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22 results for “Particle Number”
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>
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>
Number concentration and fluorescent class fraction of fluorescent and hyper-fluorescent aerosol particles measured during the Antarctic Circumnavigation Expedition
<p><strong>Dataset abstract</strong></p> <p>This dataset consists of a 5-minute time series of number concentrations of fluorescent and total aerosol particles that were measured by wideband integrated by aerosol sensor during the Antarctic Circumnavigation Expedition in the austral summer of 2016/2017. Furthermore, the dataset includes the fraction of fluorescent classes of aerosol particles, according to the ABC classification of Perring et al. (2015). The dataset provides information on fluorescent and hyper-fluorescent aerosols which were obtained by considering low and high fluorescence thresholds. For this dataset, aerosol particles that have optical diameter greater than 1 μm are considered. Since the ship’s exhaust could considerably affect the fluorescence properties of aerosol particles, in this dataset we removed the periods where it is likely that the samples were contaminated by the ship's exhaust.</p> <p><strong>Dataset contents</strong></p> <ul> <li>N_fluorescent.csv, data file, comma-separated values</li> <li>N_hyper_fluorescent.csv, data file, 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 number concentration and fluorescent class fraction of fluorescent and hyper-fluorescent aerosol particles 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>
Data for the 'Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation'
<p>All numerical data used in the manuscript <strong>“Evaluation of global simulations of aerosol particle number and cloud condensation nuclei, and implications for cloud droplet formation” </strong>by G. S. Fanourgakis et al. ACP (2019) are categorized and provided in a number of files. All files are in the hdf format. A readme file is also provided.</p> <p>These data files have been created by G. S. Fanourgakis (fanourg@uoc.gr)</p> <p>Details on the data are provided in Fanourgakis et al. Atmos. Chem. Phys. 2019 https://doi.org/10.5194/acp-2018-1340 (e-mail to <a href="mailto:mariak@uoc.gr">mariak@uoc.gr</a> ; <a href="mailto:athanasios.nenes@epfl.ch">athanasios.nenes@epfl.ch</a> )</p> <p>For an in-depth understanding of the description below, a study of the above mentioned manuscript is required.</p> <p>(A) Station model results</p> <p>The station results can be found in files with filenames of the form:</p> <p>station $MODEL.nc</p> <p>The “$MODEL” (as well as all names starting with “$”) indicates a variable, and more specifically one of the models participated in the present study. The values of this variable are tabulated in Table 1 in the readme file.</p> <p>In each file a number of computational results are provided by the specified model for all nine (9) stations that provided observational data. The name of the variable is formed as:</p> <p>st $STATION $FIELDhour st $STATION $FIELD month</p> <p>where all possible values of the variables $STATION and $FIELD are tabulated in Tables 2 and 3 in the readme file, respectively. The extension _hour denotes that hourly values for the field are provided, while the extension _month the monthly average of this quantity. For example, the variable</p> <p>st Finokalia CCN02 hour</p> <p>found in the file station_TM4-ECPL.nc, contains the hourly values of the CCN<sub>0<em>.</em>2 </sub>at the Finokalia station as computed by the TM4-ECPL model. In a similar way, in the file station_EMAC.nc, the variable below gives the monthly values of dust at Vavihill as computed with the EMAC model.</p> <p>st Vavihill DU month</p> <p>Notice also that in all files hourly and monthly data are provided for the time period from 1-1-2011 up to 31-12-2015 (60 months and 43,824 hours)</p> <p>(B) Station observational results</p> <p>There is one file that contains all observational data from Schmale et al., SCIENTIFIC DATA | 4:170003 | DOI: 10.1038/sdata.2017.3, 2017 (<a href="mailto:julia.schmale@psi.ch">julia.schmale@psi.ch</a>) and the data that were computed based on the observations (i.e. number of cloud droplets) (contact person: athanasios.nenes@epfl.ch). The file is</p> <p>station observations.nc</p> <p>while the following fields are contained in there:</p> <p>st $STATION $FIELDhour</p> <p>st $STATION $FIELD month</p> <p>The values of variables are given in the Tables 2 and 3 in the readme file. The time period covered is from 1-1-2011 up to 31-12-2015. Notice that due to the lack of observations a lot of data are missing. For missing observational data the value -9999.999 is given. Contact person for the observational data is Julia Schmale (julia.schmale@psi.ch).</p> <p>(C) Station Multi-model Median</p> <p>Monthly averages of the models can be found in the file</p> <p>station MMM.nc</p> <p>The following fields can be found in the file</p> <p>st $STATION $FIELD month median</p> <p>st $STATION$FIELD month quart25</p> <p>st $STATION$FIELD month quart75</p> <p>where the values of the variables $STATION and $FIELD can be found in Tables 2 and 3, respectively. The extension median corresponds to the multi-model median, while the quart25 and quart75 to the 25 % and 75 % quartiles, respectively.</p> <p>(D) Global model results</p> <p>In the following single file can be found for each of the models the surface distribution of various fields.</p> <p>results global models year2011.nc</p> <p>They correspond to the annual mean of the year 2011. The resolution of the grid is 1<sup>◦ </sup>× 1<sup>◦</sup>. The file contains the following variables:</p> <p>$FIELD $MODEL</p> <p>The $FIELD and $MODEL can be found in Tables 3 and 1, respectively.</p> <p>(E) Global average results</p> <p>In the file</p> <p>surface_ global_average_year2011.nc</p> <p>can be found in 5<sup>◦</sup>×5<sup>◦ </sup>resolution, the Multi-model median of surface distribution of the various fields denoted in Table 3 and their corresponding diversity. The names of the variables are formed as:</p> <p>med $FIELD</p> <p>div $FIELD</p> <p>where, ‘med’ stands for median and ‘div’ for diversity calculated as standard deviation divided by the mean of the model results.</p> <p>Tables and details on the fields provided are given in the readme file.</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>
Ultrafine particle number concentration observations perpendicular to a main street in Berlin, Germany, in summer 2017
<p>The data set was recorded in summer 2017 in Berlin, Germany. It was the basis of the paper:</p> <p>Fritz, S., Schubert, S., and Schneider, C. (2021). Measurements of spatial variability of sub-micron particle number concentrations perpendicular to a main road in a built-up area. metz. 30, 315–331. doi:10.1127/metz/2021/1058</p> <p>The measurement setup, study site, and devices used are described more detailed in the paper.</p> <p>The data sets uploaded include observations of particle number concentrations, wind direction and wind speed at ground level as well as traffic count data. The time stamp is in local time for Berlin/Germany.</p> <p>The data sets:</p> <p>coordinates.csv contains the coordinates of the measurement points used in file obs_pnc_wind. The column ‘distance’ links these two files. The values in the column ‘distance’ are the distance from the curb of the main road. The distance ‘-10’ represents the measurement point on the central strip of the main road. The two columns ‘latitude’ and ‘longitude’ provide the coordinates of the measurement points.</p> <p>obs_pnc_wind contains the observation data for particle number concentration (PNC), wind speed, and wind direction. PNC was recorded in #/cm³ with a TSI 3007 CPC at a 1-second resolution. Wind speed `ws’ [m/s] was recorded with a KESTREL 5000 with a 2-second resolution. Wind direction ‘wdir’ [°] was manually recorded as the most prominent wind direction within the 3-minute measurements per measurement point in angles of 10°. Wind direction was changed to NA (wdir_comment > 0), if it could not be determined either due to too calm wind (wdir_comment = 1) or with continuously changing wind direction (wdir_comment = 2). Along the measurement route, sometimes additional sources occurred and were recorded in the variable ‘source_type’ at the time they passed by the measurement device. Variable ‘source_comment’ provides additional information about ‘source_type’ where necessary.</p> <p>traffic_count contains the vehicles counted on the main street (Straße des 17. Juni) for each of the 72 runs. It was calculated as [vehicles/hour] based on 5-minute traffic counts. During the traffic counts, we differentiated between smaller (light duty) vehicles (<3.5 t, cars, vans, motorcycles, scooters) and bigger (heavy duty) vehicles (> 3.5 t, trucks and busses).</p>
Database used for the supervised learning of few dirty bosons with variable particle number
<p>This dataset includes files with points of different disordered potentials and their corresponding ground state energies depending on the number of particles of the system (N=1,2,3,4). It has been used to train a neural network. </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>
Particle number size distribution of fluorescent, hyper-fluorescent, and total aerosol particles measured during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>This dataset consists of a 5-minute time series of particle number concentration of fluorescent and total aerosol particles that were measured by wideband integrated by aerosol sensor during the Antarctic Circumnavigation Expedition during the austral summer of 2016/2017. For this dataset, aerosol particles within the optical diameter range of 0.5 μm to 14.5 μm were considered. In this dataset, the time periods when samples are likely contaminated by the ship’s exhaust were removed from the time series.</p> <p><strong>Dataset contents</strong></p> <ul> <li>PSD_fluorescent.csv , data file, comma-separated values</li> <li>PSD_hyper_fluorescent.csv , data file, comma-separated values</li> <li>PSD_hyper_tot.csv , data file, 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 particle number size distribution of fluorescent, hyper-fluorescent, and total aerosol particles 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>
Particle number concentrations and size distributions in the stratosphere: Implications of nucleation mechanisms and particle microphysics
<p>The data files of all figures for ACP-2022-487 entitled: "Particle number concentrations and size distributions in the stratosphere: Implications of nucleation mechanisms and particle microphysics"</p>
Particle number size distributions associated with secondary organics
<p>We provide the model data used to reproduce the figures in our manuscript titled "secondary organic aerosol formation regulates cloud condensation nuclei in the global remote troposphere". </p>
Dataset for "Aerosol mixing state, new particle formation, and cloud droplet number concentration in an urban environment "
<p>Inverted HTDMA data, parcel model input, and parcel model output associated with the manuscript. HTDMA data are from the TRACER campaign and inverted using the LSQ1 or LSQ2 method described in Petters (2021). Model input is a 4 lognormal aerosol mode representation of the aerosol size distribution during TRACER, obtained by fitting measured size distributions obtained from mobility-based and optical-based sensors. Hygroscopicity is assigned for each mode based on observed size-resolved hygroscopicity from the HTDMA measurements. Model output refers to parcel model simulations using the pyrcel model (Rothenberg and Iompar).</p> <p>Petters, M. D.: Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data, Atmos. Meas. Tech., 14, 7909–7928, https://doi.org/10.5194/amt-14-7909-2021, 2021. </p> <p>Rothenberg, D. and lompar: darothen/pyrcel: Pyrcel v1.3.2, , https://doi.org/10.5281/zenodo.8378595, 2023.563</p>
Dataset for hydrate particle number and area calculation, and hydrate coarsening videos
<p>There are three series (24 files) of data on day 4, 14, 24, 34, 44, 54, and 64:</p> <p>1. Hydrate particle number in open area (7 files):</p> <p>· 4 days-particle number.pptx</p> <p>· 14 days-particle number.pptx</p> <p>· 24 days-particle number.pptx</p> <p>· 34 days-particle number.pptx</p> <p>· 44 days-particle number.pptx</p> <p>· 54 days-particle number.pptx</p> <p>· 64 days-particle number.pptx</p> <p> </p> <p>2. Hydrate saturation calculation in porous media (15 files):</p> <p>· 4 days-area pixels.xlsx</p> <p>· 4 days-area pixels.pptx</p> <p>· 14 days-area pixels.xlsx</p> <p>· 14 days-area pixels.pptx</p> <p>· 24 days-area pixels.xlsx</p> <p>· 24 days-area pixels.pptx</p> <p>· 34 days-area pixels.xlsx</p> <p>· 34 days-area pixels.pptx</p> <p>· 44 days-area pixels.xlsx</p> <p>· 44 days-area pixels.pptx</p> <p>· 54 days-area pixels.xlsx</p> <p>· 54 days-area pixels.pptx</p> <p>· 64 days-area pixels.xlsx</p> <p>· 64 days-area pixels.pptx</p> <p>· Particle number and SH variation.xlsx</p> <p> </p> <p>3. Videos of the hydrate coarsening and sintering process (2 files):</p> <p>· Hydrate coarsening and sintering in open area.mp4</p> <p>· Hydrate coarsening and sintering in porous media.mp4</p>
Particle Number Size Distributions from Rochester NY 2002-2019
Open the record for dataset details and reuse information.
Snow particle number, aerosol concentration and 10 meter windspeed data from MOSAiC, N-ICE, Weddel Sea expeditions and chemistry transport model data (p-TOMCAT) .
<p>The folder contains data for the MOSAiC, N-ICE and Weddell sea expedition for Snow particle counter measurements. Coarse aerosol measurements from the MOSAiC expedition are also included. Simulation data from a chemistry transport model (p-TOMCAT) is also available. This version includes both .mat and .nc files</p>
Omega-3 to Reduce Diabetes Risk in Subjects With High Number of Particles That Carry "Bad Cholesterol" in the Blood
ClinicalTrials.gov study NCT04496154. IPD Sharing: NO. Countries: 0. Publications: 8.
The Effect of Vitamin D Repletion on Small Low Density Lipoprotein (LDL) Particle Number in Subjects at Elevated Cardiovascular Risk
ClinicalTrials.gov study NCT01008384. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Size-resolved particle number emissions in Beijing determined from measured particle size distributions
<p>The average daily cycle of size-resolved particle number emissions determined for the measurement station of Beijing University of Chemical Technology, Beijing, China. The average is calculated for January 2018 – March 2019. The details of the particle size distribution measurements and the method used for producing the emissions are presented by Kontkanen et al., Size-resolved particle number emissions in Beijing determined from measured particle size distributions, ACP, 2020.</p> <p>Data file contents:</p> <p>1st row contains particle diameter (unit:m).<br> 1st column contains time (unit:hour), and the following columns contain particle number emissions (unit: m<sup>-2</sup> s<sup>-1</sup>).</p> <p>For more details, contact: jenni.kontkanen@helsinki.fi</p> <p> </p>
Data of "JGR-Cluster analysis of submicron particle number size distribution at SORPES station in Yangtze River Delta of East China"
<p>Data of "JGR-Cluster analysis of submicron particle number size distribution at SORPES station in Yangtze River Delta of East China"</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.