Project Summary/Abstract: We propose to build a Research Image Repository (RIR) to house large collections of biomedical imaging data. The RIR will include datasets produced locally at Washington University: The Connectome Coordination Facility (CCF) (which itself includes the Human Connectome Project (HCP) Young Adult study, The Lifespan related projects, the Disease related projects, and assorted HCP-related projects), The Knight Alzheimer Disease Research Center (ADRC), the Adolescent Brain Cognitive Development (ABCD) Study, The Comprehensive Neuro-Oncology Data Repository (CONDR), and the clinically-based PACS image repository. In addition, copies of external data collections such as the UK Biobank, The Alzheimer's Disease Neuroimaging Initiative (ADNI), and The Cancer Image Archive (TCIA) will be maintained. The RIR includes a data management software solution that will introduce many novel features (such as `data tagging' to enrich datasets, and advanced search features) and will allow us to leverage existing storage including the Center for High Performance Computing's (CHPC) 1.4PB of BeeGFS `scratch' storage, solid-state NVMe drives integrated into the compute nodes, and 10PB of ZFS-based storage. All storage will be presented to the user as a single file-system, while data will be migrated to different performance tiers based on the storage requirements of the datasets or processing algorithms. The RIR will be integrated into the CHPC for data processing. The proposal also includes two NVIDIA DGX A100 GPU servers providing state-of-the-art GPU- based processing power. The combination of high-quality, diverse sets of biomedical imaging data with next- generation computing power will have a transformative effect on biomedical imaging processing pipelines and nowhere will the effects be more profound than in the emerging field of Deep Learning for image processing.