# Data Science Resource Core

> **NIH NIH U19** · STANFORD UNIVERSITY · 2020 · $299,638

## Abstract

The Data Science Core, based at Stanford University, is the component through which the overall
management and sharing of experimental and simulation data will be conducted. The Data Science
Core will implement the Data Science Plan by developing an integrated software framework for data
storage, analysis, and simulation, and by closely collaborating with all team members to ensure that
their research needs are met in a timely manner. This effort will require a full-time data scientist that
will be responsible for implementing and overseeing the data management framework and ensuring its
effective use among team members. The data management framework, detailed in the data science
plan, is based on a common description and storage format for experimental datasets produced by the
proposed experiments in Projects 1-4, and will include adapted versions of the existing analysis
software or new analysis tools that support the common format, software tools to efficiently extract and
represent cellular and network properties from experimental time series, and a pipeline to use data
stored in these formats to constrain our large-scale neuronal network models in Project 5. The
principal data scientist will be responsible for collaboration with external organizations that develop
scientific data formats, such as Neurodata Without Borders and the HDF Group, in order to ensure
that best technical practices are followed in development of the storage format and support software,
and for effective dissemination to the broader scientific community of all software and data generated
by the projects via a resource such as Collaborative Research in Computational Neuroscience
(CRCNS). The Data Science Core will play an important part in achieving the overall goals of the
research projects by ensuring consistent use of analysis methods, facilitating data sharing among
team members, allowing direct comparison of the outcomes of experiments performed in different labs
under different conditions, and will accelerate the development of open-source software tools to help
increase reproducibility across research teams.

## Key facts

- **NIH application ID:** 9993611
- **Project number:** 5U19NS104590-04
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** IVAN SOLTESZ
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $299,638
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9993611

## Citation

> US National Institutes of Health, RePORTER application 9993611, Data Science Resource Core (5U19NS104590-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9993611. Licensed CC0.

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