The AI-READI dataset consists of data collected from individuals with and without Type 2 Diabetes Mellitus (T2DM), harmonized across three data collection sites. It was designed with future AI/Machine Learning (AI/ML) studies in mind, including recruitment sampling procedures aimed at achieving approximately equal distribution of participants across diabetes severity, and a multi-domain data acquisition protocol (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. Some non-sensitive data will be publicly downloadable upon agreement with a license defining permitted uses. The full dataset is accessible via a Data Use Agreement (DUA). Public data include survey data, blood and urine lab results, fitness activity levels, clinical measurements (e.g., monofilament and cognitive function testing), retinal images, ECG, blood sugar levels, and environmental variables (e.g., home air quality). Controlled-access data include 5-digit ZIP code, sex, race, ethnicity, genetic sequencing data, past health records, medications, and traffic and accident reports. Enrollment is ongoing; pilot and periodic releases may not achieve balanced distributions across groups. Documentation versions correspond to dataset versions and include domain-level acquisition and processing details.