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Related Experiment Video

Updated: Jul 10, 2026

Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo
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NeuroConv: Streamlining Neurophysiology Data Conversion to the NWB Standard.

Heberto Mayorquin1, Cody Baker2, Paul Adkisson-Floro1

  • 1CatalystNeuro, Center for Open Neuroscience.

Proceedings of the Python in Science Conferences. Python in Science Conference
|July 9, 2026
PubMed
Summary

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This summary is machine-generated.

NeuroConv is an open-source Python library simplifying neurophysiology data conversion into the Neurodata Without Borders (NWB) format. This tool streamlines data sharing and reuse, promoting FAIR data practices in neuroscience research.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Data Science

Background:

  • Modern neurophysiology generates complex, multimodal datasets.
  • Standardized data formats are crucial for data sharing and reuse.
  • The Neurodata Without Borders (NWB) format addresses data standardization needs.
  • Data conversion to NWB faces challenges like format heterogeneity and metadata complexity.

Purpose of the Study:

  • To present NeuroConv, an open-source Python library for automating neurophysiology data conversion.
  • To address the bottleneck in data conversion caused by format heterogeneity and technical expertise.
  • To facilitate the adoption of FAIR data practices in neurophysiology.

Main Methods:

  • Developed an open-source Python library, NeuroConv, with a unified, modular architecture.
Keywords:
Data standardizationNeurodata without bordersNeurophysiologyScientific software

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  • Implemented format-specific DataInterfaces to abstract parsing complexity.
  • Integrated multi-stream Converters for heterogeneous data modalities.
  • Incorporated optimized writing strategies for large-scale datasets, including chunked operations and cloud storage.

    • Main Results:

    • NeuroConv automates the conversion of neurophysiology data from 47 distinct formats into NWB.
    • The library enables researchers to convert complex, multi-modal experimental sessions with minimal code.
    • NeuroConv preserves critical metadata and temporal alignment across recording systems.
    • Collaboration with over 50 neurophysiology labs informed the development.

    Conclusions:

    • NeuroConv removes technical barriers to NWB data conversion.
    • The library advances FAIR data practices in neurophysiology.
    • By facilitating data sharing and reuse, NeuroConv accelerates scientific discovery and reproducible research.