Neuroscience data analysis framework for reproducible research
Project description
spyglass
spyglass is a data analysis framework that facilitates the storage, analysis,
visualization, and sharing of neuroscience data to support reproducible
research. It is designed to be interoperable with the NWB format and integrates
open-source tools into a coherent framework.
Documentation can be found at - https://lorenfranklab.github.io/spyglass/
Installation
For installation instructions see - https://lorenfranklab.github.io/spyglass/latest/installation/
Tutorials
The tutorials for spyglass is currently in the form of Jupyter Notebooks and
can be found in the
notebooks
directory. We strongly recommend opening them in the context of jupyterlab.
Contributing
See the Developer's Note for contributing instructions found at - https://lorenfranklab.github.io/spyglass/latest/contribute/
License/Copyright
License and Copyright notice can be found at https://lorenfranklab.github.io/spyglass/latest/LICENSE/
Citation
Kyu Hyun Lee, Eric Denovellis, Ryan Ly, Jeremy Magland, Jeff Soules, Alison Comrie, Jennifer Guidera, Rhino Nevers, Daniel Gramling, Philip Adenekan, Ji Hyun Bak, Emily Monroe, Andrew Tritt, Oliver Rübel, Thinh Nguyen, Dimitri Yatsenko, Joshua Chu, Caleb Kemere, Samuel Garcia, Alessio Buccino, Emily Aery Jones, Lisa Giocomo, and Loren Frank. Spyglass: A Data Analysis Framework for Reproducible and Shareable Neuroscience Research. Neuroscience Meeting Planner. San Diego, CA: Society for Neuroscience, 2022.
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