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6,498 results for “Physicality”
Complementary figures for paper 'The ESO UVES/FEROS Large Programs of TESS OB pulsators II. On the physical origin of macroturbulence'
<p>These are figures from Appendices C and D of the paper <strong>'The ESO UVES/FEROS Large Programs of TESS OB pulsators II. On the physical origin of macroturbulence' (Serebriakova et al. Accepted on 17 Oct 2024 for publication in A&A)</strong>. Appendices C and D are moved to Zenodo following the request of the editor. The figures demonstrate MESA model properties used to compare with observed macroturbulent velocities in massive stars. Appendix C focuses on emphasising the role of opacity bumps and subsurface convection throughout evolution. Appendix D explores wave propagation in these models.</p> <p><strong>Appendix C: MESA internal profiles</strong>. Each figure contains a grid of model internal profiles of stellar properties computed with MESA. Initial mass varies in rows: 60, 20, 12, 7, and 3 Solar masses. In columns, three evolutionary stages are presented: MS, TAMS, and Hertzsprung gap. Models in Fig.1-4 were computed with Solar metallicity Z=0.014, and in Fig. 5-8 - with LMC metallicity Z=0.006. Fig. 1,5 - opacity and convective velocity VS temperature, Fig. 2,6 - opacity and convective velocity VS radius(zoomed), Fig. 3,7 - opacity and convective velocity VS radius(full), Fig. 4,8 - Sign of squared Brunt-Väisälä frequency and convective velocity as a fraction of sound speed VS radius. See the paper for details. </p> <p><strong>Appendix D: Propagation diagrams</strong>. Each figure contains a grid of propagation diagrams (shaded with g-modes and p-modes cavities) computed for the same models as before. Fig. 3 and 6 are complemented with tunnelling frequency omega_min - see 'Section 5: On wave tunnelling through subsurface convective layers' for details with examples in Fig. 8 of the paper. </p> <p> </p>
A Physical Human-Robot Interaction Dataset - TacAct
<p>This dataset is the supplementary material of the IROS 2021 article "Organization and Understanding of a Tactile Information Dataset TacAct During Physical Human-Robot Interactions".<br> The dataset was collected by a flexible supercapacitor tactile sensor installed in an imitated mechanical arm device. In a 32 × 32 grid, the sensor data can be sampled at 100 Hz (100 frames per second). A total of 12 touch actions, namely, pull, squeeze, push, hold, grasp, poke, static drag, strong hit, soft slide, scratch, soft tap, and sliding drag, were recorded. A single-action collected from a subject consists of a 32×32×N matrix (where N is the number of frames or frame length). For the same action, all subjects were asked to use as many postures as possible to apply different forces to different positions of the sensor, and repeat 20 times with each hand. Except for strongly hit and soft tap (complete in an instant, acquisition time 2 s), the duration of each action is 2 s, and the time is 4 s altogether. The experiment was conducted on 50 subjects (36 males and 14 females,ranged from 22 to 36 years and 44 were right-handed) in total, each subject consists of 480 actions (12 actions × 40 repetitions ) in total, and the dataset collected 24,000 actions from the subjects.</p>
BC Ophiolitic UMR Mineralogy, Physical Properties, and ME Geochemistry
<p>This is dataset comprises: major-element geochemistry, physical properties (magnetic susceptibility, natural remanent magnetization, density, porosity), and mineralogy (XRD and TGA) for ophiolitic ultramafic rocks in British Columbia.</p>
PhAQ: Intuitive Physics Question Answering forMulti-Modal Neural Network training
<p>Dataset for the paper:</p> <p>PhAQ: Intuitive Physics Question Answering forMulti-Modal Neural Network training</p> <p>The two proposed splits are given in zip files.</p>
Research Data and Software (re)Use Indications in High Energy Physics related Scholarly Works (Full dataset)
<p>This dataset contains research data and software (re)use indications (formal citations, informal mentions) in scholarly works related to High Energy Physics. 1,411 research and software indications were identified by a mix of approaches: use of citation discovery services and multiple search approaches in Google Scholar. The dataset contains indications by what approach the (re)use indications were found. All identified research data and software (re)use indications were classified according to their purpose, location, and elements.</p> <p>The data was collected in 2018 for a PhD thesis on research data and software (re)use indications in scholarly works.</p>
Identification of Research Data and Software (re)Use Indications in High Energy Physics related Scholarly Works (1951 - 2018)
<p>This dataset contains research data and software (re)use indications in High Energy Physics related scholarly works. A minimal random sample of scholarly works that contain highly processed research data was taken from HEPData and manually read for research data and software (re)use indications. The sample contains 368 works, which were published between 1951 and 2018.</p> <p>The data was collected in 2019 for a PhD thesis on research data and software (re)use indications in scholarly works.</p>
Capturing the Little Washita watershed water balance with a physically-based two-hydrologic-variable model
<p>Database corresponding to the research work submitted to Water Resources Research :<br> "Capturing the Little Washita watershed water balance with a physically-based two-hydrologic-variable model"<br> Fanny Picourlat (fanny.picourlat@lsce.ipsl.fr), Emmanuel Mouche, Claude Mugler</p> <p> </p> <p>"Geomorphic_Analysis" directory ------------------------------------------------------------------------------</p> <p>Little Washita geomophic analysis results. Analysis conducted on the 100 m resolution DEM (from USGS datadase, accessible at https://www.usgs.gov/core-science-systems/national-geospatial-program/small-scale-data) using a flow paths modeling algorithm developped by Maquin (2016).</p> <p> - Hillslopes_Length.csv : List of hillslopes lengths [m]<br> <br> - Hillslopes_MeanSlopes.csv : List of hillslopes mean slopes [%]<br> <br> <br> "3DREF_model_19981999" directory ------------------------------------------------------------------------------<br> Three-dimensional reference model files : exemple of the 1998-1999 water year simulation.</p> <p> - LWo.grok : Data file prepared for the pre-processor, which is then run to generate the input data files for HydroGeoSphere.</p> <p> - parameters_summary.pdf : parameters summary table.<br> <br> - "Outputs" directory ---------------------------------------------<br> Output files that form paper's figures.<br> <br> - LWo.hydrograph.Hydrographe_USGS1_07327550.dat : Streamflow at USGS1<br> - LWo.water_balance.dat : Water balance<br> - LWo.pm.dat : Subsurface domain variables for all nodes for all output times. File used for plotting depth to water table.<br> - LWo.olf.dat : Surface domain variables for all nodes for all output times. File used for plotting evapotranspiration at the closest node from the Ameriflux station.<br> <br> <br> "Hillslope_Model" directory -----------------------------------------------------------------------------------<br> Equivalent hillslope model files for the 20-year simulation.</p> <p> - LWo.grok : Data file prepared for the pre-processor, which is then run to generate the input data files for HydroGeoSphere.<br> <br> - "Outputs" directory ----------------------------------------------<br> <br> - LWo.water_balance.dat : Water balance. File used for plotting hillslope discharge and evapotranspiration.<br> - LWo.observation_well_flow.Well_1.dat : Outputs at nodes located at x=100m. File used for plotting depth to water table.<br> - LWo.pm.dat : Subsurface domain variables for all nodes for all output times. File used to extract the "post-processed" files described below.</p> <p> - "Post_processed" directory -------------------------------<br> Data extracted from the output file LWo.pm.dat.</p> <p> - TAB_xzy_sat.csv : Saturation for all nodes for all output times. Used for defining the seepage face extension Xs(t).<br> - x(t).csv : x coordinate (for all output times) of the intersection point between roots ending limit and water table. Used for defining the water table slope tan(i(t)).</p> <p><br> "Params" directory --------------------------------------------------------------------------------------------<br> Parameters files for both 3DREF model (1998-1999 simulation) and hillslope model (20-year simulation).<br> <br> - "Topography" directory --------------------------------------------<br> - maillage_hgs_corr3_riv.2dm : Horizontal 3D mesh file<br> - fichier_altitude_grok_corr3_riv_b.txt : 3D surface nodes elevation [m]<br> <br> - "Props" directory -------------------------------------------------<br> - LW.mprops : Subsurface material parameters<br> - LW.oprops : Surface domain parameters<br> - LW_LAI.etprops : Vegetation parameters<br> <br> - "Zones_vg" directory ----------------------------------------------<br> - zone_1_ele.txt : Bare soil 3D elements IDs<br> - zone_2_ele.txt : Deciduous forest 3D elements IDs<br> - zone_3_ele.txt : Evergreen forest 3D elements IDs<br> - zone_4_ele.txt : Shrubs 3D elements IDs<br> - zone_5_ele.txt : Grassland 3D elements IDs<br> - zone_6_ele.txt : Pasture 3D elements IDs<br> - zone_7_ele.txt : Crops 3D elements IDs<br> <br> - "Forcings" directory ----------------------------------------------<br> - NARR_day_9899.csv : Daily rainfall [m/s] on 1998-1999<br> - NARR_day_etp_9899.csv : Daily Potential Evapotranspiration [m/s] on 1998-1999<br> - NARR_day_9313.csv : Daily rainfall [m/s] on 1993-2013<br> - NARR_day_etp_9313.csv : Daily Potential Evapotranspiration [m/s] on 1993-2013<br> <br> - "Output_times" directory ------------------------------------------<br> - output_times_365d.csv : Output times [s] for 365 days<br> - output_times_20y.csv : Output times [s] for 20 years<br> <br> - "Rivers" directory ------------------------------------------------<br> - no_rivers_ele.txt : No river 3D elements IDs<br> - rivers_ele.txt : River 3D elements IDs<br> <br> - "LAI" directory ---------------------------------------------------<br> LAI files (1st column : time [s], 2nd column : LAI [-])<br> - LAI_2_hydro.csv : Deciduous forest LAI over one water year<br> - LAI_4_5_6_hydro.csv : Shrubs, grassland and pasture LAI over one water year<br> - LAI_winterwheat_OAlaoui.csv : Crops LAI over one water year<br> - LAI_eq_20y.csv : Equivalent LAI (for hillslope model) over 20 years<br> <br> "Analytical_Model" directory -----------------------------------------------------------------------------------</p> <p> - Analytical_model.py : Analytical model code for the 20-year simulation of the equivalent hillslope water balance.</p> <p> </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>
Conf example 48^3 x 12 T=230 MeV fullQCD 2+1 at physical point
<p>Full QCD</p> <p>2+1 rooted stout staggered fermions at physical point</p> <p>V = 48^3 x 12</p> <p>a = 0.0715 fm</p> <p>Gauge configuration saved after 5460 RHMC step (run 1) in the presence of bias multicanonic topotential.</p>
Individuals with developmental disabilities do make individual stylistic contributions to text written with physical facilitation
<p>Corpora of texts written via Facilitated Communication (FC) among two Italian centres. The texts collected resulted from a >10 years period: 16 FC users and 8 eight facilitators were involved. For each centre, 4 and 5 folders were created respectively. Those labelled as Corpus 1 and Corpus 2 refer to the original corpus. The first code (U+ number) refers to the FC user. The code that follows the underscore refers to the facilitator (F+number). The folders labelled with the underscore _A,_B and _C, contain texts written by different users with a single facilitator, split in three different ways in order to acknowledge possible diachronic stylistic changes. These corpora were analysed through Stylo package for R. </p> <p> </p>
Dataset for "Yield Estimation of the August 2020 Beirut Explosion by Using Physics-Based Propagation Simulations of Regional Infrasound"
<p>Dataset for “Yield Estimation of the August 2020 Beirut Explosion by Using<br> Physics-Based Propagation Simulations of Regional Infrasound”</p> <p>Authors: Keehoon Kim and Michael E. Pasyanos</p> <p>Lawrence Livermore National Laboratory, Livermore, CA, USA</p> <p>Description<br> This datset includes the infrasound waveform data recorded by the array at the<br> Mt. Meron (IMA) in Israel. A five-element array deployed by the National Data<br> Center of Israel (Fee et al., 2013), and all stations had Martec Tekelec MB2005<br> sensors which have a flat frequency response in the infrasound band (0.1–20Hz)<br> (Ponceau and Bosca, 2010). The infrasound data was provided by the National<br> Data Center of Israel, Soreq Nuclear Research Center, and the pressure<br> recordings only relevant to the 2020 Beirut explosion were uploaded to the<br> public repository (https://zenodo.org).</p> <p>File Description<br> IMA_array_infrasound_waveform.txt Pressure values in ASCII format for 5 stations</p> <p>Acknowledgments<br> This research was performed by the support from the U.S. Department of Energy,<br> National Nuclear Security Administration, Office of Defense Nuclear<br> Nonproliferation, Research and Development under the auspices of the U.S.<br> Department of Energy by the Lawrence Livermore National Laboratory under<br> Contract Number DE-AC52-07NA27344. This is LLNL Contribution LLNL-JRNL- 839419</p> <p>References<br> Fee, D., Waxler, R., Assink, J., Gitterman, Y., Given, J., Coyne, J., ... &<br> Grenard, P. (2013). Overview of the 2009 and 2011 Sayarim infrasound<br> calibration experiments. Journal of Geophysical Research Atmospheres, 118(12),<br> 6122-6143.</p> <p>Ponceau, D., & Bosca, L. (2010). Low-noise broadband microbarometers. In<br> Infrasound monitoring for atmospheric studies (pp. 119-140). Springer,<br> Dordrecht.</p>
A 1D coupled physical-biogeochemical model for the North Atlantic for studying vertical carbon flux parameterizations
<p>This repository provides the model output and code for analysis in the following article:</p> <p>Wang, B., & Fennel, K. (2023). An assessment of vertical carbon flux parameterizations using backscatter data from BGC Argo. <em>Geophysical Research Letters</em>, 50, e2022GL101220. <a href="https://doi.org/10.1029/2022GL101220">https://doi.org/10.1029/2022GL101220</a></p> <p> </p>
Physical state of water controls friction of gabbro-built faults
<p>Experimental data</p> <p>six columns in the CSV. file, from left to right, are: time (s), displacement (mm), friction coefficient, axial displacement (mm), pore pressure (MPa) and temperature (℃). </p>
QAMeleon Physical layer evaluation of 1x24 WSS targeting flex-grid operation simulation raw data
<p>Physical layer simulations of 1x24 WSS has been supported, targeting flex-grid operation for transmission of 16-QAM and 64-QAM at 32, 64, and 128 Gbaud </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>
A habitat connectivity reality check for fish physical habitat model results and decision making for river restoration
<ol> <li>Fish physical habitat models are a tool for guiding restoration efforts in lotic ecosystems but often they overestimate restoration outcomes because currently they do not incorporate habitat connectivity. This persistent issue can, in extreme cases, result in little or no improvement to fish populations after the restoration, wasting valuable conservation resources.</li> <li>We present a case study where practitioners applied a fish habitat model for multiple life history stages of gravel spawning fishes to a 52 kilometer stretch of the Iller River but did so at a microscale implementation (every 200 meters). This approach provided an opportunity to assess the connectivity of gravel spawning fishes to find suitable habitats for all life history stages and seasonal movements.</li> <li>We used the assessed habitat estimates (availability of distinct habitat types within the 200 m reaches) to calculate the minimum distance a fish would need to go as it hypothetically “grew up” from egg to full spawning adult. We call this technique a reality check as it results in a decisive understanding of which areas were ultimately necessary to fulfill the life cycle of gravel spawning fishes, which standard assessments do not show.</li> <li>Our results show that complete connectivity still require long movement distances for vulnerable life stages to find suitable habitat. This contradicts standard practice, as restoration schemes and decision making often assume that connectivity inherently leads to more fish production without added habitat restoration.</li> <li>We recommend practitioners should perform this habitat connectivity approach when assessments implement fish habitat suitability models at similar scales. As a result, decision makers can evaluate proposed restoration sites and measures more realistically.</li> </ol>
Deep Representation Learning of Physical Activity and Sleep Patterns During Pregnancy Identifies post-hoc Inferences Associated with Prematurity
<p><strong>Running title</strong>: series2signal gestational age "clock" for pregnancy monitoring</p> <p><strong>Summary</strong>: </p> <p>Preterm birth (PTB) is the leading cause of infant mortality globally. While research has focused on the development of predictive models for PTB, cost-effective interventions have remained understudied. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations. However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy that decays over time. In this study, we use physical activity data collected using a wearable device comprising over 181, 944 hours of data across N = 1, 083 patients. Using a new state-of-the art deep learning time-series classification architecture, we first develop a ”clock” of healthy dynamics in physical activity patterns during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also developed a novel interpretability algorithm that integrates unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and static clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy based on measures of physical activity and sleep.</p> <p>Importantly, we found that deviations from this normal ”clock” of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e − 67) and when our model overestimates GA, there are 1.44 times (P = 2.82e − 39) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043), indicating that our model assigns a more advanced GA when an individual’s daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e − 21). Combining prediction with interpretability allows us to robustly signal when activity behaviors increase or decrease the likelihood of preterm birth and advocates for the future development of clinical decision support through passive monitoring and suggestions around exercise habits and sleep patterns, which are easily implemented in low- and middle-income countries (LMICs). Beyond this particular application, the presented pipeline can be used to analyze high-fidelity time-series data in other translational studies utilizing wearable devices.</p> <p> </p> <p><strong>Data description (brief)</strong>: the raw wearables data is available as .mtn files with the GA encoded in the filename after the underscore. The processed data with sleep annotations can be loaded using the pickle module for serialized objects in python. See https://github.com/nealgravindra/wearables for examples.</p>
Data for Numerical Study on the Impact of Large Air Purifiers, Physical Distancing, and Mask Wearing in Classrooms
<p>This is a reference case setup to re-generate all the data for the paper titled "Numerical Study on the Impact of Large Air Purifiers, Physical Distancing, and Mask Wearing in Classrooms"<br> </p>
Effects of powdered cactus pear pruning amendment on the physical and hydraulic properties of two contrasting Mediterranean soils
<p>Full database.</p> <p>A production and consumption paradigm known as "circular economy" (CE) emphasizes sharing, renting, reusing, repairing, refurbishing, and, in particular, recycling materials as much as feasible. Traditional agriculture relied totally on the CE, progressively the search for maximization of yields has produced more and more by-products. Their recovery and reuse are possible with approaches that refer to the CE, for example, with the use of pruning biomasses. The cultivation of the cactus pear annually produces large quantities of pruning residues, which have been shown to be useful for the recovery and reuse of nutrients. This study investigates the hydraulic properties of benchmark soils in which this by-product is incorporated. Here we show that the amendment with powdered cactus pear pruning waste (PCPPW) positively affects soil water retention. However, observable benefits require very high amendment proportions, more than 20% by volume. These quantities make use in the open field unrealistic but offer perspectives in the horticultural and floricultural sectors. These results reveal agreement in direct comparison to what was thought to be the case previously, i.e., a decrease in soil bulk density, an increase in plant available water capacity and an increase in soil swelling. A few per cent application of PCPPW improves the drainable water capacity only in the case of not very clayey soils, where their use becomes useless. The principles of the CE are important, but they must not be pursued a priori. For example, in the use of soil amendments, the behavior in the different soils conditions the suitability of their use.</p>
Data from: "Search for New Physics in Electronic Recoil Data from XENONnT"
<p>Those files contain data from Phys. Rev. Lett. 129, 161805 (2022), "Search for New Physics in Electronic Recoil Data from XENONnT" and are made available by the XENON Collaboration. V2 added solar axion limits.</p>
ScienceDex guides
Understand access before you commit
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.