# DataJoint SciOps: A Managed Service for Neuroscience Data Workflows

> **NIH NIH R44** · VATHES INC. · 2022 · $1,084,634

## Abstract

Project summary
This SBIR proposal aims to address current challenges in data-driven neuroscience by implementing DataJoint
SciOps: a commercial service to help research labs implement computational workflows for data-intensive science
experiments. This turn-key service will organize secure data pipelines and automate analysis jobs based on scalable
cloud infrastructure while keeping the entire process transparent and reproducible. Progress in neuroscience relies
on analyzing vast amounts of complex data recorded by new generations of neurotechnologies. To analyze this data,
research teams develop advanced algorithms and share them as open-source software. These software toolchains
require advanced computing infrastructure and operations, posing a set of engineering hurdles to manage the
particular experiment workflows for data entry, acquisition, analysis, sharing, and publishing. DataJoint SciOps
is made possible by the DataJoint Elements program (NIH Grant U24 NS116470), which provides a collection of
community-curated software modules for building standardized computational workflows. These designs integrate
best-in-class open-source analysis software from leading research teams and provide integrations with neuroscience
infrastructure projects. DataJoint SciOps will effectively serve as the commercial extension of DataJoint Elements
by providing computing infrastructure, hosting, and a managed service with subject-matter expert support and
customization services.
This Direct-to-Phase II commercialization project will develop and validate a comprehensive managed service for
executing data-centric neuroscience projects with robust automated processes for data management and analysis
(Aim 1). A cloud-based software-as-a-service platform will streamline the service to enable scaling to hundreds of
labs through standardization, self-service, and process automation (Aim 2). In the process, DataJoint will partner
with Johns Hopkins University's Applied Physics Lab to integrate the platform with neuroinformatics resources and
provide collaboration interfaces (Aim 3). Jointly, the teams will ensure the transparency and reproducibility of the
managed workflows and integrate it with other data infrastructure programs in the U.S. and internationally.
With several thousand neuroscience research groups seeking to adopt advanced neurotechnology instruments and
analysis tools, the commercially operated DataJoint SciOps service will lower the technological and organizational
barriers for efficient and reproducible research.
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## Key facts

- **NIH application ID:** 10547509
- **Project number:** 1R44NS129492-01
- **Recipient organization:** VATHES INC.
- **Principal Investigator:** Dimitri Yatsenko
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,084,634
- **Award type:** 1
- **Project period:** 2022-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10547509

## Citation

> US National Institutes of Health, RePORTER application 10547509, DataJoint SciOps: A Managed Service for Neuroscience Data Workflows (1R44NS129492-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10547509. Licensed CC0.

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