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6,498 results for “Physicality”
Retail sale of alcoholic beverages in physical terms
<p><strong>The data includes retail products of different types of alcoholic beverages in Russia for 2017. Includes a breakdown by region.</strong></p> <p><strong>Here for each type of product you can see the number of sales by region. </strong></p> <p><strong>The data is collected and presented by the Federal service for alcohol market regulation of Russia.</strong></p> <p> </p> <p><strong>The posted information is formed according to the data of the Unified state automated information system. With the exception of beer, beer, cider, Poiret, Mead and alcoholic beverages sold by catering establishments. Information on retail sales in full will be provided from the report for July 2017 (taking into account the data of retail sales in rural settlements)</strong></p>
Data set used in the Article "Evaluation of Monte Carlo tools for high-energy atmospheric physics II: relativistic runaway electron avalanches"
<p>Data used for the Relativistic Runaway Electron Avalanches (RREA) simulations of the Article : "Evaluation of Monte Carlo tools for high energy atmospheric physics II: relativistic runaway electron avalanches" by D. Sarria et al.</p> <p>Includes two set of results : The Probability of Generating RREAs, and the Characterizations of RREAs.</p> <p>Link to the article : <a href="https://www.geosci-model-dev.net/11/4515/2018/gmd-11-4515-2018.html">https://www.geosci-model-dev.net/11/4515/2018/gmd-11-4515-2018.html</a></p> <p>DOI of the article: 10.5194/gmd-2018-119</p>
Nobel Prize winners in Physics, Chemistry and Medicine or Physiology 1994-2018
<p>The lists of Nobel Prize winners in Physics, Chemistry and Medicine or Physiology 1994-2018 + theirs Scopus ID's and theirs affiliations at the time of the award.</p>
Shock physics mesoscale modeling of shock stage 5 and 6 in ordinary and enstatite chondrites: modeling data
<p>These data are related to:</p> <p>Moreau, J., Kohout, T., Wünnemann K., Halodova, P., Haloda, J., 2019.<br> Shock physics mesoscale modeling of shock stage 5 and 6 in ordinary and enstatite chondrites.<br> Icarus, 332, 50-65. <a href="https://doi.org/10.1016/j.icarus.2019.06.004">https://doi.org/10.1016/j.icarus.2019.06.004</a></p> <p>Any use of these files, scripts (partial or complete) in research papers, please reference the paper above + Moreau et al. (2017, 2018) (references compiled in the above-mentioned paper).</p> <p>To use these files, you will need:<br> - authorized access to the iSALE shock physics code (iSALE-Dellen version) re-compiled with our modifications, with reference<br> to the manual in your work<br> - access to the pySALEPlot tool for iSALE users made by T. Davison acknowledged in your work<br> - running the iSALE models to generate the different jdata.dat files (average size of a jdata.file is 7 Go)<br> - python<br> - Ubuntu or macOS</p> <p> </p> <p>(more info in README.txt file)</p>
WASHTREET. Application of Structure from Motion (SfM) photogrammetric technique to determine surface elevations in an urban drainage physical model.
<p><strong>WASHTREET</strong><strong> - </strong><strong>Application of Structure from Motion (SfM) photogrammetric technique to determine surface elevations in an urban drainage physical model.</strong></p> <p>This dataset contains raw data and surface elevations results from the application of the Structure from Motion (SfM) photogrammetric technique in a 36 m<sup>2</sup> full-scale urban drainage physical model, which is placed in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain). This work is part of the <a href="https://zenodo.org/communities/washtreet">WASHTREET project</a>, where a series of high-resolution experiments were performed measuring urban surface wash-off and sediment transport through gully pots and pipes under laboratory-controlled conditions. The accurately measurement of the surface elevations is needed for a proper representation of surface flow, which is key in the detachment and transport of solids in the model surface. The dataset was used in the work developed in Naves et al. (2019) (DOI: <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a>)</p> <p>A detailed description of experimental procedure and data collected can be consulted in ‘<em>1_ExperimentalProcedure.pdf’</em>. Raw images taken as input for the SfM software are included in ‘<em>2_RawImages.zip’</em>. Then, the point cloud resulted is provided in ‘<em>3_SFM_RawPointCloud.ply</em>’. This point cloud was processed and the final elevation map with a resolution of 5 mm is included in ‘<em>4_SfM_ElevationMap(m).xyz</em>’.</p> <p>Further details of the physical model and hydraulic and sediment transport experiments can be consulted in the dataset <a href="http://doi.org/10.5281/zenodo.3233918"><em>WASHTREET - Hydraulic, wash-off and sediment transport experimental data</em></a>. In addition, raw data and runoff velocities results obtained using seeded and unseeded Particle Image Velocimetry (PIV) techniques are provided in the dataset <a href="http://www.doi.org/10.5281/zenodo.3239401">WASHTREET - PIV data</a>.</p> <p>The WASHTREET project is being developed in the scope of the PhD thesis of the first author, which is in receipt of a Spanish Ministry of Science, Innovation and Universities predoctoral grant [FPU14/01778]. The project also receive funding from the Spanish Ministry of Science, Innovation and Universities under POREDRAIN project RTI2018-094217-B-C33 (MINECO/FEDER-EU)</p> <p>Derived publications:</p> <ul> <li>Naves, J., Anta, J., Puertas, J., Regueiro-Picallo, M., & Suárez, J. (2019). Using a 2D shallow water model to assess Large-Scale Particle Image Velocimetry (LSPIV) and Structure from Motion (SfM) techniques in a street-scale urban drainage physical model. <em>Journal of Hydrology</em>, <em>575</em>, 54-65. <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a></li> <li>Naves, J., Anta, J., Suárez, J., & Puertas, J. (2020). Hydraulic, wash-off and sediment transport experiments in a full-scale urban drainage physical model. <em>Scientific Data</em>, <em>7</em>(1), 1-13.<a href="http://doi.org/10.1038/s41597-020-0384-z"> https://doi.org/10.1038/s41597-020-0384-z</a></li> </ul>
Data sets for "MELISSA: System description and spectral features of pre‐ and post‐midnight F‐region echoes. Journal of Geophysical Research: Space Physics" by Rodrigues et al.
<p>Observations used in the study "Rodrigues, F. S., Zhan, W., Milla, M. A., Fejer, B. G., de Paula, E. R., Neto, A. C., et al ( 2019). MELISSA: System description and spectral features of pre‐ and post‐midnight <em>F</em>‐region echoes. <em>Journal of Geophysical Research: Space Physics</em>, 124. <a href="https://doi.org/10.1029/2019JA027445">https://doi.org/10.1029/2019JA027445</a>."</p> <p>The uploaded files include the RTI maps measured by the MELISSA radar system between 2014 and 2018 (.tif files). They also include values of SNR versus local time and height and the spectra presented in the manuscript (.mat files).</p> <p>Please, see README.txt for additional details.</p>
Fig. 3 in Dynamics of the restoration of physical trails in the grass-cutting ant Atta capiguara (Hymenoptera, Formicidae)
Fig. 3. Restoration of the physical trails of nest 2. First and last day of survey of trail 1 (A and B, respectively), 2 (C and D, respectively) and 3 (E and F, respectively). The lines B, D, F show the location where the trail was restored.
Fig. 1 in Dynamics of the restoration of physical trails in the grass-cutting ant Atta capiguara (Hymenoptera, Formicidae)
Fig. 1. Percentage variation in the mean length of grass blades for the experimental block (continuous lines) and control block (dashed lines) of each manipulated trail of Nest 1 (A) and 2 (B). The lines of same thickness correspond to the same trail.
Fig. 2 in Dynamics of the restoration of physical trails in the grass-cutting ant Atta capiguara (Hymenoptera, Formicidae)
Fig. 2. Restoration of the physical trails of nest 1. First and last day of survey of trail 1 (A and B, respectively), 2 (C and D, respectively) and 3 (E and F, respectively). The lines on B, D, F show the location where the trail was restored.
Data-driven physics-based modeling of pedestrian dynamics
<p>Python package to create physics-based pedestrian models from crowd measurements</p> <p>Github: <a href="https://github.com/c-pouw/physics-based-pedestrian-modeling">https://github.com/c-pouw/physics-based-pedestrian-modeling</a></p>
Physical parameters for the Orion fibers
<p>Table with the physical properties determined for the 152 velocity-coherent fibers identified in the EMERGE Early ALMA Survey.</p> <p>The latter is a sample of 7 star-forming regions in Orion homogeneously surveyed with ALMA in N2H+ (1-0) at 4.5".</p> <p>The raw data was reduced with a uniform analysis using a combinaiton of custom spectral fitting routines, the identification algorithm in the PPV plane HIFIVe (Hacar et al. 2018) and the radial profile fitting routine FilChap (Suri et al. 2019).</p> <p>The header of the table includes the following parameters:</p> <ul> <li>ID: fiber identification number within the region assigned by HIFIVe during the identification process;</li> <li>x0, y0: average x-y coordinates of the fiber with respect to the reference pixel in the datacube [unit: ''];</li> <li>dV, sd_dV: median velocity dispersion (as FWHM of the spectral line) and corresponding standard deviation across the structure [unit: km s-1];</li> <li>sNT, sd_sNT: median non-thermal dispersion (in units of the sound speed) and corresponding standard deviation across the structure;</li> <li>Vlsr, sd_Vlsr: median centroid velocity and corresponding standard deviation across the structure [unit: km s-1];</li> <li>Mass: total mass of the strcuture [unit: Msun];</li> <li>points: number of fields associated to the structure;</li> <li>Tk: median kinetic temperature of the structure [unit: K];</li> <li>L: length of the structure [unit: pc];</li> <li>AR: aspect ratio (computed as L / FWHM);</li> <li>gradVx: median centroid velocity gradient along the axis knots [unit: km s-1 pc-1];</li> <li>ML: line mass of the fiber (computed as M / L) [unit: Msun pc-1];</li> <li>lgrad: mean velocity gradient across the whole axis length [unit: km s-1 pc-1];</li> <li>P: number of protostars associated to the structure;</li> <li>source: region in which the fiber was identified;</li> <li>FWHM: fiber width as derived from the fitting of its column density radial profile [unit: pc];</li> <li>N0: fiber peak column density as derived from the fitting of its column density radial profile [unit: cm-2];</li> <li>gradT: temperature gradient determined around the fiber axis [unit: K pc-1]. </li> </ul>
Fig. 2 in A physical barrier reduces capture of Euschistus servus (Hemiptera: Pentatomidae) in pheromone-baited traps near peach trees
Fig. 2. Capture of adult Euschistus servus in pheromone-baited traps placed near peach trees when the trap occurred within the enclosure formed by a 3.7-m-tall physical barrier around the peach tree, or when there was no barrier around the tree: (A) 2014 and (B) 2015.
Fig. 1. A 3.7 in A physical barrier reduces capture of Euschistus servus (Hemiptera: Pentatomidae) in pheromone-baited traps near peach trees
Fig. 1. A 3.7 × 3.7 × 3.7 m fence was erected around a peach tree to determine if this physical barrier would reduce adult Euschistus servus from reaching a pheromone-baited trap placed inside the enclosure near the tree.
Repository for: "Using automatic calibration to improve the physics behind complex numerical models: An example from a 3D lake model"
<p>Set of numerical experiments supporting the paper entitled "Using automatic calibration to improve the physics behind complex numerical models: An example from a 3D lake model" by Marina Amadori, Abolfazl Irani Rahaghi, Damien Bouffard and Marco Toffolon. Submitted to GMD. </p> <p>The folder contains: </p> <p>simulations: DYNO-PODS + Delft3D experiments on Lake Morat. See https://github.com/louisXW/DYNO-pods for more insights on DYNO-PODS and instructions for installation.</p> <p>scripts: extraction and plotting scripts</p> <p>source_code: modified Delft3D src as available at: https://github.com/eawag-surface-waters-research/Delft3D/tree/d3d4/research/surface_heat_transfer</p>
Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading"
<p>Title of dataset: Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading".</p> <p>Name/institution/contact information: Dr. Michal Bartošák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz.</p> <p>Date of data collection: The data were collected between 2021 and 2024.</p> <p>File name structure: The data consists of two files: "316L_fatigue_and_defects.xls," which contains fatigue lifetime data and defect characteristics, and an associated description file, "read_me.txt."</p> <p>See "https://doi.org/10.1016/j.ijfatigue.2024.108608" for the associated article and a detailed description of the methods.</p>
F I G U R E 1 A in A healing place: Reimagining the modern botanic garden as a physic garden
F I G U R E 1 A modern "physic garden," as I have described here: Royal Botanic Gardens Melbourne (Melbourne Gardens), one of two botanic gardens managed by Royal Botanic Gardens Victoria. Photo: Royal Botanic Gardens Victoria, July 2020.
Dataset: A review of methods to trace material flows into final products in dynamic material flow analysis - from industry shipments in physical units to monetary input-output tables (p
<p>Dynamic material flow analysis (dMFA) is widely used to model stock-flow dynamics. To appropriately represent material lifetimes, recycling potentials, and service provision, dMFA requires data about the allocation of economy-wide material consumption to different end-use products or sectors, that is, the different product stocks, in which material consumption accumulates. Previous estimates of this allocation only cover few years, countries, and product groups. Recently, several new methods for estimating end-use product allocation in dMFA were proposed, which so far lack systematic comparison. We review and systematize five methods for tracing material consumption into end-use products in inflow-driven dMFA and discuss their strengths and limitations. Widely used data on industry shipments in physical units have low spatio-temporal coverage, which limits their applicability across countries and years. Monetary input–output tables (MIOTs) are widely available and their economy-wide coverage makes them a valuable source to approximate material end-uses. We find four distinct MIOT-based methods: consumption-based, waste input–output MFA (WIO-MFA), Ghosh absorbing Markov chain, and partial Ghosh. We show that when applied to a given MIOT, the methods’ underlying input–output models yield the same results, with the exception of the partial Ghosh method, which involves simplifications. For practical applications, the MIOT system boundary must be aligned to those of dMFA, which involves the removal of service flows, sector (dis)aggregation, and re-defining specific intermediate outputs as final demand. Theoretically, WIO-MFA, applied to a modified MIOT, produces the most accurate results as it excludes massless and waste transactions. In part 2 of this work, we compare methods empirically and suggest improvements for aligning MIOT-dMFA system boundaries.</p>
Polar UVI Images (Intensity and Boundary) & Space Physical Parameters Joint Data
<p><span>The ultraviolet imager on board the Polar satellite is capable of capturing information about the polar and equatorial boundaries of the auroral oval, the overall morphology of the auroral oval, and the spatial distribution of auroral oval intensity. The intensity of the auroral oval are related to the amount of energy injected from the solar wind and magnetosphere into the polar ionosphere. And the position and size of the auroral oval boundary is also closely linked to changes in the space environment. In order to mitigate the influence of daylight on auroral images, we selected a total of 4215 images of both full, gap and incomplete aurora ovals. We utilized corresponding Interplanetary Magnetic Field (IMF), solar wind parameters, and geomagnetic index data from the NASA OMNI database to build a jointly dataset consist of the space physical parameters and the intensity and boundaries of the aurora oval.</span></p>
Precisely controlled colloids: A playground for path-wise non-equilibrium physics
<p>Particle Trajectories from Non Equilibrium Steady State (Driven) and Equilibrium state.</p> <p>Number of trajectories can be found in the Book1.xlx </p> <p>File format description on the individual *.dat files is explained in the instructions.pdf</p> <p> </p>
Physical stabilization of water-soluble PVA nanofibrous materials functionalized with biologically active substances
<p>Tissue engineering aims to develop materials that enhance biological activity and promote tissue healing and regeneration. One promising approach is to functionalize nanofibrous materials with antimicrobial substances, such as lipophosphonoxin (LPPO), and use water-soluble polymers like polyvinyl alcohol (PVA) to incorporate bioactive molecules into fibers. However, water-soluble materials often face the issue of "burst release," releasing over 90% of the active substances within the initial 24 hours. This research focuses on preparing functionalized nanofibrous materials based on PVA containing the experimental antimicrobial compound LPPO and subsequent physical stabilization of the materials using the "Heat treatment" method. The applied stabilization successfully reduced the incorporated substance's release rate by up to 50%. The resulting materials have the potential to provide functional cross-linked PVA nanofiber scaffolds for regenerative medicine applications in large and chronic skin injuries.</p>
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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.