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.
1,342
datasets available to search
ShareScore release 0.9.0
Dataset results
1,342 results for “aerosol”
Data for paper "Stratocumulus adjustments to aerosol perturbations disentangled with a causal approach"
<p>Timeseries data used for the causal effect estimation of the paper "Stratocumulus adjustments to aerosol perturbations disentangled with a causal approach".</p> <p>This dataset contains several cloud parameters and meteorological co-variates corresponding to the evolution of the South-East Atlantic stratocumulus deck for the time period January 2016 to December 2017 and the spatial domain [lon1,lon2,lat1,lat2]=[0, 10, -20, -10]. </p> <p>The processing code used to generate the timeseries data, as well as the analysis code are uploaded separately on Zenodo. The input raw satellite and reanalysis data for the processing code are from EUMETSAT (Copyright (c) (2020) EUMETSAT), NASA and COPERNICUS data (generated using Copernicus Climate Change Service information [2022]). </p> <p> </p> <p>Citations for the raw data sources: </p> <p>Finkensieper, S., Meirink, J.-F., van Zadelhoff, G.-J., Hanschmann, T., Benas, N., Stengel, M., Fuchs, P., Hollmann, R., Kaiser, J., Werscheck, M.: CLAAS-2.1: CM SAF CLoud property dAtAset using SEVIRI - Edition 2.1. Satellite Application Facility on Climate Monitoring (2020). <a href="https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002_01">https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002_01</a></p> <p>Huffman, G.J., Stocker, E.F., Bolvin, D.T., Nelkin, E.J., Tan, J.: GPMIMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06. MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) (2019). <a href="https://doi.org/10.5067/GPM/IMERG/3B-HH/06">https://doi.org/10.5067/GPM/IMERG/3B-HH/06</a>.</p> <p>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horanyi, A., Munoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Th ́ebaut, J.-N.: ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (2018). <a href="https://doi.org/10.24381/cds.adbb2d47">https://doi.org/10.24381/cds.adbb2d47</a></p> <p>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horanyi, A., Munoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Th ́ebaut, J.-N.: ERA5 hourly data on pressure levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (2018). <a href="https://doi.org/10.24381/cds.bd0915c6">https://doi.org/10.24381/cds.bd0915c6</a></p>
A Pan-European, Quantile Machine learning (QML) based, Total, Fine-Mode and Coarse-Mode Aerosol Optical Depth dataset (QML AOD))
<p>The V 1.1.0 product is an improved Aerosol Optical Depth (AOD) product based on Gap-filled MAIAC AOD, which provide first full-coverage, high-resolution monitoring of fine-mode and coarse-mode aerosols in Europe from 2003-20. This dataset has successfully rectified the previously identified issue of weak associations between satellite AOD and PM2.5 in Europe, which was primarily attributable to current limitations of AOD data. Our innovative approach has yielded stronger correlations with PM10, PM2.5, and PMcoarse than previous AOD product, laying a critical groundwork for improving PM10, PM2.5, and PMcoarse predictions in further epidemiological studies or environmental monitoring.</p> <p>We have uploaded three QML AOD datasets in Geotiff format, covering the region from -27° to 72° latitude and from -25° to 45° longitude. These datasets will be useful for researchers and policymakers to better understand the impacts of aerosols on the environment and human health.</p> <p> Note: v1.0.0 product do not include MAIAC AOD in their models.</p> <p>Please read more details in our paper </p> <h1><span>Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments</span></h1> <p><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.scitotenv.2024.170593" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.scitotenv.2024.170593</span></a></p>
Output data for "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 2: strong surfactants" by Vepsäläinen et al. (2023)
<p>Output data of the models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 2: strong surfactants" by Vepsäläinen et al. (2023).</p><p>Output data is included for 50 nm particles containing sodium myristate (c14na) and myristic acid (myristica), mixed with NaCl (nacl) in different surfactant mass fractions. Data about the critical points is also included for particles containing sodium myristate for particle size range 50-200 nm.</p><p>Plotters have been provided for the following:</p><ul><li>Part2_plotter_50_200_nm: Plots the critical supersaturations, diameters, and the relative change in cloud droplet concentrations for dry particles with 50-200 nm diameters containing c14na</li><li>Part2_plotter_50nm: Plots the Köhler curves, surface tension and partitioning factors for 50 nm particles containing c14na</li><li>Part2_plotter_50nm_myristica: Plots the Köhler curves and surface tensions for 50 nm particles containing myristica and also plots c14na for comparison (separate output files for the compounds and c14na data here is different than for the Part2_plotter_50nm plotter)</li></ul><p>Each plotter needs the user to set the location where the output files are stored. </p><p>In addition, a function is included:</p><ul><li>relative_change_in_cloud_droplet_number_conc: This function is called in "Part2_plotter_50_200_nm" and calculates the relative change in cloud droplet number concentration from the critical supersaturations.</li></ul>
Data used to create figures in the ACP Letters manuscipt "The value of remote marine aerosol measurements for constraining radiative forcing uncertainty" by Regayre et al. (2020)
<p>This dataset was created from perturbed parameter ensembles (PPEs) using the HadGEM-UKCA atmospheric composition climate model. All data needed to reproduce figures in the Regayre et al. (2020) ACP Letters article "The value of remote marine aerosol measurements for constraining radiative forcing uncertainty" are included. Other output from the PPEs can be obtained by contacting the lead author.</p> <p>The following data are included here:</p> <ul> <li>CCN measurement data degraded to match the model-measurement comparison resolution.</li> <li>Unconstrained and constrained CCN<sub>0.2</sub> output from the PPE used to make Figure 1. These compressed files contain 48 .dat files. Each .dat file contains the PPE mean, variance and 95% creidble interval data. Files are named consecutively, containing data from 90<sup>o</sup>S to 90<sup>o</sup>N at 0<sup>o</sup>E, then continuing Eastward. When combined, these files provide data for each latitude/longitude pair at the N48 spatial resolution.</li> <li>A zip file of an netcdf file containing 26-dimensional data for parameter values, used to create the sample of 1 million model variants from our statistical emulators of model output.</li> <li>A zip file containing a folder of files made of one million ones and zeros that indicate the retention/rejection criteria from applying our constraint methodology for various constraint combination scenarios, for each model variant. A value of 1 indicates the model variant was retained. Data in these files is in the same order as the unconstrained sample file of parameter values.</li> <li>Compressed files containing global, annual mean RF<sub>aci</sub> and ERF<sub>aci</sub> values for the unconstrained set of one million model variants. The compressed netcdf files contain RF (ERF), RF<sub>aci</sub> (ERF<sub>aci</sub>) and RF<sub>ari</sub> (ERF<sub>ari</sub>) values.</li> </ul>
Data to: Aerosol emission is increased in professional singing
<p>This dataset contains raw data of emitted aerosols measured via a laser particle counter. Further, R-code for statistical analyses is available. The pre-print of an article based on these data was deposited here:</p> <p>https://depositonce.tu-berlin.de/handle/11303/11491</p>
OMPS-NPP L2 LP USask Aerosol Extinction Vertical Profile swath daily V1.1
<p>The USask OMPS-LP L2 2D Aerosol v1.1 product provides stratospheric aerosol extinction retrievals performed at the University of Saskatchewan for the central slit of the Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) instrument on the Suomi-NPP satellite. The two-dimensional retrieval algorithm accounts for variation in the along orbital track dimension, retrieving an entire orbit simultaneously instead of treating each image independently. Stratospheric aerosol is retrieved from approximately the thermal tropopause to 30 km on a 1 km grid with a vertical resolution of approximately 2 km, and is assumed to be sulfate aerosol following a log-normal particle size distribution.</p> <p>Each granule contains data from the daylight portion of each orbit measured for a full month. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 160 profiles with an along orbital track sampling of 125 km. The files are written using NetCDF4.</p>
Field measurements of aerosol particles near a runway at Narita International Airport, Japan
<p>Field measurements of aerosol particles were conducted at an observation point ~180 m from the centerline of runway A (~140 m from the edge of the runway) at Narita International Airport (NRT), Japan, in February 2018. The online aerosol instruments used for the field measurements consisted of an ultrafine condensation particle counter (UCPC; Model 3776, TSI, d<sub>50</sub> = 2.5 nm), a condensation particle counter (CPC; Model 3771, TSI, d<sub>50</sub> = 10 nm), a scanning mobility particle sizer (SMPS; Model 3080, TSI), and an engine exhaust particle sizer (EEPS; TSI). The other instruments included a carbon dioxide (CO<sub>2</sub>) monitor (Model LI-840, Li-Cor Biosciences) and meteorological sensors. The sampling inlet for the UCPC, CPC, and SMPS was switched between an unheated (room temperature) mode and a 350°C heated mode every eight hours during selected time periods to measure the total and the non-volatile particles, respectively. The EEPS was operated independently from the UCPC/CPC/SMPS inlet system and it measured the unheated particle number size distributions during the entire period.</p>
Data availability for "Photolytic Radical Persistence due to Anoxia in 1 Viscous Aerosol Particles"
<p>Data availability for the paper titled "Photolytic Radical Persistence due to Anoxia in 1 Viscous Aerosol Particles" by Peter A. Alpert et al. This repository contains all data tables and files necessary to reproduce plots. Also included are open source ".hdf5” files that contain all data for X-ray microscopy images and ".dat" files having the raw data for mie resonance scattering to derive size change and mass loss. Please see the "Readme.pdf" file for more information.</p>
Datasets for: AERO-MAP: A data compilation and modelling approach to understand the fine and coarse mode aerosol composition
<p>This repository contains the data compilation, gridded datasets, model output, model source code changes and model inputs for the paper: “AERO-MAP: A data compilation and modelling approach to understand the fine and coarse mode aerosol composition “.</p> <p>The only change from the December 20, 2024 version is that a new variable "Distinct" is added which indicates whether the dataset is also included in the GHOST dataset by Bowdalo et al., 2024: https://essd.copernicus.org/articles/16/4417/2024/essd-16-4417-2024.pdf. All PM2.5 and PM10 datasets from GHOST are included in this dataset, but GHOST will be regularly updated.</p> <p> </p> <p>There are two subdirectories as tar files:</p> <p>collectoutputfiles.zip: which contains the detailed data descriptions in a csv files, gridded data in netcdf and model output in netcdf format. More details in the README file in that zipped directory.</p> <p>modelfiles.zip: which contains the Source code changes and input files needed to reproduce the simulations in the paper. More details in the README file in that zipped directory.</p>
Optical properties of marine aerosols with varying water content at wavelengths 532 and 1064 nm, modelled with a morphologically realistic aerosol model
<p>The data contain computational results obtained with the ADDA program at wavelengths 532 nm and 1064 nm, for particle sizes 0.04, 0.06, ..., 1.5 micrometers (where size = volume-equivalent dry radius), and for salt mass fractions 0.91, 0.94, 0.97, 1.00. The content of the data files is described in the README file.</p>
Dataset for "Heatwave reveals potential for enhanced aerosol formation in Siberian boreal forest"
<p>This dataset supplements the manuscript "Heatwave reveals potential for enhanced aerosol formation in Siberian boreal forest", Environmental Research Letters, 2023. </p>
Dataset for "Reduced ice loss from Greenland under stratospheric aerosol injection"
<p>Dataset for the paper "Reduced ice loss from Greenland under stratospheric aerosol injection"<br>(<em>Journal of Geophysical Research: Earth Surface</em>, 128 (11), e2023JF007112, <a href="https://doi.org/10.1029/2023JF007112">doi: 10.1029/2023JF007112</a>).</p> <p>Please see the README for details.</p> <p>V1.1.1: README and metadata updated.<br>V1.1: Scripts related to the ISIMIP-method downscaling, SEMIC code, as well as configuration and input files for SICOPOLIS and Elmer/Ice added.<br>V1: Results of new simulations that include both atmospheric and oceanic forcing.<br>V0.9.1: Crucial bug fix in the files ElmerIce_MIROC-ESM-CHEM-{RCP85,RCP45,G4}_2D_final.nc (those in V0.9 were faulty).<br>V0.9: Scalar variables: now distinguished between state and flux variables. 2D variables added.<br>V0.5: Scalar variables as functions of time.</p> <p>* * * * * * *</p> <p>Users should cite the original publication when using all or parts of these data.</p>
Meteograms of Ny-Ålesund for ICON-LEM default aerosols simulations
<p>This data contains the simulation data as meteogram from ICON-LEM simulations with ca. 600 m resolution. The output location is Ny-Ålesund. The data is for the months Aug and Oct 2021. This data was used in the PhD thesis of Theresa Kiszler. Thesis title: "Improving our understanding of cloud phase-partitioning using long-term cloud-resolving simulations of Svalbard".</p> <p>The simulation setup is is described in the method section of the paper "A Performance Baseline for the Representation of Clouds and Humidity in Cloud-Resolving ICON-LEM Simulations in the Arctic" by Kiszler et al. (2023). <a href="https://doi.org/10.1029/2022MS003299">https://doi.org/10.1029/2022MS003299</a></p>
Dataset 1 for Publication: Separation-dependent near-field effects in Mie scattering spectra of two optically trapped aerosol droplets
<p>Dataset for Publication: ASCII files of Mie spectra for each experimentally analysed run, calibrated wavelength files, and brightfield images at each interdroplet separation.</p>
Datasets for paper "Evaluating the PurpleAir monitor as an aerosol light scattering instrument"
<p>The data sets included will allow the user to reproduce the plots and analyses described in Ouimette et al. (2022). The Collocated*csv file contains data from multiple collocated PurpleAirs that sampled for a few days. The data in this file was used in the precision analysis in section 2.2.9 of the paper. The other files contain nephelometer and PurpleAir data from Mauna Loa (MLO) and Table Mountain (BOS) and DMPS size distribution files from BOS. Their contents are described in the README.TXT file.</p>
Influence of biogenic emissions from boreal forests on aerosol-cloud interactions
<p>Datasets that support the major results of the study "Influence of biogenic emissions from boreal forests on aerosol-cloud interactions".</p> <p>Acknowledgements: </p> <p>The work was supported by Academy of Finland via Center of Excellence in Atmospheric Sciences (project no. 272041), Flagship program for Atmospheric and Climate Competence Center (ACCC, 337549, 337552, 337550) and grants 317380, 320094 and 334792, 328290, 302958, 1325656, 316114, 325647, 1325681 and 341271, European Research Council Advanced Grants (227463-ATMNUCLE, 742206-ATM-GTP,) and Starting Grants (638703-COALA, 714621-GASPARCON), the Arena for the gap<br> analysis of the existing Arctic Science Co-Operations (AASCO) funded by Prince Albert Foundation Contract No 2859, and “Quantifying carbon sink, CarbonSink+ and their interaction with air quality” INAR project funded by Jane and Aatos Erkko Foundation. This work was partly supported by the Office of Science (BER), U.S. Department of Energy via BAECC<br> (Petäjä, DE-SC0010711), BAECC-SNEX (Moisseev), European Commission via projects This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 821205 (Understanding and reducing the long-standing uncertainty in anthropogenic aerosol radiative forcing, FORCeS) and ACTRIS, ACTRIS-TNA,<br> ACTRIS2, ACTRIS-IMP, BACCHUS, eLTER, ICOS, PEGASOS and Nordforsk via Cryosphere-Atmosphere Interactions in a Changing Arctic Climate, CRAICC, The BAECC SNEX was also supported by NASA Global Precipitation Measurement (GPM) Mission ground validation program. The deployment of AMF2 to Hyytiälä was enabled and supported by ARM. Argonne National<br> Laboratory's work was supported by the U.S. Department of Energy, Assistant Secretary for Environmental Management, Office of Science and Technology, under contract DE-AC02-06CH11357. The authors gratefully acknowledge the support of AMF2, SMEAR2 and the BAECC community for their support in initiating the BAECC campaign, its implementation,<br> operation, data analysis and interpretation. </p>
Output data of the models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 1: moderately surface active organics" by Vepsäläinen et al. (2022)
<p>Output data of the different models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 1: moderately surface active organics" by Vepsäläinen et al. (2022).</p> <p>Output data is included for 50 nm particles containing malonic acid (mna), succinic acid (sca) and glutaric acid (glutarica), mixed with ammonium sulphate (AS) in different organic mass fractions. </p> <p>A plotter that allows the user to plot the Köhler curves, surface tensions and organic<br> partitioning factors during droplet growth from the model output data provided is included. </p>
Long-term measurements of aerosol precursor concentrations in the Finnish sub-Arctic boreal forest
<p>This data set is connected to the article: </p> <p>Jokinen, T., Lehtipalo, K., Thakur, R. C., Ylivinkka, I., Neitola, K., Sarnela, N., Laitinen, T., Kulmala, M., Petäjä, T., and Sipilä, M.: Measurement report: Long-term measurements of aerosol precursor concentrations in the Finnish sub-Arctic boreal forest, Atmos. Chem. Phys., 2022</p>
Machine-Learning of Aerosol-Cloud-Climate Interactions Reveals an increased Cloud Fraction
<p>Data presented in the manuscript "Machine-Learning of Aerosol-Cloud-Climate Interactions Reveals an increased Cloud Fraction" by Chen et al. (2022).</p>
Data used to create figures and tables in the ACP manuscript "Two-way coupled meteorology and air quality models in Asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality" by Gao et al. (2022)
<p>This dataset contains the original data that extracted from all collected papers refering applications of two-way coupled models in Asia. It is supplied to the review paper, which titled as "Review on two-way coupled meteorology and air quality models in Asia: impacts of aerosol feedbacks on meteorology and air quality". The dataset includes three excel files (in the format of xlsx) as follows:</p> <p>1. Basic information of literatures (Table S1.xlsx)</p> <p>2. Model performance metrics (Table S2.xlsx)</p> <p>3. Quantitative results of aerosol effects on meteorological and air quality variables (Table S3.xlsx)</p> <p>4. Basic information of model setup for two-way coupled model applications in Asia (Table S4.xlsx)</p> <p>5. Summary of aerosol-induced variations of simulated shortwave and longwave radiative forcing at the bottom and top of atmosphere and in the atmosphere in Asia (Table S5.xlsx)</p> <p>.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.