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.
41
datasets available to search
ShareScore release 0.9.0
Dataset results
41 results for “Motion measurement”
Multi-year measurements of tree motion from an accelerometer on a spruce tree near Niwot Ridge, Colorado
<p>This repository includes 12 Hz three-axis acceleration data from an accelerometer mounted to the bole of a <em>Picea engelmannii</em> (engelmann spruce) next to the C-1 Ameriflux tower at Niwot Ridge LTER, Colorado, USA. The data were recorded from November 2014 through August 2020. More information on the installation can be found in Raleigh et al. (in review, Water Resources Research).</p> <p>The data are stored in netCDF files, chunked based on the collection date when the data were downloaded from the accelerometer.</p> <p><strong>File metadata:</strong></p> <p>Filename</p> <p>GCDC_L01_Raw_Data_Niwot_TreeXX_collection_YYYYMMDD.nc</p> <p>where</p> <p>XX = tree number (01 = spruce, 02 = fir)</p> <p>YYYYMMDD = year (YYYY), month (MM), and day (DD) of data collection</p> <p> </p> <p>Each netCDF includes four variables:</p> <p>1. serial_date = time increment (fractional days), as defined by Matlab: "A serial date number represents the whole and fractional number of days from a fixed, preset date (January 0, 0000) in the proleptic ISO calendar." The serial dates are in mountain standard time (MST) with no adjustments for daylight savings.</p> <p>2. Ax = acceleration in the vertical direction (counts)</p> <p>3. Ay = acceleration in the east-west direction (counts)</p> <p>4. Az = acceleration in the north-south direction (counts)</p> <p>To convert the "counts" unit to gravitational units (g), divide Ax, Ay, and Az each by 2048, as explained in the manufacturer's user manual.</p> <p> </p> <p> </p>
Multi-year measurements of tree motion from an accelerometer on a fir tree near Niwot Ridge, Colorado
<p>This repository includes 12 Hz three-axis acceleration data from an accelerometer mounted to the bole of an <em>Abies lasiocarpa</em> (subalpine fir) next to the C-1 Ameriflux tower at Niwot Ridge LTER, Colorado, USA. The data were recorded from November 2014 through August 2020. More information on the installation can be found in Raleigh et al. (in review, Water Resources Research).</p> <p>The data are stored in netCDF files, chunked based on the collection date when the data were downloaded from the accelerometer.</p> <p><strong>File metadata:</strong></p> <p>Filename</p> <p>GCDC_L01_Raw_Data_Niwot_TreeXX_collection_YYYYMMDD.nc</p> <p>where</p> <p>XX = tree number (01 = spruce, 02 = fir)</p> <p>YYYYMMDD = year (YYYY), month (MM), and day (DD) of data collection</p> <p> </p> <p>Each netCDF includes four variables:</p> <p>1. serial_date = time increment (fractional days), as defined by Matlab: "A serial date number represents the whole and fractional number of days from a fixed, preset date (January 0, 0000) in the proleptic ISO calendar." The serial dates are in mountain standard time (MST) with no adjustments for daylight savings.</p> <p>2. Ax = acceleration in the vertical direction (counts)</p> <p>3. Ay = acceleration in the east-west direction (counts)</p> <p>4. Az = acceleration in the north-south direction (counts)</p> <p>To convert the "counts" unit to gravitational units (g), divide Ax, Ay, and Az each by 2048, as explained in the manufacturer's user manual.</p> <p> </p>
Kuopio gait dataset: motion capture, inertial measurement and video-based sagittal-plane keypoint data from walking trials
<p>This dataset contains motion capture (3D marker trajectories, ground reaction forces and moments), inertial measurement unit (wearable Movella Xsens MTw Awinda sensors on the pelvis, both thighs, both shanks, and both feet), and sagittal-plane video (anatomical keypoints identified with the OpenPose human pose estimation algorithm) data.<br>The data is from 51 willing participants and collected in the HUMEA laboratory in the University of Eastern Finland, Kuopio, Finland, between 2022 and 2023. All trials were conducted barefoot.</p> <p>The file structure contains an Excel file containing information of the participants, data folders under each subject (numbered 01 to 51), and a MATLAB script.</p> <p>The Excel file has the following data for the participants:</p> <ul> <li><strong>ID</strong>: ID of the participants from 1 to 51</li> <li><strong>Age</strong>: age of the participant in years</li> <li><strong>Gender</strong>: biological sex as M for male, F for female</li> <li><strong>Leg</strong>: the participant's dominant leg, identified by asking which foot the participant would use to kick a football; R for right, L for left</li> <li><strong>Height</strong>: height of the participant in centimeters</li> <li><strong>Invalid_trials</strong>: list of invalid trials in the motion capture data (MOCAP) data, usually classified as such because the participant did not properly step on the middle force plate</li> <li><strong>IAD</strong>: inter-asis distance in millimeters, the distance between palpated left and right anterior superior iliac spine, measured with a caliper</li> <li><strong>Left_knee_width</strong>: width of the left knee from medial epicondyle to lateral epicondyle in millimeters, palpated and measured with a caliper</li> <li><strong>Right_knee_width</strong>: same as above for the right knee</li> <li><strong>Left_ankle width</strong>: width of the left ankle from medial malleolus to lateral malleolus in millimeters, palpated and measured with a caliper</li> <li><strong>Right_ankle_width</strong>: same as above for the right ankle</li> <li><strong>Left_thigh_length</strong>: the distance between the greater trochanter of the left femur and the lateral epicondyle of the left femur in millimeters, palpated and measured with a measuring tape</li> <li><strong>Right_thigh_length</strong>: same as above for the right thigh</li> <li><strong>Left_shank_length</strong>: the distance between the medial epicondyle of the femur and the medial malleolus of the tibia in millimeters, palpated and measured with a measuring tape</li> <li><strong>Right_shank_length</strong>: same as above for the right shank</li> <li><strong>Mass</strong>: mass in kilograms, measured on a force plate just before the walking measurements</li> <li><strong>ICD</strong>: inter-condylar distance of the knee of the dominant leg, measured from low-field MRI</li> <li><strong>Left_knee_width_mocap</strong>: distance between reflective MOCAP markers on the medial and lateral epicondyles of the knee in millimeters, measured from a static standing trial; -1 for missing (subject did not have those markers)</li> <li><strong>Right_knee_width_mocap</strong>: same as above for the right knee</li> </ul> <p>The folders under each subject (folders numbered 01 to 51) are as follows:</p> <ul> <li><strong>imu</strong>: "Raw" inertial measurement unit (IMU) data files that can be read with Xsens Device API (included in Xsens MT Manager 4.6, which may be unavailable these days, not sure). You won't need this if you use the data in the imu_extracted folder.</li> <li><strong>imu_extracted</strong>: IMU data extracted from those data files using the Xsens Device API, so you don't have to. <ul> <li>The data is saved as MATLAB structs where the fields are named as a sensor ID (e.g., "B42D48"). The sensor IDs and their corresponding IMU locations are as follows: <ul> <li>pelvis IMU: B42DA3</li> <li>right femur IMU: B42DA2</li> <li>left femur IMU: B42D4D</li> <li>right tibia IMU: B42DAE</li> <li>left tibia IMU: B42D53</li> <li>right foot IMU: B42D48</li> <li>left foot IMU: B42D51 (except for subjects 01 and 02, where left foot IMU has the ID B42D4E)</li> </ul> </li> <li>Some of the data are just zeros as they couldn't be read from these sensors, but under each sensor, the fields "calibratedAcceleration", "freeAcceleration", "time", "rotationMatrix", and "quaternion" contain usable data. <ul> <li>time: Contains time stamps of the measurement at each frame recorded at 100 Hz, so if you remove the first value from all values in the time vector and divide the result by 100, you will get the time in seconds from the beginning of the walking trial.</li> <li>calibratedAcceleration and freeAcceleration: Contain triaxial acceleration data from the accelerometers of the IMU. freeAcceleration is just calibratedAcceleration without the effect of Earth's gravitational acceleration.</li> <li>rotationMatrix: Orientations of the IMU as rotation matrices.</li> <li>quaternion: Orientations of the IMU as quaternions.</li> </ul> </li> </ul> </li> <li><strong>openpose</strong>: Trajectories of the keypoints identified from sagittal plane video frames, saved as json files. <ul> <li>The keypoints are from the BODY_25 model of OpenPose (https://cmu-perceptual-computing-lab.github.io/openpose/web/html/doc/md_doc_02_output.html).</li> <li>Each frame in the video has its own json file.</li> <li>You can use the function in the script "OpenPose_to_keypoint_table.m" in the root folder to read the keypoint trajectories and confidences of all frames in a walking trial into MATLAB tables. The function takes as argument the path to the folder containing the json files of the walking trial.</li> </ul> </li> <li>Note that some subjects (11, 14, 37, 49) do not have keypoint and IMU data.</li> </ul> <p>The folders under each subject are divided into three ZIP archives with 17 subjects each.</p> <p>The script "OpenPose_to_keypoint_table.m" is a MATLAB script for extracting keypoint trajectories and confidences from JSON files into tables in MATLAB.</p> <p><br><strong>Publication in Data in Brief</strong>: <a href="https://doi.org/10.1016/j.dib.2024.110841" target="_blank" rel="noopener">https://doi.org/10.1016/j.dib.2024.110841</a></p> <p><br><strong>Contact</strong>: Jere Lavikainen, jere.lavikainen@uef.fi</p>
Data Set - Laboratory measurement of the wave–induced plastic particles motion: The influence of wave period, plastic size and plastic density
<p><strong>Data set - Laboratory measurement of the wave–induced plastic particles motion: The influence of wave period, plastic size and plastic density</strong></p> <p>This data set describes the wave flume experimental data on the wave-induced plastic particles motion induced by different wave conditions and different plastic particles density and size. A manuscript is currently under review describing the analysis of the data.</p> <p>The data set is divided in two parts:</p> <p>- <strong>Wave flume hydrodynamics</strong>. With measured water surface elevation at different locations within the wave flume. These data are stored in txt files with headings describing the type of measurement, i.e. wave paddle motion, water surface elevation at different sensors, synchronization signal for the video-cameras.</p> <p>- <strong>Lagrangian trajectories. </strong>hdf5 files with information of the particles position, velocity and time (with respect to the synchronization signal in the respective hydrodynamic file) for each experiment. Two tar.gx files have been uploaded with trajectories information:</p> <p> - TOPIOS_Trajectories_FloatingParticles.tar.xz, with information of floating plastic particles and,</p> <p> - TOPIOS_Trajectories_NonfloatingParticles.tar.xz, with information of non-floating plastc particles.</p> <p>An excel file with information of filenames, cross-shore locations of sensors, plastic particles and wave conditions is also uploaded (TOPIOS_Control_exp.xlsx).</p> <p>Any question regarding the data can be addressed at jose.alsina@upc.edu</p>
Picosecond time-resolved antibunching measures nanoscale exciton motion, annihilation, and true number of chromophores
<p>The particle-like nature of light becomes evident in the photon statistics of fluorescence of single quantum systems as photon antibunching. In multichromophoric systems, exciton diffusion and subsequent annihilation occur. These processes also yield photon antibunching but cannot be interpreted reliably. Here, we develop picosecond time-resolved antibunching (psTRAB) to identify and decode such processes. We use psTRAB to measure the true number of chromophores on well-defined multichromophoric DNA-origami structures, and precisely determine the distance-dependent rates of annihilation between excitons. Further, psTRAB allows us to measure exciton diffusion in mesoscopic H- and J-type conjugated-polymer aggregates. We distinguish between one-dimensional intra-chain and three-dimensional inter-chain exciton diffusion at different times after excitation and determine the disorder-dependent diffusion lengths. Our method provides a new lens through which excitons can be studied at the single-particle level, enabling the rational design of improved excitonic probes such as ultra-bright fluorescent nanoparticles, and materials for optoelectronic devices. Here we demonstrate the raw data of DNA Origami Microscopy on which our findings based on. </p>
Assets for 'Phase correlation on the edge for estimating cloud motion' submitted to Atmospheric Measurement Techniques
<p>1. CMV-26-07-2016_ARM-SGP.gif Sample cloud motion vectors from TSI camera images over the United States Atmospheric Radiation Measurement user facility’s Southern Great Plains site.</p> <p>2. raindrop_02-01-2017_ARM-SGP.gif Rotation of cloud motion vectors from raindrop contaminated TSI camera.</p>
SNAPPING PSI surface motion measurements over selected sites presented in MDPI Remote Sensing paper "SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping"
<p>SNAPPING PSI surface motion measurements over selected sites as presented in the paper with the title "SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping" by Michael Foumelis, Jose Manuel Delgado Blasco, Fabrice Brito, Fabrizio Pacini, Elena Papageorgiou, Panteha Pishehvar and Philippe Bally on Remote Sensing Open Access Journal.</p> <p>Whenever using this dataset, please cite its original paper (<a href="https://doi.org/10.3390/rs14236075">https://doi.org/10.3390/rs14236075</a>) and include the reference to this dataset (<a href="https://doi.org/10.5281/zenodo.7369653">https://doi.org/10.5281/zenodo.7369653</a>).</p> <p>This dataset includes average Line-of-Sight velocities for the following sites and dates:</p> <table> <tbody> <tr> <td><strong>Site name</strong></td> <td><strong>Country</strong></td> <td><strong>Period</strong></td> <td><strong>Relative orbit</strong></td> <td><strong>Orbit direction</strong></td> </tr> <tr> <td>Cap-Haïtien</td> <td>Haiti</td> <td>Jan-2017 / Dec-2019</td> <td>106</td> <td>ascending</td> </tr> <tr> <td>Gran Renaissance Ethiopian Dam</td> <td>Ethiopia</td> <td>Jan-2019 / Jun-2021</td> <td>50</td> <td>descending</td> </tr> <tr> <td>La Palma Volcano</td> <td>Spain</td> <td>Jun-2019 / Dec-2021</td> <td>169</td> <td>descending</td> </tr> <tr> <td>Santorini Volcano</td> <td>Greece</td> <td>Apr-2015 / May-2021</td> <td>29</td> <td>ascending</td> </tr> <tr> <td>San Francisco</td> <td>USA</td> <td>Jan-2016 / Dec-2020</td> <td>115</td> <td>descending</td> </tr> <tr> <td>Thessaloniki International Airport (SKG)</td> <td>Greece</td> <td>Apr-2015 / Dec-2020</td> <td>102</td> <td>ascending</td> </tr> </tbody> </table>
Pre-seismic Sentinel-1 PSI surface motion measurements for the area affected by the February 2023 Türkiye–Syria earthquakes
<p>We have processed Copernicus Sentinel-1A data from 01/2019 to 01/2023 (descending track 21) over the broader area (approx. 48400 sq. km) affected by February 6, 2023, M7.8 and M7.5 earthquakes in Türkiye and Syria, utilizing the SNAPPING Persistent Scatterers Interferometry (PSI) medium resolution service of the Geohazards Exploitation Platform (GEP; <a href="https://geohazards-tep.eu">https://geohazards-tep.eu</a>).</p> <p>Measurements contain average Line-of-Sight (LoS) velocities, corresponding uncertainties, and the complete displacement time series. Please note that the original dataset of about 2M point measurements was split into parts, each containing 200k points, to facilitate easier manipulation and visualization.</p> <p>References</p> <p>[1] Foumelis, M.; Delgado Blasco, J.M.; Brito, F.; Pacini, F.; Papageorgiou, E.; Pishehvar, P.; Bally, P. SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping. Remote Sens. 2022, 14, 6075. <a href="https://doi.org/10.3390/rs14236075">https://doi.org/10.3390/rs14236075</a></p> <p>[2] SNAPPING – Surface motioN mAPPING Sentinel-1 on-demand processing service, Online tutorial, <a href="https://docs.terradue.com/geohazards-tep/tutorials/Snapping.html">https://docs.terradue.com/geohazards-tep/tutorials/Snapping.html</a>.</p>
Logistic Activity Recognition Challenge (LARa Version 03) – A Motion Capture and Inertial Measurement Dataset
<p><strong>LARa</strong><strong> Version 03</strong> is a freely accessible logistics-dataset for human activity recognition. In the “Innovationlab Hybrid Services in Logistics” at TU Dortmund University, two picking and one packing scenarios with 16 subjects were recorded using an optical marker-based Motion Capturing system (OMoCap), Inertial Measurement Units (IMUs), and an RGB camera. Each subject was recorded for one hour (960 minutes in total). All the given data have been labelled and categorised into eight activity classes and 19 binary coarse-semantic descriptions, also called attributes. In total, the dataset contains 221 unique attribute representations.</p> <p>The <strong>dataset was created according to the guideline</strong> of the following paper: “A Tutorial on Dataset Creation for Sensor-based Human Activity Recognition”, PerCom, 2023 DOI: <a href="http://dx.doi.org/10.1109/PerComWorkshops56833.2023.10150401">10.1109/PerComWorkshops56833.2023.10150401</a></p> <p>The LARa Version 03 contains a <strong>new Annotation tool </strong>for OMoCap and RGB Videos, namely, the <strong>S</strong>equence <strong>A</strong>ttribute <strong>R</strong>etrieval <strong>A</strong>nnotator (<strong>SARA</strong>). SARA, developed and modified based on the LARa Version 02 annotation tool, includes desirable features and attempts to overcome limitations as found in the LARa annotation tool. Furthermore, few features were included based on the explorative study of previously developed annotation tools, see journal. In alignment with the LARa annotation tool, SARA focuses on OMoCap and video annotations. However, it is to be noted that SARA was not intended to be a video annotation tool with features such as subject tracking and multiple subject annotations. Here, the video is considered to be a supporting input to the OMoCap annotation. We would recommend other tools for pure video-based multiple-human activity annotation, including subject tracking, segmentation, and pose estimation. There are different ways of <strong>installing the annotation tool</strong>: Compiled binaries (executable files) for Windows and Mac can be directly downloaded from here. Python users can install the tool from https://pypi.org/project/annotation-tool/ (PyPi): “pip install annotation-tool”. For more information, please refer to the “Annotation Tool - Installation and User Manual”.</p> <p><strong>Upgrade:</strong></p> <ul> <li>Annotation tool (<strong>SARA</strong>) added (for Windows and MacOS, including an installation and user manual)</li> <li>Neural Networks updated (can be used with the annotation tool)</li> <li>OMoCap data: <ul> <li>Annotation errors corrected</li> <li>Annotations reformatted, fitting the SARA annotation tool</li> <li>“additional annotated data” extended</li> <li>“Markers_Exports” added</li> </ul> </li> <li>IMU data (MbientLab and MotionMiners Sensors) <ul> <li>Annotation errors corrected</li> </ul> </li> <li>README file (protocol) updated and extended</li> </ul> <p> </p> <p><strong>If you use this dataset for research, please cite the following paper: “LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes</strong><strong>”, Sensors 2020, DOI: <a href="https://doi.org/10.3390/s20154083">10.3390/s20154083</a>.</strong></p> <p><strong>If you use the Mbientlab Networks, please cite the following paper: “From Human Pose to On-Body Devices for Human-Activity Recognition”, 25th International Conference on Pattern Recognition (ICPR), 2021, DOI: </strong><a href="https://doi.org/10.1109/ICPR48806.2021.9412283"><strong>10.1109/ICPR48806.2021.9412283</strong></a><strong>.</strong></p> <p>For any questions about the dataset, please contact Friedrich Niemann at friedrich.niemann@tu-dortmund.de.</p>
Data and code for "Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method" by Wallis et al. 2024
<p>This data and code is made available to support the article: "Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method" by Wallis et al. 2024".</p> <p>Includes: TMOC method output tide correlation, Antarctic Peninsula grounding line, DInSAR data, TMOC Code.</p> <p> </p> <p><strong>For the data:</strong></p> <p>These data are made available to acompany the article "Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method" by Wallis et al. (2024)</p> <p>This datset contains:</p> <p>AP_TMOC_tide_correlation_2019_2020.tif - Significance adjusted tide correlation values for the TMOC method for 2019-2020 for the Antarctic Peninsula.</p> <p>AP_GL_TMOC_2019_2020.shp - A continuous grounding line made from TMOC data and British Antarctic Survey Coastline Data. Intended for use by others.</p> <p>AP_GL_TMOC_2019_2020_source.shp - A discontinuous grounding line made from TMOC data and British Antarctic Survey Coastline Data including the source of each line segment.</p> <p>The folder 'Interferograms' contains the DInSAR products used in the manuscript, sorted by Sentinel-1 frame</p> <p> </p> <p><strong>For the code:</strong></p> <p>This code is made available to support the article "Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method" by Wallis et al.</p> <p>The authors take no responsibility for the quality of results derived using this code.</p> <p>This code is licensed under a Creative Commons Attribution 4.0 International Licence: http://creativecommons.org/licenses/by/4.0/</p> <p>external functions required:<br>geoimread - https://uk.mathworks.com/matlabcentral/fileexchange/46904-geoimread<br>polarstreo_inv - https://uk.mathworks.com/matlabcentral/fileexchange/32907-polar-stereographic-coordinate-transformation-map-to-lat-lon<br>CATS208 tide model and TMD 2.5 matlab toolbox - https://www.esr.org/research/polar-tide-models/tmd-software/</p> <p>The function TMOC_GL_v8 implements the TMOC method descibed in Wallis et al. 2024. This is a 'bring your own data' version.</p> <p>The script pp_folder prost-processes the outputs using the functuon LPfilt_cc</p> <p> </p>
Measuring Outcomes In Patients With Pulmonary Arterial Hypertension Not on Active Treatment (MOTION)
ClinicalTrials.gov study NCT02191137. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Strong-motion seismogeodesy by deeply coupling GNSS receivers and inertial measurement units
<p>Understanding the origin and mechanism of destructive earthquakes is predicated on the faithful recordings of their induced ground displacements near the clearly deformed epicentral regions. Global Navigation Satellite System (GNSS) receivers, such as the GPS sort, have been recognized as the best, if not the only reliable, tool to measure such large displacements. This is achieved within the GNSS receivers by continuously tracking the satellite carrier-phase signals. However, GNSS receivers are likely to lose their fidelity in recording the complete and accurate displacement waveforms in case of fierce ground motions, since their carrier-phase tracking becomes instable when strained by such persistent and high dynamic stress. We hence developed an advanced GNSS receiver architecture where the dynamic stress suffered by the carrier-phase tracking components is compensated for by an embedded inertial measurement unit consisting of one accelerometer and one gyroscope. In this case, the carrier-phase signals can be tracked steadily by GNSS receivers, and the displacement accuracy can be improved from sub-centimeter to millimeter level by about 70% when the ground accelerations reach twice the gravitational acceleration. We believe that this advanced GNSS receiver will be an excellent strong-motion seismometer recording displacements directly at a few millimeter accuracy without missing any earthquake signals, even in case of fierce ground motions.</p>
Data from: Measured perceptual nonlinearities show how ON-OFF asymmetric processing improves motion estimation in natural scenes
Animals detect motion using a variety of visual cues that reflect regularities in the natural world. Experiments in animals across phyla have shown that motion percepts incorporate both pairwise and triplet spatiotemporal correlations that could theoretically benefit motion computation. However, it remains unclear how visual systems assemble these cues to build accurate motion estimates. Here we use comprehensive measurements of fruit fly motion perception to show how flies combine local pairwise and triplet correlations to reduce variability in motion estimates across natural scenes. By generating synthetic images with statistics controlled by maximum entropy distributions, we showed that the observed improvement occurs only when light-dark asymmetries mimic natural ones. Thus, fly behavior suggests that asymmetric ON-OFF processing is tuned to the particular statistics of natural scenes. Since all animals encounter the world's light-dark asymmetries, many visual systems are likely to use asymmetric ON-OFF processing to improve motion estimation.
The intra- and inter-rater reliability of a variety of testing methods to measure shoulder range of motion, hand-behind-back and external rotation strength in healthy participants
<p>Dataset for a intra- and inter-rater reliability study of a variety of testing methods to measure shoulder range of motion, hand-behind-back and external rotation strength in healthy participants</p>
Robust CBF-based STL motion planning for socially responsible robot navigation in the presence of measurement noise
<p>Video of submitted paper entitled "Robust CBF-based STL motion planning for socially responsible robot navigation in the presence of measurement noise"</p>
Organ Motion and Early Tumor Response Measurement
ClinicalTrials.gov study NCT02139488. IPD Sharing: YES. Countries: 1. Publications: 1.
Knee Connect: Measuring Range of Motion and Gait Metrics After Total Knee Arthroplasty
ClinicalTrials.gov study NCT03651739. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
Time-motion-mode Ultrasound Diaphragm Measures in Patients With Acute Respiratory Distress in Emergency Department
ClinicalTrials.gov study NCT02273687. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data underpinning "Measuring out quasi-local integrals of motion from entanglement"
<p>Quasi-local integrals of motion are a key concept underpinning the modern understanding of many-body localisation, an intriguing phenomenon in which interactions and disorder come together. Despite the existence of several numerical ways to compute them---and astoundingly in the light of the observation that much of the phenomenology of many properties can be derived from them---it is not obvious how to directly measure aspects of them in real quantum simulations; in fact, the smoking gun of their experimental observation is arguably still missing. In this work, we propose a way to extract the real-space properties of such quasi-local integrals of motion based on a spatially-resolved entanglement probe able to distinguish Anderson from many-body localisation from non-equilibrium dynamics. We complement these findings with a new rigorous entanglement bound and compute the relevant quantities using tensor networks. We demonstrate that the entanglement gives rise to a well-defined length scale that can be measured in experiments.</p>
Feasibility of Measuring Volume of Inspiration Via Noninvasive Motion Sensors
ClinicalTrials.gov study NCT06609616. IPD Sharing: YES. Countries: 1. Publications: 0.
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.