Project Summary: The goals of the parent PREcision Care In Cardiac ArrEst - ICECAP (PRECICECAP) study are to discover novel biomarker signatures after cardiac arrest that predict treatment responsiveness and long-term recovery. Cardiac arrest is a major public health problem with high morbidity and mortality. Improving survival and functional recovery are critical public health objectives. We hypothesize that not all patients are identical and that through innovative, multi-parametric data- driven approaches we will be able to identify novel signatures to identify distinct patient subgroups. The PRECICECAP analysis plan allows us to meet our study needs but is not intrinsically generalizable or usable by others. An ever-growing number of NIH-funded projects amass and analyze related datasets from hospitalized patients and develop their own custom solutions. Such an ad hoc approach is costly, inefficient, and threatens research rigor and reproducibility. The objective of this supplement to PRECICECAP is to develop a freely-available software platform that allows artificial intelligence/machine learning (AI/ML) analysis of complex neurocritical care data and to provide a curated dataset from PRECICECAP ready for AI/ML. The knowledge learned here will be applied to help achieve NIH goals for modernizing the biomedical research data ecosystem by developing a software product that can handle AI/ML on this type of complex neurocritical care data. It will also allow the sharing of a cleaned, annotated comprehensive data set. Through a thoughtful collaboration between clinician investigators, data scientists and industry, we will take NIH-supported data from the PRECICECAP study and make it broadly available and easily usable. The project will deliver an important software tool that can be used by others conducting similar research, advancing the NIH’s mission to make complex data FAIR (Findable, Accessible, Interoperable, and Reusable). We will develop a series of modular functions (for example, to support data harmonization, annotation or visualization) that permit users to graphically construct processing pipelines maximizing automation where appropriate and allowing facile interaction with data when needed. We will develop a user-friendly dashboard interface to allow individual study sites and coordinating hubs to understand complex data, inspect key meta-data features, and identify potential errors. Modular design facilitates dynamic configuration of AI/ML architectures within the same interface, allowing individual modules to combine synergistically to maximize efficiency and reproducibility. The result will facilitate an open, wide collaboration between scientists using similar data.