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351 results for “jet”
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 6 of 6
<p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow - Database 6 / 6<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>This database presents unsteady calculated data derived from large-eddy simulations (LES) of a supersonic jet flow utilizing the FLEXI solver (https://numericsresearchgroup.org/codes.html#codes_flexi). <br>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. <br>The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. <br>These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. <br>The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file. <br>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file. <br>The database is divided into six parts. The present set of data is number six.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". <br>The numerical data presented herein were previously published in the Ph.D. Thesis "Study of Turbulent Supersonic Jet flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development".</p>
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 5 of 6
<p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow - Database 5 / 6 (Continuation of https://doi.org/10.5281/zenodo.13902381 database)<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>This database presents unsteady calculated data derived from large-eddy simulations (LES) of a supersonic jet flow utilizing the FLEXI solver (https://numericsresearchgroup.org/codes.html#codes_flexi). <br>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. <br>The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. <br>These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. <br>The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file. <br>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file. <br>The database is divided into six parts. The present set of data is number five.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". <br>The numerical data presented herein were previously published in the Ph.D. Thesis "Study of Turbulent Supersonic Jet flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development".</p>
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 2 of 6
<p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 2 / 6 (Continuation of https://doi.org/10.5281/zenodo.13902381 database)<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file.</p> <p>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file.<br>The database is divided into six parts. The present set of data is number one.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". The numerical data presented herein were previously published in the work entitled "Accuracy Assessment of Discontinuous Galerkin Spectral Element Method in Simulating Supersonic Free Jets" (https://doi.org/10.1007/s40430-024-04788-z) and the Ph.D. Thesis "Study of Turbulent Supersonic Jet Flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development".</p>
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 4 of 6
<div> <p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 4 / 6 (Continuation of https://doi.org/10.5281/zenodo.13902381 database)<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file.</p> <p>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file.<br>The database is divided into six parts. The present set of data is number one.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". The numerical data presented herein were previously published in the work entitled "Accuracy Assessment of Discontinuous Galerkin Spectral Element Method in Simulating Supersonic Free Jets" (https://doi.org/10.1007/s40430-024-04788-z) and the Ph.D. Thesis "Study of Turbulent Supersonic Jet Flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development".</p> <p> </p> </div>
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 3 of 6
<p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 3 / 6 (Continuation of https://doi.org/10.5281/zenodo.13902381 database)<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file.</p> <p>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file.<br>The database is divided into six parts. The present set of data is number one.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". The numerical data presented herein were previously published in the work entitled "Accuracy Assessment of Discontinuous Galerkin Spectral Element Method in Simulating Supersonic Free Jets" (https://doi.org/10.1007/s40430-024-04788-z) and the Ph.D. Thesis "Study of Turbulent Supersonic Jet Flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development".</p>
Dataset: River Jets versus Wave-driven Longshore Currents at River Mouths
<p>Dataset for the <em>River Jets versus Wave-driven Longshore Currents at River Mouths</em> paper</p> <p>Dataset is a result of Mike3 simulation outputs with selected fields exported in MATLAB.</p> <p>The outputs are separated by river mouth type and stored in 5-dimensional arrays, with the following data for each dimension: 1D - y dimension; 2D - x dimension; 3D – 26 conditions of jet and wave height and direction; 4D - 5 conditions of river jet; and 5D – model field outputs (bed elevation, x and y components of current velocity, significant wave height, etc.).</p> <p>Data_figures.rar contains the .jpeg export of the fields contained in the dataset.</p>
Simulated pp collisions at 13 TeV with 2 leptons + 1 b jet final state and selected benchmark Beyond the Standard Model signals
<p>This data-set is comprised of simulated events of pp collisions at 13 TeV with 2 leptons + 1 bottom jet sinal state, with HT > 500 GeV. It includes the following samples</p> <ul> <li>Standard-Model background (bkg), generated at leading order includes the sub-samples Z+Jets, ttbar, WW, WZ, and ZZ. <ul> <li>The processes were generated in kinematic regions to ensure good statistics across the whole phase space. The sampling was carried out using event generation filters at parton level as follows <ul> <li>ttbar: pT <100 GeV; pT in [100, 250] GeV; pT > 250 GeV</li> <li>The scalar sum of the pT of outgoing particles for Z+Jet: ST < 250 Gev; ST in [250, 500] GeV; ST > 500 GeV</li> <li>W/Z pT for dibosons: pT < 250 GeV; pT in [250, 500] GeV; pT > 500 GeV</li> </ul> </li> </ul> </li> <li>Vector-like T-quarks with masses 1.0, 1.2, 1.4 TeV (hq1000, hq1200, hq14000) pair produced either through the Standard-Model gluon (wohg) or through a BSM 3TeV heavy gluon (hg3000)</li> <li>tZ production through a Flavour Changing Neutral Current (fcnc) vertex</li> </ul> <p>The samples are provided with both a full set of features, or with a sanitised set of features. The sanitised features remove some accumulation at zeros from non-reconstructed objects (i.e. missing values). All samples were generated using MadGraph5 2.6.5 and the detector was simulated using Delphes 3 with the default CMS card. For the Standard-Model background, both Pythia 8.2 (with CMS CUETP8M1 underlying event tune and NNPDF 2.3 parton distribution functions) (pythia) and Herwig 7 (herwig) hadronisations are provided to compare the background simulation. For the BSM signals only Pythia is provided.</p> <p>For the details of the generation and on the differences between the two feature sets please refer to <a href="https://link.springer.com/article/10.1140%2Fepjc%2Fs10052-020-08807-w">Finding new physics without learning about it: anomaly detection as a tool for searches at colliders</a> for more details. Each file provides a train:validation:split with the ratios 1:1:1 to ensure equal statistical description of the events at each step of the machine learning workflow.</p>
Higgs to diphoton channel at least 2 jet datasets
<p>Datasets of Higgs decay to diphoton with the requirement of at least two jets contained in the events. Events are generated with either Powheg or MadGraph at particle level, then processed with Pythia for parton showering and Delphes for detector simulation.</p> <p>All samples contain ggF Higgs events with at least 2 jets. <a href="/api/files/a6021c00-f37a-423d-87fd-2a617f5845de/processed_madgraph_2j_1.0_1000000.npz?versionId=ea79229c-7665-4b4f-949c-6463471fb051">processed_madgraph_2j_1.0_1000000.npz</a> is the MadGraph sample with the nominal value of photon detector resolution. <a href="/api/files/a6021c00-f37a-423d-87fd-2a617f5845de/processed_madgraph_2j_1.2_1000000.npz?versionId=c2cbe56e-412c-49f2-8926-32cc323707df">processed_madgraph_2j_1.2_1000000.npz</a> is the MadGraph sample with the photon detector resolution scaled by a factor of 1.2 (worse resolution). <a href="/api/files/a6021c00-f37a-423d-87fd-2a617f5845de/processed_powheg_2j_1.2_200000.npz?versionId=8b1e772a-ed9f-4257-8e7f-8c60ee904d56">processed_powheg_2j_1.2_200000.npz</a> is the Powheg sample with the photon detector resolution scaled by a factor of 1.2. <a href="/api/files/a6021c00-f37a-423d-87fd-2a617f5845de/processed_madgraph_2j_syst_1000000.npz?versionId=1a490fc5-3b8e-474c-bd12-66227e868ab6">processed_madgraph_2j_syst_1000000.npz</a> is the MadGraph sample with the photon detector resolution uniformly varied from the nominal value by the factor in the range (0.5, 1.5).</p>
Single-jet datasets for particle reconstruction with deep learning
<p>Training and test datasets used for [1]* .</p> <ul> <li>singleQuarkJet_train.root : N=60649</li> <li>singleQuarkJet_test.root: N=38922</li> <li>singleGluonJet_test.root: N=38295</li> </ul> <p>The events are formed by a single initial state quark or gluon followed by parton shower generated in Pythia8 and then simulated using GEANT4 in a nearly-hermetic 6-layer calorimeter system as described in [1,2]. The branches in the ROOT files store features associated with cells, tracks, particles, pflow objects, jets, as well as edge lists for creating a graph representation of each event.<br> <br> *Note that subsets of N=50000 and N=30000 were used from the train and test samples, respectively, for the results in [1].</p> <p>[1] <a href="https://arxiv.org/abs/2212.01328">Reconstructing particles in jets using set transformer and hypergraph prediction networks</a><br> [2] <a href="https://arxiv.org/abs/2303.02101">Configurable Calorimeter Simulation for AI (COCOA)</a></p>
Data accompanying Using Neural Networks to Learn the Forced Response of the Jet-Stream to Tropospheric Temperature Tendencies
<p>Data used to train and evaluate a CNN. Details about data and the preprocessing can be found in the citation given below</p> <p>Charlotte Connolly, Elizabeth A. Barnes, Pedram Hassanzadeh, and Mike Pritchard: Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability, accepted to Artificial Intelligence for the Earth Systems 03/2023. Preprint available at <a href="https://arxiv.org/abs/2301.00496">https://arxiv.org/abs/2301.00496</a>.</p> <p>Code found at https://doi.org/10.5281/zenodo.7796266.</p> <p> </p>
JEWEL+PYTHIA simulated pp and PbPb collisions at 5020 GeV - Jet substructure variables
<p>Samples of simulated jets generated using JEWEL+PYTHIA for both Unquenched (pp) and Quenched (PbPb) cases, with the former being generated using the vacuum executable, jewel-vac, and the later using the medium executable, jewel-simple. For each case 320 000 events were produced with <span class="math-tex">\(\sqrt{s}=5020\)</span> GeV, <span class="math-tex">\(p_T \in [40, 250]\)</span> GeV, <span class="math-tex">\(|y|<2.5\)</span> . For the Quenched case, the medium settings were set to <span class="math-tex">\(\tau_i = 0.4\)</span> fm/c, <span class="math-tex">\(T_i = 440\)</span> MeV, <span class="math-tex">\(T_c = 170\)</span> MeV, and centrality <span class="math-tex">\(0-10\%\)</span>.</p> <p>Three Quenched samples are produced from the same JEWEL output eventfiles.</p> <ul> <li>One without thermal recoils</li> <li>One with thermal recoils but no background subtraction</li> <li>One with thermal recoils and background subtraction via the event-level subtraction prescription found in <a href="https://link.springer.com/article/10.1140/epjc/s10052-022-10954-1">Improved background subtraction and a fresh look at jet sub-structure in JEWEL</a></li> </ul> <p>More details about the simulation of the events and the jet variables please refer to the <a href="https://gitlab.com/lip_ml/jet-substructure-observables-ml-analysis">code repository</a> and <a href="https://arxiv.org/abs/2304.07196">Jet substructure observables for jet quenching in Quark Gluon Plasma: a Machine Learning driven analysis</a>, where they were first used.</p>
Effects of hydrogen jet fires on the erosion of tunnel road materials and lining materials
<p>This HSE test programme investigated erosive effects of an ignited high pressure hydrogen jet impinging onto concrete and tarmac structural materials. The chosen test conditions mimicked the scenario where a high-pressure release (700bar) occurs from a fuel cell hydrogen (FCH) car as a result of activation of the thermal pressure relief device (TPRD) on the fuel tank. Two nozzle sizes were used for the releases; the first had a diameter of 2.1mm (mimicking existing TPRD) and the second had a diameter of 0.57mm (mimicking a proposed alternative TPRD diameter. The reduced diameter is suggested as a strategy to reduce release hazard safety distances).</p>
An axisymmetric impinging jet under two-frequency external forcing: animations of low-order reconstruction from DMD modes of PIV data
<p>Animations of the low-order reconstruction of vortex structures dynamics in an impinging turbulent jet under external periodic forcing. The reconstruction is obtained from DMD modes of time-resolved PIV data. The flow cases correspond to the unmodulated (main case) and modulated amplitude of the forcing frequency. The modulation frequencies are set to as 1/2, 1/3, 1/4, 1/5 and 1/8 of the main one, respectively.</p>
JUMP - Data collection - Part I: Jets from a Global Climate Model.
<p>We conduct in-depth analysis of statistical flow properties from Global Circulation Model that reproduce Saturn's macroturbulence, namely large-scale zonal winds. We use a high performance Global Climate Models (GCMs), named DYNAMICO, to model the atmospheric circulation of gas giants with appropriate physical parametrizations for Saturn's atmosphere. The high-resolution model DYNAMICO solves for 3D primitive equations of motion. We ran a Saturn simulation covering 15 Saturn years using the Saturn DYNAMICO GCM. Wind fields are output every 20 Saturn days at 32 pressure levels onto 1/2° latitude-longitude grid maps. Details on this Saturn reference simulation are given in Spiga et al. (2020). In addition, to diagnose the relevant 3D dynamical mechanisms in Saturn's turbulent atmosphere, we run a set of four simulations using an idealized version of our Global Climate Model devoid of radiative transfer, with a well-defined Taylor-Green forcing and over several rotation rates (4, 1, 0.5, and 0.25 times Saturn's rotation rate). Here, we deliver a full data set, including velocity maps, at different pressure levels and time steps, from which it is possible to recompute the statistical analysis detailed in Cabanes et al. (2020). The delivered data set includes:</p> <p>Files of our (1) data collection and (2) numerical codes that lead to the statistical analysis:</p> <p>(1) Data collection:</p> <ul> <li>A PDF file named <strong>JUMP-zonal-jets-data-collection-Icarus.pdf</strong> that describes in depththe data set and the associated nomenclature.</li> <li>A netcdf file of velocity fields from our Saturn Reference Simulation (SRS) <ul> <li><strong>uvData-SRS-istep-312000-nstep-50-niz-12.nc</strong></li> <li><strong>StatisticalData.nc</strong></li> </ul> </li> <li>A netcdf file of velocity fields from idealized simulation at 4 times the Satrun's rotation rate, <ul> <li><strong>uvData-Omega-4-istep-21026.0-nstep-20-niz-8.nc</strong></li> </ul> </li> <li>A netcdf file of velocity fields from idealized simulation at 1 times the Satrun's rotation rate, <ul> <li><strong>uvData-Omega-1-istep-21026.0-nstep-20-niz-8.nc</strong></li> </ul> </li> <li>A netcdf file of velocity fields from idealized simulation at 0.5 times the Satrun's rotation rate, <ul> <li><strong>uvData-Omega-0.5-istep-20626.0-nstep-20-niz-8.nc</strong></li> </ul> </li> <li>A netcdf file of velocity fields from idealized simulation at 0.25 times the Satrun's rotation rate, <ul> <li><strong>uvData-Omega-0.25-istep-21026.0-nstep-20-niz-8.nc</strong></li> </ul> </li> </ul> <p>(2) Numerical codes:</p> <ul> <li>Codes for statistical analysis in spherical geometry are on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FPOST&sa=D&sntz=1&usg=AFQjCNFuDU0eij4XGxQfReO92CHfJz6PBA">https://github.com/scabanes/POST</a></li> </ul> <p> </p> <p><strong>Acknowledgments</strong>:</p> <p>The authors acknowledge exceptional computing support from Grand Équipement National de Calcul Intensif (GENCI) and Centre Informatique National de l’Enseignement Supérieur (CINES). All the simulations presented in this paper were carried out on the Occigen cluster hosted at CINES. This work was granted access to the High-Performance Computing (HPC) resources of CINES under the allocations A001-0107548, A003-0107548, A004-0110391 made by GENCI. The authors acknowledge funding from Agence Nationale de la Recherche (ANR), project HEAT ANR-14-CE23-0010 and project EMERGIANT ANR-17-CE31-0007. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N° 797012. Fruitful discussions with Sandrine Guerlet, Ehouarn Millour, Thomas Dubos, Frédéric Hourdin and Alexandre Boissinot from our team helped refine some discussions in the paper.</p>
JET-ILW Linear Pedestal ETG Data
<p>Data associated with paper '<a href="https://arxiv.org/abs/2004.13634">Toroidal and slab ETG instability dominance in the linear spectrum of JET-ILW pedestals</a>.' See attached Readme file for instructions.</p>
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning: W+jet large test dataset
<p>W+jet events at generator and reconstruction level, used to train analysis-specific generative models.</p> <p>Events are represented as an array of relevant high-level features. Reco objects are matched to Gen objects and a minimal selection is applied to define the generator support in the N-dim space identified by the input features.</p> <p>About 2M events, used for large-scale testing</p> <p>Details in https://arxiv.org/abs/2010.01835</p>
Animations: The Photospheric Footpoints of Solar Coronal Hole Jets
<p>This repository provides the individual animations of the SDO/AIA and HMI magnetograms of Jets 1, 2, 3, corresponding to jets 14, 32, 35 in Table 1 of the accepted manuscript, "<em>The Photospheric Footpoints of Solar Coronal Hole Jets</em>". </p> <p><strong>jet1-aia193.mp4</strong>: AIA 193A movie of Jet 1<br> <strong>jet1-hmimag.mp4</strong>: HMI magnetograms movie of Jet 1<br> <strong>jet1-aia193-hmi-contour.mp4:</strong> AIA 193A with HMI contours movie of Jet 1<br> <strong>fig1anim.mp4</strong>: Concatenation of Jet 1 movies shown in Figure 1 of the accepted manuscript.</p> <p><strong>jet2-aia193.mp4</strong>: AIA 193A movie of Jet 2<br> <strong>jet2-hmimag.mp4</strong>: HMI magnetograms movie of Jet 2<br> <strong>jet2-aia193-hmi-contour.mp4</strong>: AIA 193A with HMI contours movie of Jet 2<br> <strong>fig4anim.mp4</strong>: Concatenation of Jet 2 movies shown in Figure 4 of the accepted manuscript.</p> <p><strong>jet3-aia193.mp4</strong>: AIA 193A movie of Jet 3<br> <strong>jet3-aia304.mp4</strong>: AIA 304A movie of Jet 3<br> <strong>jet3-hmimag.mp4</strong>: HMI magnetograms movie of Jet 3<br> <strong>jet3-aia193-hmi-contour.mp4</strong>: AIA 193A with HMI contours movie of Jet 3<br> <strong>fig6anim.mp4</strong>: Concatenation of Jet 3 movies shown in Figure 6 of the accepted manuscript.<br> </p> <p> </p> <p> </p>
Pythia Generated Jet Images for Location Aware Generative Adversarial Network Training
<p>Dataset containing 872666 jet images to train Location Aware Generative Adversarial Networks (LAGAN) for High Energy Physics. Results are published in [arXiv:1701.05927].</p> <p><strong>Format</strong>:<br> HDF5 file with the following fields:</p> <ul> <li>'image' : array of dim (872666, 25, 25), contains the pixel intensities of each 25x25 image</li> <li>'signal' : binary array to identify signal (1, i.e. W boson) vs background (0, i.e. QCD)</li> <li>'jet_eta': eta coordinate per jet</li> <li>'jet_phi': phi coordinate per jet</li> <li>'jet_mass': mass per jet</li> <li>'jet_pt': transverse momentum per jet</li> <li>'jet_delta_R': distance between leading and subleading subjets if 2 subjets present, else 0</li> <li>'tau_1', 'tau_2', 'tau_3': substructure variables per jet (a.k.a. n-subjettiness, where n=1, 2, 3)</li> <li>'tau_21': tau<sub>2</sub>/tau<sub>1</sub> per jet</li> <li>'tau_32': tau<sub>3</sub>/tau<sub>2</sub> per jet</li> </ul> <p><strong>Details</strong>:</p> <ul> <li>Simulated using Pythia 8.219 at √ s = 14 TeV</li> <li>Image pre-processing using method from in L. de Oliveira et al., Jet-Images -- Deep Learning Edition [arXiv:1511.05190]</li> <li>scikit-image==0.12.0 implementation of cubic spline rotation</li> <li>Finite calorimeter granularity simulated with 0.1×0.1 grid in η and φ, with η × φ ∈ [−1.25, 1.25] × [−1.25, 1.25]</li> <li>Jet clustering with anti-k<sub>t</sub> algorithm with a radius R = 1.0 using FastJet 3.2.1; constituent re-clustering into R = 0.3 k<sub>t</sub> subjets</li> <li>Intensity of pixel = p<sub>T</sub> of cell</li> <li>60 GeV < m<sup>jet</sup> < 100 GeV</li> <li>250 GeV < p<sub>T</sub><sup>jet</sup> < 300 GeV</li> <li>Sparse images (~10% NNZ)</li> </ul> <p>Full dataset description in [arXiv:1701.05927].</p>
Dataset: Very-small-aperture 3-D infrasonic array for volcanic jet observation at Stromboli Volcano, Geophysical Journal International, Volume 229, Issue 1, Pages 459–471, https://doi.org/10.1093/gji/ggab487
<p>This is the dataset of the infrasound observation of Yamakawa et al. (GJI, 2022).</p>
Old jet engines (iPhone Lidar)
Courtesy of the Museum of Flight in Seattle, WA USA. Captured with iPhone 12 lidar and http://3dScannerApp.com (1 min scan and 2 min processing) Source: Objaverse 1.0 / Sketchfab
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.