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1,652 results for “particles”
Increased inflammation and oxidative stress caused by accumulated metal particle exposure among metro station staff, Tianjin, China, 2023
Metro is a significant part of world transport, delivering over 58 billion passengers annually. The dilution effect of particulate matter (PM) from natural ventilation was limited in underground metro stations. What's worse, train operation processes generated PM rich in heavy metals. Though PM pollution in metro stations was reported widely, there is limited evidence of the adverse health effect of metro station PM. This dataset collected urinary samples from 74 metro station staff from three different metro stations in Tianjin, China, for inflammation and oxidative stress biomarker tests to better understand the potential health effects induced by metal particulates in metro stations. Also, an indoor air quality survey was conducted simultaneously in the metro stations.
Particle concentration data from: Long-term measurement of sub-3nm particles and their precursor gases in the boreal forest
<p>The knowledge of the dynamics of sub-3nm particles in the atmosphere is crucial for our understanding of first steps of atmospheric new particle formation. Therefore, accurate and stable long-term measurements of the smallest atmospheric particles are needed. In this study, we analyzed over five years of particle concentrations in size classes 1.1–1.7 nm and 1.7–2.5 nm obtained with the Particle Size Magnifier (PSM) and three years of precursor vapor concentrations measured with the Chemical Ionization Atmospheric Pressure Interface Time-of-Flight mass spectrometer (CI-APi-ToF) at the SMEAR II station in Hyytiälä, Finland. The results show that the 1.1–1.7 nm particle concentrations have a daytime maximum during all seasons, which is due to increased photochemical activity. There are significant seasonal differences in median concentrations of 1.7–2.5 nm particles, underlining the different frequency of new particle formation between seasons. Aerosol precursor vapors have notable diurnal and seasonal differences as well. Sulfuric acid and highly oxygenated organic molecule (HOM) monomer concentrations have clear daytime maxima, while HOM dimers have their maxima during the night. HOM concentrations for both monomers and dimers are the highest during summer and the lowest during winter. Higher median concentrations during summer result from increased biogenic activity in the surrounding forest. Sulfuric acid concentrations are the highest during spring and summer, with autumn and winter concentrations being two to three times lower. A correlation analysis between the sub-3nm concentrations and aerosol precursor vapor concentrations indicates that HOMs, particularly their dimers, and sulfuric acid play a significant role in new particle formation in the boreal forest. Our analysis also suggests that there might be seasonal differences in new particle formation pathways that need to be investigated further. </p> <p> </p>
Properties and formaldehyde removal efficiency of biocarbon-MnO2 particles
<p>The dataset includes information about biocarbon particles doped with different concentrations of MnO2 photocatalyst (BC-MnO2)</p><p>Six different samples: MnO2, biocarbon, BC-MnO2-1, BC-MnO2-2, BC-MnO2-3, BC-MnO2-4</p><p>Characterization of the samples:</p><p>* SEM images collected using scanning electron microscope (Carl Zeiss SUPRA 35 VP)</p><p>* XRD data collected using Bruker D2 Phaser diffractometer</p><p>* Porosity data collected using physisorption analyzer (Autosorb iQ-XR-AG-AG). The dataset contains data about isotherms and pores size distributions from tests under nitrogen gas (meso and macro porosity) and CO2 gas (microporosity).</p><p>Formaldehyde removal potential of the samples: The raw data were generated from an electrochemical formaldehyde sensor (Stox-HCHO) at ambient conditions (temperature of 23 °C, relative humidity between 40% and 46%, and conventional visible light). The sensor was placed in a test chamber equipped with the sensor and 8 µl of formaldehyde (HCHO) solution was injected. Then the test chamber was hermetically closed and changes in formaldehyde levels were measured. The same sensor provided information about temperature and relative humidity in the test chamber. Data was aquired using TVOC-HCHO logger software. The formaldehyde removal efficiency (%) of the samples after 8h of experiment was determined from the raw data.</p>
Dataset for: Associating Mechano-electrochemical Phenomena to Stochastic Current Events in Micro-Electrochemical Cells Containing TiNb2O7 Particles
<p>This is the raw data used in a manuscript that will be submitted to ChemElectroChem. If you have any questions, please email the creators.</p>
Transparent exopolymer particle (TEP) and Coomassie stainable particle (CSP) data collected from the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>TEP are operationally defined as gel particles that are retained on 0.4 µm polycarbonate filters and stained with the cationic copper phthalocyanine dye Alcian Blue 8GX at pH 2.5. CSP are gel particles retained on 0.4 µm polycarbonate filters that are stained with a solution of Coomassie Brilliant Blue G (CCB) at pH 7.4. Seawater surface samples (5 m) were collected every 6 hours from the ship’s underway pump. In addition, vertical profiles (6 depths, generally from 5 to 100-150 m) were sampled from 19 CTD casts using a SBE 911 Plus attached to a rosette of 24 12-L PVC Niskin bottles. This dataset presents TEP and CSP from seawater samples collected from the ship’s underway pump and CTDs. Samples were collected around the Southern Ocean on the R/V Akademik Tryoshnikov in the austral summer of 2016/2017, as part of the Antarctic Circumnavigation Expedition (ACE).</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_seawater_csp.csv, data file, comma-separated values</li> <li>ace_seawater_tep.csv, data file, comma-separated values</li> <li>data_file_header_csp.txt, metadata, text</li> <li>data_file_header_tep.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This TEP and CSP dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Quality-checked horizontal particle flux data collected using a snow particle counter on board the R/V Akademik Tryoshnikov in the Southern Ocean during the austral summer of 2016/17 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Flux of particles (snow, rain and other particles including sea spray) were recorded passing through a photo-electric snow particle counter installed on board the R/V Akademik Tryoshnikov as part of the Antarctic Circumnavigation Expedition (ACE). Data were recorded from January to March 2017 in the Southern Ocean. Here we present the finalised, quality-checked, horizontal particle flux data where counts have been averaged over a one-minute period.</p> <p><strong>Dataset contents</strong></p> <ul> <li>SPC_HPF_windtrue_1min.csv, data file, comma-separated values</li> <li>SPC_HPF_windtrue_1min.png, metadata, portable network graphics</li> <li>SPC_HPF_windtrue_saveplot.py, script, Python code</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This quality-checked horizontal particle flux dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Intermediate processing stage of horizontal particle flux data collected using a snow particle counter on board the R/V Akademik Tryoshnikov in the Southern Ocean during the austral summer of 2016/17 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Flux of particles (snow, rain and other particles including sea spray) were recorded passing through a photo-electric snow particle counter installed on board the R/V Akademik Tryoshnikov as part of the Antarctic Circumnavigation Expedition (ACE). Data were recorded from January to March 2017 in the Southern Ocean. Here we present an intermediate step in data processing, with relative horizontal particle flux of particles with a size between 36 – 2000 μm averaged over one-minute periods. Data are presented in daily files.</p> <p><strong>Dataset contents</strong></p> <ul> <li>SPC_HPF_1min_YYYY_MM_DD.csv, data files, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This one-minute averaged horizontal particle flux dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Sub-micron aerosol particle size distribution collected in the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>The authors would highly appreciate to be contacted if the data is used for any purpose.</p> <p>We measured sub-micrometer aerosol particles with two scanning mobility particle spectrometers (SMPSs) between 11 and 400 nm (file name ACESPACE_submicron_aerosol_particle_size_distribution.csv) in 100 bins, and 11 and 181 nm in 77 bins - so no data entry in the remaining 23 bins - (ACESPACE_submicron_aerosol_particle_size_distribution_nano.csv) at a time resolution of five minutes during the Antarctic Circumnavigation Expedition (ACE). Particles in this size range are important for cloud formation because a sub-set of them can act as cloud condensation nuclei (CCN).</p> <p>The time series of the size distribution shows that the particle population over the Southern Ocean can be quite variable featuring three dominant modes: a new particle formation mode (11 – 30 nm); an Aitken mode (20 – 70 nm); and an accumulation mode (> 70 nm). Often a concentration minimum between the Aitken and accumulation mode can be observed. It is known as Hoppel minimum (Hoppel and Frick, 1990; 10.1016/0960-1686(90)90020-N). Typically, particles larger than this minimum act as CCN. The variability of the particle size spectrum is a result of particle sources and atmospheric processes. Sea spray generation adds larger particles likely with a peak in the mode around 200 nm. Trace gas emissions from microbial communities in the ocean, such as dimethylsulfide (DMS) will be oxidized to either sulphuric acid or methanesulfonic acid in the atmosphere which condense onto pre-existing particles, hence growing those. Sulphuric acid can also form new particles (new particle formation mode). Rain and snow will remove particles larger than the Hoppel minimum.</p> <p>The data set can be used to explore the variability of the particle size distribution in three different oceans around Antarctica (Indian, Pacific, Atlantic Oceans) and from Cape Town to Europe in relation to weather patterns, air mass trajectories, microbial activity etc. It is best used in combination with CCN data to explore the importance of particles for cloud formation. This data set cannot be used to unambiguously determine sources of particles over the southern ocean or to trace anthropogenic impact in the region.</p> <p>The data have been cleaned from the influence of the exhaust of the research vessel.</p> <p>We give five-minute average data as dN/dlog(dp), where dN is the particle number concentration per measured size bin normalized over the logarithm of the bin width. The bin width is defined as the distance between two diameters. They are spaced equally in log-space with dlog(dp) = log(d_n+1/d_n) = 1/64. To derive the total particle number concentration between 11 and 400 nm one has to integrate over the diameter range taking into account the normalization by dlog(dp).</p> <p>Temporal coverage is from December 20, 2016 to April 10, 2017. The file “ACESPACE_submicron_aerosol_particle_size_distribution.csv” covers the entire time period except between 9 and 14 January 2017 due to instrument issues. The file “ACESPACE_submicron_aerosol_particle_size_distribution_nano.csv” contains data for the period between 9 and 14 January 2017 and can be used to fill the above gap. The second data file stems from another SMPS with a smaller differential mobility analyser, hence the smaller diameter coverage.</p> <p><strong>Dataset contents</strong></p> <p>The data set contains two files with the size distribution of sub-micrometer aerosol particles. The rows are indexed by the time stamp, which is the end of the 5-minutes averaging interval. The columns are the normalized concentrations of particles in the respective size bin. See the data abstract for details.</p> <ul> <li>ACESPACE_submicron_aerosol_particle_size_distribution.csv, data file, comma-separated values</li> <li>ACESPACE_submicron_aerosol_particle_size_distribution_nano.csv, data file, comma-separated values</li> <li>ACESPACE_particle_diameter_bins.csv, metadata, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.md, metadata, text</li> </ul> <p>NaN values in a complete row denote missing values because of e.g., calibration periods, ship exhaust contamination, instrument failure. NaN values which appear individually or only in small groups reflect that data were below detection limit. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period.</p> <p><strong>Dataset license</strong></p> <p>This sub-micron aerosol particle size distribution dataset collected during ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Coarse mode aerosol particle size distribution collected in the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>The authors would highly appreciate to be contacted if the data is used for any purpose.</p> <p>We measured coarse mode aerosol particle size distributions with an aerodynamic particle sizer (APS, model TSI 3321) at a time resolution of five minutes during the Antarctic Circumnavigation Expedition (ACE). The diameter range is 0.7 to 19 µm. Particles in this size range are indicative of primary sea spray aerosol, biological particles and potentially long-range transported mineral dust. These particles are also important for cloud formation as they act as cloud condensation nuclei or ice nucleating particles, the latter especially in the case of biological particles and mineral dust.</p> <p>Typically the instrument reports data starting from particles with a diameter greater than 500 nm, however, particle number concentrations in the channels below 723 nm were overestimated, which is a common artefact with this instrument.</p> <p>The data have been cleaned from the influence of the exhaust of the research vessel. Temporal coverage is from December 20, 2016 to April 10, 2017. We give five-minute averaged data as dN/dlog(dp), where dN is the particle number concentration per measured size bin normalized over the logarithm of the bin width. The bin width is defined as the distance between two diameters. They are spaced equally in log-space with dlog(dp) = log(d_n+1/d_n) = 1/32. To derive the total particle number concentration one has to integrate over the diameter range taking into account the normalization by dlog(dp).</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_coarse_mode_aerosol_particle_size_distribution.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.md, metadata, text</li> </ul> <p>NaN values in a complete row denote missing values because of e.g., ship exhaust contamination, maintenance, instrument failure. NaN values which appear individually or only in small groups reflect that data were below detection limit. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period.</p> <p><strong>Dataset license</strong></p> <p>This coarse mode aerosol particle size distribution dataset collected during ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Aerosol particle number concentration measured over the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>The authors would highly appreciate to be contacted if the data is used for any purpose.</p> <p>We measured aerosol particle number concentration with a condensation particle counter CPC model TSI 3022 at a time resolution of 10 seconds during the Antarctic Circumnavigation Expedition (ACE). We report five-minute averaged data cleaned from exhaust gas influence. The lower cut-off of the CPC is 7 nm. Temporal coverage of the dataset is from December 20, 2016 to April 10, 2017.</p> <p>The total particle number concentration reflects aerosol particles from a variety of sources and processes. The concentrations include for example sea spray aerosol, long-range transported particles, newly formed particles and others. The variability in the concentration reflects processes such as wet removal through precipitation, new particle formation or sea spray formation.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_aerosol_particle_concentration.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.md, metadata, text</li> </ul> <p>NaN values of aerosol particle number concentration denote missing values because of e.g., ship exhaust contamination, maintenance, instrument failure. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period.</p> <p><strong>Dataset license</strong></p> <p>This aerosol particle number concentration dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Unveiling the potential of redox chemistry to form size tunable, high index silicon particles
<p>In the present work, the effect of changing the precursor ratio of silicon between sodium silicde and a hexacoordinated silicon complex to form various sizes of particles is studied. TEM images show the size difference between particles produced with different ratios. Particles produced with a 1:1 ratio are 45 nm in diameter and up to a 1:4 precursor ratio is used to make 230 nm particles. X-ray diffraction patterns confirm the presence of crystalline silicon for all sizes, while Raman spectroscopy shows how different degree of oxidation occurs thanks to different particle sizes, shifting the Raman peak. The surface chemistry is also studied to evidence the growth mechanism.</p>
Raw Data on Extracellular Particles in 613 Human and 163 Canine Diluted Plasma and Blood Samples Assessed by Interferometric Light Microscopy
<p><span>Extracellular nanoparticles (EPs) are cellular fragments. After being released in cell exterior, they become mediators of the cell-cell interaction. Their characterization in bodily fluids may reflect the clinical status of the organism. Here we present data on the number density <em>n</em> and hydrodynamic diameter <em>D</em><sub>h </sub>of EPs assessed directly in diluted plasma and blood by using a recently developed technique, Interferometric Light Microscopy (Romolo et al., 2022). The data are presented in the attached Table. </span></p> <p><span>We collected 613 blood and plasma samples from human patients with Inflammatory Bowel Disease (IBD) taken into tubes with trisodium citrate and ethylenediaminetetraacetic acid (EDTA) anticoagulants and 163 blood and plasma samples from canine patients with Brachycephalic Obstructive Airway Syndrome (BOAS). </span><span>The human study was conducted in accordance with the Declaration of Helsinki, and approved by the National Medical Ethics Committee of the Republic of Slovenia (0120-271/2022/4; KME 27 July 2022). All procedures in the animal study complied with the relevant Slovenian government regulations (Animal Protection Act, Official Gazette of the Republic of Slovenia, No. 43/2007). The animal study was approved by the Animals in Experiments Welfare Commission of the Veterinary Faculty, University of Ljubljana, approval number 18-3/2022-1. </span><span>Information regarding sample preparation is documented in the MIBlood-EV reports.</span></p> <div> <div> <div><span><a name="_msocom_1"></a></span></div> </div> </div>
Dissolved Cr concentration and stable isotope data presented in "Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ53Cr distributions in the ocean interior" (Janssen et al., 2021, EPSL).
<p>This dataset presents all of the dissolved Cr data included and discussed in “Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ<sup>53</sup>Cr distributions in the ocean interior” (Janssen et al., 2021, EPSL). Three primary datasets are included:</p> <ol> <li>Dissolved [Cr], [Cr(III)] and d53Cr in samples from shipboard particle regeneration incubations conducted in the subantarctic Southern Ocean.</li> <li>Dissolved [Cr] in porewater samples from a sediment core collected in the Tasman Sea in primarily calcareous sediments, along with [Cr] and δ<sup>53</sup>Cr in overlying bottom waters.</li> <li>3. A compilation of intermediate and deep water dissolved [Cr] and δ<sup>53</sup>Cr from seawater samples from the Southern, Pacific and Atlantic Oceans</li> </ol>
Raw Particle Number Size-Distribution Data of twin-DMPS equipped with two CPCs for nanoparticle detection for SMEAR II station, Hyytiälä, Finland, Spring 2017
<p>Raw size-Distribution data from twin-DMPS system (Aalto et al., 2001), where the nano-DMA (measuring up to 40 nm, short Hauke type DMA) is quipped with two detectors:<br> a TSI 3776 and a modified Airmodus A20 (Kangasluoma et al., 2015)</p> <p>Data acquired during in March-May 2017 at the SMEAR II station in Hyytiälä, Finland.<br> Data associated with the publication Stolzenburg, Laurila et al. (2023), Atmos. Meas. Techn., "Improved counting statistics of an ultrafine differential mobility particle size spectrometer system"</p> <p>Files DMYYDDMM_A20.Dat contain the raw DMPS data, with YYMMDD indicating the day of the measurement.<br> Data are provided alternating between data acquired with the nano-DMA and with the long-DMA, on a scan by scan basis.<br> First line of each scan cycle (for both DMAs) always indicates the start and end times of the voltage scan.<br> Second line gives the parameters related to the DMPS as given below:<br> (sheath flow in [l per min], aerosol flow in [l per min], DMA inner electrode diameter in [m], DMA outer electrode diameter in [m], DMA classification length in [m], other parameters)<br> Following lines give<br> (for long-DMA): set voltage at DMA [in V], concentration measured by TSI3772 in [per cm3]<br> (for nano_DMA): et voltage at DMA [in V], concentration measured by TSI 3776 in [per cm3], concentration measured by mod. Airmodus A20 in [per cm3]</p> <p>File dmps_data_format_specifier.text gives a conversion from voltage to diameter and indicates the measurement time at each voltage during the stepping of the DMPS.<br> Needs to be used to convert measured concentrations in counts per set-interval.</p> <p>Files GR_J_overview.xlsx gives size-distribution derived quantities during that campaign.<br> Header defines Date, Growth Rate and Formation Rate measured at different sizes [in nm] and by the two different CPCs connected to the nano-DMA.<br> Growth rates in [nm per h], formation rate in [per cm3 per s].</p> <p>Other data related to the campaign can be obtained from the corresponding author upon reasonable request.<br> juha.kangasluoma@helsinki.fi</p> <p>References:</p> <p>Stolzenburg, Laurila et al. "Improved counting statistics of an ultrafine differential mobility particle size spectrometer system",<br> Atmos. Meas. Techn., in press, 2023</p> <p>Aalto et al., "Physical characterization of aerosol particles during nucleation events",<br> Tellus B, vol. 53, pp. 344-358, 2001</p> <p>Kangasluoma et al., "Sub-3 nm Particle Detection with Commercial TSI 3772 and Airmodus A20 Fine Condensation Particle Counters",<br> Aerosol Sci. Techn., vol. 49, pp. 674-681, 2015</p>
Ice Nucleating Particle number concentration from low-volume sampling over the Southern Ocean during the austral summer of 2016/2017 on board the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract </strong></p> <p>Ice nucleating particles (INP) are a subclass of atmospheric aerosol particles, which can force heterogeneous freezing of cloud droplets at temperatures above -38 degrees C. In contrast, ice particles form from cloud droplets at temperatures below -38 degrees C due to homogeneous freezing, without INP. Due to their abundance, these particles can affect micro-physical properties of clouds, while acting as INP. During the Antarctic Circumnavigation Expedition (ACE) around the Southern Ocean, off-line filter sampling was performed. Filters were stored on the ship and analysed after the cruise at Leibniz-Institute for Tropospheric Research (TROPOS) concerning INP abundance. Here, we give INP number concentrations for sampling of 8 hour periods.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_ice_nucleating_particles_frozen_fraction_from_lowvolume_filters.csv, data file, comma-separated values</li> <li>ACESPACE_ice_nucleating_particles_number_concentration_from_lowvolume_filters.csv, data file, comma-separated values</li> <li>data_file_header_frozen_fraction.txt, metadata, text format</li> <li>data_file_header_number_concentration.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>change_log.txt, metadata, text format</li> </ul> <p><strong>Change log</strong></p> <p>v1.1 - data files updated</p> <ul> <li>change dataset title to reflect low-volume sampling method</li> <li>addition of INP number concentration data from different temperatures</li> <li>addition of fraction of frozen droplets data</li> <li>addition of field blank filter data</li> <li>create separate data_file_header files</li> <li>add change log</li> </ul> <p>v1.0 - initial release of dataset</p> <p> </p>
Data from: An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles
<p><strong> Data from: An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles</strong></p> <p>- Authors: Pedro Jimenez, Luis Chacon, Mario Merino</p> <p>- Contact email: pejimene@ing.uc3m.es</p> <p>- Date: 2024-02-09</p> <p>- Keywords: electric propulsion, plasma simulation, magnetic nozzles, implicit particle-in-cell (PIC)</p> <p>- Version: 1.2</p> <p>- Digital Object Identifier (DOI): 10.5281/zenodo.8081962</p> <p>- License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0">Open Data Commons Attribution License</a></p> <p><strong>Abstract</strong></p> <p>This dataset contains the data found in the plots of the journal article:</p> <p><a href="https://www.sciencedirect.com/science/article/pii/S0021999124000755?via%3Dihub">Pedro Jimenez, Luis Chacon, Mario Merino, "An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles"</a></p> <p>The data in this repository are the results of kinetic plasma simulations as described in the reference. For further information on the setup for the simulation please refer to the article.</p> <p><strong>Data Files</strong></p> <p>The data files are in .csv format. They were produced in Julia using <a href="http://csv.juliadata.org/stable/)">CSV.jl</a> and <a href="https://dataframes.juliadata.org/stable/">DataFrames.jl</a> libraries.</p> <p>The files are organised following the order of the figures in the article. All the plots are 1D series, the first column corresponding to the x-axis data. Y-axis data is presented in the following columns, the total number of additional columns is equal to the number of line series. The title of each series is found in the first row of the .csv files. Please find below some specificalities in certain figures:</p> <p>- The columns for the time evolution in <strong>fig6_left.csv</strong> and<strong> fig6_right.csv </strong>(corresponding to the actual left and right columns in the figure i.e. cases A and B) contain a field tag followed by the corresponding time step (e.g. phi_500).</p> <p>- Due to the different number of nodes, steady state fields for cases A and B are saved in <strong>fig8_a-f.csv</strong> while cases AF and BF are saved in <strong>fig8_a-f_fine.csv</strong>.</p> <p>The rest of the data files should be self descripting</p> <p><strong>Citation</strong></p> <p>Any works using this dataset or any part of it in any form shall cite it as follows:</p> <p>The prefered means of citation is to reference the publication asociated to the jounal article with DOI: <a href="https://doi.org/10.1016/j.jcp.2024.112826">10.1016/j.jcp.2024.112826</a></p> <p>The BibTex is also provided for the sake of convinience:</p> <pre>@article{jimenez2024implicit, title={An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles}, author={Jim{\'e}nez, Pedro and Chac{\'o}n, Luis and Merino, Mario}, journal={Journal of Computational Physics}, pages={112826}, year={2024}, publisher={Elsevier} }</pre> <p>Optionally the dataset can be cited by referencing the corresponding DOI:</p> <p><a href="https://doi.org/10.5281/zenodo.8081962">https://doi.org/10.5281/zenodo.8081962</a></p> <p><strong>Acknowledgments</strong></p> <p>This dataset was created by the [ERC-ZARATHUSTRA project](https://erc-zarathustra.uc3m.es/).</p> <p>The ERC-ZARATHUSTRA project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 950466).</p>
6D phase space of charged beam in particle accelerator
<p>The dataset is collected from HPSim (https://github.com/apphys/hpsim), an advanced, open-source tool developed at LANL, enables rapid, online simulations of multipleparticle beam dynamics is used to collect data. HPSim solves Vlasov-Maxwell equations to calculate the effects of external accelerating and focusing forces on the charged particle beam as well as space charge forces within the beam. To generate the dataset from HPSim, the RF set points (amplitude and phase) for the first four modules are randomly sampled from a uniform distribution keeping the rest of the set points of 44 modules at a mean value. Other beam and accelerator parameters, like the initial beam condition, are also set to constant realistic values. Using the RF set points as inputs to the simulation, HPSim provides a six-dimensional phase space of the charged particle beam in the form of 15 unique projections at each of the 48 accelerating section/modules of LANSCE linear accelerator. </p>
Experimental data for "Measurement Report: Influence of particle density on secondary ice production by graupel and ice pellet collisions"
<p>This dataset includes measurement data on secondary ice production due to bare graupel - bare graupel, and ice pellet - ice pellet collisions carried out in the Mainz Cold Room (M-CR) of the Johannes Gutenberg University of Mainz. </p>
Processed data and code for manuscript "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea"
<p>This repository contains the python code and processed data to reproduce analysis and figures from Rühs et al. (2024, Ocean Science): "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea".</p> <p>To reproduce the whole analysis, including the calculations of the trajectories, the following needs to be downloaded/included into a local working directory:</p> <ul> <li>the content of this repository in respective sub-directories, i.e. code (created and maintained at <a href="https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal">https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal</a>), data-proc, figs</li> <li>the original surface velocity data, to be downloaded here: <a href="https://zenodo.org/records/10879702">https://zenodo.org/records/10879702</a>, in an additional sub-directory named data-orig</li> </ul> <p>Additionally, the OceanParcels package, available via <a href="https://github.com/OceanParcels/parcels">https://github.com/OceanParcels/parcels</a> or <a href="https://anaconda.org/conda-forge/parcels">https://anaconda.org/conda-forge/parcels</a> needs to be installed in the python working environment. Then, the scripts in the code directory can be executed to re-run the trajectory simulations and analysis. Alternatively, the output in forms of figures and processed data can be accesed directly in the respective sub-directories.</p>
Size distribution of neutral and charged particles smaller than 42 nm measured over the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition (ACE).
<p>The size distribution of neutral and charged particles was measured using a neutral cluster and air ion spectrometer (NAIS) instrument. The concentration was corrected for diffusional losses in the inlet.</p> <p>The concentration and temporal dynamics of small particles is fundamental to characterize the first step of new particle formation (NPF) and growth. Moreover, naturally charged particles and ions can provide information about the role of ion induced nucleation. Newly formed particles can grow to larger sizes where they act as cloud condensation nuclei, directly affecting the Earth radiative budget and cloud properties.</p> <p>Measurements were performed on the upper deck of icebreaker Akademik Tryoshnikov along the track of the Antarctic Circumnavigation expedition. Temporal coverage is from January 22, 2017 to April 11, 2017. The concentration is reported as dN/dlog(Dp) per cubic centimetre, where Dp indicates the corresponding diameter size bin. Data were collected with one-second time resolution and averaged automatically by the acquisition software to 120 seconds before January 31 2017 and to 90 seconds after that date. The instrument was calibrated before the campaign by the manufacturer and periodically cleaned during the campaign (one time per leg).</p> <p>Pollution from the ship exhaust and other human activities (e.g. helicopter flights) was identified as described in Schmale et al., 2019 (<a href="https://doi.org/10.1175/BAMS-D-18-0187.1">https://doi.org/10.1175/BAMS-D-18-0187.1</a>) and a corresponding flag was associated to the data (with 1 meaning clean data and 0 polluted data).</p> <p> </p> <p>***** Dataset contents *****</p> <p>- 01_neutral_particles_size_distribution.csv, data file, comma-separated values</p> <p>- 02_negative_ions_size_distribution.csv, data file, comma-separated values</p> <p>- 03_positive_ions_size_distribution.csv, data file, comma-separated values</p> <p>- 04_neutral_particles_size_distribution_header.txt, metadata, text</p> <p>- 05_negative_ions_size_distribution_header.txt, metadata, text</p> <p>- 06_positive_ions_size_distribution_header.txt, metadata, text</p> <p>- README.txt, metadata, text</p> <p>Data that were missing or bad because of instrumental problems were simply removed from the file (no entry).</p>
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.