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969 results for “velocity”
Velocity of Greenland's Helheim Glacier controlled both by terminus effects and subglacial hydrology with distinct realms of influence
<p>Description of files contained in this archive:</p> <p><strong> </strong></p> <p>SHAKTI-ISSM model output</p> <p>Winter spin-up</p> <p>Helheim_SHAKTI_N_2_1yr_H10.mat</p> <ul> <li> <p>Winter base state spin-up simulation, final state serves as initial conditions for seasonal simulations</p> </li> </ul> <p><strong> </strong></p> <p>Seasonal hydrology-forced simulations</p> <p>Helheim_big_seasonal_low.mat</p> <ul> <li> <p>Seasonal simulation</p> </li> </ul> <p>Helheim_big_seasonal_firn.mat</p> <ul> <li> <p>Seasonal + firn aquifer simulation</p> </li> </ul> <p>Helheim_big_seasonal_bigmelt.mat</p> <ul> <li> <p>Enhanced melt simulation</p> </li> </ul> <p><strong> </strong></p> <p>Seasonal terminus-forced simulations</p> <p>Helheim_termforce_year2_v3.mat</p> <ul> <li> <p>Termforce simulation</p> </li> </ul> <p>Helheim_termforce_seasonal_v3.mat</p> <ul> <li> <p>Seasonal + termforce simulation</p> </li> </ul> <p>Helheim_termforce_seasonal_firn_v3.mat</p> <ul> <li> <p>Seasonal + firn aquifer + termforce simulation</p> </li> </ul> <p>Helheim_termforce_seasonal_bigmelt2_v3.mat</p> <ul> <li> <p>Enhanced melt + termforce simulation</p> </li> </ul> <p><strong> </strong></p> <p>Sensitivity simulations</p> <p>MOLHO_365d.mat</p> <ul> <li> <p>Winter base state spin-up simulation using MOLHO instead of SSA</p> </li> </ul> <p>A15_365d.mat</p> <ul> <li> <p>Winter base state spin-up simulation with flow law parameter for -15 deg C instead of -10 deg C</p> </li> </ul> <p>Helheim_br1_lr100_1yr.mat</p> <ul> <li> <p>Winter base state spin-up simulation including opening by sliding, final state serves as initial conditions for seasonal simulations</p> </li> </ul> <p>Helheim_br1_lr100_bigmelt.mat</p> <ul> <li> <p>Enhanced melt simulation including opening by sliding</p> </li> </ul> <p><strong><br><br></strong></p> <p>Model setup scripts (to be used with ISSM in MATLAB)</p> <p>runme_Helheim_inversion.m</p> <ul> <li> <p>Script to perform stress balance inversion</p> </li> </ul> <p>Helheim_CG.exp</p> <ul> <li> <p>Model domain coordinates</p> </li> </ul> <p>Greenland_clean.par</p> <ul> <li> <p>Parameter file</p> </li> </ul> <p>merra2_runoff.mat</p> <ul> <li> <p>14-day smoothed meltwater input</p> </li> </ul> <p>runme_Helheim_shaktiissm_startfrominversion_clean.m</p> <ul> <li> <p>Script to perform coupled SHAKTI-ISSM simulation, beginning from a model set up in the inversion script</p> </li> </ul> <p>runme_Helheim_continue_shaktiissm_clean.m</p> <ul> <li> <p>Script to continue a coupled SHAKTI-ISSM simulation, beginning from the end state of a previous SHAKTI-ISSM simulation</p> </li> </ul> <p>runme_Helheim_continue_seasonal_clean.m</p> <ul> <li> <p>Script to run a transient seasonal coupled SHAKTI-ISSM simulation, beginning from the end state of a winter SHAKTI-ISSM spin-up simulation</p> </li> </ul> <p><strong> </strong></p> <p>Plotting scripts</p> <p>load_models_Helheim_termforce.m</p> <ul> <li> <p>Load model output</p> </li> </ul> <p>plot_timeseries_coupled_point_paper_clean.m</p> <ul> <li> <p>Plot time series of effective pressure, velocity, and meltwater input. Also plots scatter plot of velocity vs. effective pressure</p> </li> </ul> <p>plot_pm_logscale.m</p> <ul> <li> <p>Plot difference in velocity compared to winter state with +/- log10 color scale</p> </li> </ul> <p>plot_pm_logscale_N.m</p> <ul> <li> <p>Plot difference in effective pressure compared to winter state with +/- log10 color scale</p> </li> </ul>
ESA 4DMED-Sea - Finite-size Lyapunov Exponents in the Mediterranean Sea derived from MIOST geostrophic velocities (1/24°).
<p>This product provides the Finite-size Lyapunov Exponents derived from surface geostrophic velocities result from the application of the MIOST algorithm (Ubelmann et al., 2019; https://doi.org/10.1029/2020JC016560) to altimetry L3-data (https://doi.org/10.5281/zenodo.10648981) at a resolution of 1/24° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Size Lyapunov Exponents was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/24º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>fsle (2D)</p>
Pn velocity and anisotropic tomography models, and Moho depth of the Tanlu Fault Zone
<p>Our dataset includes Pn velocity and anisotropic tomography models, and Moho depth of the Tanlu Fault Zone.</p>
ESA 4DMED-Sea - Finite-size Lyapunov Exponents in the Mediterranean Sea derived from MIOST geostrophic velocities (1/72°)
<p>This product provides the Finite-size Lyapunov Exponents derived from surface geostrophic velocities result from the application of the MIOST algorithm (Ubelmann et al., 2019; https://doi.org/10.1029/2020JC016560) to altimetry L3-data (https://doi.org/10.5281/zenodo.10648981) at a resolution of 1/72° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Size Lyapunov Exponents was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/72º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>fsle (2D)</p>
Data from: Local climate change velocities and evolutionary history explain multidirectional range shifts in a North American butterfly assemblage
<p>Species are often expected to shift their distributions either poleward or upslope to evade warming climates and colonize new suitable climatic niches. However, from 18 years of fixed transect monitoring data on 88 species of butterfly in the midwestern United States, we show that butterflies are shifting their centroids in all directions, except towards the region that is warming the fastest (southeast). Butterflies shifted their centroids at a mean rate of 4.87 km yr-1. The rate of centroid shift was significantly associated with local climate change velocity (temperature by precipitation interaction), but not with mean climate change velocity throughout the species' ranges. Species tended to shift their centroids at a faster rate towards regions that are warming at slower velocities but increasing in precipitation velocity. Surprisingly, species' thermal niche breadth (range of climates butterflies experience throughout their distribution) and wingspan (often used as a metric for dispersal capability) were not correlated with the rate at which species shifted their ranges. We observed a high phylogenetic signal in the direction species shifted their centroids. However, we found no phylogenetic signal in the rate species shifted their centroids, suggesting less conserved processes determine the rate of range shift than the direction species shift their ranges. This research shows important signatures of multidirectional range shifts (latitudinal and longitudinal) and uniquely shows that local climate change velocities are more important in driving range shifts than the mean climate change velocity throughout a species' entire range.</p>
ESA 4DMED-Sea - Finite-size Lyapunov Exponents in the Mediterranean Sea derived from 4DVARNET20 geostrophic velocities (1/24°)
<p>This product provides the Finite-size Lyapunov Exponents derived from surface geostrophic velocities result from the application of the 4DVARNET algorithm (<a href="https://isprs-annals.copernicus.org/articles/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.html" target="_blank" rel="noopener">Fablet et al., 2021</a>; resolution of the dynamical model used for the learning/training (<a href="https://github.com/ocean-next/eNATL60">eNATL60-BLB02</a>) downgraded to 1/20°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10912777) at a resolution of 1/24° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Size Lyapunov Exponents was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/24º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>fsle (2D)</p>
ESA 4DMED-Sea - Finite-size Lyapunov Exponents in the Mediterranean Sea derived from 4DVARNET20 geostrophic velocities (1/72°)
<p>This product provides the Finite-size Lyapunov Exponents derived from surface geostrophic velocities result from the application of the 4DVARNET algorithm (<a href="https://isprs-annals.copernicus.org/articles/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.html" target="_blank" rel="noopener">Fablet et al., 2021</a>; resolution of the dynamical model used for the learning/training (<a href="https://github.com/ocean-next/eNATL60">eNATL60-BLB02</a>) downgraded to 1/20°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10912777) at a resolution of 1/72° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Size Lyapunov Exponents was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/72º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>fsle (2D)</p>
ESA 4DMED-Sea - Finite-size Lyapunov Exponents in the Mediterranean Sea derived from 4DVARNET8 geostrophic velocities (1/72°)
<p>This product provides the Finite-size Lyapunov Exponents derived from surface geostrophic velocities result from the application of the 4DVARNET algorithm (<a href="https://isprs-annals.copernicus.org/articles/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.html" target="_blank" rel="noopener">Fablet et al., 2021</a>; resolution of the dynamical model used for the learning/training (<a href="https://github.com/ocean-next/eNATL60">eNATL60-BLB02</a>) downgraded to 1/8°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10908416) at a resolution of 1/72° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Size Lyapunov Exponents was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/72º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>fsle (2D)</p>
Data from: Linking in vivo muscle dynamics to in situ force-length and force-velocity reveals that guinea fowl lateral gastrocnemius operates at shorter than optimal lengths
<p>Force-length (F-L) and force-velocity (F-V) properties characterize skeletal muscle's intrinsic properties under controlled conditions, and it is thought that these properties can inform and predict <em>in vivo</em> muscle function. Here, we map dynamic <em>in vivo</em> operating range and mechanical function during walking and running, to the measured <em>in situ</em> F-L and F-V characteristics of guinea fowl (<em>Numida meleagris</em>) lateral gastrocnemius (LG), a primary ankle extensor. We use <em>in vivo</em> patterns of muscle (tendon) force, fascicle length, and activation to test the hypothesis that muscle fascicles operate at optimal lengths and velocities to maximize force or power production during walking and running. Our findings only partly support our hypothesis: <em>in vivo</em> LG velocities are consistent with optimizing power during work production, and economy of force at higher loads. However, LG does not operate at lengths on the force plateau (±5% Fmax) during force production. LG length was near L<sub>0</sub> at the time of EMG onset but shortened rapidly such that force development during stance occurred almost entirely on the ascending limb of the F-L curve, at shorter than optimal lengths. These data suggest that muscle fascicles shorten across optimal lengths in late swing, to optimize the potential for rapid force development near the swing-stance transition. This may provide resistance against unexpected perturbations that require rapid force development at foot contact. We also found evidence of passive force rise (in absence of EMG activity) in late swing, at lengths where passive force is zero <em>in situ</em>, suggesting that history dependent and viscoelastic effects may contribute to <em>in vivo</em> force development. Direct comparison of<em> in vivo </em>work loops and physiological operating ranges to traditional measures of F-L and F-V properties suggests the need for new approaches to characterize dynamic muscle properties in controlled conditions that more closely resemble <em>in vivo </em>dynamics.</p>
Dataset for the manuscript "Magneto-Ionic Control of Coercivity and Domain-Wall Velocity in Co/Pd Multilayers by Electrochemical Hydrogen-Loading"
<p>Data used to create Figures 1-5 in the manuscript "Magneto-Ionic Control of Coercivity and Domain-Wall Velocity in Co/Pd Multilayers by Electrochemical Hydrogen-Loading" and S1-S18 in the supporting information of the same manuscript. Video files belong to data for Figure 4 (b,d) and are described in the correspondig procedure file in the folder for Figure 4 .</p>
Fundamental mode Rayleigh wave group velocity dispersion data
<p>Fundamental-mode Rayleigh-wave group-velocity dispersion curves have been computed from 14706 regional waveforms (ray-paths) sampling India, Himalaya, Tibet and surrounding regions. These have been combined through ray-based travel-time tomography to compute 2D group-velocity maps at periods between 10 and 120~s. These ray-paths sample the region with unprecedented density and uniformity. The 2D maps have a lateral resolution of 3\deg x3\deg, comparable to the regional geology. The dispersion curves, at each 1\deg\ spaced node-points, are inverted to obtain a 3D isotropic shear-wave velocity structure.</p>
Data from: Bistable soft jumper capable of fast response and high take-off velocity
<p>In contrast to jumping robots made from rigid materials, soft jumpers composed of compliant and elastically deformable materials exhibit superior impact resistance and mechanically robust functionality. However, recent efforts to create stimuli-responsive jumpers from soft materials are limited in their response speed, take-off velocity, and travel distance. Here, we report a magnetic-driven, ultrafast bistable soft jumper that exhibits the highest jumping capability (jumping over 108 body heights with a take-off velocity of over 2 m/s) and the fastest response time (less than 15 ms) compared to previous soft jumping robots. The snap-through transitions between bistable states form a repeatable loop that harnesses the ultrafast release of stored elastic energy. Based on the dynamic analysis, the multimodal locomotion of the bistable soft jumper can be realized: the interwell mode of jumping and the intrawell mode of hopping. These modes are controlled by adjusting the duration and strength of the magnetic field, which endows the bistable soft jumper with robust locomotion capabilities. In addition, it is capable of jumping omnidirectionally with tunable heights and distances. To demonstrate its capability in complex environment, a realistic pipeline with amphibious terrain was established. The jumper successfully finished the simulative task of cleansing polluted water through the pipeline. The design principle and actuating mechanism of the bistable soft jumper can be further extended for other flexible systems.</p>
Figure 2. Velocity field U, t in Reconstruction of a passive tracer boundary source in an open water area
Figure 2. Velocity field U, t = 0.
Data of resulting velocity and anisotropic models, and Moho depth of the North China Craton
<pre>1. NCC_Pn_Velocity_Anisotropy_Model.txt Data of our resulting velocity and anisotropic models. Format: longitude, latitude, vel_value (km/s), ani_value (km/s), azimuth_anisotropy (degree), 2. NCC_Moho_Depth_Model.txt Data of our resulting Moho depth. Format: longitude, latitude, moho_depth (km)</pre>
Northern and central Walker Lane horizontal velocities in North America and Sierra Nevada reference frames.
<p>The data in this file contains east and north velocities covering the northern and central sections of the Walker Lane, USA. The velocities were obtained from GPS time series, available at Nevada Geodetic Laboratory (NGL): http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html (24 hour final solutions). The position time series were manually screened and corrected for offsets, and a local common-mode filter was applied following the methodology of Kreemer & Blewitt (2021, https://doi.org/10.1007/s00190-020-01466-5). Velocities in the North America frame were obtained using MIDAS, a robust median trend estimator (Blewitt et al., 2016, https://doi.org/10.1002/2015JB012552). Two sets of stations were used to rotate the velocities into the Sierra Nevada frame: CAOV, CAPV, P140, P276, P310 for the northern section and CMBB, P245, P305, P308, P512 for the central section.</p> <p>The file contains the following columns:</p> <ul> <li>sta: station ID</li> <li>lon: longitude of station (decimal degree)</li> <li>lat: latitude of station (decimal degree)</li> <li>ve_NA: east velocity in North America frame (mm/yr)</li> <li>vn_NA: north velocity in North America frame (mm/yr) </li> <li>ve_SN: east velocity in Sierra Nevada frame (mm/yr) </li> <li>vn_SN: north velocity in Sierra Nevada frame (mm/yr) </li> <li>region: denotes whether the set of stations used to rotate the velocities into the Sierra Nevada frame are located in the northern (N) or central (C) Walker Lane</li> <li>sve: east velocity uncertainty (mm/yr)</li> <li> svn: north velocity uncertainty (mm/yr)</li> <li>network: who operates the station</li> </ul>
Simple distance estimates for Gaia DR2 stars with radial velocities
<p>Bayesian distance estimates for stars with radial velocities and parallaxes published in <em>Gaia</em> DR2. Our method and prior is designed to apply to this specific subset of stars in <em>Gaia</em> DR2.</p> <p>The method is published in "Simple distance estimates for Gaia DR2 stars with radial velocities", McMillan 2018, arXiv:1806.00426</p> <p>The code used to produce the estimates is here: https://doi.org/10.5281/zenodo.1270548</p>
Datasets of microtremor arrays and phase velocities of Rayleigh waves
<p>Observed microtremor array data / Synthetic and observed phase-velocity data used in Cho and Iwata [2018, submitted to JGR].</p>
Corrective saccades influence velocity judgments and interception
<p>Here you can find the data for the paper "Corrective saccades influence velocity judgments and interception" by</p> <p>Alexander Goettker, Eli Brenner, Karl Gegenfurtner & Cristina de la Malla.</p>
Velocity-based macrorefugia for North American ecoregions
<p>Climate-change refugia, or areas of species persistence under climate change, may vary in proximity to a species' current distribution, with major implications for their conservation value. Thus, the concept of climate velocity (Loarie et al. 2009)---the speed at which an organisms must migrate to keep pace with climate change---is useful to compare and evaluate refugia. Using analog climate methods, both forward and backward velocity can be calculated, providing complementary information about spatio-temporal responses to climate change (Hamann et al. 2014, Carroll et al. 2015). In particular, backward velocity calculations can be used to identify areas of high potential refugium value for a given time period and species or ecoregion (Stralberg et al. 2018a). Refugia for a given ecoregion represent areas where the climates of that ecoregion may persist into the future. </p> <p>I used random forest model projections of future ecoregions (Stralberg et al. 2018b) to generate an index of climate-change refugia potential for individual ecoregions, using the methods outlined in Stralberg et al. (2018a). The index ranges from 0 to 1, with values close to 1 indicating overlap or very close proximity to the current mapped ecoregion, across multiple climate models. Because the random forest algorithm is a classifier that assigns an ecoregion class to every future pixel, it does not account for novel climates that are not currently found in any North American ecoregion. Of course novelty is relative and can be measured in many different ways. I calculated a multivariate environmental similarity surface (MESS) following Elith et al. (2010) to generate an index of novelty for each future ecoregion (negative values indicate dissimilarity).</p> <p>For mapping purposes, novel climates for each ecoregion, RCP, and time period were identified as those with values lower than the 1st percentile of dissimiarity values for the baseline periods:</p> <p><a href="https://drive.google.com/file/d/1mxJupbS2hQ7MNPNEycWRMYO98sBbsBsh/view?usp=sharing">1. RCP 8.5, 2080s</a></p> <p><a href="https://drive.google.com/file/d/10v2MGRyCVTrOoSMBOoBJ79XtVUDXnVoP/view?usp=sharing">2. RCP 8.5, 2050s</a></p> <p><a href="https://drive.google.com/file/d/14gfhuYI5M_NdaTcYg_rAaHm6L6GiEkVJ/view?usp=sharing">3. RCP 4.5, 2080s</a></p> <p><a href="https://drive.google.com/file/d/1xhMfck9COX0hBozB9sis1GfJwJrxGq_y/view?usp=sharin">4. RCP 4.5, 2050s</a></p> <p> </p> <p>References</p> <p>Carroll, C., J. J. Lawler, D. R. Roberts, and A. Hamann. 2015. Biotic and climatic velocity identify contrasting areas of vulnerability to climate change. PLoS ONE 10:e0140486.</p> <p>Elith, J., M. Kearney, and S. Phillips. 2010. The art of modelling range-shifting species. Methods in Ecology and Evolution 1:330-342.</p> <p>Hamann, A., D. Roberts, Q. Barber, C. Carroll, and S. Nielsen. 2015. Velocity of climate change algorithms for guiding conservation and management. Global Change Biology 21:997-1004.</p> <p>Stralberg, D., C. Carroll, J. H. Pedlar, C. B. Wilsey, D. W. McKenney, and S. E. Nielsen. 2018a. Macrorefugia for North American trees and songbirds: Climatic limiting factors and multi-scale topographic influences. Global Ecology and Biogeography 27:690-703. https://doi.org/10.1111/geb.12731 </p> <p>Stralberg, Diana. 2018b. Climate-projected distributional shifts for North American ecoregions [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1407176<br> </p>
Antisaccadic Gain and Velocity Differences Between Athletes and Non-Athletes
<p>Accurate eye movements are imperative to successfully meet the physical demands of sport. Prior literature has proposed that athletes utilize saccades to make correct perceptual responses, a process modulated by saccadic gain (SG) and velocity (SV). SG and SV are powerful measures for understanding the premotor and central circuits of saccades, and represent speed-accuracy mechanisms underlying rapid eye movement control. The current study evaluated SG differences between interceptive (INT) and strategic (STR) Division I athletes, as well as non-athletes (NON) with an antisaccade task. Thirty-six participants (12 INT, 12 STR and 12 NON) performed two, 40-trial blocks of a 20° antisaccade task, from which SG and SV were calculated. Two one-way ANOVAs were calculated to assess the differences in SG and SV between the 3 groups during an antisaccade task. There was a significant main effect of athlete type on SG (p<0.05) and SV (p<0.01). Post-hoc analyses revealed that STR had significantly lower SG and SV than INT and NON (<em>p</em><0.05). No significant differences were noted between INT and NON SG (p=0.863) and SV (<em>p</em>=0.35). This study provides novel observations in the saccadic behavior between athlete paradigms and NON. This research demonstrates that there are apparent antisaccadic gain and velocity differences between different sport paradigms.</p>
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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.