Our Mission
Neurophysiology data collection is exceptionally resource-intensive, requiring significant investment in skilled personnel, specialized equipment, and often involving animal subjects. Each dataset represents countless hours of careful experimentation and holds tremendous potential for advancing our understanding of the brain. Yet despite this value, sharing neurophysiology data remains a major challenge. Labs struggle with technical barriers in standardizing data formats, preserving critical metadata, and developing software that others can use. The result is redundant data collection across the field and isolated software development that limits scientific progress.
CatalystNeuro exists to solve this problem. We provide specialized data and software engineering expertise that enables labs to focus on scientific discovery rather than data infrastructure. By helping researchers implement data standards, develop maintainable software, and create efficient processing pipelines, we ensure that valuable neurophysiology data can be easily shared and reused. Our work accelerates the advancement of neuroscience by facilitating open science and collaboration across the global research community.
Who We Are
We are a team of Research Software Engineers specializing in open data and open software for neurophysiology research. Our international team brings together neuroscientists and software developers who are passionate about building the technical infrastructure that connects data producers, tool developers, and researchers across the neuroscience community. We understand both the scientific challenges and the engineering solutions needed to make neurophysiology data truly accessible.
Our Approach
CatalystNeuro is designed to complement academic labs by handling the data and software engineering challenges that often fall outside their core expertise. This allows researchers to concentrate on advancing and communicating scientific discoveries. Despite being a for-profit organization, we adhere to an open-source approach with permissive licensing for all software we develop, making our tools freely available to the entire scientific community. We build and maintain a comprehensive suite of open-source tools that enable labs to accelerate their analyses and streamline their data management processes.
Our Services
Data Standardization
In collaboration with NIH BRAIN Initiative-endorsed teams, we develop infrastructure that promotes collaboration across the neurophysiology community. We specialize in the Neurodata Without Borders (NWB) standard for neurophysiology data and work closely with the DANDI Archive, which provides free storage for terabytes of NWB-formatted neurophysiology data.
As core contributors to these projects, we create open-source software that makes these tools accessible:
- NWB Inspector: Scans NWB files for potential errors and best practice violations, generating comprehensive quality reports.
- NeuroConv: Automates conversion from dozens of proprietary neurophysiology data formats to NWB.
- NWB GUIDE: A graphical interface that provides a code-free solution for data conversion.
We partner with individual labs to develop customized solutions, offering both virtual and in-person training as well as contracts for tailored development work.
Software Engineering
We help labs integrate their existing analysis, visualization, and data management tools with the broader open science ecosystem via NWB and DANDI. Our team professionalizes research software through proper packaging, comprehensive testing, and thorough documentation—transforming prototype code into reliable, maintainable tools. We also design reproducible workflows for data processing and analysis, ensuring that research outputs can be shared and replicated across the scientific community.
AI in Neuroscience
We leverage artificial intelligence and machine learning to accelerate neuroscience research. Our services include developing AI agents to automate repetitive processing steps, curating datasets for building neural foundation models, and providing training in agentic code generation. These capabilities enable labs to harness modern AI tools to streamline their research workflows and extract new insights from their data.