Peer-reviewedCC BY 4.0eLife
Spyglass is a framework for reproducible and shareable neuroscience research
14 affiliations
- 1Department of Physiology, University of California, San Francisco, San Francisco, United States
- 2Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, United States
- 3Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, United States
- 4Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, United States
- 5Center for Computational Mathematics, Flatiron Institute, New York, United States
- 6Graduate Program in Neural and Behavioral Sciences, University of Tübingen, Tübingen, Germany
- 7UCSF-UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco, San Francisco, United States
- 8Medical Scientist Training Program, University of California, San Francisco, San Francisco, United States
- 9Department of Neurology, University of California, San Francisco, San Francisco, United States
- 10Sainsbury Wellcome Centre, University College London, London, United Kingdom
- 11DataJoint, Houston, United States
- 12Department of Electrical and Computer Engineering, Rice University, Houston, United States
- 13Centre de Recherche en Neuroscience de Lyon, CNRS, Bron, France
- 14Allen Institute for Brain Science, Seattle, United States
Abstract
Scientific progress depends on reliable and reproducible results. Progress can be accelerated when data are shared and re-analyzed to address new questions. Current approaches to storing and analyzing neural data involve bespoke formats and software that make replication and reuse of data difficult. To address these challenges, we created Spyglass, an open-source data management and analysis framework written in Python. Spyglass provides reproducible pipelines for common neuroscience analyses and sharing of raw data, intermediate analyses, and final results within and across labs. Spyglass uses the Neurodata Without Borders (NWB) standard and includes pipelines for spectral filtering, spike sorting, pose tracking, and neural decoding. Spyglass can be extended to apply existing and newly developed pipelines to datasets from multiple sources. We demonstrate these features in the context of a cross-laboratory replication by applying advanced state space decoding algorithms to publicly available data. New users can try out Spyglass on a Jupyter Hub hosted by HHMI and 2i2c: https://spyglass.hhmi.2i2c.cloud/.
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BibTeX
@article{lee2026spyglass,
title = {{Spyglass is a framework for reproducible and shareable neuroscience research}},
author = {Kyu Hyun Lee and Eric L Denovellis and Ryan Ly and Jeremy Magland and Jeff Soules and Alison E Comrie and Daniel P Gramling and Jennifer A Guidera and Rhino Nevers and Philip Adenekan and Chris Brozdowski and Samuel R Bray and Emily Monroe and Ji Hyun Bak and Michael E Coulter and Xulu Sun and Emrey Broyles and Donghoon Shin and Sharon Chiang and Cristofer Holobetz and Andrew Tritt and Oliver Rübel and Thinh Nguyen and Dimitri Yatsenko and Joshua Chu and Caleb Kemere and Samuel Garcia and Alessio Paolo Buccino and Loren Frank},
journal = {eLife},
year = {2026},
month = nov,
doi = {10.7554/elife.108089},
url = {https://elifesciences.org/articles/108089},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Spyglass is a framework for reproducible and shareable neuroscience research
AU - Kyu Hyun Lee
AU - Eric L Denovellis
AU - Ryan Ly
AU - Jeremy Magland
AU - Jeff Soules
AU - Alison E Comrie
AU - Daniel P Gramling
AU - Jennifer A Guidera
AU - Rhino Nevers
AU - Philip Adenekan
AU - Chris Brozdowski
AU - Samuel R Bray
AU - Emily Monroe
AU - Ji Hyun Bak
AU - Michael E Coulter
AU - Xulu Sun
AU - Emrey Broyles
AU - Donghoon Shin
AU - Sharon Chiang
AU - Cristofer Holobetz
AU - Andrew Tritt
AU - Oliver Rübel
AU - Thinh Nguyen
AU - Dimitri Yatsenko
AU - Joshua Chu
AU - Caleb Kemere
AU - Samuel Garcia
AU - Alessio Paolo Buccino
AU - Loren Frank
PY - 2026
DA - 2026/11/03
JO - eLife
DO - 10.7554/elife.108089
UR - https://elifesciences.org/articles/108089
AB - Scientific progress depends on reliable and reproducible results. Progress can be accelerated when data are shared and re-analyzed to address new questions. Current approaches to storing and analyzing neural data involve bespoke formats and software that make replication and reuse of data difficult. To address these challenges, we created Spyglass, an open-source data management and analysis framework written in Python. Spyglass provides reproducible pipelines for common neuroscience analyses and sharing of raw data, intermediate analyses, and final results within and across labs. Spyglass uses the Neurodata Without Borders (NWB) standard and includes pipelines for spectral filtering, spike sorting, pose tracking, and neural decoding. Spyglass can be extended to apply existing and newly developed pipelines to datasets from multiple sources. We demonstrate these features in the context of a cross-laboratory replication by applying advanced state space decoding algorithms to publicly available data. New users can try out Spyglass on a Jupyter Hub hosted by HHMI and 2i2c: https://spyglass.hhmi.2i2c.cloud/.
SN - 2050-084X
ER -