| Name | spyglass-neuro JSON |
| Version |
0.5.2
JSON |
| download |
| home_page | None |
| Summary | Neuroscience data analysis framework for reproducible research |
| upload_time | 2024-04-22 23:06:04 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.10,>=3.9 |
| license | Copyright (c) 2020-present Loren Frank
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
COPYRIGHT HOLDERS BEsq LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
data analysis
datajoint
electrophysiology
kachery
neuroscience
nwb
reproducible
research
sortingview
spike sorting
spikeinterface
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# spyglass
[](https://github.com/LorenFrankLab/spyglass/actions/workflows/workflow.yml)
[](https://badge.fury.io/py/spyglass-neuro)
[Demo](https://spyglass.hhmi.2i2c.cloud/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FLorenFrankLab%2Fspyglass-demo&urlpath=lab%2Ftree%2Fspyglass-demo%2Fnotebooks%2F01_Insert_Data.ipynb&branch=main) | [Installation](https://lorenfranklab.github.io/spyglass/latest/installation/) | [Docs](https://lorenfranklab.github.io/spyglass/) | [Tutorials](https://github.com/LorenFrankLab/spyglass/tree/master/notebooks) | [Citation](#citation)
`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.
Try out a demo [here](https://spyglass.hhmi.2i2c.cloud/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FLorenFrankLab%2Fspyglass-demo&urlpath=lab%2Ftree%2Fspyglass-demo%2Fnotebooks%2F01_Insert_Data.ipynb&branch=main)!
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.
Documentation can be found at -
[https://lorenfranklab.github.io/spyglass/](https://lorenfranklab.github.io/spyglass/)
## Installation
For installation instructions see -
[https://lorenfranklab.github.io/spyglass/latest/installation/](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](https://github.com/LorenFrankLab/spyglass/tree/master/notebooks)
directory. We strongly recommend opening them in the context of `jupyterlab`.
## Contributing
See the
[Developer's Note](https://lorenfranklab.github.io/spyglass/latest/contribute/)
for contributing instructions found at -
[https://lorenfranklab.github.io/spyglass/latest/contribute/](https://lorenfranklab.github.io/spyglass/latest/contribute/)
## License/Copyright
License and Copyright notice can be found at
[https://lorenfranklab.github.io/spyglass/latest/LICENSE/](https://lorenfranklab.github.io/spyglass/latest/LICENSE/)
## Citation
> 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., Broyles, E., Shin, D., Chiang, S., Holobetz, C., 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).
Raw data
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"description": "# spyglass\n\n[](https://github.com/LorenFrankLab/spyglass/actions/workflows/workflow.yml)\n[](https://badge.fury.io/py/spyglass-neuro)\n\n\n\n[Demo](https://spyglass.hhmi.2i2c.cloud/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FLorenFrankLab%2Fspyglass-demo&urlpath=lab%2Ftree%2Fspyglass-demo%2Fnotebooks%2F01_Insert_Data.ipynb&branch=main) | [Installation](https://lorenfranklab.github.io/spyglass/latest/installation/) | [Docs](https://lorenfranklab.github.io/spyglass/) | [Tutorials](https://github.com/LorenFrankLab/spyglass/tree/master/notebooks) | [Citation](#citation)\n\n`spyglass` is a data analysis framework that facilitates the storage, analysis,\nvisualization, and sharing of neuroscience data to support reproducible\nresearch. It is designed to be interoperable with the NWB format and integrates\nopen-source tools into a coherent framework.\n\nTry out a demo [here](https://spyglass.hhmi.2i2c.cloud/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FLorenFrankLab%2Fspyglass-demo&urlpath=lab%2Ftree%2Fspyglass-demo%2Fnotebooks%2F01_Insert_Data.ipynb&branch=main)!\n\nFeatures of Spyglass include:\n\n- **Standardized data storage** - Spyglass uses the open-source\n [Neurodata Without Borders: Neurophysiology (NWB:N)](https://www.nwb.org/)\n format to ingest and store processed data. NWB:N is a standard set by the\n BRAIN Initiative for neurophysiological data\n ([R\u00fcbel et al., 2022](https://doi.org/10.7554/elife.78362)).\n- **Reproducible analysis** - Spyglass uses [DataJoint](https://datajoint.com/)\n to ensure that all analysis is reproducible. DataJoint is a data management\n system that automatically tracks dependencies between data and analysis\n code. This ensures that all analysis is reproducible and that the results\n are automatically updated when the data or analysis code changes.\n- **Common analysis tools** - Spyglass provides easy usage of the open-source\n packages [SpikeInterface](https://github.com/SpikeInterface/spikeinterface),\n [Ghostipy](https://github.com/kemerelab/ghostipy), and\n [DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) for common analysis\n tasks. These packages are well-documented and have active developer\n communities.\n- **Interactive data visualization** - Spyglass uses\n [figurl](https://github.com/flatironinstitute/figurl) to create interactive\n data visualizations that can be shared with collaborators and the broader\n community. These visualizations are hosted on the web and can be viewed in\n any modern web browser. The interactivity allows users to explore the data\n and analysis results in detail.\n- **Sharing results** - Spyglass enables sharing of data and analysis results\n via [Kachery](https://github.com/flatironinstitute/kachery-cloud), a\n decentralized content addressable data sharing platform. Kachery Cloud\n allows users to access the database and pull data and analysis results\n directly to their local machine.\n- **Pipeline versioning** - Processing and analysis of data in neuroscience is\n often dynamic, requiring new features. Spyglass uses *Merge tables* to\n ensure that analysis pipelines can be versioned. This allows users to easily\n use and compare results from different versions of the analysis pipeline\n while retaining the ability to access previously generated results.\n- **Cautious Delete** - Spyglass uses a `cautious delete` feature to ensure that\n data is not accidentally deleted by other users. When a user deletes data,\n Spyglass will first check to see if the data belongs to another team of\n users. This enables teams of users to work collaboratively on the same\n database without worrying about accidentally deleting each other's data.\n\nDocumentation can be found at -\n[https://lorenfranklab.github.io/spyglass/](https://lorenfranklab.github.io/spyglass/)\n\n## Installation\n\nFor installation instructions see -\n[https://lorenfranklab.github.io/spyglass/latest/installation/](https://lorenfranklab.github.io/spyglass/latest/installation/)\n\n## Tutorials\n\nThe tutorials for `spyglass` is currently in the form of Jupyter Notebooks and\ncan be found in the\n[notebooks](https://github.com/LorenFrankLab/spyglass/tree/master/notebooks)\ndirectory. We strongly recommend opening them in the context of `jupyterlab`.\n\n## Contributing\n\nSee the\n[Developer's Note](https://lorenfranklab.github.io/spyglass/latest/contribute/)\nfor contributing instructions found at -\n[https://lorenfranklab.github.io/spyglass/latest/contribute/](https://lorenfranklab.github.io/spyglass/latest/contribute/)\n\n## License/Copyright\n\nLicense and Copyright notice can be found at\n[https://lorenfranklab.github.io/spyglass/latest/LICENSE/](https://lorenfranklab.github.io/spyglass/latest/LICENSE/)\n\n## Citation\n\n> 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., Broyles, E., Shin, D., Chiang, S., Holobetz, C., Tritt, A., R\u00fcbel, 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).\n\n*\\* Equal contribution*\n\nSee paper related code [here](https://github.com/LorenFrankLab/spyglass-paper).\n",
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