# Data-Science Core

> **NIH NIH U19** · UNIVERSITY OF ROCHESTER · 2020 · $417,812

## Abstract

Project Summary
This application proposes to use theory-driven experimental design, with advanced techniques for neural
recording, data analysis, and computational modeling, to investigate the neural mechanisms, circuits, and
representations underlying the perceptual process of causal inference in space and time. The multidisciplinary
nature of the proposed work requires close collaboration among consortium members. The Data Science Core
will facilitate this collaboration and provide the tools necessary to handle and analyze the large-scale neural
data collected in the proposed experiments. To achieve these goals, the Data Science Core will rely on
existing infrastructure, open standards, and open-source software as much as possible. Aim 1 will establish a
unified data standard, and data exchange and storage infrastructure, using the architecture established by the
International Brain Laboratory, which stores metadata in a relational, searchable database, and experimental
and processed data on a separate file server. Github will enable joint development, exchange, and
documentation of the code underlying data preprocessing, processing, and analysis. To relate data to models,
voltages recorded experimentally must be transformed into standardized spike times and counts, without
artifacts or confounds. Aim 2 will develop a principled, transparent, and reproducible pipeline for this
preprocessing and apply it to all neurophysiological data generated in Projects B and C. The first stage will
eliminate electrical and behavioral artifacts and convert voltages into spike times and local field potentials. The
second stage will use a statistical model of neural activity to identify and label potential outliers. This pipeline
will produce annotated and cleaned data in a standardized format that can be used to perform reliable
analyses, model fitting, and hypothesis tests. Aim 3 will combine cutting-edge methods and convert them to
software tools that can be reliably applied to new data. Most of this effort will be applied to variants of latent-
state discovery techniques that jointly fit the influence of stimuli, model-driven hypothesized latent states, and
unobserved latent states such as slow fluctuations. The central work of this aim is to implement those tools,
help the team apply them to the data generated by the collaboration, and refine them for public use. Aim 4 is to
share the experimental data with the wider research community by uploading the relevant portions of the data
to public and freely accessible repositories. Code, documentation, and use cases will be made public on
Github. The use of standard data structures, open standards, and open-source software will ensure barrier-free
access, ease of use, and reproducibility for neuroscience researchers. With the help of a full-time data scientist
hired to manage these efforts, the Data Science Core will build on established data storage and analysis
standards and methods to produce cleaned and sta...

## Key facts

- **NIH application ID:** 10047609
- **Project number:** 1U19NS118246-01
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** Jan Drugowitsch
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $417,812
- **Award type:** 1
- **Project period:** 2020-08-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10047609

## Citation

> US National Institutes of Health, RePORTER application 10047609, Data-Science Core (1U19NS118246-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10047609. Licensed CC0.

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