# Spyglass

![Figure 1](./images/fig1.png)

**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 of Spyglass include:

- **Standardized data storage** - Spyglass uses the open-source
    [Neurodata Without Borders: Neurophysiology (NWB:N)](https://www.nwb.org/)
    format to ingest and store processed data. NWB:N is a standard set by the
    BRAIN Initiative for neurophysiological data
    ([Rübel et al., 2022](https://doi.org/10.7554/elife.78362)).
- **Reproducible analysis** - Spyglass uses [DataJoint](https://datajoint.com/)
    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](https://github.com/SpikeInterface/spikeinterface),
    [Ghostipy](https://github.com/kemerelab/ghostipy), and
    [DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) for common analysis
    tasks. These packages are well-documented and have active developer
    communities.
- **Interactive data visualization** - Spyglass uses
    [figurl](https://github.com/flatironinstitute/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](https://github.com/flatironinstitute/kachery-cloud), 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](./installation.md) and
[tutorials](./notebooks/index.md) to help you get started with Spyglass. We
recommend running the notebooks yourself. They can be downloaded from GitHub
[here](https://github.com/LorenFrankLab/spyglass).

## Diving Deeper

The [API Reference](./api/index.md) provides a detailed description of all the
tables and class functions in Spyglass via python docstrings. Potential
contributors should also read the [Developer Guide](./contribute.md). Those
interested in in hosting a Spyglass instance for their own data should read the
[database management guide](./misc/database_management.md).

We have a series of additional docs under the [misc](./misc/index.md) folder
that may be helpful. Our [changelog](./CHANGELOG.md) highlights the changes that
have been made to Spyglass over time and the [copyright](./LICENSE.md) 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](https://doi.org/10.1101/2024.01.25.577295 ).

*\* Equal contribution*

See paper related code [here](https://github.com/LorenFrankLab/spyglass-paper).

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