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Dataset results
12 results for “speed of adaptation”
Movies (II) for "3D Space-Time Adaptive Hybrid Simulations of Magnetosheath High-Speed Jets"
<p>Movies for the aforementioned paper showing magnetic field lines, dynamic pressure and plasma density in global magnetospheric simulations. NW and SW refer to quasi-radial northward and southward IMF. </p> <p>https://doi.org/10.1029/2020JA029035</p>
Rivals Reloaded - Adapting to Sample-Based Speed–Accuracy Trade-Offs Through Competitive Pressure: Data
<p>Data and codebook for experiment described in publication titled Rivals Reloaded - Adapting to Sample-Based Speed–Accuracy Trade-Offs Through Competitive Pressure published in Journal of Experimental Psychology: Learning, Memory, and Cognition authored by Linda McCaughey, Johannes Prager and Klaus Fiedler </p>
Demographic feedbacks during evolutionary rescue can slow or speed adaptive evolution
<p>Populations declining toward extinction can persist via genetic adaptation in a process called evolutionary rescue. Predicting evolutionary rescue has applications ranging from conservation biology to medicine, but requires understanding and integrating the multiple effects of a stressful environmental change on population processes. Here we derive a simple expression for how generation time, a key determinant of the rate of evolution, varies with population size during evolutionary rescue. Change in generation time is quantitatively predicted by comparing how intraspecific competition and the source of maladaptation each affect the rates of births and deaths in the population. Depending on the difference between two parameters quantifying these effects, the model predicts that populations may experience substantial changes in their rate of adaptation in both positive and negative directions, or adapt consistently despite severe stress. These predictions were then tested by comparison to the results of individual-based simulations of evolutionary rescue, which validated that the tolerable rate of environmental change varied considerably as described by analytical results. We discuss how these results inform efforts to understand wildlife disease and adaptation to climate change, evolution in managed populations, and treatment resistance in pathogens.</p>
Demographic feedbacks during evolutionary rescue can slow or speed adaptive evolution
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Data for: Quantifying human adaptation to a novel split-belt walking condition after broad experience at different belt speeds
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Livorno, Urban driving, autonomous speed adaptation approaching intersection
<p><strong>Scenario description</strong>:</p> <p>Test session for AD+connected car and connected cars approaching an intersection regulated by a "smart" traffic light with a stereocamera able to detect jaywalking.</p> <p><strong>Session description</strong>:</p> <p>A "smart" traffic light (with stereocamera) sends SPaT and MAP messages describing the topology and the actual status of the traffic light. If a jaywalking occurrence is detected (pedestrian crossing with the red light) a DENM message is sent to warn vehicles of the presence of the pedestrian to other connected vehicles. The the hazard warning is also sent to the oneM2M platform on the cloud. An AD vehicle consumes the information and autonomously adapts its speed in order to cross the intersection without violating the traffic light phases, or even stop to avoid collision with pedestrian. The influence of other vehicles moving in front is considered too. Goal is to record data for the technical evaluation.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Plantar Somatosensory Restoration Enhances Gait, Speed Perception, and Motor Adaptation
<p>These are the data/code needed to reproduce our results.</p> <p>Below is a description of the data stored inside 'DATA.mat'.</p> <p>- Baseline: Walking at 0.5 m/s (tied-belt)</p> <ul> <li>Controls: 6 able-bodied participants</li> <li>LLA01, LLA02, & LLA03: 3 participants used sensory neuroprosthesis</li> <li>SL_DominantLimb: Dominant leg's step length (SL)</li> <li>SL_NonDominantLimb: Non-dominant leg's step length</li> <li>SL_Symmetry: Step length symmetry between dominant and non-dominant legs</li> <li>ST_DominantLimb: Dominant leg's stance time (ST)</li> <li>ST_NonDominantLimb: Non-dominant leg's stance time</li> <li>ST_Symmetry: Stance time symmetry between dominant and non-dominant legs</li> <li>GRFx_DominantLimb: Mediolateral ground reaction force (GRF) generated from dominant leg</li> <li>GRFy_DominantLimb: Anteroposterior GRF generated from dominant leg</li> <li>GRFz_DominantLimb: Vertical GRF generated from dominant leg</li> <li>GRFx_NonDominantLimb: Mediolateral GRF generated from non-dominant leg</li> <li>GRFy_NonDominantLimb: Anteroposterior GRF generated from non-dominant leg</li> <li>GRFz_NonDominantLimb: Vertical GRF generated from non-dominant leg</li> <li>COP_COM_x: Mediolateral distance between center of pressure (COP) and center of mass (COM) trajectories</li> <li>COP_COM_y: Anteroposterior distance between center of pressure and center of mass trajectories</li> <li>AMx: Whole-body angular momentum (AM) in the sagittal plane</li> <li>AMy: Whole-body AM in the frontal plane</li> </ul> <p>- StimOff/On: Condition under which sensory neuroprosthesis turned off/on</p> <ul> <li>SL_ProstheticLimb: Prosthetic leg's step length</li> <li>SL_IntactLimb: Intact leg's step length</li> <li>SL_Symmetry: Step length symmetry between Prosthetic and Intact legs</li> <li>ST_ProstheticLimb: Prosthetic leg's stance time</li> <li>ST_IntactLimb: Intact leg's stance time</li> <li>ST_Symmetry: Stance time symmetry between Prosthetic and Intact legs</li> <li>GRFx_ProstheticLimb: Mediolateral GRF generated from Prosthetic leg</li> <li>GRFy_ProstheticLimb: Anteroposterior GRF generated from Prosthetic leg</li> <li>GRFz_ProstheticLimb: Vertical GRF generated from Prosthetic leg</li> <li>GRFx_IntactLimb: Mediolateral GRF generated from Intact leg</li> <li>GRFy_IntactLimb: Anteroposterior GRF generated from Intact leg</li> <li>GRFz_IntactLimb: Vertical GRF generated from Intact leg</li> <li>COP_COM_x: Mediolateral distance between center of pressure and center of mass trajectories</li> <li>COP_COM_y: Anteroposterior distance between center of pressure and center of mass trajectories</li> <li>AMx: Whole-body angular momentum in the sagittal plane</li> <li>Amy: Whole-body angular momentum in the frontal plane</li> </ul> <p>- MAT: Motor adaptation task (a 2:1, 1.0 m/s:0.5 m/s, belt speed perturbation for 10 minutes)</p> <ul> <li>COMVy: Forward velocity of the body's center of mass </li> </ul> <p>- SJTpre/post: Symmetry judgment task (verbally announcing whether they perceived both limbs at the same speed) before/after performing the MAT</p> <ul> <li>SymmetryResponse_Early/Late: Verbal response from the participant on whether the treadmill belts were at the same speed (at early/late SJT)</li> <li>ResponseDealy_Early/Late: Time delay to the verbal response (at early/late SJT)</li> </ul>
Speed Manipulated Adaptive Rehabilitation Therapy Bike for Parkinson's Disease
ClinicalTrials.gov study NCT05361200. IPD Sharing: NO. Countries: 1. Publications: 4.
Effect of Long-lasting Adaptation to Endurance and Speed-power Training on Plasma Free Amino Acids Concentration
ClinicalTrials.gov study NCT05672758. IPD Sharing: YES. Countries: 1. Publications: 34.
Speed trajectory data from adaptive eco-driving applications
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Data from: Speed of adaptation and genomic footprints of host-parasite coevolution under arms race and trench warfare dynamics
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RNA-seq of seven altitude-adapted female speed skaters before and after exercise
GEO Series GSE164890. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing.
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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.