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135 results for “Multimodal dataset”
Datasets for "MGTCOM: Community Detection in Multimodal Graphs"
<p>These are the datasets used in <em><strong>"MGTCOM: Community Detection in Multimodal Graphs"</strong></em></p> <p>The dataset preparation code can be found in <a href="https://github.com/EgorDm/MGTCOM">our repository</a>.</p> <p>Each dataset consists of a heterogenous graph with additional edge or node timestamps and preprocessed feature vectors.</p> <p>For each dataset, the files are split into raw and processed folders.<br> * `<em>raw</em>` folder: contains a set of parquet files with formatted raw dataset data. Files follow the naming convention `node_<name>` or `edge_<from>_<rel_name>_<to>`.<br> * `<em>processed</em>` folder: contains preprocessed datasets in <a href="https://pytorch-geometric.readthedocs.io/en/latest/">pytorch geometric</a> graph data format</p>
N20EM dataset for multimodal lyric transcription
<p>N20EM dataset for multimodal lyric transcription, proposed in our ACM MM 2022 paper, MM-ALT: A Multimodal Automatic Lyric Transcription System. This dataset contains recordings of three modalities: audio, video, and IMU motion signal. </p> <p>Our paper's camera ready version: https://arxiv.org/abs/2207.06127</p> <p>Project website: https://n20em.github.io/</p> <p><strong>Note: </strong></p> <ol> <li><strong>Once you download the dataset, we assume you have read and agreed with the <a href="https://drive.google.com/file/d/1te7AxPTSGAdyqqNtkfjFbtCv4ydwgcOF/view?usp=sharing">Terms and Conditions</a>.</strong></li> <li><strong>Commercial usage is strictly prohibited.</strong></li> </ol> <p>Please cite our work as:</p> <p>@inproceedings{gu2022mm, title={MM-ALT: A multimodal automatic lyric transcription system}, author={Gu, Xiangming and Ou, Longshen and Ong, Danielle and Wang, Ye}, booktitle={Proceedings of the 30th ACM International Conference on Multimedia}, pages={3328--3337}, year={2022} }</p> <p> </p>
Gender annotations for Multimodal Opinion-level Sentiment Intensity dataset (MOSI)
<p>Annotations of perceived gender (female/male) for all files of the Multimodal Opinion-level Sentiment Intensity dataset (MOSI) [ arXiv:1606.06259]. The annotations were done by a single German and English speaking male annotator.</p>
Dataset for "Suppressing transverse mode instability through multimode excitation in a fiber amplifier"
<p>Numerical and theoretical data associated with "Suppressing transverse mode instability through multimode excitation in a fiber amplifier" (doi.org/10.1073/pnas.2217735120). </p>
A Multimodal Dataset on Stainless Steel for Electrochemical Corrosion Studies: Optical Microscopy and Linear Sweep Voltammetry
<p>The upload includes optical and electrochemical data for corrosion experiments.</p> <p>This dataset presents the results of an experimental study conducted to investigate the electrochemical behavior of electropolished Stainless Steel 316L (SS316L) samples immersed in NaCl solutions. The combination of Linear Sweep Voltammetry (LSV) and optical microscopy techniques was employed to gather comprehensive insights into the electrochemical processes occurring on the surface of the stainless steel samples.</p> <p>The samples used in the experiment were electropolished SS316L, chosen for its widely recognized corrosion resistance properties and frequent application in various industrial sectors. LSV was performed on the samples in a potential range of -0.5V to 1.35V, (vs 3.4M KCl Ag/AgCl). NaCl solutions with concentrations of 5mM, 10mM, and 50mM were prepared to simulate different electrolyte conditions.</p> <p>Two different scan rates, 50mV/s and 100mV/s, were applied during the LSV experiments to observe the effect of varying scan rates on the electrochemical behavior of the SS316L samples. The scan rates were chosen to cover a range commonly encountered in electrochemical studies.</p> <p>List of experiments:</p> <ul> <li> 5 mM solution, 100mV/s scan rate</li> <li> 10 mM solution, 50mV/s scan rate</li> <li> 10 mM solution, 100mV/s scan rate</li> <li> 50 mM solution, 50mV/s scan rate</li> <li> 50 mM solution, 100mV/s scan rate</li> </ul> <p>The dataset is accompanied by animated plots. The top left plot shows electrochemistry data, bottom left - average normalized intensity and derivative of intensity. Top right - original optical images, bottom right - normalized images.</p> <p>The scale for optical images: 1px = 480 nm. Axes on images are in pixels</p> <p>Jupyter notebook with the code, used to create videos included. We recommend opening the Jupyter notebook file in a Python 3 environment.<br> </p>
Multimodal Toxic Memes Detection Dataset
<p>The dataset for training and evaluating multimodal toxic memes detection models. Contains images, extracted texts and toxicity labels. Images are collected from popular Russian Telegram channels and labelled with respect to <a href="https://transparency.fb.com/policies/community-standards">Facebook Community Standards</a>.</p>
ASSIST-IoT Multimodal Fall Detection Dataset
<p>Multimodal dataset for fall detection. Includes acceleration data collected from a tag and two smartwatches, and location reported by the tag. More details about the data collection procedure can be found in <code>notes.md</code>.</p> <p><strong>Contents</strong></p> <p>The repository contains:</p> <ul> <li><code>data/location_data.csv</code> and <code>data/full_acceleration</code> – preprocessed acceleration and location data from 10 participants and mannequin simulated falls with target variable identified</li> <li><code>data/subsampled_acceleration_data.csv</code> – subsampled acceleration dataset used for training the AI model</li> <li><code>notes.md</code> – description of activities performed and notes from data collection</li> <li><code>videos</code> – reference videos for performed activities</li> </ul> <p><strong>Authors</strong></p> <ul> <li><a href="https://orcid.org/0000-0002-2543-9461">Piotr Sowiński</a> – research methodology, data collection and processing</li> <li><a href="https://orcid.org/0000-0003-3217-1050">Monika Kobus</a> – research methodology, data collection</li> <li><a href="https://orcid.org/0000-0003-4295-3005">Anna Dąbrowska</a> – research methodology, methodological supervision</li> <li><a href="https://orcid.org/0000-0003-1524-7877">Kajetan Rachwał</a> – data collection</li> <li><a href="https://orcid.org/0000-0002-7109-891X">Karolina Bogacka</a> – research methodology</li> <li><a href="https://orcid.org/0000-0002-9572-2705">Krzysztof Baszczyński</a> – research methodology, data collection</li> <li><a href="https://orcid.org/0000-0002-3080-0303">Anastasiya Danilenka</a> – research methodology, data collection and processing</li> </ul> <p><strong>Acknowledgements</strong></p> <p>This work is part of the <a href="https://assist-iot.eu/">ASSIST-IoT project</a> that has received funding from the EU’s Horizon 2020 research and innovation programme under grant agreement No 957258.</p> <p>The <a href="https://www.ciop.pl/en">Central Institute for Labour Protection – National Research Institute</a> provided facilities and equipment for data collection.</p> <p><strong>License</strong></p> <p>The dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>
Dataset and Data analysis "Multimodal vibrational studies of drug uptake in vitro: Is the whole greater than the sum of their parts?"
<p>Data Analysis for the publication 10.1002/jbio.202000264.</p> <p>It is divided in three different folders describing three different part of the data analysis:</p> <p><strong>A. DATA TREATMENT RAMAN (Folder 1)</strong></p> <p><em>1. Import data using the Import_Raman script.<br> 2. Plot Spectra and integrate DOX band<br> Figure 1A<br> Figure 1B<br> 3. PCA<br> Figure 1D<br> Figure 1C<br> SM 1<br> 4. PLS<br> Figure 1F<br> Figure 1E</em></p> <p><strong>B. ANALYSIS OF IR DATA AND MULTIMODAL IR-RAMAN OF DOX UPTAKE (Folder 2)</strong></p> <p><em>1 Load Data IR<br> 2 Exploratory Analysis IR<br> Figure 2A<br> 3 PCA <br> SM 2<br> 4. Partial Least Squares vs time<br> Figure 2C<br> Figure 2B<br> 5. Partial Least Squares vs Raman Signal<br> Figure 2E<br> Figure 2D<br> 6. Make Averages and clean up Data for DATA Fusion<br> IR<br> Raman<br> 7. 2DCORR<br> Figure 3B<br> 8. MCR_ALS WITH DATA FUSION<br> Fitting of the concentration of Raman using the method in [9].<br> MCR-ALS<br> Figures 4 A, B and C</em></p> <p> </p> <p><strong>C. SIMULATION (Folder 3)</strong></p> <p><em>1. Load Raman DATA<br> 2. Simulate Raman DAta<br> 3. Load and simulate IR Data<br> 4. 2D corr<br> Figure 3A</em></p> <p> </p> <p>. Each folder contains a .mlx with the data analysis performed. Figures numbering corresponds to the one found in the article.</p>
Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (2000-2024)
<p>The data contains simulation results from 2000-2024, 25 years total.</p> <p>You can access the remaining part of the dataset via Qingchen Xu and Lu Li (2025) using the following reference:</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1950-1974) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671245</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1975-1999) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671253</p>
Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1975-1999)
<p>The data contains simulation results from 1975-1999, 25 years total.</p> <p>You can access the remaining part of the dataset via Qingchen Xu and Lu Li (2025) using the following reference:</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1950-1974) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671245</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (2000-2024) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671254</p>
Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1950-1974)
<p>The data contains simulation results from 1950-1974, 25 years total.</p> <p>You can access the remaining part of the dataset via Qingchen Xu and Lu Li (2025) using the following reference:<br>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1975-1999) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671253</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (2000-2024) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671254</p>
Plains zebra 2019 multimodal communication dataset
<p>Multimodality is a virtually ubiquitous feature of communication. With the increasing interest in how animals, including humans, use multimodal and multicomponent signals in social interactions, there is an acute need for standardized and rigorous tools that will allow us to visualize, and analyze these signals as they occur in naturalistic interactions as a complex, integrated system. Network theory is a powerful methodology for intuitively visualizing and investigating the relationships between entities. Here, we propose a new application of network theory for analyzing multimodal communication. Using a case study of natural multimodal interactions in wild plains zebras (<em>Equus quagga</em>), we introduce the descriptive power of network metrics by providing an objective set of metrics to: (a) describe the relationships between simultaneously produced signals within and between modalities; and (b) infer signal meaning and function. Here we make available the multimodal communication data collected during our 2019 field season.</p>
Multimodal Agricultural Aerial and Ground Robotics Simulation Dataset
<p><strong>Dataset description</strong></p><p>This dataset was generated using an aerial robot and a ground robot in the Webots simulator with the <a href="https://github.com/opendr-eu/opendr/tree/master/projects/python/simulation">OpenDR agricultural dataset generator tool</a>.</p><p>It consists of 13980 RGB images and their semantic segmentation counterparts taken at different lighting conditions and robot positions in an agricultural field. It also includes the annotation data comprised of the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box. Furthermore, it includes gps and inertial unit sensor data for UAV and gps, inertial and lidar sensor data for UGV.</p><p><strong>Folder configuration</strong></p><p>The dataset contains 4 folders for different lighting conditions:</p><ul><li>noon cloudy</li><li>noon stormy</li><li>dawn cloudy</li><li>dusk</li></ul><p>Each contains UAV and UGV folders. UAV folder includes:</p><ul><li>annotations: contains segmented images in JPG files and annotations in TXT files.</li><li>camera: contains generated RGB images.</li><li>gps: contains the three-axis location of global positioning sensor saved in TXT files.</li><li>inertial unit: contains the inertial unit date in TXT files.</li></ul><p>UGV folder includes:</p><ul><li>annotations: contains segmented images in JPG files and annotations in TXT files.</li><li>front_bottom_camera: contains generated RGB images.</li><li>Hemisphere_v500: contains the three-axis location of the global positioning sensor saved in TXT files.</li><li>imu_robotti: contains the inertial unit date in TXT files.</li><li>velodyne: contains lidar data in PCD files.</li></ul><p><strong>Data format</strong></p><p>The dataset includes</p><ul><li>The inertial measurement TXT files include Euler angles in order of Roll, Pitch, and Yaw.</li><li>The GPS measurement TXT files include the robot position in x, y, and z order.</li><li>Object annotation TXT files include the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box at each line for the corresponding frame.</li></ul><p><strong>File naming convention</strong></p><p>Each data is named "s_i{_segmented, _annotation}.ext", where:</p><ul><li><strong>s</strong> denotes the simulated time in seconds.</li><li><strong>i</strong> denotes the index counting every 10ms of simulated time.</li><li><strong>ext</strong> denotes the extension, "jpg" for images, "pcd" for lidar, and "txt" for the rest.</li><li>Labels <strong>_segmented</strong> and <strong>_annotation </strong>appended to the name for segmentation image and object annotations, respectively.</li></ul><p>Each segmented image uses the following RGB color mapping:</p><ul><li>Tree: 0.1, 0.4, 0.0</li><li>Apple Tree: 0.85, 0.49, 0.57</li><li>Cow: 0.380, 0.220, 0.137</li><li>Sheep: 0.937, 0.921, 0.862</li><li>Fox: 0.992, 0.376, 0.086</li><li>Barn: 0.625, 0.293, 0.226</li><li>Cat: 0.870, 0.580, 0.0</li><li>Deer: 0.415, 0.364, 0.302</li><li>Human: 1.0, 0.855, 0.672</li></ul>
Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry
<p>This repository contains the data associated with: "Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry" (see here: <a href="https://rxn4chemistry.github.io/multimodal-spectroscopic-dataset/">https://rxn4chemistry.github.io/multimodal-spectroscopic-dataset/</a>)</p>
PCLMM: A multimodal dataset for Patronizing and Condescending Language detection
<p>The PCLMM dataset is sourced from Bilibili, the largest online community for young people in China. Our work aims to uncover microaggressions targeted at vulnerable groups, including discriminatory and patronizing language expressions, as well as facial expression features. PCLMM contains six main vulnerable communities :</p> <ol> <li>Disabled individuals</li> <li>Women</li> <li>The elderly</li> <li>Children</li> <li>Single-parent families</li> <li>Low-income groups</li> </ol> <p>The PCLMM dataset contains 715 videos, totaling 21 hours of content, with an average video length of 1.80 mins and a frame rate of 30 FPS, comprising 2.3M frames. Approximately 27.4% of the videos were labeled as patronizing (label-1), aligning with the distribution of PCL data on internet platforms.</p> <p>If you would like to learn more about our PCL field (a branch of toxic speech detection) and PCLMM dataset, please refer to this paper, accepted by ICASSP 2025: </p> <p><a href="https://ieeexplore.ieee.org/abstract/document/10890580">https://ieeexplore.ieee.org/abstract/document/10890580</a></p> <p>The code implementation of the paper can be found in our repository: <a href="https://github.com/dut-laowang/PCLMM" target="_new" rel="noopener">https://github.com/dut-laowang/PCLMM</a></p> <p><strong>Updation</strong></p> <p><em><strong>February 9, 2025</strong></em> – The Annotation_Subset.csv containing the original video links has been updated.</p>
Datasets for Multimodal Biosensing for Vestibular Network-Based Cybersickness Detection
<p>These are the datasets about the experimental group (N=20, where N is the sample size, that is, 20 participants in total) and control group (N=20, where N is the sample size, that is, 20 participants in total) for our published paper entitled "Multimodal Biosensing for Vestibular Network-Based Cybersickness Detection", DOI: 10.1109/JBHI.2021.3134024. </p> <p>1. What do these datasets include?</p> <p>There are 40 participants' data in 40 folders, respectively. Each folder includes EEG data, Questionnaires, and answers as well as other non-EEG biometrics and memory test result of the cognitive task.</p> <p><br> 1.1 EEG data<br> All EEG data is either *.easy file or *.info file. The *.easy file is the raw EEG data. The *.info file is the information (e.g., EEG montage) paired with those raw EEG data. The *.info file can be simply readable by any Notepad software. The *.easy file can be readable by Neuroelectrics's plugin for EEGLAB (https://www.neuroelectrics.com/wiki/index.php/EEGLAB). Note, most of the participants' EEG data is a single easy file, but due to technical problems, few participants' EEG data consists of some separated easy files. Please use the function 'merge dataset' in the EEGLAB to merge them, and then use the following markers to extract segments of interest.</p> <p>All received EEG markers are integer numbers. Specifically, the markers are as follows:</p> <p>For control group:<br> a) The 'target' stimuli in the cognitive task: 16<br> b) The 'distractor' stimuli in cognitive task:32<br> c) The end of Baseline_1(that is, before the first cognitive task): 37<br> d) The end of Baseline_2(that is, before the round 1 neutral task): 47<br> e) The end of Baseline_3(that is, before the round 2 neutral task): 57<br> f) The end of Baseline_4(that is, before the second cognitive task): 67<br> g) The end of FMS reporting during the round 1 neutral task: 100<br> h) The end of FMS reporting during the round 2 neutral task: 101</p> <p>For experimental group:</p> <p>a) The 'target' stimuli in the cognitive task: 16<br> b) The 'distractor' stimuli in cognitive task:32<br> c) The end of Baseline_1(that is, before the first cognitive task): 37<br> d) The end of Baseline_2(that is, before the tunnel travel task): 47<br> e) The end of Baseline_3(that is, before the rollercoaster task): 57<br> f) The end of Baseline_4(that is, before the second cognitive task): 67<br> g) The end of FMS reporting during the Tunnel travel task: 100<br> h) The end of FMS reporting during the Rollercoaster task: 101</p> <p>1.2 Questionnaires</p> <p>1.2.1 SSQ<br> All SSQ questionnaires are named as xxx_QuestionnaireResult.csv, where xxx stands for the timestamps.</p> <p>1.2.2 FMS</p> <p>For control group:<br> All FMS questionnaires are named as xxx_Nature_FMS_backup.csv or xxx_Bio-data and FMS Nature.csv, where xxx stands for the timestamps and 'Nature' refers to the vection-free neutral task. The FMS scores in the xxx_FMS_backup.csv and xxx_Bio-data and FMS 'Task name'.csv are the exactly same. Unlike the xxx_Bio-data and FMS 'Task name'.csv contains the non-EEG biometrics as well, the xxx_FMS_backup.csv is pure FMS scores and easy to read.</p> <p>For experimental group:</p> <p>All FMS questionnaires are named as xxx_Gabor_FMS_backup.csv or xxx_Rollercoaster_FMS_backup.csv or xxx_Bio-data and FMS GaborRacer.csv or xxx_Bio-data and FMS Rollercoaster.csv, where xxx stands for the timestamps and Gabor or GaborRacer refer to the tunnel travel task. The FMS scores in the xxx_FMS_backup.csv and xxx_Bio-data and FMS 'Task name'.csv are the exactly same. Unlike the xxx_Bio-data and FMS 'Task name'.csv contains the non-EEG biometrics as well, the xxx_FMS_backup.csv is pure FMS scores and easy to read.</p> <p>1.3 Non-EEG biometrics<br> All non-EEG biometrics are named as xxx_Bio-data 'Task name'.csv, where 'Task name' refers to 'VA' (that is the cognitive task), or 'Nature' (for the control group) or 'GaborRacer' (for the experimental group) or 'Rollercoaster' (for the experimental group). The column information is as follows:<br> Column A: time<br> Column Q: PPG raw data (the left index finger)<br> Column R: Fingertip temperature (the left middle finger)<br> Column S: Forehead temperature<br> Column T: VR hermetic space temperature</p> <p>The data in other columns are not used in this study.</p> <p>1.4 The memory test result of the cognitive task</p> <p>Memory test result: xxx_CheckMemoryResult.csv, where xxx stands for the timestamps.</p> <p>Column A: time</p> <p>Column B: memory test result (Correct/Wrong)</p> <p>Column C: the number of the target (green sunfish)</p> <p>Column D: the reported number of the target in the memory test</p>
MMFlood: A Multimodal Dataset for Flood Delineation from Satellite Imagery
<p>MMFlood is remote sensing dataset derived from Sentinel-1 (VV-VH), MapZen (DEM) and OpenStreetMap (Hydrography). It provides a complete and well-rounded set of data specifically designed for flood events, focusing on three main features: worldwide distribution, manually validated annotations and multiple modalities.</p> <ul> <li>1748 pairs of SAR images and pixel-level annotations exclusively based around flooded areas and taking into account 95 flood events in 42 different countries around the world, in a time span ranging from 2014 to 2021.</li> <li>Image size <2000x2000, image resolution of 20m .</li> <li>Manually verified flood events across the world with raw SAR acquisitions derived from Sentinel-1.</li> <li>Includion of Digital Elevation Model (DEM) images.</li> </ul>
Multimodal dataset: Protein Function Prediction using STRING data & COVID19 Mortality Model by EI
<p>The PFP.zip file contains 1. 5 well-formated GO terms dataset for EI, 2. STRING data 3. GO term annotation. The last two could be merged by the 'generate_data.py' script in https://github.com/GauravPandeyLab/ensemble_integration</p> <p>The covid19_model_built.zip contained the EI model built based on the COVID-19 Mortality dataset, the detail of usage are here:.</p>
PiH Dataset for Determining Exception Context in Assembly Operations form Multimodal Data
<p>PiH Dataset used in Simonič, M.; Majcen Hrovat, M.; Džeroski, S.; Ude, A.; Nemec, B. Determining Exception Context in Assembly Operations from Multimodal Data. <em>Sensors</em> <strong>2022</strong>, <em>22</em>, 7962. https://doi.org/10.3390/s22207962</p> <p>The dataset consists of color images of different outcomes of the PiH task as well as the corresponding Cartesian pose of the robot end-effector and force torque data. Data is organized into two folders, representing one of the two possible insertion slots. In each of the folders, data is further split into the following cases:<br> - error in insertion target position ranging from -10 to 10 mm in x direction in 1 mm steps,<br> - error in insertion target position ranging from -10 to 10 mm in y direction in 1 mm steps,<br> - no positional error.</p> <p>Multiple attempts were made for each case.</p> <p>Each entry has unique date-time tag and comprises: RGB image (.jpg) and .mat file with <em>states </em>object that contains reference and measured target pose in Cartesian space (positions and quaternions) and raw force-torque sensor data and force-torque data transformed to the tool frame.</p> <p>The experiments were performed with Franka Emika Panda collaborative robot. For acquisition of image data an Intel Realsense D435 RGB-D camera has been utilized. </p>
Rings Insertion Dataset for Determining Exception Context in Assembly Operations form Multimodal Data
<p>Rings Insertion Dataset used in Simonič, M.; Majcen Hrovat, M.; Džeroski, S.; Ude, A.; Nemec, B. Determining Exception Context in Assembly Operations from Multimodal Data. <em>Sensors</em> <strong>2022</strong>, <em>22</em>, 7962. https://doi.org/10.3390/s22207962</p> <p>The dataset consists of color images of different outcomes of the ring insertion task as well as the corresponding Cartesian pose of the robot end-effector and force torque data. Data is organized into four folders, representing one of the possible insertion slots. In each of the folders, data is further split into the following cases:<br> - error in insertion target position ranging from -3 to 3 mm in x direction in 1 mm steps,<br> - error in insertion target position ranging from -3 to 3 mm in y direction in 1 mm steps,<br> - no positional error,<br> - other unidentified error (bad insertion).</p> <p>Multiple attempts were made for each case.</p> <p>Each entry has unique date-time tag and comprises: RGB image (.jpg) and .mat file with <em>states </em>object that contains reference and measured target pose in Cartesian space (positions and quaternions) and raw force-torque sensor data and force-torque data transformed to the tool frame.</p> <p>The experiments were performed with Franka Emika Panda collaborative robot. For acquisition of image data an Intel Realsense D435 RGB-D camera has been utilized. </p>
ScienceDex guides
Understand access before you commit
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.