# Data Science Core

> **NIH NIH U19** · UNIVERSITY OF ROCHESTER · 2022 · $405,788

## Abstract

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.

## Key facts

- **NIH application ID:** 10516499
- **Project number:** 1U19NS128613-01
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** Douglas H Kelley
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $405,788
- **Award type:** 1
- **Project period:** 2022-08-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10516499

## Citation

> US National Institutes of Health, RePORTER application 10516499, Data Science Core (1U19NS128613-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10516499. Licensed CC0.

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