Spyglass: a data analysis framework for reproducible and shareable neuroscience research

Kavli Affiliate: Loren Frank

| Authors: Kyu Hyun Lee, Eric L. Denovellis, Ryan Ly, Jeremy Magland, Jeff Soules, Alison E Comrie, Daniel P Gramling, Jennifer A Guidera, Rhino Nevers, Philip Adenekan, Chris Brozdowski, Samuel R Bray, Emily Monroe, Ji Hyun Bak, Michael Coulter, Xulu Sun, Emrey Broyles, Donghoon Shin, Sharon Chiang, Cristofer Holobetz, Andrew Tritt, Oliver Ruebel, Thinh Nguyen, Dimitri Yatsenko, Joshua Chu, Caleb Kemere, Samuel Garcia, Alessio Buccino and Loren M Frank

| Summary:

Abstract Sharing data and reproducing scientific results are essential for progress in neuroscience, but the community lacks the tools to do this easily for large datasets and results obtained from intricate, multi-step analysis procedures. To address this issue, we created Spyglass, an open-source software framework designed to promote the shareability and reproducibility of data analysis in neuroscience. Spyglass integrates standardized formats with reliable open-source tools, offering a comprehensive solution for managing neurophysiological and behavioral data. It provides well-defined and reproducible pipelines for analyzing electrophysiology data, including core functions like spike sorting. In addition, Spyglass simplifies collaboration by enabling the sharing of final and intermediate results across custom, complex, multi-step pipelines as well as web-based visualizations. Here we demonstrate these features and showcase the potential of Spyglass to enable findable, accessible, interoperable, and reusable (FAIR) data management and analysis by applying advanced state space decoding algorithms to publicly available data from multiple laboratories.

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