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.
1,328
datasets available to search
ShareScore release 0.9.0
Dataset results
1,328 results for “motion”
Multi-temporal Structure from Motion ponit clouds of riparian vegetation
<p>the dataset consists of three pointclouds and two NIR orthomosaics generated through a Structure from Motion standard workflow of the same forested area. The study area is typical riparian habitat vegetation. The data were acquired in different phenological stages:</p> <p>the first acquisition was realised in leaves-off conditions (march 2020)</p> <p>The second acquisition was realised in June 2020</p> <p>the third acquisition was realised in July 2020.</p> <p>Reference system: WGS84/32N [EPGS: 32632]</p> <p>For further information regarding the data processing please refer to https://doi.org/10.3390/rs13091756<br> </p>
Dataset for generating LOD3 building models from structure-from-motion and semantic segmentation
<p>This repository contains the codes for computing geometrical digital twins as LOD3 models for buildings, using a structure from motion and semantic segmentation. The methodology hereby implements was presented in the paper [Generating LOD3 building models from structure-from-motion and semantic segmentation" by Pantoja-Rosero et., al. (2022)] (<a href="https://doi.org/10.1016/j.autcon.2022.104430">https://doi.org/10.1016/j.autcon.2022.104430</a>)</p>
California Peak Ground Motion Dataset
<p>Dataset of peak ground motion recordings from M3-M7 earthquakes in California from 2011-2022 compiled as part of USGS Award G21AP10284. Each row corresponds to a different ground motion record with the following field:</p> <ul> <li>evid: USGS ComCat event ID</li> <li>evmag: event magnitude</li> <li>evlon: event longitude</li> <li>evlat: event latitude</li> <li>evdep: event depth (km)</li> <li>net: network name of recording site</li> <li>sta: station name of recording site</li> <li>stlon: longitude of recording site</li> <li>stlat: latitude of recording site</li> <li>dist[km]: source-site distance in km</li> <li>pga[%g]: peak ground acceleration in %g</li> <li>pgv[cm/s]: peak ground velocity in cm/s</li> </ul> <p>Disclaimer: The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey.</p>
E2-Create Motion Bank Dataset
<p>The dataset contains motion capture and video recordings of six professional dancers performing solo dance improvisations. The improvisations represent the dancer's interpretations of four different task instructions "Unusual Poses, Movement Vocabulary, Movement Qualities, Improvisation to Music".</p> <p>The dataset forms part of a small study that has been conducted to compare movement representation employed by professional dancers in contemporary dance with those used for the computational acquisition and analysis of movement. The dataset also includes three interviews that have been conducted with some of the dancers who where involved in the motion capture recordings. These interviews address how the dancers interpreted the task instructions and what relevance the given tasks had to them.</p> <p> The dataset has been created at Motion Bank, Fachhochschule Mainz, in 2020. </p>
Suppression of 1/f noise in graphene due to non-scalar mobility fluctuations induced by impurity motion
<p>Experimental dataset for article ''Suppression of 1/f noise in graphene due to non-scalar mobility fluctuations induced by impurity motion'', <a href="https://doi.org/10.48550/arXiv.2112.11933">arXiv:2112.11933</a></p>
The Influence of Active and Passive Motion Experience on Infants' Visual Prediction Ability
<p>Data set of the article: The Influence of Active and Passive Motion Experience on Infants’ Visual Prediction Ability</p>
Figure 5. Forward walking image sequence-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>The behavior module determines the target position and orientation according to the results<br> of localization and the sensor measurements, and then constructs an action series which consists of<br> the elementary gaits to realize omni directional walking. The implementation of forward walking is<br> applying Virtual Slope Walking in the sagittal plane with the Lateral Swing Movement for lateral<br> stability. The sideward walking and turning is realized by carefully designing the key frames. All of<br> above gait is generated by connecting the key frames with smooth sinusoids. The forward walking<br> speed of PERSIA Humanoid Robot is 25cm/s. The image sequences of forward walking are shown<br> in Figure 5.</p>
Figure 2. Mechanical construction of the PERSIA humanoid robots-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>Figure 2 shows one of the constructions used for our robots. Knee joints are considered to<br> bend in both directions which help faster response of the robot in backward walking. Efforts have<br> been made to hold the proportions as much as possible human like. The PERSIA robot is 38cm tall<br> and weighs about 1.6 kg. It has 18 degrees of freedom: 5 in each leg, 3 in each hand and 2 in head.<br> To facilitate exchange of the players, all robots use mechanically the same structure.</p>
Figure 4. (a)Our Humanoid soccer robot, (b) Overview of the Control System-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>Figure 4 shows the block diagram of the software which runs in the robot’s main processor.<br> The program consists of 4 main blocks:<br> • Hardware Interface: Contains all low level routines to access hardware of the robot including<br> sensors and actuators.<br> • Vision: Contains image processing algorithms such as recognition of landmarks and other<br> object. Self localization is done using particle filtering. Particles are scored by comparing a<br> simulated image from each particle with the current frame captured by camera. Using<br> “Sampling-Importance Resampling” method, a new distribution of the particles is created after<br> each step.<br> Particles are also updated using a motion model. Final distribution of the particles converges to<br> the real pose of the robot.<br> • Planning: Planning system of the robot is based on a multi layer, and multi thread structure.<br> The layers are named Strategy, Role, Behavior and Motion. Each layer contains a Scenario<br> which runs in parallel with the scenarios in the other layers. A scenario in a higher level can<br> terminate and change the scenario running in the lower level; however it is usually done in<br> synchronization with the lower level scenario to avoid conflicts and instabilities. (Such as<br> stopping the walking motion while one of the feet is still in the air).<br> • Network: Mainly responsible for the wireless communication of the robot with the other robots<br> or the referee box. This is done via WLAN.<br> • Motion Control: manages all the actuators of the robot, and controls locomotion or any other<br> action of the robot according to the requests from Cognition.<br> • Sensor Control: manages other sensors, and interacts with the Sub-Controller.</p>
Figure 8. Artificial Intelligence Algorithm-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>This module receives information from Artificial Intelligent unit. Total functions about<br> Robot Behavior such as stability motors actions, robot path planning, turn camera, walking,<br> shooting, dribbling; motion and etc are controlled in this section.</p>
Figure 1. PERSIA Humanoid Robot in Robocup IranOpen2010 Competition-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>In this paper, we will at first describe the general hardware design of the PERSIA Humanoid<br> Robocup Team, (section 2) and after that focus on our scientific approaches in sensor fusion and<br> learning (section 3). Finally, section 4 concludes this paper. This document describes the current<br> state of the project as well as the intended development for the RoboCup 2010 competition.</p>
Figure 3. (a) Our Humanoid soccer robot, (b) Overview of the Control System-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>The PERSIA Humanoid robot designed for has multipurpose capability. This robot<br> equipped with main board for motion control, vision sensor, other balancing sensors, servo motors<br> and etc. Figure 3 shows picture of the robot and overview of the Persia humanoid robot control<br> system.</p>
Data products from "oMEGACat II - Photometry and proper motions for 1.4 million stars in Omega Centauri and its rotation in the plane of the sky"
<p>This repository contains the data products of the publication:<br><a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240403722H/abstract"> Häberle et al (2024): "oMEGACat II - Photometry and proper motions for 1.4 million stars in Omega Centauri and its rotation in the plane of the sky"</a></p> <p>A detailed description of the data products and their creation is given in the accompanying paper.</p> <p>The data products include:</p> <ul> <li>The astrometric catalog with position and proper motion information for around 1.4 million sources within the half-light radius of Omega Centauri. We provide the catalog in both the .fits and .mrt format.</li> <li>The 7 photometric catalogs (for the 7 different used Hubble Space Telescope filters), both in .fits and .mrt format.</li> <li>The 7 deep, stacked image mosaics (one for each filter) in .fits format</li> <li>In addition, we include an IPython Notebook with basic usage examples for all these files.</li> </ul> <p>Please cite the catalog paper <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240403722H/abstract">Häberle et al. (2024)</a> when using this work.</p> <p>In case of any questions, feel free to contact us using <a href="mailto:haeberle@mpia.de">haeberle@mpia.de</a></p> <p> </p> <p> </p>
Dataset of Human Hand Motion Planning
<p>This dataset contains 544 human hand motion trajectories in a point-to-point reaching experiment. The purpose of this dataset is to provide human demonstrations for imitation-learning- and reinforcement-learning -based robot motion planning. Refer to 'ReadMe.md' in the zip file for details about the format and usage of the dataset.</p>
Supplement - Structure from Motion Metadata and Outcomes
<p>Ground control points used to ensure Structure from Motion (SfM) terrain models were georectified (Westoby et al. 2012; Wolf 2021) using Emlid R2 RTK (real-time kinematic) GNSS (global navigation satellite system) system consisting of a base station set up over an established known point (established with Canadian Geodetic Survey of Natural Resources Canada (NRCAN) service Canadian Spatial Reference System Precise Point Positioning (CSRS- PPP)) and a rover. </p> <p>Once the ground control points were surveyed, aerial drone images were acquired. We created flight polygons in Drone Deploy. Pictures were captured with a DJI Mavic II drone with minimum 80 % overlap of photos. Drone Deploy was chosen because it has an option to account for the doming error commonly found in models created from drone imagery and structure from motion (SfM). The doming effect is a systematic error that impacts the DEMs vertical component and can provide errors larger than the usual centimeter level (Sanz-Ablanedo et al. 2020). Generally, each site was flown once in fall of 2020 and once in spring of 2021. </p> <p>We created orthorectified images and digital terrain models using Agisoft Metashape, a photogrammetric processing software application that uses SfM. We followed the workflow outlined in Bywater-Reyes and Pratt-Sitaula (2022). Once processed, orthorectified imagery and Digital Elevation Models (DEMs) were exported to ArcGIS Pro for additional analysis. Data collection metadata and postprocessing outcomes can be found in this Zenodo repository.</p>
Dataset: Garrett Motion Inc. (GTX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Silicon Motion Technology Corporation (SIMO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
FIGURE 3 in Forelimb motion and orientation in the ornithischian dinosaurs Styracosaurus and Thescelosaurus, and its implications for locomotion and other behavior
FIGURE 3. Stratigraphic distribution of ornithopod and basal ornithischian ichnogenera (after Lockley et al., 2003, 2009; Stanford et al., 2004; Díaz-Martínez et al., 2015; Salisbury et al., 2016), with time-calibrated phylogeny of Ornithopoda (after McDonald, 2012; Dieudonné et al., 2020; Kobayashi et al., 2021). Blue parts of the cladogram and blue manus and pes prints indicate taxa and ichnotaxa with manus enclosed in a mitten-like sheath of soft tissue. Striped blue and black on the cladogram indicates uncertainty: known fossils don't include enough of the manus to determine whether the fingers were enclosed in a mitten-like sheath of soft tissue. The unnamed tracks from Spain are those described by Pérez-Lorente et al. (1997).
FIGURE 1 in Forelimb motion and orientation in the ornithischian dinosaurs Styracosaurus and Thescelosaurus, and its implications for locomotion and other behavior
FIGURE 1. Right pectoral girdle and forelimb bones of the holotype of Styracosaurus albertensis (CMN 344) and motion at the shoulder. A. Right scapula and coracoid in lateral view. B–D. Humerus in lateral (B), posterior (C), and anterior (D) views, with broken white line indicating edge of humeral head. E–F. Radius and ulna in proximal (E) and distal (F) views. G. Range of parasagittal motion at the shoulder in lateral view. H. Range of motion at the shoulder in dorsal view. I. Range of transverse motion at the shoulder in anterior view, with radius and ulna included; the broken line indicates the humerus in the approximate position of full elevation through the transverse plane, and the unbroken line indicates the humerus in the position that was used for photographing it in position 3. J. Range of parasagittal and transverse motion at the shoulder in lateral view, with radius and ulna included. K–M. Fleshed out reconstructions of S. albertensis in anterior view in habitual posture for standing and locomotion (K), in anterior view with forelimbs in sprawling posture (L), and in lateral view with forelimbs in habitual posture for standing and locomotion (M). 1 – 3, positions 1 – 3 (see Materials and Methods for description), c, coracoid; g, glenoid cavity; h, humerus; hh, humeral head; r, radius; s, scapula; u, ulna.
FIGURE 2 in Forelimb motion and orientation in the ornithischian dinosaurs Styracosaurus and Thescelosaurus, and its implications for locomotion and other behavior
FIGURE 2. Right pectoral girdle and forelimb bones of Thescelosaurus sp. (NCSM 15728) and motion at the shoulder. A. Right scapulocoracoid in lateral view. B–D. Humerus in lateral (B), posterior (C), and anterior (D) views, with broken white line indicating edge of humeral head. E–F. Radius and ulna in proximal (E) and medial (F) views. G. Motion at the shoulder in lateral view. H. Transverse motion at the shoulder in anterior view. I–J, Skeletons of Thescelosaurus sp. NCSM 15728 (I) and CMN 8537 (J), showing that the curvature of the anterior dorsal vertebrae positions the forelimb such that it can reach the ground when the sacrum is horizontal. K, Tracing of several of the bones of CMN 8537 (vertebral centra, femur, tibia + fibula + proximal tarsals, metatarsus + distal tarsal, scapulocoracoid, humerus, radius + ulna, and carpus + metacarpus), with corrections of the dislocations at the hip and posterior dorsum in the preserved skeleton, and with heavy lines representing the long axis of the sacrum and the surface of the ground, showing that the forelimb can reach the ground and can also be retracted to avoid the ground during bipedal locomotion. L, Fleshed-out reconstruction of Thescelosaurus sp. posed as in K. 1–3, positions 1–3 (see Materials and Methods for description), c, coracoid; ca, carpals; g, glenoid cavity; h, humerus; hh, humeral head; r, radius; s, scapula; u, ulna.
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.