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274 results for “collision”
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>
Wall collision of deformable bubbles in the creeping flow regime (Supporting Data)
<p>The dataset contains sample numerical results of a bubble colliding with a solid wall at small Reynolds number, pertaining to the manuscript under the same title, "Wall collision of deformable bubbles in the creeping flow regime", published in the European Journal of Mechanics - B/Fluids.</p>
R Data for CPBS Report 23SDSU01 - Urban Demographic Shift of Pedestrian and Bicyclist Collisions, Equity, and Police Enforcement
<p>Data for the statistical program R.</p>
Simulated sPHENIX Time-Projection Chamber (TPC) Data in Central Au-Au Collisions at sqrt[s] = 200 GeV, outer layer group
<p>This is the dataset we used to train the 2D and 3D Bicephalous Convolutional Autoencoders (BCAEs) described in "Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data" published in the 9th International Workshop on Data Analysis and Reduction for Big Scientific Data (https://drbsd.github.io/).<br> </p><p>To untar the file, run `tar -xvzf outer.tgz`. </p><p><br>The <i>Time Projection Chamber (TPC)</i> is a hollow cylinder. Along the radial dimension, the TPC is composed of 48 cylindrical layers of small sensors, which are grouped into three layer groups: inner, middle, and outer. Each layer group has 16 consecutive layers. On each TPC layer, the voxels are presented as a rectangular grid with rows along the z (or horizontal) direction and columns along the azimuthal direction. Within one layer group, all layers have the same number of rows and columns. This allows us to represent the ADC values from one layer group as a 3D array.</p><p>The data released here focuses on the <i>outer layer group</i>, where the array of ADC values has shape (16, 2304, 498) in the radial, azimuthal, and horizontal orders. <br><br>The full voxel data are divided into 24 equal-size non-overlapping sections: 12 along the azimuthal direction (30 degrees per section) and 2 along the horizontal direction (divided by the transverse plane passing the collision point). We call one such section a <i>TPC wedge</i>. The array of ADC values from each TPC wedge in the outer layer has shape (16, 192, 249), listed in radial, azimuthal, and horizontal directions, respectively. The TPC wedges are used as the direct input to the deep neural network compression algorithms.</p><p>We simulated 1310 events for central <i>sqrt[s]=200 GeV Au-Au collisions with 170kHz pile-up</i>. The data were generated with the HIJING event generator and Geant4 Monte Carlo detector simulation package integrated with the sPHENIX software framework.</p><p>The simulated TPC readout (ADC values) from these events are represented in a 10-bit unsigned integer in [0, 1023]. To reduce unnecessary data transmission between detector pixels and front-end electronics, a zero-suppression algorithm has been applied. ADC values below 64 are suppressed to zero as most of them are noise. The zero compression makes the TPC data sparse at about 10% nonzero occupancy.<br><br>We divide the 1310 total events into 1048 events for training and 262 for testing. Each event contains 24 outer-layer wedges. Thus, the training partition contains 25152 TPC outer-layer wedges, while the testing portion has 6288 wedges. The compression algorithm compresses each wedge independently.<br><br>The dataset has the following structure:</p><ol><li>24 subfolders with the name `12-2_[azimuthal section]-[horizontal section]` where the [azimuthal section] is labeled by an integer in [0, 11] and the [horizontal section] is labeled by either 0 or 1. Each file in one of the subfolders has the name in the format "AuAu200_170kHz_10C_Iter2_[simulation id].xml_TPCMLDataInterface_[event id within simulation].npy". There are 131 simulations, and each simulation contains 10 <i>independent</i> events (and hence the 1310 total events as mentioned above). Each [event id within simulation] is an integer in [0, 9].</li><li>train.txt: a list of all TPC wedges for the training split.</li><li>text.txt: a list of all TPC wedges for the test split.</li></ol><p>Note that the dataset is <i>split by events</i>. That is, if a TPC wedge from an event is in the train split, all 24 wedges from the same event will all be in the train split. The same holds for the test split.</p>
The role of collision speed, cloud density, and turbulence in the formation of young massive clusters via cloud-cloud collisions
<p>This repository contains the essential unprocessed data to recreate the results in this paper (10.1093/mnras/staa2857; <a href="https://ui.adsabs.harvard.edu/abs/2020MNRAS.499.1099L/abstract">Liow and Dobbs 2020</a>), which contains the following directories:</p> <p><strong>initial_conditions </strong></p> <ul> <li>This directory contains the initial condition dumpfiles. There are 24 main models in this paper. They are named XY_abc, where 'X' is either 'B', 'C', or 'D', representing low, standard, and high initial cloud density respectively; 'Y' is either '1' or '2', representing low and high turbulence; 'abc' is a three-digit number representing the collision Mach number.</li> <li>Additional models in appendices have prefixes attached to the filenames. The prefixes are either 'R' (resolution test models; Appendix A), 'T' (random turbulent seeds models; Appendix B), or 'M' (massive clouds models; Appendix D). The value after 'R' shows the number of SPH particles used, a.k.a. resolution.</li> <li>The dumpfiles were produced using PHANTOM v1.3.0 (Price et al. 2018; https://phantomsph.bitbucket.io/) in binary format and can be visualised using SPLASH v2.8.0 (Price 2007; https://splash-viz.readthedocs.io/en/latest/intro.html) with code units 100,000 MSun and 10 pc. </li> </ul> <p><strong>ten_percents</strong> </p> <ul> <li>This directory contains the dumpfiles at t<sub>10%</sub>, the time when 10% of the gas mass is converted to sink mass. Most analysis in the paper was done at this snapshot. They can be visualised using SPLASH similar to the initial condition dumpfiles.</li> </ul> <p><strong>ev_files</strong></p> <ul> <li>This directory contains ASCII files that track the global properties (energies, total linear/angular momentum, etc) throughout the simulation. The suffixes '_0?' right before '.ev', not relevant to the analysis, show the number of times the simulations were continued. </li> </ul> <p><strong>all_turbulent_fields</strong></p> <ul> <li>This directory contains turbulent fields used in most models ('turb001' and 'turb002') and in the random turbulent seed models (otherwise), written in binary format. See 'README' for the random seeds used, and 'velfield.f' for the code to generate the turbulent fields.</li> </ul> <p>Not included in this repository are the intermediate dumpfiles and the sink particle data, but the data shown in this repository is sufficient to reproduce the time and snapshots in the paper. Post-processed data (e.g. global star formation rates, sink particle information at t<sub>10% </sub>and other specific snapshots) and results are not included here to preserve data authenticity.</p> <p>Readers are welcome to contact the authors for the post-processed data and more information.</p>
Data for Collision Induced Spin-Orbit Relaxation of Highly Vibrationally Excited NO Near 1 K
<p>Data for "<strong>Collision Induced Spin-Orbit Relaxation of Highly Vibrationally Excited NO Near 1 K" published in Natural Sciences</strong></p>
Collision-induced dissociation of protonated uracil water clusters probed by molecular dynamics simulations
<p>This dataset contains the input and output files associated to the work of "Collision-induced dissociation of protonated uracil water clusters probed by molecular dynamics simulations"</p> <p> </p> <p> </p>
Minimizer collision statistics (BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches in Genome Analysis)
<p>This dataset includes the statistics for the minimizers that generate the same hash value (i.e., collisions). The hash values are generated using a low-collision hash function and the SimHash technique in BLEND.</p> <p> </p> <p>*collision_stats.txt files include the overall collision statistics for a tool and configuration of the tool (i.e., the number of colliding minimizer pairs with a certain edit distance and their ratio to all number of collisions). For example blend_n3_collision_stats.txt shows the statistics for BLEND where the number of neighbors is set to 3 when running BLEND.</p> <p>_sim.csv files include all minimizer pairs with the same hash value and the edit distance between them.</p> <p> </p>
K-mer collision statistics (BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches in Genome Analysis)
<p>This dataset contains 1,077 FASTA files and CSV files. Each FASTA file includes 25-character long sequences similar to each other.</p> <p>We have a CSV file for each tool (i.e., minimap2 and BLEND) and configuration (i.e., different number of neighbors in BLEND). CSV files include the non-identical k-mer pairs (16-mers) that generate the same hash value (i.e., collisions). These k-mers are extracted from sequences that are similar to each other. In each line, we show the hash value of the k-mers, the actual sequene pairs that the k-mers are extracted from, k-mer pairs that generate the same hash value, and the edit distance between these k-mers.</p> <p> </p>
Data for "Mid-air Collisions Control the Wavelength of Aeolian Sand Ripples"
<p>The results of the numerical simulations mentioned in the paper. </p> <p>The package "ripple formation" contains the bed surface data of the ripple formation simulations. These cases are simulated in a 1m x 0.01m x 0.3m cuboid domain. The initial bed is a flat granular surface of 0.02m in thickness. The density of the fluid rho_f=1.263 kg/m^3; the density of particles rho_p=2650 kg/m^3; the particle diameter d=2.5e-4 m; the kinetic viscosity of the fluid nu=1.49e-5 (m^2)/s. All the settings are based on the wind-blowing sand situation of earth environment. Different wind velocities are tested. "u3, u35, u4, u45..." represent the wind velocity u*=0.3, u*=0.35, u*=0.4, u*=0.45, etc.</p> <p>Variables in the surface.plt file:</p> <p>PX: location in the fluid direction. PY: location in the transverse direction. PZ: bed surface elevation at (PX, PY). DP: not used in this paper.</p> <p>The packages with prefix "preripple-" contain the particle information derived from the pre-rippled simulations. All the settings of them are similar to those in the ripple formation simulations. The right part of the package name declares the changed setting. For example, package "preripple-d200" contains the cases run with a particle diameter d=200 µm (or 2e-4 m), "preripple-rho_f05" contains the cases run with a fluid density rho_f=0.5 kg/m^3. For each run, the wave number of the pre-rippled surface varies every 20 seconds from k=50 to k=500.</p> <p>Variables in the particle_loc.plt file:</p> <p>XP, YP, ZP: particle location. DP: particle diameter. UP, VP, WP: particle velocity. FK: particle flight distance. FZ: particle height from the bed surface. FH: maximum particle height from the bed surface. FG: mid-air colliding counter. FT: particle flight duration time.</p>
Quantum state resolved molecular dipolar collisions over four decades of energy - online data
<p>Data for submission of "Quantum state resolved molecular dipolar collisions over four decades of energy"</p> <p>Authors:</p> <p>Guoqiang Tang<sup>1</sup>, Matthieu Besemer<sup>1</sup>, Stach Kuijpers<sup>1</sup>, Gerrit C. Groenenboom<sup>1</sup>,<br> Ad van der Avoird<sup>1</sup>, Tijs Karman<sup>1</sup>**, Sebastiaan Y.T. van de Meerakker<sup>1</sup>**</p> <p><sup>1</sup>Radboud University, Institute for Molecules and Materials<br> Heijendaalseweg 135, 6525 AJ Nijmegen, the Netherlands</p> <p>**To whom correspondence should be addressed;<br> E-mail: basvdm@science.ru.nl, t.karman@science.ru.nl</p>
Smartphone and rightward collisions
<p>This zip file contains data sets of the paper "smartphones and rightward collisions", submitted to Laterality. Please read "readme.txt" for details. </p>
Experimental study of the response of sheared E × B flow to varying ion-neutral collisions
<p>The experimental data is to support the plots within the paper "Experimental study of the response of sheared E × B flow to varying ion-neutral collisions"</p>
Data repository for Jarvis et al. (2023): The probabilistic nature of dune collisions in 2D"
<p>This is a data repository associated with Jarvis et al. (2023) "The probabilistic nature of dune collisions in 2D" published in Earth Surface Dynamics.<br> </p>
Weekly road traffic collision (AXA Mexico) & weekly road traffic deaths
<p><span>Dataset 1 (AXA collisions 2015–2019) was curated and used to evaluate the effect of two road traffic regulations implemented in Mexico City in 2015 and 2019 on collisions using an interrupted time series analysis. </span>Collisions data came from insurance collision claims (January 2015 to December 2019). The dataset contains 8 variables: year (anio_n), week (semana), count of total collisions per week (c_total), count of collisions resulting in injury per week (c_p_lesion), binary variable to identify the 2015 intervention (limit), binary variable to identify the 2019 intervention (limit1), the number of weeks from baseline (time), an estimate of the number of insured vehicles per week (veh_a_cdmx).</p> <p><span>Dataset 2 (Road traffic deaths 2013–2019) was curated and used to evaluate the effect of two road traffic regulations implemented in Mexico City in 2015 and 2019 on mortality using an interrupted time series analysis. </span>Mortality data came from vital registries collated by the Mexican Institute for Geography and Statistics, INEGI, (January 2013 to December 2019). The dataset contains 7 variables: year (anio_ocur), week (semana), count of traffic-related deaths per week (def_trans), binary variable to identify the 2015 intervention (limit), binary variable to identify the 2019 intervention (limit1), the number of weeks from baseline (time) and an estimate of the Mexico City population per week (pob_tot_p).</p>
Developing a Method to Automatically Extract Road Boundary and Linear Road Markings from MMS Point Cloud using OBB Collision Detection Techniques
<p>This video demonstrates the application of our method in a software tool for constructing road boundaries and lane marking data.</p>
COLLISION RELAPSE Trial
ClinicalTrials.gov study NCT05861505. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Carotenoids for Collision Athletes
ClinicalTrials.gov study NCT06270680. IPD Sharing: NO. Countries: 1. Publications: 10.
COLLISION XL: Unresectable Colorectal Liver Metastases (3-5cm): Stereotactic Body Radiotherapy vs. Microwave Ablation
ClinicalTrials.gov study NCT04081168. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
COLLISION Trial - Colorectal Liver Metastases: Surgery vs Thermal Ablation
ClinicalTrials.gov study NCT03088150. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
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Allen Brain Atlas
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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.