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.
1,574
datasets available to search
ShareScore release 0.9.0
Dataset results
1,574 results for “atmospheres”
The Tracing Convective Momentum Transport in Complex Cloudy Atmospheres Experiment - Level 1
<p>The first field campaign from the Tracing Convective Momentum Transport in Complex Cloudy Atmospheres experiment project (CMTRACE) took place in Cabauw, the Netherlands, between September 13th and October 3rd 2021. During this field campaign, two cloud radars and one wind lidar were operated with a similar scanning strategy for deriving wind speed and direction profiles from near the surface up to cloud tops. Here we provide the daily Level 1 data from each instrument. At this level, several processing steps were applied to the raw data to minimize offsets, reduce the number of spurious data and derive wind speed and direction profiles; however, the data from each instrument is kept on its original spatial and temporal resolution. The raw data is available for the users on request from the corresponding author.</p> <p><strong>Prefix identificaiton:</strong></p> <p>Lidar data: cmtrace_cabauw_wls200-218<br> Scanning radar data: cmtrace_cabauw_rpg_radar_35-94<br> Vertically pointing radar data: cmtrace_cabauw_rpg_radar_94</p> <p> </p>
Datasets for investigation of the impact of afforestation on the atmospheric water cycle over the Loess Plateau
<p>These datasets are the processed and refined data that support and lead to the described results and allow other readers to assess the conclusions in the paper, entitled “Large-scale Afforestation Enhances Precipitation by Intensifying the Atmospheric Water Cycle over the Chinese Loess Plateau”.</p>
Data used in: 'Atmospheric impacts of chlorinated very short-lived substances over the recent past – Part 1: Stratospheric chlorine budget and the role of transport' by Bednarz et al. (2022)
<p>Data used in: 'Atmospheric impacts of chlorinated very short-lived substances over the recent past – Part 1: Stratospheric chlorine budget and the role of transport' by Bednarz et al. (2022), which has been accepted for publication in Atmospheric Chemistry and Physics.</p> <p> </p>
Influence of Large-scale Land-sea Atmosphere Interaction on Ozone Pollution in Coastal Cities in the Northern Bohai Sea
<p><strong>O3_obs </strong>includes ozone observations for Qinhuangdao (QHD), Jinzhou (JZ), Yingkou (YK), Dalian (DL) from 29 August to 5 September 2017, and the information of four sites including station code, longitude and latitude. <strong>O3_sim</strong> includes ozone simulation in the four sites extracted according to location of them. <strong>Met_obs</strong> and <strong>Met_sim</strong> include the observations of 2 m temperature (℃), 2 m relative humidity (RH2) and 10 m wind speed for the 4 stations from 29 August to 5 September 2017, and the information of four stations including station code and their location. <strong>Slp_wind_9km.nc</strong> is mean sea-level pressure and wind in Phase Ⅰ and Phase Ⅱ. <strong>O3_wind_9km.nc</strong> is mean simulated surface ozone mixing ratios and wind at 10 m in 19:00-09:00 LT and 10:00-18:00 LT during Phase Ⅰ and Phase Ⅱ. <strong>Process_contribution </strong>includes mean surface O<sub>3</sub> mixing ratios and O<sub>3</sub> contribution at the bottom level in Phase Ⅰ, Phase Ⅱ, and at different heights (AGL) in Phase Ⅱ in four sites, respectively. <strong>O3_source_site</strong> includes time series of O<sub>3 </sub>source in QHD, JZ, YK, and DL. <strong>Mean_source_base_27km.nc </strong>is the mean O­<sub>3</sub> contribution in Phase Ⅰ and Phase Ⅱ from five primary exogenous source regions. <strong>Mean_source_control_27km.nc</strong> is the O<sub>3</sub> contribution in Phase Ⅱ from the BTH and NEC emissions in Phase I, in which BTH and NEC’s emissions in Phase Ⅱ are set zero. <strong>Trjectory_conc_pa</strong> includes three trajectories analyzed in this work and vertical O<sub>3</sub> and NO<sub>X</sub> mixing ratios, and the chemical generations and consumptions of O<sub>3</sub> within the air masses along the trajectories.</p>
Generators of Architectural Atmosphere Symposium
<p>This dataset is an output of the ‘Generators of Architectural Atmosphere’ Symposium, an Interfaces event of the Academy of Neuroscience for Architecture (ANFA), sponsored by the EU’s Horizon 2020 MSCA Program — RESONANCES Project, the Perkins Eastman Studio, and the 2020 Regnier Chair. The symposium was hosted in the College of Architecture, Planning and Design (APDesign), Kansas State University, Manhattan (Kansas, USA), on April 12, 2022. Speakers: Bob Condia (Kansas State University), Elisabetta Canepa (University of Genoa and Kansas State University), Kutay Güler (Kansas State University), and Tiziana Proietti (Oklahoma University).</p> <p><br> Recent advances in science confirm many of the architect’s expert intuitions opening new doors to the perception of space and the meaning of architectural and urban design. The symposium ‘Generators of Architectural Atmosphere’ presented to an audience of students, educators, architects, and scientists a conversation about human perception of design and building, specifically speaking to the significance of atmosphere, mood, architectural proportion, and virtual reality.</p> <p><br> This dataset is made of six files:<br> no. 1 dataset summary (.pdf)<br> no. 1 symposium poster (.pdf)<br> no. 4 videos containing speakers’ presentations (.mp4).</p> <p><br> Recorded videos of each lecture are also available on the RESONANCES project website (www.resonances-project.com/harvest) and its YouTube channel (UCk32skDiT4Bz1AHnltT51Yg).</p>
Dataset for the ``Fast atmospheric response to a cold oceanic mesoscale patch in the north-western tropical Atlantic" publication
<p>The dataset presented here contains the files needed to produce the results presented in the publication "Fast atmospheric response to a SST mesoscale cold patch in the north-western subtropical Atlantic" submitted to the <em>Journal of Geophysical Research: Atmospheres</em>. The scripts that read and produce these files are publicly available at <a href="https://github.com/ClauClouds/SST-impact/">https://github.com/ClauClouds/SST-impact/</a> and can also be found in this repository (code_python.zip). This Zenodo data repository includes the following datasets:</p> <ul> <li> <p>Radiosonde data from 2-3 February 2020 (Stephan et al., 2021)</p> </li> <li> <p>Doppler lidar, and ARTHUS Raman lidar variables data from 2-3 February 2020,</p> </li> <li> <p>GOES-East (Geostationary Operational Environmental Satellite - East) Binary Cloud Mask (BCM) and Cloud Optical Depth (COD) products, provided at 2 km grid spacing every 10 minutes. They come from the GOES-R Advanced Baseline Imager (ABI) (Schmit et al., 2017), available at <a href="https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data">https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data</a> and they are provided for the 2-3 February 2020.</p> </li> <li> <p>Multi-scale Ultra-high Resolution (MUR) product (JPL MUR MEaSUREs Project, 2015,183 (Chin et al., 2017)) averaged between the 2nd and 3rdfor the 2nd of February 2020. The MUR product is an analysis product provided on a daily basis that combines different satellite (infrared at high and medium resolutions and microwave products) and in-situ data (Chin et al., 2017).</p> </li> <li> <p>W-band radar data post-processed for the purposes of the publication. The original W-band radar data used are publicly accessible at <a href="https://howto.eurec4a.eu/merian_cloudradar.html">https://howto.eurec4a.eu/merian_cloudradar.html</a> and can be downloaded via <a href="https://eurec4a.aeris-data.fr/">AERIS data portal</a>. See more details and specific DOI below.</p> </li> </ul> <p>The present dataset is structured as follows:</p> <ul> <li> <p>diurnal_cycle_removed_vars: files containing the time series of the variables without noise and diurnal cycle (filenames with extended dates 20200202 and 20200203)</p> </li> <li> <p>diurnal_cycle: files containing the diurnal cycle of each variable used in the publication</p> </li> <li> <p>binned_sst_vars: files containing variables binned in terms of SST, used to derive the plots in the paper.</p> </li> <li> <p>satellite_data: a folder containing all satellite data used in the publication</p> </li> </ul> <p>Additional data used in the publication, that are processed via the scripts contained in the link mentioned above, are available online at the following urls:</p> <ul> <li> <p>cloud radar observations can be directly obtained from the public dataset identifiable via DOI: <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a> (Acquistapace et al., 2022)</p> </li> <li> <p>ASCAT wind field data and corresponding MUR SST data are available from the NASA JPL PODAAC platform (<a href="https://podaac.jpl.nasa.gov/">https://podaac.jpl.nasa.gov/</a>)</p> </li> <li> <p>hourly ERA5 (Hersbach et al., 2020) gridded fields (available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form, last accessed March 2022) of the following variables: SST, water vapor mixing ratio, air temperature, and horizontal wind components. </p> </li> </ul> <p><br> </p> <p>References;</p> <p>Acquistapace et al., 2022, ESSD, <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a>.</p> <p>Schmit, T. et al., 2017, QJRMS, <a href="https://doi.org/10.1175/BAMS-D-15-00230.1">https://doi.org/10.1175/BAMS-D-15-00230.1</a></p> <p>Hersbach et al., 2020, QJRMS, <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803">https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803</a></p> <p>Stephan et al., 2021, ESSD, <a href="https://doi.org/10.5194/essd-13-491-2021">https://doi.org/10.5194/essd-13-491-2021</a></p> <p>Chin, T. M. et al., (2017), RS, <a href="https://doi.org/10.1016/j.rse.2017.07.029">https://doi.org/10.1016/j.rse.2017.07.029</a></p>
Inputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data
<p>The dataset contains the inputs of the notebook "Met Office UKV high-resolution atmosphere model data" published in The Environmental Data Science Book.</p> <p>The input data refer to a subset of single sample data file for 1.5 m temperature as part of the Met Office contribution to the COVID 19 modelling effort.</p> <p>The full dataset was available for download from the Met Office Azure (https://metdatasa.blob.core.windows.net/covid19-response-non-commercial/). The full dataset was available for download under the terms of non-commercial purposes.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samantha V. Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>Met Office Informatics Lab (creator)</p> </li> <li> <p>Microsoft (support)</p> </li> <li> <p>European Regional Development Fund (support)</p> </li> </ul> <p><em>Dataset authors</em></p> <ul> <li> <p>Met Office</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Theo McCaie. Met office and partners offer data and compute platform for covid-19 researchers. URL: <a href="https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f">https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f</a>.</p> </li> </ul> <p><strong>Note this data should be used only for non-commercial purposes.</strong></p>
Climatological data from mechanistic model experiments of Boljka and Birner (2022/3; npj Climate and Atmospheric Science)
<p>Some climatological output data from mechanistic dry dynamical core model experiments used for the paper of Boljka and Birner (2022/3): "Potential impact of tropopause sharpness on the structure and strength of the general circulation", npj Climate and Atmospheric Science. For more details see the manuscript. </p>
Atmospheric Distribution of HCN from Satellite Observations and 3-D Model Simulations - TOMCAT data
<p>This repository contains the model data from the paper "Atmospheric Distribution of HCN from Satellite<br> Observations and 3-D Model Simulations" submitted to ACP.</p> <p>The files contains the monthly mean hydrogen cyanide (HCN) mixing ratios modelled using the TOMCAT 3-D offline chemical transport model with a horizontal resolution of 2.8° × 2.8° with 60 hybrid σ-pressure levels from the surface to ~60 km.</p>
Signatures of Nitrogen Chemistry in Hot Jupiter Atmospheres - Posteriors
<p>Supplementary material for 'Signatures of Nitrogen Chemistry in Hot Jupiter Atmospheres', ApJL, 2017.</p> <p>Contains the posterior probability distributions resulting from atmospheric retrievals of WASP-31b, WASP-63b, and HD 209458b.</p>
Earth's atmosphere protects the biosphere from nearby supernovae
<p>Dataset for manuscript: "Earth’s atmosphere protects the biosphere from nearby supernovae".</p> <p>Communications Earth & Environment</p> <p>DOI: <a href="https://doi.org/10.1038/s43247-024-01490-9" target="_blank" rel="noopener noreferrer">10.1038/s43247-024-01490-9</a></p> <div><span>CONTRIBUTORS: </span>Theodoros Christoudias; Jasper Kirkby; Dominik Stolzenburg; Andrea Pozzer; Eva Sommer; Guy P. Brasseur; Markku Kulmala; Jos Lelieveld</div>
Monthly methane emissions estimated with the atmospheric inversion model CarbonTracker Europe - CH4
<p>Monthly estimates of global methane emissions from CarbonTracker Europe - CH4 (CTE-CH4). CTE-CH4 is a Bayesian inversion framework based on an ensemble Kalman filter algorithm using the Eulerian global atmospheric transport model TM5. The gridded fluxes are available with a resolution of 1.0x1.0 degrees and in units of kgCH4/m2/month. The gridded flux file contains variables for posterior fluxes from soils (bio_flux_opt) and anthropogenic sources (anth_flux_opt) and the total posterior flux (total_flux_opt). Priors used: Anthropogenic: EDGAR v6, biosphere/wetlands (soils): LPX-Bern DYPTOP v1.4, Ocean: Weber et al. (2019), Biomass burning: GFED v4.1, Termites: VISIT. A more detailed setup of the inversion is documented in Erkkilä, A., Tenkanen, M., Tsuruta, A., Rautiainen, K., and Aalto, T.: Environmental and Seasonal Variability of High Latitude Methane Emissions Based on Earth Observation Data and Atmospheric Inverse Modelling, Remote Sensing, 15, https://doi.org/10.3390/rs15245719, 2023. Note: Fluxes are optimised at 1.0x1.0 degrees in northern high latitudes (USA, Canada, Europe and Russia), but are also provided here at the same resolution for other regions.</p>
Dataset associated with Banks et al.: "Dust aerosol from the Aralkum Desert influences the radiation budget and atmospheric dynamics of Central Asia"
<p>This dataset contains the COSMO-MUSCAT simulation output for the 'Dustbelt' (DUBLT) scenarios of Central Asian dust aerosol and associated radiative effects described by the paper "Radiative cooling and atmospheric perturbation effects of dust aerosol from the Aralkum Desert in Central Asia", written by Banks et al. and submitted to ACP in 2023. The paper was renamed "Dust aerosol from the Aralkum Desert influences the radiation budget and atmospheric dynamics of Central Asia" in 2024.</p>
Components of spore capture device in "A simple mechanism for uncrewed aircraft bioaerosol sampling in the lower atmosphere"
<p>These STL files enable the 3D printing of the referenced spore capture device. The complete device can be assembled following printing of the: (1) petri dish holder base; (2) lid; and (3) flange. The STL file extension stands for stereolithography, colloquially referred to as Standard Triangle Language or Standard Tessellation Language, and is a popular file format for 3D printing. The 3D models were created, and can be viewed, with CAD software.</p>
Data for "A simple mechanism for uncrewed aircraft bioaerosol sampling in the lower atmosphere"
<p>Colony count data collected from Petri dishes, as described in "A simple mechanism for uncrewed aircraft bioaerosol sampling in the lower atmosphere." See the associated article for more information.</p>
Dataset for Spectral scaling of unstably-stratified atmospheric flows: turbulence anisotropy and the low frequency spread
<p>30 min turbulence statistics and spectra of 13 datasets from flat to highy complex terrain. Data only cover unstable stratification. </p> <p>Dataset is a companion to the manuscript Charrondiere, C., Stiperski, I., 2024: Spectral scaling of unstably-stratified atmospheric flows: turbulence anisotropy and the low frequency spread. Quarterly Journal of the Royal Meteorological Society, https://doi.org/10.1002/qj.4811<strong><br></strong></p> <p> </p>
Models and Datasets for "Extracting Paleoweather from Paleoclimate: A Deep Learning Reconstruction of Northern Hemisphere Summertime Atmospheric Blocking over the Last Millennium"
<p><strong>Associated publication:</strong> <em>Karamperidou, C., Extracting Paleoweather from Paleoclimate: A Deep Learning Reconstruction of Northern Hemisphere Summertime Atmospheric Blocking over the Last Millennium, Nature Communications Earth & Environment, (2024)</em></p> <p> </p> <p><strong>This repository contains:</strong></p> <ul> <li>the architecture and weights of PaleoBlockNet v1.0</li> <li>the following ensemble DL reconstructions of JJA frequency of blocked days inferred by PaleoBlockNet: <ol> <li>the 10-member NTREND-based DL reconstruction; uses as input the NTREND DA N.Hemisphere MJJA surface temperature anomaly by King et al. (2021)</li> <li>the 100-member PHYDA-based DL reconstruction; uses as input the PHYDA JJA surface temperature anomaly by Steiger et al. (2018)</li> <li>the 12-member LME-based DL reconstruction; uses as input the CESM-LME surface temperature anomaly; this is a sensitivity experiment (see publication for details).</li> </ol> </li> <li>Integrated Gradients that assign importance to the input features for PaleoblockNet's blocking inferences </li> <li>train-validate-test samples to use with sample scripts from the Gituhub repo github/ckaramp-research/paleoblocknet</li> </ul> <p> </p> <p><strong>If you use this dataset, please cite the associated publication and the present repository.</strong></p> <p>To <strong>interactively explore</strong> the datasets, a web interface has been developed and can be accessed at <a href="https://www2.hawaii.edu/~ckaramp/paleoblocknet">https://www2.hawaii.edu/~ckaramp/paleoblocknet</a></p> <p>Contact the author Christina Karamperidou (<a title="Karamperidou Research Group" href="https://www2.hawaii.edu/~ckaramp" target="_blank" rel="noopener">https://www2.hawaii.edu/~ckaramp</a>) for more information about the details of these datasets.</p>
Sonora 2018: Cloud-free, solar composition, solar C/O substellar atmosphere models and spectra
<p>These models for non-irradiated, substellar mass objects belong to the Sonora 2018 model series, to be described in Marley et al., currently in preparation for submission to Astrophysical Journal.</p> <p>This particular set of model atmosphere structures and associated spectra are for cloudless, solar metallicity, solar C/O ratio objects (relative to Lodders (2010) abundances) with <span class="math-tex">\(3.25 \le \log g \le 5.5\)</span> and <span class="math-tex">\(200 \le T_{\rm eff} \le 2400\,\rm K.\)</span> "Rainout" chemical equilibrium is assumed. Spectra describe the emergent flux from the top of the atmosphere of the object, units in file header. Profiles give temperature and pressure through the model radiative-convective equilibrium structure. Volume mixing ratios for a few gasses of interest are also tabulated. Additional gas species are included in the calculation.</p> <p>Filename specifies <span class="math-tex">\(T_{\rm eff}\)</span> and gravity (in mks units). </p> <p>"co1.0" in version and spectra header nomenclature refers to 1.0 times the solar C/O ratio. Y is the He mass fraction. f_hole is a cloud parameter for cloudy models, not relevant to these cloudless models.</p> <p>The two "flux table" files give fluxes, in mJy, for various standard filter bandpasses of interest for each model case.</p> <p> </p> <p> </p> <p> </p>
Dataset for "Sources and sinks of carbonyl sulfide inferred from atmospheric observations at the Lutjewad tower"
<p>Measurements that are used in "Sources and sinks of carbonyl sulfide inferred from atmospheric observations at the Lutjewad tower”. The dataset includes mole fraction measurements of COS, CO<sub>2</sub>, CO and H<sub>2</sub>O made in Lutjewad (the Netherlands) and Hyytälä (Finland) between 2014 and 2018, and measurements of <sup>222</sup>Rn, SF<sub>6</sub> and meteorological parameters in Lutjewad.</p>
Long-term moss monitoring network for atmospheric deposition in Germany, link to research data and scientific software
<p>Research data and scientific software related to a study that aims to restructure a long-term monitoring network using moss as biomonitor for atmospheric deposition in Germany. Data from the European Moss Survey 2005 and a statistically based methodology including a decision support system were used to design the spatial network for the 2005 survey.</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.