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Dataset . 2018
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2018
License: CC BY
Data sources: ZENODO
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ZENODO
Dataset . 2018
License: CC BY
Data sources: Datacite
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DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

Authors: Mathis, Alexander; Mamidanna, Pranav; Cury, Kevin M.; Abe, Taiga; Murthy, Venkatesh N.; Mathis, Mackenzie Weygandt; Bethge, Matthias;

DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

Abstract

This data entry contains annotated mouse data from the DeepLabCut Nature Neuroscience paper. This data entry contains a public release of annotated mouse data from the DeepLabCut paper. The trail-tracking behavior is part of an investigation into odor guided navigation, where one or multiple wildtype (C57BL/6J) mice are running on a paper spool and following odor trails. These experiments were carried out by Alexander Mathis & Mackenzie Mathis in the Murthy lab at Harvard University. Data was recorded by two different cameras (640×480 pixels with Point Grey Firefly (FMVU-03MTM-CS), and at approximately 1,700×1,200 pixels with Grasshopper 3 4.1MP Mono USB3 Vision (CMOSIS CMV4000-3E12)) at 30 Hz. The latter images were cropped around mice to generate images that are approximately 800×800. Here we share 1066, frames from multiple experimental sessions observing 7 different mice. Pranav Mamidanna labeled the snout, the tip of the left and right ear as well as the base of the tail in the example images. The data is organized in DeepLabCut 2.0 project structure with images and annotations in the labeled-data folder. The names are pseudocodes indicating mouse id and session id, e.g. m4s1 = mouse 4 session 1. Code for loading, visualizing & training deep neural networks available at https://github.com/DeepLabCut/DeepLabCut.

{"references": ["Dataset associated with Mathis, A., Mamidanna, P., Cury, K.M. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat Neurosci 21, 1281\u20131289 (2018). https://doi.org/10.1038/s41593-018-0209-y"]}

Keywords

pose estimation; deep learning; deeplabcut; mouse; trail-tracking; open field

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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