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13 results for “global gridded dataset”
LAI_TS_Val: LAI time-series validation datasets in the 1-km pixel grid at global scale from 2001 to 2011
<p>Leaf area index (LAI), which is defined as one half of the total green leaf area per unit ground surface area, is a critical structural variable for quantifying the exchange processes of energy and matter between the land surface and atmosphere, it is thus identified as a key parameter in most terrestrial ecosystem models. To acquire long-term LAI records at the global scale, several remote sensing LAI products have been generated from various satellite sensors. However, assessing the uncertainties associated with these LAI products through comparisons with independent ground-truth measurements is pivotal for an effective application of products. Many sites from global networks have collected and provided invaluable ground LAI measurements covering a wide range of biome types and spatial variabilities. These site-based LAI measurements have been obtained about 30 years (1990-now). However, the spatial scale mismatch between site and pixel observations restricts the utilization of LAI measurements for product time-series validation. This datasets were generated from site-based LAI measurements of FLUXET and Chinese Ecosystem Research Network (CERN), using the proposed GUGM (Grading and Upscaling of Ground Measurements) method to resolve the scale-mismatch issue between site and sensor observations and maximize the utility of time-series of site-based LAI measurements, which can achieve the goal of product time-series validation. This GUGM approach first ingests both high-resolution images and site-based LAI measurements to capture the spatiotemporal variability in the product pixel grid. Then, a strategy was employed to grade the spatial representativeness of LAI measurements in the product pixel grid. For those LAI measurements which cannot be directly used in the validation of products, a strategy was adopted to calculate the spatial upscaling coefficient based on site-based LAI measurements and aggregated high-resolution reference maps to derive reliable LAI time-series validation datasets. The GUGM method has been applied to the site-based LAI measurements to generate global time-series LAI validation datasets from 2001 to 2011 in the 1 km pixel grid. The datasets include 28 sites which are mainly located in North America and Asia, providing 924 validation data in total. Among these sites, 16 sites with 508 (55.0%) validation data were obtained for forest, while 11 sites with 341 (36.9%) validation data and one site with 75 (8.1%) were obtained for crops and grasses, respectively. This datasets were saved in two formats: *.xls and *.kmz and each format was zipped for 63 KB and 31 KB, respectively.</p>
A 20-year (1998-2017) global sea surface dimethyl sulfide gridded dataset with daily resolution
<p>This dataset contains (1) the matched and binned data used for constructing an artificial neural network (ANN) ensemble model to simulate the sea surface concentration of dimethyl sulfide (DMS); (2) the simulated global daily sea surface concentrations of DMS ranging from 1998 to 2017 by ANN model and the calculated total transfer velocities (Kt) and sea-to-air fluxes. The input variables of this ANN ensemble model include chlorophyll <em>a</em>, sea surface temperature (SST), mixed layer depth (MLD), nitrate, phosphate, silicate, dissolved oxygen (DO), downward short-wave radiation (DSWF), and sea surface salinity (SSS). The spatial resolution of the simulated dataset is 1°×1°. The units of DMS concentration, Kt, and flux are nmol L–1, m d–1, and μmol S m–2 d–1, respectively.</p> <p><strong>Update Note</strong></p> <ol> <li>In Version 4.0 and earlier versions, the sea ice cover data (from the OISST dataset) used to calculate Kt and DMS flux contained certain time periods with completely missing values, which were incorrectly replaced with zeros. This led to a significant overestimation of Kt and DMS flux in polar regions where sea ice coverage exists. The missing data periods include: November 27–28, 2011; January 7–9, 2016; April 18 to June 30, 2016; and January 7 to February 28, 2017. This issue was resolved beginning with Version 5.0 through the use of updated sea ice data.</li> <li>Compared to Version 5.0, the current version introduces a correction to a bug in the DMS concentration simulation. When input data were missing—primarily in polar regions—the DMS concentration should have been flagged as missing. However, it was previously assigned a value of 1.6031 nM in Version 5.0. In the current version, these values are now replaced with -999 to indicate missing data.</li> </ol>
A global gridded CO2 flux dataset inferred from OCO-2 retrievals using the GONGGA inversion system (v2025)
<p><strong>Data Description</strong></p> <p>Here we provide a global monthly CO2 flux dataset at 1° × 1° spatial resolution for the period 2014.9-2024.12. The dataset is generated using the GONGGA (Global ObservatioN-based system for monitoring Greenhouse GAs) inversion system by assimilating OCO-2 (Observing Carbon Observatory 2) v11.2r column CO2 retrievals that scaled to the WMO X2019 standard. The dataset contains fluxes from biosphere (Net Ecosystem Exchange, NEE) (both prior and posterior), ocean (both prior and posterior), biomass burning emissions and fossil fuel emissions.</p> <p>We also provide the posterior model simulated values corresponding to all measurements contained in the lastest release of NOAA’s ObsPack database (obspack_co2_1_GLOBALVIEWplus_v10.1_2024-11-13 and obspack_co2_1_NRT_v10.1_2025-02-07).</p> <p><strong>Change from v2024</strong></p> <ul> <li>Assimilation of OCO-2 v11.2r retrievals</li> <li>Update of prior fluxes</li> </ul> <p><strong>Data version specification</strong></p> <p>v202x.ori refers to original GONGGA flux data with 3-hourly time resolution and 2° latitude × 2.5° longitude spatial resolution, v202x refers to GONGGA flux data resampled to monthly time resolution and 1° latitude × 1° longitude spatial resolution for facilitating comparisons with other GCP inversion results.</p> <p><strong>Article citation</strong></p> <p>Jin, Z., Wang, T., Zhang, H., Wang, Y., Ding, J., Tian, X., Constraint of satellite CO2 retrieval on the global carbon cycle from a Chinese atmospheric inversion system. Science China Earth Sciences, 2023, 66: 609-618, doi: 10.1007/s11430-022-1036-7.</p> <p>Jin, Z., Tian, X., Wang, Y., Zhang, H., Zhao, M., Wang, T., Ding, J., and Piao, S.: A global surface CO2 flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system, Earth System Science Data, 2024, 16: 2857-2876, doi: 10.5194/essd-16-2857-2024.</p>
Data for: Downscaled gridded global dataset for Gross Domestic Product (GDP) per capita at purchasing power parity (PPP) over 1990-2022
<p>This dataset provides a gridded dataset for GDP per capita at purchasing power parity (PPP) downscaled to an admin 2 level (43,501 admin units). The dataset is based on reported subnational admin data (from 89 countries and 2,708 subnational units) and spans three decades from 1990 to 2022. </p> <p>The dataset is presented in details in the following publication. <strong><em>Please cite this paper when using data. </em></strong></p> <p>Kummu, M., Kosonen, M. & Masoumzadeh Sayyar, S. 2025. Downscaled gridded global dataset for gross domestic product (GDP) per capita PPP over 1990–2022. Scientific Data 12: 178. <a href="https://doi.org/10.1038/s41597-025-04487-x" target="_blank" rel="noopener">https://doi.org/10.1038/s41597-025-04487-x</a></p> <p><strong>Code is available</strong> at: <a href="https://github.com/mattikummu/griddedGDPpc" target="_blank" rel="noopener">https://github.com/mattikummu/griddedGDPpc </a></p> <p> </p> <p><strong>The following data is given (formats in brackets)</strong></p> <ul> <li>GDP per capita (PPP) at admin 0 level (national) (GeoTIFF, gpkg, csv)</li> <li>GDP per capita (PPP) at admin 1 level (at the level of reporting, either admin 1 level or admin 0 level) (GeoTIFF, gpkg, csv)</li> <li>GDP per capita (PPP) at admin 2 level (downscaled from admin 1 level) (GeoTIFF, gpkg, csv)</li> <li>Total GDP (PPP), downscaled admin 2 level GDP per capita (PPP) multiplied by gridded population count, with three resolutions: 30 arc-sec, 5 arc-min, and 30 arc-min (GeoTIFF) </li> <li>Input data for the script that was used to generate the data above (code_input_data.zip). Code available at https://github.com/mattikummu/griddedGDPpc </li> </ul> <p><strong>Files are named as follows</strong><br><em>Format</em>: raster data (GeoTIFF) starts with rast_*, polygon data (gpkg) with polyg_*, and tabulated with tabulated_*. <br><em>Admin levels:</em> adm0 for admin 0 level, adm1 for admin 1 level, and adm2 for admin 2 level<br><em>Product type:</em> GDP per capita at purchasing power parity (PPP): _gdp_perCapita_; and total GDP at purchasing power parity (PPP): _gdp_tot_</p> <p> </p> <p><strong>Metadata </strong></p> <p><em>Grids for GDP per capita data:</em></p> <p>Resolution: 5 arc-min (0.083333333 degrees) (for admin 2 level also 30 arc-min, 0.5 degree, resolution is provided)</p> <p>Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax) </p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: Multiband geotiff; each band for each year over 1990-2022 </p> <p>Unit: USD in 2017 international dollars</p> <p> </p> <p><em>Grids for total GDP:</em></p> <p>Resolution: 30 arc-sec, 5 arc-min or 30 arc-min</p> <p>Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax) </p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: Multiband geotiff; each band for each year over 1990-2022 (5 arc-min, 30 arc-min) or for each five years 1990, 1995, ... 2015, 2020 (30 arc-sec)</p> <p>Unit: USD in 2017 international dollars</p> <p> </p> <p><em>Geospatial polygon (gpkg) files: </em></p> <p>Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax) </p> <p>Temporal extent: annual over 1990-2022</p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: gkpk </p> <p>Unit: USD in 2017 international dollars</p>
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>
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>
Global Gridded Dataset of Average-state Specific Yield
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A reference dataset of global gridded average-state specific yield
<p>The uploaded data is related with our manuscript "A reference dataset of global gridded average-state specific yield".</p>
Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015
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G4D-DOC: A global four-dimensional gridded dataset of ocean dissolved oxygen concentrations retrieval from Argo profiles
<p>Based on temperature and salinity observations from Argo floats, this dataset uses machine-learning methods to reconstruct global ocean dissolved oxygen (DO) concentrations.<br><strong>This version only provides monthly-scale netCDF format data for everyone's use. If you need other time scales, please check previous versions.</strong></p> <h2>Spatiotemporal Characteristics</h2> <ul> <li> <p><strong>Time range:</strong> 2005–2022, <strong>monthly</strong> fields.</p> </li> <li> <p><strong>Geographic range:</strong> Global oceans <strong>excluding the Arctic Ocean</strong>, from 90°S to 84°N and 180°W to 180°E.</p> </li> <li> <p><strong>Horizontal resolution:</strong> 1° × 1° (regular grid).</p> </li> <li> <p><strong>Vertical levels (26):</strong> 10, 20, 30, 40, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1750, 1995dbar .</p> </li> </ul> <h2>Data Format & Conventions</h2> <ul> <li> <p><strong>Format:</strong> NetCDF4</p> </li> <li> <p><strong>Coordinate conventions:</strong></p> <ul> <li> <p><code>lat</code> (Y axis): 89.5 → −89.5 (descending)</p> </li> <li> <p><code>lon</code> (X axis): converted to <strong>−180 → 180</strong> (1° centers)</p> </li> <li> <p><code>depth</code>: ascending (matching the 26 target levels)</p> </li> </ul> </li> <li> <p><strong>Units:</strong> DO in <strong>μmol/kg</strong> (<code>umol kg-1</code>).</p> </li> </ul> <h2>Variables & Dimensions</h2> <ul> <li> <p><strong>Variables kept:</strong> <code>DO</code>, <code>depth</code>, <code>lat</code>, <code>lon</code> (with a single-valued <code>time</code> coordinate).</p> </li> <li> <p><strong>DO dimensions:</strong> <code>(time, depth, lat, lon)</code>.</p> </li> </ul> <h2>Filenames</h2> <ul> <li> <p><strong>Pattern:</strong> <code>G4D_DOC_YYYY_MM.nc</code><br><em>Example:</em> <code>G4D_DOC_2005_07.nc</code> contains the field for <strong>July 2005</strong>.</p> </li> </ul> <h2>Citation & Disclaimer</h2> <p>Please cite the dataset and relevant literature when using it in publications or products.<br>Recommended citation (example):</p> <blockquote> <p>Xue, C., & Wang, Z. (2025). <em>A global four-dimensional gridded dataset of ocean dissolved oxygen concentrations retrieval from Argo profiles</em> (Monthly NetCDF version). Zenodo. <a target="_new" rel="noopener">https://doi.org/</a>10.5281/zenodo.13920233</p> </blockquote> <p>The data producers are not responsible for any losses arising from data use. Map boundaries or masks do not imply official positions.</p> <h2>Contacts</h2> <ul> <li> <p><strong>Cunjin Xue</strong> — <a rel="noopener">xuecj@aircas.ac.cn</a></p> </li> <li> <p><strong>Zhenguo Wang</strong> — <a rel="noopener">zgwang24@m.fudan.edu.cn</a></p> </li> </ul>
Discrete Global Grid System-based Flow Routing Datasets in the Amazon and Yukon Basins
<p>ISEA3H DGGS-based flow routing datasets in the Amazon and Yukon River Basins.</p>
GGWS-PCNN: A global gridded wind speed dataset (1973/01-2021/12; Ongoing Update)
<p><strong>Profile of the dataset</strong></p> <ul> <li>The GGWS-PCNN is a global gridded monthly dataset of 10-m wind speed based on an artificial intelligence algorithm (the partial convolutional neural network), observations from weather stations (the HadISD dataset), and 34 climate models from CMIP6.</li> <li>It has a resolution of 1.25° × 2.5° (latitude × longitude). We will update this dataset as soon as the new HadISD version is accessible.</li> <li>For more details about the dataset and its reconstructed processes, please see our paper "<strong>An artificial intelligence reconstruction of global gridded surface winds</strong>" published in the <em>Science Bulletin</em>.</li> </ul> <p><strong>Notice</strong></p> <ul> <li>The HadISD discovered an issue in the wind data after 2013. So in their version 3.3.0.202201p and later, they fixed this issue. Find the website<strong> </strong><a href="https://www.metoffice.gov.uk/hadobs/hadisd/">Met Office Hadley Centre observations datasets</a> for more details.</li> <li>Due to the limitations of existing AI algorithms in reconstructing data with many missing values, our product has a small number of outliers (e.g. wind speeds less than zero or very high), most of which are located in the Antarctic region. We recommend you remove these outliers before using this dataset.</li> </ul> <p><strong>Reference</strong></p> <p>Lihong Zhou, Haofeng Liu, Xin Jiang, et al. (2022). <a href="https://www.researchgate.net/publication/363806982_An_artificial_intelligence_reconstruction_of_global_gridded_surface_winds">An artificial intelligence reconstruction of global gridded surface winds</a>. Science Bulletin.</p>
Dataset of global gridded monthly crop coefficient, yearly and monthly blue-to-total water footprint ratio, and national unit blue and green water footprints of maize (2000-2021)
<p>The data includes monthly <span><span>crop coefficient</span></span>, yearly and monthly blue-to-total water footprint ratio at a 5 arcminute spatial scale, and the unit water footprint at an annual national (regional) scale of global maize.</p>
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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)
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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.