What NeuroData does

NeuroData is an academic research collective led by Principal Investigator Joshua Vogelstein, PhD, operating within the Johns Hopkins University Department of Biomedical Engineering in Baltimore, Maryland. Its stated mission is to understand and improve animal and machine intelligences worldwide by designing, building, studying, and applying statistical machine learning and big data science techniques. The lab combines neuroscience, statistics, and computer science research, with extensive collaborations with experimental neuroscientists at multiple scales and across international institutions in England, France, Germany, and at Argonne National Laboratory.

The collective's core output is an open-source software ecosystem covering graph statistics (GraSPy/graspologic), multivariate hypothesis testing (hyppo), progressive/lifelong learning (ProgLearn), random forest methods (SPORF), supervised dimensionality reduction (LOL), image registration (CloudReg), MR-based connectome estimation (m2g), scalable graph analytics (FlashGraph/FlashX), and NUMA-optimized k-means (knor). Supporting data infrastructure includes the Open Connectome Project, MRI Cloud, Zebromes, and ndcloud, a commercial-cloud deployment of the lab's tools. The lab also operates active training programs, including MSE/PhD/dual-degree pathways and the NeuroData Design course, and disseminates research through peer-reviewed publications, arXiv/bioRxiv preprints, and recurring workshops (NeuroNex, BigNeuro, NeuroReproducibility Hackashop, Neurostorm, Global Brain Workshop, Brains and Bits).

NeuroData's business model is non-commercial. All code is released under the MIT license and all data is open access, with distribution via GitHub, PyPI, conda, and CRAN. The organization is funded primarily through research grants from agencies including NSF and NIH, with no paid customer base, no commercial pricing, and no disclosed venture capital. Customer/operating segments are the academic neuroscience and machine learning research community, graduate students, and downstream data scientists who self-serve the open-source tooling.

Name

NeuroData

Legal name

NeuroData

Website

https://neurodata.io

Company type

Private

Founded year

2011

Operating status

Operating

Headcount range

11–50 employees

Short description

NeuroData is an academic research collective at Johns Hopkins University that develops open-source statistical machine learning and big data science software (GraSPy, hyppo, ProgLearn, SPORF, CloudReg, m2g) and open-access connectome datasets for the global neuroscience and machine learning research community.

Ownership category

akta.pro rank

Product category

Scientific Computing Software for Neuroscience

NAICS

Computer Systems Design and Related Services (5415), Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (5182)

SIC

Services-Prepackaged Software (7372), Services-Computer Programming, Data Processing, Etc. (7370)

akta.pro primary industry

Bioinformatics & Multi-omics Analysis Software (HLAGAJAD)

Keywords

• Connectomics analysis software
• Open-source machine learning
• Neuroscience data analytics
• Statistical graph analytics
• Brain imaging informatics

Headquarters

HQ city

Pasadena

HQ country

United States

HQ region

North America

Offices2 records

Markets served

GTM type

B2B

Offering type

Software

Cost components

Personnel, Technology or R&D, Operations, Infrastructure, Marketing or Sales

Revenue model

1. Grant Funding: Primarily funded through research grants from NSF, NIH, and other funding agencies. The lab maintains active grant funding through multiple programs.

Pricing tiers

Go-to-market motion2 records

Distribution channels3 records

Marketing channels4 records

Core offering

NeuroData develops and distributes an open-source software ecosystem for neuroscience and machine learning research, centered on statistical machine learning, big-data analytics, and connectomics. Its deliverables include Python packages such as graspologic/GraSPy (graph statistics), hyppo (multivariate/non-parametric hypothesis testing including Multiscale Graph Correlation), ProgLearn (progressive/lifelong learning), SPORF (random forests), LOL (dimensionality reduction), and CloudReg (image registration), together with open-access data platforms like the Open Connectome Project, MRI Cloud, and Zebromes, all made freely available under MIT-licensed open-source terms.

Product overview

NeuroData is a research collective offering an open-source software ecosystem alongside open-access data projects for understanding animal and machine intelligences. The product portfolio centers on statistical machine learning and big data science tools, including the core Python packages hyppo (multivariate hypothesis testing), ProgLearn (lifelong learning), graspovic/GraSPy (graph statistics), SPORF (random forests), LOL (dimensionality reduction), and CloudReg (image registration), deployed via ndcloud and supported by data platforms including the Open Connectome Project, MRI Cloud, and Zebromes. Additional tools include FlashGraph/FlashX (scalable analytics), knor (k-means clustering), and MEDA (matrix analysis). The ecosystem is community-built and integrates multiple open-source projects designed for neuroscience research, connectomics, and machine learning applications.

Differentiator

Problem solved

Functional benefit

Brands

• Open Connectome Project: Open source software stack of Web-services that store, analyze and visualize large imaging datasets for neuroscience research.
• ProgLearn
• GraSPy
• hyppo
• graspologic

Products and services

• Open Connectome Project Open-access data platform offering Web-services to store, analyze, and visualize large brain imaging datasets, integrated with NeuroData's open-source tooling for neuroscience research.
• ProgLearn (Progressive Learning) Open-source Python package implementing representation ensembling algorithms for synergistic lifelong learning, enabling models to improve performance across many disparate sequential tasks without catastrophic forgetting.
• hyppo Open-source Python package providing multivariate hypothesis testing algorithms, including Multiscale Graph Correlation (MGC), for universally consistent testing on high-dimensional and non-Euclidean data.
• graspologic (GraSPy) Open-source Python package of graph statistics utilities and algorithms for the processing and analysis of graphs, particularly aimed at statistical connectomics.
• SPORF (Sparse Projection Oblique Randomer Forests) Open-source improved random forest algorithm providing better accuracy and scaling than prior implementations across a standard suite of more than 100 benchmark problems.
• CloudReg Open-source non-linear affine and deformable image registration tool designed for neuroimaging data.
• LOL (Linear Optimal Low-rank projection) Open-source supervised dimensionality reduction method using linear optimal low-rank projection to improve classification performance on high-dimensional data.
• m2g (MR Graph Analysis) Open-source tool that uses diffusion MRI data from individual subjects to estimate human connectomes reliably and at scale.
• ndcloud (NeuroData Cloud) Cloud deployment platform that hosts NeuroData's open-source tools and the Open Connectome Project, providing scalable access via the commercial cloud ecosystem.
• FlashGraph (FlashX) Open-source scalable analytics tool for big-data analytics on graphs, part of the FlashX big-data analytics tools for graphs and matrices deployed at scale.
• knor (NUMA-Optimized K-means) Open-source, highly optimized library for computing k-means in parallel, with accelerations for NUMA architectures.
• MEDA (Matrix Exploratory Data Analysis) Open-source software implementing the top 10 recommended analyses and visualizations for matrix-valued data.
• VESICLE Open-source synapse detection framework for analyzing serial electron microscopy data (now subsumed by VESICLE-CNN-2).
• MRI Cloud Open-access MRI brain imaging data project providing curated neuroimaging datasets to the neuroscience research community.
• Zebromes Open-access zebrafish brain imaging data project supporting connectomics research.
• Neurodata Notebooks Collection of Jupyter notebooks created by NeuroData lab members to support reproducible research and analysis workflows.
• Decision Forests vs. Deep Networks (DFDN) Open research resource providing conceptual and empirical comparisons of decision forests and deep networks across tabular, vision, and audition data domains.

Quantifiable outcome

• Open-source contributions to SciPy, igraph, neuroglancer, C-PAC, and other major projects

Named customers1 record

Segments3 records

Ideal customer profiles2 records

Technology focussed Yes

API detail

Has API

No

API docs

API detail

Core technology

AI maturity

App detail

AI capability9 records

Feature9 records

Partnerships

Eight partnerships are on record, tiered core, major and minor.

• Johns Hopkins University - Biomedical EngineeringcoreStrategic or Co-development PartnerPrimary academic affiliation. JHU BME is rated #1 in the world by US News & World Reports. Lab operates within this department, recruiting graduate students through MSE, PhD, and dual degree programs.
• Experimental Neuroscience LabscoreStrategic or Co-development PartnerCollaborates extensively with experimental neuroscientists who design experiments and collect data. Students work with data acquisition institutions/labs for 1-2 weeks to months.
• International Research Labs (England, France, Germany)majorStrategic or Co-development PartnerCollaborates with labs in England, France, Germany for comparative connectomics and data acquisition research.
• Argonne National LaboratorymajorStrategic or Co-development PartnerCollaborates with researchers at Argonne National Laboratory on computational neuroscience projects.
• SciPy/Open Source CommunitymajorTechnology or IntegrationContributed state-of-the-art algorithms to SciPy including quadratic_assignment() and multiscale_graphcorr() (MGC) for hypothesis testing.
• NSF NeuroNexmajorStrategic or Co-development PartnerNSF-funded research network for neuroscience building collaborative research infrastructure.
• Kavli Neuroscience Discovery InstitutemajorStrategic or Co-development PartnerHosts Global Brain Workshop and other events at Johns Hopkins Kavli Neuroscience Discovery Institute.
• Open Synaptome ProjectminorStrategic or Co-development PartnerCollaborative project on single-synapse analysis of diverse populations of CNS synapses.

Scale indicators3 records

Recent moves6 records

Expansion highlights6 records

Others

• NetworkX: Widely-used open-source Python library for graph/network analysis. Compares as adjacent open-source tooling that NeuroData's graspologic extends with statistical methods for connectomics.
• scikit-learn: Dominant open-source Python ML library. Compares as a community-driven, open-source scientific Python project that NeuroData's packages (hyppo, ProgLearn, SPORF) complement and sometimes contribute into.
• International Neuroinformatics Coordinating Facility (INCF): International organization coordinating standards, infrastructure, and training for neuroscience data analysis. Compares as an ecosystem-level peer — NeuroData's open-source stack and workshop program serve overlapping scientific communities.
• Cosyne community: Leading computational and systems neuroscience conference/community where NeuroData's PI has been spotlighted. Compares as the academic community and venue ecosystem within which NeuroData competes for visibility and talent.

Broad incumbents

• Janelia Research Campus (HHMI): HHMI's large neuroscience research campus producing connectomics datasets (e.g., FlyWire), open-source analysis tools, and computational methods. Comparable in open-science, connectomics, and tool-building, but at an order-of-magnitude larger scale.
• DeepMind Neuroscience Research: Industry AI lab with active neuroscience research using deep learning to model brain function and connectivity. Comparable at the methodological level (ML for neuroscience) but with vastly greater compute, headcount, and proprietary commercial framing.
• Allen Institute for Brain Science: Large-scale nonprofit brain-mapping initiative producing open datasets, atlases, and analysis tools for neuroscience. Directly comparable mission (open science in connectomics and brain data) but operates at vastly greater scale and budget as an established institution.

Direct peers

• Sebastian Seung Lab (Princeton): Academic research group focused on mapping neural connectomes and developing crowdsourced/ML analysis tools (e.g., Eyewire). Directly comparable in combining open citizen-science platforms, connectomics data, and statistical ML for neuroscience.
• Blue Brain Project (EPFL): EPFL-led effort to build digital reconstructions and simulations of mouse and human brain circuitry using statistical and ML methods. Comparable in combining open-source tooling with connectomics-focused research, though with stronger simulation emphasis than NeuroData.
• nilearn: Open-source Python package for neuroimaging data analysis (fMRI, structural MRI). Comparable as a community-built Python library for neuroscience data analysis with overlapping user base and similar permissive licensing model.

Market position

Strengths5 records

Weaknesses5 records

Competitive moat5 records

Key risks6 records

Key highlights7 records

Customer concentration

• Xx.com/neuro_data
• GitHubgithub.com/neurodata

Revenue estimate

Valuation estimate

Number of profiles

Profiles1 record

Funding overview

Funding rounds

Investors

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M&A

Investments

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Frequently asked questions about NeuroData