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.
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Composition
What do the instances represent?
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.
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.
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.
Flagship Dataset of Type 2 Diabetes from the AI-READI Project
Flagship Dataset of Type 2 Diabetes from the AI-READI Project
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.
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.
Role
Name
ORCID
Affiliation
Contributor
AI-READI Consortium
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AI-READI Consortium
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.
Description
Early releases (pilot/periodic) may not fully achieve intended balanced distributions across severity groups.
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.
Description
Elements under controlled access include 5-digit ZIP code, sex, race, ethnicity, genetic sequencing data, past health records, and medications.
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.
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.
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.
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.
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.
Description
Data collected and harmonized across three participating collection sites by AI-READI study teams.
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.
Description
Harmonization across sites; domain-specific preprocessing described in the documentation portal (file formats, data standards, metadata, and example outputs).
Description
Domain-specific cleaning and quality control steps as described in the documentation; details vary by data modality.
Description
Domain-specific annotation/labeling (e.g., clinical assessments, test results) documented per modality.
Description
AI-READI Consortium (dataset stewardship and documentation).
FAIRhub data portal (hosting and access workflows).
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Uses
What (other) tasks could the dataset be used for?
Response
AI/ML method development and analysis for T2DM using multimodal clinical, imaging, wearable, and survey data.
Description
Multimodal representation learning; longitudinal modeling for glycemic control; risk stratification; phenotype discovery; causal inference and prognostic modeling in T2DM.
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.
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.
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.
Prior versions remain available on the FAIRhub portal (e.g., v1.0.0 and v2.0.0), with documentation aligned to each dataset version.
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Maintenance
How will the dataset be maintained?
2.0.0
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