Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

2,009

datasets available to search

ShareScore release 0.9.0

Dataset results

2,009 results for “Global data”

Learn how ShareScore rates datasets ↗

zenodo48/100

Global restoration opportunities in tropical rainforest landscapes - Supplementary Materials - Spatial Data Layers

Global restoration opportunities in tropical rainforest landscapes Sci Adv 5 (7), eaav3223 DOI: 10.1126/sciadv.aav3223 <a href="https://advances.sciencemag.org/content/5/7/eaav3223">https://advances.sciencemag.org/content/5/7/eaav3223</a> Supplementary Materials <a href="https://advances.sciencemag.org/content/suppl/2019/07/01/5.7.eaav3223.DC1">https://advances.sciencemag.org/content/suppl/2019/07/01/5.7.eaav3223.DC1</a> Spatial Data layers: <a href="https://doi.org/10.5281/zenodo.3233495">https://doi.org/10.5281/zenodo.3233495</a> _OutR10: r_10.img → Global restoration opportunity score (ROS) r_10_sc.img → Global restoration opportunity score (ROS) – rescaled 0-1 r_10_nt_sc.img → Neo Tropic restoration opportunity score (ROS) – rescaled 0-1 r_10_aa_sc.img → Australiasia restoration opportunity score (ROS) – rescaled 0-1 r_10_at_sc.img → Afro Tropic restoration opportunity score (ROS) – rescaled 0-1 r_10_im_sc.img → Indo Malay restoration opportunity score (ROS) – rescaled 0-1 r_10_nt_sc.img → Neo Tropic restoration opportunity score (ROS) – rescaled 0-1   _OutBasics: r_1.img → Study Area r_2.img → Restorable Area r_3.img → Restoration Benefits r_4.img → Restoration feasibility _OutCountry: r_10_XXX_sc.tif → restoration opportunity score (ROS) for country XXX – rescaled 0-1 _OutHotspots: r_10_hotspot_XXX_hotspot_area_sc.tif → restoration opportunity score (ROS) for conservation hotspot area XXX – rescaled 0-1 r_10_hotspots_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in conservation hotspots _OutKBA: r_10_XXX_sc.tif → restoration opportunity score (ROS) for Key Biodiversity Area XXX – rescaled 0-1 r_10_kba_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in Key Biodiversity Areas _OutAichi: r_10_aichi_XXX.tif → Top 15% area of with highest restoration opportunity score (ROS) in country XXX r_10_aichi.img → Top 15% area of with highest restoration opportunity score (ROS) global _OutBonn: r_10_XXX_Bonn.img → Area with highest restoration opportunity score (ROS) in country XXX according to their Bonn Challenge commitments   _OutParis: r_10_at_paris.img → Area with highest restoration opportunity score (ROS) in Afro Tropic Nationally Determined Contributions to the Paris Climate Agreement r_10_im_paris.img → Area with highest restoration opportunity score (ROS) in Indo Malay Nationally Determined Contributions to the Paris Climate Agreement r_10_nt_paris.img → Area with highest restoration opportunity score (ROS) in Neo Tropic Nationally Determined Contributions to the Paris Climate Agreement _OutTEOW: r_10_ECOREGION_XXX_sc.tif → restoration opportunity score (ROS) for Ecoregion XXX – rescaled 0-1 r_10_ECOREGION_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in Ecoregions   _OutAll alltargets.img → Area with highest restoration opportunity score (ROS) according to all targets (excluded from the paper)

opencc-by-4.0Jun 2019View details →

zenodo48/100

Data for the 'Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation'

All numerical data used in the manuscript “Evaluation of global simulations of aerosol particle number and cloud condensation nuclei, and implications for cloud droplet formation” by G. S. Fanourgakis et al. ACP (2019) are categorized and provided in a number of files. All files are in the hdf format. A readme file is also provided. These data files have been created by G. S. Fanourgakis (fanourg@uoc.gr) Details on the data are provided in Fanourgakis et al. Atmos. Chem. Phys. 2019 https://doi.org/10.5194/acp-2018-1340  (e-mail to <a href="mailto:mariak@uoc.gr">mariak@uoc.gr</a> ; <a href="mailto:athanasios.nenes@epfl.ch">athanasios.nenes@epfl.ch</a> ) For an in-depth understanding of the description below, a study of the above mentioned manuscript is required. (A) Station model results The station results can be found in files with filenames of the form: station $MODEL.nc The “$MODEL” (as well as all names starting with “$”) indicates a variable, and more specifically one of the models participated in the present study. The values of this variable are tabulated in Table 1 in the readme file. In each file a number of computational results are provided by the specified model for all nine (9) stations that provided observational data. The name of the variable is formed as: st $STATION $FIELDhour st $STATION $FIELD month where all possible values of the variables $STATION and $FIELD are tabulated in Tables 2 and 3 in the readme file, respectively. The extension _hour denotes that hourly values for the field are provided, while the extension _month the monthly average of this quantity. For example, the variable st Finokalia CCN02 hour found in the file station_TM4-ECPL.nc, contains the hourly values of the CCN0.2 at the Finokalia station as computed by the TM4-ECPL model. In a similar way, in the file station_EMAC.nc, the variable below gives the monthly values of dust at Vavihill as computed with the EMAC model. st Vavihill DU month Notice also that in all files hourly and monthly data are provided for the time period from 1-1-2011 up to 31-12-2015 (60 months and 43,824 hours) (B) Station observational results There is one file that contains all observational data from Schmale et al., SCIENTIFIC DATA | 4:170003 | DOI: 10.1038/sdata.2017.3, 2017 (<a href="mailto:julia.schmale@psi.ch">julia.schmale@psi.ch</a>) and the data that were computed based on the observations (i.e. number of cloud droplets) (contact person: athanasios.nenes@epfl.ch). The file is station observations.nc while the following fields are contained in there: st $STATION $FIELDhour st $STATION $FIELD month The values of variables are given in the Tables 2 and 3 in the readme file. The time period covered is from 1-1-2011 up to 31-12-2015. Notice that due to the lack of observations a lot of data are missing. For missing observational data the value -9999.999 is given. Contact person for the observational data is Julia Schmale (julia.schmale@psi.ch). (C) Station Multi-model Median Monthly averages of the models can be found in the file station MMM.nc The following fields can be found in the file st $STATION $FIELD month median st $STATION$FIELD month quart25 st $STATION$FIELD month quart75 where the values of the variables $STATION and $FIELD can be found in Tables 2 and 3, respectively. The extension median corresponds to the multi-model median, while the quart25 and quart75 to the 25 % and 75 % quartiles, respectively. (D) Global model results In the following single file can be found for each of the models the surface distribution of various fields. results global models year2011.nc They correspond to the annual mean of the year 2011. The resolution of the grid is 1◦ × 1◦. The file contains the following variables: $FIELD $MODEL The $FIELD and $MODEL can be found in Tables 3 and 1, respectively. (E) Global average results In the file surface_ global_average_year2011.nc can be found in 5◦×5◦ resolution, the Multi-model median of surface distribution of the various fields denoted in Table 3 and their corresponding diversity. The names of the variables are formed as: med $FIELD div $FIELD where, ‘med’ stands for median and ‘div’ for diversity calculated as standard deviation divided by the mean of the model results. Tables and details on the fields provided are given in the readme file.

opencc-by-4.0Jul 2019View details →

zenodo48/100

Supplementary data: Impact of a global temperature rise of 1.5 degrees Celsius on Asia's glaciers

Supplementary data to <a href="http://doi.org/10.1038/nature23878">Kraaijenbrink, Bierkens, Lutz and Immerzeel, 2017. Impact of a global temperature rise of 1.5 degrees Celsius on Asia’s glaciers, Nature.</a> Model code can be found <a href="https://doi.org/10.5281/zenodo.2548689">here</a>. Please note that all data is provided in 7z-archives. To extract the data use the open source software <a href="http://www.7-zip.org/">7zip</a>.   Model input: Raster data The raster data that is required to run the model is available for the entire High Mountain Asia (complete-hma.7z) and for each <a href="https://www.glims.org/RGI/">RGI v5.0</a> sub-region (<region-name>.7z). The 7z-archives hold separate folders for each glacier, which are named by RGI glacier ID. The rasters for each glacier are in GeoTIFF format, have a 30 m resolution, are in local UTM projection (WGS84 datum), and are clipped to the RGI glacier extent. Rasters present for each glacier are: <pre>classification.tif  The debris classification made in google earth engine. debris-thickness-50cm.tif  Debris thickness estimation based on Landsat 8 surface temperature. ice-thickness.tif  Ice thickness determined using the Glabtop2 model ls8-composite-b456.tif  Landsat 8 warmest-pixel optical composite (bands RED, NIR, SWIR1) ls8-composite-tsurf.tif  Landsat 8 warmest-pixel surface temperature composite srtm-elevation.tif  SRTM 1 arc second elevation data srtm-slope.tif  Slope of the SRTM 1 arc second data</pre>   Model input: RDS data The general model input data (mbg-model-rds-data.7z) is stored in R’s binary RDS format and <a href="https://www.r-project.org/">R</a> is required to open and read the data. Files present in the 7z-archive are: <pre>dP_factors_2006-2100.rds  Precipitation changes (delta factors) up to 2100 dT_degrees_2006-2100.rds  Temperature changes (Kelvin) up to 2100 glacier-data.rds  Glacier centroids with current climate and mass balance input ostrem_meancurve.rds  The Östrem curve used by the model rgi-subregions.rds  RGI sub-region polygons for Asia</pre>   Output data Region-aggregated output is available in ESRI Shapefile format for the RGI sub-regions, major river basins, and for a 1×1 degree grid (output-shapefiles.7z). The attribute tables of all shapefiles hold data on the occurrence of debris as well as current glacier area and volume, and volume projections for the end of century. The available shapefile attributes are: <pre>count number of glaciers a_total total glacier area (m2) a_debris glacier area covered by debris (m2) a_ela glacier area below modelled ELA (m2) a_ela_deb glacier area below modelled ELA covered by debris (m2) v_total total glacier volume (m3) v_debris glacier volume covered by debris (m3) v_ela glacier volume below modelled ELA (m3) v_ela_deb glacier volume below modelled ELA covered by debris (m3) m_total_gt total glacier mass (gigaton) volST_EOC volume remaining in end of century under a stable current temperature vol15_EOC volume remaining in end of century under 1.5 degree scenario vol26_EOC volume remaining in end of century for the RCP2.6 model ensemble vol45_EOC volume remaining in end of century for the RCP4.5 model ensemble vol60_EOC volume remaining in end of century for the RCP6.0 model ensemble vol85_EOC volume remaining in end of century for the RCP8.5 model ensemble</pre>

opencc-by-4.0Sep 2017View details →

zenodo48/100

Supplementary Data: Axion global fits with Peccei-Quinn symmetry breaking before inflation using GAMBIT

Description of Supplementary Data This record contains the samples used to create the figures (excluding validation and prior dependence plots) and to derive most of the results in Hoof et al., “Axion global fits with Peccei-Quinn symmetry breaking before inflation using GAMBIT” (available on the <a href="https://arxiv.org/abs/1810.07192">arXiv</a>). Please contact the authors if you are interested in other samples, YAML files or plotting scripts. This record consists of <ul> <li>21 <code>YAML</code> files (6 for <code>T-Walk</code>, 15 for <code>Diver</code>). Running <code>./gambit -f path/to/YAML/file.yaml</code> in the GAMBIT directory will start the scan. However, most users might want to adjust the output file name and directory as well as the settings for the samplers to their systems.</li> <li>21 <code>hdf5</code> files (6 for <code>T-Walk</code>, 15 for <code>Diver</code>). These files contain the actual samples and were compressed using the <code>tar</code> format.</li> <li>Two example <code>pip</code> files (<code>2_QCDAxion_10M1.pip</code> for <code>Diver</code> samples, <code>2_QCDAxion_3041.pip</code> for <code>T-Walk</code> samples) for producing plots from the corresponding <code>hdf5</code> files, using <a href="https://github.com/patscott/pippi"><code>pippi</code></a> and <code>functions.py</code>.</li> </ul> The files follow the naming scheme <code>V_ModelName_[S][C][I][R][E]</code> plus one of the extensions <code>.yaml</code>, <code>.hdf5.tar.gz</code>, or <code>.pip</code>. <ul> <li><code>V</code>: This internal version number can be ignored, but should be quoted when asking for help with the plotting scripts</li> <li><code>ModelName</code>: Corresponds to the axion models in the paper (GeneralALP, QCDAxion, DFSZAxion_I, DFSZAxion_II, KSVZAxion)</li> <li><code>S</code>: Scanner (<code>S=1</code>: <code>Diver</code>, <code>S=3</code>: <code>T-Walk</code>)</li> <li><code>C</code>: Switch to include (<code>C=1</code>) or exclude (<code>C=0</code>) the White Dwarf cooling hints</li> <li><code>I</code>: Setting for the initial misalignment angle θi (<code>I=4</code>: flat prior on θi with values in [-3.1415, 3.1415]). <code>I=M</code> is used to indicate that the file includes merged samples from other scans in addition to the corresponding <code>I=4</code> scan.</li> <li><code>R</code>: Setting for the DM relic density likelihood (<code>R=1</code>: upper limit, <code>R=2</code>: matching the DM density)</li> <li><code>E</code>: Extra digit for the anomaly ratio E/N; only for KSVZAxion models (<code>E=1</code>: 0, <code>E=2</code>, 2/3, <code>E=3</code>: 5/3, <code>E=4</code>: 8/3), DFSZAxion-I models (<code>E=1</code>: 8/3), DFSZAxion-II models (<code>E=2</code>: 2/3), or some GeneralALP files (<code>E=a</code>: “QCD-like setting” with β = 7.94, Tcrit = 147 MeV; <code>E=b</code>: “Simple ALP-like setting” with β = 0, Tcrit irrelevant)</li> </ul> For convenience, we provide a mapping between the figures in the paper and the <code>hdf5</code> files: <ul> <li>Fig. 1: none</li> <li>Figs 2 - 11: Validation plots</li> <li>Figs 12 + 13: 2_GeneralALP_10M2</li> <li>Fig. 14: 2_GeneralALP_10M2a, 2_GeneralALP_10M2b</li> <li>Fig. 15: 2_QCDAxion_10M1, 2_QCDAxion_10M2</li> <li>Fig. 16: 2_QCDAxion_3041, 2_QCDAxion_3042</li> <li>Figs 17 + 18: 2_QCDAxion_10M1, 2_QCDAxion_10M2, 2_QCDAxion_30M1, 2_QCDAxion_30M2</li> <li>Fig. 19: 3_KSVZAxion_10M11, 3_KSVZAxion_10M12, 3_KSVZAxion_10M13, 3_KSVZAxion_10M14, 3_DFSZAxion_I_10M11, 3_DFSZAxion_II_10M12</li> <li>Fig. 20: 2_QCDAxion_10M1, 3_KSVZAxion_10M11, 3_KSVZAxion_10M12, 3_KSVZAxion_10M13, 3_KSVZAxion_10M14, 3_DFSZAxion_I_10M11, 3_DFSZAxion_II_10M12</li> <li>Fig. 21: 2_QCDAxion_11M1, 2_QCDAxion_11M2</li> <li>Fig. 22: 2_QCDAxion_3141, 2_QCDAxion_3142</li> <li>Figs 23 + 24: 2_QCDAxion_3041, 2_QCDAxion_3042, 2_QCDAxion_3141, 2_QCDAxion_3142</li> <li>Fig. 25: 2_QCDAxion_3141, 2_QCDAxion_3142</li> <li>Fig. 26: 2_QCDAxion_11M1, 3_DFSZAxion_I_11M11, 3_DFSZAxion_II_11M12</li> <li>Fig. 27: 2_QCDAxion_3141, 3_DFSZAxion_I_31411, 3_DFSZAxion_II_31412</li> <li>Fig. 28: Validation plot</li> <li>Fig. 29: Prior dependence plot</li> </ul> A few caveats to keep in mind: <ul> <li>The YAML files are designed to work with <code>GAMBIT 1.3.1</code>, and the pip files are tested with <code>pippi 2.1</code>, commit 1a08644. They may or may not work with later versions of either software (these working versions/commits can always be obtained via the <code>git</code> history).</li> <li>The <code>pip</code> files will produce an approximately complete, but very basic version of plots in the paper. Re-creating all the plots in the paper requires various manual, undocumented interventions such as additions, deletions and combination of the plotting scripts created by <code>pippi</code>. Users wishing to reproduce the more advanced plots in the paper should contact the authors for tips, scripts, or experiment for themselves.</li> </ul>

opencc-by-4.0Oct 2018View details →

zenodo48/100

Supplementary Data: Global rise in forest fire emissions linked to climate change in the extratropics

Supplementary Data for the paper "Global rise in forest fire emissions linked to climate change in the extratropics" by Jones et al. (2024, Science). The records include mapped pyromes and data and code used to delineate the pyromes. <h3>Mapped Pyromes</h3> The data records include mapped pyromes in three forms: <ol> <li>Shapefile (Jones_etal_2024_Global_Forest_Pyromes.shp.zip). Vector features in shapefile format containing data fields pyrome ID and pyrome name. The zipped file contains .shp, .dbf, .prj, .shx files.</li> <li>Lower-resolution NetCDF (Jones_etal_2024_Global_Forest_Pyromes_Qdeg.nc). NetCDF version 4 file containing gridded values of pyrome ID at quarter-degree resolution.</li> <li>Higher-resolution NetCDF (Jones_etal_2024_Global_Forest_Pyromes_005deg.nc). NetCDF version 4 file containing gridded values of pyrome ID at 0.05 degree resolution.</li> </ol> Shapefiles are accessible via GIS programmes such as QGIS or ArcGIS. All files .shp, .dbf, .prj, .shx files must be stored in a single directory NetCDF files can be access by a variety of programming languages such as Python and R. For quick visualisations and access to the data structure, we suggest using the Panoply  tool https://www.giss.nasa.gov/tools/panoply/. <h3>Correlation Data</h3> The data records (Correlation_Qdeg.zip) include gridded quarter-degree correlations between forest burned area (BA) and each of the following variables: <ul> <li>Fire weather index</li> <li>Atmospheric instability (continuous Haines index)</li> <li>Lightning flash density</li> <li>Soil moisture</li> <li>Vegetation productivity (Normalised Difference Vegetation Index)</li> <li>Population density</li> <li>Cropland cover</li> <li>Pasture cover</li> <li>Road density</li> <li>Potential fuel loads - surface fuels</li> <li>Potential fuel loads - shrub fuels</li> <li>Potential fuel loads - canopy and ladder fuels</li> <li>Terrain ruggedness index</li> <li>Forest area density</li> </ul> The BA data derive from MODIS MCD64A1 collection 6.1 (Giglio et al., 2018). BA data for forests is masked using the MODIS MOD44B product (DiMiceli et al., 2021) with a 30% tree cover threshold. The predictor data derive from multiple sources as desribed by Jones et al. (2024). See Supplementary Methods and Materials. The gridded correlations data are provided in Hierarchical Data Format version 5 (.hdf5) files, zipped to Correlation_Qdeg.zip. File names describe the variables used. Cropland_Pasture_Qdeg.hdf5 contains data for both cropland and pasture. Each file contains layers describing the Spearman's rho (ρ) correlation coefficient and the related p-value. As explained and justified by Jones et al. (2024), the correlation structure used depends on the variable (see Supplementary Methods and Materials) as per the following categories: <ul> <li>Fire Weather Index, Atmospheric Instability, and Lightning Flash Density: Monthly correlation between forest BA and each variable across all fire season months in the period 2001-2021 at the quarter-degree resolution.</li> <li>Soil Moisture: Inter-annual correlation between (i) mean soil moisture during the fire season and (ii) accumulated forest BA during the fire season at quarter-degree resolution across years 2001-2021. </li> <li>Vegetation Productivity (NDVI): Inter-annual correlation between (i) mean NDVI during the prior growing season and (ii) accumulated forest BA during the fire season at quarter-degree resolution across years 2001-2021. </li> <li>Population Density, Cropland Cover, Pasture Cover, Road Density, Terrain Ruggedness Index, Forest Area Density: Spatial correlation between mean annual forest BA and each variable across the 0.05° cells within each quarter-degree cell during 2001-2021.</li> </ul> Note that these grids are provided for insights into spatial variation in the input correlation data. Pyromes are defined based on correlations fitted on the spatial scale of Olson ecoregions, not quarter-degree grid cells (see further details below). <h3>Clustering Code</h3> DEMO_Clustering.zip contains R Statistics code for clustering forest ecoregions into pyromes based on correlations observed between forest BA and 14 predictors at regional level. The Input directory contains a .RData data frame with correlations between forest BA and each predictor for ecoregions. For demonstrative purposes the code is applied to cluster forest ecorgions of North America into pyromes. The Regions directory contains ecoregions of North America in shapefile format. The Output directory contains output generated by M. Jones, which can be used for validation purposes once other users have trialled the code.

opencc-by-4.0Oct 2023View details →

zenodo48/100

Global peatland, bare rock and bare sand extent at 100 m to 1 km spatial resolution based on multisource data

Ensemble estimate of the global distribution of <a href="https://en.wikipedia.org/wiki/Peatland">peatlands</a> / extent (peatland.extent_wri.gfw.peatgrids_p). This is a simple average from three (3) sources of data: <ol> <li><a href="https://data.globalforestwatch.org/datasets/gfw::global-peatlands/about">WRI Global Peatlands extent map</a> at 30-m (250-m effective);</li> <li><a href="https://doi.org/10.5281/zenodo.12559238">PEATGRIDS</a> at 1-km;</li> <li><a href="https://globalpeatlands.org/new-online-global-peatland-map-asian-peatlands-story-map-presenting-best-peatlands-mapping">Global Peatlands Map 2.0</a> produced by the Global Peatlands Initiative;</li> </ol> The average between the three sources is an extent map with value 0–100%. The refence period is 2000–2020, although probably most of data is based on pre 2010. For more details about the source data please refer to the cited references below. Bare rock and bare sand estimates are based on the following two sources of data: <ol> <li><a href="https://land.copernicus.eu/en/products/global-dynamic-land-cover">Copernicus GLC land cover</a> at 100-m for 2015 and 2019;</li> <li><a href="https://lcz-generator.rub.de/global-lcz-map">Local Climate zones</a> map at 100-m for 2018;</li> </ol> Two classes are considered: (1) probability of occurrence of bare rock (bare.rock_glc.gfz_p), (2) probability of occurrence of bare sand i.e. shifting sand (bare.soil.sand_glc.gfz_p). We recommend using only the 1-km data for spatial modeling. The time-series of bare areas (bare.areas_esa.cci_p) are based on the <a href="https://climate.esa.int/en/odp/#/project/land-cover">ESA CCI Land Cover time-series</a> (2000–2022) 300-m resolution data; also available at 1-km resolution based on "average" resampling.

opencc-by-4.0Oct 2024View details →

zenodo48/100

Global Carbon Budget 2022, surface ocean fugactiy of CO2 (fCO2) and air-sea CO2 flux of individual Global ocean biogechemical models and surface ocean fCO2-based data-products

Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (data-products). There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs.   Note: These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. The best estimates of the annual global ocean carbon sink, based on the native grids of data-products and GOBMs and with the adjustments described in the Global Carbon Budget 2022 (https://doi.org/10.5194/essd-14-4811-2022, section C3), are available in the Global Carbon Budget 2022 spreadsheet. The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 13 of the Global Carbon Budget 2022 paper (https://doi.org/10.5194/essd-14-4811-2022), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.17 GtC yr-1, Tropics: 0.16 GtC yr-1, South: 0.32 GtC yr-1, see GCB 2022 paper, section 2.4.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because adjustments were applied only for global fluxes. What is in the files? (1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly): fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: north, tropics, south fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude area: Area per pixel, dimensions: latitude, longitude area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south (2) The files for the GOBMs contain the following fields, for simulation A (‘contemporary simulation’, including effects of rising CO2, climate change and variability) and simulation B (‘control simulation’, constant CO2, no climate change and variability). Temporal resolution: monthly fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude area: Area per pixel, dimensions: latitude, longitude (3) One file ‘GCB-2022_OceanModel_RegionalBreakdown_1959-2021.nc’ with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual. Fair data use statement: The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use. Citation: Please cite the Global Carbon Budget 2022 (Friedlingstein et al., 2022, ESSD, https://doi.org/10.5194/essd-14-4811-2022) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2022 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720). Acknowledgement: Please add the following text in the acknowledgement of your paper: “We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output.” Co-authorship: An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication. Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make additional output from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudget.org/

opencc-by-4.0Dec 2021View details →

zenodo48/100

Data supplement for "Land use intensification increasingly drives the spatiotemporal patterns of the global human appropriation of net primary production in the last century"

This data supplements the publication "Land use intensification increasingly drives the spatiotemporal patterns of the global human appropriation of net primary production in the last century" by Thomas Kastner, Sarah Matej, Matthew Forrest, Simone Gingrich, Helmut Haberl, Thomas Hickler, Fridolin Krausmann, Gitta Lasslop, Maria Niedertscheider, Christoph Plutzar, Florian Schwarzmüller, Jörg Steinkamp, Karl-Heinz Erb. For details, please refer to the included readme file and to the publication (<a href="https://doi.org/10.1111/gcb.15932">https://doi.org/10.1111/gcb.15932</a>) In this new Version 1.01, we changed the file structure to make the data more accessible, we added data on means across modulations as used in the paper, and we include csv files with national totals for the different HANPP components.

opencc-by-4.0Sep 2021View details →

zenodo48/100

Data for Marine Ecological Niche Models, for 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species

Data for Ecological Niche Models: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species.

opencc-by-4.0Nov 2022View details →

zenodo48/100

Global taxonomic occurrence grids using GBIF data for species distribution models.

To achieve large geographic coverage, species occurrence databases that are composed of ad hoc species data collections such as that provided by the Global Biodiversity Information Facility (GBIF) are often used. A drawback to using these data is their geographic sampling bias, in which some regions are more intensively sampled than others, while other areas have very little to none reported sampling effort. Uneven sampling effort can mislead conclusions about biodiversity patterns and species distributions (Gotelli & Colwell, 2001; Lobo, 2008). Here we provide taxonomic occurrence grids to help mitigate the effects of sampling bias in species distribution modeling. These grids can be used to exclude areas of (a custom-defined) low sampling effort from the background when sampling for pseudo-absences’ (Phillips et al., 2009; Barbet-Massin et al.,2012). The occurrence grids have a 1 degree spatial resolution using WGS 84 as the geographic coordinate system. Each 1 degree grid cell contains the number of records present in GBIF corresponding to a specific taxonomic group: plants, mammals, reptiles, amphibians, birds and molluscs. To construct the occurrence grids, we used the 1- by 1-degree world latitude and longitude vector grid provided by ESRI (Redlands, California). It has a custom license which permits it reuse as long as ESRI is cited. It was downloaded from : <a href="https://www.arcgis.com/home/item.html?id=f11bcdc5d484400fa926dcce68de3df7">https://www.arcgis.com/home/item.html?id=f11bcdc5d484400fa926dcce68de3df7</a> To map spatial sampling effort, the number of georeferenced occurrences corresponding to each taxonomic group contained by each 1- by 1-degree grid cell were counted. The grids were then converted to GeoTIFFs. The raster values correspond to the number of occurrences reported for the grid cells. For the purposes of the <a href="https://osf.io/7dpgr/">TrIAS project</a>, grid cells with fewer than 5 occurrences were removed. The TrIAS taxonomic occurrence grids are used as inputs to the TrIAS risk modelling and mapping workflow: https://github.com/trias-project/risk-modelling-and-mapping. Full (with all grid cells containing at least one occurrence) taxonomic occurrence grids are also provided. GBIF data for each taxonomic group were downloaded using the following criteria: “Basis of Record”: Observation, Machine Observation, Human Observation, Specimen, Material sample, Literature Occurrence, Unknown evidence., "HasCoordinate is true", "HasGeospatialIssue is false", "TaxonKey is Amphibia", "Year 1975-2005". Raster Attributes <table> <tbody> <tr> <td> Attribute </td> <td> Description </td> </tr> <tr> <td> OID </td> <td> numeric row ID </td> </tr> <tr> <td> Value </td> <td> the number of records contained in the grid cell </td> </tr> <tr> <td> Count </td> <td> the number of times the value appears in the raster </td> </tr> </tbody> </table>     The extent of each taxonomic occurrence grid: <ul> <li> longitude -180.0; latitude -90.0 (southwest corner) </li> <li> longitude 180.0; latitude 90.0 (northeast corner) </li> </ul>   Files: TrIAS taxonomic occurrence grids amphib_1deg_min5.tif birds_1deg_min5.tif mammals_1deg_min5.tif molluscs_1deg_min5.tif reptiles_1deg_min5.tif   Raw taxonomic occurrence grids amphib_1deg_grid.tif birds_1deg_grid.tif mammals_1deg_grid.tif molluscs_1deg_grid.tif reptiles_1deg_grid.tif

opencc-by-4.0Jan 2023View details →

zenodo48/100

Global mangrove soil carbon data set at 30 m resolution for year 2020 (0-100 cm)

Global soil organic carbon stocks in mangrove forests at 30 m resolution, and predicted for 2020 using spatiotemporal ensemble machine learning. Soil organic carbon stock (t/ha) was derived using predictions of soil organic carbon content and bulk density (BD) to 1 m soil depth, which were then aggregated to calculate soil organic carbon stocks. The "mangroves_tiles_SOC_predictions_2020.zip" file contains predictions of SOC content, Bulk Density (BD) and aggregated SOC stocks (t/ha) for 0—100 cm depth interval. Example of a tile: <ul> <li>089E_21N (89E to 90E, 21N to 22N): <ul> <li>sol_db.od_mangroves.typology_m_30m_s0..100cm_2020_global_v0.1.tif = predicted BD aggregated to 0—100 cm;</li> <li>sol_soc.wpct_mangroves.typology_m_30m_s0..0cm_2020_global_v1.1.tif = predicted SOC content (%) at 0 cm depth (surface soil);</li> <li>sol_soc.wpct_mangroves.typology_m_30m_s0..100cm_2020_global_v1.1.tif = predicted SOC content (%) for 0—100 cm;</li> <li>sol_soc.tha_mangroves.typology_m_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha (mean value);</li> <li>sol_soc.tha_mangroves.typology_l.std_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha lower 95% probability prediction interval;</li> <li>sol_soc.tha_mangroves.typology_u.std_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha upper 95% probability prediction interval;</li> </ul> </li> </ul> Example of a tile: <ul> <li>class      : RasterLayer</li> <li>dimensions : 4004, 4004, 16032016  (nrow, ncol, ncell)</li> <li>resolution : 0.00025, 0.00025  (x, y)</li> <li>extent     : 88.9995, 90.0005, 20.9995, 22.0005  (xmin, xmax, ymin, ymax)</li> <li>crs        : +proj=longlat +datum=WGS84 +no_defs</li> <li>source     : sol_db.od_mangroves.typology_m_30m_s0..0cm_2002_global_v0.1.tif</li> </ul> To load global mosaics Soil Carbon t/ha Maps (0—100cm) as COGs directly into QGIS or similar, best use: <ul> <li> <a href="https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_m_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_m_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif</a> </li> <li> <a href="https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_l.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_l.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif</a> </li> <li> <a href="https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_u.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_u.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif</a> </li> </ul>

opencc-by-4.0Mar 2023View details →

zenodo48/100

Bee Interaction Data from Global Biotic Interactions

New versions of this dataset are found at: <a href="https://doi.org/10.5281/zenodo.16689326">https://doi.org/10.5281/zenodo.16689326</a>   This repository includes the following: <ol> <li>interactions-GloBI-September-14-2021.tsv.gz: a full version of the Global Biotic Interactions downloaded on September 14, 2021. No data transformations have occurred on this dataset after the download</li> <li>globi_bee_data.sh: Shell script for extracting bee records using bee family names from the full version of Global Biotic Interactions</li> <li>all_bee_data_unique.txt: a file that includes only bee interactions, based on extracting bee names from interactions-GloBI-September-14-2021.tsv.gz</li> </ol> Global Biotic Interactions (GloBI - https://globalbioticinteractions.org) aims to simplify access to existing records of species interactions, such as predator-prey, plant-pollinator, and virus-host interactions. To achieve this, GloBI follows a process where existing, versioned datasets on species interactions are transformed into various aggregate formats, including tsv, csv, neo4j, rdf/nquad, and darwin core-ish archives, with applied name maps included for explicit taxonomic linking. GloBI owes its success to researchers, collections, projects, and institutions that openly share their datasets. Whenever you use this data, please credit the original data contributors, including citing the specific datasets used in derivative work. Each species interaction record in GloBI is linked to a reference and dataset citation. If you have any suggestions on how to make it easier to cite original datasets, you are welcome to join a discussion on https://globalbioticinteractions.org or related projects. Introduction to Global Bee Interaction Data The dataset available here includes all bee interactions recorded in the <a href="https://www.globalbioticinteractions.org/">Global Biotic Interactions</a> (GloBI; Poelen et al. 2014) index as of September 21, 2021. These interactions are gathered quarterly by the <a href="http://big-bee.net/">Big Bee Project </a>(Seltmann et al. 2021) from various sources, including natural history collections, community science observations (such as iNaturalist), and scientific literature. The dataset covers a wide range of bee interactions, including flower visitation, parasitic interactions (such as mite and viral interactions), and lecty, among others. The dataset is filtered for unique records based on interaction description and source citation to ensure accuracy and consistency. For other versions of the bee interaction dataset, please refer to <a href="https://zenodo.org/record/7315159">Seltmann, 2022</a>. Data Description Please see the <a href="https://www.globalbioticinteractions.org/process">integration process page</a> to better understand how Global Biotic Interactions combines datasets from various sources. The complete interaction dataset for all species can be accessed via <a href="https://www.globalbioticinteractions.org/data">https://www.globalbioticinteractions.org/data</a> and the <a href="https://doi.org/10.5281/zenodo.3950589">GloBI Community Zenodo publication</a>. Dataset column names definitions <a href="https://api.globalbioticinteractions.org/interactionFields">https://api.globalbioticinteractions.org/interactionFields</a> or <a href="https://api.globalbioticinteractions.org/interactionFields">https://api.globalbioticinteractions.org/interactionFields</a> References Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. <a href="https://doi.org/10.1016/j.ecoinf.2014.08.005">https://doi.org/10.1016/j.ecoinf.2014.08.005</a> Katja C. Seltmann. (2022). Global Bee Interaction Data (v2.02) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7315159">https://doi.org/10.5281/zenodo.7315159</a> Seltmann KC, Allen J, Brown BV, Carper A, Engel MS, Franz N, Gilbert E, Grinter C, Gonzalez VH, Horsley P, Lee S, Maier C, Miko I, Morris P, Oboyski P, Pierce NE, Poelen J, Scott VL, Smith M, Talamas EJ, Tsutsui ND, Tucker E (2021) Announcing Big-Bee: An initiative to promote understanding of bees through image and trait digitization. Biodiversity Information Science and Standards 5: e74037. <a href="https://doi.org/10.3897/biss.5.74037">https://doi.org/10.3897/biss.5.74037</a>

opencc-zeroSep 2021View details →

zenodo48/100

Figure data and code used in Technical comment on "Fairness considerations in global mitigation investments"

The package contains the data and code to create the figure in the associated technical comment in Science published at <a href="https://www.science.org/doi/10.1126/science.adg5893">https://www.science.org/doi/10.1126/science.adg5893</a>

opencc-by-4.0May 2023View details →

zenodo48/100

Data for Figure 8 of Publication "Additional global climate cooling by clouds due to ice crystal complexity"

This repository contains the data to produce Figure 8 in the paper: "Järvinen, E., Jourdan, O., Neubauer, D., Yao, B., Liu, C., Andreae, M. O., Lohmann, U., Wendisch, M., McFarquhar, G. M., Leisner, T., and Schnaiter, M.: Additional global climate cooling by clouds due to ice crystal complexity, Atmos. Chem. Phys., 18, 15767–15781, https://doi.org/10.5194/acp-18-15767-2018, 2018." Note that the scripts are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.8095469)

opencc-by-4.0Jun 2023View details →

zenodo48/100

Data for figures in the Publication "The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2"

This repository contains the data to produce figures for the paper: "Lohmann, U. and Neubauer, D.: The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2, Atmos. Chem. Phys., 18, 8807–8828, https://doi.org/10.5194/acp-18-8807-2018, 2018." Note that the scripts are to be found in the accompanying package (https://doi.org/10.5281/zenodo.8183412)

opencc-by-4.0Jul 2023View details →

zenodo48/100

L3Pilot Global User Acceptance Survey, Second Phase Data

The research leading to these results received funding from the European Commission Horizon 2020 programme under the project L3Pilot (L3Pilot.eu), grant agreement number 723051. The L3Pilot Global User Acceptance Survey investigated the acceptance of SAE Level 3 (L3) conditionally automated cars. Survey data was collected in two phases. This dataset contains the data from the second phase of the survey with responses collected from 9 countries on five continents. This document contains information about the survey methodology and coding of the variables. For a detailed description of the first and second phase survey methodology, please consult L3Pilot deliverable D7.1 ‘Annual quantitative survey about user acceptance towards ADAS and vehicle automation’ by Nordhoff et al. (2021). If you use the dataset, please cite it as: L3Pilot (2023). L3Pilot Global User Acceptance Survey, Second Phase Data. <a href="https://doi.org/10.5281/zenodo.8389718">https://doi.org/10.5281/zenodo.8389718</a> For further information, please contact: <a href="mailto:user-survey@eict.de">user-survey@eict.de</a>

opencc-by-4.0Sep 2023View details →

edi48/100

Data for Forb diversity globally is harmed by nutrient enrichment but can be rescued by large mammalian herbivory

Forbs (“wildflowers”) are important contributors to grassland biodiversity and services, but they are vulnerable to environmental changes that affect their coexistence with grasses. In a factorial experiment at 94 sites on 6 continents, we tested the global generality of several broad predictions arising from previous studies: (1) Forb cover and richness decline under nutrient enrichment, particularly nitrogen enrichment, which benefits grasses at the expense of forbs. (2) Forb cover and richness increase under herbivory by large mammals, especially when nutrients are enriched as grazing will release forbs from decreased grass competition under fertilization. (3) Forb richness and cover are less affected by nutrient enrichment and herbivory in more arid climates, because water limitation reduces the impacts of competition with grasses. We found strong evidence for the first, partial support for the second, and no support for the third prediction. Forb richness and cover are reduced by nutrient addition, with nitrogen having the greatest effect; forb cover is enhanced by large mammal herbivory, although only under conditions of nutrient enrichment and high herbivore intensity; and forb richness is lower in more arid sites, but is not affected by consistent climate-nutrient or climate-herbivory interactions. We also found that nitrogen enrichment disproportionately affects forbs in certain families (Asteraceae, Fabaceae). Our results underscore that anthropogenic nitrogen addition is a major threat to grassland forbs and the ecosystem services they support, but grazing under high herbivore intensity can offset these nutrient effects. For associated r code that goes along with this dataset, please refer to the following Zenodo repository: https://zenodo.org/records/14207290

openCustomFeb 2025View details →

edi48/100

Global data set of long-term summertime vertical temperature profiles in 153 lakes

Climate change and other anthropogenic stressors have led to long-term changes in the thermal structure, including surface temperatures, deepwater temperatures, and vertical thermal gradients, in many lakes around the world. Though many studies highlight warming of surface water temperatures in lakes worldwide, less is known about long-term trends in full vertical thermal structure and deepwater temperatures, which have been changing less consistently in both direction and magnitude. Here, we present a globally-expansive data set of summertime in-situ vertical temperature profiles from 153 lakes, with one time series beginning as early as 1894. We also compiled lake geographic, morphometric, and water quality variables that can influence vertical thermal structure through a variety of potential mechanisms in these lakes. These long-term time series of vertical temperature profiles and corresponding lake characteristics serve as valuable data to help understand changes and drivers of lake thermal structure in a time of rapid global and ecological change.

openCC0Sep 2022View details →

edi48/100

MCR LTER: Coral Reefs: Coral bleaching and mortality in July 2019; data for Speare et al. 2021 Global Change Biology

These data are from field surveys conducted at seven sites at 10m depth on the outer reef of Mo’orea following a marine heatwave and coral bleaching event in the Austral Summer of 2019. These data describe the size, percent of the colony that was bleached, and the percent of the colony that recently dead for corals in the genera Acropora and Pocillopora. At six sites (LTER 1-6) coral colony size was quantified using ordinal size bins and observers collected data on all coral colonies > 5cm diameter. At one site on the north shore of Mo’orea (LTER Experimental Site) coral colony size was measured to the nearest centimeter. At this site researchers did two separate sets of surveys, one to collect data on all corals > 5cm diameter, and one to collect data on all individuals ≤ 5cm diameter. Additionally, data on survivorship of newly-settled coral recruits on coral settlement tiles are included. Tiles were deployed at 10m depth at one site on the outer reef of Moorea. Survivorship of coral recruits between March and July was assessed in 2017 and 2019. These data are in support of a publication Speare et al. (2021) Global Change Biology. The manuscript title and author list are as follows: Size-dependent mortality of corals during marine heatwave erodes recovery capacity of a coral reef. Kelly E. Speare, Thomas C. Adam, Erin M. Winslow, Hunter S. Lenihan, Deron E. Burkepile This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2021). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.

openCC (other)Nov 2021View details →

zenodo44/100

Eddy Kinetic Energy in the Arctic Ocean from a High-resolution Global Simulation with 1-km Arctic (data).

Data for the "Eddy Kinetic Energy in the Arctic Ocean from a High-resolution Global Simulation with 1-km Arctic".

opencc-by-4.0Apr 2020View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record