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351 results for “jet”
Data for the paper "Longitudinal Filtering, Sponge Layers, and Equatorial Jet Formation in a General Circulation Model of Gaseous Exoplanets"
<p>GCM data for the paper "Longitudinal Filtering, Sponge Layers, and Equatorial Jet Formation in a General Circulation Model of Gaseous Exoplanets" published in Monthly Notices of the Royal Astronomical Society. Data are in the pp format used by the Met Office Unified Model GCM and can be loaded using the Iris python package.<br><br>The individual files correspond to simulations done in the following parts of the paper<br><br>eta75_study.tgz - Simulations from Section 3.1.1 investigating changing the value of t_K with eta_s=0.75<br>Keff_flat.tgz - The simulation with a flattened Keff, as outlined in Section 3.1.2<br>eta9_study.tgz - Simulations from Section 3.2 investigating increasing eta_s to 0.9<br>resolution_study.tgz - The simulations from the resolution study found in Appendix B.</p>
ATLAS OmniFold 24-Dimensional Z+jets Open Data
<div>These datasets contain the unbinned, twenty-four-dimensional ATLAS Z+jets differential cross-section measurement presented in <a href="https://cds.cern.ch/record/2899105" target="_blank" rel="noopener">CERN-EP-2024-132</a>. The measurements are presented as Pandas DataFrames in HDF5 format, and they are accompanied by MC predictions formatted as Numpy arrays. Measurements are provided both for "pseudo-data", i.e. a validation MC sample with truth- and reco-level quantities that has been reweighted to match data, as well as real data. </div> <div> </div> <div><strong>Important: </strong>Before using this data, please consult the <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024" target="_blank" rel="noopener">documentation & example notebooks</a>. </div> <div> </div> <div>The signal process is inclusive Z→μμ production with a fiducial region defined in the boosted regime: p_T^μμ > 200 GeV. </div> <div> </div> <div>In total, 24 Z+jets kinematic observables are measured:</div> <ul> <li>p_T, η, and ϕ of each of the two muons (6 observables)</li> <li>The p_T and rapidity of the dimuon system: p_T^μμ, y^μμ (2 observables)</li> <li>The 4-momenta (p_T, y, ϕ, m) of the two leading charged particle jets (8 observables)</li> <li>The number of (charged) constituents and n-subjettiness quantities τ_1, τ_2, and τ_3 for each of the same two jets (8 observables)</li> </ul> <div>The dimuon system p_T and y can be obtained from the muon kinematics, but they are included for convenience. The observables are labeled by 1 and 2 for leading and subleading in p_T, respectively. </div>
List and movies of Equatorial Coronal-Hole Jets
<p>Here is the supplement material for a paper on "Multiwavelength Study of Equatorial Coronal-Hole Jets" by Kumar et. al (2019), ApJ</p> <p>http://adsabs.harvard.edu/abs/2019arXiv190200922K</p> <p> </p>
Jet stream controls on driven European climate extremes and agricultural agriculture
<p>The code is used to analyze data and produce the main figures for the manuscript (Xu et al., Jet stream controls on European climate and agriculture since 1300 CE. Nature, 2024, https://doi.org/10.1038/s41586-024-07985-x). We used three temperature-sensitive tree-ring maximum chronologies from Europe to reconstruct the North-Atlantic-Europe Jet Stream Latitude (EU JSL). The EU JSL can capture the variability of dipolar patterns in temperature and precipitation extremes between Southern Europe and Northern Europe in summer (July-August). We extended the EU JSL to 1300 CE based on a multiple linear regression transfer function, which can explain the total variance of about 38.5% over the instrumental period (1945-2005, from NCEP reanalysis dataset V1.0). We then explore the relationships between EU JSL and temperature and precipitation over a long-term scale. We also explored the relationships between EU JSL extremes, crop failure events, and some social events over the past seven centuries. We found that the climate and societal extremes have been influenced by a summertime climatic dipole between northwestern and southeastern Europe, driven by EU JSL. Using the reconstruction, we explore relationships between EU JSL and climate and societal extremes using independent historical documentary datasets. We highlight the imprint of the EU JSL climate dipole on not only historical climate extremes, but also on the dipole's biophysical (e.g., grape harvest, wildfire), economic (e.g., wine quality and grain price), and even demographic (e.g., mortality and epidemics) impacts.</p> <p>The code is for the data analysis and visualization. We used R 4.2 version in the Windows 11 platform. Before re-running the code, please read the following instructions:</p> <p>1. Download all the input data and the R folder (all of the code). Please put the input data into a folder named input. We uploaded the temp and precip reconstruction data for the European domain.<br>2. Create a similar structure for the folders.<br>3. Before running the code, please unzip the crop data in the folder "./input/crop/*.zip", two large crop NetCDF fromat dataset.<br>4. Before running the main code "test all.R", please set you-work-path and run "setwd ("YOURPATH")" command line. <br>5. Some results or data in the output folder can be produced when run the code, you can also directly input them from the input folder in the next step to save time.<br>6. For the visualization, some of the arrangements are finished in Adobe Illustrator or Adobe Photoshop.<br>7. This is the main code for the manuscript, we would be improved or modified during the peer review.<br>8. If you have any questions or warnings during the re-run, please get in touch with me (guobaoxu@nwu.edu.cn; xgb234@lzb.ac.cn).</p> <p>If you have any suggestions or comments, please also let me know.</p> <p>Best wishes,<br>Guobao</p>
ELF/VLF data for Sprites, Gigantic Jets, and Rocket-Triggered Lightning
<p>This data set is broadband ELF and VLF data that shows transient events in the paper "ELF/VLF Radio Bursts Associated with Transient Luminous Events: Radio Metrics." These data are stored in files with two file types, '.mat' and '.bin'. The files encode recording parameters in the filename: these parameters are the Station (SS), UT start date (YYMMDD), and UT start time (HHMMSS). The '.mat' files are standard Matlab -V4 file format. The '.bin' files are data extracted from the original Matlab data file and also encode an offset time in seconds from the beginning of the originating file (ssss). Finally, all files indicate the antenna channel (ANT) with '_000' indicating magnetic north-south VLF, '_001' indicating magnetic east-west VLF, '_002' indicating magnetic north-south ELF, and '_003' indicating magnetic east-west ELF. All data is stored as short 16-bit signed integers. All VLF signals are sampled with 100 kHz sample rate. ELF at Arrival Heights (AH), Sondrestromfjord (SF), and Stanford Dish (SD) is sampled at 5 kHz. ELF at Chistochina (CH) and Palmer Station (PA) is sampled at 10 kHz. Using the nomenclature above, the '.mat' files are named: 'SSYYMMDDHHMMSS_ANT.mat'. The '.bin' files are named: 'SSYYMMDDHHMMSS_ssss_ANT.bin'.</p>
PYTHIA Jet Datasets for cDDPM Unfolder
<p>Datasets of QCD jets used for studying unfolding in "Towards Universal Unfolding using Denoising Diffusion Probabilistic Models" consist of two different detector simulation frameworks:</p> <h3>Data-driven detector smearing:</h3> <ul> <li>Events were generated using PYTHIA 8.3 for proton-proton collisions at √s = 14 TeV</li> <li>Several physics processes were simulated: <ul> <li>ttbar production with various PDFs (CT14lo, NNPDF23, CTEQ6L1)</li> <li>Z+jets (Z → μμ) with CT14lo, NNPDF23, CTEQ6L1</li> <li>W+jets (W → μν) with CT14lo, NNPDF23, CTEQ6L1</li> <li>Dijet production</li> <li>Leptoquark production</li> </ul> </li> <li>Jets with radius parameter R = 0.4 were reconstructed using the anti-kT algorithm at particle-level ("gen_jets"), and then detector effects were applied ("reco_jets")</li> <li>Detector effects were simulated using ATLAS 8 TeV calibration data-derived jet resolution functions for pT, η, and φ</li> <li>Phase space bias was applied to some samples to enhance high-pT statistics: (pT_hat/pT_ref)^a with pT_ref = 100 GeV and a = 5</li> </ul> <h3> </h3> <h3>DELPHES CMS detector simulation:</h3> <ul> <li>Events were generated using PYTHIA 8.3 for proton-proton collisions at √s = 14 TeV</li> <li>Physics processes included: <ul> <li>ttbar production with CTEQ6L1</li> <li>Z+jets (Z → μμ) with CTEQ6L1</li> <li>W+jets (W → μν) with CTEQ6L1</li> <li>Dijet production with CTEQ6L1</li> <li>Leptoquark production with CTEQ6L1</li> </ul> </li> <li>Events were passed through DELPHES 3.4.2 fast detector simulation using the CMS detector configuration</li> <li>Jets with radius parameter R = 0.4 were reconstructed using the anti-kT algorithm at both particle level ("gen_jets") and detector level ("reco_jets")</li> <li>Phase space bias was applied to some samples using (pT_hat/pT_ref)^a with pT_ref = 100 GeV and a = 5</li> </ul> <p> </p> <p>For both frameworks, each dataset consists of several arrays containing jet kinematic information (pT, η, φ, E, px, py, pz) at both truth ("gen_jets") and detector ("reco_jets") level. Additional features such as event identifiers ("event_num") are included to enable reconstruction of event-level observables.</p>
Unstable gas flow in a flat channel under the influence of a transverse force field: self-oscillations of the jet
<p>For a description of the task, see the original paper.</p> <div> <div> <div> <div>For each video, there are the meaning of force (F), Section and Subsection of the article where this video is mentioned, as well as the number of Figure from the article corresponding to this calculation. The Mach number is 0.29 for all the calculations.</div> </div> </div> </div> <p>(1) "Video-1.avi": </p> <p>F = 2.5, </p> <p>Section "Main modeling results"</p> <p>Subsection "Weak force field"</p> <p>Figure 2</p> <p> </p> <p>(2) "Video-2.avi": </p> <p>F = 4, </p> <p>Section "Main modeling results"</p> <p>Subsection "Strong force field"</p> <p>Figure 3</p> <p> </p> <p>(3) "Video-3.avi": </p> <p>F = 3, </p> <p>Section "Main modeling results"</p> <p>Subsection "Average force field"</p> <p>Figure 4</p> <p> </p> <p>(4) "Video-4.avi": </p> <p>F = 7 (continuous force field, see Figure 5), </p> <p>Section "Main modeling results"</p> <p>Subsection "Continuous force field"</p> <p>Figure 6</p>
Integration-based Extraction and Visualization of Jet Stream Cores - Demo Data
<p>Demo data for the publication "Integration-based Extraction and Visualization of Jet Stream Cores", containing the meteorological attirbutes for September 01, 2016 at 00:00. The data is derived from ERA5.</p> <p>The ERA5 data is courtesy of the European Centre for Medium-Range Weather Forecasts (ECMWF) and is documented here: <a href="https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation">https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation</a> The data is available under the Copernicus License Agreement: <a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</a></p>
Data from: Nucleation of jet engine oil vapours is a large source of aviation-related ultrafine particles
<p>Data of Figures 1-3</p>
VatsalSy/IITR-ThesisTemplate: Understanding of the mutual interactions between liquid jets: entrainment and sheet formation
<p>Thesis template for IIT Roorkee Master and Ph.D. theses</p>
Methotrexate degradation in artificial wastewater using non-thermal pencil plasma jet
<p>Supplementary Material</p>
Data for "Tracking the as yet unknown nearfield evolution of a shallow, neutrally-buoyant plane jet over a sloping bottom boundary"
<p>This file gives the datasets of our manuscript "Tracking the as yet unknown nearfield evolution of a shallow, neutrally-buoyant plane jet over a sloping bottom boundary".</p>
Are Dipolarization Fronts a Typical Feature of Magnetotail Plasma Jets Fronts?
<p>Additional files</p>
Data set for "Tracking the as yet unknown nearfield evolution of a shallow, neutrally-buoyant plane jet over a sloping bottom boundary"
<p>Data set for the paper titled "Tracking the as yet unknown nearfield evolution of a shallow, neutrally-buoyant plane jet over a sloping bottom boundary".</p>
Empirical models of "Magnetosheath Jets Over Solar Cycle 24: An Empirical Model"
<p>The empirical models of jet observations rates as a function of solar wind parameters used in the article "Magnetosheath Jets Over Solar Cycle 24: An Empirical Model" by L.Vuorinen, A. T. LaMoury, H. Hietala, and F. Koller. Please contact L. Vuorinen (lakavu@utu.fi) for additional information.</p>
Jet feature data from PAMIP model simulations
<p>Author: Yvonne Anderson</p> <p>Contact: ee22ya@leeds.ac.uk</p> <p>Dataset created: 21/08/2023</p> <p>Paper title: Minimal influence of future Arctic sea ice loss on North Atlantic jet stream morphology</p> <p> </p> <p><strong>Dataset information</strong></p> <p>CSV files contain arrays of daily jet feature data for all ensemble member winters for a given model.</p> <p>Dimensions of the arrays are (number of ensemble members, 90 winter days).</p> <p><strong>Filename structure</strong></p> <p>Filenames of CSV files can be interpreted as: timeperiod_jetfeature_model.csv</p> <p><strong>Example filename structure</strong></p> <table> <thead> <tr> <th scope="col">Time period</th> <th scope="col">Jet feature</th> <th scope="col">Model</th> <th scope="col">Example filename</th> </tr> </thead> <tbody> <tr> <td>Present-day</td> <td>Latitude</td> <td>AWI-CM-1-1-MR</td> <td>present-day_jet_latitude_AWI-CM-1-1-MR.csv</td> </tr> <tr> <td>Future</td> <td>Speed</td> <td>HadGEM3-GC31-MM</td> <td>future_jet_speed_HadGEM3-GC31-MM.csv</td> </tr> </tbody> </table> <p><strong>Jet feature description</strong></p> <p>Jet feature data are for the largest mass jet region found on each day of winter, where jet mass is the area weighted jet speed.</p> <p>The jet features and corresponding units contained in the csv files are as follows:</p> <table> <thead> <tr> <th scope="col">Jet feature</th> <th scope="col">Units</th> </tr> </thead> <tbody> <tr> <td>Latitude</td> <td>°</td> </tr> <tr> <td>Speed</td> <td>ms<sup>-1</sup></td> </tr> <tr> <td>Mass</td> <td>ms<sup>-1</sup></td> </tr> <tr> <td>Tilt</td> <td>°</td> </tr> <tr> <td>Area</td> <td>m<sup>2</sup></td> </tr> </tbody> </table> <p><strong>Time periods</strong></p> <p>Time periods are present-day and future, which refer to simulations forced by present-day and future sea ice concentrations, from which the jet features have been extracted.</p> <p><strong>Models</strong></p> <p>Models are AWI-CM-1-1-MR, CanESM5, FGOALS-f3-L, HadGEM3-GC31-MM, IPSL-CM6A-LR and MIROC6 from the Polar Amplification Model Intercomparison Project (PAMIP; https://doi.org/10.5194/gmd-12-1139-2019)</p> <p><strong>Spatial and temporal information</strong></p> <p>Arrays contain daily jet feature data that has been constrained to the North Atlantic region (0-60 &deg; W, 15-75 &deg; N) and to the winter period (December, January and February)</p> <p><strong>Prior processing</strong></p> <ul> <li>Original dataset: netcdf files of daily zonal wind data from Polar Amplification Model Intercomparison Project simulations forced by present-day and future sea ice concentrations</li> <li>850 hPa wind speed data was extracted and regridded to 2.81 ° x 2.81 ° resolution</li> <li>Constrained to North Atlantic region and winter period</li> <li>Wind speed data was filtered using a 10-day Lanczos filter with a 61 day window</li> <li>Jet feature data was extracted for each day in ensemble member winters and saved to numpy arrays</li> </ul> <p><strong>Example code for loading jet variables from csv file</strong></p> <p>To generate a numpy array of jet variable arrays contained in the csv file:</p> <pre><code class="language-python">loaded_jet_variable_arrays = np.genfromtxt((path_to_file/filename.csv'), delimiter=',')</code></pre> <p>To combine arrays for all ensemble member winters, which allows plotting of daily jet feature distributions:</p> <pre><code class="language-python">jet_variable_array_all_winters = np.concatenate(loaded_jet_variable_arrays)</code></pre> <p> </p> <p> </p>
Self-consistent MHD simulation of jet launching in a neutron star - white dwarf merger: Complimentary material
<p>Complementary material to the paper.</p> <p>Movies showing different magnitudes during the evolution of the neutron-star white-dwarf merger. All the movies show slices through the orbital plane (on the left) and perpendicular to the orbital plane (on the right right) centred on the neutron star. The region where the gravitational potential is softened around the neutron star is outlined by a black-dashed line.<br> The different videos show the following magnitudes: the ratio between the magnetic pressure and gas pressure (beta), the density, the entropy, the absolute value of the magnetic field, the radial velocity with respect to the neutron-star and the temperature.</p> <p>For every magnitude there are two videos (_01rsol and _003rsol) showing them in a box of 0.1 and 0.03 solar radii respectively.</p>
Needle-free Jet Injection of Reduced-dose, Intradermal, Influenza Vaccine in >= 6 to < 24-month-old Children
ClinicalTrials.gov study NCT00386542. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparison of Lung Deposition With the Aeroneb Solo Adapter and a Standard Jet Nebulizer by SPECT-CT
ClinicalTrials.gov study NCT02298101. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Pain Perception With a Comfort-ın Jet Injection and Conventional Dental Injection
ClinicalTrials.gov study NCT04682080. IPD Sharing: Not stated. Countries: 1. Publications: 14.
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OpenNeuro
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