Project Summary The Johns Hopkins University (JHU) seeks to strengthen the coordination of innovative research and practice efforts in maternal health through collaboration with the National Institutes of Health (NIH) and their Implementing a Maternal Health and Pregnancy Outcomes Vision for Everyone (IMPROVE) initiative grantees. The overarching goals of this project are to establish and maintain a Maternal Health Data Innovation and Coordination Hub to support Maternal Health Research Centers of Excellence, and to facilitate the reuse of the data they generate. The project will be implemented by a multidisciplinary team of maternal health experts and biostatisticians at JHU’s Bloomberg School of Public Health, and informatics and data science specialists at JHU’s School of Medicine, with support from an Experts’ Bureau comprised of subject matter experts in health equity, bioethics, health economics, patient safety, patient and family engagement in research. Key project activities are to establish and maintain a secure, cloud-based coordination platform with controlled access, and a public-facing Data Hub website; develop common data elements using a modified Delphi approach; support the use of a common data model; provide data collection and analysis tools with integrated quality assurance workflows; provide support for statistical analyses using traditional and artificial intelligence/machine learning techniques; prepare and share data with NIH repositories; provide technical assistance and skills coaching, training, and professional development opportunities to Research Centers/IMPROVE grantees. Our proposal has technical and conceptual areas of innovation. Most notably, the proposed integration of the Data Hub with an existing research coordination platform with demonstrated feasibility -- JHU’s Precision Medicine Analytics Platform (PMAP). It utilizes the Observational Health Data Science and Informatics (OHDSI) open-source community and the Observational Medical Outcomes Partnership (OMOP), employed by large NIH-funded research. OMOP is based upon standard clinical terminologies; enables extraction, ingestion, collation of variables of interest into an observational research registry; and has the capability for data storage, security, analysis, and transfer among participating sites. Also innovative are the proposed training and career development opportunities, including tuition scholarships, data challenge awards, and a mentorship program. We anticipate that these activities will lead to short-term and intermediate outcomes (e.g. improved data science capabilities; generation of findable, accessible, interoperable, and reusable data), which, over the long-term, will advance research to improve maternal health outcomes and promote equity. Process and outcomes evaluations will ascertain the extent to which our project is successfully supporting Research Centers. Data science methods and findings from research projects will be disseminated...