As part of the feasibility phase of the Translator program, we have developed a disease-agnostic framework and approach for openly exposing clinical data that have been integrated at the patient- and visit-level with environmental exposures data: the Integrated Clinical and Environmental Exposures Service (ICEES). We have validated ICEES and demonstrated the service’s ability to replicate and extend published findings on asthma, while also supporting open team science, accelerated translational discovery, and integration with the broader Translator ecosystem. This proposal aims to move ICEES from prototype to development via creation of an ICEES+ Knowledge Provider (KP). Specifically, we aim to address three major challenges that we have identified through research and development (R&D) of the prototype ICEES in an effort to improve the quality, value, and impact of query answers and assertions. Specific Aim 1. Advance the rigor of insights and assertions that ICEES provides. Our prototype ICEES currently provides the ability to dynamically define cohorts and conduct simple statistical associations to examine bivariate relationships between feature variables. Recently, we have identified an approach to extend the bivariate functionalities to support multivariate analysis of the data. For the proposed work, we will apply multivariate analyses, including traditional statistical methods (e.g., regression models) and machine learning methods (e.g., bayesian neural network models, variational autoencoder models), and systematically quantify the extent of data loss and analytic bounds when algorithms are imposed on the ICEES+ KP open application programming interface (API) versus the Institutional Review Board (IRB)– protected, fully identified, pre-binned, underlying integrated feature tables. The overall goal is to provide users with more rigorous insights and estimates of the robustness, validity, accuracy, and specificity of knowledge and assertions generated via the ICEES+ KP OpenAPI. Specific Aim 2. Address issues related to space–time and causality. Clinical and environmental data are inherently spatiotemporal, with observations or events that are contingent on space and time and may be causally related. For the proposed work, we will evaluate and implement technical approaches (e.g., ICEES+ design modifications), spatiotemporal statistical algorithms (e.g., conditional auto-regression), recurrent neural network models, and causal inference models. As part of this effort, we will derive insights from and contribute real-world evidence to support Causal Activity Models and Adverse Outcome Pathways. We also will explore approaches for incorporating into ICEES+ nationwide public data on school exposures—data that will allow us to begin to address patient mobility. Specific Aim 3. Evaluate the security of the ICEES+ KP to ensure that patient privacy is preserved as new capabilities are enabled. ImPACT is an NSF-funded package of tools and services that provides end-to-end infrastructure and support for privacy-assured research and computation on sensitive data. Over the award period, we will implement and evaluate ImPACT security protocols, focusing initially on application of the ImPACT secure multiparty computation (SMC) algorithm as a method to support secure multi-institutional sharing of data on rare diseases and events—a functionality that is not currently supported by ICEES. In addition, we will evaluate other ImPACT security protocols, working under the guidance of a security advisor and in the context of driving use cases and capabilities developed under Specific Aims1 and 2. Importantly, the project aims will be driven by three use cases and associated high-value queries designed to complement and extend our asthma-focused work on the prototype ICEES: (1) an asthma cohort from the Environmental Polymorphism Registry (EPR) at the National Institute for Environmental Health Sciences (NIEHS); (2) a primary ciliary disease cohort (PCD) from the UNC PCD Registry; and (3) a drug-induced liver injury (DILI) cohort from the National DILI Network. These use cases will invoke new diseases, new data types, new organ systems, new institutions, and new queries, thereby stress-testing the ICEES framework and approach and moving it from prototype to development as the ICEES+ KP.