User-ready tools and scalable workflows for INCLUDE datasets in the cloud: advancing brain imaging data management and analytics

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PROJECT SUMMARY With rapidly growing medical datasets and data repositories, there is a critical need for technical infrastructures to make data sharing and usability feasible. Cloud ecosystems provide computationally efficient resources for large-scale data handling and analytics. While cloud-based platforms have been developed for molecular and clinical data processing, there is no single end-to-end platform for processing biomedical imaging data. Moreover, there is no standardized system to enable interoperable work between molecular, clinical, and imaging data pipelines. We propose to (Aim 1) integrate existing open-source neuroimaging software into a centralized, cloud-based platform for brain imaging data (Flywheel). This will provide scalable, automated, and publicly accessible tools to process and analyze data from standard imaging modalities, and supply streamlined pipelines for standardization and privacy-protection (de-identification) for submission to long-term repositories. In addition, we will (Aim 2) establish data representation, ontology, and workflows to allow for interoperability between currently incompatible molecular and imaging systems (CAVATICA and Flywheel). This will allow the use of rich multi-modal datasets in scientific research on brain pathology and development. The present proposal leverages collaboration between inter-disciplinary teams of software developers and biomedical analysts and researchers, and their complementary expertise in computer science, cloud computing, and neuroimaging and molecular research. Overall, this project will provide the infrastructure and methods to support large-scale data hosting and management for the neuroscience and bioinformatics communities, which will support scientific efforts including multi-institutional research and clinical trials.