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1,328 results for “motion”
User Study Data for "Perception of Ultrasound Haptic Focal Point Motion"
<p>Data from two experiments about the perception of ultrasound haptic feedback.</p>
Geometry and tool motion planning for curvature adapted CNC machining
<p>Examples of 5-axis CNC machining tool paths and corresponding g-codes for a concave, convex and a freeform surface milling with a toroidal cutter (supported information for the paper "Geometry and tool motion planning for curvature adapted CNC machining", DOI 10.1145/3450626.3459837).</p> <p>In the 'path.txt' files, each line contains three numbers that are Euclidean coordinates of the contact points; in the 'positions.txt' files, each line contains six numbers: the first three being the coordinates of the centers of the torus and the other three being the coordinates of the unit axis vector of the tool, pointing outside the surface. </p>
Motion Capture Data for Hand Motion Embodiment
<h1>Dataset</h1> <p>A dataset of human manipulation actions recorded with a motion capture system.</p> <p>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>.</p> <p>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.</p> <h1>Publication</h1> <p>This dataset was introduced in the following paper:</p> <p>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.</p> <p>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>.</p> <p>If you use the dataset, please cite the paper as:</p> <blockquote> <p>@INPROCEEDINGS{Fabisch2022,<br> author={Fabisch, Alexander and Uliano, Manuela and Marschner, Dennis and Laux, Melvin and Brust, Johannes and Controzzi, Marco},<br> booktitle={2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)}, <br> title={A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations}, <br> year={2022},<br> pages={801--808},<br> doi={10.1109/Humanoids53995.2022.10000165}<br>}</p> </blockquote> <h1>Ethics Approval</h1> <p>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.</p> <h1>Origin and Funding</h1> <p>This dataset is provided by the Robotics Innovation Center, DFKI GmbH.</p> <p>This work was supported by the European Commission under the Horizon 2020 framework program for Research and Innovation (project acronym: APRIL, project number: 870142).</p>
QUIQI II Dataset - Statistical analyses of motion-corrupted MRI relaxometry data
<p>QUIQI II package includes supporting material for the scientific article by Corbin et al. entitled ‘Statistical analyses of motion-corrupted MRI relaxometry data’.</p> <p>The complete support package for the QUIQI method includes:</p> <ol> <li>A copy of the original analysis code used to compile the results presented in the original scientific publication (doi: 10.5281/zenodo.7612032) </li> <li>A subset of the data used in the original publication for computation of the results. This data also includes a set of analysis results obtained by running the code described in 1. on the provided data.</li> </ol> <p><br> The combination of 1. and 2. allows users to replicate the computation of the provided analysis results.<br> <strong>The material provided here only concerns part 2. of the QUIQI support package described above - subset of the data used in the original publication.</strong></p>
DeepDance: Motion capture data of improvised dance (2019)
<p><em>When using this resource, please cite Wallace, B., Nymoen, K., Martin, C.P & Tøressen, J. DeepDance: Motion capture data of improvised dance (2019) (version 2.0). Zenodo 10.5281/zenodo.5838178</em></p> <p><strong>Abstract</strong></p> <p>This dataset comprises full-body motion capture of improvised dance as well as corresponding audio files. 30 dancers were recorded individually, improvising to six different audio files. The motion was captured in units of mm at 240Hz using a Qualisys infra-red optical system. The experiment was carried out at the University of Oslo in October 2019. For each dancer, 3 performances are recorded for each musical piece, resulting in 540 1-minute motion capture files. The dataset was collected for use as training data in deep learning for motion generation. This dataset also includes MATLAB code to visualize the motion capture files.</p> <p> </p> <p><strong>Music</strong></p> <ul> <li>Skarphedinsson, M. Wallace, B. (2019). “Song a”</li> <li>Skarphedinsson, M. Wallace, B. (2019). “Song b”</li> <li>Skarphedinsson, M. Wallace, B. (2019). “Song c”</li> <li>Skarphedinsson, M. Wallace, B. (2019). “Song d”</li> <li>Skarphedinsson, M. Wallace, B. (2019). “Song f”</li> <li>LaClair, J. Bounce. Jesse LaClair, (2018) <em>Referenced here as “Song e”</em></li> </ul> <p> </p> <p><strong>Data Description</strong></p> <p>The following data types are provided:</p> <ul> <li>Motion (marker position): Recorded with Qualisys Track Manager and saved as tab-separated .tsv files.</li> <li>Stimuli: audio .wav files containing 1 minute of the tracks described above.</li> <li>MATLAB script for animating the tsv files. (requires the MoCap Toolbox)</li> </ul> <p>Note: Recordings which contained errors such as missing markers have been replaced by subject 001. </p> <p><strong>Acknowledgements</strong></p> <p>This work was partially supported by the Research Council of Norway through its Centres of Excellence scheme, project number 262762.</p> <p> </p> <p><strong>Conflicts of Interest</strong></p> <p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p> <p> </p>
Free energy simulations of receptor-binding domain opening in the SARS-CoV-2 spike indicate a barrierless transition with slow conformational motions
<p>This online data set accompanies the manuscript entitled "Free energy<br> simulations of receptor-binding domain opening in the SARS-CoV-2 spike<br> indicate a barrierless transition with slow conformational motions."</p> <p>The dataset is composed of the following files:</p> <p>* pmf0-now.dcd -- pmf63-now.dcd : molecular dynamics trajectory frames in<br> each of the 64 umbrella sampling windows, from which water has been<br> removed to save space</p> <p>* s1am_0-now.pdb -- s1am_63-now.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, from which water has been removed,<br> corresponding to the trajectory data above</p> <p>* view -- Visual Molecular Dynamics command script to load a trajectory, <br> e.g., in Linux, use "vmd -e view"</p> <p>* s1am_0-cg.dcd -- s1am_63-cg.dcd : molecular dynamics<br> trajectory frames in each of the 64 umbrella sampling windows, coarse-grained to<br> 1 bead per residue.</p> <p>* s1am_0-cg.pdb -- s1am_63-cg.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, corresponding to the coarse-grained trajectory<br> data above.</p> <p>* viewcg -- Visual Molecular Dynamics command script to load a<br> coarse-grained trajectory, e.g., in Linux, use "vmd -e viewcg"</p> <p>* 0readme -- brief instructions on how to view the trajectories</p> <p>* colors.vmd -- utility script for VMD</p> <p>* covmacros.vmd -- VMD script to define coronavirus spike subdomains</p> <p>* fe.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the free energy profiles</p> <p>* diff.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the diffusion and mean first passage times calculations</p> <p>* pca-qha.zip -- ZIP archive that contains the data and Matlab analysis files<br> to compute the autocorrelation functions of trajectory displacements<br> along principal/quasiharmonic modes</p> <p>Each ZIP archive contains a "0readme" file with brief instructions, and also the <br> results of the calculations<br> </p>
The effect of normal stress oscillations on fault slip behavior near the stability transition from stable to unstable motion
<p>Tectonic fault zones are subject to normal stress variations with a wide range of spatio-temporal scales. Stress perturbations cover a wide range of frequencies and amplitudes from high frequency seismic waves generated by earthquakes to low frequency transients associated with solid Earth tides. These perturbations can reactivate critically stressed faults and trigger earthquakes. Here, we describe lab experiments to illuminate the physics of such changes in friction and the mechanics of earthquake triggering and fault reactivation. Friction tests were done in a double direct shear configuration for conditions near the stability transition from stable to unstable motion. We studied simulated fault gouge composed of quartz powder and conducted experiments at reference normal stress from 10 to 13.5 MPa. After shearing to steady state sliding, we applied sinusoidal normal stress oscillations of amplitude 0.5 to 2 MPa, and period of 0.5 to 50 s. We performed numerical simulations using measured values of rate/state friction (RSF) parameters to assess our data. Our results show that low frequency stress oscillations cause a Coulomb-like response of shear strength that transitions from stable slip to slow lab earthquakes as frequency increases. At the critical frequency predicted by RSF we observe periodic stick-slip behavior. Perturbations of high amplitude and short period weaken the fault, while lower amplitudes strengthen the fault. We find that a modified RSF formulation is able to accurately match our laboratory data. Our findings highlight the complex effects of stress perturbations for fault strength and the mode of fault slip.</p> <p>The data are uploaded are structured as follow:</p> <p>1) For each experiment a .txt file of the datafile that is recorded from the machine (raw data) and a binary file containing the elaborated data (data_rp). The experiments information are listed in experiment_info.txt</p> <p>2) The folder <a href="https://zenodo.org/api/files/89fe30fb-a2cb-4fcb-b9df-db80583fc652/codes_results.zip">codes_results.zip</a> contain the codes of the data analysis and the related results </p> <p>The data are analyzed using rawPy that can be found at <a href="https://github.com/marcoscuderi/rawPy">https://github.com/marcoscuderi/rawPy</a></p> <p>For any additional information please do not hesitate to contact the corresponding author Federico Pignalberi at federico.pignalberi@uniroma1.it</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>
Age-related humerothoracic, scapulothoracic, and glenohumeral kinematics during elevation and rotation motions
<p>Age affects gross shoulder range of motion (ROM), but biomechanical changes over a lifetime are typically only characterized for the humerothoracic joint. Suitable age-related baselines for the scapulothoracic and glenohumeral contributions to humerothoracic motion are needed to advance understanding of shoulder injuries and pathology. Notably, biomechanical comparisons between younger or older populations may obscure detected differences in underlying shoulder motion. Kinematics derived from healthy subjects aged <35 years and >45 years using biplane fluoroscopy and skin-marker motion analysis quantified humerothoracic, scapulothoracic, and glenohumeral motion during 3 static poses (resting neutral, internal rotation to L4-L5, and internal rotation to maximum reach) and 2 dynamic activities (scapular plane abduction and external rotation in adduction). These data are available for download to aid researchers and clinicians in characterizing non-pathologic shoulder motion during common clinical ROM activities. </p>
First motion data and focal mechanism solutions of 108 earthquakes occurred between 1928 and 2019 in the Southeastern Alps
<p>This dataset contains the P-wave polarities readings (FPS_polarities_input.zip) and the focal mechanisms (FPFIT_solution.pdf, FPFIT_solution.csv) obtained by the FPFIT algorithm (Reasenberger and Oppenheimer, 1985) of 108 earthquakes with 1.9 ≤ M<sub> </sub>≤ 4.8 occurring between 1928 and 2019 in the Southeastern Alps area (latitude 45°N-47.5°N and longitude 10°E-15°E). The preferred solution for each earthquake has been reported in the focal mechanism catalogue of Saraò et al. (2020).</p> <p>The first polarities used to compute the focal mechanisms were manually picked from seismograms of the National Institute of Oceanography and Applied Geophysics (OGS) northeastern Italy seismic and deformation network (Priolo et al., 2005; Bragato et al., 2011, Bragato et al., 2020)or extracted from the Bulletin of the International Seismological Centre the Seismological Bulletin of Slovenia. The polarities were also read from the seismograms archived in various Italian and European seismological observatories, many of which are no longer operating (Osservatorio meteorico-sismico nel Seminario - Chiavari; ENEL, Osservatorio Ximeniano - Florence, Osservatorio Astronomico "Brera" -Milan, &nbspDipartimento di Fisica dell’Università di Padova - Padua,;Osservatorio S. Domenico – Prato, Osservatorio meteoro-sismico nel Santuario di N.S. - Oropa, Osservatorio Bina - Perugia, Osservatorio "Valerio"- Pesaro, Osservatorio meteorico-sismico nel Collegio Alberoni - Piacenza, Osservatorio Meteorico Istituto Fisica - University of Siena,Sismografi Lungo Periodo di Mantovani (Bologna, Bolzano, Grosseto, Naples, Olbia, Palermo, Turin), Osservatorio meteorico-sismico nel Seminario Maggiore - Treviso, Osservatorio meteorico-sismico nel Seminario Patriarcale – Venice, Ljubljana, Munich, Stuttgart, Vienna).</p> <p>For more details</p> <p>Saraò, A., Sugan, M., Bressan, G., Renner, G., and Restivo, A.: A focal mechanism catalogue of earthquakes that occurred in the southeastern Alps and surrounding areas from 1928–2019, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2020-369, in review, 2021.</p> <p> </p> <p>References:</p> <p>Bragato, P.L., Di Bartolomeo, P., Pesaresi, D., Plasencia Linares, M., and Saraò A.: Acquiring, archiving, analyzing and exchanging seismic data in real time at the Seismological Research Center of the OGS in Italy, Ann. Geophys. 54, 67–75, https://doi.org/10.4401/ag-4958, 2011.</p> <p>Bragato P.L., P. Comelli, A. Saraò, D. Zuliani, L. Moratto, V. Poggi, G. Rossi, C. Scaini, M. Sugan, C. Barnaba, P. Bernardi, M. Bertoni, G. Bressan, A. Compagno, P. Di Bartolomeo, E. Del Negro, P. Fabris, M. Garbin, M. Grossi, A. Magrin, E. Magrin, D. Pesaresi, B. Petrovic, M.P. Plasencia Linares, M. Romanelli, A. Snidarcig, L. Tunini, S. Urban, E. Venturini and S. Parolai (2020). The OGS- North-Eastern Italy Seismic and Deformation Network: current status and outlook. Submitted to Seism. Res. Lett. </p> <p>Priolo, E., Barnaba, C., Bernardi, P., Bernardis, G., Bragato, P.L., Bressan, G., Candido, M., Cazzador, E., Di Bartolomeo, P., Durì, G., Gentili, S., Govoni, A., Klinc, P., Kravanja, S., Laurenzano, G., Lovisa, L., Marotta, P., Michelini, A., Ponton F., Restivo, A., Romanelli, A., Snidarcig, A., Urban, S., Vuan, A., Zuliani, D.: Seismic monitoring in northeastern Italy: A ten-year experience, Seismol. Res. Lett., 76, 446–454, https://doi.org/10.1785/gssrl.76.4.446, 2005.</p> <p>Reasenberg, P., Oppenheimer, D.: FPFIT, FPPLOT and FPPAGE: Fortran computer programs for calculating and displaying earthquake fault-plane solutions, Open-File Rep., 85-739, USGS, Menlo Park, 109 pp., 1985.</p> <p>Saraò A., Sugan M., Bressan G., Renner G., Restivo A., 2020: Focal mechanisms of Southeastern Alps and surroundings, doi: 10.5281/zenodo.4284971 .</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>
A Kalman Filter Approach to the Fusion of Acceleration, GNSS position and Rotation Sensor Data from Robot Motions
<p><strong>GNSS data:</strong></p> <ul> <li>Instrument: Javad antenna and Septentrio receiver</li> <li>sampling rate: 100 Hz</li> <li>Bandwidth of loop filter: auto adjust</li> <li>Relative positioning </li> <li>Baseline: ultra short with distance of 5 m</li> <li>files in Rinex format: Rover (moving antenna) and Base (stationary antenna), .20G (GLONASS Navigation data), .20N (GPS Navigation data), .20L (Galileo Navigation data), .20O (Observations)</li> </ul> <p><strong>Accelerometer data:</strong></p> <ul> <li>Instrument: EpiSensor and Centaur Digitizer</li> <li>Sampling rate: 250 Hz</li> <li>Unit: counts</li> <li>unfiltered</li> <li>file: XKUK_centaur-6_1233_20200908_114500.seed</li> </ul> <p><strong>Angular rate data:</strong></p> <ul> <li>Instrument: IMU KvH 1750 (includes accelerometer and rotational sensor)</li> <li>Sampling rate: 250 Hz</li> <li>Unit gyro: rad/s</li> <li>Unit accelerometer: g (gravitational acceleration)</li> <li>file: LOGGING_1750_IMU_1308K004_11_57_25_250.csv</li> </ul> <p><strong>Robot Feedback:</strong></p> <ul> <li>Instrument: KUKA model AGILUS KR 6 R900 sixx</li> <li>Sampling rate: 250 Hz</li> <li>Unit translation: m</li> <li>Unit rotation: degree</li> <li>files: kuka_motion_*.txt, 1-4 are consecutive in time.</li> </ul> <p><strong>Experiments:</strong></p> <ul> <li>T: translations, R: rotations, XL, L, S denote the relative amplitudes</li> <li>10 experiments: TLRXL, TLRXL, TLRL, TLRL, TLRS, unfinished TLRS, TLRS, TLRS, TSRS, TSRS (Robot feedback (1,2), angular rate, GNSS data)</li> <li>9 experiments: TLRXL, TLRXL, TLRL, TLRL, TLRS, unfinished TLRS, TLRS, TSRS, TSRS (Robot feedback (3,4), accelerometer data</li> </ul>
Non-local interactions in collective motion
<p>The collective motion of animal groups often exhibits velocity-velocity correlations between nearest neighbours, with the strongest velocity correlations observed at the shortest inter-animal spacings. This may have been a motivational factor in the development of models based primarily on short-ranged interactions. Here we ask whether such observations necessarily mean that the interactions are short-ranged. We develop a minimal model of collective motion capable of supporting interactions of arbitrary range and show that it represents a counter-example: the strongest velocity correlations emerge at the shortest distances, even when the interactions are explicitly non-local.</p>
Data from Churan et al. Action-dependent processing of self-motion in parietal cortex of macaque monkeys
<p><strong>Animals</strong></p> <p>Two adult male monkeys (macaca mulatta) participated in the study. Single-unit recordings were done using standard tungsten microelectrodes (FHC, Bowdoin, USA) with an impedance of ~2 MΩ at 1 kHz that were positioned by an hydraulic micromanipulator (MO-95, Narishige, Tokyo, Japan). A stainless-steel guiding tube was used for transdural penetration and support of the electrode. The neuronal signal was processed using a commercial system (Alpha Omega, Nof HaGalil, Israel). It was band-pass filtered (cut-off frequencies at 500 Hz and 8000 Hz) and sampled at 44 kHz.</p> <p><strong>Apparatus</strong></p> <p>During recordings, the monkeys were sitting head-fixed in a primate chair in a dark room, and their eye-position was monitored at 1000 Hz using a video-based eye tracker (EyeLink 1000, SR Research, Ottawa, Canada). The chair was positioned at a distance of 97 cm from a semi-transparent screen (size 160 cm x 90 cm, subtending the central 79 deg x 50 deg of the visual field) on which the visual stimuli were back-projected using a PROPixx-projector (VPixx Technologies, St-Bruno de Montarville, Canada) running at a resolution of 1920 x 1080 pixels and at a frame rate of 100 Hz. A custom-made touch sensor (length 10 cm, diameter 1 cm) was integrated into the monkey chair in front of the monkey and its status was monitored online at a sampling rate of 1 kHz.</p> <p><strong>Data processing</strong></p> <p>Single units were isolated using a semi-manual spike sorter (Plexon Inc, Dallas, Texas). To this end we used a threshold on the electrode signal that was set manually to separate the action potentials from noise. The samples that exceeded the threshold were further analyzed using principal components as well as other features that were derived from the signal (like local maxima and minima). Then clusters of samples with similar properties were identified visually and each defined as representing a single unit. For a detailed description of the sorting process see the offline User Guide (Plexon, 2020).</p> <p>Further description of the Methods, see: Churan et al. 2021, doi: 10.1152/jn.00049.2021</p> <p><strong>Data:</strong></p> <p>The file '<strong>data_active_passive.mat</strong>' contains following variables:</p> <p>monkey: code for the tested monkey (1=monkey S, 2=monkey O)</p> <p>baseline: Mean and standard deviation of the activity in a time window of 150 ms to 20 ms before the press of the button.</p> <p>reaction: Mean time between the switch of the color of the fixation point from red to green and the time of the button press.</p> <p>anti_p: Significance of a one sided t-test between the baseline activity and activity 200 ms to 0 ms prior to the onset of stimulus motion.</p> <p>p_win (a (1-3),b (1-3),c (1-3),n(1-110)): 4D matrix containing p-values of t-tests</p> <p>a:</p> <p>1: Was preparatory activity significantly higher in the passive relative to the active condition?</p> <p>2: Was preparatory activity significantly lower in the passive relative to the active condition?</p> <p>3: Was the tonic motion response (200 ms to 500 ms after motion onset) significantly different between the active and the passive conditions?</p> <p>b:</p> <p>1: Calculation was made based on all motion directions</p> <p>2: Calculation was made based on the preferred motion direction</p> <p>3: Calculation was made based on the flanking motion directions</p> <p>c:</p> <p>1: Calculation was made based on all presented delays</p> <p>2: Calculation was made based on the shorter set of delays (500 ms to 700 ms)</p> <p>3: Calculation was made based on the longer set of delays (701 ms to 1000 ms)</p> <p>n: number of the investigated neuron</p> <p>psth_alldir: cell array containing the PSTHs (obtained by convolving each spike with a Gaussian as described in the manuscript) in a time window between 1000 ms before and 800 ms after the onset of motion (in 1 ms steps). PSTHs were calculated based on data from all tested directions. Each cell array consists of 4 elements containing different conditions:</p> <p>1: active condition</p> <p>2: passive condition shorter set of delays (500 ms to 700 ms)</p> <p>3: passive condition longer set of delays (701 ms to 1000 ms)</p> <p>4: passive condition all delays</p> <p>psth_bestdir: same as above - using only the preferred direction</p> <p>psth_nbestdir: same as above - using only the flanking directions</p> <p>d_alldir: cell array containing the continuous d-prime (as described in the manuscript) in a time window between 1000 ms before and 800 ms after the onset of motion (in 1 ms steps). d' were calculated based on data from all tested directions. Each cell array consists of 4 elements containing different conditions:</p> <p>1: active condition</p> <p>2: passive condition shorter set of delays (500 ms to 700 ms)</p> <p>3: passive condition longer set of delays (701 ms to 1000 ms)</p> <p>4: passive condition all delays</p> <p>d_bestdir: same as above - using only the preferred direction</p> <p>d_nbestdir: same as above - using only the flanking directions</p> <p>The file '<strong>timecourse_preparatory.mat</strong>' contains the cell array 'd_alldir_preparatory' that consists of 201 elements. Each of the elements contains PSTHs of 23 neurons that have exhibited significant preparatory activity in the passive condition in a time window 1000 ms to 0 ms before the motion onset. Each of the 201 elements describes a specific range of delays between button press and motion onset. This delay range is always a 100 ms wide sliding window, e.g. the element 1 represents delays between 500 and 600 ms, in element 2, the delays are between 501 and 601 ms and so on with the last element (201) representing delays between 700 and 800 ms.</p> <p>Some example code that re-creates most of the figures from the manuscript and that may serve as a starting point for further exploration of the data is available on request from the corresponding author.</p>
Data from Study: Respiratory Motion Correction of PET using MR-Constrained PET-PET Registration
<p>This dataset contains the data used to arrive at the conclusions in the research article <em>Respiratory Motion Correction of PET using MR-Constrained PET-PET Registration</em>, by Balfour et al [<em>BioMedical Engineering OnLine</em> 2015, <strong>14</strong>:85].</p> <p>This study was based upon motion-affected PET images simulated from real dynamic MR image volumes, simulated and reconstructed using the Software for Tomographic Image Reconstruction ("STIR", see http://stir.sourceforge.net/). This dataset includes data from MR scans of 4 healthy volunteers (male, aged 22-33).</p> <p>Three types of data are provided, which should be sufficient for repeating the findings of the study:</p> <ul> <li>Reconstructed PET image volumes, split into 6 respiratory bins ("gates") for each simulation</li> <li>The dynamic 3D MR volumes used to derive the respiratory motion of each volunteer</li> <li>Text files outline which dynamics have NOT been used for PET simulation - these are the ones used to make the motion model in the study</li> </ul> <p>These MR volumes were registered and combined with the head-foot position of the right hemidiaphragm to form a respiratory motion model, which was subsequently used to constrain PET to PET image registration, attempting to correct for the motion in the PET images.</p> <p>For more detailed information regarding the method, please refer to the article.</p> <p>The PET data is split into several sub-categories:</p> <ul> <li>Volunteer ID (4 possibilities, anonymised)</li> <li>Lesion position (9 possibilities - see article for locations)</li> <li>Lesion diameter, in millimetres (10 or 14 mm)</li> <li>Respiratory gate number, ranging from 1 (most inhaled) to 6 (most exhaled)</li> </ul> <p>Note that there are two types of each simulation: with motion, and without motion. These are included in the respective zip files for each volunteer ID.</p> <p> </p>
Development and impact analysis of ground motion datasets for potential strong-to-great seismic scenarios in Chinese mainland
<p>地震动情景数据对于评估地震灾害损失至关重要,并且是协作式多学科地震风险分析和区域灾害预防的基础要素。这项研究根据地震灾害分区数据确定了 50 个潜在的地震成因位置,并得到了地质和地震学证据的支持。根据潜在的损坏程度选择了四个震级(Mw 6.5、Mw 7.0、Mw 7.5 和 Mw 8.0),最终建立了 200 个地震情景。该数据集包括峰值地面加速度 (PGA)、峰值地面速度 (PGV) 和地震强度,是使用之前在应急响应中验证的强大 GMPE 生成的。这为灾害预防、减灾和城市规划提供了有用的支持,使其适用于许多分析需求。</p> <p><strong><em>注意: </em> 这是对高风险地震断层地震情景的模拟,旨在帮助决策者采取主动措施来减轻潜在地震的影响。它还为相关研究人员提供了一组可用的数据资源。请不要使用这些数据来生成或传播错误信息!</strong></p>
OCDetect - A Real-World Dataset to Detect Handwashing in Daily-Life using Wrist Motion Data from Wearables
<p>Handwashing detection is a relevant research topic with applications in healthcare and professional environments. While usually related to hygiene improvement, handwashing detection could also be used to support individuals with obsessive-compulsive disorder (OCD). For these individuals, compulsive, long, and frequent handwashing has a negative impact. An automated system could spot compulsive handwashing in real-time and augment the therapy process. No activity recognition datasets containing in-the-wild-recorded compulsive handwashing are available. With this work, we present the OCDetect Dataset, the first dataset with unscripted, compulsive handwashing. It contains recordings from inertial measurement units (IMUs) of 22 participants over 28 days, with ~3000 recorded hand washes. For each hand wash, we supply its user-annotated kind (compulsive / routine). We provide an overview of related datasets and describe the recording, cleaning, labeling, and final features of our dataset. We reach a maximum F1 score of 0.77 (avg.: 0.33, chance level: 0.03) when spotting handwashing from all background activities on unseen participants. Our dataset and code for the reproduction of our results are publicly available.</p>
Different spectral sensitivities of ON- and OFF-motion pathways enhance the detection of approaching color objects in Drosophila - Processed Data
<p>Processed data and code for plotting figures for the paper:</p><p>"Different spectral sensitivities of ON- and OFF-motion pathways enhance the detection of approaching color objects in Drosophila", by Kit D. Longden, Edward M. Rogers, Aljoscha Nern, Heather Dionne, Michael B. Reiser.</p><p>Data (compressed results folder) and plotting code (compressed src folder) are MATLAB files (see READ_ME for version information and toolboxes). The Source Data excel file also contains the data plotted in the paper figures.</p>
Retrograde Motion of Mars
<p>Winner in the 2023 IAU OAE Astrophotography Contest, category Still images with smartphones-mobile devices: Retrograde Motion of Mars, by Rob Kerby Guevarra.</p> <p>This image captures the celestial waltz of Mars as it demonstrates its intriguing retrograde motion against the background of fixed stars. This event, when Mars appears to backtrack in its orbit, arises from the different speeds at which Earth and Mars orbit the Sun. Earth’s faster movement occasionally positions it ahead of Mars, creating the illusion of the Red Planet moving in reverse from our perspective. This retrograde motion occurs when Mars is on the other side of the sky from the Sun, when it is said to be in opposition. Following Mars from 14 August 2022 to 5 April 2023, this smartphone image stands as a testament to perseverance and precision in the tranquil setting of Bataan, Philippines. Enduring unpredictable weather and ever-shifting celestial alignments, the photographer meticulously captured each shot at regular intervals of five to eight days. The process involved aligning 35 distinct images of Mars, taken without any external lens or telescope, alongside a stacked background image composed of 54 frames lasting 15 seconds each, portraying the starry expanse. Fusing these images involved precisely aligning them and cropping Mars in order to centre its position, revealing its retrograde movement against the backdrop of stars. This intricate process, blending the images seamlessly into the background by masking, highlights the planet’s unique motion. In the lower right corner, the Pleiades star cluster is visible.</p> <p>Credit: Rob Kerby Guevarra/IAU OAE (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>)</p>
Motion Policy Networks
<p>This is the pretrained model, training data set, evaluation problem sets, and sample real robot data accompanying Motion Policy Networks, an end-to-end neural model that can be used to generate collision-free, smooth motion from just a single depth camera observation. The training data set consists of over 3 million motion planning problems for a Franka Panda arm in over 500,000 environments. </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.