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9 results for “particle matter”
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>
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>
Cosmological Initial Conditions (dark matter particles) for 85Mpc^3
<p>Files representing the initial conditions of Dark Matter Particles at z=40 for ENZO-MHD cosmological simulation of a comoving 85Mpc^3 volume, simulated with 1024^3 cells and 1024^3 DM particles. The data are in hdf5 format and they were generated using the mpgrafic code.</p> <p>More details of the simulations and on it cosmological parameter can be found at:</p> <ul> <li>https://ui.adsabs.harvard.edu/abs/2021Galax...9..109V/abstract</li> <li>https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.5350V/abstract</li> <li>https://ui.adsabs.harvard.edu/abs/2017CQGra..34w4001V/abstract</li> </ul>
Data and code for "Salomon et al. 2024: Effects of dissolved organic matter on the toxicity of micro- and nanoplastic particles to Daphnia - a meta-analysis."
<div> <p>All data and R code for</p> <p><strong>Salomon S, Grubmüller E, Kropf P, Nickl E, Rühl A, Weigel S, Becker F, Antonio Vital AL, Laforsch C, Schott M, Mair MM. (2024). Effects of dissolved organic matter on the toxicity of micro- and nanoplastic particles to <em>Daphnia</em> - a meta-analysis. <em>Microplastics and Nanoplastics</em>. (<a href="https://doi.org/10.1186/s43591-024-00088-4" target="_blank" rel="noopener">https://doi.org/10.1186/s43591-024-00088-4</a>)</strong></p> <p><em>Abstract</em></p> <p>Effects of micro- and nanoplastic particles (MNP) on organisms have been increasingly reported in recent years, with a large number of studies conducted on water fleas of the genus <em>Daphnia</em>. Most of the available studies used pristine particles that have not been exposed to the environment or to organic substances. In natural environments, however, organic substances like dissolved organic matter (DOM) attach to the MNP, forming an ecocorona on the particles’ surface. How the formation of an ecocorona influences MNP toxicity is still uncertain. While some studies suggest that DOM can mitigate the negative effects of MNP on organisms, other studies did not find such associations. In addition, it is unclear whether the DOM attached to the particles’ surface is attenuating the effects of MNP directly or whether co-exposure with DOM solved in the medium attenuates MNP toxicity indirectly, for instance by increasing Daphnia‘s resilience to stressors in general. To draw more solid conclusions about the direction and size of the mediating effect of DOM on MNP-associated immobilization in <em>Daphnia</em> spp., we synthesized evidence from the published literature and compiled 305 data points from 13 independent studies. The results of our meta-analysis show that the toxic effects of MNP are likely reduced in the presence of certain types of DOM in the exposure media. We found similar mediating effects when MNP were incubated in media containing DOM before the exposure experiments, although to a lesser extent. Future studies designed to disentangle the effects of DOM attached to the MNP from the general effects of DOM in the exposure medium will contribute to a deeper mechanistic understanding of MNP toxicity in nature and enhance the reliability of MNP risk assessment.</p> </div>
Figures -using Particle Swarm Optimization (PSO)-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>The performance of WML quantification is evaluated using clustering algorithms. When the<br> image is pre-processed, contrast of the image is enhanced. The resulting enhanced image is<br> clustered using the effective clustering algorithms. Figure 3 represents the input image for WML<br> detection. In order to increase robustness, the noisy medical image is pre-processed. Figure 4<br> depicts the pre-processed image. Bright contrast stretching, which is one of the image enhancement<br> (pre-processing) techniques is applied. After pre-processing the enhanced image is subjected to<br> clustering. Three clustering models are proposed to provide accurate results.</p> <p>All scans obtained from different image clustering models are manually ranked based on<br> values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and<br> GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO)<br> are applied on a dataset of 208 images and ranking is done in terms of under detected, over<br> detected, properly detected as shown in figure 8 and figure 9. The number of images detected<br> properly in GFCM is comparatively high than FCM and GPC.</p>
Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution - Part B - Particle Number Concentrations - Dataset
<p>This repository contains the data used for the analysis of the paper "Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution - Part B - Particle Number Concentrations (PNC)" which is under submission.</p> <p> </p> <p>The experimental conditions and the instruments used are detailed in Bulot, F.M.J.; Russell, H.S.; Rezaei, M.; Johnson, M.S.; Ossont, S.J.J.; Morris, A.K.R.; Basford, P.J.; Easton, N.H.C.; Foster, G.L.; Loxham, M.; Cox, S.J. Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution. <em>Sensors</em> <strong>2020</strong>, <em>20</em>, 2219. https://doi.org/10.3390/s20082219</p> <p>The files are available in .csv and in .rds (for R) formats. For details about the measurement equipment used<br> during this study, please refer to the methods section of the paper.</p> <p> </p> <p>sensors_raw.csv contains the following headers:</p> <ul> <li>Bin0 to Bin15: Alphasense OPC-R1 particle number concentrations for different size bins</li> <li>Bin[1-3-5-7]MToF: mean time of flight of particles within the corresponding size bins of the Alphasense OPC-R1</li> <li>Checksum: checksum of the Alphasense OPC-R1</li> <li>SFR: sample flow rate of the Alphasense OPC-R1</li> <li>Humidity: relative humidity measured by the Alphasense OPC-R1</li> <li>Temperature: temperature measured by the Alphasense OPC-R1</li> <li>SamplingPeriod: sampling period of the Alphasense OPC-R1</li> <li>gr03um, gr05um, gr10um, gr25um, gr50um, gr100um: PNC measured by the Plantower PMS5003</li> <li>n05, n1, n25, n4, n10: PNC measured by the Sensirion SPS30</li> <li>humidity: relative humidity measured by a Sensirion SHT-3x</li> <li>temperature: temperature measured by a Sensirion SHT-3x</li> <li>sensor: id of the sensors</li> <li>site: name of the air quality monitor hosting the sensors</li> <li>exp: name of the experiment conducted</li> <li>source: source used to generate PM (incense or candle)</li> <li>variation: whether the sensors were exposed to stable or peak concentrations of PM pollution</li> <li>date: date in format yyyy-mm-dd HH:MM:SS</li> </ul> <p>For more explanations about the fields of individual sensors, please refer to their manual (Alphasense OPC-R1: https://kolegite.com/EE_library/datasheets_and_manuals/sensors/OPC/072-0500_OPC-R1_manual_issue_1_250219.pdf ; Plantower PMS5003: https://www.aqmd.gov/docs/default-source/aq-spec/resources-page/plantower-pms5003-manual_v2-3.pdf ; Sensirion SPS30: https://sensirion.com/products/catalog/SPS30/)</p> <p> </p> <p>ops.csv and ops.rds contains the readings from the OPS with the following cut sizes for the bins:</p> <ul> <li>Bin 1 Cut Point (um),0.300</li> <li>Bin 2 Cut Point (um),0.374</li> <li>Bin 3 Cut Point (um),0.465</li> <li>Bin 4 Cut Point (um),0.579</li> <li>Bin 5 Cut Point (um),0.721</li> <li>Bin 6 Cut Point (um),0.897</li> <li>Bin 7 Cut Point (um),1.117</li> <li>Bin 8 Cut Point (um),1.391</li> <li>Bin 9 Cut Point (um),1.732</li> <li>Bin 10 Cut Point (um),2.156</li> <li>Bin 11 Cut Point (um),2.685</li> <li>Bin 12 Cut Point (um),3.343</li> <li>Bin 13 Cut Point (um),4.162</li> <li>Bin 14 Cut Point (um),5.182</li> <li>Bin 15 Cut Point (um),6.451</li> <li>Bin 16 Cut Point (um),8.031</li> <li>Bin 17 Cut Point (um),10.000</li> </ul> <p>nanotracer.csv and nanotracer.rds contain the measurements from the Nanotracer:</p> <ul> <li>N.1.: particles/cm3</li> <li>dp_avg.1.: mean diameter of the particles (nm)</li> <li>P.1.:</li> <li>S_al.1.: Lung Deposited Surface Area in um2/cm3</li> </ul> <p> </p> <p>experimental_conditions.csv and experimental_conditions.rds contain the end dates and start dates of each of the experiment conducted.</p> <p> </p> <p> </p> <p>"pm100_cf1","pm10_cf1","pm25_cf1"</p> <p> </p> <p> </p>
Madrid Hyperlocal Particle Matter Data
<p>This directory contains the csv files corresponding to the co-location field tests performed at three reference stations of the air quality network of the Madrid City Council, aiming to validate a new hyperlocal IoT particle matter device that integrates an innovative dryer to correct the error due to the hygroscopic growth in high relative humidity conditions. In concrete, three devices have been deployed in the Escuelas Aguirre, Cuatro Caminos and Mobile Unit during July/August 2021. Each csv has the following columns:</p> <p>- date: date of measurement</p> <p>- PM10_Reference: PM10 concentration according to reference station</p> <p>- PM2.5_Reference: PM2.5 concentration according to reference station</p> <p>- PM1_Reference: PM1 concentration according to reference station</p> <p>- TIME: date in currentmillis</p> <p>- PM1: PM1 concentration according to hyperlocal IoT device</p> <p>- PM2.5: PM2.5 concentration according to hyperlocal IoT device</p> <p>- PM10: PM10 concentration according to hyperlocal IoT device</p> <p>- TEMEPERATURE_INT: temperature inside the hyperlocal IoT device</p> <p>- HUMIDITY_INT: humidity inside the hyperlocal IoT device</p> <p>- TEMEPERATURE_AMB: ambient temperature</p> <p>- HUMIDITY_AMB: ambient humidity</p>
QCD Uncertainties in Particle Spectra from Dark Matter Annihilation (updated data can be found in GitHub: https://github.com/ajueid/qcd-dm.github.io.git)
<p>************************************************************************************************</p> <p>QCD Uncertainties on Particle Spectra from Dark Matter Annihilation</p> <p><strong>Please check the updated data at GitHub: https://github.com/ajueid/qcd-dm.github.io.git</strong></p> <p>Authors: Simone Amoroso, Sascha Caron, Adil Jueid, Roberto Ruiz de Austri, and Peter Skands</p> <p>If you use these tables, please cite:</p> <p>S. Amoroso et al. arXiv: 1812.07424 [hep-ph], JCAP05(2019)007</p> <p>************************************************************************************************</p> <p>We provide the spectra of stable particles in dark matter annihilation, in the galactic region or beyond, in a tabulated form using PYTHIA8 version 8235. In addition to the central prediction, we estimate for the first time the QCD uncertainties both due to hadronization as well as to showering. The uncertainties on the spectra are provided in separate tables. A wide range of dark matter masses from 10 GeV to 100 TeV is covered. We consider 11 primary annihilation channels:</p> <p>DM DM -> e+e-, mu+ mu-, tau tau, qq (q=u,d,s), cc, bb, tt, WW, ZZ, gg, and hh.</p> <p>Each file contains 13 columns: the dark matter mass, the fraction x -- defined as the kinetic energy of the particle divided by the DM mass -- in the logarithmic scale, and dN/dLog_10(x) for 11 primary channels. The provided tables correspond to the dN/dLog_10(x) of Standard Model stable particles, i.e. of photons, positrons, electron anti-neutrinos, muon anti-neutrinos and tau anti-neutrinos.</p> <p>The work on the spectra of anti-protons is ongoing (please come back soon). </p> <p>For each particle species, we provide twelve tables which can be found in zip format. The notation of the different tables is given below:</p> <p> 1) The table corresponding to the central prediction for the spectra is denoted by 'AtProduction-Hadronization1-$TYPE.dat' with $TYPE=Nuel, Numu, Nuta, Ga which refers to the three flavours of neutrinos, and photons respectively.</p> <p> 2) There are nine tables corresponding to the different variations of the light quark fragmentation function's parameters. These tables are denoted by 'AtProduction-Hadronization$h-$TYPE.dat' with h=2,..,10.</p> <p> 3) The particle spectra corresponding to the variations of the shower evolution scale (mu_R) are denoted by 'AtProduction-Shower-Var$s-$TYPE.dat' with s=1,2 corresponds to 1/2 mu_R and 2 mu_R. </p> <p><em><strong>IMPORTANT:</strong></em></p> <p> i) Uncertainty on the spectra, from hadronization, is obtained from the envelope of all the variations (including the central prediction).</p> <p> ii) In the variations of the parton shower evolution scale, the parameters of the hadronization function are fixed to their central value.</p> <p> iii) In principle, showering uncertainties are uncorrelated to hadronization uncertainties. To obtain the full uncertainty, one might combine those uncertainties in quadrature.</p> <p><em><strong>If you use the data on the site, please cite:</strong></em></p> <p>Simone Amoroso, Sascha Caron, Adil Jueid, Roberto Ruiz de Austri, Peter Skands, "Estimating QCD uncertainties in Monte Carlo event generators for gamma-ray dark matter searches," <strong>JCAP 05 (2019) 007</strong>, arXiv: 1812.07424.</p> <p><em><strong>In addition, if you use the data corresponding to shower uncertainties, please cite:</strong></em></p> <p>S. Mrenna and P. Skands, "Automated Parton-Shower Variations in Pythia 8,'' <strong>Phys. Rev. D 94 (2016) no.7</strong>, 074005, arXiv:1605.08352 [hep-ph].</p> <p><em><strong>Finally, please cite the paper of M. Cirelli et al. if you use their data for comparison or other tasks:</strong></em></p> <p>M.Cirelli, G.Corcella, A.Hektor, G.Hütsi, M.Kadastik, P.Panci, M.Raidal, F.Sala, A.Strumia, "PPPC 4 DM ID: A Poor Particle Physicist Cookbook for Dark Matter Indirect Detection'', <strong>JCAP 1103 (2011) 051</strong>, arXiv 1012.4515, Erratum: <strong>JCAP 1210 (2012) E01</strong>.</p> <p>Contact: <em>Adil Jueid</em> <adil.hep@gmail.com></p>
Datasets - Role of Secondary Organic Matter on Soot Particle Toxicity in Reconstituted Human Bronchial Epithelia Exposed at the Air–Liquid Interface
<p>Vuesz files corresponding to Figure2 and 3 of the following article: <a href="https://doi.org/10.1021/acs.est.2c03692">https://doi.org/10.1021/acs.est.2c03692</a>. Veusz is an open-source software which can be downloaded here: <a href="https://veusz.github.io/">Veusz – a scientific plotting package</a>.</p>
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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.