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292 results for “physical performance”
Maternal and genetic correlations between morphology and physical performance traits in a small captive primate, Microcebus murinus
<p>Physical performance traits are key components of fitness and direct targets of selection. Maternal effects are important components of integrated phenotypes in a variety of species. Yet their contribution to variation in performance, and phenotypes closely associated with performance, remains poorly understood. We used an animal model approach to quantify the contribution of maternal effects to performance trait variation (in bite force and pull strength) and the relationships between performance and the relevant underlying morphology in <i>Microcebus murinus</i>. We show that bite force is heritable (h<sup>2</sup>~0.23), and that maternal effects are also important source of variation, resulting in a medium inclusive heritability (IH<sup>2</sup>~0.47). Grip strength presented a rather low and non-significant narrow-sense heritability suggesting a higher selective pressure on this trait. Genetic correlations between performance traits and their associated morphometric traits were significant and high (0.47 bite force-head width; 0.48 grip strength-radius length), as was the maternal correlation for bite force-head width (0.75). Further studies evaluating the heritability of performance for other taxa and the role of maternal effects are badly needed to better understand the drivers of variation in performance ultimately allowing for a better understanding of the importance of these types of traits in an evolutionary context.</p>
Evaluation of high-resolution WRF simulation in urban areas - Effect of different physics schemes on simulation performance in the Rhine-Main-Neckar area
<p>This dataset contains data sampled from a WRF sensitivity study saved in NetCDF format. The study was run over 4 months of the year 2020. The folders contain the following data:</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Datasets</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>wrf_met_sample_full.nc</td> <td>all</td> <td>WRF meteorology sampled at the 19 weather stations in the simulation domain</td> </tr> <tr> <td>wrf_met_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>wrf_met_sample_full_ucmheights.nc</td> <td>only 2020_12</td> <td>Same as wrf_met_sample_full.nc but only for the run using the vertical layer distribution of UCM</td> </tr> <tr> <td>wrf_met_sample_full_and_quant_ucmheights.nc</td> <td>only 2020_12</td> <td>Same as wrf_met_sample_full_and_quant.nc but only for the run using the vertical layer distribution of UCM</td> </tr> <tr> <td>wrf_pblh_sample_full.nc</td> <td>all</td> <td>WRF PBLH (and custom PBLH_RIB) sampled at the 2 radio sonde stations in the simulation domain</td> </tr> <tr> <td>wrf_pblh_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>era5_met_sample_full.nc</td> <td>all</td> <td>ERA5 meteorology sampled at the 19 weather stations in the simulation domain</td> </tr> <tr> <td>era5_met_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>era5_pblh_sample_full_and_quant.nc</td> <td>all</td> <td>WRF PBLH (and custom PBLH_RIB) sampled at the 2 radio sonde stations in the simulation domain, resampled onto the measured meteorology and with summary statistics</td> </tr> </tbody> </table> <p>Each of these files contains the samples and statistics as NetCDF Variables. These Variables have multiple dimensions, which describe the individual datapoints. For the WRF samples, these dimensions are:</p> <table> <tbody> <tr> <td><strong>Dimension</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Time</td> <td>time since start of simulation</td> </tr> <tr> <td>station_id</td> <td>the ID of the station where the sample was taken (meteo - length 19, PBLH - length 2)</td> </tr> <tr> <td>pbl</td> <td>Planetary Boundary Layer scheme (Bou-Lac / MYJ / YSU)</td> </tr> <tr> <td>lsm</td> <td>Land Surface Model scheme (N / NMP)</td> </tr> <tr> <td>slm</td> <td>Surface Layer Model scheme (MM5 / MO)</td> </tr> <tr> <td>urb</td> <td>Urban Parametrization scheme (SLUCM / BEP)</td> </tr> </tbody> </table> <p>Not all combinations between different simulation schemes exist, so some values in the NetCDF Variables are NaNs.</p>
Mental health, physical health, training load and subjective performance during the COVID-19 pandemic – a Swiss elite athletes' cohort study
<p>Dataset of Swiss elite athletes (n=203) participating in a repeated online survey evaluating mental and physical health factors, as well as training and performance related metrics. After the first survey during the first lockdown between April and May 2020, there were monthly follow-up surveys over a 6-month period.</p>
Database of Urogynecological and obstetric history associated with lower limb physical performance in women
<p>Database: Urogynecological and obstetric history associated with lower limb physical performance in women</p>
BRAIN Journal-Brain signal analysis using EEG and Entropy to study the effect of physical and mental tasks on cognitive performance-Figure 2. Energy-VAS subjective measures for the participants (n=12) under two conditions (control and exercise involved cognitive task).
<p>As shown in Figure 2, it was found that there was a statistically significant interaction in the<br> percentage of mental fatigue between the condition type and time-on-task factor times (F(6, 22) =<br> 492.19, p < 0.001) as well as there was a significant main effect of time-on-task (time5 to time30)<br> (F(5, 22) = 463.794, p < 0.001). In addition, there was also a significant main effect in the condition type (F (1, 22) = 713.133, p < 0.001) which represented a large effect size. For the physical fatigue<br> subjective measure, there was a significant difference between the two experimental conditions (p <<br> 0.001), and within the subject test times (p < 0.001). However, there was no significant difference<br> between the means of the concentration visual analogue scale for these two experimental conditions<br> (p = 0.057) despite a significant difference (p < 0.001) in the time-on-task repeated measures.</p>
BRAIN Journal-Brain signal analysis using EEG and Entropy to study the effect of physical and mental tasks on cognitive performance-Figure 1. The 10-20 international system electrode placement showing the EEG electrode placement
<p>For the EEG analysis, the average power in the theta band (4 – 8 Hz), and alpha band (8 – 12<br> Hz) were computed at the frontal and parietal electrodes Fz and Pz respectively. Next, the ratio of<br> these two powers was determined, and named the ‘cognitive ratio’ as several researchers found that<br> the fronto-parietal network play important roles in cognitive activities.</p>
Data set of paper Model-Driven System-Performance Engineering for Cyber-Physical Systems
<p>This data set contains the raw and processed data of the paper <em>Model-Driven System-Performance Engineering for Cyber-Physical Systems</em>, published in the proceedings of ESWEEK’21.</p>
Recording and analysing physical control variables used in clarinet playing: A Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT)
<p>Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.</p>
Maternal and genetic correlations between morphology and physical performance traits in a small captive primate, Microcebus murinus
Open the record for dataset details and reuse information.
Impact of light intensity on sugar maple leaf physical traits and consequences for caterpillar preference and performance
Open the record for dataset details and reuse information.
Tramadol effects on physical performance and sustained attention during a 20-min indoor cycling time-trial: A randomised controlled trial
<p>Objectives: To investigate the effect of tramadol on performance during a 20-min cycling time-trial (Exper- iment 1), and to test whether sustained attention would be impaired during cycling after tramadol intake (Experiment 2). Design: Randomized, double-blind, placebo controlled trial. Methods: In Experiment 1, participants completed a cycling time-trial, 120-min after they ingested either tramadol or placebo. In Experiment 2, participants performed a visual oddball task during the time-trial. Electroencephalography measures (EEG) were recorded throughout the session. Results: In Experiment 1, average time-trial power output was higher in the tramadol vs. placebo condition (tramadol: 220 W vs. placebo: 209 W; p < 0.01). In Experiment 2, no differences between conditions were observed in the average power output (tramadol: 234 W vs. placebo: 230 W; p > 0.05). No behavioural differences were found between conditions in the oddball task. Crucially, the time frequency analysis in Experiment 2 revealed an overall lower target-locked power in the beta-band (p < 0.01), and higher alpha suppression (p < 0.01) in the tramadol vs. placebo condition. At baseline, EEG power spectrum was higher under tramadol than under placebo in Experiment 1 while the reverse was true for Experiment 2. Conclusions: Tramadol improved cycling power output in Experiment 1, but not in Experiment 2, which may be due to the simultaneous performance of a cognitive task. Interestingly enough, the EEG data in Experiment 2 pointed to an impact of tramadol on stimulus processing related to sustained attention. Trial registration: EudraCT number: 2015-005056-96.</p>
Motor performance in violin bowing: Effects of attentional focus on acoustical, physiological and physical parameters of a sound-producing action
<p>Violin bowing is a specialised sound-producing action, which may be affected by psychological performance techniques. In sport, attentional focus impacts motor performance, but limited evidence for this exists in music. We investigated the effects of attentional focus on acoustical, physiological, and physical parameters of violin bowing in experienced and novice violinists. Attentional focus significantly affected spectral centroid, bow contact point consistency, shoulder muscle activity, and novices’ violin sway. Performance was most improved when focusing on tactile sensations through the bow (somatic focus), compared to sound (external focus) or arm movement (internal focus). Implications for motor performance theory and pedagogy are discussed.</p>
Data for "A benchmarking method to rank the performance of physics-based earthquake simulations"
<p>This repository contains the datasets and codes supplementary to the article "<strong>A benchmarking method to rank the performance of physics-based earthquake simulations</strong>" submitted to <em>Seismological Research Letters</em>.</p> <p>The datasets include the codes to run the ranking analyses, inputs and outputs for the RSQSim earthquake simulation cases explained in the paper: a single fault and the fault system of the Eastern Betics Shear Zone (simulations from Herrero-Barbero et al. 2021). The results and data are stored in a separate folder for each case study presented in the paper: "Single fault" and "EBSZ". Each folder contains a series of subfolders and a Python script to run the ranking analysis for that specific case study. The script contains the default path references to read all necessary input files for the analysis and automatically save all the outputs. The subfolders are:</p> <p><strong>./Inputs: </strong>This folder contains the input files required for the RSQSim simulations. This includes:</p> <p>a. The fault model ("Nodes_RSQSim.flt" and "EBSZ_model.csv" for the single fault and EBSZ cases, respectively), which specifies the coordinate nodes of the fault triangular meshes and fault properties such as rake (º) and slip rate (m/yr).</p> <p>b. Neighbor file ("neighbors.dat"/"neighbors.12") that contains lists of triangular patches of the fault model that are neighboring. This file is used in RSQSim.</p> <p>c. Input parameter file ("Input_Parameters.txt"): this file specifies the parameters that are variable in each catalogue. This file is just for information purposes and is not used for the calculations.</p> <p>d. Parameter file(s) to run the RSQSim calculations.</p> <p>*For the single fault, this file is common ("test_normal.in") and is updated during the calculation when executing the "Run.sh" file in the terminal when running RSQSim. This file contains a script that loops through the input parameters a, b and normal stress explored in the study and changes the input parameter file accordingly in each iteration.</p> <p>*For the EBSZ, this file is specific for each simulation ("param_EBSZ_(n).in"), as each simulation was run separately.</p> <p>e. (Only for the EBSZ case) Input paleoseismic data for the paleorate benchmark. One file ("coord_sites_EBSZ.csv") contains a list of UTM coordinates of each paleoseismic site in the EBSZ and another ("paleo_rates_EBSZ.csv") contains the mean recurrence intervals and annual paleoearthquake rates in those sites (data from Herrero-Barbero et al., 2021).</p> <p><strong>./Simulation_models:</strong> contains several subfolders, one for each simulated catalogue (96 for the single fault case and 11 for the EBSZ). Each subfolder contains data that is read by the ranking code to perform the analysis. </p> <p>*For the single fault, the folder names follow the structure "model_(normal stress)<em>(a)</em>(b)". </p> <p>*For the EBSZ, the folder names are "cat-(n)".</p> <p><strong>./Ranking_results: </strong>contains the outputs of the ranking analysis, which are two figures and one text file.</p> <p>*Figure 1 ("Final_ranking.pdf"): visualization of the final ranking analysis for all models against the analyzed benchmarks.</p> <p>*Figure 2 ("Parameter_sensitivity.pdf"): visualization of the final and benchmark performance versus the input parameter of the models.</p> <p>*Text file ("Ranking_results.txt"): contains the final and benchmark scores of each simulation model. This file is outputted so the user can reproduce and customize their own figures with the ranking results.</p> <p>To use the ranking codes in you own datasets, please replicate the folder structure explained above. Use the code that best suits your data: use the one for the single fault if you wish not to use the paleorate benchmarks, and use the EBSZ one if you wish to include these data in your analysis. At the beginning of the respective codes (before the "Start" block comment) you will find the variables where the file names of the fault model and paleoseismic data are indicated. Change them to adapt it to your data. There you can also assign weights to the respective benchmarks in the analysis (default is set at equal weight for all benchmarks).</p> <p>For updates of the code please visit our GitHub: https://github.com/octavigomez/Ranking-physics-based-EQ-simulations</p>
Replication data for: "Effectiveness of iso-inertial resistance training on eccentric and concentric power, physical performance, and risk of falls in physically active middle-older adults: a randomised controlled trial"
<p>Replication data for: "Effectiveness of iso-inertial resistance training on eccentric and concentric power, physical performance, and risk of falls in physically active middle-older adults: a randomised controlled trial"</p> <p>This folder contains 4 files:</p> <p>1) Database that contains the values for concentric and eccentric power measured with both iso-inertial and gravitational systems (Dataset_power.xlsx)</p> <p>2) Database that contains the values for the Short Physical Performance Battery (SPPB) and Get Up and Go (GUG) test (Dataset_SPPB_GUG.xlsx)</p> <p>3) R Software script used to analyse file 1 (Iso-inertial analysis_power.R)<br> <br>4) R Software script used to analyse file 2 (Iso-inertial analysis_SPPB_GUG.R)</p>
Permeable pavement hydraulic performance and clogging experiments using a full-scale urban drainage physical model
<p>This dataset contains the results from 15 tests conducted used a physical model in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain) as part of the POREDRAIN project.</p> <p><br>The objective of the tests is to analyse the hydraulic performance of a porous asphalt layer of the PA-16 type and the impact of clogging on the hydrological behaviour and water quality of the effluent. The porous asphalt was used to retrofit an impervious concrete surface of a 36 m² full-scale street section physical model, which consist of a rainfall simulator placed over the street surface. The behaviour of the porous asphalt layer was assessed by adding surface sediment loads between simulated rainfall events. Stormwater flow discharges were collected from two gully pots and an outlet lateral channel. </p>
Dataset for: Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE
<p><strong>Based on the Github respostitory: <a href="https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main">https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main</a></strong></p> <p>This repository contains the data used for the paper "Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE"<br> You'll find the csv files for the three simulated (Hydrus 1D) scenarios explained in the paper. These files were processed from the 'Nod_Inf.out' files to csv format.</p> <p><strong>Acknowledgments</strong><br> The publicly available data used for this study (scenario 1 & 2) as well as the code for the second PINN architecture (based on Dr. Maziar Raissi PINN code) and the code used to transform “Nod_inf.out” files from Hydrus 1D to csv files created by Dr. Toshiyuki Bandai and Dr. Teamrat A. Ghezzehei were helpfulfor this study.</p>
Data from: Sexual selection for extreme physical performance in a polygynous bird is associated with exceptional sex differences in oxygen carrying capacity
<p>In many animal species, males compete for access to fertile females. The resulting sexual selection leads to sex differences in morphology and behaviour, but may also have consequences for physiology. Pectoral sandpipers are an arctic breeding polygynous shorebird in which males perform elaborate displays around the clock and move over long distances to sample potential breeding sites. We examined the oxygen carrying capacity of pectoral sandpipers, measured as the volume percentage of red blood cells in blood (haematocrit, Hct). We found a remarkable sex difference in Hct levels, with males having much higher values (58.9 ± 3.8 SD) than females (49.8 ± 5.3 SD). While Hct values of male pectoral sandpipers are notable for being among the highest recorded in birds, the sex difference we report is unprecedented and more than double that of any previously described. We also show that Hct values declined after arrival to the breeding grounds in females, but not in males, suggesting that males maintain an aerobic capacity during the mating period equivalent to that during trans-hemispheric migration. We conclude that sexual selection for extreme physical performance in male pectoral sandpipers has led to exceptional sex differences in oxygen carrying capacity.</p>
Effects of a Rehabilitation Program on Physical Performance and Disease Self-management in Rheumatoid Arthritis.
ClinicalTrials.gov study NCT01307787. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The ADAPT-study: Measuring Physical Performance Using Wearable Sensors in Parkinson's Disease and COPD (ADAPT)
ClinicalTrials.gov study NCT05756075. IPD Sharing: YES. Countries: 1. Publications: 1.
Immersive Virtual Reality and Physical Exercise on Cognitive and Functional Performance in Hospitalized Older Patients
ClinicalTrials.gov study NCT06340282. IPD Sharing: YES. Countries: 1. Publications: 2.
ScienceDex guides
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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.