Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
351
datasets available to search
ShareScore release 0.9.0
Dataset results
351 results for “jetting”
Secondary Vertex Finding in Jets Dataset
<p>This is a dataset for developing secondary vertex finding algorithms.</p> <p>The dataset consists of jets sampled from <span class="math-tex">\( pp\rightarrow t\bar{t}\)</span> events at <span class="math-tex">\(\sqrt{s}=14\)</span> TeV. The events are generated with Pythia8 and a basic detector simulation is performed with Delphes emulating a detector similar to ATLAS.</p> <p>Each jet has a set of associated reconstructed tracks. the task of secondary vertex finding is to partition this set according to the point of origin of the particle that created that track.</p> <p>The dataset includes a label for each track that indicates which vertex it originated from. This label is an arbitrary integer - within one jet a pair of tracks originated from the same point if their labels are equal, and from different points otherwise.</p> <p>charged particle tracks are represented by 6 perigee parameters and their covariance matrix. Noise is added to the track perigee parameters with gaussian smearing. The track parameters resolution depends on the transverse momentum and pseudo-rapidity of the track. The covariance matrix is diagonal in this simplified track smearing model.</p>
Figure Data for the paper: "Observation of the onset of a blue jet into the stratosphere"
<p>Data files for the figures in Neubert et al. (2019): "Observation of the onset of a blue jet into the stratosphere".</p> <p>The data is given per panel of the respective figure. Figure and panel are indicated in the file name.</p> <p>The photometer data is given as one time sequence without the separation in frames, while the camera data is stored per frame. The different frames have their own tab in the .xlsx files.</p> <p> </p> <p>More information on the data of this study is given in the main paper.</p>
Jet engine
Jet Engine on display at the Science and Technology Center Source: Objaverse 1.0 / Sketchfab
The unification of relativistic jets
<p>Dataset of the cited paper. The explanations of the columns are at the end of each file. Two files, one for the Active Galactic Nuclei (quasars, BL Lac Objects, radio-loud Narrow-Line Seyfert 1 Galaxies) and the other for Galactic Binaries (stellar-mass black holes, accreting neutron stars.</p>
Supplementary data for paper: Extremum seeking to control the amplitude and frequency of a pulsed jet for bluff body drag reduction
<p>Data accompanying the Experiments in Fluids paper entitled: Extremum seeking to control the amplitude and frequency of a pulsed jet for bluff body drag reduction. http://dx.doi.org/10.1007/s00348-016-2243-4.</p> <p>Zipped files contain the folders of data, while the .mat files contain Matlab scripts to generate the plots in the paper.</p>
Data for "Tracking the nearfield evolution of an initially shallow, neutrally-buoyant plane jet over a sloping bottom boundary"
<p>This folder contains all the data set for figures in the paper titled as "Tracking the nearfield evolution of an initially shallow, neutrally-buoyant plane jet over a sloping bottom boundary". </p>
Experimental measurements of the effects of surface roughness on large-scale downburst-like impinging jets at the WindEEE Dome laboratory
<p>Thunderstorm downbursts originate as negatively buoyant currents of cold air descending from cumulonimbus clouds. Upon impacting the ground, a strong radial outflow develops with maximum wind velocities occurring at the near-ground level. These types of flows pose serious hazard to the natural and built environment. Their restricted time and spatial extent as well as their intermittent and non-Gaussian fluctuating nature make them extremely challenging to be recorded and analyzed through classic full-scale measurements in nature. Alongside synoptic-scale extra tropical cyclones, downbursts govern the wind climate at the mid-latitude areas around the globe. Recent trends in climate change studies suggest both an increased intensity as well as frequency of occurrence of these events. Therefore, their scientific comprehension urges serious consideration.</p> <p>In the context of the project THUNDERR “Detection, simulation, modelling and loading of thunderstorm outflows to design wind-safer and cost-efficient structures”, financed by the European Research Council (ERC) Advanced Grant 2016 (grant No. 741273, P.I. Prof. Giovanni Solari, University of Genoa), an extensive experimental campaign was recently conducted at the WindEEE Dome wind chamber. This campaign focused on measuring downburst-like flows (DLFs) generated by large-scale impinging jets. The dataset presented here encompasses a portion of the measurements collected during this comprehensive experimental initiative. Specifically, this series of tests aimed to unravel the role of surface roughness in potentially altering the configuration and dynamics of the overall radial outflow. The term "surface roughness" pertains to the ground patch under examination, encompassing both the natural features of the terrain and any obstacles, such as buildings, present on the ground. While surface roughness plays a decisive role in changing the shape and magnitudes of the wind speed vertical profiles for extra-tropical cyclones, the scientific literature has not yet thoroughly addressed its impact on downburst winds, which are different being dominated by intense vortex dynamics.</p> <p>Impinging jets, considered representative for the simulation of downburst like flow (DLF), are here simulated as transient phenomena through the opening and closing of the bell-mouth that connects the test chamber and the upper plenum of the dome, the latter being pressurized before releasing the jet. As a result, the velocity records exhibit a distinct pattern, featuring a sudden ramp-up of velocity, followed by a velocity peak, a statistically-stationary phase, and ultimately, a gradual velocity deceleration—mirroring the behavior observed in real-world scenarios.</p> <p>The database consists of six ASCII tab-delimited text files, denoted as ‘windspeedDB89z0eq007.txt’, ‘windspeedDB89z0eq020.txt’, ‘windspeedDB89z0eq320.txt’, ‘windspeedDB124z0eq007.txt’, ‘windspeedDB124z0eq020.txt’, and ‘windspeedDB124z0eq320.txt’, aligning with the two jet intensities and three rough surfaces employed in the experiments. These filenames correspond to: (i) centerline jet velocities at the nozzle outlet section, with values of <em>Wjet</em> = 8.9 and 12.4 m/s (indicated as “<em>Wjet</em>” in the database files); (ii) equivalent full-scale roughness lengths <em>z0eq</em> = 0.007, 0.020, 0.32 m (“<em>z0eq</em>” in the database files, see details below). Each file encompasses wind speed timeseries, detailed as follows:</p> <p>The three-component velocity measurements were recorded by means of 11 Cobra probes (sampling frequency 2,500 Hz) mounted on a stiff mast. The heights (<em>z</em>) of the probes were <em>z</em> = 0.040, 0.070, 0.100, 0.125, 0.150, 0.200, 0.300, 0.400, 0.500, 0.700, 1.000 m above the surface. Within the database files, the wind speed linked to various heights is labeled as “<em>v_zXXXXmm</em>”. In this notation, '<em>v</em>' designates the velocity component: longitudinal ‘<em>U</em>’ (along the horizontal axis of the probe), corresponding to the radial outflow of the downburst, with a positive value when the flow is directed toward the probe. Transversal, ‘<em>V</em>’, represents the velocity component transverse to the probe's centerline axis, having a positive value when the flow is directed right-to-left concerning an observer facing the probe's head. The vertical component is denoted as ‘<em>W</em>’ with a positive value indicating an upward direction. The term “XXXX” signifies the height of the probe, specified in millimeters (mm). The mast with the Cobra probes was subsequently positioned at ten radial <em>r</em> distances with respect to the jet impingement position in the range <em>r/D</em> (<em>D</em> = 3.2 m is the jet diameter) between 0.2–2.0 with an increment of 0.2. Note that the position <em>r/D</em> = 0.8 was adjusted to <em>r/D</em> = 0.75. This modification was necessary due to irregularities on the chamber floor at <em>r/D</em> = 0.8, which could have otherwise introduced bias into the measurements. The radial distance is identified with “<em>r/D_distance</em>” in the dataset files. The ceiling height of the testing chamber is <em>H</em> = 3.75 m, which leads to <em>H/D</em> > 1 allowing for a full vertical development of the downburst radial outflow. For every <em>r/D</em> position, each experiment with the same initial condition (i.e., <em>Wjet</em>) was repeated 10 times (“<em>repetition#</em>” in the database files) to inspect the repeatability of the tests and their variance. Each velocity record lasted 12 s (12 × 2,500 = 30,000 samples) and the duration of the downburst-like part of the record varied between 3–5 s. Overall, 6,600 total time series (2 <em>Wjet</em> × 3 rough surface × 10 repetitions × 10 <em>r/D</em> positions × 11 heights <em>z</em>) of downburst-like outflows were recorded during this set of experimental tests.</p> <p>The reported accuracy of Cobra probes from the manufacturer is +/- 0.5 m/s and +/- 1° for velocity and yaw/pitch angles respectively, up to approximately 30% of turbulence intensity. All velocity magnitudes below 1 m/s were removed and converted to NaN (Not a Number) in the database due to the poor accuracy of Cobra probes for velocities below this threshold. In addition, some velocity values were reported as null in the instrument readings due to the incoming flow being outside the probe's spatial cone of measurement (+/- 45° in respect to the probe horizontal axis). These values are flagged as NULL values in the database. This notation aligns with that utilized in a preceding database of measurements collected within the same experimental campaign at the WindEEE Dome (Canepa et al., 2021; <a href="https://doi.org/10.1594/PANGAEA.931205">https://doi.org/10.1594/PANGAEA.931205</a>).</p> <p>DLFs were tested on three different surfaces: (i) WindEEE Dome bare floor; (ii) Carpet; (iii) Artificial grass. A 1 m × 8 m rectangular section was selected from each of the three surfaces for testing purposes. Each surface was positioned with a 1 m offset relative to the geometric location of the jet impingement. It was identified by an equivalent full-scale roughness length, “<em>z0eq</em>”based on matching atmospheric boundary layer profiles measured in WindEEE in boundary layer mode with standard ESDU (Engineering Science Data Unit) profiles. A total of 15 different Atmospheric Boundary Layer (ABL)-like profiles were tested inside the chamber by varying the rotation-per-minute (rpm) of the fans across the 4 rows of the 60-fan wall—a peripheral wall of the hexagonal WindEEE Dome chamber comprising a matrix of 4 × 15 (rows × columns) fans that is used to produce ABL -like flows. A specific configuration of the 60-fan wall and a length scale of 1:200 were chosen based on correlation analysis between physically reproduced ABL profiles and curve fitting through Eq. A1.8 of the ESDU 82026. This scale is deemed suitable for both ABL and downburst winds produced at the laboratory. Through a linear fitting of the measured data on the <em>U – ln(z)</em> chart, employing the logarithmic law-of-the-wall (dependent on roughness length <em>z0</em> and friction velocity <em>u*</em>), the equivalent roughness lengths for the three surfaces were determined: <em>z0eq</em> = 0.007, 0.020, 0.320 m for the WindEEE Dome bare floor, carpet, and artificial grass, respectively.</p> <p>In summary, each experimental velocity time series consists of 30,000 rows, with 33 columns detailing the three velocity components (<em>U</em>, <em>V</em>, <em>W</em>) across the 11 Cobra probe heights. Columns 34 to 37 provide information on the repetition number, radial position of measurement, equivalent full-scale roughness length, and jet intensity. The subsequent timeseries within the dataset refer to the parameters in columns 34 to 37, each one spanning its entire range in the specified order.</p> <p>Researchers can leverage this database to validate and calibrate numerical and analytical models of thunderstorm winds, in addition to interpreting full-scale measurements of the phenomenon. It also serves as a valuable resource for the fluid dynamics community, particularly those interested in the physical comprehension of downscaled flows or the surface flow dynamics of large Reynolds number impinging jets.</p>
Multiscale large-eddy simulations of a low-level jet interacting with a wind farm and terrain (vertical slices of potential temperature)
<p>Multiscale large-eddy simulations of a low-level jet interacting with a wind farm and terrain (vertical slices of potential temperature) using the WRF-LES-GAD approach. Supplementary material part of the paper "Influence of simple terrain on the spatial variability of a low-level jet and wind farm performance in the AWAKEN field campaign", submitted to the Wind Energy Science journal.</p>
Multiscale large-eddy simulations of a low-level jet interacting with a wind farm and terrain (vertical slices of wind speed)
<p>Multiscale large-eddy simulations of a low-level jet interacting with a wind farm and terrain (vertical slices of wind speed) using the WRF-LES-GAD approach. Supplementary material part of the paper "Influence of simple terrain on the spatial variability of a low-level jet and wind farm performance in the AWAKEN field campaign", submitted to the Wind Energy Science journal.</p>
Multiscale large-eddy simulations of a low-level jet interacting with a wind farm and terrain (wind speed at 90 m AGL)
<p>Multiscale large-eddy simulations of a low-level jet interacting with a wind farm and terrain (wind speed at 90 m AGL) using the WRF-LES-GAD approach. Supplementary material part of the paper "Influence of simple terrain on the spatial variability of a low-level jet and wind farm performance in the AWAKEN field campaign", submitted to the Wind Energy Science journal.</p>
The effect of opening angle on shock patterns in supersonic jets
<p>Movies in the form of animated gifs.</p> <p>Corresponding to figures, table and notation of paper submitted to MNRAS with the same title.</p> <p>Movies of jets exiting a circular nozzle with a</p> <p>Half-opening angle A in degrees,</p> <p>Mach number M, </p> <p>overpressure relative to ambient K and</p> <p>density D relative to the ambient medium. </p> <p>All simulations run to 200 time units.</p> <p>Adiabatic gas with specific heat ratio of 5/3. </p> <p>These are all pressure movies with the ambient pressure set to 0.6., ambient sound speed and density both unity.</p> <p> </p>
Movies for Atomizing pulsed jet paper
Open the record for dataset details and reuse information.
Z'/QCD Jets for Mass Generalisation
<p>A collection of 10 Z'/QCD jet datasets used in <a href="https://arxiv.org/abs/2111.06047"><em>Meta-learning and data augmentation for mass-generalised jet taggers</em></a>. Each dataset contains 500K Z' jets and 500K QCD jets.</p> <p><strong>Simulation</strong></p> <p>Events are simulated with <em>MadGraph5 2.8.0</em>, showered in <em>Pythia 8.244</em>, then passed to <em>Delphes 3.4.2</em> with the default CMS card. For the signal, we use a simple <a href="https://feynrules.irmp.ucl.ac.be/wiki/WZPrimeAtNLO\#no1">Z' model</a> from <em>FeynRules</em>. In each event, the leading <span class="math-tex">\(R=1\)</span> anti-kt jet is selected.<strong> </strong></p> <p><strong>Cuts</strong></p> <p>Parton-level cuts of <span class="math-tex">\(|\eta_J|, |\eta_{Z'}|<2.0\)</span> and <span class="math-tex">\(\not\!\!E_T>1.2\)</span> TeV are applied in <em>MadGraph</em>. Cuts of <span class="math-tex">\(|m_J-m_{Z'}|<m_{Z'}/4\)</span> and <span class="math-tex">\(p_{T}>1.2\)</span> TeV are applied after detector simulation.</p> <p><strong>Preprocessing</strong></p> <p>Jets are preprocessed by first translating all constituents in the rapidity-azimuth plane such that the leading constituent lies at the origin. A rotation is then applied to position the centre of momentum below the origin.</p> <p><strong>Format</strong></p> <p>Each jet is stored as a list of constituent information in the format <span class="math-tex">\((p_T/p_{T,J}, \eta', \phi', g(\texttt{pdg_id}))\)</span> where <span class="math-tex">\(\eta'\)</span> and <span class="math-tex">\(\phi'\)</span> respectively denote pseudorapidity and azimuthal angle coordinates after the mentioned transformations and <span class="math-tex">\(g\)</span> maps Particle Data Group Monte Carlo identifiers to small floats. Specifically:</p> <table align="left"> <thead> <tr> <th scope="row"><strong>Particle</strong></th> <th scope="col"><span class="math-tex">\(\gamma\)</span></th> <th scope="col"><span class="math-tex">\(e^+\)</span></th> <th scope="col"><span class="math-tex">\(e^-\)</span></th> <th scope="col"><span class="math-tex">\(\mu^+\)</span></th> <th scope="col"><span class="math-tex">\(\mu^-\)</span></th> <th scope="col">Neutral Hadron</th> <th scope="col"><span class="math-tex">\(\pi^-\)</span></th> <th scope="col"><span class="math-tex">\(\pi^+\)</span></th> <th scope="col"><span class="math-tex">\(K^-\)</span></th> <th scope="col"><span class="math-tex">\(K^+\)</span></th> <th scope="col"><span class="math-tex">\(\bar{p}\)</span></th> <th scope="col"><span class="math-tex">\(p\)</span></th> </tr> </thead> <tbody> <tr> <th scope="row">pdg_id</th> <td>22</td> <td>-11</td> <td>11</td> <td>-13</td> <td>13</td> <td>0</td> <td>-211</td> <td>211</td> <td>-321</td> <td>321</td> <td>-2212</td> <td>2212</td> </tr> <tr> <th scope="row">g( pdg_id )</th> <td>0.05</td> <td>0.15</td> <td>0.25</td> <td>0.35</td> <td>0.45</td> <td>0.55</td> <td>0.65</td> <td>0.75</td> <td>0.85</td> <td>0.95</td> <td>1.05</td> <td>1.15</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p>The arrays are serialised and saved in <em>TFRecord</em> format allowing for efficient interfacing with <em>Tensorflow. </em>To avoid<em> </em>memory issues associated with loading a large number of datasets, each dataset is split into 100 shards.</p>
Data Files: Auroral Jets Experiment---JGR: Akbari2021Resonant
<p>The zip file includes seven data files with .csv extension. The data files include electric fields (mV/m), magnetic field perturbations (nT), and Langmuir Probe data for two sounding rockets, along with flight trajectory information. In all files, time is Universal Time (UT).</p> <p> </p>
Data for: Alfvén Pulse-Driven Spicule-like jets in the presence of thermal conduction and ion-neutral collision in a two-fluid regime
<p>The uploaded folder consists of the numerical simulation data and analyses routines of two-fluid JOANNA code that studied the Alfvén pulse-driven spicule-like jets in the presence of thermal conduction and Ion-neutral collision. The code produced the data in .xmf and .h5 formats, which are available for the analysis in the Data folder. The slices folder within Data consists of grid information in X- and -Y, as well as time. Apart from that, these slices consist of the temporal variations of various physical variables, e.g., pressure, density, velocity for ions and neutrals, magnetic field, etc. These slices are utilized in making the distance-time maps as presented in Figs 4-5 in the paper. Each physical variable is finally converted from code units to physical units (S.I or C.G.S. as required) and presented in the paper. The data and its analysis tree are self-descriptive, and each folder contains the instruction files in this context.</p>
Data for droplet frequencies in atomizing pulsed jet
Open the record for dataset details and reuse information.
Simulations of Pulsed Over-Pressure Jets: Formation of Bellows and Ripples in Galactic Environments
<p>Movies in the form of animated gifs.</p> <p>Corresponding to figures, tables and notation of paper submitted to MNRAS with the same title.</p> <p>Movies of jets exiting a circular nozzle with an overpressure K and density E relative to the ambient medium. </p> <p>The Mach number is set to 2 and an initial ramp up inspeed over 10 time units.</p> <p>All simulations run to 200 time units.</p> <p>These are Mach 2 jets with superimposed velocity pulsations with pulse period P and amplitude V-1( so V1.4 is 40% and V2 is 100% relative amplitude) A ramp up of the velocity from 0 is applied over an initial period R. Adiabatic gas with specific heat ratio of 5/3. </p> <p>The animated graphs called PROFILES here are radial cross-cuts of the physical parameters as a function of time.</p> <p> </p> <p>The pressure movies are collated into four streams according to the over-pressure and density.</p> <p>Within each zipped folder are movies with pulse periods of 2, 10 and 40 time units.</p> <p> </p> <p>The fixed speed movies are non-pulsed and both density and pressure movies are included.</p> <p> </p> <p>The preview movie is chosem to illustrate the ripples in the environment.</p> <p> </p> <p> </p> <p> </p>
PAIReD Jet Tagging Dataset
<p>Training and testing dataset used in "PAIReD jet: A multi-pronged resonance tagging strategy across all Lorentz boosts" (arXiv:2311.11011).</p> <p>This dataset corresponds to approximately 2% of the dataset used in the article. Five types of jets are simulated: AK4, AK8, AK15, PAIReD, and PAIReDEllipse. Each tar file contains one type of jet. Jets are split into train and test datasets. Three processes, namely ZHbb, ZHcc, and Z+jj are included. The Higgs mass in the training dataset is varied between 10-500 GeV, while the test dataset contains jets arising from a 125 GeV Higgs.</p> <p> </p> <p> </p>
Disentangling forced trends in the North Atlantic jet from natural variability using deep learning
<p>Code used to train the LLAE to obtain forced trends in the North Atlantic jet stream and to produce plots.</p>
Bayesian Uncertainty Quantification and Optimization of Jet Grout Column Diameter Prediction
<p><span>This dataset includes the jet grout data compiled from published case histories for Bayesian Uncertainty Quantification and Optimization of Jet Grout Column Diameter Prediction.</span></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.