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1,506 results for “objects”
FEater dataset: A molecular fragment dataset to benchmark the robustness of 3D flexible object recognition
<p>This dataset is associated with the work: Benchmarking the robustness of the correct identification of flexible 3D objects using common machine learning models</p> <pre><code># Original FEater-Single and FEater_Dual dataset. FEater_Single ├── TestSet_coord.h5 ├── TrainingSet_coord.h5 └── ValidationSet_coord.h5 FEater_Dual ├── TestSet_coord.h5 ├── TrainingSet_coord.h5 └── ValidationSet_coord.h5 # Non-redundant baseline dataset FEater_Baseline ├── TestSet_Dual.h5 ├── TestSet_Single.h5 ├── TrainingSet_Dual.h5 └── TrainingSet_Single.h5 # FEater-Single and FEater_Dual in different sample size FEater_Mini200 ├── Mini200_Dual.h5 └── Mini200_Single.h5 FEater_Mini400 ├── Mini400_Dual.h5 └── Mini400_Single.h5 FEater_Mini800 ├── Mini800_Dual.h5 └── Mini800_Single.h5</code></pre> <p>For further details of the usage, please visit the original GitHub repository: <a title="FEater_repo" href="https://github.com/miemiemmmm/FEater" target="_blank" rel="noopener">https://github.com/miemiemmmm/FEater</a></p>
Tiny Robotics Dataset and Benchmark for Continual Object Detection
<p>Dataset for <strong>TiROD</strong>: Tiny Robotics Dataset and Benchmark for Continual Object Detection<br><br>Official Website -> <a href="https://pastifra.github.io/TiROD/">https://pastifra.github.io/TiROD/</a></p> <p>Code -> <a href="https://github.com/pastifra/TiROD_code">https://github.com/pastifra/TiROD_code</a></p> <p>Video -> <a href="https://www.youtube.com/watch?v=e76m3ol1i4I">https://www.youtube.com/watch?v=e76m3ol1i4I</a></p> <p>Paper -> <a href="https://arxiv.org/abs/2409.16215">https://arxiv.org/abs/2409.16215</a></p>
Object-based evaluation of precipitation systems in convection-permitting regional climate simulation over eastern China
<p>Data used in the manuscript "<strong>Object-based evaluation of precipitation systems in convection-permitting regional climate simulation over eastern China</strong>" which was submitted to Journal of Geophysical Research: Atmospheres. </p>
Replication package for: Taking the Fed at its Word: A New Approach to Estimating Central Bank Objectives using Text Analysis
<p>This zip file contains all of the programs and data necessary to replicate the results, tables, and figures <br> in the following paper:</p> <p>Shapiro, Adam H., and Daniel J. Wilson (2021). "Taking the Fed at its Word: A New Approach to Estimating Central Bank Objectives using Text Analysis," forthcoming at Review of Economic Studies.</p>
Evidence of an internal model of friction when controlling kinetic energy at impact to slide an object along a surface toward a target
<p>This repository contains zip files for data (Data.zip) and Matlab functions library (Library.zip), and the Matlab script (Matlab_Script_for_Cumputed_data.m) used in the Famié et al PLOSONE study. In addition, the <em>README FIRST MATLAB program.pdf</em> file explains the Matlab code and data organization. </p>
Data and scripts for the article on 'Affectedness and Differential Object Marking in Turkish and Uzbek', published in Linguistics
<p>Data and scripts for the article on 'Affectedness and Differential Object Marking in Turkish and Uzbek', published in Linguistics; see the publication for details</p>
Grape bunch and vine trunk dataset for Deep Learning object detection.
<p>Grape bunch and vine trunk dataset containing images and annotations for Deep Learning object detection.</p>
A collection of X-ray projections of 131 pieces of modeling clay containing stones for machine learning-driven object detection
<p><strong>Summary</strong></p> <p>This submission contains a collection of 235800 X-ray projections of 131 pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as an extensive and easy-to-use training dataset for supervised machine learning driven object detection. The ground truth locations of the stones are included. The data is supplementary material to the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022].</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></p> <p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p> <p><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections have been corrected with flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images). Both the X-ray projections and the ground truth images are resized to 128x128 pixels. The raw data is made available in another (larger) submission for complete reproduction (<a href="https://zenodo.org/record/5866228">https://zenodo.org/record/5866228</a>). All images are stored in .tif format. The data for samples with 5-8 stones are put in a separate folder from the data with 0-3 stones. The size of the completely unpacked dataset is 19.6 GB.</p> <p><strong>NOTE</strong>: Because the dataset consists of 471600 files, fully extracting the dataset may take a while. Therefore, an additional and significantly smaller zip-file is included for previewing the data, with one X-ray projection for each sample.</p> <p> </p> <p><strong>Additional Links</strong><br> These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow enabling deep learning for X-ray based foreign object detection", 2022 (in preparation)<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p>If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.</p>
DLOFTBs - Deformable Linear Objects Tracking with B-splines - dataset
<p>A dataset associated with the paper "DLOFTBs - Fast Tracking of Deformable Linear Objects with B-splines".</p> <p>Contains 2D images and ROS bags with 3D videos of deformable linear objects manipulated by humans and robots.</p>
Dataset for benchmarking Multiple Object Tracking and Segmentation (MOTS) in an apple orchard field.
<p>A dataset of temporally consistent apple images and labels taken using UAVs and a wearable sensor in an orchard, consisting of 86000 manually annotated apple instances and 1700 frames annotated in the MOTS (Multi-object Tracking and Segmentation) style.</p> <p>Sequence 0-5 are used for training. Sequence 6-8 are used for testing/validation. Sequence 10-12 are the testing datasets that have "ignore regions" overlays.</p> <p>The code used in the paper can be found on <a href="https://git.wur.nl/said-lab/rt-obj-tracking/">our GitLab.</a></p>
SingleCellExperiment Object MIS-C data (Hoste et al., 2022)
<p>Data from https://rupress.org/jem/article/219/2/e20211381/212918/TIM3-TRBV11-2-T-cells-and-IFN-signature-in</p> <p> </p>
Multimodal Sensory Learning for Object Manipulation
<p><strong>Multimodal Manipulation Learning Database</strong></p> <p>The dataset consists of data recordings for object manipulation with audio-tactile sensory feedback for object handover. It captures the auditory and tactile signals of a Kuka IIWA robot with an Allegro hand holding a plastic container containing different materials. The robot manipulates the container with vertical shaking and rotation motions. The data consists of force/pressure measurements on the Allegro hand using a Tekscan tactile skin sensor, auditory signals from a microphone, and the joints data of the IIWA robot and the Allegro hand joints. </p> <p><strong>Dataset</strong></p> <p>Each datafile is a rosbag file containing the data recording from one trial of a robot motion with one material, with rostopics on the following data:</p> <ul> <li>Kuka IIWA 7 Joint data: /iiwa/TorqueController/command /iiwa/eePose /iiwa/joint_states</li> <li>Allegro hand joint data: /allegro_hand_right/joint_states</li> <li>Tekscan sensor recording (tactile force/pressure sensor data on hand): /tekscan/frame</li> <li>Audio data (for microphone attached to hand): /audio/audio /audio/audio_info</li> <li>Experiment information: /trialInfo <ul> <li>which contains: <ul> <li>trial information (motion type, speed, etc.)</li> <li>start/stop of different phases of the trials</li> </ul> </li> </ul> </li> </ul> <p><strong>Motion Types</strong></p> <p>The database contains recordings for the robot executing two different motion types: vertical shaking of the object and rotation of the object.</p> <p><strong>Materials</strong></p> <p>The database contains recordings for 5 different material classes in the plastic container, as shown below: empty, vitamins, gummies, cornflakes, and rice. We used approximately the same volume of each material for each trial. We tested each material class and motion combination for a total of 10 different experimental conditions and collected 30 trials for each condition.</p> <p>The vertical motion dataset was entirely collected on 2021/08/25. The rotation dataset was split into two day. The empty, gummies and rice class data was collected on 2021/08/26. The vitamins and cornflakes classes were collected on 2021/09/13.</p> <p><strong>Database Setup</strong></p> <p>The database consists of the data in two formats: annotated ('annotated_bags_mml.zip') and unannotated/numbered filenames ('numbered_bags_mml.zip') datasets. The data in the two datasets are identical- the annotated filename dataset has the experimental descriptions in the filename directly (as described below).</p> <p>The annotated filenames dataset ('annotated_bags_mml.zip') consists of a single directory with all 300 rosbag datafiles (10 experimental conditions, 30 trials each). Each rosbag (<code>.bag</code>) is saved in the directory, with filename specified ('Date Recorded YYYYMMDD' + '_motion' + '_material' + '_trialID' + '.bag'). Motion Types are: {'vertical', 'rotation'}. Materials are: {'empty', 'cornflakes', 'gummies', 'rice', 'vitamins'}. For each experimental condition, there are 30 datafiles with trial IDs from 0-29.</p> <p>All data recordings for the vertical motion have filenames: '20210825_vertical_+ 'material' + 'trialID' +'.bag). For the rotation motion, the empty, gummy and rice classes have filenames: '20210826_rotation_+ 'material' + 'trialID' +'.bag). For cornflakes and vitamins classes, the filenames are: '20210913_rotation_+ 'material' + 'trialID' +'.bag).</p> <p>The numbered/unannotated file dataset ('numbered_bags_mml.zip') consists of the same 300 data files as in the annotated dataset except here the filenames are numbered '{000-299}.bag'. The directory contains a spreadsheet ('annotations.csv') listing the experimental descriptions for each file name. The columns of the xls spreadsheet are {'Bagfile name', 'Year', 'Month', 'Day', 'Motion/Movement (mvt_type)', 'Material', 'Trial ID'}, where {Year, Month, Day} refer to the date that trial data was collected (either 2021/08/25, 2021/08/26, or 2021/09/13). </p>
Object for single-cell mouse HSC development data
<p>This file contains SingleCellExperiment object generated from mouse HSC development data, including raw and log-normalized counts, gene regulatory network inferred by PUIC, transcription factor activity matrix estimated by metaRegulon.</p>
Instances and solutions for the multi-depot electric vehicle scheduling problem with the objective of minimizing the fleet size (EVSP-MD-FS)
<p>The set of instances and corresponding solutions, which were used in the computational study of the paper “Multi-depot electric vehicle scheduling in in-plant production logistics considering non-linear charging models”.</p>
BIM Learning Objects Compass
<p>The figure contains the learning compass applied to BIM Learning Objects.</p>
Objective identification of high-wind features within extratropical cyclones using a probabilistic random forest (RAMEFI). Part I: Method and illustrative case studies - Video Supplement
<p>These videos provide examples of application of RAMEFI (RAndom-forest based MEsoscale wind Feature Identification), a new objective identification of high-wind features within extratropical cyclones, for twelve selected case studies.</p>
Multi-objective optimization of equation of state molecular parameters: SAFT-VR Mie models for water
<p>Supporting document containing information on the Pareto points and thermodynamic calculations.</p>
Dopamine activity in the tail of the striatum, DeepLabCut and MoSeq during novel object exploration
<p>In this study, we characterized dynamics of novelty exploration using multi-point tracking (DeepLabCut) and behavioral segmentation (MoSeq). Mice were habituated in an arena, and then a object was placed at the corner of the arena. We compared 4 groups of mice: one with presentation of a novel object (stimulus novelty), one with a presentation of a familiar object (contextual novelty), one with presentation of a novel object after ablation of dopamine neuorns that project to the tail of the striatum (TS), and one with presentation of a novel object after sham surgery. With a separate group of mice, dopamine activity in TS was recorded during novelty exploration.</p>
TPDL2022 - Experiment - Research Objects Data
<p>This is the collection of research objects used in TPDL2022. It contains workflow research objects downloaded form the platform workflowhub (https://www.workflowhub.eu)</p>
Models & Unity Task for Shape of U: The non-monotonic relationship between object-location memory and expectedness
<p>These are the fitted models as well as the Unity task for <em>Shape of U: The non-monotonic relationship between object-location memory and expectedness</em> to appear <em>Psychological Science</em>.</p> <p>For more information please see <a href="https://github.com/JAQuent/schemaVR">here</a>. Non-registered versions of these files can also be downloaded via <a href="https://github.com/JAQuent/schemaVR/blob/master/downloads_and_moves_files_from_osf.R">this script</a>.</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.