No single pre-defined target; resource intended for diverse AI/ML tasks.
Sampling Strategies
Is Sample
True
Is Random
False
Source Data
Prospective recruitment across three data collection sites.
Is Representative
Not explicitly; recruitment targeted diabetes severity balance.
Why Not Representative
Enrollment is ongoing; pilot and periodic releases may not be balanced across groups.
Strategies
Recruitment aimed at approximately equal distribution across diabetes severity.
Identification
People with T2DM
People without T2DM
Diabetes severity strata
Distribution
Recruitment aimed at approximately equal distribution across diabetes severity; pilot/updates may not be balanced.
Description
Public dataset downloadable under an agreement-defined license; full dataset accessible via Data Use Agreement (DUA) through FAIRhub data portal.
Description
Pilot release completed; periodic updates planned (versions referenced include v1.0.0 and v2.0.0 in documentation).
🔍
Collection Process
How was the data acquired?
ai-readi-flagship-t2dm
AI-READI Flagship Dataset of Type 2 Diabetes
AI-READI Dataset
A harmonized, multi-modal dataset collected across three data collection sites from individuals with and without Type 2 Diabetes Mellitus (T2DM). The dataset was designed to enable future AI/Machine Learning studies, with recruitment and sampling aimed at approximately equal distribution across diabetes severity and a standardized data acquisition protocol spanning multiple domains (survey data, physical measurements, clinical data, imaging data, wearable device data, etc.). The goal is to better understand salutogenesis (the pathway from disease to health) in T2DM. A public dataset (non-sensitive) is downloadable under an agreement-defined license; the full dataset, which includes sensitive elements, is available via a data use agreement (DUA). Enrollment is ongoing; the pilot data release and subsequent updates may not achieve balanced distribution across all groups.
AI
Machine Learning
Type 2 Diabetes
T2DM
Multi-modal
Survey data
Clinical measurements
Imaging data
Retinal images
ECG
Wearable devices
Blood glucose
Laboratory results
Environmental data
Data harmonization
FAIR
AI-READI Project
Response
Provides an AI/ML-ready, harmonized, multi-domain cohort resource that enables analyses not feasible with existing sources such as claims or EHR data alone.
Role
Name
ORCID
Affiliation
Contributor
-
AI-READI Project
External Resources
Dataset landing and access via FAIRhub data portal (documentation references this portal).
Future Guarantees
Not specified.
Archival
Not specified.
Restrictions
Full dataset requires entering into a Data Use Agreement (DUA).
Description
Dataset includes sensitive personal health data under controlled access (via DUA).
Description
5-digit ZIP code
Sex
Race
Ethnicity
Genetic sequencing data
Past health records
Medications
Traffic and accident reports
Description
Multi-domain protocol including direct measurements, surveys, imaging, and device data.
Data include directly observed, participant-reported, and derived elements.
Was Directly Observed
True
Was Reported By Subjects
True
Was Inferred Derived
True
Was Validated Verified
Not specified; harmonized protocol across three sites.
Description
Standardized clinical examinations and measurements.
Surveys/questionnaires.
Imaging (e.g., retinal photography).
Wearable/device data collection.
Laboratory assays (blood and urine).
Description
Data collected and harmonized across three data collection sites (site personnel not specified).
Description
Pilot study phase; enrollment is ongoing with periodic data release updates.
Description
Documentation highlights AI-readiness and ethical practices; specific IRB details not provided on this page.
Description
Documentation provides domain-specific processing details (file formats, standards, metadata, example outputs) per data domain.
Description
Domain sections describe data processing and harmonization; specific cleaning steps not detailed on this page.
Description
Not a labeled benchmark; domain documentation may include derived variables/annotations where applicable.
Description
AI-READI Project (contact via project documentation "Contact Us"; GitHub repository referenced for docs).
Description
Public release excludes sensitive personal health information; controlled-access data include quasi-identifiers and sensitive clinical/genomic elements.
Downstream AI/ML analyses across multiple domains (e.g., modeling from survey, clinical, lab, imaging, wearable, and environmental signals); not limited to tasks feasible with claims/EHR alone.
Description
Multimodal risk prediction, phenotype discovery, causal inference, and digital biomarker research in T2DM and metabolic health.
Description
Ongoing enrollment and potential imbalance in early releases may affect model fairness/generalization across demographic and severity groups.
Description
Some non-sensitive data are publicly available for download upon agreeing to a license that defines permitted use.
Full dataset requires execution of a Data Use Agreement (DUA).
📤
Distribution
How will the dataset be distributed?
Description
Documentation provides a version selector to navigate datasets/documentation versions (e.g., v1.0.0 and v2.0.0).
🔄
Maintenance
How will the dataset be maintained?
2.0.0
2025-09-08
Description
Pilot data release with periodic updates to subsequent releases; documentation supports version navigation (e.g., v1.0.0, v2.0.0).
Generated on 2025-11-09 10:17:35 using Bridge2AI Data Sheets Schema