Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
247
datasets available to search
ShareScore release 0.9.0
Dataset results
247 results for “PM2.5”
Global surface O3, NO2, HCHO, and PM2.5 concentrations estimated from deep learning from 2019 to 2023
Open the record for dataset details and reuse information.
Future intensification of co-occurrences of heat, PM2.5 and O3 extremes in China and India despite stringent air pollution controls
<p>This dataset provides WRF-Chem simulation outputs of the manuscript titled "Future intensification of co-occurrences of heat, PM2.5 and O3 extremes in China and India despite stringent air pollution controls".</p>
Daily 1 km PM2.5 concentration distribution in China from 2016 to 2020
<p>Daily 1 km PM2.5 concentration distribution in China from 2016 to 2020.</p> <p>The dataset contains the daily 1 km PM2.5 dataset generated in the article <em>''PM2.5 estimation and its relationship with NO2 and SO2 in China from 2016 to 2020''</em> in the "<em>International Journal of Digital Earth</em>" journal.</p> <p>The file's name format is as follows: "pm25_date.tif" e.g., "pm25_20160101.tif" 20160101 represents 1 Jan 2016.<br>The format of the files is Geotiff.<br>The projection is WGS-84.<br>The spatial resolution is 0.01°.<br>The nodata value is -9999.<br>Due to the storage limit of the Zenodo platform, we convert the original Float data type of the files to Integer data type. If you need original files with Float data type, please get in touch with Huangyuan Tan (tanhuangyuan@whu.edu.cn).</p>
Europe SSP air quality PM2.5 (VD Scaled) O3 mortality output
Open the record for dataset details and reuse information.
PM2.5 Chemical characterisation campaign at Medellín from April 2019 to October 2022
<p>This dataset consists of a file "GHYGAM-Campaign_PM25-Chemistry_April2019-October2022_Medellin.xlsx", which contains data from the PM2.5 chemistry characterisation campaign conducted in Medellín at latitude 6.2372341 and longitude -75.610466 from April 2019 to October 2022 by GHYGAM (Grupo de Higiene y Gestión Ambiental - Politécnico Jaime Isaza Cadavid). The file includes the daily (noon-to-noon) concentration of each compound measured in ug/m^3 units, categorized by compound type as the sheet names:</p> <p>- <strong>PM2.5</strong>: this contains the concentration [ug/m^3] of PM2.5 measured by a Low-Vol and a High-Vol PM2.5 sampler. <br>- <strong>Minerals</strong>: this contains the concentration [ug/m^3] of Be, Na, Mg, Al, Si, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Ag, Cd, Sb, Ba, Hg, Pb and Minerals; this last is the total minerals concentration.<br>- <strong>Carbonaceous Matter</strong>: this contains the concentration [ug/m^3] of different species of Organic-Carbon (OC1, OC2, OC3, OC4, OC5) and Elemental-Carbon (EC1, EC2, EC3, EC4, EC5, EC6), pyrogenic Carbon (PyC), total Organic-Carbon (OC), total Elemental-Carbon (EC), total Carbon (C). This sheet also includes the ratio OC/EC, which is a non-dimensional index. <br>- <strong>Anions</strong>: this contains the concentration [ug/m^3] of F^-, Cl^-, NO3^-, SO4^2- and total anions as "Anions".<br>- <strong>Cations</strong>: this contains the concentration [ug/m^3] of K^+, Mg^2+, Ca^2+, Na^+ and total cations (Cations).<br>- <strong>Levoglucosan</strong>: Levoglucosan, Mannosan, Galactosan, Galactose, Glucose, Mannose, Glycerol, Inositol, Threitol, Mannitol, Arabinose, Xylose.</p> <p>The file also includes:</p> <p>- Calculated SOC: contains the concentration [ug/m^3] of the calculated secondary Organic Carbon (SOC). This is different from the other compounds calculated based on measured concentrations of OC and EC in this case. This sheet also contains SOC/OC, which is a non-dimensional index. <br>- DtE: provides information on the distance in days to various Long-range transport (LRT) air pollutants events caused by volcanic degassing, dust storms, and biomass-burning emissions</p> <p>For more detailed information about the campaign and the LRT events, please refer to (<a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-695/">https://doi.org/10.5194/egusphere-2024-695</a>; Velásquez-García et al., 2024)[in publication process]. For further questions, please contact Miriam Gómez-Marín at (mgomez@elpoli.edu.co or miryamgomezmarin@gmail.com)[campaign leader].</p> <p><strong>Financial support.</strong> This campaign has been supported by the Ministry of Science, Technology, and Innovation (grant no. 2020000100410); Ecopetrol (grant no. 3017481); the International Atomic Energy Agency (grant no. RLA7023); the United Nations (grant no. RLA7023) and the Área Metropolitana de Medellín y el Valle de Aburrá (grant nos. 734, 787, and 671). </p>
Data for manuscript ''The Impact of Ammonium on the Distillation of Organic Carbon in PM2.5''
<p>Data for manuscript ''The Impact of Ammonium on the Distillation of Organic Carbon in PM2.5'' submitted to Geophysical Research Letters</p>
Identifying contributors to PM2.5 simulation biases of chemical transport model using fully connected neural networks
<p>The processed data and codes in the study are included. </p> <p><strong>Source data:</strong></p> <p>The training and testing dataset is composed of observed and simulated data of pollutants and meteorology in the BTH and YRD regions in the whole year of 2015. The processed datasets used for training are named as "dataset_BTH" and "dataset_YRD" in the folder.</p> <ul> <li><em>The hourly observed pollution data</em> are from China National Urban Air Quality Real-time Release Platform of the National Environmental Monitoring Station</li> <li><em>The hourly simulated pollutants data</em> comes from the output of WRF-CMAQv5.2 (spatial resolution of 27 km).</li> <li><em>Meteorological observation data</em> is provided by China Meteorological Data Service Centre</li> <li><em>The meteorological simulation data</em> comes from the simulation results of the WRF model</li> </ul> <p><strong>Codes:</strong></p> <ul> <li>preprocessing of raw CMAQ data, observed pollution data and meteorological data</li> <li>bulid and train process of fully connected neural networks</li> <li>calculation of correlation between variables</li> <li>feature selection method</li> <li>contribution analysis</li> </ul>
AOD-dry PM2.5 Relationship with Consideration of Aerosol Type and Hygroscopicity
<p>The "dataset1_NGAI training" file is AERONET data for training NGAI index.</p> <p>The "dataset2_AOD_PM2.5" for raw relationships between AOD and PM2.5</p> <p>The "dataset2_AOD_PM2.5_RH" for improving the relationships through filtering aerosol types and RH consideration.</p> <p> </p>
COMPOSITIONAL SPATIO-TEMPORAL PM2.5 MODELLING IN WILDFIRES (R SCRIPT AND DATASET)
<p>The present R script and dataset were used in the assessment of a spatio-temporal PM<sub>2.5</sub> model in wildfire events with a limited number of monitoring stations using a compositional approach (CoDa). </p>
Strengthened PM2.5 air quality improvement and health benefits by synergies of carbon peak, carbon neutrality, and clean air policies in China
<p>Dataset and code used in this research: (1) emission, major air pollutants (i.e., SO2, NOx, PM25, NMVOCs, NH3), and CO2 emissions during 2020-2060 under the scenario ensembles (i.e., reference, clean air, on-time peak-clean air, on-time peak-net zero-clean air, early peak-net zero-clean air). (2) PM2.5 exposure (NetCDF, 0.1×0.1), future PM2.5 concentrations (2025, 2030, 2035, 2040, 2045, 2050, 2055, 2060) under the scenario ensembles, re-gridded from the corresponding CMAQ simulations. (3) population, future population grid under the SSP1 scenario, re-gridded from SSP Datasets (<a href="http://clima-dods.ictp.it/Users/fcolon_g/ISI-MIP/">http://clima</a><a href="http://clima-dods.ictp.it/Users/fcolon_g/ISI-MIP/">-</a><a href="http://clima-dods.ictp.it/Users/fcolon_g/ISI-MIP/">dods.ictp.it/Users/fcolon_g/ISI</a><a href="http://clima-dods.ictp.it/Users/fcolon_g/ISI-MIP/">-</a><a href="http://clima-dods.ictp.it/Users/fcolon_g/ISI-MIP/">MIP/</a>). (4) death, PM2.5-related premature deaths (2025, 2030, 2035, 2040, 2045, 2050, 2055, 2060) under the scenario ensembles. (5) code for premature death calculation, with the method of GBD2019. (6) code for re-grid PM2.5 concentrations from CMAQ output.</p>
Highly time−resolved PM2.5 data over Xi'an, China
<p>Chemical species of high−time resolution PM<sub>2.5</sub> collected during wintertime in Xi’an, China, a typical city in the northwest region, representative of relatively serious air pollution in China.</p>
Chemical compositions of PM1 and PM2.5 collected online at PKUERS site in Beijing during three seasons from 2016 to 2018
<p>This dataset provides the chemical composition of PM<sub>1</sub> and PM<sub>2.5 </sub>measured in urban Beijing. The sampling site is PKUERS which is located on the campus of Peking University. The dataset was collected by an online aerosol mass spectrometer covering three seasons (early autumn, winter, and spring) from 2016 to 2018.</p>
Seattle Children's Deidentified Health Outcomes with Residential PM2.5 Exposure (2006 - 2020)
<p>This dataset in .rda format contains all the health outcomes used in UW/Seattle Children's Hospital study that evaluated the association between wildfire smoke days and hospital encounters. The dataset includes deidentified health outcomes (categorized by APR-DRGs and ICD codes), clinical information regarding the encounter, patient residential county and zip code, and exposure variables (PM2.5 and Humidex) linked by patient residential zip code. </p>
Ukraine's PM2.5 in 2019
<p>Global (GL) Annual PM2.5 Grids from MODIS, MISR, and SeaWiFS Aerosol Optical Depth (AOD), v4.03 (1998 – 2019) is a dataset provided by NASA Socioeconomic Data and Applications Center (SEDAC) and is part of the SEDAC Environmental Indicators (SEI).</p>
Quantifying the contributions of atmospheric processes and meteorology to severe PM2.5 pollution episodes during the COVID-19 lockdown in the Beijing-Tianjin-Hebei, China
<p>Data</p>
Datasets for " Evaluation of the TOF-ACSM-CV for PM1.0 and PM2.5 measurements during the RITA-2021 field campaign"
<p>For the description of the dataset, please see the readme file included in the compressed file.</p>
High-time-resolution chemical composition and source apportionment of PM2.5 in northern Chinese cities: implications for policy
<p>Three real-time measurement campaigns were conducted in Xi’an, Shijiazhuang, and Beijing to investigate the chemical characteristics and source contributions of PM<sub>2.5</sub> and explore the formation progress of heavy pollution for policy implications. Chemeical components of PM<sub>2.5</sub> including OA, NO<sub>3</sub><sup>-</sup>, SO<sub>4</sub><sup>2-</sup>, NH<sub>4</sub><sup>+</sup>, Cl-, BC, and elements were monitored by Q-ACSM, AE33, and Xact, respectively. The results show that chemical compositions of PM<sub>2.5 </sub>in the three cities are all dominated by organic aerosol (OA) and nitrate (NO<sub>3</sub><sup>-</sup>). And results of source apportionment analyzed by the hybrid environmental receptor model (HERM) show that the secondary nitrate plus sulfate contributed higher to PM<sub>2.5</sub> compared to other primary sources. Morevoer, the potential formation mechanisms of secondary aerosol in three cities were further explored by establishing the correlations between secondary nitrate plus sulfate and aerosol liquid water content (ALWC), and O<em><sub>x</sub></em> (O<sub>3</sub> + NO<sub>2</sub>). The results show that photochemical oxidation and aqueous-phase reaction were two important pathways of secondary aerosol fromation. Additionally, this study compared the changes in chemical composition and source contributions of PM<sub>2.5</sub> in past decades, and suggested that not only the activities of clean energy replacements for the rural household is urgently enhanced to reduce the primary source emissions in northern China, but also collaborative control ozone and particulate matter need to be contiouslty promoted to weaken the atmosphere oxidation capacity for reducing secondary aerosol formation.</p>
In-vehicle exposure to NO2 and PM2.5: A comprehensive assessment of controlling parameters and reduction strategies to minimise personal exposure
<p>In-vehicle and on-road (ambient) PM2.5 measurements in different car cabins from Birmingham, UK. This dataset was used for the publication: "In-vehicle exposure to NO2 and PM2.5: A comprehensive assessment of controlling parameters and reduction strategies to minimise personal exposure, Science of The Total Environment, 2023,165537, <a href="https://doi.org/10.1016/j.scitotenv.2023.165537">https://doi.org/10.1016/j.scitotenv.2023.165537</a></p> <p>The NO2 data has been uploaded in a previous publication: "NO2 levels inside vehicle cabins with pollen and activated carbon filters: A real world targeted intervention to estimate NO2 exposure reduction potential" Science of The Total Environment, 2023, 860, 160395, <a href="https://doi.org/10.1016/j.scitotenv.2022.160395">https://doi.org/10.1016/j.scitotenv.2022.160395</a> and are available in zenodo <a href="https://doi.org/10.5281/zenodo.7388363">https://doi.org/10.5281/zenodo.7388363</a></p>
Chinese all-day hourly PM2.5 dataset in 2020
<p>Chinese all-day hourly PM2.5 dataset with a resolution of 0.1×0.1°;if need PM2.5 dataset in 2020 with a resolution of 0.01×0.01° (over 800 GB), please connect with Dr.Li (siwei.li@whu.edu.cn)</p>
Air Pollution (PM2.5) on Accelerated Atherosclerosis: A Montelukast Interventional Study in Modernizing China
ClinicalTrials.gov study NCT04762472. IPD Sharing: YES. Countries: 1. Publications: 16.
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.