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.
2,170
datasets available to search
ShareScore release 0.9.0
Dataset results
2,170 results for “clouds”
Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations
<p>This dataset contains a comprehensive set of quality-controlled remote sensing observations of low-level mixed-phase clouds collected at the high Arctic site of Ny-Ålesund, between 10 October 2021 and 31 December 2022. Cornerstones of the dataset are observations from a 35-GHz polarimetric scanning Doppler cloud radar and a 94-GHz zenith-pointing Doppler cloud radar. Radar data are complemented with thermodynamic retrievals from a microwave radiometer, liquid base height from a ceilometer and wind fields from large-eddy simulations. All data have undergone extensive quality control, especially the cloud radar data, which are accurately calibrated, matched, and corrected for gas and liquid-hydrometeor attenuation, ground clutter and range folding. This dataset is especially suited for cloud microphysical studies, and the high number of events included allows for the compiling of robust statistics. The dataset is accompanied by a data descriptor article, which is available at <a href="https://doi.org/10.5194/essd-15-5427-2023" target="_blank" rel="noopener">doi.org/10.5194/essd-15-5427-2023</a>.</p> <p> </p> <p><strong>Dataset overview</strong><br>The files include only low-level mixed-phase cloud (LLMPC) events, as well as the 2 hours preceding and following events. Each file contains an individual event, unless multiple events are less than 4 hours apart, in which case they are combined into the same file. LLMPC events are detected by requiring that ice and liquid phase coexist in a cloud layer with top below 2500 m for at least one hour. All radar variables observed in zenith (Doppler moments at 35 and 94 GHz, linear depolarization ratio (LDR) at 35 GHz), as well as microwave radiometer retrievals (temperature (T), liquid water path (LWP), integrated water vapor (IWV)), liquid base height from the ceilometer, and model data (horizontal wind speed and direction) are brought to the same time and range grids (respectively named ‘time_zen’ and ‘range_zen’ in the files). Off-zenith radar variables (reflectivity, differential reflectivity (ZDR), maximum spectral ZDR (sZDRmax), correlation coefficient (RhoHV), differential phase shift (PhiDP), and specific differential phase (KDP)) are stored on separate coordinates (named ‘time_slant’ and ‘range_slant’). All derived corrections are already applied to the data, and stored in the files, in case the user is interested in reconstructing the original data. A number of flags have been included in the files: in particular ‘MPC_detected’ indicates whether a LLMPC event was detected, and ‘liquid_attenuation_correction_flag_zen’ and ‘liquid_attenuation_correction_flag_slant’ indicate whether radar reflectivities were corrected for attenuation due to liquid hydrometeors. Liquid attenuation corrections should be especially taken into account when computing the dual-wavelength ratio (i.e., the difference between reflectivity at 35 GHz and at 94 GHz, both expressed in dBZ), and performing quantitative analyses of reflectivity fields.</p>
Data for "Detection of large-scale cloud microphysical changes within a major shipping corridor after implementation of the IMO 2020 fuel sulfur regulations"
<p>Processed data used for the manuscript "Detection of large-scale cloud microphysical changes within a major shipping corridor after implementation of the IMO 2020 fuel sulfur regulations".</p> <p>Includes input data for kriging algorithm as "SSF1deg_shipkrige_Terra.nc" and output data files as "Data_Terra_[VAR]_[YEAR]_C_M[MONTH].nc" for [VAR] Acld (overcast albedo) or cer (cloud droplet effective radius), [YEAR] the starting year of a three-year period starting with 2002 and ending at 2020 or "clim" for the 2002-2019 climatology, and [MONTH] 1to12 (annual mean) or 9to11 (austral spring).</p> <p>For the output data, "Obs" is the original data, "Est" is the mean counterfactual field obtained via kriging, "lowEst" and "highEst" are the 2.5th and 97.5th percentiles of the kriged fields for each grid box, "krSims" stores the results of the 5,000 simulated kriged fields, "Semivariance" is the binned empirical variogram values, "pVal" is the raw field significance (not adjusted for multiple testing), "nOut" is the number of individually significant grid boxes, "tran" is the transform applied (none for cer, logit for Acld), "iniPhi" and "iniSigma2" are the initial values for the fitted variogram, "Phi" and "Sigma2" are the fitted values using weighted least squares, and "parSel" is the list of selected regressors for the mean function that minimize the Bayesian information criterion.</p>
Clouds and Seasonality on Terrestrial Planets with Varying Rotation Rates
<p>Base Isca namelists used to construct experiment grid and figure plotting notebooks. Includes .zip files that contain post-processed data used to produce the figures—note that .ipynb notebooks have not had file structure changed to match files in this directory, which will need to be changed if the script is re-run (plots are viewable in notebooks).</p>
Data for the publication "Future warming exacerbated by aged soot effect on cloud formation"
<p>This repository contains the data for the paper:</p> <p>"Ulrike Lohmann, Franz Friebel, Zamin A. Kanji, Fabian Mahrt, Amewu A. Mensah and David Neubauer: Future warming exacerbated by aged soot effect on cloud formation. <em>Nat. Geosci.</em> <strong>13, </strong>674–680 (2020). https://doi.org/10.1038/s41561-020-0631-0"</p> <p>Each tar-file contains the data (or instructions how to obtain the data) to reproduce a figure or table in our paper.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.3457878)</p> <p>Also note that Extended Data Fig. 1 is identical to Supplementary Fig. S4 as well as that Extended Data Table 1 is identical to Supplementary Table S2.</p>
Corrections for Geostationary Cloud Liquid Water Path Using Microwave Imagery
<p>Netcdf files containing a set of correctional factors for GOES-16 and GOES-17 cloud liquid-water path (LWP). The correctional factors for both satellites are fractional corrections of microwave imager LWP divided by GOES-16/17 LWP at a given solar zenith, GOES sensor zenith, relative azimuth (solar - sensor zenith), and low-cloud fraction.</p> <p>Uncorrected GOES-16/17 LWP is derived from GOES retrieved cloud-optical thickness and cloud-top effective radius, and it is multiplied by the corresponding correctional factor (i.e. the bins which the uncorrected values are in).</p>
NPM3D dataset with instance labels used in paper "Toward Accurate Instance Segmentation in Large-scale LiDAR Point Clouds"
<p>NPM3D (https://npm3d.fr/paris-carla-3d) consists of mobile laser scanning (MLS) point clouds collected in four different regions in the French cities of Paris and Lille, where each point has been annotated with two labels: one that assigns it to one out of 10 semantic categories and another one that assigns it to an object instance. When inspecting the data, we found 9 cases where multiple tree instances had not been separated correctly (i.e., they had the same ground truth instance label). These cases were manually corrected using the CloudCompare software (https://www.cloudcompare.org), and 35 individual tree instances were obtained. Our variant of the dataset with 10 semantic categories and enhanced instance labels is publicly available.</p>
Data for ECHAM-HAM in the Publication "Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions"
<p>This repository contains 3-hourly data of the ECHAM-HAM experiment in the paper:</p> <p>"Saponaro, G., Sporre, M. K., Neubauer, D., Kokkola, H., Kolmonen, P., Sogacheva, L., Arola, A., de Leeuw, G., Karset, I. H. H., Laaksonen, A., and Lohmann, U.: Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions, Atmos. Chem. Phys., 20, 1607–1626, https://doi.org/10.5194/acp-20-1607-2020, 2020."</p>
TEAMx-PC22 (TEAMx pre-campaing 2022) - KITcube cloud radar vertical winds in Kolsass
<p>The RPG FMCW dual-pol dual-frequency cloud radar was operated in Kolsass. This data set contains vertical wind speed in 10 s temporal resolution for both frequencies, 94 GHz and 35 GHz. Vertical wind measurements are interrupted by PPI scans, thus there are regular gaps. The data set covers the period from May, 18th, through Sept., 22th, 2022.There were technical problems which caused partly very long measurement gaps especially in the second half of the period. Data are stored as one NetCDF file.<br> </p>
TEAMx-PC22 (TEAMx pre-campaing 2022) - KITcube cloud radar horizontal winds (from PPI) in Kolsass
<p>The RPG FMCW dual-pol dual-frequency cloud radar was operated in Kolsass. This data set contains wind speed and wind direction in 10 min. temporal resolution for both frequencies, 94 GHz and 35 GHz. The wind is determined from PPI at 70 degree elevation via an unfolding VAD algorithm (see Pierre Tabary, Georges Scialom, and Urs Germann. Real-time retrieval of the wind from aliased velocities measured by doppler radars. J. Atmos. Oceanic Technol., 18 (6):875–882, June 2001.) PPIs were performed from May, 31st, through Aug, 26th, 2022. There were technical problems which caused partly very long measurement gaps especially in the second half of the period. Data are stored as one NetCDF file.<br> </p>
Data for "Realistic representation of mixed-phase clouds increases future climate warming
<p>Data to reproduce the figures from Hofer et al. (2023) <a href="https://www.researchsquare.com/article/rs-2981113/v1">Realistic representation of mixed-phase clouds increases future climate warming</a></p>
Dataset for "Droplet collection efficiencies inferred from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions"
<p>This dataset in includes MODIS-CloudSat CFODD reference data, the updated Warm Rain Diagnostics implemented in COSPv2.0, RANSAC regression analysis, and figure production scripts associated with the manuscript “Droplet collection efficiencies estimated from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions”<br> Authors: Beall, Charlotte, M.; Ma, Po-Lun; Christensen, Matthew W.; Mülmenstädt, Johannes; Varble, Adam; Suzuki, Kentaroh; Michibata, Takuro<br> Journal: Atmospheric Chemistry & Physics (submitted, 2023)</p>
Supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations"
<p>This dataset is a supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations", available at <a href="http://doi.org/10.5281/zenodo.7803064">doi.org/10.5281/zenodo.7803064</a>. The additional variables here included are: slow edge velocity, fast edge velocity, and eddy dissipation rate (EDR). All variables are stored on the same time and range grids adopted for the main dataset. Similarly, the event selection and file structure are identical to those of the main dataset.<br><br>Slow and fast edge velocities are derived from Doppler spectra recorded by the zenith-pointing 94-GHz cloud radar. The slow (fast) edge velocity is calculated as the velocity associated with the slowest (fastest) Doppler bin above the peak noise level, belonging to a spectral cluster whose width is at least 5 Doppler bins.<br><br>The EDR is retrieved following the approach by Borque et al. (2016; <a href="http://doi.org/10.1002/2015JD024543">doi.org/10.1002/2015JD024543</a>), using as input the slow edge velocity, and model horizontal wind speed from the main dataset. EDR is retrieved in 5 minute intervals, up to a maximum range of 3 km.<br><br>The detailed documentation of the variables here included can be found in the Supporting Information to the following publication: <a href="https://doi.org/10.1029/2023GL106599" target="_blank" rel="noopener">doi.org/10.1029/2023GL106599</a>.</p>
Supplementary material for "Inverse Modeling of the Initial Stage of the 1991 Pinatubo Volcanic Cloud Accounting for Radiative Feedback of Volcanic ash" paper
<p>Supplementary material for "Inverse Modeling of the Initial Stage of the 1991 Pinatubo<br>Volcanic Cloud Accounting for Radiative Feedback of Volcanic ash" by A. Ukhov, <br>G. Stenchikov, S.Osipov, N. Krotkov, N. Gorkavyi, C. Li, O. Dubovik, and A. Lopatin.</p> <p>Corresponding author: Alexander Ukhov, alexander.ukhov@kaust.edu.sa</p> <p>Contents<br>0. This 'README' file</p> <p>1. Emission profiles for ash and SO2<br> 1.1 In pickle and txt format, when radiative feedback is accounted for:<br> 1.1.1 Files 'ash_2d_emission_profiles_rad_on' [Mt/sec] and 'ash_2d_emission_profiles.txt' [Mt/(m sec)]<br> 1.1.2 Files 'so2_2d_emission_profiles_rad_on' [Mt/sec] and 'so2_2d_emission_profiles.txt' [Mt/(m sec)]</p> <p> 1.2 In pickle format, when radiative feedback is not accounted for:<br> 1.2.1 Files 'ash_2d_emission_profiles_rad_off' [Mt/sec]<br> 1.2.2 Files 'so2_2d_emission_profiles_rad_off' [Mt/sec]</p> <p>2. python script 'draw_supplementary_profiles.py' plots inverted emission profiles <br> (in pickle format) and their time integrated variants.</p> <p>3. WRF-Chem output file 'wrfout_d01_1991-06-16_00:00:00' in netcdf format contains <br> 3-D fields of ash, sulfate, and SO2 concentrations at 0000 UTC on 16 of June. <br> Instructions on how to process WRF-Chem output are available at the Appendix of [1].</p> <p>4. WRF-Chem domain grid description in the file 'wrf_small_grid.txt'. This file can be<br> used for conservative interpolation of 3-D fields to another grid, for example <br> using 'cdo remapcon'.</p> <p>There are two options: <br>1. Use inverted ash and SO2 emission profiles (see p.1 and p.2)<br>2. Use ash, sulfate, and SO2 concentrations from WRF-Chem output file (see p.3 and p.4)<br> as initial conditions for another run.</p> <p><br>References:<br>1. Ukhov, A., Ahmadov, R., Grell, G., and Stenchikov, G.: Improving dust simulations<br> in WRF-Chem v4.1.3 coupled with the GOCART aerosol module, <br> Geosci. Model Dev., 14, 473–493, https://doi.org/10.5194/gmd-14-473-2021, 2021.</p> <p>2. Ukhov et. al, Enhancing Volcanic Eruption Simulations with the WRF-Chem v4.7.x</p>
Leaf traits plus growth and dieback data for cloud forest epiphytes in a cloud exclusion treatment at Wayqecha Biological Station, Peru
Morphological traits [specific leaf area, stomatal length and density, leaf thickness, cuticle thickness and hydrenchymal layer thickness] and physiological traits [stomatal conductance, minimum leaf conductance and foliar water uptake capacity] along with pressure volume curves were measured at the Wayqecha Biological Station, Peru. Vascular epiphytes were sampled from a control plot and fog reduction treatment plot constructed from two 30 m tall aluminum towers, 40 m apart with panels of polyethylene mesh strung between the towers. Measurements of leaf morphological traits were taken in June-July 2022 in the control and treatment plots to assess plasticity to fog reductionin the fifth year of the experiment. Samples of branches and leaves for each individual approximately 1-2 meters off the ground were collected in the plot, close enough to the curtain to maximize any effects of fog interception. Growth and dieback data were also collected species of vascular and non-vascular epiphytes attached to wooden transplant boards in the control and treatment plot in 2017. Rates of growth and dieback were recorded for vascular epiphytes from 2017-2019 and for non-vascular epiphytes from 2018-2019. The 2017 data collection took place in November while data for 2018 and 2019 were collected in June.
Forest metrics derived from the 2008 Lidar point clouds, includes canopy closure, percentile height, and stem mapping for the Andrews Experimental Forest.
There are three types of forest metrics within this database. They all are derived from the raw Lidar point clouds using the FUSION software. The three types are canopy closure, height metric, and stem mapping. The canopy closure and height metric grids cover a variety of canopy heights and grid cell sizes. 1. Canopy closure: This metric measures the canopy closure of a given horizontal cell above a given vertical threshold (height break). Canopy closure can inform many landscape models and provide insight on how much light will reach the forest floor. 2. Height Metric: This metric measures the height at which a given percent of the first return points are below. This analysis is done in a given grid cell size. Height metrics give various statistics of the elevation above ground for a given set of Lidar points. In forested landscapes, first return height metrics describe the forest canopy. 3.This stem map locates the approximate center of all trees in the HJ Andrews Research Forest greater than 10 meters. In addition to the stem location, a canopy radius is also provided. FUSION and TreeVaWA software programs were used to develop this data. Watershed Sciences, Inc. (WS) collected Light Detection and Ranging (LiDAR) data from HJ Andrews and the Willamette National Forest (NF) on August 10th and 11th 2008. Total area for this AOI is 17,705 acres. The total area of delivered LiDAR including 100 m buffer is 19,493 acres.
Daily MODIS snow cover maps for the European Alps from 2002 onwards at 250m horizontal resolution along with a nearly cloud-free version
<p><strong>NOTE: We discovered some errors in the data for images after February 2019. They will be fixed in version >= 1.1.x, until then, usage of the data after Feb 2019 is not advised. The rest of the data is fine.</strong></p> <p> </p> <p>This is the data to the same-titled Data paper, which can be found at <a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>.</p> <p>Along with auxilary files for the <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">cloudremoval package</a>, and example scripts on how to access chunks of the data.</p> <p>The files contain:</p> <ol> <li><strong>python-cloudremoval-aux-data.tar.gz</strong> : auxilary data (altitude, aspect, ...) to run the cloudremoval module which can be found at <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal</a></li> <li><strong>python-example-data-access.html </strong>: Example script how to access parts of the data using python</li> <li><strong>R-example-data-access.html</strong> : Example script how to access parts of the data using R</li> <li><strong>zenodo_01_original.tar.gz</strong> : time series of snow cover maps, developed at the Institute for Earth Observation, Eurac Research, Bolzano, Italy. More information in same-title Data paper (<a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>), and for algorithm at <a href="https://doi.org/10.3390/rs5010110">https://doi.org/10.3390/rs5010110</a>.</li> <li><strong>zenodo_02_cloudremoval.tar.gz</strong> : time series of cloud filtered maps, based on 2. above, using code mentioned in 1. More information in same-titled Data paper.</li> </ol> <p> </p> <p>The maps are GeoTIFF with integer based values:</p> <p>0 = no data; 1 = snow; 2 = land; 3 = cloud; 4&5 = water bodies / nodata</p> <p> </p> <p>Version history:</p> <p>1.0.0 : initial upload<br> 1.0.1 : changes after revision of Data paper<br> 1.0.2 : added example scripts</p> <p> </p> <p> </p>
Figure 7 in The Trichoptera of Panama. VI. Seven new species of microcaddisflies (Insecta: Trichoptera: Hydroptilidae) from Mount Totumas Cloud Forest and Biological Reserve
Figure 7. Ochrotrichia anticheirion sp. n., male genitalia. A) Segments VIII–IX, lateral (base of phallus crosshatched). B) Segments IX–X, dorsal. C) Inferior appendages, ventral. D) Phallus, lateral. E) Phallus, dorsal.
Figure 8 in The Trichoptera of Panama. VI. Seven new species of microcaddisflies (Insecta: Trichoptera: Hydroptilidae) from Mount Totumas Cloud Forest and Biological Reserve
Figure 8. Rhyacopsyche totuma sp. n., male genitalia. A) Segments IX–X, lateral. B) Segments IX–X, dorsal. C) Inferior appendages, ventral. D) Phallus, lateral. E) Phallus, dorsal.
Figure 5 in The Trichoptera of Panama. VI. Seven new species of microcaddisflies (Insecta: Trichoptera: Hydroptilidae) from Mount Totumas Cloud Forest and Biological Reserve
Figure 5. Metrichia calla sp. n., male genitalia. A) Segments IX–X, lateral (base of phallus crosshatched). B) Segments IX–X, dorsal. C) Segment IX, ventral. D) Phallus, lateral. E) Phallus, dorsal.
Figure 4 in The Trichoptera of Panama. VI. Seven new species of microcaddisflies (Insecta: Trichoptera: Hydroptilidae) from Mount Totumas Cloud Forest and Biological Reserve
Figure 4. Metrichia brocha sp. n., male genitalia. A) Segments VII–X, lateral (base of phallus crosshatched). B) Segments IX–X, dorsal. C) Segment IX, ventral. D) Phallus, lateral. E) Phallus, dorsal.
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.