# AI READI all combined d4d

Datasheet for Dataset - Human Readable Format

🎯

## Motivation

Why was the dataset created?

Purposes

- Response
    Better understand salutogenesis (the pathway from disease to health) in Type 2 Diabetes Mellitus (T2DM) and enable downstream AI/ML analyses across multimodal health data.

📊

## Composition

What do the instances represent?

Instances

1. Representation
     Human participants with and without Type 2 Diabetes Mellitus (T2DM).
   Instance Type
     Multiple modalities per participant, including survey responses, physical/clinical measurements, imaging (retinal), physiological signals (ECG), wearable device time series (e.g., CGM), lab results, and environmental variables.
   Data Type
     Mix of raw and processed data depending on domain; harmonized across three collection sites under a standardized acquisition protocol.
   Sampling Strategies
     - Strategies
         - Stratified recruitment to achieve approximately equal distribution across diabetes severity.
       Is Representative
         - Not fully representative in early releases due to ongoing enrollment.
       Why Not Representative
         - Pilot and periodic releases may not achieve balanced distribution across groups while enrollment is ongoing.

Subpopulations

- Identification
    - Subpopulations are identifiable by diabetes severity, sex, race, and ethnicity (among others).
  Distribution
    - Recruitment targeted approximately equal distribution across diabetes severity; actual distributions in pilot/periodic releases may vary during ongoing enrollment.

Distribution Formats

- Description
    - Access via FAIRhub data portal; public subset (non-sensitive) downloadable under Health Data License; controlled-access components available via data use agreement.
    - Data modalities include tabular (surveys, labs, clinical measures), images (retinal), physiological signals (ECG), and time series (CGM); specific file formats documented per domain.

Distribution Dates

- Description
    - 2024 11 08 (version 2.0.0; DOI
        10.60775/fairhub.2)
    - 2024 05 03 (version 1.0.0; DOI
        10.60775/fairhub.1)

🔍

## Collection Process

How was the data acquired?

ID *
doi:10.60775/fairhub.2
Name
Flagship Dataset of Type 2 Diabetes from the AI-READI Project
Title
Flagship Dataset of Type 2 Diabetes from the AI-READI Project
Description
The AI-READI flagship dataset consists of multimodal data collected from individuals with and without Type 2 Diabetes Mellitus (T2DM), harmonized across three collection sites. The study was designed for AI/ML readiness, with recruitment aimed at approximately equal distribution across diabetes severity and a standardized acquisition protocol spanning multiple domains (survey data, physical measurements, clinical data, imaging, wearable device data, etc.). Public data (non-sensitive) are available under a Health Data License; full data (including sensitive elements) are available under a data use agreement. The dataset supports research into salutogenesis (pathways from disease to health) in T2DM and is versioned with periodic updates as enrollment continues.
Language
English
Page
https://doi.org/10.60775/fairhub.2
Keywords

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

Created By

- AI-READI Consortium

Issued
2024-11-08
Addressing Gaps

- Response
    Provides an ethically sourced, harmonized, multimodal dataset for T2DM that is not feasible to obtain from claims/EHR alone; recruitment targeted balanced distribution across diabetes severity.

Creators

Role | Name | ORCID | Affiliation
--- | --- | --- | ---
Contributor | AI-READI Consortium | - | AI-READI Consortium

Sampling Strategies

- Strategies
    - Stratified sampling by diabetes severity across three data collection sites under a harmonized protocol.
  Is Representative
    - Not fully representative in early releases due to ongoing enrollment.
  Why Not Representative
    - Interim releases may have imbalances across groups while recruitment continues.

Anomalies

- Description
    - Early releases (pilot/periodic) may not fully achieve intended balanced distributions across severity groups.

External Resources

- External Resources
    - Documentation portal for dataset details and domain-specific processing guidance (https://docs.aireadi.org/docs/2)
    - Some environmental exposure variables (e.g., traffic and accident reports) linked via participant geography may rely on external data sources.
  Restrictions
    - External data sources used for environmental variables may carry their own terms of use or licensing; consult source providers as applicable.

Confidential Elements

- Description
    - Elements under controlled access include 5-digit ZIP code, sex, race, ethnicity, genetic sequencing data, past health records, and medications.

Sensitive Elements

- Description
    - Health-related data including genetic data, medications, past health records.
    - Demographic attributes such as race and ethnicity.
    - Geolocation at 5-digit ZIP code granularity.

Is Deidentified

- Description
    - Publicly released subset excludes sensitive personal health data and is de-identified per project policies; controlled-access components include potentially re-identifiable elements and require a data use agreement.

Acquisition Methods

1. Description
     - Data comprise directly observed clinical measurements and device outputs (e.g., ECG, retinal imaging, CGM), participant-reported survey responses, and variables derived/harmonized for analysis.
   Was Directly Observed
     Yes (e.g., clinical measurements, imaging, device signals).
   Was Reported By Subjects
     Yes (e.g., surveys).
   Was Inferred Derived
     Yes (harmonized/derived variables for analysis across sites and domains).
   Was Validated Verified
     Collected under standardized, harmonized protocols across sites; domain-specific processing and quality steps are documented.

Collection Mechanisms

- Description
    - Standardized multi-site protocol using clinical procedures and hardware (e.g., ECG systems, retinal imaging cameras), wearable sensors (e.g., CGM), surveys, and harmonized data processing pipelines.

Data Collectors

- Description
    - Data collected and harmonized across three participating collection sites by AI-READI study teams.

Collection Timeframes

- Description
    - Enrollment and data collection are ongoing; Version 1.0.0 released 2024-05-03 (pilot), Version 2.0.0 released 2024-11-08, with periodic updates anticipated.

Preprocessing Strategies

- Description
    - Harmonization across sites; domain-specific preprocessing described in the documentation portal (file formats, data standards, metadata, and example outputs).

Cleaning Strategies

- Description
    - Domain-specific cleaning and quality control steps as described in the documentation; details vary by data modality.

Labeling Strategies

- Description
    - Domain-specific annotation/labeling (e.g., clinical assessments, test results) documented per modality.

Maintainers

- Description
    - AI-READI Consortium (dataset stewardship and documentation).
    - FAIRhub data portal (hosting and access workflows).

🚀

## Uses

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

Tasks

- Response
    AI/ML method development and analysis for T2DM using multimodal clinical, imaging, wearable, and survey data.

Other Tasks

- Description
    - Multimodal representation learning; longitudinal modeling for glycemic control; risk stratification; phenotype discovery; causal inference and prognostic modeling in T2DM.

Future Use Impacts

- Description
    - Interim imbalance across groups in pilot/periodic releases and the inclusion/exclusion of sensitive attributes across access tiers may affect fairness and generalizability. Users should assess representativeness and potential bias before deployment and implement mitigations as needed.

Discouraged Uses

- Description
    - Any attempt to re-identify participants or to use the dataset outside the scope of the Health Data License or data use agreement terms.

License And Use Terms

Description
  - Publicly available, non-sensitive data are distributed under a Health Data License.
  - Full dataset access (including sensitive data such as ZIP code, genetic data, health records, and medications) requires entering into a data use agreement.

📤

## Distribution

How will the dataset be distributed?

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

Description
  - Prior versions remain available on the FAIRhub portal (e.g., v1.0.0 and v2.0.0), with documentation aligned to each dataset version.

🔄

## Maintenance

How will the dataset be maintained?

Version
2.0.0
Updates

Description
  - Periodic updates planned as enrollment continues and additional data become available; major releases are versioned on the FAIRhub portal with corresponding documentation.

Generated on 2025-11-09 10:17:35 using Bridge2AI Data Sheets Schema