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63 results for “Pose Estimation”
MODYS-video: 2D Human pose estimation data and Dyskinesia Impairment Scale scores from children and young adults with dyskinetic cerebral palsy
<p>The dataset contains the 2D coordinates in pixels of body landmarks (wrists, ankles, shoulders, hips, knees and ankles) extracted from 188 videos of 34 children with dyskinetic cerebral palsy using DeepLabCut [1] and appertaining clinical scores of the Dyskinesia Impairment Scale (DIS) [2].</p> <p>The videos were collected during the item “lying in rest” and “sitting in rest” of the DIS at three time points during a clinical trial on the effect of intrathecal baclofen [3]. Children had a mean age of 14y2m (SD 4.0), 26 were male. Their gross motor function classification system level ranged from IV-V and their manual ability classification system level from III-V. Original videos have length of 4-35 seconds with a resolution of 720x575 pixels and are sampled with 25 Hz. We added stick figures to complement the data for context and ease of understanding. They were created from the 2D coordinates that were extracted with a likelihood >0.8.</p> <p>Clinical scoring was performed by three trained experts (according to the DIS) on the original videos. Within the items “lying in rest” and “sitting in rest” the amplitude and duration of dystonia and choreoathetosis of the trunk, proximal right arm, proximal left arm, proximal right leg and proximal left leg are scored on a 0-4 ordinal scale and calculated towards a percentage score between 0-1.</p> <p>The dataset can be used in a machine learning approach to automatically assess dystonia and choreoathetosis of children with dyskinetic cerebral palsy using 2D coordinates of body points extracted from videos.</p> <p> </p> <p>References:</p> <p>1. Mathis, A., et al., DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat Neurosci, 2018. 21(9): p. 1281-1289.</p> <p>2. Monbaliu, E., et al., The dyskinesia Impairment Scale: a new instrument to measure dystonia and choreoathetosis in dyskinetic cerebral palsy. Dev Med Child Neurol, 2012. 54: p. 278-283.</p> <p>3. Bonouvrie, L.A., et al., The Effect of Intrathecal Baclofen in Dyskinetic Cerebral Palsy: The IDYS Trial. Ann Neurol, 2019. 86: p. 79-90.</p> <p> </p> <p> </p>
Tango Spacecraft Dataset for Monocular Pose Estimation
<p><strong>Reference Paper:</strong></p> <p><a href="https://doi.org/10.1016/j.actaastro.2023.01.012"><strong>M. Bechini, M. Lavagna, P. Lunghi, Dataset generation and validation for spacecraft pose estimation via monocular images processing, Acta Astronautica 204 (2023) 358–369</strong></a></p> <p><a href="https://www.researchgate.net/publication/361924362_Spacecraft_Pose_Estimation_via_Monocular_Image_Processing_Dataset_Generation_and_Validation">M. Bechini, P. Lunghi, M. Lavagna. "Spacecraft Pose Estimation via Monocular Image Processing: Dataset Generation and Validation". In 9th European Conference for Aeronautics and Aerospace Sciences (EUCASS)</a></p> <p><strong>General Description:</strong></p> <p>The "<em>Tango Spacecraft Dataset for Monocular Pose Estimation</em>" dataset here published should be used for relative pose estimation tasks. It is split into 30002 train images and 3002 test images representing the Tango spacecraft from Prisma mission, being the largest publicly available dataset of synthetic space-borne noise-free images tailored to pose estimation tasks (up to our knowledge). The label of each image gives relative quaternion (in scalar-last format) between Tango and the camera (hence the relative position of the target with respect to the camera in camera reference frame) and the relative position of Tango with respect to the camera in camera reference frame. More information on the dataset split and on the label format are reported below. </p> <p><strong>Images Information:</strong></p> <p>The dataset comprises 30002 synthetic grayscale images of Tango spacecraft from Prisma mission that serves as train set, while the test set is formed by 3002 synthetic grayscale images of Tango spacecraft from Prisma mission in PNG format. About 1/6 of the images both in the train and in the test set have a non-black background, obtained by rendering an Earth-like model in the raytracing process used to define the images reported. The images are noise-free to increase the flexibility of the dataset. The illumination direction of the spacecraft in the scene is uniformly distributed in the 3D space in agreement with the Sun position constraints. The dataset contains also a .txt file with the parameters of the camera used to generate the images.</p> <p><br> <strong>Labels Information:</strong></p> <p>Labels in the SPEED and SPEED+ dataset format are here provided in separated JSON files. The files are formatted per each image as in the following example:</p> <ul> <li> filename : tango_img_1 # name of the image to which the data are referred</li> <li> q_TRG2CAM : [qx qy qz qw] # relative quaternion from Target to Camera reference frame</li> <li> t_CAM2TRG : [x, y, z] # relative position of Tango with respect to the camera expressed in meters</li> </ul> <p>Notice that for making the usage of the dataset easier, both the training set and the test set are split in two folders containing the images with earth as background and without background.</p> <p><strong>VERSION CONTROL</strong></p> <ul> <li>v1.0: This version contains the dataset (both train and test) of full scale images with relative pose annotations. These images have width=height=1024 pixels. The position of tango with respect to the camera is randomly selected from a uniform distribution, but it is ensured the full visibility in all the images. </li> </ul> <p>Note: this dataset contains the same images of the <em>"Tango Spacecraft Wireframe Dataset Model for Line Segments Detection"</em> v2.0 full-scale (DOI: <a href="https://doi.org/10.5281/zenodo.6372848">https://doi.org/10.5281/zenodo.6372848</a>) and also "<em>Tango Spacecraft Dataset for Region of Interest Estimation and Semantic Segmentation</em>" v1.0 (DOI: <a href="https://doi.org/10.5281/zenodo.6507863">https://doi.org/10.5281/zenodo.6507863</a>) and they can be used together by combining the annotations of the relative pose and the ones of the reprojected wireframe model of Tango, with also the ones of the ROI. <strong>These three datasets give the most comprehensive dataset of space borne synthetic images ever published</strong> (up to our knowledge).</p>
I-MuPPET: Interactive Multi-Pigeon Pose Estimation and Tracking (Videos)
<p>This data entry contains the multi-pigeon video sequences from the <a href="https://urs-waldmann.github.io/i-muppet/"><strong>I-MuPPET project page</strong></a>.</p> <p>This data contains four video sequences showing one, two, three and four pigeons in our <a href="https://www.exc.uni-konstanz.de/collective-behaviour/research/facilities/imaging-barn/">imaging barn</a> at the <a href="https://www.exc.uni-konstanz.de/collective-behaviour/">Centre for the Advanced Study of Collective Behaviour</a>. The experiments were carried out by Hemal Naik, Máté Nagy, Fumihiro Kano and Iain D. Couzin and were approved by the Regierungspräsidium Freiburg under the permit number Az. 35-9185.81/G-19/107.</p> <p>Data was recorded by two Vicon Vue 2 cameras (1920x1080 pixels) at 50 Hz.</p> <p>Code for I-MuPPET available at <a href="https://github.com/urs-waldmann/i-muppet/">https://github.com/urs-waldmann/i-muppet/</a>.</p>
I-MuPPET: Interactive Multi-Pigeon Pose Estimation and Tracking (Tracking Benchmark)
<p>This data entry contains the multi-pigeon video sequences with ground truth for the quantitative tracking evaluation from the <a href="https://urs-waldmann.github.io/i-muppet/docs/muppet_paper.pdf"><strong>I-MuPPET GCPR 2022 paper</strong></a>.</p> <p>This data contains 24 video sequences showing up to four pigeons in our <a href="https://www.exc.uni-konstanz.de/collective-behaviour/research/facilities/imaging-barn/">imaging barn</a> at the <a href="https://www.exc.uni-konstanz.de/collective-behaviour/">Centre for the Advanced Study of Collective Behaviour</a>. The experiments were carried out by Hemal Naik, Máté Nagy, Fumihiro Kano and Iain D. Couzin and were approved by the Regierungspräsidium Freiburg under the permit number Az. 35-9185.81/G-19/107.</p> <p>Data was recorded by two Vicon Vue 2 cameras (1920x1080 pixels) at 50 Hz.</p> <p>The ground truth for the quantitative tracking evaluation was created by Urs Waldmann.</p> <p>Code for I-MuPPET available at <a href="https://github.com/urs-waldmann/i-muppet/">https://github.com/urs-waldmann/i-muppet/</a>.</p>
Supplementary materials for "Phonetic differences between affirmative and feedback head nods in German Sign Language (DGS): A pose estimation study"
<div> <pre>This is the supplementary data for the article "Phonetic differences between affirmative and feedback head nods in German Sign Language (DGS): A pose estimation study" by Anastasia Bauer, Anna Kuder, Marc Schulder and Job Schepens.<br><br>The supplementary data consists of three components, stored in separate directories:<br>- <code>annotations/</code>: The manual annotations of head nod categories, produced by Anna Kuder and Anastasia Bauer.<br>- <code>pose_analysis/</code>: Code and input/output files for the pose-based automatic analysis of phonetic attributes head nods, produced by Marc Schulder.<br>- <code>statistical_analysis/</code>: Code for the statistical analysis of the other two components and for the creation of related figures, produced by Job Schepens.<br><br>For further details, see the README files of the respective directories.</pre> </div>
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - Corner Clamp Part 1
<p>@article{schieber2024asdf,<br> title={ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation},<br> author={Schieber, Hannah and Li, Shiyu and Corell, Niklas and Beckerle, Philipp and Kreimeier, Julian and Roth, Daniel},<br> journal={arXiv preprint arXiv:2403.16400},<br> year={2024}<br>}</p>
Additional raw video and pose estimation data of top view open field mouse behavior recordings after diazepam injections
<p>This repository contains raw data for 32 different open field recordings of mice. These include top view raw video .mp4 files (Videos.zip and Videos_2.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from a single diazepam injection experiment at Roche, where animals were injected with saline, 1 mg/kg, 2 mg/kg or 3 mg/kg of diazepam. The METADATA_ROCHE.csv or METADATA_ROCHE.xlsx files contain all grouping variables and help linking the pose estimation files (located in data/) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how we have used this data.</p>
Additional raw video and pose estimation data of top view mouse behavior recordings (marble burying test, light-dark box, fear conditioning box) of acute and chronic stress models
<p>This repository contains raw data for 296 different behavioral recordings of mice (marble burying test, light-dark box, fear conditioning box). These include top view raw video .mp4 files (Videos.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from multiple different experiments. The METADATA.csv or METADATA.xlsx files contain all grouping variables and help linking the pose estimation files (located in multiple subfolders of /data) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how this data has be used by us.</p>
CRESENT: a CRatEr-baSed pose estimation datasEt for cisluNarlocated spacecrafT
<h3>CRESENT is first introduced in the paper “Robust Perspective-n-Crater for Crater-based Camera Pose Estimation” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.</h3> <p><strong>Overview:</strong></p> <p>This dataset contains images produced by The University of Adelaide using PANGU Planet Surface Simulation Software developed by the Space Technology Centre at the University of Dundee, Scotland.</p> <p>High-resolution lunar DEMs from the PDS data node were rendered in PANGU, and images were taken above the lunar surface at an altitude of 100km with varying angles off nadir to mimic the expected conditions of a lunar orbiter surface surveillance mission.</p> <p>The dataset contains images taken above four different surface regions on the Moon, each within a region of 45 degrees latitude and 45 degrees longitude.</p> <p><strong>Data organisation:</strong></p> <p>There are four root folders, each containing images produced under one of the four lunar regions:</p> <p><em>LDEM_x_yE_l_mN/</em></p> <p>where x and y are the latitude bounds (degrees) of the lunar region and l and m are the longitude bounds (degrees) of the lunar region, rendered in PANGU. Note that due to the high resolution of the DEMs, any region of the Moon that was outside these latitude and longitude bounds was not rendered (and the surface will appear cut off/black at the boundaries of these regions).</p> <p>Within each of the lunar region folders, there are seven subfolders:</p> <p><em>LDEM_x_yE_l_mN_float_60fov_1024_1024_ideg_off_nadir/</em></p> <p>where i is the viewing angle (degree) off nadir the image was taken at, where i is either 0, 10, 20, 30, 40, 50, or 60 degrees. Each subfolder contains a folder of images, a poses.csv file of ground truth poses, a calibration file and a file detailing the specifics of the rendered LDEM. Note that each image taken within each sub-directory of each lunar region folder will have the same number of files, each located at the same position in the Selenographic reference frame, but at different angles off nadir. For example,<em> LDEM_-90_-45E_0_45N/LDEM_-90_-45E_0_45N_float_60fov_1024_1024_0deg_off_nadir/</em> and <em>LDEM_-90_-45E_0_45N/LDEM_-90_-45E_0_45N_float_60fov_1024_1024_60deg_off_nadir/</em> will both have the same number of images in their <em>images/</em> subdirectory, and each image file number in these directories was taken at the same camera position, but at viewing angles of 0 degrees and 50 degrees off nadir, respectively.</p> <p>Each line in the poses file contains the X, Y, Z position (m) and the yaw, pitch, roll angles (degrees) of the camera in the selenographic reference frame. There are the same number of lines in the poses file as images in each subfolder, e.g., the first line of the poses file corresponds to the XYZ yaw pitch roll pose of the camera that generated <em>images/0.png</em>, the second line of the poses file corresponds to the pose of the camera of <em>images/1.png</em>, etc.</p> <p><strong>How to cite:</strong></p> <p>Users of this dataset are requested to cite the following paper.</p> <p>Reference String</p> <p><em>McLeod, S. et al. Robust Perspective-n-Crater for Crater-based Camera Pose Estimation in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (June 2024)</em></p> <p>Bibtex</p> <p><code>@InProceedings{Mcleod_2024_CVPR,</code></p> <p><code>author = {Mcleod, Sofia and Chng, Chee Kheng and Ono, Tatsuharu and Shimizu, Yuta and Hemmi, Ryodo and Holden, Lachlan and Rodda, Matthew and Dayoub, Feras and Miyamoto, Hirdy and Takahashi, Yukihiro and Kasai, Yasuko and Chin, Tat-Jun}, </code></p> <p><code>title = {Robust Perspective-n-Crater for Crater-based Camera Pose Estimation}, </code></p> <p><code>booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, </code></p> <p><code>month = {June}, </code></p> <p><code>year = {2024}, </code></p> <p><code>pages = {6760-6769} </code></p> <p><code>}</code></p> <p><strong>DISCLAIMER</strong>:</p> <p><em>The dataset provided is a lower resolution version of the original and is intended for academic research purposes only, consistent with the terms of the Open Access License that applies to the usage of this dataset. Please contact <a href="mailto:tat-jun.chin@adelaide.edu.au">tat-jun.chin@adelaide.edu.au</a> if you require the higher resolution versions of the dataset.</em></p>
I-MuPPET: Interactive Multi-Pigeon Pose Estimation and Tracking (Dataset)
<p>This data entry contains the annotated single pigeon data from the <a href="https://urs-waldmann.github.io/i-muppet/docs/muppet_paper.pdf"> <strong>I-MuPPET GCPR 2022 paper</strong></a>.</p> <p>This data contains our annotated single pigeon data with RGB images and seven distinct keypoint annotations. The experiments were carried out by Hemal Naik, Máté Nagy, Fumihiro Kano and Iain D. Couzin and were approved by the Regierungspräsidium Freiburg under the permit number Az. 35-9185.81/G-19/107.</p> <p>Data was recorded by two Vicon Vue 2 cameras (1920x1080 pixels) at 50 Hz, four Vantage 5 and 26 Vero 2.2 sensors.</p> <p>The dataset was originally created during the Ph.D. thesis work of Hemal Naik. The method to reproduce the setup and dataset can be found at <a href="https://mediatum.ub.tum.de/?id=1554403">https://mediatum.ub.tum.de/?id=1554403</a>. An updated version of this dataset with 4K resolution (3D-POP) is available at <a href="https://doi.org/10.17617/3.HPBBC7">https://doi.org/10.17617/3.HPBBC7</a>. The code base is available at <a href="https://github.com/alexhang212/dataset-3dpop"> https://github.com/alexhang212/dataset-3dpop</a>. The users can reproduce annotations or improve them by adding more key points.</p> <p>Code for I-MuPPET available at <a href="https://github.com/urs-waldmann/i-muppet/"> https://github.com/urs-waldmann/i-muppet/</a>.</p>
Early-life sleep disruption impairs subtle social behaviours in prairie voles: a pose-estimation study
<p><span>Early life sleep disruption (ELSD) has been shown to have long-lasting effects on social behaviour in adult prairie voles (Microtus ochrogaster), including impaired expression of pair bonding during partner preference testing. However, due to the limitations of manual behaviour tracking, the effects of ELSD across the time course of pair bonding have not yet been described, hindering our ability to trace mechanisms. Here, we used pose estimation to track prairie voles during opposite-sex cohabitation, the process leading to pair bonding. Male-female pairs were allowed to interact through a mesh divider in the home cage for 72 h, providing variables of body direction, distance-to-divider and locomotion speed. We found that control males displayed periodic patterns of body orientation towards females during cohabitation. In contrast, ELSD males showed reduced duration and ultradian periodicity of these body orientation behaviours towards females. Furthermore, in both sexes, ELSD altered spatial and temporal patterns of locomotion across the light/dark cycles of the 72-h recordings. This study allows a comprehensive behavioural assessment of the effects of ELSD on later life sociality and highlights subtle prairie vole behaviours. Our findings may shed light on neurodevelopmental disorders featuring sleep disruption and social deficits, such as autism spectrum disorders.</span></p>
Raw video and pose estimation data of top view open field mouse behavior recordings of acute and chronic stress models
<p>This repository contains raw data for 411 different open field recordings of mice. these include top view raw video .mp4 files (Videos.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from multiple different experiments. The METADATA.csv or METADATA.xlsx files contain all grouping variables and help linking the pose estimation files (located in multiple subfolders of /data) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how this data has be used by us.</p>
Raw video and pose estimation data of top view open field mouse behavior recordings after yohimbine injections
<p>This repository contains raw data for 32 different open field recordings of mice. these include top view raw video .mp4 files (Videos.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from a single yohimbine injection experiment at Roche, where animals were injected with saline, 1mg/kg,3mg/kg or 6mg/kg of yohimbine. The METADATA.csv or METADATA.xlsx files contain all grouping variables and help linking the pose estimation files (located in data/Yohimbine_Roche) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how we have used this data</p>
Dataset for: Estimating pregnancy rate from blubber progesterone levels of a blindly biopsied beluga population poses methodological, analytical and statistical challenges
<p class="MsoBodyText"><span>Beluga (<em>Delphinapterus leucas</em>) from the St. Lawrence Estuary, Canada, have been declining since the early 2000s, suggesting recruitment issues as a result of low fecundity, abnormal abortion rates or poor calf or juvenile survival. Pregnancy is difficult to observe in cetaceans, making the ground-truthing of pregnancy estimates in wild individuals challenging. Blubber progesterone concentrations were contrasted among 62 SLE beluga with a known reproductive state (i.e., pregnant, resting, parturient, and lactating females), that were found dead in 1997–2019. The suitability of a threshold obtained from decaying carcasses to assess reproductive state and pregnancy rate of freshly-dead or free-ranging and blindly-sampled beluga was examined using three statistical approaches and two datasets (135 freshly-harvested carcasses in Nunavik, and 65 biopsy-sampled SLE beluga). Progesterone concentrations in decaying carcasses were considerably higher in known-pregnant (mean </span><span>±</span><span> sd: </span><span>365 </span><span>±</span><span> 244 ng g<sup>-1</sup> of tissue) </span><span>than resting (</span><span>3.1 </span><span>±</span><span> 4.5 ng g<sup>-1</sup> of tissue</span><span>) or lactating (</span><span>38.4 </span><span>±</span><span> 100 ng g<sup>-1</sup> of tissue</span><span>) females. An approach based on statistical mixtures of distributions and a logistic regression was compared to the commonly-used, fixed threshold approach (here, 100 ng g<sup>-1</sup>) for discriminating pregnant from non-pregnant females. The error rate for classifying individuals of known reproductive status was the lowest for the fixed threshold and logistic regression approaches, but the mixture approach required limited <em>a priori</em> knowledge for clustering individuals of unknown pregnancy status. Mismatches in assignations occurred at lipid content <10% of sample weight. Our results emphasize the importance of reporting lipid contents and progesterone concentrations in both units (ng g<sup>-1 </sup>of tissue and ng g<sup>-1</sup> of lipid) when sample mass is low. By highlighting ways to circumvent potential biases in field sampling associated with capturability of different segments of a population, this study also enhances the usefulness of the technique for estimating pregnancy rate of free-ranging population. </span></p>
Rethinking pose estimation in crowds: overcoming the detection information bottleneck and ambiguity
<p>#############</p><p>Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity, ICCV 2023</p><p>#############</p><p> </p><p>Authors: Zhou, Mu and Stoffl, Lucas and Mathis, Mackenzie Weygandt and Mathis, Alexander</p><p>Affiliation: EPFL</p><p>Date: October, 2023</p><p> </p><p>Here we provide neural networks weights for the best models in our article "Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity", ICCV 2023. Each model has the naming convention "dataset"-"modeltype".pth</p><p>These pth files can be loaded with PyTorch. The code to load and use the models is available at The code to load and use the models is available at: <a href="https://github.com/amathislab/BUCTD">https://github.com/amathislab/BUCTD</a></p><p><strong>Note: The weights for OCHuman</strong>, are called COCO-* as one only trains on COCO. So OCHuman-X := COCO-X</p><p>We also share the predictions from various bottom-up models to reproduce the training stored in *.json format (compressed as zip files). See our repository for more details.</p><p> </p><p>Link to the ICCV article: </p><p><a href="https://openaccess.thecvf.com/content/ICCV2023/papers/Zhou_Rethinking_Pose_Estimation_in_Crowds_Overcoming_the_Detection_Information_Bottleneck_ICCV_2023_paper.pdf">https://openaccess.thecvf.com/content/ICCV2023/papers/Zhou_Rethinking_Pose_Estimation_in_Crowds_Overcoming_the_Detection_Information_Bottleneck_ICCV_2023_paper.pdf</a></p><p> </p><p>The weights and predictions are released with Creative Commons Attribution 4.0 license. The code is released under the Apache 2.0 license, see https://github.com/amathislab/BUCTD </p><p><i>If you find our weights, code or ideas useful, please cite:</i></p><p> </p><p>@InProceedings{Zhou_2023_ICCV,</p><p> author = {Zhou, Mu and Stoffl, Lucas and Mathis, Mackenzie Weygandt and Mathis, Alexander},</p><p> title = {Rethinking Pose Estimation in Crowds: Overcoming the Detection Information Bottleneck and Ambiguity},</p><p> booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},</p><p> month = {October},</p><p> year = {2023},</p><p> pages = {14689-14699}</p><p>}</p><p> </p>
Early-life sleep disruption impairs subtle social behaviours in prairie voles: a pose-estimation study
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Dataset for: Estimating pregnancy rate from blubber progesterone levels of a blindly biopsied beluga population poses methodological, analytical and statistical challenges
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mRI: multi-modal 3d human pose estimation dataset using mmwave, rgb-d, and inertial sensors
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OpenApePose: a database of annotated ape photographs for pose estimation
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Example body pose estimation and tracking
<p>Example body pose tracking of representative C. elegans wild type, egl-4 (n477 lf) and egl-4 (ad450 gf) animals for both the early and late time points in our behavioral assay.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.