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1,652 results for “particles”
Data for Li et al., Coupling remote sensing and particle tracking to estimate trajectories in large water bodies, International Journal of Applied Earth Observation and Geoinformation, 2022
<p>This data set contains four parts:</p> <p>1) compressed folder with input parameters and results for the hydrodynamic model</p> <p>2) compressed folder with input parameters and results for the particle tracking</p> <p>3) compressed folder with satellite data </p> <p>4) code used in the article for hydrodynamic model, particle tracking and image processing</p> <p>Each folder contains a readme file,</p>
Dataset for the publication "Size matters: small biochar particles hardly disintegrate under cryo-stress"
<p>Corresponding dataset for the OA publication "Small biochar particles hardly disintegrate under cryo-stress" published in Geoderma</p>
Supplementary Data: Cosmological constraints on decaying axion-like particles: a global analysis
<p><strong>Supplementary Data</strong></p> <p><em>Cosmological constraints on decaying axion-like particles: a global analysis</em></p> <p>This record contains the supplemetary data for the GAMBIT article, "Cosmological constraints on decaying axion-like particles: a global analysis". </p>
Data for the publication "Retrieving ice-nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations"
<p>This repository contains the data for the paper:</p> <p>Wieder, J., Ihn, N., Mignani, C., Haarig, M., Bühl, J., Seifert, P., Engelmann, R., Ramelli, F., Kanji, Z. A., Lohmann, U., and Henneberger, J.: Retrieving ice nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-67, in review, 2022.</p> <p>More information can be found in the README files.</p> <p>Note that the scripts to reproduce the figures of the publication are available on request.</p>
Synthetic cryo electron microscopy single particle images containing biomolecular complexes with continuous conformational variability used for validating DeepHEMNMA method and validation results
<p>This archive contains a synthetic dataset used for validating DeepHEMNMA method and the validation results. DeepHEMNMA is a deep learning extension of HEMNMA approach for analyzing continuous conformational variability of biomolecular complexes in cryo electron (cryo-EM) microscopy single particle images. We provide a training set of 20,000 images and an inference set of 50,000 images. The training images were used (1) to estimate the conformational and rigid-body parameters with HEMNMA and (2) to train the neural network using the parameters previously estimated with HEMNMA (the file with the HEMNMA-estimated parameters is provided). The inference images were used to infer the parameters with the trained neural network. Also, we provide (1) the input PDB structure, its normal modes, and the conformational and rigid-body parameters used to synthesize the 20,000 training images (ground-truth parameters) and (2) the conformational and rigid-body parameters inferred from the set of 50,000 inference images.</p> <p>The DeepHEMNMA method and the method for synthesizing images have been fully described in the following article: "Hamitouche I and Jonic S (2022), DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images. Front Mol Biosci 9, 965645. <a href="https://doi.org/10.3389/fmolb.2022.965645">https://doi.org/10.3389/fmolb.2022.965645</a> (in press)". Additionally, this article describes a test of DeepHEMNMA using one experimental cryo-EM dataset (available in EMPIAR database under the accession code EMPIAR-10016). </p>
Particle number concentration and meteorological measurements at Berlin-Tegel Airport during its closure
<p>Observation data of particle number concentrations (PNC) on the airfield of Berlin-Tegel Airport (TXL) during its closure. PNC was recorded with a Grimm EDM465 UFPC, including meteorological parameters measured with a Lufft WS600-UMB.</p> <ul> <li>observations between 20. October 2020, 14:44 LT and 3 December 2020, 03:03 LT</li> <li>time interval: 5 seconds</li> <li>air inlet of CPC at 1.4 m above ground</li> <li>weather sensor at 1.3 m above the ground.</li> <li>Location: 52,561 North, 13,320 East</li> <li>Variables: <ul> <li>particle number concentration in particles/cm³ (pnc)</li> <li>wind speed in m/s (ws)</li> <li>wind direction in ° (wdir)</li> <li>air temperature in °C (temp)</li> <li>relative humidity in % (rh)</li> <li>air pressure in hPa (pressure)</li> <li>precipitation in mm (pcpn)</li> <li>date as local time</li> <li>phase: whether the airport was still open ("TXL_open") or already closed ("TXL_closed")</li> <li>no data available between between 15.11.2020 02:15:00 and 18.11.2020 11:14:55 due to hardware issues</li> </ul> </li> </ul>
Oscillatory compression with different frequencies of 2D, dense, soft particle suspensions
<p>This repository contains 5 datasets of cyclically compressed hydrogel packings inside microfluidic channels, with different oscillation frequencies, observed using a microscope. This repository contains the raw data (images) as well as analyzed data of the particles tracked over time. The data format closely resembles information you might obtain from 2D DEM simulations, and could, therefore, be used to calibrate DEM simulations of the compaction of soft particles.</p> <p>The "Readme.md" file contains more in-depth information about the experimental setup, experiments and data structure.</p>
Multiple Particle Tracking Data from Neonatal Organotypic Rat Brain Slices
<p>The data includes statistical features generated from raw multiple particle tracking data from videos collected during three independent experiments: (1) 5 different brain regions, (2) 3 different treatment conditions in the brain, and (3) 5 different brain ages. </p> <p> </p> <table> <thead> <tr> <th scope="col">Feature</th> <th scope="col">Model Abbreviation</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>alpha</td> <td>alpha</td> <td>Exponent of the anomalous diffusion equation.</td> </tr> <tr> <td>Effective diffusion coefficient</td> <td>D_fit</td> <td>Coefficient of the anomalous diffusion equation</td> </tr> <tr> <td>Kurtosis</td> <td>kurtosis</td> <td>The fourth moment of the projected positions on the dominant eigenvector of the radius gyration tensor (T).</td> </tr> <tr> <td>Asymmetry1</td> <td>asymmetry1</td> <td>Characterizes the asymmetry of the trajectory. Asymmetry1 equals 0 for circularly symmetric trajectories and 1 for linear trajectories.</td> </tr> <tr> <td>Asymmetry2</td> <td>asymmetry2</td> <td>The ratio of the smaller to larger principal radius of gyration.</td> </tr> <tr> <td>Asymmetry3</td> <td>asymmetry3</td> <td>An asymmetry feature that accounts for non-cylindrically symmetric point distributions.</td> </tr> <tr> <td>Aspect ratio</td> <td>AR</td> <td>The ratio of the kong and short side of the trajectory's minimum bounding rectangle. Perfectly symmetric trajectories have an aspect ratio of 1, and aspect ratio increases as trajectories become more elongated. </td> </tr> <tr> <td>Elongation</td> <td>elongation</td> <td>An estimation of amount of extension of the trajectory from its centroid. </td> </tr> <tr> <td>Boundedness</td> <td>boundedness</td> <td>Boundedness quantifies how much a particle with diffusion coefficient <em>D<sub>eff</sub></em> is restricted by a circular confinement of radius <em>r</em> when diffusing for a period of time <span class="math-tex">\(N\Delta t \)</span></td> </tr> <tr> <td>Fractal Dimension</td> <td>fractal_dim</td> <td>Fractal dimension is a measure of how "complicated" a self similar figure is. </td> </tr> <tr> <td>Trappedness</td> <td>trappedness</td> <td>The probability (<span class="math-tex">\(\textit{P}_{\textit{t}} \)</span>) that a particle with duffusion coefficient <em>D<sub>eff</sub></em> is trapped in a region (<em>r<sub>0</sub></em>) for a period of time <span class="math-tex">\(N\Delta t \)</span>. </td> </tr> <tr> <td>Efficiency</td> <td>efficiency</td> <td>The ratio of the squared net displacement to the sum of step lengths. </td> </tr> <tr> <td>Straightness</td> <td>straightness</td> <td>The ratio of the net displacement to the sum of step lengths. </td> </tr> <tr> <td>MSD Ratio</td> <td>MSD_ratio</td> <td>MSD ratio characterizes the shape of the MSD curve. For Brownian motion, it is 0; For restricted motion it is < 0; For directed motion it is > 0. </td> </tr> <tr> <td>Frames</td> <td>frames</td> <td>The total number of frames the trajectory spans. </td> </tr> <tr> <td>Effective Diffusion Coefficient 1</td> <td>Deff1</td> <td>Effective diffusion coefficient at 0.33 s.</td> </tr> <tr> <td>Effective Diffusion Coefficient 2</td> <td>Deff2</td> <td>Effective diffusion coefficient at 3.3s. </td> </tr> </tbody> </table> <p>Mean values were calculated based on surrounding datapoints for alpha, D_fit, kurtosis, asymmetry1, asymmetry2, asymmetry3, AR, elongation, boundedness, fractal_dim, trappedness, efficiency, straightness, MSD_ratio, Deff2, and Deff2. </p> <p> </p>
Solute particle near a nanopore: influence of size and surface properties on the solvent-mediated forces: ftDFT code
<p>This is the complete ftDFT code used to generate the results reported in our Nov 2017 Nanoscale article "Solute particle near a nanopore: influence of size and surface properties on the solvent-mediated forces",DOI: 10.1039/C7NR07218J. </p>
Backscattering from randomly placed Dirichlet particles
<p><strong>Summary:</strong></p> <p>A simulated data set of backscattered waves, governed by the 2D wave equation, from randomly placed Dirichlet particles. The particles are circular with 0.2 < radius < 2.0 and 1% < volume fraction < 20%. The incident wave is of the form e<sup>i k (x - x<sub>R</sub> - t)</sup> where the particles are placed in the halfspace x>0 and the backscattering is emitted and received at (x<sub>R</sub>, 0) with x<sub>R</sub> = -10.0. The data named bunny and bunnytest is for incident waves with 0 < k < 1 (where the term e<sup>- i k t</sup> factors out). The data named train and test is for incident waves of the form e<sup>0.1</sup><sup>(x - xR - t)^2</sup>, with 9.5 < t < 98. </p> <p><strong>Format:</strong></p> <p>An example of how to use the data is given in [4]. To load the data requires the packages [1] and [2] in the language Julia v0.5> [3]. When using load(filename) the data will automatically be loaded as an array of type FrequencySimulation or of type TimeSimulation. </p> <p>[1] https://github.com/jondea/MultipleScattering.jl</p> <p>[2] https://github.com/JuliaIO/JLD.jl </p> <p>[3] https://julialang.org/</p> <p>[4] https://github.com/gowerrobert/MultipleScatteringLearnMoments/tree/master/examples</p>
Three-dimensional Reconstructions and Quantitative Indicators for colloidal particles in Dry and Liquid Conditions in Scanning Transmission Electron Microscope (STEM)
<p>This dataset accompanies the research presented in the paper:</p> <div>Esteban, D.A., Wang, D., Kadu, A., Olluyn, N., Iglesias, A.S., Perez, A.G., Casablanca, J.G., Nicolopoulos, S., Liz-Marzán, L.M. and Bals, S., 2023. Liquid phase fast electron tomography unravels the true 3D structure of colloidal assemblies. <em>arXiv preprint arXiv:2311.05309</em>. [<a href="https://arxiv.org/pdf/2311.05309" target="_blank" rel="noopener">link</a>]</div> <p>It provides a comprehensive collection of three-dimensional reconstructions and quantitative descriptors for small colloidal particles. These gold nanoparticles are arranged in tetrahedral and other intricate geometries under both dry and liquid conditions. The dataset contains 3D reconstructions and quantitative indicators such as centroids, volumes, surface areas, solidity measures, and principal axis lengths for assemblies with 4, 5, and 6 particles. </p> <p>The dataset includes: <code>N4_dry_dart.rec</code> and <code>N4_liquid_dart.rec</code> for the 3D reconstructions of an assembly with 4 particles in dry and liquid conditions respectively; <code>N4_quant_descriptors_dry.mat</code> and <code>N4_quant_descriptors_liquid.mat</code> providing quantitative descriptors for these conditions. Similar files are provided for assemblies with 5 and 6 particles, such as <code>N5_dry_dart.rec</code>, <code>N5_liquid_dart.rec</code>, <code>N5_quant_descriptors_dry.mat</code>, <code>N5_quant_descriptors_liquid.mat</code>, and the corresponding files for N6. </p> <p>This dataset can be used to study the structural dynamics of nanoparticle assemblies and studies in colloidal chemistry, materials science, and nanotechnology. The <code>.rec</code> files can be visualized using volume rendering software (e.g. Amira or Avizo), while the <code>.mat</code> files contain structured data for analysis in MATLAB. The supporting code and scripts for this dataset are available on the GitHub repository: <a href="https://github.com/ajinkyakadu/LiquidET_NatComm2024" target="_new" rel="noreferrer">https://github.com/ajinkyakadu/LiquidET_NatComm2024</a>. </p>
Data for the paper "Particle method for the numerical simulation of the path-dependent McKean-Vlasov equation"
<p>This deposit contains the data obtained by the method described in [A. Bernou, Y. Liu, Particle method for the numerical simulation of the path-dependent McKean-Vlasov equation, 2024]. The notebooks used to generate them through a suitable Euler scheme can be find at https://github.com/ArmdBrn/McKean_PathDep, along with files containing the estimated errors. <br>The two models considered are:<br>- a modified Ornstein-Uhlenbeck model with path-dependency;<br>- a model of neural masses with intrinsic potentiation leading to path-dependent dynamics. </p>
BetaEddyOne: A long-lived 1.5 layer quasigeostrophic eddy on a beta plane, with Lagrangian particles
<p>BetaEddyOne is a yearlong simulation of a large, nonlinear oceanic eddy under 1.5 layer quasigeostrophic dynamics on a beta plane, initially located at 24˚N. The simulation is run at 512 x 256 resolution and is seeded with Lagrangian particles with one particle per grid point. </p> <p>This simulation is indended for use as a common test case for eddy-related diagnostics and analysis methods. It approximately replicates the eddy analyzed in detail in Early, Samelson, and Chelton (2011), <a href="https://doi.org/10.1175/2011JPO4601.1">https://doi.org/10.1175/2011JPO4601.1</a>. The simulation was created using the WaveVortexModel (Early, Lelong, and Sundermeyer, 2021, <a href="https://doi.org/10.1017/jfm.2020.995">https://doi.org/10.1017/jfm.2020.995</a>), the code for which is available on GitHub at <a href="https://github.com/Energy-Pathways-Group/GLOceanKit">https://github.com/Energy-Pathways-Group/GLOceanKit</a>. This particular simulation was created for use in the paper</p> <p>Lilly, J. M., J. Feske, B. Fox-Kemper, and J. J. Early (2024). Integral theorems for the gradient of a vector field, with a fluid dynamical application. <em>Proceedings of the Royal Society of London, Series A</em>. <strong>480</strong> (2293): 20230550, 1–30. <a href="https://doi.org/10.1098/rspa.2023.0550">doi 10.1098/rspa.2023.0550</a>.</p> <p>The figure shows a snapshot of the model's vertical vorticity. </p> <p> </p>
Computational models for kaolinite nano-particles (Generations 1-3) and their comprehensive FTIR spectra
<p>The dataset contains a large number of computational models and detailed spectral comparison, fitting, and deconvolution of a large set of FTIR data for crystalline and exfoliation kaolinite, nano-kaolinite and halloysite, nano-halloysite samples.<br> The <strong>G1.xyz</strong>, <strong>G2.xyz</strong>, and <strong>G3.xyz</strong> files contain the initial structures for the first three generations of nano-kaolinite molecules.<br> The compressed folder <strong>SVP-def2TZVP.zip</strong> contains the structural information relevant for comparing and contrasting the performance a double-zeta (SVP) and triple-zeta (TZVP) basis sets.<br> The <strong>edge_protonation.zip</strong> folder guides the reader through the stepwise evaluation of various edge protonation models and shows the final converged results.<br> The <strong>full_optimization.zip</strong> folder summarizes the stationary structure calculations at various levels of theory carried out for the G2 model.<br> </p>
The role of particle, energy and momentum losses in 1D simulations of divertor detachment
<p>Source code, inputs, simulation outputs, analysis scripts and figures used in the paper</p> <p>"The role of particle, energy and momentum losses in 1D simulations of divertor detachment" by B D Dudson, J Allen, T Body, B Chapman, C Lau, L Townley, D Moulton, J Harrison, B Lipschultz.</p> <p>Questions to benjamin.dudson@york.ac.uk .</p> <p>This version is before submission to journal.</p> <p> </p> <p> </p>
Measurements of Ice Nucleating Particles in Beijing, China - Data and processing code
<p>Dataset needed to replicate findings published in the journal article "Measurements of Ice Nucleating Particles in Beijing, China, published in the Journal of Geophysical Research. The dataset contains the following:</p> <p>1. Data files containing raw data from a Continuous Flow Diffusion Chamber - Ice Activation Spectrometer (CFDC-IAS), in comma-delimited format).</p> <p>2. Data and processing files for analysis of backward air trajectories as an Igor Pro 8 packed experiment package file. Igor Pro is available from www.wavemetrics.com and a free 30-day trial version can be used to export data to other formats.</p> <p>3. Data and processing files for analysis of CFDC, APS and meteorological data, including data in the form of waves, as part of an Igor Pro 8 packed experiment package.</p>
PEPT data for Understanding the effect of fluid viscosity in Vertical Stirred Mills using the Positron Emission Particle Tracking (PEPT) approach
<p>The raw PEPT data collected for the paper "Understanding the effect of fluid viscosity in vertical stirred mills using the positron emission particle tracking (PEPT) approach." The paper is the first to use the PEPT technique to investigate the effect of fluid viscosity on the efficiency of the grinding process.</p> <p>This data can be post-processed using the PEPT-ML library and used in isolation or it can be used to calibrate an equivalent simulation. The simulation template is available on GitHub and the link to this is under the Software tab. Each file is labelled by the fluid viscosity and attritor speed used in the experiment, The data for a single run is often split across files but can be combined by the PEPT-ML library.</p>
Data for: Klein et al., Viscosity of aqueous ammonium nitrate--organic particles: Equilibrium partitioning may be a reasonable assumption for most tropospheric conditions, egusphere-2024-1459
<p><strong>Experimental data </strong></p> <p>This folder contains the experimental and modelled data to the figures shown in the main manuscript and Appendix.</p> <p>Figure 3B AIOMFAC-VISC (AIOMFAC-VISC modelling of sucrose)</p> <p>Figure 3B Experimental (Viscosity measurements of sucrose)</p> <p>Figure 4 (Viscosity measurements of ammonium nitrate - sucrose - water mixtures)</p> <p>Figure 5 A and C (Viscosity estimations of ammonium nitrate - sucrose - water mixtures using mixing rules)</p> <p>Figure 5 B and D (Viscosity estimations of ammonium nitrate - sucrose - water mixtures using AIOMFAC-VISC)</p> <p>Figure 6 (Viscosity estimations of inorganic - sucrose - water mixtures using mixing rules)</p> <p>Figure 7 (Mixing times for ammonium nitrate - sucrose - water and Toluene SOA - sucrose - water aerosol particles for varies cities) </p> <p>Figure A2 (Viscosity estimations of ammonium nitrate - sucrose - water mixtures using a mass fraction based mixing rules)</p>
Data from: "DarkSide-20k sensitivity to light dark matter particles"
<p>These files provide expected limits from the preprint of arXiv:2407.05813, "DarkSide-20k sensitivity to light dark matter particles" and are made available by the DarkSide-20k Collaboration.</p>
Fast calculation methods for the magnetic field of particle lattices: Datasets and scripts
<div>*********************************************** README.txt **************************************************</div> <div> </div> <div>Title: Fast calculation methods for the magnetic field of particle lattices: </div> <div>Datasets and scripts</div> <div>Version: 1.0</div> <div>Date of Release: 2024/10/11</div> <div>Identifier: doi:10.5281/zenodo.13930969</div> <div>Permalink: http://dx.doi.org/10.5281/zenodo.13930969</div> <div> </div> <div>*************************************************************************************************************</div> <div> </div> <div>Associated publication: I. Royo-Silvestre, D. Gandia, J. J. Beato-López, E. Garaio, C. Gómez-Polo </div> <div>"Fast calculation methods for the magnetic field of particle lattices" </div> <div>(paper yet to be published)</div> <div> </div> <div>Link to publication: (paper yet to be published)</div> <div> </div> <div>Suggested citation: Please reference the associated publication above when using any datasets or</div> <div> materials described in this README file.</div> <div> </div> <div>Contact information: Isaac Royo Silvestre, </div> <div>Universidad Pública de Navarra, </div> <div>Pamplona, Spain, </div> <div>isaac.royo@unavarra.es</div> <div> </div> <div>License: CC BY 4.0</div> <div> </div> <div>------------------------------------------------------------------------------------------------------------</div> <div> </div> <div>This directory contains the following datasets and supplementary materials:</div> <div> </div> <div> ------------------------------</div> <div> SCRIPTS</div> <div> ------------------------------</div> <div> </div> <div> - scripts.zip Matlab scripts (compressed zip file) used to calculate the magnetic field of </div> <div>lattices of magnetic particles by analytical and semianalytical methods (more information in the associated paper) </div> <div> </div> <div> --------------------------------</div> <div> DATASETS</div> <div> --------------------------------</div> <div> </div> <div> - data.zip: Tabular data required to plot curves (compressed zip file) in csv format,</div> <div>also data used to obtain average values</div> <div> </div> <div> </div> <div>Specific documentation of each file is described in readme files.</div> <div> </div> <div>Refer to the original manuscript (see above) for additional information regarding the collection and generation of these data.</div> <div> </div> <div>------------------------------------------------------------------------------------------------------------</div> <div> </div> <div> ---------------------------------------------------------------------</div> <div> DOCUMENTATION FOR 'scripts.zip'</div> <div> ---------------------------------------------------------------------</div> <div> </div> <div> The zip file contains another readme.txt file (that explains the content of the zip file in detail), </div> <div>and multiple .m files. m files are Matlab scripts, text files that can be read using any text editor. However it has to be executed via Matlab, scripts contain documentation as comments.</div> <div> </div> <div> ---------------------------------------------------------------</div> <div> DOCUMENTATION FOR 'data.zip'</div> <div> ---------------------------------------------------------------</div> <div> </div> <div> The zip file contains another readme.txt file (that explains the content of the zip file in detail), </div> <div>multiple .dat files with data used to obtain averaged valus (see format in the readme.txt </div> <div>contained in the zip), and a folder "curves".</div> <div>The curves folder contains tabular data in .csv files, these files can be used to plot the curves</div> <div>in the manuscript.</div> <p> </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.