DOI: 10.5281/zenodo.10618947
title: SuperAnimal-TopViewMouse-5K

description: Introduction

This dataset supports Ye et al. 2024 Nature Communications. Please cite this dataset and paper if you use this resource. Please also see Ye et al. 2024 for the full DataSheet that accompanies this download, including the meta data for how to use this data is you want to compare model results on benchmark tasks. Below is just a summary. Also see the dataset licensing below.

Data

SuperAnimal-TopViewMouse-5K includes the following datasets:

- CSI, BM, EPM, LDB, OFT See full details at (1) and in (2). 

- BlackMice See full details at (3).

- WhiteMice Courtesy of Prof. Sam Golden and Nastacia Goodwin. See details in SIMBA (4). TriMouse See full detailsat (5). 

- DLC-Openfield See full details at (6). 

- Kiehn-Lab-Openfield, Swimming, and treadmill Courtesy of Prof. OleKiehn, Dr. Jared Cregg, and Prof. Carmelo Bellardita; see details at (7). 

- MausHaus We collected video data from fivesingle-housed C57BL/6J male and female mice in an extended home cage, carried out in the laboratory of Mackenzie Mathisat Harvard University and also EPFL (temperature of housing was 20-25C, humidity 20-50%). Data were recorded at 30Hzwith 640 × 480 pixels resolution acquired with White Matter, LLC eV cameras. Annotators localized 26 keypoints across 322frames sampled from within DeepLabCut using the k-means clustering approach (8). All experimental procedures for micewere in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and approved bythe Harvard Institutional Animal Care and Use Committee (IACUC) (n=1 mouse), and by the Veterinary Office of the Cantonof Geneva (Switzerland; license GE01) (n=4 mice).

Here is an image with examples from the datasets, the distribution of images per dataset, and the keypoint guide.

Ethical Considerations

• Data was collected with IUCAC or other governmental approval. Each individual dataset used in training reports the ethics approval they obtained.

Caveats and Recommendations

• Please note that each training dataset was labeled by separate labs and different individuals, therefore while we map names to a unified pose vocabulary, there will be annotator bias in keypoint placement (See Ye et al. 2024 for our Supplementary Note on annotator bias).

• Note the dataset is primarily using C56Blk6/J mice and only some CD1 examples.

License

Modified MIT.

Copyright 2023-present by Mackenzie Mathis, Shaokai Ye, and contributors. 

Permission is hereby granted to you (hereafter "LICENSEE") a fully-paid, non-exclusive,and non-transferable license for academic, non-commercial purposes only (hereafter “LICENSE”)to use the "DATASET" subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantialportions of the Software:

This data or resulting software may not be used to harm any animal deliberately.

LICENSEE acknowledges that the DATASET is a research tool. THE DATASET 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 BE 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 DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET.

If this license is not appropriate for your application, please contact Prof. Mackenzie W. Mathis (mackenzie@post.harvard.edu) for a commercial use license.

Please cite Ye et al if you use this DATASET in your work.

References

1. Oliver Sturman, Lukas von Ziegler, Christa Schläppi, Furkan Akyol, Mattia Privitera, Daria Slominski, Christina Grimm, Laetitia Thieren, Valerio Zerbi, Benjamin Grewe, et al. Deep learning-based behavioral analysis reaches human accuracy and is capable of outperforming commercial solutions. Neuropsychopharmacology, 45(11):1942–1952, 2020.2. Lukas von Ziegler, Oliver Sturman, and Johannes Bohacek. Videos for deeplabcut, noldus ethovision X14 and TSE multi conditioning systems comparisons. https://doi.org/10.5281/zenodo.3608658. Zenodo, January 2020.3. Isaac Chang. Trained DeepLabCut model for tracking mouse in open field arena with topdown view. https://doi.org/10.5281/zenodo.3955216. Zenodo, July 2020.4. Simon RO Nilsson, Nastacia L. Goodwin, Jia Jie Choong, Sophia Hwang, Hayden R Wright, Zane C Norville, Xiaoyu Tong, Dayu Lin, Brandon S. Bentzley, Neir Eshel, Ryan J McLaughlin, and Sam A. Golden. Simple behavioral analysis (simba) – an open source toolkit for computer classification of complex social behaviors in experimental animals. bioRxiv, 2020.5. Jessy Lauer, Mu Zhou, Shaokai Ye, William Menegas, Steffen Schneider, Tanmay Nath, Mohammed Mostafizur Rahman, Valentina Di Santo, Daniel Soberanes, Guoping Feng, Venkatesh N. Murthy, George Lauder, Catherine Dulac, Mackenzie W. Mathis, and Alexander Mathis. Multi-animal pose estimation, identification and tracking with deeplabcut. Nature Methods, 19:496 – 504, 2022.6. Alexander Mathis, Pranav Mamidanna, Kevin M Cury, Taiga Abe, Venkatesh N Murthy, Mackenzie Weygandt Mathis, and Matthias Bethge. Deeplabcut: markerless pose estimation of user-defined body parts with deep learning. Nature neuroscience, 21:1281–1289, 2018.7. Jared M. Cregg, Roberto Leiras, Alexia Montalant, Paulina Wanken, Ian R. Wickersham, and Ole Kiehn. Brainstem neurons that command mammalian locomotor asymmetries. Nature neuroscience, 23:730 – 740, 2020