Data Science Core

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Abstract, Data Science Core The goal of this proposal is to characterize and quantify how neural circuits control cerebrospinal fluid (CSF) flow and solute clearance during sleep and wake, in both mice and humans. Achieving that goal will require combining multimodal data from mathematical models and various experiments in both species. The Data Science Core will provide the essential infrastructure and produce innovative data methods to enable powerful synergy among the Projects. First, because the Projects will produce enormous data sets, and because the data will be multimodal (MRI, two-photon imaging, simulation results, EEG, and more), productive collaboration will require careful attention to storing, organizing, processing, analyzing, and internally sharing the data. Aim 1 is to provide data infrastructure and staffing for seamless integration of all Projects, for synergistic code development and sharing, and for rapid analysis of multimodal data via efficient workflows, always leveraging existing best practices. Second, discovering the causal links among neural control, CSF flow, and solute clearance will require tools and analyses that do not yet exist. Aim 2 is to build transformative tools leveraging quantitative, data-driven methods to measure CSF flow, its drivers, their neural control mechanisms, and the resulting efflux. Third, the novel data and tools produced in Projects 1-4 will have potential to advance the field, enabling a range of new scientific questions, especially if they are disseminated widely. Aim 3 is to document the software tools we develop, annotate the data we produce, and share both publicly. The Data Science Core will build on existing methods and tools previously developed by the co-PIs, take advantage of best practices in the field, and combine the expertise of experienced data scientists to build novel tools. Documented code will be shared via GitHub. We will use a two-tiered system for sharing data internally, in which each Project will leverage their existing data workflows and keep some data locally; meanwhile, data shared among Projects will be stored on the BlueHive supercomputer at the University of Rochester, available to all U19 participants. We will develop a clear organizational structure for shared data, storing metadata in sidecar files and implementing a searchable database to facilitate collaboration and interaction among the Projects. We will keep a searchable stock list of tools provided by the Virus Core, along with delivery times and histories. Annotated data will be shared publicly via repositories upon publication. A full-time data scientist will be employed to lead day-to-day Core activities, along with two PhD students.