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Automatically convert neurophysiology data to NWB

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Table of Contents

About

NeuroConv is a Python package for converting neurophysiology data in a variety of proprietary formats to the Neurodata Without Borders (NWB) standard.

Features:

  • Reads data from 50 popular neurophysiology data formats and writes to NWB using best practices.
  • Extracts relevant metadata from each format.
  • Handles large data volume by reading datasets piece-wise.
  • Minimizes the size of the NWB files by automatically applying chunking and lossless compression.
  • Supports ensembles of multiple data streams, and supports common methods for temporal alignment of streams.

Installation

We always recommend installing and running Python packages in a clean environment. One way to do this is via conda environments:

conda create --name <give the environment a name> --python <choose a version of Python to use>
conda activate <environment name>

To install the latest stable release of neuroconv though PyPI, run:

pip install neuroconv

To install the current unreleased main branch (requires git to be installed in your environment, such was via conda install git), run:

pip install git+https://github.com/catalystneuro/neuroconv.git@main

NeuroConv also supports a variety of extra dependencies that can be specified inside square brackets, such as

pip install "neuroconv[openephys, dandi]"

which will then install extra dependencies related to reading OpenEphys data as well as the usage of the DANDI CLI (such as automatic upload to the DANDI Archive).

You can read more about these options in the main installation guide.

Documentation

See our ReadTheDocs page for full documentation, including a gallery of all supported formats.

Citing NeuroConv

If you use NeuroConv in your research, please cite our paper:

Mayorquin, H., Baker, C., Adkisson-Floro, P., Weigl, S., Trapani, A., Tauffer, L., Rübel, O., & Dichter, B. (2025). NeuroConv: Streamlining Neurophysiology Data Conversion to the NWB Standard. Proceedings of the 24th Python in Science Conference (SciPy 2025). https://doi.org/10.25080/cehj4257

BibTeX

@inproceedings{mayorquin2025neuroconv,
  title={NeuroConv: Streamlining Neurophysiology Data Conversion to the NWB Standard},
  author={Mayorquin, Heberto and Baker, Cody and Adkisson-Floro, Paul and Weigl, Szonja and Trapani, Alessandra and Tauffer, Luiz and R\"ubel, Oliver and Dichter, Benjamin},
  booktitle={Proceedings of the 24th Python in Science Conference},
  year={2025},
  month={July},
  doi={10.25080/cehj4257}
}

License

NeuroConv is distributed under the BSD3 License. See LICENSE for more information.

Project links

Data verified by PyPI on Feb 13, 2026
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Key dates

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  • Released:
Newer release available (0.10.2)

3 maintainers

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Avatar for bendichter from gravatar.com bendichter Avatar for h.mayorquin from gravatar.com h.mayorquin Avatar for pauladkisson from gravatar.com pauladkisson

Credits

Author: Cody Baker, Szonja Weigl, Heberto Mayorquin, Paul Adkisson-Floro, Luiz Tauffer, Alessandra Trapani

GitHub Statistics

Data verified by PyPI on Feb 13, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

BSD License (BSD 3-Clause License)

Requires

Python >=3.10

Provides Extra

dandi compressors aws csv excel text audio sleap deeplabcut fictrac video lightningpose medpc behavior ecephys-minimal alphaomega axon axona biocam blackrock cellexplorer edf intan kilosort maxwell mcsraw mearec neuralynx neuroscope openephys phy plexon plexon2 spike2 spikegadgets spikeglx tdt whitematter ecephys abf icephys image fiber-photometry ophys-minimal brukertiff caiman cnmfe extract minian femtonics hdf5 hdf5imaging inscopix micromanagertiff miniscope sbx scanbox scanimage scanimage-legacy sima suite2p tdt-fp thor tiff ophys full

Tags

nwb NeurodataWithoutBorders

Classifiers

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