Synthetic AI-READI Dataset for T2DM Research

作者：Nicholas Jackson, Natalia Espinosa Dice, Chao Yan, Zhuohang Li, Xiaoqian Jiang, Aaron Lee, Bradley Malin · 发表于：Zenodo (CERN European Organization for Nuclear Research) · 年份：2026 · DOI：10.5281/zenodo.21497875 · 研究领域：Computer science、Information retrieval、Artificial intelligence、Data mining

This record archives a synthetic multimodal dataset for type 2 diabetes, derived from the AI-READI dataset (v3.0.0; https://doi.org/10.60775/fairhub.3). The synthetic data comprise three independently generated modules — retinal optical coherence tomography (OCT) images, retinal fundus photographs, and tabular clinical records — formatted to match the structure of the original AI-READI data. Because the data are entirely synthetic, they contain no records corresponding to real individuals and are intended to support method development, benchmarking, education, and reproducible research without the access constraints of the source dataset. This Zenodo record is the citable archival version of the dataset. The data are additionally available through Amazon Web Services Marketplace: https://aws.amazon.com/marketplace/pp/prodview-mxp3i7s3ugkxqFor details of the dataset's construction and validation, see the associated publication [citation / DOI to be added on acceptance].