Find research datasets worth reusing

Herbarium specimen image of Rhododendron roxieanum var. cucullatum (Hand.-Mazz.) D.F.Chamb., part of the collection of Royal Botanic Garden Edinburgh

Herbarium specimen image of Sorbus ochracea (Hand.-Mazz.) J.E.Vidal, part of the collection of Royal Botanic Garden Edinburgh

Herbarium specimen image of Sorbus epidendron Hand.-Mazz., part of the collection of Royal Botanic Garden Edinburgh

Herbarium specimen image of Taraxacum mongolicum Hand.-Mazz., part of the collection of Royal Botanic Garden Edinburgh

Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published. Content of this deposition: - A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values. - A JPEG image file of the scanned herbarium sheet. - A lossless TIFF image from which the JPEG image has been derived. - Two PNG files containing segmented image overlays of the scanned herbarium sheet. The _all extension indicates that all labels, color charts and pieces of text have received a different color against a black background color. The _sel extension indicates that these elements are white if they're barcode labels, yellow if they're color charts and red if they're anything else.

opencc-by-4.0Oct 2019View details →

Raw data supporting Identifying invertebrates from pitfall, flight interception traps and hand collecting

Raw data supporting identifying invertebrates from pitfall, flight interception traps and hand collecting: comparing metabarcoding with traditional methods. Two step PCRs were performed on each sample replicate using modified primers mICOIintF and jgHCO2198 followed by the Nextera XT index kit v2 Set A (Illumina). The pool was loaded onto an illumina MiSeq using a MiSeq Reagent Kit v2 500 cycle kit (Illumina), with 10% Phi-X to generate 250-bp paired-end reads.

opencc-by-4.0Sep 2023View details →

Clothing Dataset for Second-Hand Fashion

<h1>Second-Hand Fashion Dataset</h1> <h2>Update Sep. 19th, 2024</h2> - Some problematic and duplicate images have been removed from version 2. - All "gold dataset" data from `station1` and `station3` has been moved to a single `test100` folder. - JSON errors have been fixed - all JSON files should be parsed correctly now. The new dataset has 31,638 items (+ about 100 items in `test100` folder) instead of the 31,997 items in version 2. <h2>Overview</h2> The dataset originates from projects focused on the sorting of used clothes within a sorting facility. The primary objective is to classify each garment into one of several categories to determine its ultimate destination: reuse, reuse outside Sweden (export), recycling, repair, remake, or thermal waste.  The dataset has 31,638 clothing items, a massive update from the 3,000 items in version 1. The dataset collection started under the Vinnova funded project "AI for resource-efficient circular fashion" in Spring, 2022 and involves collaboration among three institutions: RISE Research Institutes of Sweden AB, Wargön Innovation AB, and Myrorna AB. The dataset has received further support through the EU project, CISUTAC (cisutac.eu). <h2>Project page </h2> - Webpage: <a title="Project: Second-hand Fashion" href="https://fnauman.github.io/second-hand-fashion/">second-hand-fashion</a> - Contact: farrukh.nauman@ri.se <h2>Dataset Details</h2> - The dataset contains 31,638 clothing items, each with a unique item ID in a datetime format. The items are divided into three stations: `station1`, `station2`, and `station3`. The `station1` and `station2` folders contain images and annotations from Wargön Innovation AB, while the `station3` folder contains data from Myrorna AB. Each clothing item has three images and a JSON file containing annotations.  - Three images are provided for each clothing item:   1. Front view.   2. Back view.   3. Brand label close-up. About 4000-5000 brand images are missing because of privacy concerns: people's hands, faces, etc. Some clothing items did not have a brand label to begin with. - Image resolutions are primarily in two sizes: `1280x720` and `1920x1080`. The background of the images is a table that used a measuring tape prior to January 2023, but later images have a square grid pattern with each square measuring `10x10` cm.  - Each JSON file contains a list of annotations, some of which require nuanced interpretation (see `labels.py` for the options):     - `usage`: Arguably the most critical label, usage indicates the garment's intended pathway. Options include 'Reuse,' 'Repair,' 'Remake,' 'Recycle,' 'Export' (reuse outside Sweden), and 'Energy recovery' (thermal waste). About 99% of the garments fall into the 'Reuse,' 'Export,' or 'Recycle' categories.     - `trend`: This field refers to the general style of the garment, not a time-dependent trend as in some other datasets (e.g., Visuelle 2.0). It might be more accurately labeled as 'style.'     - `material`: Material annotations are mostly based on the readings from a Near Infrared (NIR) scanner and in some cases from the garment's brand label.     - Damage-related attributes include:         - `condition` (1-5 scale, 5 being the best)         - `pilling` (1-5 scale, 5 meaning no pilling)         - `stains`, `holes`, `smell` (each with options 'None,' 'Minor,' 'Major').                   Note: 'holes' and 'smell' were introduced after November 17th, 2022, and stains previously only had 'Yes'/'No' options. For `station1` and `station2`, we introduced additional damage location labels to assist in damage detection:  <pre><code> "damageimage": "back", "damageloc": "bottom left", "damage": "stain ", "damage2image": "front", "damage2loc": "None", "damage2": "", "damage3image": "back", "damage3loc": "bottom right", "damage3": "stain"</code></pre>         Taken from `labels_2024_04_05_08_47_35.json` file. Additionally, we annotated a few hundred images with bounding box annotations that we aim to release at a later date.     - `comments`: The comments field is mostly empty, but sometimes contains important information about the garment, such as a detailed text description of the damage.  - Whenever possible, ISO standards have been followed to define these attributes on a 1-5 scale (e.g., `pilling`). - Gold dataset: 100 garments  were annotated multiple times by different annotators for **annotator agreement comparisons**. These 100 garments are placed inside a separate folder `test100`. - The data has been annotated by a group of expert second-hand sorters at Wargön Innovation AB and Myrorna AB.  - Some attributes, such as `price`, should be considered with caution. Many distinct pricing models exist in the second-hand industry:   - Price by weight   - Price by brand and demand (similar to first-hand fashion)   - Generic pricing at a fixed value (e.g., 1 Euro or 10 SEK)      Wargön Innovation AB does not set the prices in practice and their prices are suggestive only (`station1` and `station2`). Myrorna AB (`station3`), in contrast, does resale and sets the prices.  <h3>Comments</h3> - We received feedback on our version 1 that some images were too blurry or had poor lighting. The image quality has slightly improved, but largely remains similar to release 1.  - Some users did not prefer a `tar.gz` format that we uploaded in version 1 of the dataset. We have now switched to `.zip` for convenience. - Extra care was taken not to leak personal information. This is why you will not see any entries for `annotator` attribute in the JSON files in station1/sep2023 since people used their real names. Since then, we used internally assigned IDs.  - Many brand images contained people's hands, faces, or other personal information. We have removed about 4000-5000 brand images for privacy reasons.  - Please inform us immediately if you find any personal information revelations in the dataset:     - Farrukh Nauman (RISE AB): `farrukh.nauman@ri.se`,      - Susanne Eriksson (Wargön Innovation AB): `susanne.eriksson@wargoninnovation.se`,       - Gabriella Engstrom (Wargön Innovation AB): `gabriella.engstrom@wargoninnovation.se`. We went through 100k images four times to ensure no personal information is leaked, but we are human and can make mistakes. <h2>Partners</h2> The data collection for this dataset has been carried out in collaboration with the following partners: 1. <a href="https://www.ri.se/en">RISE Research Institutes of Sweden AB</a>: RISE is a leading research institute dedicated to advancing innovation and sustainability across various sectors, including fashion and textiles. 2. <a href="https://wargoninnovation.se/en/">Wargön Innovation AB</a>: Wargön Innovation is an expert in sustainable and circular fashion solutions, contributing valuable insights and expertise to the dataset creation. 3. <a href="https://www.myrorna.se/">Myrorna AB</a>: Myrorna is Sweden's oldest chain of stores for collecting clothes and furnishings that can be reused.  <h2>License</h2> CC-BY 4.0. Please refer to the LICENSE file for more details.  <h2>Acknowledgments</h2> This dataset was made possible through the collaborative efforts of RISE Research Institutes of Sweden AB, Wargön Innovation AB, and Myrorna AB, with funding from Vinnova and support from the EU project CISUTAC. We extend our gratitude to all the expert second-hand sorters and annotators who contributed their expertise to this project.

opencc-by-4.0Sep 2022View details →

Motion Capture Data for Hand Motion Embodiment

<h1>Dataset</h1> A dataset of human manipulation actions recorded with a motion capture system. A Qualisys motion capture system was used to record the data. We tracked individual finger movements as well as the position and orientation of the right hand. Some recordings contain additional markers at the back, shoulder, and elbow. The motion capture setup is explained <a href="https://dfki-ric.github.io/hand_embodiment/motion_capture_setup.html">here</a>. The dataset contains the original recordings of manipulation actions as well as metadata with annotations of relevant parts of the recordings (labels, start, end). Recordings are exported from the Qualisys Track Manager (QTM) as tab-separated value (TSV) files. Metadata is provided in JSON format. Related software is available at <a href="https://github.com/dfki-ric/hand_embodiment">github.com/dfki-ric/hand_embodiment</a>, which also contains code to load and use the dataset. <h1>Publication</h1> This dataset was introduced in the following paper: Alexander Fabisch, Manuela Uliano, Dennis Marschner, Melvin Laux, Johannes Brust, Marco Controzzi: "A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations", Proceedings of IEEE-RAS International Conference on Humanoid Robots 2022. It is available from <a href="https://arxiv.org/abs/2203.02778">arxiv.org</a> as a preprint or from <a href="https://ieeexplore.ieee.org/document/10000165">IEEE</a>. If you use the dataset, please cite the paper as: <blockquote> @INPROCEEDINGS{Fabisch2022,   author={Fabisch, Alexander and Uliano, Manuela and Marschner, Dennis and Laux, Melvin and Brust, Johannes and Controzzi, Marco},   booktitle={2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)},    title={A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations},    year={2022},   pages={801--808},   doi={10.1109/Humanoids53995.2022.10000165} } </blockquote> <h1>Ethics Approval</h1> Experimental protocols were approved by the ethics committee of the University of Bremen. Written informed consent was obtained from all participants for participation in the study and to publish this dataset. <h1>Origin and Funding</h1> This dataset is provided by the Robotics Innovation Center, DFKI GmbH. This work was supported by the European Commission under the Horizon 2020 framework program for Research and Innovation (project acronym: APRIL, project number: 870142).

opencc-by-4.0Feb 2023View details →

Present day human hand grasping the same artifact by hand and hafted

Examples of a present day human hand demonstrating a precision grip (top left) when grasping an artifact by hand and a power "squeeze" grip (top right) when grasping a hafted artifact (both palmar view). In turquoise (first metacarpal) and purple (trapezium) are the present day human and Neanderthal bones forming the trapeziometacarpal complex at the base of the thumb and responsible for its movements.

opencc-by-4.0Apr 2022View details →

DECIMER - Hand-drawn molecule images dataset

DECIMER - Hand-drawn molecule images dataset The translation of images of chemical structures into machine-readable representations of the depicted molecules is known as optical chemical structure recognition (OCSR). There has been a lot of progress over the last three decades in this field, but the development of systems for the recognition of complex hand-drawn structure depictions is still at the beginning. Currently, there is no data for the systematic evaluation of OCSR methods on hand-drawn structures available. Here we present DECIMER - Hand-drawn molecule images, a standardised, openly available benchmark dataset of 5088 hand-drawn depictions of diversely picked chemical structures. Every structure depiction in the dataset is mapped to a machine-readable representation of the underlying molecule. The dataset is openly available and published under the CC-BY 4.0 licence which applies very few limitations. We hope that it will contribute to the further development of the field.

opencc-by-4.0Feb 2023View details →

Time Series Data of Gaze, Head Pose, Hand Pose, and Object Positions for Object Approaches with a Given Intention

This data set comprises time series data of gaze, head pose, hand pose, and object positions for object approaches with a given intention. The data was captured in the context of the following publication: <ul> <li>Michael Fennel, Serge Garbay, Antonio Zea, Uwe D. Hanebeck, Intention Estimation with Recurrent Neural Networks for Mixed Reality Environments, Proceedings of the 26th International Conference on Information Fusion (Fusion 2023) (under review)</li> </ul> A Microsoft Hololens 2 was used for recording the data at 60 fps under the modalities explained in detail in the above-mentioned paper. The file names are structured as follows: <ul> <li>1st/2nd: <ul> <li>The data with "1st" contains approaches to randomly placed objects on a grid, which are rendered in augmented reality. The user is informed about the object to approach using a visual cue. This corresponds to Section IV-A.</li> <li>The data with "2nd" contains approaches to real objects placed statically in a room. The user is informed about the object to approach using a voice command.</li> </ul> </li> <li>unfiltered: Contains all approaches, including those where the user disrespects the given commands. Filtering is done as described in the paper.</li> <li>train/val/test: The first dataset was split in a 70/20/10 ratio for training, validation, and test.</li> </ul> Each data set contains the following columns. In each approach, 5 objects numbered from i=0 to i=4 are present. <ul> <li>General: <ul> <li>time: in seconds</li> <li>subject: consecutive subject number</li> <li>handedness: left (1), right (0)</li> <li>trial: consecutive trial number per subject</li> <li>target_label: index of the object to approach (0 to 4)</li> </ul> </li> <li>Data in world coordinates: <ul> <li>head_{x,y,z}: head position</li> <li>head_quat_{w,x,y,z}: head orientation quaternion</li> <li>W_gaze_{x,y,z}: gaze direction</li> <li>W_r_hand_{x,y,z}: right hand position</li> <li>W_r_hand_quat_{w,x,y,z}: right hand orientation quaternion</li> <li>W_l_hand_{x,y,z}: left hand position</li> <li>W_l_hand_quat_{w,x,y,z}: left hand orientation quaternion</li> <li>W_object_i_{x,y,z}: position of object i</li> <li>W_object_i_quat {w,x,y,z}: orientation quaternion of object i</li> </ul> </li> <li>Data in egocentric coordinates (head coordinate system). This data is provided for convenience and can be derived from the other data: <ul> <li>gaze_{x,y,z}: gaze direction</li> <li>r_hand_{x,y,z}: right hand position</li> <li>r_hand_quat_{w,x,y,z}: right hand orientation quaternion</li> <li>l_hand_{x,y,z}: left hand position</li> <li>l_hand_quat_{w,x,y,z}: left hand orientation quaternion</li> <li>object_i_{x,y,z}: position of object i</li> <li>object_i_quat {w,x,y,z}: orientation quaternion of object i</li> </ul> </li> </ul> Acknowledgment: This work was supported by the <a href="https://robdekon.de/">ROBDEKON</a> project of the German Federal Ministry of Education and Research.

opencc-by-4.0Feb 2023View details →

Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound - Human Hand: Wave Patterns and Perception

Human Hand: Wave Patterns and Perception This dataset is part of a larger repository (DOI: 10.5281/zenodo.5248082) which houses links to the data used in the publication "Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound" (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">Reardon et al., 2023</a>). If you use these data please cite our publication (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">http://www.science.org/doi/10.1126/sciadv.adf2037</a>). This dataset contains the in vivo response of a single participant's hand to focused ultrasound (UHEV1, Ultrahaptics) scanned in a zigzag path from the wrist to the distal end of digit 2 (and vice-versa). The data is provided as .mat files. The files are separated via longitudinal scanning speed, vl = 1, 2, 4, 7, 11 m/s. At all speeds, the ultrasound focus was modulated transverse to its primary motion direction at a speed, vmod of +-2.5 m/s yielding a zigzag path (2 cm path width). The longitudinal speed is designated in the filename. The direction of scanning - either from the wrist to the distal end of digit 2 (Distal direction) or from the distal end of digit 2 to the wrist (Proximal direction) - is also designated in the filename. Written, informed consent was gathered from the participant in this study, and the protocol was approved by the human subjects committee of our institution. Details about our experimental procedure can be found in our publication. IMPORTANT - The data is the unprocessed output from a laser doppler vibrometer (PSV-500, Polytec). The data is NOT time-aligned and must be reconstructed using the reference signal and the map of the measurement locations.   Data Fields y (NxMx2) - 3D array containing the skin velocity normal to the laser doppler vibrometer (in m/s) at N measurement locations for M timepoints and 2 repetitions ref (NxMx2) - 3D array containing a reference voltage signal taken from the ultrasound phased array. The beginning of the reference signal can be used to time-align each of the measurements and repetitions fs - Laser doppler vibrometer sampling rate (in Hz) measurementLocations (Nx3) - 3D locations on the hand (x,y,z; in m) for each of N measurement locations     BehavioralDataset.zip Contains the responses from three different perception experiments on tactile motion direction discrimination. The experiments are provided in three separate files; the results are provided as a MATLAB table. Written, informed consent was gathered from all participants in this study, and the protocol was approved by the human subjects committee of our institution. Details about our experimental procedure can be found in our publication. In the first experiment, SSW_PrimaryDataset.mat, participants (N=12) identified the direction of the focused ultrasound as either moving from the wrist to the end of digit 2 (Distal direction) or from the end of digit 2 to the wrist (Proximal direction). The second experiment, SSW_SecondaryDataset-Zigzag.mat, was nearly identical to the first experiment, except we cyclically repeated the stimuli such that the total integrated time in which the stimulus was applied to the skin was approximately constant between all of the different scan speeds. The participants (N=3) identified the motion direction of the focused ultrasound as either "Distal" or "Proximal" under two conditions - one in which there was no delay between our cyclical repeats (No Delay condition) and a second in which there was a 500 ms time delay between subsequent repetitions (With Delay condition). The third file, SSW_SecondaryDataset-Circle.mat, presents the pilot results (N=1) of a similar tactile motion experiment, except with circular trajectories (radius 2.8 cm) drawn on the palm of the hand in either a clockwise or counterclockwise direction. The stimuli were also repeated cyclically (with and without delay between repetitions), similar to experiment two.     Table Fields - SSW_PrimaryDataset.mat Participant - Participant label Speed - Longitudinal speed, *vl*, of the focused ultrasound stimulus (in m/s) Response - Participant response as a binary 0 (Distal direction) or 1 (Proximal direction) Direction - True direction of the stimulus as a binary 0 (Distal direction) or 1 (Proximal direction) isCorrect - Indicates whether the participant's response matches the true stimulus direction Repetition - Stimuli were block randomized and "Repetition" refers to how many times the participant has seen that specific stimulus ResponseLabel - Participant response as either "Distal" or "Proximal" DirectionLabel - True label of the stimulus as either "Distal" or "Proximal" Plays - Number of times the participant felt the stimulus before selecting a response   Table Fields - SSW_SecondaryDataset-Zigzag.mat Participant - Participant label Speed - Longitudinal speed, *vl*, of the focused ultrasound stimulus (in m/s) Response - Participant response as a binary 0 (Distal direction) or 1 (Proximal direction) Direction - True direction of the stimulus as a binary 0 (Distal direction) or 1 (Proximal direction) isCorrect - Indicates whether the participant's response matches the true stimulus direction Repetition - Stimuli were block randomized and "Repetition" refers to how many times the participant has seen that specific stimulus ResponseLabel - Participant response as either "Distal" or "Proximal" DirectionLabel - True label of the stimulus as either "Distal" or "Proximal" Condition - Indicates the experimental condition ("NoDelay" or "WithDelay")   Table Fields - SSW_SecondaryDataset-Circle.mat Participant - Participant label Speed - Linear speed of the focused ultrasound stimulus along the circular trajectory (in m/s) Response - Participant response as a binary 0 (Counterclockwise direction) or 1 (Clockwise direction) Direction - True direction of the stimulus as a binary 0 (Counterclockwise direction) or 1 (Clockwise direction) isCorrect - Indicates whether the participant's response matches the true stimulus direction Repetition - Stimuli were block randomized and "Repetition" refers to how many times the participant has seen that specific stimulus ResponseLabel - Participant response as either "Counterclockwise" or "Clockwise" DirectionLabel - True label of the stimulus as either "Counterclockwise" or "Clockwise" Condition - Indicates the experimental condition ("NoDelay" or "WithDelay")

opencc-by-4.0Jun 2023View details →

VRGestures: Controller and Hand Gesture Datasets for Virtual Reality

15 VR Controller gestures 11 one-handed VR Hand Gestures for each hand 2 two-handed VR Hand Gestures

opencc-by-4.0Jul 2021View details →

Dataset: Shell Commands Used by Participants of Hands-on Cybersecurity Training

This repository contains supplementary materials for the following journal paper: Valdemar &Scaron;vábenský, Jan Vykopal, Pavel Seda, Pavel Čeleda. Dataset of Shell Commands Used by Participants of Hands-on Cybersecurity Training. In Elsevier Data in Brief. 2021. <a href="https://doi.org/10.1016/j.dib.2021.107398">https://doi.org/10.1016/j.dib.2021.107398</a> <ul> </ul> How to cite If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link). <pre><code>@article{Svabensky2021dataset, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and Seda, Pavel and \v{C}eleda, Pavel}, title = {{Dataset of Shell Commands Used by Participants of Hands-on Cybersecurity Training}}, journal = {Data in Brief}, publisher = {Elsevier}, volume = {38}, year = {2021}, issn = {2352-3409}, url = {https://doi.org/10.1016/j.dib.2021.107398}, doi = {10.1016/j.dib.2021.107398}, }</code></pre> The data were collected using a logging toolset referenced <a href="https://zenodo.org/record/5126693">here</a>. Attached content <ol> <li>Dataset (data.zip). The collected data are attached here on Zenodo. A copy is also available in <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/datasets/commands">this repository</a>.</li> <li>Analytical tools (toolset.zip). To analyze the data, you can instantiate the toolset or <a href="https://gitlab.ics.muni.cz/muni-kypo/tools/commands-elk">this project for ELK</a>.</li> </ol> Version history <ul> <li>Version 1 (<a href="https://zenodo.org/record/5137355">https://zenodo.org/record/5137355</a>) contains 13446 log records from 175 trainees. These data are precisely those that are described in the associated journal paper. Version 1 provides a snapshot of the state when the article was published.</li> <li>Version 2 (<a href="https://zenodo.org/record/5517479">https://zenodo.org/record/5517479</a>) contains 13446 log records from 175 trainees. The data are unchanged from Version 1, but the analytical toolset includes a minor fix.</li> <li>Version 3 (<a href="https://zenodo.org/record/6670113">https://zenodo.org/record/6670113</a>) contains 21762 log records from 275 trainees. It is a superset of Version 2, with newly collected data added to the dataset.</li> <li>The current Version 4 (<a href="https://zenodo.org/record/8136017">https://zenodo.org/record/8136017</a>) contains 21459 log records from 275 trainees. Compared to Version 3, we cleaned 303 invalid/duplicate command records.</li> </ul>

opencc-by-4.0Sep 2023View details →

CHOC: The CORSMAL Hand-Occluded Containers dataset

CORSMAL Hand-Occluded Containers (CHOC) is an image-based dataset for category-level 6D object pose and size estimation, affordance segmentation, object detection, object and arm segmentation, and hand+object reconstruction. The dataset has 138,240 pseudo-realistic composite RGB-D images of hand-held containers on top of 30 real backgrounds (mixed-reality set) and 3,951 RGB-D images selected from the <a href="https://corsmal.eecs.qmul.ac.uk/containers_manip.html">CORSMAL Container Manipulation (CCM)</a> dataset (real set). CHOC-AFF is the subset that focuses on the problem of visual affordance segmentation. CHOC-AFF consists of the RGB images, the object and arm segmentation masks, and the affordance segmentation masks. The images of the mixed-reality set are automatically rendered using <a href="https://www.blender.org/">Blender</a>, and are split into 129,600 images of handheld containers and 8,640 images of objects without hand. Only one synthetic container is rendered for each image. Images are evenly split among 48 unique synthetic objects from three categories, namely 16 boxes, 16 drinking containers without stem (nonstems) and 16 drinking containers with stems (stems), selected from <a href="https://shapenet.org/">ShapeNetSem</a>. For each object, 6 realistic grasps were manually annotated using <a href="https://graspit-simulator.github.io/">GraspIt!</a>: bottom grasp, natural grasp, and top grasp for the left and right hand. The mixed-reality set provides RGB images, depth images, segmentation masks (hand and object), normalised object coordinates images (only object), object meshes, annotated 6D object poses (orientation and translation in 3D with respect to the camera view), and grasp meshes with their <a href="https://mano.is.tue.mpg.de/">MANO</a> parameters. Each image has a resolution of 640x480 pixels. Background images were acquired using an <a href="https://en.wikipedia.org/wiki/Intel_RealSense">Intel RealSense D435i</a> depth camera, and include 15 indoor and 15 outdoor scenes. All information necessary to re-render the dataset is provided, namely backgrounds, camera intrinsic parameters, lighting, object models, and hand + forearm meshes, and poses; users can complement the existing data with additional annotations. Note: The mixed-reality set was built on top of previous works for the generation of synthetic and mixed-reality datasets, such as <a href="https://hassony2.github.io/obman.html">OBMan</a> and <a href="https://geometry.stanford.edu/projects/NOCS_CVPR2019/">NOCS-CAMERA</a>. The images of the real set are selected from 180 representative sequences of the CCM dataset. Each image contains a person holding one of the 15 containers during a manipulation occurring in the video prior to a handover (e.g., picking up an empty container, shaking an empty or filled food box, or pouring a content into a cup or drinking glass). For each object instance, sequences were chosen under four randomly sampled conditions, including background and lighting conditions, scenarios (person sitting, with the object on the table; person sitting and already holding the object; person standing while holding the container and then walking towards the table), and filling amount and type. The same sequence is selected from the three fixed camera views (two side and one frontal view) of the CCM setup (60 sequences for each view). Fifteen sequences exhibit the case of the empty container for all fifteen objects, whereas the other sequences have the person filling the container with either pasta, rice or water at 50% or 90% of the full container capacity. The real set has RGB images, depth images and 6D pose annotations. For each sequence, the 6D poses of the containers are manually annotated every 10 frames if the container is visible in at least two views, resulting in a total of 3,951 annotations. Annotations of the 6D poses for the intermediate frames are also provided by using interpolation.   Contacts For enquiries, questions, or comments, please contact <a href="mailto:alessio.xompero@gmail.com?subject=CHOC">Alessio Xompero</a>. For enquiries, questions, or comments about CHOC-AFF, please contact <a href="mailto:tommaso.apicella@edu.unige.it?subject=CHOC-AFF%20%2F%20ACAnet">Tommaso Apicella</a>.   References If you work on Visual Affordance Segmentation and you use the subset CHOC-AFF, please see the related work on <a href="https://apicis.github.io/projects/acanet.html">ACANet </a>and also cite: Affordance segmentation of hand-occluded containers from exocentric images T. Apicella, A. Xompero, E. Ragusa, R. Berta, A. Cavallaro, P. Gastaldo IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023   Additional resources <a href="https://corsmal.eecs.qmul.ac.uk/pose.html">Webpage</a> of 6D pose estimation using CHOC <a href="https://github.com/CORSMAL/CHOC-dataset-toolkit">Toolkit</a> to parse and inspect the dataset, or generate new data   Release notes 2023/09/10 - Added object affordance segmentation masks 2023/02/08 - Fixed NOCS maps due to a missing rotation during the generation - Fixed annotations to include the missing rotation 2023/01/09 - Fixed RGB_070001_80000 (wrong files previously) 2022/12/14 - Added a mapping dictionary from grasp-IDs to their corresponding MANO-parameters-IDs to grasp.zip - Added object meshes with the NOCS textures/material in object_models.zip - Fixed folder name in annotations.zip - Updated README file to include these changes and fix a typo in the code block to unzip files

OpenNeuro40/100

Hands on analysis tutorial

Open the record for dataset details and reuse information.

openCC0Jan 2019View details →

opencc-by-4.0Jul 2019View details →

Supplementary Data: A Frequentist Analysis of Three Right-Handed Neutrinos with GAMBIT

Supplementary Data  A Frequentist Analysis of 3 Right-Handed Neutrinos with GAMBIT. The files in this record contain data for the frequentist global fit of a model with three right-handed neutrinos using the <a href="http://gambit.hepforge.org">GAMBIT</a> tool.

opencc-by-4.0Aug 2020View details →

MIDAS hand-annotated news articles

This dataset was produced in 2020 from the data collected throughout 2019 for the development of the MIDAS project (http://www.midasproject.eu/) The data is distributed throughout 5 topics: - EUS: Childhood Obesity (UC Basque Country) - FIN: Mental Health (UC Finland) - IRE: Diabetes (UC Ireland) - NIR: Children in Care (UC Northern Ireland) - INF: Infectious Diseases including Coronavirus (UC Influenzanet) The available data comes in 3 kinds and file formats: TXT - the source of news including ID, title and body of text CSV - the hand annotation of the news articles in TXT with 5 to 10 MeSH headings JSON - the input file for the evaluation of the classifier, including the title, news article body and MeSH heading IDs (available from https://www.ncbi.nlm.nih.gov/mesh/) The CSV files with name starting in "f1_", "pr_", "re_" are the results of the F1/Precision/Recall evaluation for each of the cases. ## AUTHORS Joao Pita Costa, Anthony Staines, Jarmo Pääkkönen, Jenni Konttila, Joseba Bidaurrazaga, Oihana Belar, Christine Henderson ## ACKNOWLEDGMENTS This work was supported by the European Commission H2020 project MIDAS (G.A. nr. 727721).  ## LICENSE This dataset is licensed over Creative Commons.

opencc-by-4.0Feb 2020View details →

Data for: Geometrical parameters for musculoskeletal modeling of hand

Dataset to be linked with not yet published manuscript "Geometrical parameters for musculoskeletal modeling of hand" In musculoskeletal modelling, parameters identification, such as the exact position and trajectories of muscle attachments, is a crucial issue. The main goal of this study was to calculate the position, attachment dimensions and cross section areas of twenty-five extrinsic and intrinsic hand muscle complexes. We integrated measurements taken from cadaveric preparations, magnetic resonance imaging and mathematical theory. Sixteen cadaveric preparations were dissected to draw up the anatomical maps including the position of muscle attachments, dimensions, shapes, cross section areas and variations. The magnetic resonance imaging of cadaveric upper extremity was performed to reconstruct the geometry of all bones and hand muscles. Using these outcomes, the muscle attachments and cross section areas were extracted and verified using the obtained morphological and morphometric analysis. The exact trajectories of muscle lines of action were computed using the modified weighted k-means method and Hungary algorithm. This work introduces a new approach to acquiring musculoskeletal modelling data in general and contributes extensive dataset to the hand musculature modelling in particular. Acknowledgments This work was supported by the project n. 182 “Obstetrics 2.0 - Virtual models for the prevention of injuries during childbirth” realised within the frame of the Program INTERREG V-A: Cross- border cooperation between the Czech Republic and the Federal State of Germany Bavaria, Aim European Cross-border cooperation 2014-2020. The realisation is supported by financial means of the European Regional Development Fund (85 % of the costs) and the state budget of the Czech Republic (5 %). KI is part-funded by project No. CZ.02.1.01/0.0/0.0/16_019/0000787 “Fighting INfectious Diseases“, awarded by the Ministry of Education, Youth and Sports of the Czech Republic, financed from The European Regional Development Fund.

opencc-zeroJan 2021View details →

IODP Expedition 368X Hand held XRF

Elemental concentrations in section halves, unpowdered discrete samples (e.g., a thin section billet), or powdered discrete samples were measured by a handheld (portable) X-ray fluorescence spectrometer (pXRF). Data are presented by element, and it should be noted that for section-half or unpowdered discrete samples, the concentrations are not calibrated and should be used appropriately (even though they are reported in units).