Skip to content

Spyglass

Figure 1

Spyglass is an open-source software framework designed to offer reliable and reproducible analysis of neuroscience data and sharing of the results with collaborators and the broader community.

Features

Features of Spyglass include:

  • Standardized data storage - Spyglass uses the open-source Neurodata Without Borders: Neurophysiology (NWB:N) format to ingest and store processed data. NWB:N is a standard set by the BRAIN Initiative for neurophysiological data (Rübel et al., 2022).
  • Reproducible analysis - Spyglass uses DataJoint to ensure that all analysis is reproducible. DataJoint is a data management system that automatically tracks dependencies between data and analysis code. This ensures that all analysis is reproducible and that the results are automatically updated when the data or analysis code changes.
  • Common analysis tools - Spyglass provides easy usage of the open-source packages SpikeInterface, Ghostipy, and DeepLabCut for common analysis tasks. These packages are well-documented and have active developer communities.
  • Interactive data visualization - Spyglass uses figurl to create interactive data visualizations that can be shared with collaborators and the broader community. These visualizations are hosted on the web and can be viewed in any modern web browser. The interactivity allows users to explore the data and analysis results in detail.
  • Sharing results - Spyglass enables sharing of data and analysis results via Kachery, a decentralized content addressable data sharing platform. Kachery Cloud allows users to access the database and pull data and analysis results directly to their local machine.
  • Pipeline versioning - Processing and analysis of data in neuroscience is often dynamic, requiring new features. Spyglass uses Merge tables to ensure that analysis pipelines can be versioned. This allows users to easily use and compare results from different versions of the analysis pipeline while retaining the ability to access previously generated results.
  • Cautious Delete - Spyglass uses a cautious delete feature to ensure that data is not accidentally deleted by other users. When a user deletes data, Spyglass will first check to see if the data belongs to another team of users. This enables teams of users to work collaboratively on the same database without worrying about accidentally deleting each other's data.

Getting Started

This site hosts both installation instructions as part of our tutorials to help you get started with Spyglass. We recommend running the notebooks yourself. They can be downloaded from GitHub here.

Diving Deeper

The API Reference provides a detailed description of all the tables and class functions in Spyglass via Python docstrings.

To highlight some of the key features of Spyglass and some features added to DataJoint, we have a series of articles on Spyglass features.

Our developer guide provides an overview of development practices for either contributing to the project itself or setting up custom pipelines for your own analysis.

Our changelog highlights the changes that have been made to Spyglass over time and the copyright page contains license information.

Citing Spyglass

Lee, K.H.*, Denovellis, E.L.*, Ly, R., Magland, J., Soules, J., Comrie, A.E., Gramling, D.P., Guidera, J.A., Nevers, R., Adenekan, P., Brozdowski, C., Bray, S., Monroe, E., Bak, J.H., Coulter, M.E., Sun, X., Tritt, A., Rübel, O., Nguyen, T., Yatsenko, D., Chu, J., Kemere, C., Garcia, S., Buccino, A., Frank, L.M., 2024. Spyglass: a data analysis framework for reproducible and shareable neuroscience research. bioRxiv. 10.1101/2024.01.25.577295.

* Equal contribution

See paper related code here.