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6,498 results for “Physicality”
Family Type, Mental and Physical Health of in-school Female Adolescents in Ado-Ekiti, Nigeria
<p>This dataset is based on a cross-sectional survey conducted in Ado-Ekiti, Nigeria among in-school female adolescents. The general objective of the study was to examine the influence of family type on the physical and mental health of in-school female adolescents. The data were obtained in 2017 from in-school female adolescents aged 10-18 years old. The respondents were randomly selected from four purposively selected secondary schools. Two schools were selected from the government-owned public schools and two from schools owned by individuals or private organizations. Nigeria operates a six-year secondary education; three years at the junior secondary school (JSS 1-3) and three years at the senior secondary (SS 1-3). The population for this study excluded students in SS3 because they had just concluded their terminal examination at the time of this study. Using the formula proposed by (Krejcie & Morgan, 1970) for deriving a small sample when the population is known, a sample size of 383 was derived from a population of 1656 female students in the four schools. With 10% added to adjust for non-response, a total of 421 students were involved in the study. The number of respondents allocated to each school was proportional to the size of the female students' population in each school. In each school, respondents were assigned to classes proportional to the size, and the particular respondents were identified using a random number. Data were collected with a self-administered structured questionnaire which was piloted before the actual survey. The data is saved in Stata format. </p> <p>A scale for measuring self-reported mental well was designed using questions adapted from Ross, Mirowsky, & Goldsteen (1990) and (Langton & Berger, 2011). The scale reliability coefficient was 0.85. The students were asked to state how often they experience 22 conditions such as feeling sad, discouraged, lonely, hopeless, worthless, wishing you were dead, having trouble concentrating, having difficulty sleeping, crying, and worried among others. </p> <p>Physical health was measured using a scale generated with questions adapted from (Langton & Berger, 2011; Ross et al., 1990). The students were asked to state the frequency of experiencing seven conditions in the month before the survey: feeling sick, tired, dizzy, having chest pain, a headache, muscle or joint pain, and stomach ache. The response options were every day, more than two times, two times, once, and never, graded 1-5.</p> <p>Family type was categorised in four ways: 1) two-parent and single parent. Two parents comprised of couples who were legally married and those who were living together in a consensual union whereas single parents comprised never married, widowed, divorced, and separated mother or father. 2) two-parent (monogamous), two-parent (polygynous), and single parent 3) two-parent, single father, and single mother 4) two-parent, never married, widowed, divorced, and separated</p>
Data: Physical constraints on thermoregulation and flight drive morphological evolution in bats
<p>Body size and shape fundamentally determine organismal energy requirements by modulating heat and mass exchange with the environment and the costs of locomotion, thermoregulation, and maintenance. Ecologists have long used the physical linkage between morphology and energy balance to explain why the body size and shape of many organisms vary across climatic gradients, e.g., why larger endotherms are more common in colder regions. However, few modeling exercises have aimed at investigating this link from first principles. Body size evolution in bats contrasts with the patterns observed in other endotherms, probably because physical constraints on flight limit morphological adaptations. Here, we develop a biophysical model based on heat transfer and aerodynamic principles to investigate energy constraints on morphological evolution in bats. Our biophysical model predicts that the energy costs of thermoregulation and flight, respectively, impose upper and lower limits on the relationship of wing surface area to body mass (S-MR), giving rise to an optimal S-MR at which both energy costs are minimized. A comparative analysis of 278 species of bats supports the model’s prediction that S-MR evolves toward an optimal shape and that the strength of selection is higher among species experiencing greater energy demands for thermoregulation in cold climates. Our study suggests that energy costs modulate the mode of morphological evolution in bats—hence shedding light on a long-standing debate over bats’ conformity to ecogeographical patterns observed in other mammals—and offers a procedure for investigating complex macroecological patterns from first principles.</p>
Dataset of the manuscript "Problems and Solutions in Applying Continuous Integration and Delivery to 20 Open-Source Cyber-Physical Systems"
<p>This archive contains the artifacts (datasets) for the manuscript "Problems and Solutions in Applying Continuous Integration and Delivery to 20 Open-Source Cyber-Physical Systems"</p>
Overview of PASTA Questionnaires: Physical Activity through Sustainable Transport Approaches (PASTA): protocol for a multi-centre, longitudinal study
<p>Overview of questionnaires used in the PASTA project (Physical Activity through Sustainable Transport Approaches (PASTA))</p> <p>English version</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° runoff over the conterminous United States, 1980–2015
<p>This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the surface and subsurface runoff. The file name has four parts: the variable collection, the used parameterization, the time scale, and the suffix.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° ET over the conterminous United States, 1980–2015
<p>This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration). The file name has four parts: the variable collection, the used parameterization, the time scale, and the suffix.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° terrestrial water storage over the conterminous United States, 1980–2015
<p>This dataset contains the 1980–2015 monthly terrestrial water storage simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the total terrestrial water storage and its constituents (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the variable collection, the used parameterization, the time scale, and the suffix.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</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>
Dataset of the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"
<p><br> #Dataset Package for the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"</p> <p><br> Description of the content:</p> <p><br> 1) "1_RQ-CPS-bugs-Taxonomy" folder contains all the main experimental data concerning the issues sampled and analyzed from all the Projects considered in the study,<br> including row-data on the taxonomy validtion steps.<br> <br> - Under "the sub-folder "1_Taxonomy-Raw-data" are reported the row-data concerning the taxonomy validtion steps </p> <p><br> 2) "2_Scripts" contains all scripts used to generate the issue data and sampled issue raw-data in the previous folders: </p> <p><br> - "setup.md" file in the folder describes how to set=up and run the script used for collecting and sampling the issues for the validation steps:<br> <br> - runJSONtoCSV.sh<br> - JSONtoCSV.py<br> - generateListOfAllSamples.py<br> - generateAllSamples.r<br> <br> Under "the sub-folder "1_Scripts/1_Data_Collection":<br> <br> <br> 3) "3_Final Taxonomy" folder contains the final Table representation (also reported in the previous folder) and main figures of the CPSs Bugs Taxonomy.</p>
A comparison of the World Health Organisation's HEAT model results using a non-linear physical activity dose response function with results from the existing tool
<p>Datasets relating to the Wellcome Open Research publication of the same name.</p>
NMMA: A nuclear-physics and multi-messenger astrophysics framework to analyze binary neutron star mergers
<p>Data release associated with the preprint "<em>NMMA: A nuclear-physics and multi-messenger astrophysics framework to analyze binary neutron star mergers</em>"</p> <p>Data includes:</p> <p>EOS files:</p> <ul> <li>5000 eos files with radius (km), mass (Msun), and tidal deformability as columns stored under eos/eos_data</li> <li>prior probabilities for the EOSs are stored in eos/eos_prior_probability.dat</li> </ul> <p>Posterior samples:</p> <ul> <li>Posterior samples based on the analysis of GW170817 and AT2017gfo stored in posterior_samples/GW170817-AT2017gfo_posterior_samples.dat</li> <li>Posterior samples based on the analysis of GW170817, AT2017gfo, and the afterglow of GRB170817A are stored in posterior_samples/GW170817-AT2017gfo-GRB170817A_afterglow_posterior_samples.dat</li> </ul> <p> </p>
JetClass: A Large-Scale Dataset for Deep Learning in Jet Physics
<p>JetClass is a new large-scale dataset to facilitate deep learning research in jet physics. It consists of 100M jets for training, 5M for validation and 20M for testing. The dataset contains 10 classes of jets, simulated with MadGraph + Pythia + Delphes. <br> <br> A detailed description of the JetClass dataset is presented in the paper <a href="https://arxiv.org/abs/2202.03772">Particle Transformer for Jet Tagging</a>. An interface to use the dataset is provided in <a href="https://github.com/jet-universe/particle_transformer">https://github.com/jet-universe/particle_transformer</a>.</p>
A physically interpretable data-driven surrogate model for wake steering
<p>PALM input files for the simulations performed in the study "A physically interpretable data-driven surrogate model for wake steering" by Sengers et al. (2022). </p> <p>The PALM code is available at <a href="https://palm.muk.uni-hannover.de/">https://palm.muk.uni-hannover.de</a><br> Additional information to the input files is given in the README file</p> <p>Cite this as:<br> B.A.M. Sengers (2022). Dataset: A physically interpretable data-driven surrogate model for wake steering. https://doi.org/10.5281/zenodo.6821164</p>
Continuous Integration and Delivery Practices for Cyber-Physical Systems: An Interview-Based Study - Online Dataset
<p>This package contains the online dataset of the manuscript:</p> <p>Continuous Integration and Delivery Practices for Cyber-Physical Systems: An Interview-Based Study</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° runoff over the conterminous United States, 1980–2015 (time-merged version)
<p>This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the surface and subsurface runoff. The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° ET over the conterminous United States, 1980–2015 (time-merged version)
<p>This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° terrestrial water storage over the conterminous United States, 1980–2015 (time-merged version)
<p>This dataset contains the 1980–2015 monthly terrestrial water storage simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the total terrestrial water storage and its constituents (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
"A physics-based model for wind turbine wake expansion in the atmospheric boundary layer"
<p>Vahidi, Dara, and Fernando Porté-Agel. "A physics-based model for wind turbine wake expansion in the atmospheric boundary layer." <em>Journal of Fluid Mechanics</em> 943 (2022).</p>
COVID-19, physical activity, and health
<p>Dataset related to the project on COVID-19, physical activity, and health</p> <p>This dataset includes:</p> <p><strong>1) A codebook (including the name of the main variables)</strong></p> <p>--> "code_book_covid.xlsx"</p> <p><strong>2) The anonymous data set</strong></p>
Data and morphometric results from a physical experiment simulating washover deposition
<p>This portfolio includes two sets of data, used and explained in Lazarus, Williams & Goldstein (preprint: <a href="https://doi.org/10.31223/X5JH1X">https://doi.org/10.31223/X5JH1X</a>):</p> <ol> <li>washover morphometry measured from a physical experiment to simulate washover deposition, conducted at the Total Environment Simulator (TES) at the University of Hull ('TES_washover_morphometry.csv');</li> <li>the terrestrial laser-scan data in which those morphometric characteristics of washover were measured ('Run01.zip' – 'Run22.zip'), with a table of experimental conditions for each trial (run).</li> </ol> <p> </p> <p><strong>Washover morphometry dataset</strong></p> <p><em><strong>TES_washover_morphometry.csv</strong></em> – Washover morphometry from the 22 experimental trials in the TES at Hull. Dataset includes: deposit ID, throat width (m), deposit width (m), intrusion length (m), deposit area (m<sup>2</sup>), deposit volume (m<sup>3</sup>), deposit perimeter (m), built fraction, forcing mode ("inundation" or "wave-driven"), and a dataset label ("TES").</p> <p> </p> <p><strong>Table of experimental conditions</strong></p> <p><em><strong>TES_parameters_trial_log_full_v2.pdf</strong></em> – Table of experimental conditions for each trial (run).</p> <p> </p> <p><strong>Data folders</strong></p> <p>These folders contain terrestrial laser-scan data from washover experiments at the Total Environment Simulator (TES) at the University of Hull. These files were used to identify and measure the individual washover deposits in each experimental trial.</p> <p>Each folder (Run01–Run22) contains data (in 'e57' file format) for a specific trial, and includes the following:</p> <p><em><strong>RunX_DOD.e57</strong></em> – This is a point cloud of the difference between the Pre_Processed and Post_Processed files. This is produced by subtracting the 'post' values from the 'pre' values.</p> <p><em><strong>RunX_Post_Processed.e57</strong></em> – This is a point cloud of the topography at the end of the experimental run, created from the raw data file by trimming out extraneous points.</p> <p><em><strong>RunX_Post_Raw.e57</strong></em> – This is a point cloud of the topography at the end of the experimental run, created in FARO Scene to merge two individual point clouds (covering the entire interior of the TES) into a non-proprietary file format.</p> <p><em><strong>RunX_Pre_Processed.e57</strong></em> – This is a point cloud of the topography prior to the experimental run, created from the raw data file by trimming out extraneous points.</p> <p><em><strong>RunX_Pre_Raw.e57</strong></em> – This is a point cloud of the topography prior to the experimental run, created in FARO Scene to merge two individual point clouds (covering the entire interior of the TES) into a non-proprietary file format.</p>
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)
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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.