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added NWB conversion tool description to the JOSS manuscript
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| Original file line number | Diff line number | Diff line change |
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| @software{vispy, | ||
| author = {Luke Campagnola and | ||
| Eric Larson and | ||
| Almar Klein and | ||
| David Hoese and | ||
| Siddharth and | ||
| Cyrille Rossant and | ||
| Adam Griffiths and | ||
| Nicolas P. Rougier and | ||
| asnt and | ||
| Kai Mühlbauer and | ||
| Alexander Taylor and | ||
| MSS and | ||
| Talley Lambert and | ||
| sylm21 and | ||
| Alex J. Champandard and | ||
| Max Hunter and | ||
| Thomas Robitaille and | ||
| Mustafa Furkan Kaptan and | ||
| Elliott Sales de Andrade and | ||
| Karl Czajkowski and | ||
| Lorenzo Gaifas and | ||
| Alessandro Bacchini and | ||
| Guillaume Favelier and | ||
| Etienne Combrisson and | ||
| ThenTech and | ||
| fschill and | ||
| Mark Harfouche and | ||
| Michael Aye and | ||
| Casper van Elteren and | ||
| Cedric GESTES}, | ||
| title = {vispy/vispy: Version 0.11.0}, | ||
| month = jul, | ||
| year = 2022, | ||
| publisher = {Zenodo}, | ||
| version = {v0.11.0}, | ||
| doi = {10.5281/zenodo.6795163}, | ||
| url = {https://doi.org/10.5281/zenodo.6795163} | ||
| author = {Luke Campagnola and | ||
| Eric Larson and | ||
| Almar Klein and | ||
| David Hoese and | ||
| Siddharth and | ||
| Cyrille Rossant and | ||
| Adam Griffiths and | ||
| Nicolas P. Rougier and | ||
| asnt and | ||
| Kai Mühlbauer and | ||
| Alexander Taylor and | ||
| MSS and | ||
| Talley Lambert and | ||
| sylm21 and | ||
| Alex J. Champandard and | ||
| Max Hunter and | ||
| Thomas Robitaille and | ||
| Mustafa Furkan Kaptan and | ||
| Elliott Sales de Andrade and | ||
| Karl Czajkowski and | ||
| Lorenzo Gaifas and | ||
| Alessandro Bacchini and | ||
| Guillaume Favelier and | ||
| Etienne Combrisson and | ||
| ThenTech and | ||
| fschill and | ||
| Mark Harfouche and | ||
| Michael Aye and | ||
| Casper van Elteren and | ||
| Cedric GESTES}, | ||
| title = {vispy/vispy: Version 0.11.0}, | ||
| month = jul, | ||
| year = 2022, | ||
| publisher = {Zenodo}, | ||
| version = {v0.11.0}, | ||
| doi = {10.5281/zenodo.6795163}, | ||
| url = {https://doi.org/10.5281/zenodo.6795163} | ||
| } | ||
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| @article{bento, | ||
| abstract = {The study of naturalistic social behavior requires quantification of animals' interactions. This is generally done through manual annotation---a highly time-consuming and tedious process. Recent advances in computer vision enable tracking the pose (posture) of freely behaving animals. However, automatically and accurately classifying complex social behaviors remains technically challenging. We introduce the Mouse Action Recognition System (MARS), an automated pipeline for pose estimation and behavior quantification in pairs of freely interacting mice. We compare MARS's annotations to human annotations and find that MARS's pose estimation and behavior classification achieve human-level performance. We also release the pose and annotation datasets used to train MARS to serve as community benchmarks and resources. Finally, we introduce the Behavior Ensemble and Neural Trajectory Observatory (BENTO), a graphical user interface for analysis of multimodal neuroscience datasets. Together, MARS and BENTO provide an end-to-end pipeline for behavior data extraction and analysis in a package that is user-friendly and easily modifiable.}, | ||
| article_type = {journal}, | ||
| author = {Segalin, Cristina and Williams, Jalani and Karigo, Tomomi and Hui, May and Zelikowsky, Moriel and Sun, Jennifer J and Perona, Pietro and Anderson, David J and Kennedy, Ann}, | ||
| citation = {eLife 2021;10:e63720}, | ||
| abstract = {The study of naturalistic social behavior requires quantification of animals' interactions. This is generally done through manual annotation---a highly time-consuming and tedious process. Recent advances in computer vision enable tracking the pose (posture) of freely behaving animals. However, automatically and accurately classifying complex social behaviors remains technically challenging. We introduce the Mouse Action Recognition System (MARS), an automated pipeline for pose estimation and behavior quantification in pairs of freely interacting mice. We compare MARS's annotations to human annotations and find that MARS's pose estimation and behavior classification achieve human-level performance. We also release the pose and annotation datasets used to train MARS to serve as community benchmarks and resources. Finally, we introduce the Behavior Ensemble and Neural Trajectory Observatory (BENTO), a graphical user interface for analysis of multimodal neuroscience datasets. Together, MARS and BENTO provide an end-to-end pipeline for behavior data extraction and analysis in a package that is user-friendly and easily modifiable.}, | ||
| article_type = {journal}, | ||
| author = {Segalin, Cristina and Williams, Jalani and Karigo, Tomomi and Hui, May and Zelikowsky, Moriel and Sun, Jennifer J and Perona, Pietro and Anderson, David J and Kennedy, Ann}, | ||
| citation = {eLife 2021;10:e63720}, | ||
| date-modified = {2022-10-05 13:18:49 -0400}, | ||
| doi = {10.7554/eLife.63720}, | ||
| editor = {Berman, Gordon J and Wassum, Kate M and Gal, Asaf}, | ||
| issn = {2050-084X}, | ||
| journal = {eLife}, | ||
| keywords = {social behavior, pose estimation, machine learning, computer vision, microendoscopic imaging, software}, | ||
| month = {nov}, | ||
| pages = {e63720}, | ||
| pub_date = {2021-11-30}, | ||
| publisher = {eLife Sciences Publications, Ltd}, | ||
| title = {The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice}, | ||
| url = {https://doi.org/10.7554/eLife.63720}, | ||
| volume = 10, | ||
| year = 2021, | ||
| bdsk-url-1 = {https://doi.org/10.7554/eLife.63720}} | ||
| doi = {10.7554/eLife.63720}, | ||
| editor = {Berman, Gordon J and Wassum, Kate M and Gal, Asaf}, | ||
| issn = {2050-084X}, | ||
| journal = {eLife}, | ||
| keywords = {social behavior, pose estimation, machine learning, computer vision, microendoscopic imaging, software}, | ||
| month = {nov}, | ||
| pages = {e63720}, | ||
| pub_date = {2021-11-30}, | ||
| publisher = {eLife Sciences Publications, Ltd}, | ||
| title = {The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice}, | ||
| url = {https://doi.org/10.7554/eLife.63720}, | ||
| volume = 10, | ||
| year = 2021, | ||
| bdsk-url-1 = {https://doi.org/10.7554/eLife.63720} | ||
| } | ||
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| @misc{rastermap, | ||
| author = {C. Stringer and M. Pachitariu}, | ||
| title = {rastermap}, | ||
| year = {2020}, | ||
| author = {C. Stringer and M. Pachitariu}, | ||
| title = {rastermap}, | ||
| year = {2020}, | ||
| publisher = {GitHub}, | ||
| journal = {GitHub repository}, | ||
| url = {https://github.com/MouseLand/rastermap} | ||
| journal = {GitHub repository}, | ||
| url = {https://github.com/MouseLand/rastermap} | ||
| } | ||
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| @misc{vidio, | ||
| author = {J. Bohnslav}, | ||
| title = {VidIO: simple, performant video reading and writing in python}, | ||
| year = {2020}, | ||
| author = {J. Bohnslav}, | ||
| title = {VidIO: simple, performant video reading and writing in python}, | ||
| year = {2020}, | ||
| publisher = {GitHub}, | ||
| journal = {GitHub repository}, | ||
| url = {https://github.com/jbohnslav/vidio} | ||
| journal = {GitHub repository}, | ||
| url = {https://github.com/jbohnslav/vidio} | ||
| } | ||
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| @misc{umap, | ||
| doi = {10.48550/ARXIV.1802.03426}, | ||
| url = {https://arxiv.org/abs/1802.03426}, | ||
| author = {McInnes, Leland and Healy, John and Melville, James}, | ||
| keywords = {Machine Learning (stat.ML), Computational Geometry (cs.CG), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, | ||
| title = {UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction}, | ||
| doi = {10.48550/ARXIV.1802.03426}, | ||
| url = {https://arxiv.org/abs/1802.03426}, | ||
| author = {McInnes, Leland and Healy, John and Melville, James}, | ||
| keywords = {Machine Learning (stat.ML), Computational Geometry (cs.CG), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, | ||
| title = {UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction}, | ||
| publisher = {arXiv}, | ||
| year = {2018}, | ||
| year = {2018}, | ||
| copyright = {arXiv.org perpetual, non-exclusive license} | ||
| } | ||
|
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| @misc{petrucco_2020_3925903, | ||
| author = {Petrucco, Luigi}, | ||
| title = {Mouse head schema}, | ||
| month = jul, | ||
| year = 2020, | ||
| publisher = {Zenodo}, | ||
| doi = {10.5281/zenodo.3925903}, | ||
| url = {https://doi.org/10.5281/zenodo.3925903} | ||
| } | ||
| author = {Petrucco, Luigi}, | ||
| title = {Mouse head schema}, | ||
| month = jul, | ||
| year = 2020, | ||
| publisher = {Zenodo}, | ||
| doi = {10.5281/zenodo.3925903}, | ||
| url = {https://doi.org/10.5281/zenodo.3925903} | ||
| } | ||
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| @article{NWB, | ||
| article_type = {journal}, | ||
| title = {The Neurodata Without Borders ecosystem for neurophysiological data science}, | ||
| author = {Rübel, Oliver and Tritt, Andrew and Ly, Ryan and Dichter, Benjamin K and Ghosh, Satrajit and Niu, Lawrence and Baker, Pamela and Soltesz, Ivan and Ng, Lydia and Svoboda, Karel and Frank, Loren and Bouchard, Kristofer E}, | ||
| editor = {Colgin, Laura L and Jadhav, Shantanu P}, | ||
| volume = 11, | ||
| year = 2022, | ||
| month = {oct}, | ||
| pub_date = {2022-10-04}, | ||
| pages = {e78362}, | ||
| citation = {eLife 2022;11:e78362}, | ||
| doi = {10.7554/eLife.78362}, | ||
| url = {https://doi.org/10.7554/eLife.78362}, | ||
| abstract = {The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB’s impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.}, | ||
| keywords = {Neurophysiology, data ecosystem, data language, data standard, FAIR data, archive}, | ||
| journal = {eLife}, | ||
| issn = {2050-084X}, | ||
| publisher = {eLife Sciences Publications, Ltd} | ||
| } |
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