Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,163
datasets available to search
ShareScore release 0.9.0
Dataset results
1,163 results for “demonstration”
2D Array of Pulse Coupled Oscillators Driving Cilia to Demonstrate Metachronal Waves
<p>The video demonstrates metachornal waves from an 2D array of delay locked pulse coupled oscillators, each mapping to a cilia.<br>Within the triangle structure, the oscillators are coupled to their nearest neighbors.</p>
Demonstration of Stretching an Elastic Sample with Electric Contacts on a Self-Built Table
<p>This video demonstrates the stretching of an elastic sample equipped with electric contacts. The experiment takes place on a table constructed by the presenter as part of a project funded by the National Science Centre in Poland. Watch as the sample undergoes controlled deformation, showcasing the functionality of the setup. The samples used are ITO/ZnO/SnO/ITO/PET (in the video) and ITO/ZnO/Co3O4/ITO/Kapton (in the GIF).</p>
Assembly Dataset for Contact Based tasks for Learning from Demonstration
<h1>Assembly Dataset for Contact Based Tasks for Learning from Demonstration</h1> <p>The Dataset is a detailed collection of motion and visual data during kinesthetic demonstration gathered for the HARTU project, specifically for two use cases: PCL (Philips Consumer Lifestyle) assembly of electric shaver's body to lackering fixture and Tofas (Türk Otomobil Fabrikası A.Ş) bearing assembly of the brakes. This dataset supports research and development for Learning from Demonstrations of contact based tasks in robotics, focusing on motion planning, control, and computer vision applications related to contact based assembly tasks.</p> <h2>Dataset Contents</h2> <h3>1. Use Cases</h3> <ul> <li><strong>PCL Assembly</strong>: Demonstrations related to the two assembly tasks in the PCL assembly process.</li> <li><strong>Tofas Assembly</strong>: Demonstrations related to the two assembly tasks in the Tofas assembly process.</li> </ul> <h3>2. Motion Information</h3> <p>The motion data provides detailed information about the movement and operation of the dual arm KUKA robot. This includes:</p> <ul> <li><strong>Cartesian Positions</strong>: The positions of the robot's end effectors in 6D Cartesian coordinates, position in 3D (x, y, z) and orientation represented as quaternion (x,y,z,w).</li> <li><strong>Joint Positions</strong>: The joint angles or positions of each joint in the robot's arms.</li> <li><strong>Velocity</strong>: The speed of movement in both Cartesian positions and joint positions.</li> <li><strong>Force/Torque</strong>: The forces and torques experienced by the robot's end effectors during the demonstrations measured using FT sensor and torques measured from joints. </li> <li><strong>Gripper Actions</strong>: Information about the actions performed by the robot's grippers, such as open, close during assembly operation.</li> </ul> <h3>3. Camera Information</h3> <p>The visual data is captured using an intel Realsense d455 RGB-D camera, which provides both color and depth information for each frame. This includes:</p> <ul> <li><strong>RGB Data</strong>: Color images captured during the demonstrations, providing visual context for the robot's actions.</li> <li><strong>Depth Data</strong>: Depth images that represent the distance of objects from the camera, useful for 3D reconstruction and spatial analysis.</li> </ul> <h3>4. Data Format</h3> <p>The dataset is stored in HDF5 files, a hierarchical data format that allows for efficient storage and retrieval of large datasets. Each HDF5 file contains:</p> <ul> <li><strong>Motion Data</strong>: Cartesian positions, joint positions, velocities, forces, and gripper actions.</li> <li><strong>Camera Data</strong>: RGB and depth images.</li> </ul> <p>In addition, ROS bags are recorded during each demonstration, capturing all the relavant ROS topics published during the demonstrations for easy playback and analysis.</p> <h2>Format and Structure</h2> <p>The dataset is organized into directories corresponding to individual demonstrations for each use case. Again each use case is recorded for complete execution and step wise execution. Each directory contains the following files:</p> <ul> <li><strong>demo.hdf5</strong>: An HDF5 file containing motion and camera data recorded during demonstration.</li> <li><strong>rosbag2*</strong>: A ROS bag folder containing metadata and db3 file capturing required ROS topics during the demonstration.</li> </ul> <h2>Resources for Working with HDF5 and ROS Bags</h2> <h3>HDF5 Resources</h3> <ul> <li><strong>HDF5 Viewer</strong>: <a href="https://www.hdfgroup.org/downloads/hdfview/">HDFView</a> is a tool for browsing and editing HDF5 files. </li> <li><strong>Python Library</strong>: <a href="http://www.h5py.org/" target="_new" rel="noreferrer">h5py</a> is a Python library for interacting with HDF5 files.</li> </ul> <h3>ROS Bag Resources</h3> <ul> <li><strong>ROS Bag Tutorials</strong>: <a target="_new" rel="noreferrer">ROS Wiki</a></li> <li><strong>rqt_bag</strong>: A GUI plugin for visualizing ROS bag files.</li> </ul> <h2>Usage</h2> <p>This dataset can be used for LfD applications, including but not limited to:</p> <ul> <li><strong>Robotics Motion Planning</strong>: Developing and testing algorithms for controlling dual arm robots in industrial assembly tasks.</li> <li><strong>Robot Vision</strong>: Training and evaluating models for object recognition, tracking, and 3D reconstruction in assembly processes.</li> <li><strong>Force Control</strong>: Studying the interaction forces between the robot and its environment during assembly tasks.</li> </ul> <h2>Licensing and Citation</h2> <p>Please refer to the repository's LICENSE file for terms of use. If you use this dataset in your research, please cite it as per the citation details given.</p> <h2>Contact</h2> <p>For questions or further information, please contact Vamsi Origanti at vamsi.origanti@dfki.de.<br><br><br><br>The HARTU project supports this work. This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101092100.</p>
Movement Primitive Diffusion Demonstration Data
<p>Training data for the experiments described in the paper "Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects".</p> <p>Movement Primitive Diffusion (MPD) is a diffusion-based imitation learning method for high-quality robotic motion generation that focuses on gentle manipulation of deformable objects.</p> <p>@article{Scheikl2024MPD, author={Scheikl, Paul Maria and Schreiber, Nicolas and Haas, Christoph and Freymuth, Niklas and Neumann, Gerhard and Lioutikov, Rudolf and Mathis-Ullrich, Franziska}, title={Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects}, journal={IEEE Robotics and Automation Letters}, year={2024}, volume={9}, number={6}, pages={5338-5345}, doi={10.1109/LRA.2024.3382529}, }</p>
Tomographic data for testing, demonstrating, and developing methods of removing ring artifacts
<p>These tomographic data were used for demonstrating our methods of eliminating ring artifacts published in Optics Express, <em>Nghia T. Vo, Robert C. Atwood, and Michael Drakopoulos, "Superior techniques for eliminating ring artifacts in X-ray micro-tomography," <strong>26</strong>, 28396-28412 (2018)</em><em>. </em>In sinogram, the artifacts appear as straight lines or stripe artifacts. The data have many types of stripe artifacts: full stripes, partial stripes, unresponsive stripes, fluctuating stripes, and blurry stripes. They are very useful for testing and developing methods of removing ring artifacts.</p> <p>Documentation: <a href="https://sarepy.readthedocs.io/">https://sarepy.readthedocs.io/</a></p> <p>Python implementations of these methods:</p> <p><a href="https://github.com/nghia-vo/sarepy">https://github.com/nghia-vo/sarepy</a></p> <p>In Tomopy:</p> <p><a href="https://tomopy.readthedocs.io/en/latest/api/tomopy.prep.stripe.html">https://tomopy.readthedocs.io/en/latest/api/tomopy.prep.stripe.html</a></p> <p>In Savu:</p> <p><a href="http://github.com/DiamondLightSource/Savu/tree/master/savu/plugins/ring_removal">https://github.com/DiamondLightSource/Savu/tree/master/savu/plugins/ring_removal</a></p> <p>In Algotom:</p> <p><a href="https://github.com/algotom/algotom/blob/master/algotom/prep/removal.py">https://github.com/algotom/algotom/blob/master/algotom/prep/removal.py</a> </p>
Raw Experimental Data for work presented in 'Leveraging Chaos for Wave-Based Analog Computation: Demonstration with Indoor Wireless Communication Signals'
<p>This is the raw experimental data for the work presented in 'Leveraging Chaos for Wave-Based Analog Computation: Demonstration with Indoor Wireless Communication Signals', to be published in Physical Review X.</p> <p> </p> <p>https://journals.aps.org/prx/accepted/dc07aKdcFa91ea06d2949139dac733fa62ce1c02c</p> <p> </p> <p>See the README files and sample pieces of codes for an explanation of the data.</p>
Consistent Modeling of GS 1826-24 X-Ray Bursts for Multiple Accretion Rates Demonstrates the Possibility of Constraining rp-process Reaction Rates
<p>MESA inlists and run_star_extras associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018arXiv180505552M/abstract">Meisel (2018)</a>. MESA version 9793.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/1538-4357/aac3d3">10.3847/1538-4357/aac3d3</a></p>
Tomographic data for demonstrating distortion correction methods
<p>Datasets were used to demonstrate the improvement of a tomographic data after distortion correction was applied.</p> <p>Details in the conference paper: </p> <p>Nghia T. Vo, Robert C. Atwood, and Michael Drakopoulos "Preprocessing techniques for removing artifacts in synchrotron-based tomographic images", Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111131I (10 September 2019); https://doi.org/10.1117/12.2530324</p> <p>Distortion coefficients were calculated using vounwarp package: </p> <p><a href="https://github.com/DiamondLightSource/vounwarp/blob/master/vounwarp/examples/example_01.py">https://github.com/DiamondLightSource/vounwarp/blob/master/vounwarp/examples/example_01.py</a></p> <p>Slices can be reconstructed using the <a href="https://github.com/DiamondLightSource/Savu">Savu</a> pipeline: <a href="https://github.com/DiamondLightSource/Savu/blob/master/savu/plugins/corrections/dark_flat_field_correction.py">flat-field correction</a>, <a href="https://github.com/DiamondLightSource/Savu/blob/master/savu/plugins/corrections/distortion_correction_dev.py">distortion correction</a>, <a href="https://github.com/DiamondLightSource/Savu/blob/master/savu/plugins/centering/vo_centering.py">center of rotation calculation</a>, <a href="https://github.com/DiamondLightSource/Savu/blob/master/savu/plugins/ring_removal/remove_all_rings.py">ring artifact removal</a>, and<a href="https://github.com/DiamondLightSource/Savu/blob/master/savu/plugins/reconstructions/astra_recons/astra_recon_gpu.py"> FBP reconstruction</a>.</p>
Real-Time High-Rate GNSS Displacements: Performance Demonstration During the 2019 Ridgecrest, CA Earthquakes
<p>Post-processed and real-time GNSS displacement waveforms for the 2019 Ridgecrest earthquakes. Waveforms are for the M6.4 and M7.1 events recorded at 1 and 5Hz sample rates. Data are in miniSEED format, channel codes LYE, LYN, LYZ correspond to east, north, and up respectively. Displacement units are meters and time is UTC. The data are trimmed 60s before the USGS origin times for<br> the earthquakes. No filtering has been applied.</p> <p>A paper describing the data has been submitted to SRL. In the meantime if you use the data please cite our <a href="https://eartharxiv.org/pdxqw/">preprint on the EarthArXiv</a> as:</p> <p>Melgar, D., TI Melbourne, BW Crowell, J Geng, W Szeliga, C Scrivner, M Santillan, DER Goldberg. 2019. Real-time High-rate GNSS Displacements: Performance Demonstration During the 2019 Ridgecrest, CA Earthquakes. EarthArXiv, doi:10.31223/osf.io/pdxqw.</p>
GitHub Pull Request Demonstration by Alexandr Smagin
<p>In this demonstration, NLU student Alexandr Smagin walks us through pull requests for the TOPS SCHOOL GitHub repository. You can watch the video below or find a link in the 'Additional details' section.</p>
How to Drawjectory? - Trajectory Planning using Programming by Demonstration (Reproducibility Package)
<p>The experiments were conducted following the scenario description from <em>scenario_description.pdf. </em>The scripts containing the DSL commands used for programming the trajectories can be found under <em>/dsl_scripts.</em></p> <p>The evaluation was conducted using the R (v3.6.3 and tested on 4.4.1 too) programming language. The main script is <em>experiments/evaluation.R</em> which includes and sources all other necessary scripts. To re-run this script, make sure to open the R project (<em>experiments/xperiment.Rproj</em>) and have the following packages installed (note: other versions may also work): </p> <ul> <li>jsonlite (1.8.8)</li> <li>magrittr (2.0.3)</li> <li>dplyr (1.1.4)</li> <li>ggplot2 (3.5.1)</li> <li>wesanderson (0.3.7)</li> <li>pracma (2.4.4)</li> <li>SimilarityMeasures (1.4)</li> <li>progress (1.2.3)</li> </ul>
High resolution reanalysis for the northern Adriatic Sea (CADEAU project - CMEMS Demonstration 32-DEM-L5)
<p><span><span>This dataset includes the output of the high resolution reanalysis carried out in the framework of the CADEAU project (CMEMS Demonstration 32-DEM-L5). </span></span></p> <p><span><span>The reanalysis covers the northern Adriatic Sea for the 2006-2017 time period; the simulation is characterized by a horizontal resolution of 1/128° and 27 non-equally spaced vertical layers.</span></span></p> <p><span><span>The dataset includes the following physical and biogeochemical variables (averaged every 5 days): (1) salinity (S), temperature (T), chlorophyll (Chla), nitrate (N3n), phosphate (N1p).</span></span></p>
Freedom of Spelling Demonstration
<p>This video demonstrates the performance and behind the scenes of the magic effect generated with the use of constraint programming called Freedom of Spelling to be published on the "27th International Conference on Principles and Practice of Constraint Programming (CP 2021)".</p>
Biomedical prototype for human movement data collection and basic collection procedure demonstrations
<p>Video explaining the usage of the 1st prototype of the device produced as well as basic collection procedure example.</p> <p>The video was originally published on <a href="https://www.youtube.com/watch?v=tWhNt0iaYUY">https://www.youtube.com/watch?v=tWhNt0iaYUY</a></p>
Virtual Storage Plant (VSP) preliminary demonstration of Load Frequency Control (LFC)
<p>This the dataset of preliminary demostration of using distributed control with consensus algorithm for utilizing a virtual storage plant for automatic load frequency control. The dataset contains raw data from 3 experiments (6 units in a VSP) for 5 scenarios: TC_5.02.01, TC_5.02.02, TC_5.02.03, TC_5.02.04, TC_5.02.05.</p> <p>The experiments were conducted at the University of Zagreb's Smart Grid Laboratory (SGLab).</p>
Virtual Storage Plant (VSP) preliminary demonstration of active power control
<p>This the dataset of preliminary demostration of using distributed control with consensus algorithm for dispatching a virtual storage plant. The dataset contains raw data from 4 experiments (3, 4, 5 and 6 units in a VSP) for 5 scenarios: TC_5.02.01, TC_5.02.02, TC_5.02.03, TC_5.02.04, TC_5.02.05.</p> <p>The experiments were conducted at the University of Zagreb's Smart Grid Laboratory (SGLab).</p> <p> </p>
Demonstration that the special relativity theory is wrong and that the Earth (as well as any other mass) is surrounded b y a media.
<p>This is a demonstration that the inertial reference frames of the special theory of relativity are wrong when applied to the associated wave of a particle. It follows that the Earth must be surrounded by a media, which is the reference for all velocities on the surface of the Earth. The same media is of course surrounding all masses.</p>
Playbacks of Asian honey bee stop signals demonstrate referential inhibitory communication
<p>Referential communication provides a sophisticated way in which animals can communicate information about their environment. Previously, research demonstrated that honey bee stop signals encode predator danger in their fundamental frequency and danger context in their duration. Here, we show that these signals also encode danger in their vibrational amplitude. Stop signals elicited by the more dangerous predator, the large hornet (<em>Vespa</em><em>mandarinia</em>) had significantly 1.5-fold higher vibrational amplitudes than those elicited by the small hornet predator (<em>Vespa</em><em>velutina</em>). We measured the freezing vibrational response thresholds, and show that these natural signals exceed response thresholds. Finally, with artificial playbacks of the vibratory stop signal, we demonstrate that these signals referentially encode the danger that foragers experience at food source. Stop signals elicited by the larger and significantly more dangerous predator (<em>V.</em><em>mandarinia</em>) were significantly 1.4-fold more inhibitory than stop signals elicited by the smaller and less dangerous predator (<em>V.</em><em>velutina</em>). This dataset contains all of the data used for the statistical analyses reported in this paper and to generate the figures.</p>
Spraints demonstrate small population size and reliance on fishponds for Eurasian otter (Lutra lutra) in Hong Kong
<p><span>Lack of data on population sizes and resource requirements are major impediments to the effective conservation of rare species globally. The conservation of the Eurasian otter (Lutra lutra) in Hong Kong reflects many of these key challenges for elusive and difficult-to-study mammals. It is a rare carnivore that has narrowly escaped extirpation, now surviving within a human-dominated environment. Using sign surveys and spraint analysis, we recorded only 40 fresh spraints from 246 otter signs locations, over four months of intensive sampling across two years. Records were restricted to the Mai Po wetlands, confirming this as the core area for Hong Kong's otter population. Molecular analysis and microsatellite genotyping identified a minimum of seven individuals, two pairs of which were likely related. The genetic and sign data together strongly indicate a small population. Fish dominated the otter diet, highlighting the importance of fishpond habitats as a premium foraging resource. Given the rapid changes surrounding the Mai Po area (especially the new Northern Metropolis Development Strategy), maintaining quality and connected habitats, in addition to sustaining commercial fishponds will be key to otter recovery and long-term population viability in Hong Kong.</span></p>
Demonstration of Simulation Tools for Electricity Markets considering Power Flow Analysis
<p>Two novel publicly available web services for electricity market (EM) simulation and study and power flow evaluation and validation are proposed, namely the Electricity Market Service (EMS) and Power Flow Service (PFS). EMS enables the simulation of two auction-based algorithms and the execution of three wholesale EMs. PFS provides the creation and evaluation of electrical grids from the transmission to distribution grids. Combining both services one can simulate EMs from wholesale to local markets and test if the results are compatible with a specific electrical grid. This dataset publishes the input and output data of a case study simulation scenario.</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.