ABSTRACT The project, Integrated Stress Response in Human Islets During Early Type 1 Diabetes (T1D), hypothesizes that the activation of the integrated stress response and formation of stress granules is an early cellular response initiating β cell stress in T1D that determines cell survival and can be monitored in pre- and early-T1D individuals with minimal invasiveness. A multidisciplinary Team science approach is being taken to test this hypothesis, collecting a large suite of heterogenous data, such as mRNA, lipidomics, proteomics and immunologic measurements. Machine learning is being used to extract a multi-biomarker panel to aid in stratifying stress in human islets and translating these findings to individuals at-risk for T1D and new-onset T1D. Although we are formatting the multi-omics data for this specific machine learning task within the parent grant, the data being generated, as well as our data collected from prior collaborations, are not generally AI/ML-ready for general application of methods. They are however excellent candidates to be used as “flagship” datasets for AI/ML readiness, both to test novel AI/ML approaches to tackle data pre-processing challenges and to extract molecular signatures of T1D. These two gaps in analyses are the central themes of two aims. The first aim focuses on the generation of AI/ML ready omics datasets that are properly annotated to address challenges in sparsity and bias, such as imputation and batch correction. The second aim focuses AI/ML ready multi-omic datasets to enable new studies in using machine learning to elicit biomarkers and pathway-level molecular signatures from the data focused on standard AI/ML methods, as well as those specialized for small sample size. Dataset machine learning model cards will be utilized to better enable to AI/ML research communities to utilize these datasets in an efficient manner. For both aims there is a key focus on generating reusable software approaches to generate data packages that can be directly imported into the most common AI/ML packages and released to the AI/ML community through a variety of resources that enable feedback to continually improve and refine the AI/ML readiness software development plan.