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89 results for “Kinematic data”
Data from: Effects of chemoradiation and tongue exercise on swallow biomechanics and bolus kinematics
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Data from: Kinematic and hydrodynamic analyses of turning manoeuvres in penguins: Body banking and wing upstroke generate the centripetal force
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Data from: Kinematic trajectories in response to speed perturbations in walking suggest modular task-level control of leg angle and length
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Data from: Comparison between the kinematics for kangaroo rat hopping on a solid versus sand surface
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Data from: Area 2 of primary somatosensory cortex encodes kinematics of the whole arm
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Kinematic data of humans performing the critical stability task
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Data from: Depth dependent dive kinematics suggest cost-efficient foraging strategies by tiger sharks
Tiger sharks Galeocerdo cuvier are a keystone, top-order predator that are assumed to engage in cost-efficient movement and foraging patterns. To investigate the extent to which patterns of oscillatory diving by these animals conform to these patterns, we used a biologging approach to model their cost of transport. High-resolution biologging tags with tri-axial sensors were deployed on 21 tiger sharks at Ningaloo Reef for durations of 5-48 hours. Using overall dynamic body acceleration (ODBA) as a proxy for energy expenditure, we modelled the cost of transport of oscillatory movements of varying geometries in both horizontal and vertical planes for tiger sharks. The cost of horizontal transport was minimized by descending at the lowest possible angle and ascending at an angle of 5-14°, meaning that vertical oscillations conserved energy compared to swimming at a level depth. Reduction of vertical travel costs occurred at steeper angles. The absolute dive angles of tiger sharks increased between inshore and offshore zones, presumably to reduce the cost of transport while continuously hunting for prey in both benthic and surface habitats. Oscillatory movements of tiger sharks conform to strategies of cost-efficient foraging, and shallow inshore habitats appear to be an important habitat for both hunting prey and conserving energy while travelling.
Data from: A 2.6‐g sound and movement tag for studying the acoustic scene and kinematics of echolocating bats
1. To study sensorimotor behaviour in wild animals, it is necessary to synchronously record the sensory inputs available to the animal, and its movements. To do this, we have developed a biologging device that can record the primary sensory information and the associated movements during foraging and navigating in echolocating bats. 2. This 2.6 -gram tag records the sonar calls and echoes from an ultrasonic microphone, while simultaneously sampling fine-scale movement in three dimensions from wideband accelerometers and magnetometers. In this study, we tested the tag on an European noctula (Nyctalus noctula) during target approaches and on four big brown bats (Eptesicus fuscus) during prey interception in a flight room. 3. We show that the tag records both the outgoing calls and echoes returning from objects at biologically relevant distances. Inertial sensor data enables the detection of behavioural events such as flying, turning, and resting. In addition, individual wing-beats can be tracked and synchronized to the bat's sound emissions to study the coordination of different motor events. 4. By recording the primary acoustic flow of bats concomitant with associated behaviours on a very fine time-scale, this type of biologging method will foster a deeper understanding of how sensory inputs guide feeding behaviours in the wild.
Gait data of idiopathic toe walking patients with pre & post sessions in surgical or conservative treatment group & healthy participants including ankle kinematics & kinetics
<p>This dataset contains gait data of 29 Idiopathic Toe Walking (ITW) patients and 21 Healthy participants from the Kinesiology Laboratory database. For the ITW population, the gait data of two clinical gait analyses were included, one before and one after surgical or conservative treatment. The dataset includes the trajectory of lower body markers, ankle kinematics and moments as well as calculated gait parameters. The markers' trajectory, ankle kinematics and moments are stored in c3d files, a biomechanics standard file format. The ankle kinematics and moments are also stored in the following csv or xlsx files: <em>ControlGroup_DataPerCycle.csv</em>, <em>ITW_DataPerCycle.xlsx</em> & <em>ITW_Matched_DataPerCycle.xlsx</em>; one row corresponding to the data of one gait cycle. The gait parameters were computed from the kinematics & kinetics data and were stored in the following xlsx files: <em>ITW_WithWithoutSurg.xlsx</em> & <em>ITW_WithWithoutSurg_matched.xlsx</em>; one row corresponding to the data of one clinical gait analysis.</p>
Kinematic and morphological data from: Trophic guilds of suction-feeding fish are distinguished by their characteristic hydrodynamics of swimming and feeding
<p>Suction-feeding in fish is a ubiquitous form of prey capture whose outcome depends both on the movements of the predator and the prey, and on the dynamics of the surrounding fluid, which exerts forces on the two organisms. The inherent complexity of suction-feeding has challenged previous efforts to understand how the feeding strikes are modified when species evolve to feed on different prey types. Here, we utilize the concept of dynamic similarity, commonly applied to understanding the mechanisms of swimming, flying, walking, and aquatic feeding. We characterize the hydrodynamic regimes pertaining to 1) the forward movement of the fish (ram), and 2) the suction flows for feeding strikes of 71 species of acanthomorph fish. A discriminant function analysis revealed that feeding strikes of zooplanktivores, generalists, and piscivores could be distinguished based on their hydrodynamic regimes. Furthermore, a phylogenetic comparative analysis revealed that there are distinctive hydrodynamic adaptive peaks associated with zooplanktivores, generalists, and piscivores. The scaling of dynamic similarity across species, body sizes, and feeding guilds in fish indicates that elementary hydrodynamic principles govern the trophic evolution of suction-feeding in fish.</p>
Density, kinematic viscosity, surface tension, distillation curve, and flash point data of diesel-biodiesel blends
<p>This dataset contains measurement data for density, kinematic viscosity, surface tension, distillation curve, initial boiling point, and flash point for different biodiesel samples blended with standard diesel fuel. Properties of coconut oil methyl ester, palm oil methyl ester, and biodiesel from waste cooking oil were evaluated in terms of temperature and biodiesel volume fraction. For a detailed description of the measurements and further information, please see the published paper.</p>
kinematic and force data from 11 humans interacting with a "cup of coffee"
<p>The dataset contains the kinematic and force data from 11 human participants when interacting with a virtual object that resembles a "cup of coffee". Further models that mimic human data are also provided. Codes to process and generate figures for the research paper are also available. </p> <p>Research article:<br> "Body mechanics, optimality, and sensory feedback in the human control of complex objects"</p> <p>by Reza Sharif Razavian, Mohsen Sadeghi, Salah Bazzi, Rashida Nayeem, and Dagmar Sternad</p> <p>Northeastern University</p>
Characterization of Road Condition with Data Mining Based on Measured Kinematic Vehicle Parameters, Data
<p>Data for Paper "Characterization of Road Condition with Data Mining Based on Measured Kinematic Vehicle Parameters"</p>
Data from: Encoding of locomotion kinematics in the mouse cerebellum
The cerebellum is involved in coordinating motor behaviour, but how the cerebellar network regulates locomotion is still not well understood. We characterised the activity of putative cerebellar Purkinje cells, Golgi cells and mossy fibres in awake mice engaged in an active locomotion task, using high-density silicon electrode arrays. Analysis of the activity of over 300 neurons in response to locomotion revealed that the majority of cells (53%) were significantly modulated by phase of the stepping cycle. However, in contrast to studies involving passive locomotion on a treadmill, we found that a high proportion of cells (45%) were tuned to the speed of locomotion, and 19% were tuned to yaw movements. The activity of neurons in the cerebellar vermis provided more information about future speed of locomotion than about past or present speed, suggesting a motor, rather than purely sensory, role. We were able to accurately decode the speed of locomotion with a simple linear algorithm, with only a relatively small number of well-chosen cells needed, irrespective of cell class. Our observations suggest that behavioural state modulates cerebellar sensorimotor integration, and advocate a role for the cerebellar vermis in control of high-level locomotor kinematic parameters such as speed and yaw.
Data files for the Kinematic Driver-Aerosol intercomparison
<p>The Kinematic Driver-Aerosol (KiD-A) intercomparison was established to test the hypothesis that detailed warm microphysical schemes provide a benchmark for lower-complexity bulk microphysics schemes. KiD-A is the first intercomparison to compare multiple Lagrangian cloud models (LCMs), size bin-resolved schemes, and double-moment bulk microphysics schemes in a consistent 1D dynamic framework and box cases. The motivation and description of intercomparison, along with overview of the schemes included and the intercomparison results are presented in Hill et al (2023).</p> <p>Here we provide details of the raw data that was produced during the intercomparison by participants and used to generate the results and plots that are discussed in Hill et al (2023).</p> <p>The data for the intercomparison are provided in the kid-a_0d_1d_final_submission.tar.gz. To access the data, untar and unzip the file using</p> <pre><code class="language-bash">$ tar -xzvf kid-a_0d_1d_final_submission.tar.gz</code></pre> <p>Once untarred and unzipped, the following directory structure is available</p> <ul> <li>kid-a_pub/kida_0d_data contains all the data for the 0-D box simulations</li> <li>kid-a_pub/kida_1d_data contains all the data for the 1-D column simulations</li> </ul> <p>Within kida_0d_data and kida_1d_data, there are further directories, which are specific to each submission. Tables 1 and 2 define the directories for each submission, with model names that relate to the model naming in Hill et al. (2023), and a full description of the model types can be found in Hill et al. (2023). The specific directories for each model contain NetCDF output from the KiD model. In addition, some participants also included namelists that were used to run KiD and produce the output. Where this was submitted, these namelist files are also included.</p> <table> <caption>Table 1: Scheme names as defined in Hill et al. (2023), scheme type, and 1-D column model data directory (kida 1d data)</caption> <tbody> <tr> <td>Model</td> <td>Description</td> <td>1-D data directory</td> </tr> <tr> <td>LCM-FH</td> <td>Lagrangian microphysics</td> <td>hoffman_1d_ensemble</td> </tr> <tr> <td>LCM-MA</td> <td>Lagrangian microphysics</td> <td>mirek_lm_1d</td> </tr> <tr> <td>UWLCM</td> <td>Lagrangian microphysics</td> <td>piotr_uw_lcm</td> </tr> <tr> <td>bin2d</td> <td> <p>Eulerian bin scheme</p> </td> <td> <p>lebo_bin2d_solute_tests</p> </td> </tr> <tr> <td>TAU-bin</td> <td>Eulerian bin scheme</td> <td>hill_kida_taubin_results_v2</td> </tr> <tr> <td>MSSG-bin</td> <td>Eulerian bin scheme</td> <td>onishi_mssg_1d_v2</td> </tr> <tr> <td>MG2</td> <td>Two-moment bulk scheme</td> <td>mg2_4th_sub</td> </tr> <tr> <td>CASIM (cond/evap)</td> <td>Two-moment bulk scheme</td> <td>hill_1D_CE_casim_result</td> </tr> <tr> <td>CASIM (precip)</td> <td>Two-moment bulk scheme</td> <td>hill_1D_casim_v0.5</td> </tr> <tr> <td>LIMA</td> <td>Two-moment bulk scheme</td> <td>vie_lima_bulk_kida_results</td> </tr> <tr> <td>LIMA-KK</td> <td>Two-moment bulk scheme</td> <td>vie_limakk_bulk_kida_results</td> </tr> </tbody> </table> <table> <caption>Table 2: Scheme names as defined in Hill et al. (2023), scheme type, and 0-D box model data directory (kida 0d data).</caption> <tbody> <tr> <td>Model</td> <td>Description</td> <td>0-D data directory</td> </tr> <tr> <td>LCM-FH (cond)</td> <td> <p>Lagrangian microphysics</p> </td> <td> <p>hoffman_kida_results/box_cond</p> </td> </tr> <tr> <td>LCM-FH (coll)</td> <td>Lagrangian microphysics</td> <td>hoffman_kida_results/box_coll</td> </tr> <tr> <td>LCM-MA (cond)</td> <td>Lagrangian microphysics</td> <td>mirek_lm_0d/ZERO-D_v1.0/CE</td> </tr> <tr> <td>LCM-MA (coll)</td> <td>Lagrangian microphysics</td> <td>mirek_lm_0d/ZERO-D_v1.0/CO</td> </tr> <tr> <td>UWLCM (coll)</td> <td>Lagrangian microphysics</td> <td>piotr_lm_0d</td> </tr> <tr> <td>bin2d (cond)</td> <td>Eulerian bin scheme</td> <td>lebo_bin2d/cond</td> </tr> <tr> <td>bin2d (coll)</td> <td>Eulerian bin scheme</td> <td>lebo_bin2d/coll</td> </tr> <tr> <td>TAU-bin (cond)</td> <td>Eulerian bin scheme</td> <td>hill_kida_taubin_result/cond</td> </tr> <tr> <td>TAU-bin (coll)</td> <td>Eulerian bin scheme</td> <td>hill_kida_taubin_result/coll</td> </tr> </tbody> </table> <p> </p> <p>Hill, A. A., Lebo, Z. J., Andrejczuk, M., Arabas, S., Dziekan, P., Field, P., Gettelman, A., Hoffmann, F., Pawlowska, H., Onishi, R., & Vi ́e, B. (2023). Toward a Numerical Benchmark for Warm Rain Processes, Journal of the Atmospheric Sciences (published online ahead of print 2023). doi:https://doi.org/10.1175/JAS-D-21-0275.1</p>
Data from The Kinematics, Metallicities, and Orbits of Six Recently Discovered Galactic Star Clusters with Magellan/M2FS Spectroscopy
<p>Pace, Koposov, Walker et al 2023</p> <p>Catalog level data products from Magellan/M2FS and AAT/AAOmega spectroscopy (fits, npy formats) including membership information</p> <p>Summary table (Table 2) in machine readable formats (npy, fits, and csv formats)</p> <p>Updates (v2): The original M2FS sample had a bug in the application of the systematic errors. All errors have been corrected. The summary table has been updated accordingly. </p>
Data from: Walking with wider steps changes foot placement control, increases kinematic variability and does not improve linear stability
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Data from: Integrating morphology and kinematics in the scaling of hummingbird hovering metabolic rate and efficiency
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Data from: Direct measurement of swimming and diving kinematics of giant Atlantic bluefin tuna (Thunnus thynnus)
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Data from: Depth dependent dive kinematics suggest cost-efficient foraging strategies by tiger sharks
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.