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708 results for “Global dataset”
Dataset: Heritage Global Inc. (HGBL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Global X Video Games & Esports ETF (HERO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Coinbase Global, Inc. (COIN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
3S-GEOPROF-COMB: A Global Gridded Dataset for Cloud Vertical Structure from combined CloudSat and CALIPSO observations
<p>Global cloud dataset from combined spaceborne radar and lidar.</p> <p>This repository contains the 3S-GEOPROF-COMB product, a globally-gridded dataset for cloud vertical structure retrieved from hybrid active remote sensing (CloudSat radar and CALIPSO lidar) reported at 240 m vertical resolution. Science variables include vertical cloud fraction and vertically-integrated cloud cover for various geometrical criteria (i.e. high, middle, low, and thick clouds, along with with unique high, middle, and low cloud cover variants).</p> <p>A Python notebook showing how to work with the dataset is available <a href="https://github.com/bertrandclim/3S-GEOPROF-COMB/blob/main/notebooks/brief_intro.ipynb">on GitHub</a>, as is the source code used to produce the data product.</p> <p>Our product is calculated from the latest release (R05) of per-orbit (level 2) combined cloud mask profiles in 2B-GEOPROF-LIDAR with additional data from 2B-GEOPROF. Validation and a complete description of the data product is given in the paper <a href="https://doi.org/10.5194/essd-16-1301-2024">"A Global Gridded Dataset for Cloud Vertical Structure from Combined CloudSat and CALIPSO Observations"</a> (Earth System Science Data).</p> <p>Please cite "Bertrand, L., Kay, J. E., Haynes, J., and de Boer, G.: A global gridded dataset for cloud vertical structure from combined CloudSat and CALIPSO observations, Earth Syst. Sci. Data, 16, 1301–1316, https://doi.org/10.5194/essd-16-1301-2024, 2024."</p> <p>The files contained in each folder are given via the following format:</p> <p><strong> instruments_frequency_resolution.zip</strong></p> <ul> <li><strong>instruments:</strong> <ul> <li><strong>radarlidar:</strong> the standard product, computed from merged geometrical profiles of hydrometeor occurrence</li> <li><strong>radaronly:</strong> computed solely from CloudSat radar profiles, otherwise processing is identical. For when users need to determine which instrument is responsible for observations of interest.</li> <li><strong>lidaronly: </strong>computed solely from CALIPSO lidar profiles, otherwise processing is identical. For when users need to determine which instrument is responsible for observations of interest.</li> </ul> </li> <li><strong>frequency:</strong> <ul> <li><strong>monthly:</strong> data files report fields aggregated over a 1-month period</li> <li><strong>seasonal:</strong> data files report fields aggregated over a 3-month period (DJF, MAM, JJA, SON)</li> </ul> </li> <li><strong>resolution:</strong> <ul> <li><strong>2.5x2.5: </strong>each grid box spans 2.5 degrees latitude and 2.5 degrees longitude</li> <li><strong>5x5:</strong> each grid box spans 5 degrees latitude and 5 degrees longitude</li> <li><strong>10x10:</strong> each grid box spans 10 degrees latitude and 10 degrees longitude</li> </ul> </li> </ul> <p>Each folder contains a netCDF data file and a cloud cover quicklook plot image file for each time period over the 2006-2019 data record. Individual files are named according to the following format:</p> <p><strong> timeperiod_instruments_datastream_version.nc (or .png)</strong></p> <ul> <li><strong>timeperiod: </strong>the time step at the given frequency, either e.g. 2006-08 (August 2006) or 2012-DJF (December 2012 to February 2013).</li> <li><strong>instruments</strong><strong>:</strong> the instruments used in the data product as a whole, always CSCAL (CloudSat and CALIPSO).</li> <li><strong>datastream:</strong> either 3S-GEOPROF-COMB (COMBined radar and lidar), 3S-GEOPROF-COMB-RO (the auxiliary Radar Only variant of the product), or 3S-GEOPROF-COMB-LO (the auxiliary Lidar Only variant of the product)</li> <li><strong>version:</strong> current release is v8.4</li> </ul> <p>The product handles the 2011 CloudSat battery anomaly, after which the satellite only collects data in the sunlit portion of its orbit, by allowing users to subsample the pre-anomaly period to mimic the post-anomaly collection patterns. This allows users to estimate the effect of the reduced sampling on their analyses or apply a consistent sampling mode to the entire dataset. This option is provided to users via the "<strong>doop</strong>" dimension. Dimension coordinate value "All cases" reports variables computed using all observations, while "DO-OP observable" reports variables using only input data that either were or would have been collected in DO-OP mode (i.e. the pre-DO-OP period is subsampled to DO-OP collection patterns).</p>
EcoregionsTreeFinder – a global dataset documenting observations of 48,129 tree species in 828 terrestrial ecoregions
<p>Check this article for a description of the methods used to develop the EcoregionsTreeFinder. Together with the citation for this Zenodo archive, it is the suggested citation for the database.</p> <p>Kindt, R. and Pedercini, F. (2025), EcoregionsTreeFinder—A Global Dataset Documenting the Abundance of Observations of >45,000 Tree Species in 828 Terrestrial Ecoregions. Global Ecol Biogeogr, 34: e70064. <a href="https://doi.org/10.1111/geb.70064">https://doi.org/10.1111/geb.70064</a></p> <p>Use this shinyapp to filter native tree species for a particular ecoregion or to see ecoregions where a species is expected to be native: <a href="https://patspo.shinyapps.io/EcoregionsTreeFinder/" target="_blank" rel="noopener">https://patspo.shinyapps.io/EcoregionsTreeFinder/</a></p> <p> </p> <p>The database was created from observation records filtered from: GBIF.org (16 March 2021) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.77gcvq" target="_blank" rel="noopener">https://doi.org/10.15468/dl.77gcvq</a></p> <p> </p> <p><strong>Funding </strong></p> <p>Development of the EcoregionsTreeFinder was supported by the <strong>Bezos Earth Fund</strong> via the Quality Tree Seed for Africa project, by <strong>Norway's International Climate and Forest Initiative</strong> via the Provision of Adequate Tree Seed Portfolio in Ethiopia (PATSPO) project, by the <strong>Darwin Initiative</strong> via project DAREX001 of Developing a Global Biodiversity Standard certification for tree-planting and restoration, by the <strong>Green Climate Fund</strong> via the Readiness proposal Burkina Faso and TREPA projects, and by the <strong>International Climate Initiative</strong> via the Right Tree for the Right Place and Right Purpose (RTRPRP) project.</p> <p> </p>
Shingle example self-consistent source dataset for global domain generation
<p>Self-consistent source dataset for the Shingle project -- an approach and software library for the generation of boundary representation from arbitrary geophysical fields and initialisation for anisotropic, unstructured meshing (see https://www.shingleproject.org for more information).</p>
Global datasets for duration of frozen ground with snow cover and without snow cover
<p>These are the main datasets associated with the submitted manuscript--Climate change causes functionally colder winters for snow cover-dependent organisms. The datasets include duration of frozen ground with snow cover (Dsc) and without snow cover (Dfwos) for the historical (1982-2014) and future (2071-2100) periods, which are available with GeoTIFF format at 5-km resolution. Dsc and Dfwos are defined as the number of days during the frozen season when frozen ground is covered by snow or not, which are calculated using AVHRR/MODIS snow cover product and NASA MEaSUREs Global Record of Daily Landscape Freeze/Thaw Status dataset.</p>
D-PLACE dataset derived from 'Global Multi-resolution Terrain Elevation Data 2010'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Global Multi-resolution Terrain Elevation Data courtesy of the U.S. Geological Survey (Downloaded 14 Jul 2014)</p> </blockquote>
D-PLACE dataset derived from Kreft and Jetz 2007 'Global patterns and determinants of vascular plant diversity'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Kreft H, Jetz W. Global patterns and determinants of vascular plant diversity. Proc Natl Acad Sci. 2007;104: 5925–5930.</p> </blockquote>
Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses - Supporting Dataset
<p>This dataset contains the files used to substantiate the outcomes of the publication <em>"Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses"</em>. </p> <p>It includes the output of 250 random model realizations of an aquifer thermal energy storage system in a thick productive aquifer (Case 1). It also includes the output of 500 random model realizations of an aquifer thermal energy storage system in a shallow alluvial aquifer (Case 2 part 1 and part 2).</p> <p>If there is interest in generating new output, the datset also includes the model input files for both cases.</p> <p>(Scripts to process the output data or to generate new output data can be found in the corresponding GitHub repository: https://github.com/lukatas/ATES_SensitivityAnalyses.git )</p>
Global Ski Resort Rankings Dataset
<div> <div>A comprehensive dataset containing crowdsourced rankings of nearly all ski resorts worldwide. The dataset includes detailed information on each resort, such as location, snowfall, number of lifts and slopes, total slope length, and vertical drop. The dataset is updated regularly as more votes are collected<br><br><a title="Ski resorts ranking " href="https://rank-tank.com/datasets">Ski resorts ranking </a></div> </div>
GLAD42 - Global Locust Attack Dataset for 42 countries
<p>This dataset is compiled by collecting Locust attack data of 42 countries from 1985 to 2020 from FAO and environemntal features data for the same countries and same time period from TerraClimate website.</p> <p>This dataset have Target variable Locustpresent which has two classes yes and no. Moreover independent features have coutries, regions, start year, start date, Precipitation, Soil moisture and maximum temperature. This dataset is useful for predicting locust attack across 42 countries </p>
TSSCXG-17: Global Gridded Dataset of Surface Ocean pCO2 and Air-Sea CO2 Flux (1993-2020)
<p>This dataset presents a global gridded reconstruction of the partial pressure of CO2 (pCO2) in the surface ocean and the corresponding air-sea CO2 flux, covering the period from 1993 to 2020. Developed to enhance understanding of climate change and the global carbon cycle, this dataset addresses gaps in oceanic carbon flux data through innovative machine learning techniques. The reconstruction process integrates in situ observations, satellite data, and reanalysis products, employing a three-step algorithm involving dimensionality reduction, clustering, and regression.</p>
SeasFire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth System
<p>The <strong>SeasFire Cube</strong> is a scientific datacube for seasonal fire forecasting around the <strong>globe</strong>. Apart from seasonal fire forecasting, which is the aim of the SeasFire project, the datacube can be used for several other tasks. For example, it can be used to model teleconnections and memory effects in the earth system. Additionally, it can be used to model emissions from wildfires and the evolution of wildfire regimes.<br> <br> It has been created in the context of the <a href="https://seasfire.hua.gr/">SeasFire project</a>, which deals with "<em>Earth System Deep Learning for Seasonal Fire Forecasting</em>" and <strong>is funded by the European Space Agency (ESA) </strong> in the context of ESA Future EO-1 Science for Society Call.<br> <br> It contains <strong>21 years</strong> of data (2001-2021) in an <strong>8-days</strong> time resolution and <strong>0.25 degrees grid</strong> resolution. It has a diverse range of seasonal fire drivers. It expands from atmospheric and climatological ones to vegetation variables, socioeconomic and the target variables related to wildfires such as burned areas, fire radiative power, and wildfire-related CO2 emissions.</p> Datacube properties <table><tbody><tr> <th> <p><strong>Feature</strong></p> </th> <th> <p><strong>Value</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>Spatial Coverage</p> </td> <td> <p>Global</p> </td> </tr> <tr> <td> <p>Temporal Coverage</p> </td> <td> <p>2001 to 2021</p> </td> </tr> <tr> <td> <p>Spatial Resolution</p> </td> <td> <p>0.25 deg x 0.25 deg</p> </td> </tr> <tr> <td> <p>Temporal Resolution</p> </td> <td> <p>8 days</p> </td> </tr> <tr> <td> <p>Number of Variables</p> </td> <td> <p>54</p> </td> </tr> <tr> <td> <p>Tutorial Link </p> </td> <td> <p><a href="https://github.com/SeasFire/seasfire-datacube">https://github.com/SeasFire/seasfire-datacube</a></p> </td> </tr> </tbody> </table> <table> <tbody><tr> <th>Full name</th> <th>DataArray name</th> <th>Unit</th> <th>Contact *</th> </tr> </tbody><tbody> <tr> <th>Dataset: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview">ERA5 Meteo Reanalysis Data</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Mean sea level pressure</th> <td>mslp</td> <td>Pa</td> <td>NOA</td> </tr> <tr> <th>Total precipitation</th> <td>tp</td> <td>m</td> <td>MPI</td> </tr> <tr> <th>Relative humidity</th> <td>rel_hum</td> <td>%</td> <td>MPI</td> </tr> <tr> <th>Vapor Pressure Deficit</th> <td>vpd</td> <td>hPa</td> <td>MPI</td> </tr> <tr> <th>Sea Surface Temperature</th> <td>sst</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Skin temperature</th> <td>skt</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Wind speed at 10 meters</th> <td>ws10</td> <td>m*s-2</td> <td>MPI</td> </tr> <tr> <th>Temperature at 2 meters - Mean</th> <td>t2m_mean</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Temperature at 2 meters - Min</th> <td>t2m_min</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Temperature at 2 meters - Max</th> <td>t2m_max</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Surface net solar radiation</th> <td>ssr</td> <td>MJ m-2</td> <td>MPI</td> </tr> <tr> <th>Surface solar radiation downwards</th> <td>ssrd</td> <td>MJ m-2</td> <td>MPI</td> </tr> <tr> <th>Volumetric soil water level 1</th> <td>swvl1</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th> <table> <tbody> <tr> <th>Volumetric soil water level 2</th> </tr> </tbody> </table> </th> <td>swvl2</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th>Volumetric soil water level 3</th> <td>swvl3</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th>Volumetric soil water level 4</th> <td>swvl4</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th>Land-Sea mask</th> <td>lsm</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th>Dataset: Copernicus <p><a href="http://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview">CEMS</a></p> </th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Drought Code Maximum</th> <td>drought_code_max</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Drought Code Average</th> <td>drought_code_mean</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Fire Weather Index Maximum</th> <td>fwi_max</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Fire Weather Index Average</th> <td>fwi_mean</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="http://confluence.ecmwf.int/display/CKB/CAMS%3A+Global+Fire+Assimilation+System+%28GFAS%29+data+documentation">CAMS: Global Fire Assimilation System (GFAS)</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Carbon dioxide emissions from wildfires</th> <td>cams_co2fire</td> <td>kg/m²</td> <td>NOA</td> </tr> <tr> <th>Fire radiative power</th> <td>cams_frpfire</td> <td>W/m²</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://climate.esa.int/en/projects/fire/data/">FireCCI - European Space Agency’s Climate Change Initiative</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Burned Areas from Fire Climate Change Initiative (FCCI)</th> <td>fcci_ba</td> <td>ha</td> <td>NOA</td> </tr> <tr> <th>Valid mask of FCCI burned areas</th> <td>fcci_ba_valid_mask</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th><br> Fraction of burnable area</th> <td>fcci_fraction_of_burnable_area</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Number of patches</th> <td>fcci_number_of_patches</td> <td>N</td> <td>NOA</td> </tr> <tr> <th>Fraction of observed area</th> <td>fcci_fraction_of_observed_area</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Dataset: Nasa MODIS <a href="https://lpdaac.usgs.gov/products/mod11c1v006/">MOD11C1</a>, <a href="https://lpdaac.usgs.gov/products/mod13c1v006/">MOD13C1</a>, <a href="https://lpdaac.usgs.gov/products/mcd15a2hv006/">MCD15A2</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Land Surface temperature at day</th> <td>lst_day</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Leaf Area Index</th> <td>lai</td> <td>m²/m²</td> <td>MPI</td> </tr> <tr> <th>Normalized Difference Vegetation Index</th> <td>ndvi</td> <td>unitless</td> <td>MPI</td> </tr> <tr> <th>Dataset: Nasa SEDAC <a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-adjusted-to-2015-unwpp-country-totals-rev11">Gridded Population of the World (GPW), v4</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Population density</th> <td>pop_dens</td> <td>persons per square kilometers</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="http://www.globalfiredata.org/data.html">Global Fire Emissions Database (GFED)</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Burned Areas from GFED (large fires only)</th> <td>gfed_ba</td> <td>hectares (ha)</td> <td>MPI</td> </tr> <tr> <th>Valid mask of GFED burned areas</th> <td>gfed_ba_valid_mask</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th>GFED basis regions</th> <td>gfed_region</td> <td>N</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="http://gwis.jrc.ec.europa.eu/apps/country.profile/downloads">Global Wildfire Information System (GWIS)</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Burned Areas from GWIS</th> <td>gwis_ba</td> <td>ha</td> <td>NOA</td> </tr> <tr> <th>Valid mask of GWIS burned areas</th> <td>gwis_ba_valid_mask</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://psl.noaa.gov/data/climateindices/list/">NOAA Climate Indices</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Arctic Oscillation Index</th> <td>oci_ao</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Western Pacific Index</th> <td>oci_wp</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Pacific North American Index</th> <td>oci_pna</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>North Atlantic Oscillation</th> <td>oci_nao</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Southern Oscillation Index</th> <td>oci_soi</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Global Mean Land/Ocean Temperature</th> <td>oci_gmsst</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Pacific Decadal Oscillation</th> <td>oci_pdo</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Eastern Asia/Western Russia</th> <td>oci_ea</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>East Pacific/North Pacific Oscillation</th> <td>oci_epo</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Nino 3.4 Anomaly</th> <td>oci_nino_34_anom</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Bivariate ENSO Timeseries</th> <td>oci_censo</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://www.esa-landcover-cci.org/">ESA CCI</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Land Cover Class 0 - No data</th> <td>lccs_class_0</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 1 - Agriculture</th> <td>lccs_class_1</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 2 - Forest</th> <td>lccs_class_2</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 3 - Grassland</th> <td>lccs_class_3</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 4 - Wetlands</th> <td>lccs_class_4</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 5 - Settlement</th> <td>lccs_class_5</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 6 - Shrubland</th> <td>lccs_class_6</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 7 - Sparse vegetation, bare areas, permanent snow and ice</th> <td>lccs_class_7</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 8 - Water Bodies</th> <td>lccs_class_8</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://ecoregions.appspot.com/">Biomes</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Dataset: Calculated</th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Grid Area in square meters</th> <td>area</td> <td>m²</td> <td>NOA</td> </tr> </tbody> </table> <p>*The datacube specifications (temporal, spatial resolution, chunk size) have been set up by the Max Planck Institut (MPI) team. For the variables that the contact is MPI, Lazaro Alonso (lalonso <at> bgc-jena.mpg.de) has led the efforts to collect and process them. For the variables that the contact is NOA, Ilektra Karasante (ile.karasante <at> noa.gr) has led the efforts to collect and process them.</p>
GlobalNO2_AIT: 0.1° Annual Resolution Global Ground-level NO2 Dataset
<p>The GlobalNO2_AIT dataset provides a comprehensive annual resolution of ground-level nitrogen dioxide (NO2) concentrations at a spatial resolution of 0.1° across global land areas. This dataset is generated using advanced machine learning techniques, integrating various satellite and ground-based observations to enhance accuracy and coverage. It facilitates the study of air quality, atmospheric chemistry, and the impacts of NO2 on human health and the environment. The dataset is essential for researchers, policymakers, and environmental organizations aiming to analyze trends, evaluate pollution mitigation strategies, and model air quality impacts.</p>
OEMC Hackathon 2023: Global FAPAR Modeling Dataset (including raster data)
<p>Dataset organized by the <a href="https://earthmonitor.org/">Open-Earth-Monitor (OEMC) project</a> within the context of <a href="http://www.kaggle.com/competitions/oemc-hackathon-eu-land-cover-classification/overview">Hackathon 2023</a>.</p> <p>The dataset contains monthly mean FAPAR values aggregated by each ground station. FAPAR represents the fraction of the incoming (photosynthetic active) radiation that is absorbed by vegetation, and is given in the range <code>0-1</code>. It is a measure of vegetation health and ecosystem functioning, and a key parameter in light use efficiency models that model primary productivity.</p> <p>For each monthly FAPAR value, a set of covariates / features were extracted from <strong>32</strong> raster spatial layers, including including satellite (spectral bands and indices) and temperature images (land surface temperature), climate images (precipitation) and digital terrain model (slope and elevation). The features are organized by columns, unique data points in time are identified by the <code>sample_id</code> column, and data points points belonging to the same location are identified by <code>station_number</code>.</p> <p><strong>Column names:</strong></p> <ul> <li><code>sample_id</code>: unique identifier of datapoint</li> <li><code>station</code>: ground station number</li> <li><code>fapar</code>: monthly mean FAPAR</li> <li><code>month</code>: month of measurement</li> <li><code>modis_{..}</code>: NDVI, EVI, reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared) based on <a href="https://lpdaac.usgs.gov/products/mod13q1v061/">MOD13Q1</a></li> <li><code>modis_lst_day_p{..}</code>: Land surface temperatures daytime of percentiles 5th, 50th and 95th based on <a href="https://lpdaac.usgs.gov/products/mod11a2v061/">MOD11A2</a></li> <li><code>modis_lst_night_p{..}</code>: Land surface temperatures nighttime of percentiles 5th, 50th and 95th based on <a href="https://lpdaac.usgs.gov/products/mod11a2v061/">MOD11A2</a></li> <li><code>wv_yearly_p{..}</code>: Water vapour aggregated yearly by percentiles 25th, 50th and 75th based on derived from <a href="https://zenodo.org/record/8226282">MCD19A2</a></li> <li><code>wv_monthly_lt_p{..}</code>: Water vapour aggregated long-term monthly by percentiles 25th, 50th and 75th based on <a href="https://zenodo.org/record/8226282">MCD19A2</a></li> <li><code>wv_monthly_lt_sd</code>: Water vapour aggregated long-term monthly standard deviation based on <a href="https://zenodo.org/record/8226282">MCD19A2</a></li> <li><code>wv_monthly_ts_raw</code>: Water vapour monthly time series based on <a href="https://zenodo.org/record/8226282">MCD19A2</a></li> <li><code>wv_monthly_ts_smooth</code>: Water vapour monthly time series smoothed using the Whittaker method based on <a href="https://zenodo.org/record/8226282">MCD19A2</a></li> <li><code>accum_pr_monthly</code>: Monthly accumulated precipitation based on <a href="https://doi.org/10.1038/sdata.2017.122">CHELSA timeseries</a></li> <li><code>dtm_{..}</code>: Several DTM derivatives (Elevation, Slope, aspect (sine, cosine), curvature (up- and downslope), openness (negative, positive), compound topographic index (cti), valley bottom flatness (vbf)) based on <a href="https://ui.adsabs.harvard.edu/abs/2017AGUFM.H12C..04Y">MERIT DEM</a></li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>train.csv</strong>: Training set with 3,461 rows and 36 columns, including sample id (<code>sample_id</code> - index column), ground station (<code>station</code>), reference month (<code>month</code>), measured FAPAR (<code>fapar</code>), and 32 features / covariates</li> <li><strong>test.csv</strong>: Test set with 4,939 rows and 34 columns, including sample id (<code>sample_id</code> - index column), ground station (<code>station</code>), reference month (<code>month</code>) and 32 features / covariates</li> <li><strong>sample_submission.csv</strong>: a sample submission file with 4,939 rows and 2 columns, including sample id (<code>sample_id</code> - index column) and measured FAPAR (<code>fapar</code>)</li> </ul>
Supplementary dataset for "Global Climatology of the Daytime Surface Cooling of Urban Parks Using Satellite Observations"
<p>This dataset supplements the paper "Global Climatology of the Daytime Surface Cooling of Urban Parks Using Satellite Observations" in Geophysical Research Letters by Agathangeldis et al.</p> <ul> <li>The file "parks.gpkg" contains the boundaries of the parks used in the study, along with the Surface Park Cool Island (SPCI) intensity and additional metadata as attributes.</li> <li>The file "parks.csv" includes the same information as "parks.gpkg" but without the geospatial information.</li> <li>The file "seasonal_values" provides the SPCI intensity for each park, broken down by season.</li> </ul>
Global Drainage Basin Morphology (GDBM) dataset
<p>The Global Drainage Basin Measurement (GDBM) dataset contains morphometric information about 254,966 large river channels, classified into Koppen-Geiger climate sub zones. These data can be used to explore global trends in river morphology and climate.</p>
Global atmospheric particle formation from CERN CLOUD measurements: nucleation rate dataset
<p>Data S1 for paper "Dunne et al, (the CLOUD collaboration), Global atmospheric particle formation from CERN CLOUD measurements, Science 354 6316 (2016). Data contains nucleation rates and chamber conditions (including precursor gas concentrations) for inorganic binary and ternary (H2SO4-H2O) and (H2SO4-NH3-H2O) neutral and ion-induced nucleation measurements presented in the paper. These data were originally attached as supplemental to the paper, but are not currently available on the Science website (as of October 2024). </p> <p>Relative humidity units are percent. Care is needed to interpret all the data, for example not all ammonia values quoted were measured directly, some are inferred. See the supplementary materials of the paper for more discussion, and users of data at relative humidity other than (38+/-5)% are advised to discuss with the contact author Hamish Gordon. Ion production rate and nucleation rate units are per cm3 per second. The sulfuric acid units can be interpreted by noting that the value of 632 in the first data entry is 6.32x10^8 cm-3. </p> <p>Please cite the original Science article if you use these data: https://www.science.org/doi/10.1126/science.aaf2649</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 1
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: unused productive wilderness areas (WILD-core); productive wilderness areas that are sporadically used at very low intensity (WILD-periphery); unused unproductive wilderness areas (WILD-nps); forestry areas, mainly coniferous (FO-con); forestry areas, mainly non-coniferous (FO-ncon); settlements, urban areas and infrastructure (BU-builtup)</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.