And Versioned Documentation
        https://docs.aireadi.org/docs/2/citation

Other Tasks

- Name
    potential-uses
  Description
    - Multi-modal integrative modeling, phenotype characterization, prognostic/risk modeling, and cross-domain exploratory analyses in T2DM and related conditions (subject to license/DUA).

Future Use Impacts

- Name
    fairness-and-generalizability
  Description
    - Potential impacts from evolving cohort composition (e.g., interim imbalance across groups) should be considered to mitigate fairness and generalizability risks in downstream models.

License And Use Terms

Name
  licensing-and-dua
Description
  - Public subset available under a Health Data License; users must accept the license.
  - Controlled-access subset requires a Data Use Agreement and approval.
  - Access workflow includes login, diabetes research eligibility, required training, research purpose statement, license acceptance, and data selection.

📤

## Distribution

How will the dataset be distributed?

DOI
doi:10.60775/fairhub.2
License
Health Data License
Version Access

Name
  versioning-and-persistence
Description
  - Older Versions Remain Accessible And Citable Via Their Dois (e.g., V1.0.0
      10.60775/fairhub.1).

🔄

## Maintenance

How will the dataset be maintained?

Version
2.0.0
Last Updated On
2024-11-08
Updates

Name
  update-plan
Description
  - Periodic updates to add new participants/modalities and to correct issues; communicated via FAIRhub versioning and documentation updates.

Generated on 2025-11-09 10:17:34 using Bridge2AI Data Sheets Schema