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387 results for “global map”
The global 30-m mangrove canopy height map for 2020
<p><span>The global mangrove canopy height map with a resolution of 30 m for 2020 (GlobeMCH_2020_30m_v1) was generated by integrating the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), Sentinel-2 optical images and other ancillary data based on Google Earth Engine (GEE) platform. The coordinate system of the GlobeMCH_2020_30m_v1 is World Geodetic System 1984 (WGS 84) and the unit of the mangrove canopy height value is meter. The GlobeMCH_2020_30m_v1 was divided into 101 files, and the range of each file is 11°×11° (10° + 1° buffer).</span></p>
Global gridded maps of yield potential of the Global Yield Gap Atlas (GYGA)
<p>A complete description of maps' methods, accuracy, strengths, and limitations is available in <a href="https://doi.org/10.1038/s43016-024-01029-3">Aramburu-Merlos et al. (Nat. Food, 2024)</a>. </p> <p>Briefly, we combined site-specific yield potential estimates of the <a href="https://www.yieldgap.org/">Global Yield Gap Atlas</a> with gridded environmental predictors in a machine-learning metamodel to generate global maps of yield potential at a 30-arc-second resolution for maize, wheat, and rice, separately for irrigated and rainfed conditions. Model predictions were restricted to their <a href="https://doi.org/10.1111/2041-210X.13650">area of applicability</a> and lands harvested with the given crop and water regime condition (harvested area > 0.5% according to <a href="https://doi.org/10.7910/DVN/PRFF8V">SPAM v2.0</a>).</p> <p> </p>
Telescopus finkeldeyi Haacke, 2013 DAMARA TIGER SNAKE Telescopus finkeldeyi Haacke 2013:281. Holotype: TM 53542 (collector J.A. van Rooyen). Type locality: "Rössing Uranium mine area, Swako- mund [sic] district (2214Db) Namibia." Global conservation status (IUCN): Not Evaluated. Global distribution: The species is known from Angola and Namibia. Ocurrences in Angola (Map 364): The species occurs in southwestern Angola. Namibe: "5 km north Namibé" [-15.20000, 12.15000] (Haacke 2013:285). Taxonomic and distributional notes: Some earlier records of T. semiannulatus polystictus in Namibia actually refer to this recently described species. MAP 364. Distribution of Telescopus finkeldeyi in Angola. in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
Telescopus finkeldeyi Haacke, 2013 DAMARA TIGER SNAKE Telescopus finkeldeyi Haacke 2013:281. Holotype: TM 53542 (collector J.A. van Rooyen). Type locality: "Rössing Uranium mine area, Swako- mund [sic] district (2214Db) Namibia." Global conservation status (IUCN): Not Evaluated. Global distribution: The species is known from Angola and Namibia. Ocurrences in Angola (Map 364): The species occurs in southwestern Angola. Namibe: "5 km north Namibé" [-15.20000, 12.15000] (Haacke 2013:285). Taxonomic and distributional notes: Some earlier records of T. semiannulatus polystictus in Namibia actually refer to this recently described species. MAP 364. Distribution of Telescopus finkeldeyi in Angola.
The global 30-m forest canopy height map for 2020
<p>The global forest canopy height map with a resolution of 30 m for 2020 (GlobeFCH_2020_30m_v1) was generated by integrating the new-generation space-borne LiDAR (Global Ecosystem Dynamics Investigation, GEDI; Ice, Cloud, and Land Elevation Satellite-2, ICESat-2), Sentinel-1 SAR images, Sentinel-2 optical images and other ancillary data based on Google Earth Engine (GEE) platform. The coordinate system of the GlobeFCH_2020_30m_v1 is World Geodetic System 1984 (WGS 84) and the unit of the forest canopy height value is centimeter. The GlobeFCH_2020_30m_v1 was divided into 305 files, and the range of each file is 10°×10°.</p>
Global maps of agricultural expansion potential at a 300 m resolution
<p><strong>Global </strong><strong>maps of agricultural expansion potential</strong><strong> at a 300 m resolution </strong></p> <p>This repository contains data from “Global maps of agricultural expansion potential at a 300 m resolution” study.</p> <p><strong>Abstract:</strong></p> <p>The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using Artificial Neural Network (ANN) models relating locations of recent past conversions (2007-2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with <50% crops) to various predictor variables reflecting topography, climate, soil and accessibility. Cross-validation of the models indicated good performance with Area Under the Curve (AUC) values of 0.88-0.93. Hindcasting of the models from 1992 to 2006 revealed a similar high performance (AUC of 0.83-0.91), indicating that our maps provide representative estimates of current agricultural conversion potential provided that the drivers underlying agricultural expansion patterns remain the same. Our maps can be used to downscale projections of global land change models to more fine-grained patterns of future agricultural expansion, which is an asset for global environmental assessments.</p> <p><strong>Data description:</strong></p> <p>We provide here raster maps of agricultural expansion potential for three categories of agriculture - (i) cropland only, (ii) mosaics with >50% crops, and (iii) mosaics with <50% crops. The source for delineating categories was the ESA CCI land cover data. ESA CCI land cover data recognizes additional categories of agricultural land, however some of them have limited spatial coverage. For that reason, we merged the rainfed cropland and irrigated cropland categories into a single category - cropland only, where a grid cell is largely dominated by crops. Rainfed croplands account for 87% of the this category, while irrigated croplands account for the remaining 13%. Mosaic categories were defined in the same way as in the ESA CCI land cover dataset. Numerical designations of these categories in the ESA CCI land cover dataset are 10, 20, 30, and 40 for rainfed, irrigated, mosaics with >50% crops, and mosaics with <50% crops, respectively.</p> <p>Global maps are provided at the spatial resolution of 10 arc-seconds (~300 meters at the equator). These files are available for three categories in the main folder with the filename prefix "Agri_potential_mosaic_*<em>"</em>. The numerical value in the file name refers to the agricultural category type (10 - cropland only, 30 - mosaics with >50% crops, and 40 - mosaics with <50% crops). In addition to the 10 arc-second layers, we provide aggregated layers with the spatial resolution of 30 arc-seconds, 5 and 10 arc-minutes, for coarse-grained applications and less computationally-intensive analyses. We provide the aggregated layer maps for the minimum, median, mean/average, and maximum values of the aggregated 10 arc-seconds values within the coarser cells. There are in total 9 files provided for each of the aggregated spatial resolutions.</p> <p><strong>Repository content:</strong></p> <p>Full resolution layers:<br> - “Agri_potential_mosaic_10.tif” is the global raster map for cropland only category at the spatial resolution of 10 arc-seconds.<br> - “Agri_potential_mosaic_30.tif” is the global raster map for mosaics with >50% crops category at the spatial resolution of 10 arc-seconds.<br> - “Agri_potential_mosaic_40.tif” is the global raster map for mosaics with <50% crops category at the spatial resolution of 10 arc-seconds.<br> - "readme.txt" is the text file with the basic description and the metadata for the repository.</p> <p>Aggregated layers:<br> This folder contains files with a different spatial resolution (30s, 5m, 10m; see argument "RESL" below).</p> <p>File names for the aggregated maps contain the following information: “Agri_potential_aggregated_RESL_TYPE_CATG.tif”</p> <p>- "RESL" is the spatial resolution of the layer. Value is either "30s", "5m", or "10m", corresponding to spatial resolution of 30 arc-second, 5 arc-minutes, and 10 arc-minutes.</p> <p>- "TYPE" is the type of aggregated values. Value is either "min", "avg", "med", or "max", corresponding to the minimum, mean, median, and maximum values of the aggregated 10 arc-seconds values within the coarser cells.</p> <p>- "CATG" is the category of agricultural land. Value is either "10", "30", or "40", where category 10 is cropland only, category 30 is mosaics with >50% crops, and category 40 is mosaics with <50% crops.</p> <p>Raster metadata:</p> <p>Driver: GTiff<br> Projection proj4string: +proj=longlat +ellps=WGS84 +no_defs</p> <p><strong>Notes on use:</strong></p> <p>Our conversion potential maps are useful for researchers and practitioners interested in downscaling projections of global land change models to a more fine-grained patterns of future agricultural expansion, or interested in assessing the locations and effects of future agricultural expansion, for example in integrated assessment modelling or biodiversity impact modelling. When coupling outputs with integrated assessment modelling, our maps need to be combined with estimates of the expected future demands for agricultural land per socio-economic region. In such a coupled approach, our global conversion potential maps can be used to spatially allocate the additional agricultural land demands. In this context, it is important to note that the modelled relationships between the agricultural conversions and our set of predictors may result in non-zero probabilities also in areas that are highly unlikely to be converted into agriculture, such as urban areas or strictly protected nature reserves. This implies that users of our maps may need to implement an additional map layer that masks areas unavailable for agricultural expansion. We also stress that our maps represent agricultural conversion potential conditional on the predictor variables that we included, implying that our maps do not capture the possible influences of other potentially relevant predictors. For example, our conversion potential models and maps do not account for permafrost, which may pose significant challenges to possible agricultural expansion to higher latitudes in response to climate change.</p>
Global dataset for "Global leaf-trait mapping based on optimality theory "
<p>This repository contains Global data used for “<em>Global leaf-trait mapping based on optimality theory</em><strong>” </strong>published in GEB.</p> <ol> <li>Global_Maps_SLA represents climatology of published Global SLA used for comparison (details products see table 1 and figure 4).</li> <li>Global_Maps_Na represents climatology of published Global Narea used for comparison (details see table 1 and figure 4).</li> <li>Global_Maps_Nmass represents climatology of published Global Nmass used for comparison (details see table 1 and figure 4).</li> <li>TS_SLA is simulated time-series of <em>SLA</em> based on optimality theories from 1992 to 2015</li> <li>TS_Na is simulated time-series of <em>Narea </em>based on optimality theories from 1992 to 2015</li> <li>TS_Nmass is simulated time-series of <em>Nmass </em>based on optimality theories<em> </em> from 1992 to 2015</li> <li>TS_LMA_decidudous is simulated time-series of deciduous <em>LMA</em> based on optimality theories from 1982 to 2016</li> <li>TS_LMA_evergreen is simulated time-series of evergreen <em>LMA</em> based on optimality theories from 1982 to 2016</li> <li>TS_Vcmax25 is simulated time-series of Vcmax25 based on optimality theories from 1982 to 2016</li> </ol>
Map of the global wildland-urban interface
<p>The wildland-urban interface (WUI) is where buildings and wildland vegetation meet or intermingle. It is where human-environmental conflicts and risks are concentrated, including the loss of houses and lives to wildfire, habitat loss and fragmentation, and the spread of zoonotic diseases. However, a global analysis of the WUI has been lacking.</p> <p>This dataset features a global, 10 m resolution map of the wildland-urban interface that was developed in a recent study by the authors of this dataset (see corresponding publication).</p> <p><strong>Temporal extent</strong></p> <p>The data contains data representative for ca. 2020.</p> <p><strong>Data format and units</strong></p> <p>The data are organized in tiles of 100 km x 100 km and follow the EQUI7 tiling grid and projection system. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Please consider the generation of image pyramids before using *.vrt files.</p> <p>The raster dataset contains Wildland-urban interface (WUI) data (one layer), 10 m spatial resolution, 8 discrete classes:</p> <p>1 - Forest/Shrubland/Wetland-dominated Intermix WU</p> <p>2 - Forest/Shrubland/Wetland-dominated Interface WUI</p> <p>3 - Grassland-dominated Intermix WUI</p> <p>4 - Grassland -dominated Interface WUI</p> <p>5 - Non-WUI: Forest/Shrub/Wetland-dominated</p> <p>6 - Non-WUI: Grassland-dominated</p> <p>7 - Non-WUI: Urban</p> <p>8 - Non-WUI: Other</p> <p>In addition, the data contain tabular data on WUI area, population and biomass in the WUI, as well as wildfire area and people affected by wildfire in the WUI per world region, country, subnational administrative unit and biome.</p> <p>The data also contain the key algorithm for WUI mapping (also accessible here: https://github.com/franzschug/global_wildland_urban_interface).</p> <p><strong>Further information</strong></p> <p>For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit the website of SILVIS lab, University of Wisconsin-Madison (http://silvis.forest.wisc.edu/globalwui) to learn more about the Wildland-Urban Interface.</p> <p>The data can be interactively visualizes in a web viewer <a href="https://geoserver.silvis.forest.wisc.edu/geodata/fast/globalwui/">here.</a></p> <p><strong>Corresponding publication</strong></p> <p>Schug, Franz<sup>*</sup>; Bar-Massada, Avi; Carlson, Amanda R.; Cox, Heather; Hawbaker, Todd J.; Helmers, David; Hostert, Patrick; Kaim, Dominik; Kasraee, Neda K.; Martinuzzi, Sebastián; Mockrin, Miranda H.; Pfoch, Kira A.; Radeloff, Volker C. The global wildland-urban interface, DOI: 10.1038/s41586-023-06320-0</p> <p><strong>Funding</strong></p> <p>This research was funded by the NASA Land Cover and Land Use Change Program under agreement 80NSSC21K0310.</p>
Data for detailed temporal mapping of global human modification from 1990 to 2017
<p>Data on the extent, patterns, and trends of human land use are critically important to support global and national priorities for conservation and sustainable development. To inform these issues, we created a series of detailed global datasets for 1990, 1995, 2000, 2005, 2010, 2015, and 2017 to evaluate temporal changes and spatial patterns of land use modification of terrestrial lands (excluding Antarctica). These data were calculated using the degree of human modification approach that combines the proportion of a pixel of a given stressor (i.e. footprint) times the intensity of that stressor (ranging from 0 to 1.0). Our novel datasets are detailed (0.09 km^2 resolution), temporally consistent (for 1990-2015, every 5 years), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. We also provide a dataset that represents ~2017 conditions and has 14 stressors for an even more comprehensive dataset, but the 2017 results should not be used to calculate change with the other datasets (1990-2015). <strong>Note that because of repo file size limits, the datasets for the for the HM overall for 1990 and 1995, as well as major stressors for all years, are located <a href="https://drive.google.com/drive/folders/1D1-S_IuPuPrduBSwCiRfY8b4kPjtho2f?usp=drive_link">this</a> Google Drive. </strong></p> <p>This version 1.5 provides the following updates:</p> <ol> <li> <p>Datasets are provided for each of the 6 stressor groups: built-up areas (BU), agricultural/timber harvest (AG), extractive energy and mining (EX), human intrusions (HI), natural system modifications (NS), and transportation & infrastructure (TI), available now at 300 m resolution for each of the time steps in the 1990-2015 time series.</p> </li> <li> <p>It provides the addition datasets for the years 1995 and 2005, calculated using linear interpolation when stressor data do not provide data at the specific year.</p> </li> <li> <p>The ESA 150 m water-mask dataset (<a href="https://www.mdpi.com/2072-4292/9/1/36">Lamarche et al. 2017</a>) was used to provide better and more consistent alignment of datasets at the ocean-land-inland water interfaces.</p> </li> <li> <p>The built-up stressor uses an updated version of the Global Human Settlement Layer (v2022A).</p> </li> <li> <p>Values provided are 32-bit floating point values, with human modification values ranging from 0.0 to 1.0.</p> </li> </ol> <p>For more details on the approach and methods, please see: Theobald, D. M., Kennedy, C., Chen, B., Oakleaf, J., Baruch-Mordo, S., and Kiesecker, J.: Earth transformed: detailed mapping of global human modification from 1990 to 2017, Earth Syst. Sci. Data., https://doi.org/10.5194/essd-2019-252, 2020.</p> <p>Version 1.5 was completed in collaboration with the Center for Biodiversity and Global Change at Yale University and supported by the E.O. Wilson Biodiversity Foundation. </p>
High-resolution global map of closed-canopy coconut palm
<p>The file ‘GlobalCoconutLayer_2020_v1-2.zip’ contains 878 raster tiles of 100x100 km in geotiff format. The raster files are the result of a convolutional neural network that classified Sentinel-1 and Sentinel-2 annual composites into a coconut palm layer for the year 2020. The images have a spatial resolution of 20 meters and contain two classes: <br> [0] Other land covers that are not coconut palm.<br> [1] Coconut palm.</p> <p>The file ‘GlobalCoconutLayer_2020_densityMap_1km_v1-2.zip’ contains the 20-meter coconut palm classification aggregated to 1 km. The value of each pixel represents the coconut palm area (in squared meters) within the 1-km pixel. </p> <p>The file ‘Validation_points_GlobalCoconutLayer_2020_v1-2.shp’ includes the 10,200 points that were used to validate the product. Each point includes the attribute ‘Class’, which is the class assigned by visual interpretation of sub-meter resolution images, and the attribute ‘predClass’, which reflects the predicted class by the convolutional neural network. The ‘predClass’ values are the same as the raster files:<br> [0] Other land covers that are not coconut palm.<br> [1] Coconut palm.<br> The attribute ‘Class’ contains the following values: <br> [0] Land cover could not be determined because sub-meter resolution data was not available.<br> [1] Other land covers that are not coconut palm.<br> [2] Sparse coconut palm. Low density of coconut palms; between 1 and 4 coconut palms within the 20-meter pixel.<br> [3] Dense open-canopy coconut palm; more than 4 coconut palms within the 20-meter pixel but coconut trees do not reach the full canopy closure. <br> [4] Closed -canopy coconut palm; more than 4 coconut palms within the 20-meter pixel and coconut palms fully cover the ground.<br> [5] Palm species that are not coconut palm.</p> <p> </p> <p>Changelog v1-2:</p> <p>- Pixels classified as class ‘coconut’ were reclassified to class ‘other’ in West Bengal.</p>
Global Exposure Map
<p>The Global Exposure Map (v2023.1) presents the geographic distribution of residential, commercial and industrial buildings. The number of buildings and total replacement cost (in millions of USD$) are presented on a hexagonal grid, with a spacing of 0.30 x 0.36 decimal degrees (approximately 1,000 km2 at the equator). The datasets employed to develop this exposure map were provided by national institutions, or developed within the scope of regional programs or bilateral collaborations. This global map and the underlying databases are based on best available and publicly accessible datasets and models.</p>
Data from: Comparing methods for mapping global parasite diversity
Open the record for dataset details and reuse information.
Data from: Detailed temporal mapping of global human modification from 1990 to 2017
<p>Data on the extent, patterns, and trends of human land use are critically important to support global and national priorities for conservation and sustainable development. To inform these issues, we created a series of detailed global datasets for 1990, 2000, 2010, 2015, and 2017 to evaluate temporal and spatial trends of land use modification of terrestrial lands (excluding Antarctica). Our novel datasets are detailed (0.09 km2 resolution), temporally consistent (for 1990-2015), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. We also provide a dataset for ~2017 with 14 stressors for an even more comprehensive dataset. Also provided is a land/water mask to support subsequent analyses.</p> <p>Please also be sure to check your spam folder if you do not receive an email with the link from Dryad, which is provided because of the large file size.</p>
Global soil saturated hydraulic conductivity map using random forest in a Covariate-based GeoTransfer Functions (CoGTF) framework at 1 km resolution
<p>The global Ksat map at 1 km resolution was developed by harnessing the technological advances in machine learning and availability of remotely sensed surrogate information such as terrain, climate, vegetation, and soil covariates. We merge concepts of predictive soil mapping with a large data set of Ksat measurements and local information (soil, vegetation, climate) into covariate-based “Geo Transfer Functions'' (CoGTFs) to generate global estimates of Ksat values (to highlight the impact of Geo-referenced covariates including various remote sensing maps, we use the term Geotransfer function GTF and not pedotransfer function PTF; in the latter case, typically only soil properties are used to estimate Ksat).</p> <p>The Ksat dataset is provided in GeoTIFF format. A total of 4 files that represent different soil depths (0, 30, 60, and 100 cm) are provided. The Ksat values are log-transformed (log10 Ksat) and cm/day was selected as a standardized unit.</p> <p>The Global Ksat training dataset used for this study is available here:<br> <a href="https://doi.org/10.5281/zenodo.3752721">https://doi.org/10.5281/zenodo.3752721</a></p> <p>The R code used for this study is available here:<br> <a href="https://github.com/ETHZ-repositories/Ksat_mapping_2020">https://github.com/ETHZ-repositories/Ksat_mapping_2020</a></p> <p>For more details / to cite this dataset please use:</p> <ul> <li>Gupta, S., Lehmann, P., Bonetti, S., Papritz, A., and Or, D., (2020): <strong>Global prediction of soil saturated hydraulic conductivity using random forest in a Covariate-based Geo Transfer Functions (CoGTF) framework</strong>. Journal of Advances in Modeling Earth Systems,<strong> </strong>13(4), e2020MS002242. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002242</li> </ul> <p>Other datasets related to this project:</p> <p>The Global vG training dataset is available here:</p> <p><a href="https://doi.org/10.5281/zenodo.5547338">10.5281/zenodo.5547338</a></p> <p>Examples of using this dataset to generate van Genuchten parameters maps can be found in <a href="https://doi.org/10.5281/zenodo.6343570">10.5281/zenodo.6343570</a>.</p> <p>The study was supported by ETH Zurich (Grant ETH-18 18-1). We would like to thank Zhongwang Wei, Samuel Bickel and Simone Fatichi (ETH Zurich) for insightful discussions.</p> <p> </p> <p> </p>
Global Sectoral GDP map at 30'' resolution (SectGDP30) v2.0
<p>- This dataset provides global sector-specific GDP distribution maps (in GeoTIFF format) with a 30-second spatial resolution. It allocates GDP at the 30-arcsecond grid level for three sectors (services, industry, and agriculture) by the distribution of country-level GDP data using high-resolution land cover map.<br>- The source GDP data for allocation is based on nominal GDP for the years 2010, 2015, and 2020, obtained from the World Bank. As the high-resolution land cover map, it uses the built-up area and non-residential area data by the Global Human Settlement Layer (Pesaresi and Politis, 2022) for the service and industrial sectors and the Global cropland map by Potapov et al. (2022) for the agriculture sector. Detailed descriptions of the data creation methodology can be found in Shoji et al. (In Review).<br>- Each pixel represents the monetary value of added value generated by economic activity hypothetically occurring within that pixel. The unit of each pixel value is in millions of USD (current prices for 2010, 2015, and 2020).</p> <p>(Updated to v2.0 on July 1, 2025)</p> <p>This update includes a major change to the spatial allocation method for service and agricultural GDP. For details on the new GDP mapping methodology, please refer to Shoji et al. (In Review). There are no changes to the industrial GDP map.</p> <p> </p> <p>Reference:<br>- Pesaresi M, Politis P.: GHS-BUILT-S R2022A: GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975–2030). European Commission, Joint Research Centre (JRC), 2022.<br>- Potapov P, Svetlana T, Matthew CH, Alexandra T, Viviana Z, Ahmad K, Xiao-Peng S, Amy P, Quan S, Jocelyn C.: Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nature Food 3: 19–28, 2022.<br>- Shoji T, Kajiyama K, Yamazaki D, Kita Y, Watanabe M.: Global spatially-distributed sectoral GDP map for disaster risk analysis. In Review.</p>
Annual global grided livestock mapping from 1961 to 2021
<p>We release <strong>annual global livestock density maps (1961–2021) at 5-km resolution</strong> for eight groups: <strong>cattle, buffaloes, sheep, goats, horses, pigs, chickens, and ducks</strong>. Each raster represents <strong>heads per km²</strong> on a 5-km grid and is produced by a Random Forest workflow trained on multi-source predictors and constrained by theoretical suitability masks. We also provide a matched <strong>per-pixel, per-year uncertainty layer</strong> (0–1) that integrates (i) temporal extrapolation with local sample support, (ii) feature completeness by species/year, and (iii) MESS-like environmental similarity to the training domain. Uncertainty is<strong> </strong>higher in earlier decades (1960s–1990s) and data-sparse regions; users should consult the accompanying uncertainty layers when analyzing long-term trends or making regional inferences.</p>
Mapping Global Nitrogen Mineralization Rates: A Climate-Soil Perspective
<p>The file "Ecosystem_β.tif" represents the spatial distribution of nitrogen mineralization rates in global ecosystems (cropland, grassland, and forest) under different climate models (SSP1-2.6, SSP2-4.5, SSP5-8.5). "Cropland_β.tif" represents the spatial distribution of cropland ecosystems across various SSPβ scenarios. "Grassland_β.tif" represents the spatial distribution of grassland ecosystems under different SSPβ scenarios. "Forest_β.tif" represents the spatial distribution of forest ecosystems across different SSPβ scenarios.</p>
Mapping and modelling global mobility infrastructure stocks, material flows and their embodied greenhouse gas emissions - Data
<p>Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. </p><p>The data in this repository show the material stocks contained in global mobility infrastructure networks at the country-level and mapped at 5arcmins, as well as country-level estimates of material flows for maintenance, replacement and expansion of those infrastructures, and the associated GHG emissions from materials production. This repository contains all data as shown in figures of the article, including the GeoTIFF files for figure 3, and the supplementary data file containing full country-level results.</p><p><strong>Data</strong><br>This dataset includes the following data:</p><ul><li>Global maps of material stocks in mobility infrastructure networks at 5 arcmins, separate for all roads, all rail-based infrastructure, as well as in total and per capita</li><li>Global country-level material stock estimates for mobility infrastructures</li><li>Global country-level estimates of material flows and associated GHG emissions for materials production</li><li>Material intensity in mass per area of road (kg/m²) per road type</li><li>Material intensity in mass per area of railway track (kg/m²) per railway type</li><li>Material intensity in mass per area (kg/m²) per bridges and tunnels</li></ul><p>Material intensity factors are available for iron and steel, concrete, asphalt, aggregate (sand & gravel), timber, and other.</p><p><strong>Further information</strong><br>This dataset complements the following scientific article:</p><p>Wiedenhofer, Dominik, André Baumgart, Sarah Matej, Doris Virág, Gerald Kalt, Maud Lanau, Danielle Densley Tingley, u. a. "Mapping and Modelling Global Mobility Infrastructure Stocks, Material Flows and Their Embodied Greenhouse Gas Emissions". <i>Journal of Cleaner Production</i>, November 2023, 139742. <a href="https://doi.org/10.1016/j.jclepro.2023.139742">https://doi.org/10.1016/j.jclepro.2023.139742</a>.</p><p>For further information please see the publication. You can also contact Dominik Wiedenhofer <a href="mailto:dominik.wiedenhofer@boku.ac.at">dominik.wiedenhofer(a)boku.ac.at</a> and visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project: <i>MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</i></p><p><strong>Funding</strong><br>This research was funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). </p>
ADAPT Global Solar Magnetic Maps - 2010 Sep 18-20 (w/ & w/o farside active region input)
<p>ADAPT (Air Force Data Assimilative Photospheric flux Transport) model global solar magnetic maps using HMI magnetograms with ("wfar") and without ("orig") estimated farside active region flux, for the 3 day period: September 18-20, 2010. The farside emergence of NOAA AR11109 (approximately on 18sep2010, based on STEREO observations of farside) is estimated by modeling the HMI vector observation on the east-limb (i.e., at a CMD of approximately -61.5 degrees) back ~6 days. </p>
Surface drifters and high resolution global simulations mapping of internal tide surface energy
<p>File " gdp_energy.nc " contains surface semidiurnal internal tides binned-averaged energy levels estimated from the Global Drifter Program dataset. </p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSV_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface meridional velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSU_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface zonal velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSV_hf_binned_dl2.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface meridional velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 2deg x 2deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSU_hf_binned_dl2.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface zonal velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 2deg x 2deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides binned-averaged kinetic energy levels estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg .</p> <p>For all files semidiurnal signal is obatined from band-pass filtering.</p>
Global map of foliar N:P
<p>Map associated to the manuscript:</p> <p><span>Vallicrosa, H.</span>, <span>Sardans J.</span>, <span>Maspons J.</span>, & <span>Peñuelas J.</span> (<span>2022</span>). <span>Global distribution and drivers of forest biome foliar nitrogen to phosphorus ratios (N:P)</span>. <em>Global Ecology and Biogeography</em>, <span>31</span>, <span>861</span>–<span>871</span>. <a href="https://doi.org/10.1111/geb.13457">https://doi.org/10.1111/geb.13457</a></p> <p> </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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