Three-dimensional anatomic modeling and simulation (3D M&S) in cardiovascular (CV) disease have become a crucial component of treatment planning, medical device design, diagnosis, and FDA approval. Comprehensive, curated 3-D M&S databases are critical to enable grand challenges, and to advance model reduction, shape analysis, and deep learning for clinical application. However, large-scale open data curation involving 3-D M&S present unique challenges; simulations are data intensive, physics-based models are increasingly complex and highly resolved, heterogeneous solvers and data formats are employed by the community, and simulations require significant high-performance computing resources. Manually curating a large open-data repository, while ensuring the contents are verified and credible, is therefore intractable. We aim to overcome these challenges by developing broadly applicable automated curation data science to ensure model credibility and accuracy in 3-D M&S, leveraging our team’s expertise in CV simulation, uncertainty quantification, imaging science, and our existing open data and open source projects. Our team has extensive experience developing and curating open data and software resources. In 2013, we launched the Vascular Model Repository (VMR), providing 120 publicly-available datasets, including medical image data, anatomic vascular models, and blood flow simulation results, spanning numerous vascular anatomies and diseases. The VMR is compatible with SimVascular, the only fully open source platform providing state-of-the-art image-based blood flow modeling and analysis capability to the CV simulation community. We propose that novel curation science will enable the VMR to rapidly intake new data while automatically assessing model credibility, creating a unique resource to foster rigor and reproducibility in the CV disease community with broad application in 3D M&S. To accomplish these goals, we propose three specific aims: 1) Develop and validate automated curation methods to assess credibility of anatomic patient-specific models built from medical image data, 2) Develop and validate automated curation methods to assess credibility of 3D blood flow simulation results, 3) Disseminate the data curation suite and expanded VMR. The proposed research is significant and innovative because it will 1) enable rapid expansion of the repository by limiting curator intervention during data intake, leveraging compatibility with SimVascular, 2) increase model credibility in the CV simulation community, 3) apply novel supervised and unsupervised approaches to evaluate anatomic model fidelity, 4) leverage reduced order models for rapid assessment of complex 3D data. This project assembles a unique team of experts in cardiovascular simulation, the developers of SimVascular and creator of the VMR, a professional software engineer, and radiology technologists. We will build upon our successful track record of launching and supporting open source ...