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708 results for “Global dataset”
Supplementary material 1 from: Pérez-Luque AJ, Zamora R, Bonet FJ, Pérez-Pérez R (2015) Dataset of MIGRAME Project (Global Change, Altitudinal Range Shift and Colonization of Degraded Habitats in Mediterranean Mountains). PhytoKeys 56: 61-81. https://doi.org/10.3897/phytokeys.56.5482
Table S1: Explanation note: Information about transects of the project. Elevation in m a.s.l. Type: AM = Altitudinal migration; FO = Forest; MH = Marginal Habitat. Subtype: AC-e: Abandoned Cropland: edge; AC-i: Abandoned Cropland: inside; Pp-e: Pine plantations: edge; Pp-i: Pine plantations: inside; TE: Treeline Ecotone. Locality: CA = Robledal de Cáñar; SJ = Robledal de San Juan.
The global biophysical potential for mangrove restoration dataset
<p>This dataset is the restoration potential area estimates, restoration potential index scores and ecosystem service values (carbon and fisheries) to accompany the paper "The global biophysical potential for mangrove restoration" - Worthington et al. (In Review). </p> <h2>Description of files</h2> <p><strong>R Scripts & Data</strong></p> <ul> <li>This folder contain several R scripts and datafiles used to calculate the values in Worthington et al. (In Review)</li> </ul> <p><strong>Mangrove_Typology_v3_Composite</strong></p> <ul> <li>This folder contains a shapefile that is the spatial framework of the research. Full details of the mangrove typology can be found at <a href="../records/8340259">A global biophysical typology of mangroves version 3</a>. <ul> <li>The data in 'Data Exports' can be joined to the mangrove spatial typology shapefile using the 'ID' column.</li> </ul> </li> </ul> <p><strong>Data Exports</strong></p> <ul> <li>This folder contains several data exports that summarise the data behind Worthington et al., (In Review) <ul> <li>Unit_Area_Data.csv: This spreadsheet has area statistic data for each of the 3983 mangrove typological units.</li> <li>Restoration_Index_Data.csv: This spreadsheet has the restoration potential index data for each of the 3983 mangrove typological units.</li> <li>Fisheries_Benefits_Data.csv: 3) Fisheries_Benefits_Data.csv: This spreadsheet has the potential additional individuals of 37 mangrove-affiliated marine fish and invertebrate species of commercial importance whose populations are estimated to increase with mangrove restoration.</li> <li>AGB_Carbon_Benefits_Data.csv: This spreadsheet has the potential secured and restored aboveground biomass (AGB) carbon stock (MgC) over a 40-year time horizon from restoration of mangroves.</li> <li>SOC_Carbon_Benefits_Data.csv: his spreadsheet has the potential secured and restored soil carbon stock (MgC, top 1 m) over a 40-year time horizon from restoration of mangroves</li> <li>Country_Statistics.csv: This spreadsheet summarises the restoration, fisheries and carbon benefits at the national level.</li> </ul> </li> </ul>
Basalt_Source_Insight_global_melts_dataset
<p><span>The global data set used for the present study consists of<span> </span>928 major element analysis of experimental melts of peridotite (100 wt% normalized), transitional and mafic lithology with or without volatiles compiled by Yang et al. (2019).</span></p> <p><span>Yang, Z.-F., Li, J., Jiang, Q.-B., Xu, F., Guo, S.-Y., Li, Y., Zhang, J., 2019. Using Major Element Logratios to Recognize Compositional Patterns of Basalt: Implications for Source Lithological and Compositional Heterogeneities. Journal of Geophysical Research: Solid Earth 124, 3458–3490. https://doi.org/10.1029/2018JB016145</span></p>
Supplementary Datasets for 'Reducing climate change impacts from the global food system through diet shifts'
<p>Supplementary Datasets for <em>Reducing climate change impacts from the global food system through diet shifts.</em></p>
The 4-km monthly global air-sea carbon flux density dataset from 2000 to 2018
<p>We combined 24.6 million ocean observation data points, terabyte-level remote sensing images, and petabyte-level reanalysis data to create a standardized sample set containing more than 3 million records. We proposed a spatiotemporal feature-embedding machine-learning method to solve the issues of sparse data, missing data, and discontinuous changes existing in most current research. Our approach enables efficiently exploiting these massive datasets, leading to the first fully continuous and monthly global ocean partial pressure dataset covering the years 2000 to 2018. Based on the dataset, we presented a depiction of the 4-km monthly global air-sea carbon flux density that encompassed coastal oceans and characterized the ocean carbon budget for the period of 2000 to 2018 by integrating open datasets, including atmosphere partial pressure, wind speed, sea surface temperature, sea surface salinity.</p>
GIMMS FPAR4g, a global dataset of the fraction of absorbed photosynthetically active radiation for 1982—2022
<p><span>The fourth generation of the GIMMS FPAR dataset (GIMMS FPAR4g, version 1.0) offers a temporal resolution of half a month and a spatial resolution of 1/12<span>°</span>, spanning from 1982 to 2022. It was developed based on the PKU GIMMS NDVI that eliminates the orbital drift and sensor degradation issues and high-quality Sensor-Independent FPAR using a combination of a machine learning algorithm and a pixel-wise multi-sensor records integration approach. We have created a separate file in GeoTIFF format for each scene. There are 24 scenes for each year, each of which represents FPAR for half a month. Each scene is associated with a quality control layer inherited from PKU GIMMS NDVI (version: solely). Additionally, we provide global images showcasing the correlation coefficient and RMSE of the pixel-wise regression models for data users. The FPAR4g solely dataset (1982—2015) which has not been calibrated with SI FPAR is also stored in the same repository. It is strongly recommended that users of the GIMMS FPAR4g product read the Readme file before use to ensure proper handling of the fill values and QC flags within the dataset.</span></p>
Global Gross Primary Productivity (GPP) Dataset 2020 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2020</p> <p>Region: Global (in MODIS tile grid)</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publications: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE:</strong> This dataset has been produced in the MODIS tile format, to allow comparison to existing products. </p>
Dataset - Global site-specific health impacts of fossil energy, steel mills, oil refineries and cement plants
<div> <div> <p>Climate change and particulate matter air pollution present major threats to human well-being by causing impacts on human health. Both are connected to key air pollutants such as carbon dioxide (CO<span><span><span>2</span></span></span>), primary fine particulate matter (PM<span><span><span>2.5</span></span></span>), sulfur dioxide (SO<span><span><span>2</span></span></span>), nitrogen oxides (NO<span><span><span>x</span></span></span>) and ammonia (NH<span><span><span>3</span></span></span>), which are primarily emitted from energy-intensive industrial sectors. We present the first study to consistently link a broad range of emission measurements for these substances with site-specific technical data, emission models, and atmospheric fate and effect models to quantify health impacts caused by nearly all global fossil power plants, steel mills, oil refineries and cement plants. The resulting health impact patterns differ substantially from far less detailed earlier studies due to the high resolution of included data, highlighting in particular the key role of emission abatement at individual coal-consuming industrial sites in densely populated areas of Asia (Northern and North-Eastern India, Java in Indonesia, Eastern China), Western Europe (Germany, Belgium, Netherlands) as well as in the US. Of greatest health concern are the high SO<span><span><span>2</span></span></span> emissions in India, which stand out due to missing flue gas treatment and cause a particularly high share of local health impacts despite a limited number of emission sites. At the same time, the massive infrastructure and export capacity build-up in China in recent years is taking a substantial toll on regional and global health and requires more stringent regulation than in the rest of the world due to unfavorable environmental conditions and high population densities. The current phase-out of highly emitting industries in Europe is found not to have started with sites having the greatest health impacts. Our detailed site-specific emission and impact inventory is able to highlight more effective alternatives and to track future progress.</p> </div> </div>
'Reconciling Surface Deflections From Simulations of Global Mantle Convection' : Numerical model dataset
<p><strong>Output data from TERRA simulations included in 'Reconciling Surface Deflections From Simulations of Global Mantle Convection'</strong></p> <p>Dataset includes:</p> <ul> <li>Full density field (`NC_visc_dens_037.tar.gz`)</li> <li>Radial stresses (directory `radial_stresses`)</li> <li>Spherical harmonic coefficients for density field (`density_sph.037`)</li> <li>Radial viscosity factors (`visc.dat`)</li> </ul> <p>Radial stresses are calculated at depths of:</p> <ul> <li>0 km (surface)</li> <li>45 km</li> <li>180 km</li> <li>270 km</li> </ul> <p>and with various amounts of shallow structure removed:</p> <ul> <li>NC_DT_rmir0 - No shallow structure removed</li> <li>NC_DT_rmir1 - 45 km removed</li> <li>NC_DT_rmir2 - 90 km removed</li> <li>NC_DT_rmir3 - 135 km removed</li> <li>NC_DT_rmir5 - 225 km removed</li> <li>NC_DT_rmir7 - 270 km removed</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>
Datasets for "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations"
<p>This repository provides the datasets for the publication "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations".</p>
MUSES 500m Global Annual Gross and Net Primary Productivity Dataset
<p>The MUltiscale Satellite remotE Sensing (MUSES) 500m global annual vegetation productivity dataset includes gross primary productivity (GPP) and net primary productivity (NPP) data from 2001 to 2019. GPP and NPP were estimated with a light use efficiency (LUE) model and GLASS leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) products.</p> <p>The MUSES product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/).</p> <p>The detail information of the MUSES 500m global annual GPP and NPP dataset is as below:</p> <p>Name: MUSES 500m global annual GPP and NPP dataset</p> <p>Period: 2001-2019</p> <p>Projection: Sinusoidal projection;</p> <p>Spatial resolution: 463.3127165 m;</p> <p>Temporal resolution: annual;</p> <p>Data format: zipped GeoTiff file;</p> <p>Data type: unsigned short integer (16bit);</p> <p>Image size: 86400 columns, 36000 rows</p> <p>Upper left coordinates: ULX = -20015109.354 m, ULY = 10007554.677 m;</p> <p>Scale factor: 10. NPP = DN / scale factor; GPP = DN / scale factor;</p> <p>Unit: gC/m<sup>2</sup>/yr.</p> <p> </p> <p>Citation (Please cite these papers when these data are used)</p> <p>1. Wang, J.M., Sun, R., Zhang, H.L., Xiao, Z.Q., Zhu A.R., Wang, M.J., Yu, T., Xiang, K.L., New global MuSyQ GPP/NPP remote sensing products from 1981 to 2018. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14, 5596-5612.</p> <p>2. Wang, M.J.; Sun, R.; Zhu, A.R.; Xiao, Z. Q. Evaluation and Comparison of Light Use Efficiency and Gross Primary Productivity Using Three Different Approaches. Remote Sensing. 2020, 12, 1003.</p> <p>3. Yu, T.; Sun, R.; Xiao, Z.Q. ;Zhang , Q.; Liu, G.; Cui, T.X.; Wang, J.M. Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data. Remote sensing. 2018, 10, 327.</p>
Dataset for the ARON-2 trial, paper Global real-world experiences with pembrolizumab in advanced urothelial carcinoma after platinum-basedchemotherapy: the ARON-2 study.
<p>Dataset for the ARON-2 trial, paper: Global real-world experiences with pembrolizumab in advanced urothelial carcinoma after platinum-basedchemotherapy: the ARON-2 study.</p>
A global 0.05° gross primary productivity of sunlit and shaded leaves dataset via combining two-leaf light use efficiency model with random forest over 2002~2020
<p>The TL-CRF model generated a global 0.05´0.05° product for eight-day gross primary productivity (GPP) of sunlit and shaded canopies from 2002 to 2020 by embedding the random forest (RF) submodule into the two-leaf light use efficiency (TL-LUE) model while considering the seasonal differences in the clumping index. The RF technique was used to integrate various environmental stress factors including meteorological, hydrological, soil properties, and elevation, thereby improving the overall scale of the complex environmental conditions to the maximum LUE. This novel GPP product could support further research on spatial and temporal patterns of the carbon cycle and its association with climate change.</p> <p> </p> <p> </p> <p>Variable: GPP, GPP<sub>sh</sub>, and GPP<sub>su</sub></p> <p>Spatial coverage: global</p> <p>Temporal coverage: 2002 to 2020</p> <p>Spatial resolution: 0.05×0.05°</p> <p>Temporal resolution: eight-day</p> <p>Unite: g C m<sup>−2</sup> d<sup>−1</sup></p> <p>Data format: raster (.tif)</p>
Global Dataset for GRASS GIS
<p><strong>Global Dataset for GRASS GIS</strong><br>This geospatial dataset contains global raster and vector data. The top level directory <em>global-dataset</em> is a GRASS GIS location for the World Geodetic System 1984 (WGS84) with <a href="https://epsg.io/4326">EPSG code 4326</a>. Inside the location there is the <em>PERMANENT</em> mapset, a license file, and readme file.</p> <p><strong>Instructions</strong><br>Install <a href="https://grass.osgeo.org/">GRASS GIS</a>, unzip this archive, and move the location into your <a href="https://grass.osgeo.org/grass77/manuals/grass_database.html">GRASS GIS database</a> directory. If you are new to GRASS GIS read the <a href="https://grass.osgeo.org/documentation/first-time-users/">first time users guide.</a></p> <p><strong>Data Sources</strong></p> <ul> <li><a href="https://www.naturalearthdata.com/">Natural Earth</a></li> <li><a href="https://www.protectedplanet.net/">Protected Planet</a></li> <li><a href="https://visibleearth.nasa.gov/">NASA Blue Marble</a></li> <li><a href="https://www.worldwildlife.org/pages/hydrosheds">WWF HydroSHEDS</a></li> <li><a href="https://www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-world">WWF Terrestrial Ecoregions of the World</a></li> </ul> <p><strong>License</strong><br>This dataset is licensed under the <a href="https://opendatacommons.org/licenses/pddl/index.html">ODC Public Domain Dedication and License 1.0 (PDDL)</a> by Brendan Harmon and Paulo van Breugel.<br> </p>
Global Rayleigh wave amplitude dataset (van Heijst and Woodhouse, 1997, 1999).
<p>The global vertical-component Rayleigh wave amplitude dataset made by Hendrik van Heijst and John Woodhouse.</p> <p>See README for information.</p> <p>Citations:</p> <p>van Heijst, H.J. and Woodhouse, J., 1997. Measuring surface-wave overtone phase velocities using a mode-branch stripping technique. Geophysical Journal International, 131(2), pp.209-230.</p> <p>Jan van Heijst, Hendrik, and John Woodhouse. "Global high-resolution phase velocity distributions of overtone and fundamental-mode surface waves determined by mode branch stripping." Geophysical Journal International 137.3 (1999): 601-620.</p>
Dataset from: "Global research trends of BRUE (brief resolved unexplained event) or formerly ALTE (apparent life-threatening event): A comprehensive visualization and bibliometric analysis from 1988 to 2024"
Open the record for dataset details and reuse information.
GDCLD:A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images
<p>GDCLD : A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images</p> <p>Fang, C., Fan, X., Wang, X., Nava, L., Zhong, H., Dong, X., Qi, J., and Catani, F.: A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-239, in review, 2024.</p> <p> </p> <p>Data description:</p> <p> </p> <p>The training dataset and the validation dataset are composed of UAV, PlanetScope, Gaofen-6 and Map World images of the 5 earthquake regions of Luding, Nippes, Hokkaido, Jiuzhaigou and Mainling. There is no overlapping area in each TIFF. The training dataset and the validation dataset are randomly divided at a ratio of approximately 0.75:0.25.</p> <p> </p> <p>train_dataset:</p> <p>train_data: The train dataset part of the GDCLD data set contains 11162 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 3) (TIFF).</p> <p>train_label: The train dataset part of the GDCLD data set contains 11162 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 1) (TIFF).</p> <p> </p> <p>Validation_dataset</p> <p>val_data: The validation dataset part of the GDCLD data set contains 4459 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 3) (TIFF).</p> <p>val_label: The validation dataset part of the GDCLD data set contains 4459 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 1) (TIFF).</p> <p> </p> <p>Test_dataset (Lushan, Sumatra, Mesetas and Palu dataset)</p> <p>This package contains the original files of remote sensing images from three sources: UAV, Map World, and PlaneScope belonging to the Lushan, Sumatra, Mesetas and Palu earthquake regiones, which are used to display the test area.<br><br>Future work:<br>The future work includes additional landslide data that the authors will continue to upload. In this 2.0 version update, we have added UAV imagery and interpreted data for loess landslides triggered by the December 2023 M6.2 earthquake in Gansu, China, with a resolution of 0.1 m. Due to authorization constraints, we can only provide PNG files without geographic coordinates. Additionally, this update includes PlanetScope imagery of landslides induced by heavy rainfall in Guangdong, China, in 2024, as well as PlanetScope imagery and landslide labels for events triggered by the Hualien earthquake in Taiwan.<br><br>Please note that this landslide dataset is publicly available exclusively for scientific research purposes and must not be used for commercial purposes.</p>
Dataset for "Global Sensitivity Analysis of Nitric Oxide-Related Chemical Reaction Rates in the Global Ionosphere Thermosphere Model"
<p>This repository contains all the datasets and scripts used to generate the figures in the paper tilted "Global Sensitivity Analysis of Nitric Oxide-Related Chemical Reaction Rates in the Global Ionosphere Thermosphere Model". The data and scripts are organized according to the figure numbers to facilitate ease of use and reproducibility.</p> <p><strong>Directory Structure</strong><br>Each figure has its own dedicated folder. Inside each folder, you will find:</p> <p><strong>Data files: </strong>These files contain the dataset used to generate the corresponding figure.<br><strong>Scripts: </strong>Python or other relevant scripts needed to process the data and create the figure.<br><strong>README.txt: </strong>Each figure folder includes a separate README.txt file that provides detailed instructions on how to run the scripts.<br><strong>How to Use</strong><br>Navigate to a Figure's Folder: Locate the folder corresponding to the figure number you are interested in (e.g., Fig_01, Fig_02, etc.).</p> <p>Read the README.txt: Open the README.txt file in that folder. It contains specific instructions on how to execute the code and generate the figure, along with any necessary setup details.</p> <p><strong>Run the Scripts:</strong> Follow the instructions in the README.txt file to run the script(s) and generate the figure.</p>
Dataset for "Estimating high-resolution profiles of wind speeds from a global reanalysis dataset using TabNet"
<p>The dataset supports the article "Estimating high-resolution profiles of wind speeds from a global reanalysis dataset using TabNet", which is accepted to be published in the Environmental Data Science journal. <br><br>The description of the files is as follows:</p> <ol> <li>ERA5.nc: <br> <ul> <li>Dimensions: (location: 11, time: 166560)<br>Coordinates:<br> longitude (location) float32 ...<br> latitude (location) float32 ...<br> * time (time) datetime64[ns] 2000-01-01 ... 2018-12-31T23:00:00<br> year (time) int64 ...</li> <li>Variables: 10ws, 100ws, 100alpha, 975ws, 950ws, 975wsgrad, 950wsgrad, zust, i10fg, t2m, skt, stl1, d2m, msl, blh, cbh, ishf, ie, tcc, lcc, cape, cin, bld, t_975, t_950, 2mtempgrad, sktempgrad, dewtempsprd, 975tempgrad, 950tempgrad, sinHR, cosHR, sinJDAY, cosJDAY, 10ws_delta1, 10ws_delta2, 10ws_delta3, 10ws_delta4, 10ws_delta5, 10ws_delta6, 100ws_delta1, 100ws_delta2, 100ws_delta3, 100ws_delta4, 100ws_delta5, 100ws_delta6, 975ws_delta1, 975ws_delta2, 975ws_delta3, 975ws_delta4, 975ws_delta5, 975ws_delta6, 950ws_delta1, 950ws_delta2, 950ws_delta3, 950ws_delta4, 950ws_delta5, 950ws_delta6</li> </ul> </li> <li>2000.nc: <ul> <li>Dimensions: (obs: 11, time: 8784, heightAboveGround: 12)<br>Coordinates:<br> lat (obs) float64 ...<br> lon (obs) float64 ...<br> * time (time) datetime64[ns] 2000-01-01 ... 2000-12-31T23:00:00<br> * heightAboveGround (heightAboveGround) float64 10.0 15.0 ... 400.0 500.0<br>Dimensions without coordinates: obs<br>Data variables:<br> data (obs, time, heightAboveGround) float64 ...</li> </ul> </li> <li> 2001.nc: <ul> <li>Dimensions: (obs: 11, time: 8760, heightAboveGround: 12)<br>Coordinates:<br> lat (obs) float64 ...<br> lon (obs) float64 ...<br> * time (time) datetime64[ns] 2001-01-01 ... 2001-12-31T23:00:00<br> * heightAboveGround (heightAboveGround) float64 10.0 15.0 ... 400.0 500.0<br>Dimensions without coordinates: obs<br>Data variables:<br> data (obs, time, heightAboveGround) float64 ...</li> <li>Data is the wind speed at multiple height levels</li> </ul> </li> </ol>
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.