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Supplementary Data: Axion global fits with Peccei-Quinn symmetry breaking before inflation using GAMBIT
<p><strong>Description of Supplementary Data</strong></p> <p>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., <em>“Axion global fits with Peccei-Quinn symmetry breaking before inflation using GAMBIT”</em> (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.<br> <br> This record consists of</p> <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> <p>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>.</p> <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 (<em>GeneralALP</em>, <em>QCDAxion</em>, <em>DFSZAxion_I</em>, <em>DFSZAxion_II</em>, <em>KSVZAxion</em>)</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 <em>θ<sub>i</sub></em> (<code>I=4</code>: flat prior on <em>θ<sub>i</sub></em> 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 <em>E/N</em>; only for <em>KSVZAxion</em> models (<code>E=1</code>: 0, <code>E=2</code>, 2/3, <code>E=3</code>: 5/3, <code>E=4</code>: 8/3), <em>DFSZAxion-I</em> models (<code>E=1</code>: 8/3), <em>DFSZAxion-II</em> models (<code>E=2</code>: 2/3), or some <em>GeneralALP</em> files (<code>E=a</code>: “QCD-like setting” with <em>β</em> = 7.94, <em>T<sub>crit</sub></em> = 147 MeV; <code>E=b</code>: “Simple ALP-like setting” with <em>β</em> = 0, <em>T<sub>crit</sub></em> irrelevant)</li> </ul> <p>For convenience, we provide a mapping between the figures in the paper and the <code>hdf5</code> files:</p> <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> <p>A few caveats to keep in mind:</p> <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>
Global and regional glacier mass changes from 1961 to 2016
<p>Supplementary data tables with the results from Zemp et al. (2019) entitled "<em><strong>Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016</strong></em>", Nature:</p> <p><strong>Data Tables 1a-t | Temporal variabilities for glaciological clusters based on variance decomposition model. </strong>Annual results are made available as csv-files for all 20 cluster: Zemp_etal_results_clusters.zip</p> <p><strong>Data Tables 2a-t | Regional and global mass balance and mass change results from 1961-2016. </strong>Annual results are made available as csv-files for all 19 regions (cf. RGI 6.0) as well as for the global sum: Zemp_etal_results_regions_global.zip</p> <p><strong>Data Table 3 | Glacier mass changes for Central Europe from 1961-2016. </strong>Annual results as in Data Table 2 but with examples for multi-year error calculations are made available as Excel-file: Zemp_etal_results_region-CEU_errorcalcs.xlsx</p> <p><strong>Note</strong>: The corresponding full sample of glaciological and geodetic observations for individual glaciers are publicly available from the World Glacier Monitoring Service: http://doi.org/10.5904/wgms-fog-2018-11</p> <p><strong>Version 1.1</strong><br> This version provides the results from the glaciological clusters (Data Tables 1a-t) as used by Zemp et al. (2019). In addition, it contains corrected results for region Iceland (Data Table 2, region_6_ISL) and correspondingly for the global sum (Data Table 2, global); the results for the other regions remain unchanged. For more details on the correction of the regional results for Iceland, see Zemp et al. (2019, Nature), Author Correction.</p> <p><strong>Version 1.0</strong><br> Data tables containing the results for the 20 glaciological clusters (Data Tables 1a-t) as well as for the 19 regions and global sums (Data Tables 2a-t) related to the publication by Zemp et al. (2019, Nature). We note that this version erroneously contains pre-release versions of results for most of the glaciological clusters that were not used in Zemp et al. (2019, Nature).</p> <p> </p>
ECCO Iter22 Global Ocean State Estimate - 1 January 2004 to 30 April 2005
<p>Time series of global ocean temperature, salinity, and sound speed derived from the “Estimating the Circulation and Climate of the Ocean" (ECCO) program Iter22 state estimates. The sound speed fields were computed for simulation of acoustic propagation over basin scales or longer in a realistic oceanic environment. These estimates were computed in 2010 by the JPL-MIT-SIO ECCO program. <br>Original link: http://ecco2.jpl.nasa.gov/data9/cube/iter22/lat_lon/quart_80S_80N/THETA/ , now defunct.</p> <p>The solution is mesoscale permitting. The solution was obtained on a cube sphere grid between 80S and 80N with 18-km horizontal grid spacing and 50 vertical levels (Menemenlis et al., 2005, NASA supercomputer improves prospects for ocean <br>climate research, Eos Trans., AGU 86, 89, 95–96.). State estimates were averaged over a 3-day interval. File 003 is averaged over 2004/1/1 -- 2004/1/3. Three-day-mean temperature and salinity profiles from the iter22 solution were provided on 1/4 degree <br>grid for the period 1 January 2004 to 30 April 2005. There are 162 snapshots at 3-day intervals. </p> <p>Depths were decimated to the standard 33 depths of the World Ocean Atlas to 5500 m. YearDay 1 is 1 January 1992. The number of the filename indicates the yearday in 2004. In situ temperature was computed from model potential temperature. Sound speed was computed using the Del Grosso sound speed equation. Original model profiles descended only to the model sea floor. Temperature, salinity and sound speed were filled in on a uniform grid using nearest neighbor to 5500 m depth. Values on a regular grid make life easier. Product documented in Dushaw and Menemenlis, 2014, Antipodal acoustic thermometry: 1960, 2004, Deep Sea Research Part I: Oceanographic Research Papers, 86, 1–20, https://doi.org/10.1016/j.dsr.824 2013.12.008.</p> <p>Each snapshot is stored as a netcdf 4 file. Latitude, Longitude, Depth, and YearDay variables given separately in sspgrid.nc . <br>N.B.: Values in the files are stored as 32-bit or 16-bit integers to save disk space:</p> <p>Sound Speed: saved as "round( (c-1000)*1000 )", so to get actual c: c=1000. + double(c)/1000. <br>Sound speed is stored to 3 decimal places as a 32-bit integer.</p> <p>Temperature: saved as "round( (T-10)*1000 )", so to get actual T: T=10. + double(T)/1000. <br>Temperature is stored to 3 decimal places as a 16-bit integer. Note that abyssal temperature can sometimes be negative.</p> <p>Salinity: saved as "round( (S-10)*1000 )", so to get actual S: S=10. + double(S)/1000. <br>Salinity is stored to 3 decimal places as a 16-bit integer.</p> <p>Data directory also has two matlab routines: get_section.m and dist.m. get_section.m shows how to load the files, compute the physical variable from the stored value, and compute a section of ssp, T, or S. dist.m is a utility for computing geodesics; it relies on R. Pawlowitz's m_map package which can be downloaded freely from his University of Vancouver web page.</p> <p>$ md5sum *tgz <br>53ca3621f422b89c599c92fbab71d2fc S_ecco_iter22.tgz (2.99 GB)<br>a9e9109b6dae7355bf2fa0926d436d9a ssp_ecco_iter22.tgz (6.09 GB)<br>0cb8d1c2cdee4c7377353dff722ece34 T_ecco_iter22.tgz (4.33 GB)</p>
Global wildland-urban interface maps in 2000, 2010, and 2020, based on GlobeLand30
<p>This dataset provides global wildland-urban interface (WUI) maps at a spatial resolution of <strong>30 meters </strong>for the years <strong>2000, 2010, and 2020</strong>. The WUI is defined as areas where the 200-meter buffers of urban areas (characterized by artificial surfaces) intersect with the 400-meter buffers of wildland areas, including forests, shrublands, and grasslands. These maps are produced based on land cover classification results from the GlobeLand30 datasets.</p> <p><strong>Projection Information:</strong><br>The projection information aligns with GlobeLand30 standards:</p> <ul> <li><strong>Projection:</strong> UTM (Universal Transverse Mercator) for latitudes from S85 to N85, using a 6-degree zone system without zone numbers.</li> <li><strong>Polar Azimuthal Projection:</strong> Applicable for latitudes from S85 to N90 and N85 to N90, with the projection surface intersecting at the South and North Poles.</li> </ul> <p><strong>Naming Convention:</strong><br>The file naming convention is as follows:</p> <div> <div> <div> <div><strong>WUI_LHH_VV_YYYYlc030.tif</strong></div> <div> </div> </div> </div> </div> <p>Where:</p> <ul> <li><strong>L</strong> = Latitude code (N for the Northern Hemisphere, S for the Southern Hemisphere)</li> <li><strong>HH</strong> = Number of UTM zone</li> <li><strong>VV</strong> = Starting latitude of the tile (each tile crosses 5° latitude)</li> <li><strong>YYYY</strong> = Year mapped</li> <li><strong>lc</strong> = Land cover abbreviation</li> <li><strong>030</strong> = Spatial resolution of 30 meters</li> </ul> <p><strong>Example File Name:</strong><br>For instance, the file named <strong>WUI_n15_45_2020lc030.tif</strong> can be interpreted as follows:</p> <ul> <li><strong>WUI</strong>: Wildland-Urban Interface dataset</li> <li><strong>n</strong>: Northern latitude</li> <li><strong>15</strong>: UTM zone 15</li> <li><strong>45</strong>: Starting latitude of 45 degrees</li> <li><strong>2020</strong>: Product year of 2020</li> <li><strong>lc</strong>: Land cover classification</li> <li><strong>030</strong>: Spatial resolution of 30 meters</li> </ul>
Supplementary Data: Global rise in forest fire emissions linked to climate change in the extratropics
<p>Supplementary Data for the paper "Global rise in forest fire emissions linked to climate change in the extratropics" by Jones et al. (2024, <em>Science</em>).</p> <p>The records include mapped pyromes and data and code used to delineate the pyromes.</p> <h3><strong>Mapped Pyromes</strong></h3> <p>The data records include mapped pyromes in three forms:</p> <ol> <li><strong>Shapefile</strong> (Jones_etal_2024_Global_Forest_Pyromes.shp.zip). Vector features in shapefile format containing data fields <em>pyrome ID</em> and <em>pyrome name</em>. The zipped file contains .shp, .dbf, .prj, .shx files.</li> <li><strong>Lower-resolution NetCDF </strong>(Jones_etal_2024_Global_Forest_Pyromes_Qdeg.nc). NetCDF version 4 file containing gridded values of <em>pyrome ID</em> at quarter-degree resolution.</li> <li><strong>Higher-resolution NetCDF</strong> (Jones_etal_2024_Global_Forest_Pyromes_005deg.nc). NetCDF version 4 file containing gridded values of <em>pyrome ID</em> at 0.05 degree resolution.</li> </ol> <p>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</p> <p>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/.</p> <h3><strong>Correlation Data</strong></h3> <p>The data records (Correlation_Qdeg.zip) include gridded quarter-degree correlations between forest burned area (BA) and each of the following variables:</p> <ul> <li><em><strong>Fire weather index</strong></em></li> <li><em><strong>Atmospheric instability (continuous Haines index)</strong></em></li> <li><em><strong>Lightning flash density</strong></em></li> <li><em><strong>Soil moisture</strong></em></li> <li><em><strong>Vegetation productivity (Normalised Difference Vegetation Index)</strong></em></li> <li><em><strong>Population density</strong></em></li> <li><em><strong>Cropland cover</strong></em></li> <li><em><strong>Pasture cover</strong></em></li> <li><em><strong>Road density</strong></em></li> <li><em><strong>Potential fuel loads - surface fuels</strong></em></li> <li><em><strong>Potential fuel loads - shrub fuels</strong></em></li> <li><em><strong>Potential fuel loads - canopy and ladder fuels</strong></em></li> <li><em><strong>Terrain ruggedness index</strong></em></li> <li><em><strong>Forest area density</strong></em></li> </ul> <p>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.</p> <p>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.<em> Cropland_Pasture_Qdeg.hdf5 </em>contains data for both cropland and pasture. Each file contains layers describing the Spearman's rho (ρ) correlation coefficient and the related p-value.</p> <p>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:</p> <ul> <li><strong><em>Fire Weather Index, Atmospheric Instability, and Lightning Flash Density:</em></strong> 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><strong><em>Soil Moisture:</em></strong> 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><strong><em>Vegetation Productivity (NDVI):</em></strong> 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><strong><em>Population Density, Cropland Cover, Pasture Cover, Road Density, T</em></strong><strong><em>errain Ruggedness Index, Forest Area Density: </em></strong>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> <p>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).</p> <h3><strong>Clustering Code</strong></h3> <p>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 <em>Input</em> 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 <em>Regions</em> directory contains ecoregions of North America in shapefile format. The <em>Output</em> directory contains output generated by M. Jones, which can be used for validation purposes once other users have trialled the code.</p>
Global peatland, bare rock and bare sand extent at 100 m to 1 km spatial resolution based on multisource data
<p>Ensemble estimate of the global distribution of <a href="https://en.wikipedia.org/wiki/Peatland">peatlands</a> / extent (<strong>peatland.extent_wri.gfw.peatgrids_p</strong>). This is a simple average from three (3) sources of data:</p> <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> <p>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.</p> <p>Bare rock and bare sand estimates are based on the following two sources of data:</p> <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> <p>Two classes are considered: (1) probability of occurrence of bare rock (<strong>bare.rock_glc.gfz_p</strong>), (2) probability of occurrence of bare sand i.e. shifting sand (<strong>bare.soil.sand_glc.gfz_p</strong>). We recommend using only the 1-km data for spatial modeling.</p> <p>The time-series of bare areas (<strong>bare.areas_esa.cci_p</strong>) 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. </p>
RRING Global Survey Research Dataset (WP3)
<p>The RRING Work Package 3 (WP3) objective was to clarify how Research Funding Organisations (RFOs) and Research Performing Organisations (RPOs) operated within region-specific research and innovation environments. It explored how they navigated the governance and regulatory frameworks for Responsible Research and Innovation (RRI), as well as offering their perspectives on the entities responsible for RRI-related policy and action in their locales.</p> <p>This data set covers the global survey research part, which was designed to contextualise how RPOs and RFOs interacted within the research environment and with non-academic stakeholders. Countries were grouped according to the UNESCO regions of the world and key results per region are listed below. For a detailed analysis and further findings of the work completed under WP3 of the RRING project, please refer to the full deliverable document "State of the Art of RRI in the Five UNESCO World Regions" [link to be inserted].</p> <p> </p> <p><strong>European and North American States</strong></p> <ul> <li>‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of ensuring ethical principles were applied in R&I (92%), followed by diverse perspectives (88%), and gender equality (79%). Including ethnic minorities was the area which garnered the least attitudinal support (71%). Respondents took the most practical steps towards engaging with diverse perspectives (63%), and the least towards inclusion of ethnic minorities (24%).</li> <li>‘Anticipative and reflective’: Respondents widely agreed (82%) with the importance of ensuring R&I work does not cause concerns for society, but only 37% confirmed they had taken practical steps to ensure this.</li> <li>‘Open and transparent’: Vast majorities of respondents agreed on the importance of keeping R&I methods open and transparent (94%), with 65% also confirming they take practical steps to do this. An equally high number agreed on the importance of making the results of R&I work accessible to as wide a public as possible (94%), and 68% confirmed this through their reported actions. This indicated the smallest value-action gap of all RRI measures for respondents from European and North American countries. Attitudinal agreement on the importance of making data freely available to the public was lower (83%), as was the practical action aspect for this measure (45%).</li> <li>‘Responsive and adaptive to change’: Most respondents agreed (89%) that it was important to ensure their work addresses societal needs, and 62% confirmed that they take practical steps towards this aim.</li> </ul> <p> </p> <p><strong>Latin American and Caribbean States</strong></p> <ul> <li>‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of gender equality in R&I (86%), followed by ensuring ethical principles are applied (85%), and diverse perspectives incorporated (83%). Including ethnic minorities was the area which garnered the least attitudinal support (77%). Respondents took the most practical steps towards ensuring ethical principles guide their work (50%), and the least towards including ethnic minorities (25%), but the smallest value action gap was found for gender equality.</li> <li>‘Anticipative and reflective’: Respondents agreed (79%) that it is important to ensure R&I work does not cause concerns for society, but only 29% confirmed they had taken practical steps to ensure this.</li> <li>‘Open and transparent’: The majority of respondents agreed on the importance of keeping R&I methods open and transparent (89%), with 45% indicating they had taken practical action. A majority also agreed on the importance of making the results of R&I work accessible to as wide a public as possible (88%), and 44% backed this up with practical action. Attitudinal agreement on the importance of making data freely available to the public was slightly lower (81%), as was the practical action aspect for this measure (35%).</li> <li>‘Responsive and adaptive to change’: Most respondents agreed (84%) that it was important to ensure their work addresses societal needs, and 49% confirmed that they take practical steps towards this aim.</li> </ul> <p> </p> <p><strong>Asian and Pacific States</strong></p> <ul> <li>‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of ensuring ethical principles were applied in R&I (90%), followed by diverse perspectives (89%), and gender equality (86%). Including ethnic minorities was the area which garnered the least attitudinal support (76%). Respondents took the most practical steps towards engaging with diverse perspectives (65%), and the least towards including ethnic minorities (30%).</li> <li>‘Anticipative and reflective’: Respondents widely agreed (78%) with the importance of ensuring R&I work does not cause concerns for society, and 42% confirmed they had taken practical steps to ensure this.</li> <li>‘Open and transparent’: The majority of respondents agreed on the importance of keeping R&I methods open and transparent (91%), with 58% indicating they take practical steps to do this. A majority also agreed on the importance of making the results of R&I work accessible to as wide a public as possible (89%), and 64% backed this up with practical action. Attitudinal agreement on the importance of making data freely available to the public was lower (79%), as was the practical action aspect for this measure (40%).</li> <li>‘Responsive and adaptive to change’: Most respondents agreed (92%) that it was important to ensure their work addresses societal needs, and 69% confirmed that they take practical steps towards this aim. This was the RRI measure with the smallest valueaction gap for respondents from the Asian and Pacific region.</li> </ul> <p> </p> <p><strong>Arab States</strong></p> <ul> <li>‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of ensuring ethical principles were applied in R&I (93%), followed by diverse perspectives (81%), and gender equality (85%). Including ethnic minorities was the area which garnered the least attitudinal support (74%). Respondents took the most practical steps towards engaging with diverse perspectives (66%), which equated to one of two equally small value-action gaps for respondents from Arab states, and the least practical steps towards inclusion of ethnic minorities (22%).</li> <li>‘Anticipative and reflective’: A high proportion of respondents (85%) agreed that it is important to ensure R&I work does not cause concerns for society. However, only 38% confirmed they had taken practical steps to ensure this.</li> <li>‘Open and transparent’: The majority of respondents agreed on the importance of keeping R&I methods open and transparent (89%), with 59% also confirming they take practical steps to do this. A majority also agreed on the importance of making the results of R&I work accessible to as wide a public as possible (90%), and 66% backed this up with practical action. Ensuring public accessibility of research results was the second of two measures with equally small value-action gaps. Attitudinal agreement on the importance of making data freely available to the public was much lower (78%), which also reflected the practical action aspect for this measure (49%).</li> <li>‘Responsive and adaptive to change’: Most respondents agreed (96%) that it was important to ensure their work addresses societal needs, and 68% confirmed that they take practical steps to achieve this.</li> </ul> <p><strong>African States</strong></p> <ul> <li>‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of ensuring engagement with diverse perspectives and expertise in R&I (91%), followed by ensuring ethical principles are applied (90%), and gender equality (89%). Including ethnic minorities was the area which garnered the least attitudinal support (74%). Respondents took the most practical steps towards ensuring ethical principles guide their work (57%), and the least towards including ethnic minorities (32%).</li> <li>‘Anticipative and reflective’: The majority of respondents (85%) agreed that it is important to ensure R&I work does not cause concerns for society, with 59% confirming that they take practical steps to ensure this.</li> <li>‘Open and transparent’: A high proportion of respondents agreed on the importance of keeping R&I methods open and transparent (90%), with 54% also confirming they take practical steps to do this. A majority also agreed on the importance of making the results of R&I work accessible to as wide a public as possible (86%), and 56% backed this up with practical action. Attitudinal agreement on the importance of making data freely available to the public was significantly lower (73%), as was the practical action aspect for this measure (38%).</li> <li>‘Responsive and adaptive to change’: Respondents mostly agreed (92%) that it was important to ensure their work addresses societal needs, and 64% confirmed that they take practical steps towards this aim. This was the RRI measure with the smallest valueaction gap for respondents from African states.</li> </ul> <p> </p> <p><em>Note: Please refer to the "RRING WP3 - Survey Data Documentation" document for detailed instructions on how to use this dataset.</em></p>
Global Ionosphere Maps of vertical electron content combined in real-time from the RT-GIMs of CAS, CNES, UPC-IonSAT, and WHU International GNSS Service (IGS) centers (from Dec 1, 2020, to March 1, 2021)
<p>The datasets consists on 91 daily files, in IONEX format (<a href="http://ftp.aiub.unibe.ch/ionex/draft/ionex11.pdf">http://ftp.aiub.unibe.ch/ionex/draft/ionex11.pdf</a>) , corresponding to three months of global ionospheric maps (GIM) of vertical total electron content (VTEC) computed in real-time from the assessed and combined real-time GIMs generated by four analysis centers. Indeed, the Real-Time Working Group (RTWG) of International GNSS Service (IGS) is dedicated to providing high-quality data, high-accuracy products for Global Navigation Satellite System (GNSS) navigation, positioning, timing, and Earth observations. As one of the important part of real-time products, the IGS combined Real-Time Global Ionosphere Map (RT-GIM) have been generated by real-time weighting technique with the help of RT-GIMs from IGS real-time ionosphere centers including the Chinese Academy of Sciences (CAS), Centre National d’Etudes Spatiales (CNES), Universitat Politècnica de Catalunya (UPC), and Wuhan University (WHU). Compared with IGS rapid Global Ionosphere Maps (GIMs) (corg, ehrg, emrg, esrg, igrg, jprg, uhrg, uprg, uqrg, whrg) and IGS final combined GIM (igsg), the IGS combined RT-GIM (irtg) is equivalent to the post-processed GIMs and even better than some rapid GIMs. The IGS RT-GIMs are reliable sources of real-time global VTEC information and has great potential for real-time applications including range error correction for transionospheric radio signals (such as GNSS positioning, search and rescue, air traffic, radar altimetry, and radioastronomy), the monitoring of space weather (such as geomagnetic and ionospheric storms, ionospheric disturbance) and detection of natural hazards on a global scale (such as hurricanes/typhoons, ionospheric anomalies associated with earthquakes)</p>
ZooBase: A global synthesis of marine zooplankton species occurrences.
<p><em><strong>Description of the methods used to implement the present ZooBase dataset (extarct from Section A.2 from the Methods of Benedetti et al., 2021).</strong></em></p> <p>A new dataset of global zooplankton species occurrences was compiled in a comparable fashion to that put together for phytoplankton (Righetti et al., 2020). Prior to retrieving the occurrence data online, we first identified the phyla (Order/Class/Family) that comprise the bulk of extant oceanic zooplankton communities: Copelata (i.e. appendicularians), Ctenophora, Cubozoa (i.e. box jellyfish), Euphausiidae (i.e. krill), Foraminifera, Gymnosomata (i.e. sea angels, pteropods), Hydrozoa (i.e. jellyfish), Hyperiidea (i.e. amphipods), Myodocopina (i.e. ostracods), Mysidae (i.e. small pelagic shrimps resembling krill), Neocopepoda, Podonidae and <em>Penilia</em> <em>avirostris</em> (i.e. cladocerans), Sagittoidea (i.e. chaetognaths), Scyphozoa (i.e. jellyfish), Thaliacea (i.e. salps, doliolids and pyrosomes), Thecosomata (i.e. pteropods), and four families of pelagic Polychaeta (i.e. worms) that are often found in the zooplankton and whose species are known to display holoplanktonic lifecycles (Tomopteridae, Alciopidae, Lopadorrhynchidae, Typhloscolecidae). The presence data associated with species belonging to these groups were retrieved from OBIS and GBIF between the 12/04/2018 and the 18/04/2018 using online queries via the R packages RPostgreSQL, robis and rgbif. Since the Neocopepoda infra-class comprise several thousands of benthic and parasitic taxa (https://copepodes.obs-banyuls.fr/en/), a preliminary selection of the non-parasitic planktonic species had to be carried out prior to the online downloading using the species list of Razouls et al. (https://copepodes.obs-banyuls.fr/en/) as a reference. The spatial distributions of the groups cited above were first inspected using GBIF’s and OBIS’s online mapping tools to evaluate the potential number of overlapping observations between the two databases. As a result of their relatively low contributions to total observations/diversity, and very high overlap between databases, the occurrences of Cladocera and Polychaeta were retrieved from OBIS only (which usually harbours more occurrences). On top of the data collected from OBIS and GBIF, the copepod occurrences from Cornils et al. (2018) and the pteropod occurrences from the MAREDAT initiative (Buitenhuis et al., 2013) were added to the dataset. We discarded records that: (i) presented at least one missing spatial coordinate, (ii) were associated with an incomplete sampling date (d/m/y), (iii) were associated with a year of collection older than 1800, (iv) were not associated with any sampling depth, (v) were not identified down to the species level. Occurrences associated with grid cells shallower than 10m were removed (bathymetry data from ETOPOv1 at a 15min resolution, downloaded using the 'marmap' R package). Finally, every species name was then carefully examined and compared to the taxonomic reference list of the World Register of Marine Species (WoRMS; <a href="http://www.marinespecies.org">http://www.marinespecies.org</a>) for all taxa. The AphiaID and the Status were retreived from WoRMS based on the ScientificName. To remove the duplicate occurrences due to the highly overlapping source archives (GBIF and OBIS), a unique occurrenceID was given to each record based on rounded spatial coordinates (closest 0.1°x0.1°), rounded depth layer (10m depth layers), month and year of the occurrence and the acccpted species name (e.g., AphiaID).</p> <p><strong>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862923. This output reflects only the author’s view, and the European Union cannot be held responsible for any use that may be made of the information contained therein.</strong></p>
Crop-specific global fertilizer application rates from "Closing yield gaps through nutrient and water management"
<p>Crop-specific global maps of N, P2O5, and K2O fertilizer application rates circa the year 2000 from the following paper:</p> <p>Mueller, ND, JS Gerber, M Johnston, DK Ray, N Ramankutty, and JA Foley. 2012. Closing yield gaps through nutrient and water management. <em>Nature</em> <strong>490</strong>: 254–257</p> <p>Data are provided at five arc-minute resolution and are saved as netcdf files. Fertilizer application rates are estimated from reconciling various national and subnational data sources. See the Supplementary Information from the 2012 paper for a full description of data sources and methods. Data quality for each grid cell is described in a map layer. Files containing the text "totalcons" sum nutrient consumption across crops per grid cell, using crop harvested areas from Monfreda et al. 2008 Global Biogeochemical Cycles. For maize, wheat, and soybean N application rates, additional maps and csv files (containing the text "politboundaries") identify the political units around the world containing unique information. Crops and crop group categories are consistent with those utilized in Monfreda et al. 2008 Global Biogeochemical Cycles.</p>
SInAS: A global dataset of native and alien distributions of alien species
<p>The SInAS dataset represents a collection of regional lists of alien (also called non-native or non-indigenous) species and includes information about their native ranges, alien ranges, invasion status for alien ranges, habitats and year of first record. This dataset has been generated by standardising and integrating large global databases of alien species occurrences using the SInAS workflow version 2.0. </p> <p>The SInAS dataset is described in more detail in the following scientific article, which need to be cited when using this dataset:</p> <p>Gómez-Suárez, M., Laeseke, P., and Seebens, H. (submitted) A global dataset of native and alien distributions of alien species </p> <p>The code to generate the dataset is stored on Github (https://github.com/hseebens/SInAS) with releases available on Zenodo (https://doi.org/10.5281/zenodo.3763221).</p>
OCNET global daily Chlorophyll-a products
<p>We constructed the Ocean Chl-a Reconstruction Neural Ensemble Network (OCNET) model to generate global daily chlorophyll-a (Chl-a) concentration data. The model utilized the climatological data from the Ocean Color Climate Change Initiative (OCCCI) version 6 as the background field and the NOAA MSL12 spatiotemporally continuous daily-scale data as the target dataset. Sea surface temperature (SST), salinity (SAL), photosynthetically available radiation (PAR), and sea surface pressure (SSP) were selected as the primary environmental factors influencing phytoplankton growth and distribution for data reconstruction. Ultimately, we developed a daily-scale global ocean surface Chl-a concentration dataset for the period 2001–2023, with a spatial resolution of 0.25°. This dataset is spatiotemporally continuous, spans a long time period, and shows high consistency with satellite data products in regions with available data. It also addresses the issue of severe data gaps in satellite daily-scale products, providing critical information for long-term and large-scale studies of marine phytoplankton. Compared with traditional interpolation methods, the OCNET model fully leverages the environmental information provided by ERA5 reanalysis data and MODIS satellite data to reconstruct Chl-a concentration data. Moreover, the model is not limited by the size of the marine area or the temporal coverage of the original Chl-a concentration dataset. Provided that reliable environmental variable data are available, the model can serve as an important reference for reconstructing historical Chl-a data and predicting future changes in Chl-a concentration.</p> <p>The latitude and longitude of the left bottom corner, the number of rows and columns, and grid cells information are all included in each netCDF file. In addition to the Chl-a data generated by the OCNET model, we also provide the OCNET model algorithm (based on Matlab version 2022b), example datasets, and the detection results of Marine High Chlorophyll-a (Chl-a) Events. </p>
The Global Carbon Project's fossil CO2 emissions dataset
<p>The <a href="https://www.globalcarbonproject.org/">Global Carbon Project</a> (GCP) has been publishing estimates of global and national fossil CO2 emissions since 2001. In the first instance these were simple re-publications of data from another source, but over subsequent years refinements have been made in response to feedback and identification of inaccuracies. In this article (PDF document) we describe the history of this process leading up to the methodology used in the 2025 release of the GCP's fossil CO2 dataset.</p> <p>The fossil CO2 emissions dataset is included in both its standard, absolute form, and per capita, with associated metadata files in JSON format. A file indicating the source(s) of each data point is also provided.</p> <p>This is the initial release of the 2025 dataset.</p>
Global Claims Dataset
<p>Collection of claims collected from different fact-checking websites, covering various languages and topics. Described in "Global Claims: A Multilingual Dataset of Fact-Checked Claims with Veracity, Topic, and Salience Annotations"</p>
SM2RAIN-Climate (1998-2021): monthly global satellite rainfall dataset
<p><strong>SM2RAIN-Climate</strong> rainfall product is a new long-term global scale rainfall product developed by using the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture product v06.1 as input into the SM2RAIN algorithm (<em>Brocca et al., 2014; 2019</em>). The SM2RAIN-Climate global rainfall dataset is generated in the period 1998-2021 with monthly temporal and 1° spatial resolutions, which provide the opportunity for climatological studies.</p> <p>Four different SM2RAIN-Climate datasets are provided in NetCDF format. For each dataset, the spatial grid (latitude and longitude), the rainfall values, and the mask type is defined in each NetCDF file. Two different masks are the temperature mask in data post-processing and a threshold value (percentage of missing data) taking into account missing data within a month. Depending on the application, the user can select the more suitable product.</p> <p>Details on the dataset development is provided as:</p> <p>Mosaffa, H., Filippucci, P., Massari, C., Ciabatta, L., & Brocca, L. (2023). SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies. <em>Scientific Data</em>, <em>10</em>(1), 749. <a href="https://doi.org/10.1038/s41597-023-02654-6"><em>https://doi.org/10.1038/s41597-023-02654-6</em></a></p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The work is supported by the Open-Earth-Monitor Cyberinfrastructure project that has received funding from the European Union's Horizon Europe research and innovation programme (grant agreement no. 101059548) and by the European Space Agency through the Digital Twin Earth Hydrology project (grant no. ESA 4000129870/20/I-NB - CCN N. 1) and the 4DMED Hydrology project (grant no. ESA 4000136272/21/I-EF).</p>
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
<p><strong>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).</strong><br> 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. </p> <p><strong>Note: </strong>These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. <strong>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.</strong></p> <p>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.</p> <p><strong>What is in the files?</strong></p> <p>(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):</p> <p><br> fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: north, tropics, south<br> fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br> sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br> area: Area per pixel, dimensions: latitude, longitude<br> area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south</p> <p>(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</p> <p>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br> sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br> area: Area per pixel, dimensions: latitude, longitude</p> <p><br> (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.</p> <p><br> <strong>Fair data use statement:</strong><br> The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.<br> <strong>Citation:</strong> 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).<br> <strong>Acknowledgement:</strong> 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.”<br> <strong>Co-authorship: </strong>An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.</p> <p><br> Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional output</strong> 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/</p> <p> </p>
Data supplement for "Land use intensification increasingly drives the spatiotemporal patterns of the global human appropriation of net primary production in the last century"
<p>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.</p> <p>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>)</p> <p>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.</p>
Datasets for greenhouse gasses emissions and removals from inventories and global models over Africa
<p>This file includes the data from Mostefaoui et al. (ESSD, under submission), for 54 countries African countries</p> <p> The data includes: </p> <p>(1) CO2 fluxes from global models - satellite inversions and Dynamic Global Vegetation Models (DGVM) -, and from a collection of national inventories for LULUCF, GFEDv4 and FAO data.</p> <p> DGVM values are the median of 14 models, consistent with the Global Carbon Budget 2020 (https://essd.copernicus.org/articles/12/3269/2020/) LULUCF UNFCCC corrected values are from Grassi <a href="https://priv-bx-myremote.tech.ec.europa.eu/preprints/essd-2022-104/,DanaInfo=.aetugDhuwm0xto76O48y,SSL+">https://essd.copernicus.org/preprints/essd-2022-104/</a> </p> <p>(2) CH4 fluxes from global models consistent with the Global Methane Budget 2020 (https://essd.copernicus.org/articles/12/1561/2020/)</p> <p>(3 N2O fluxes from global models (three inversions)</p> <p>For further methodological details, see Mostefaoui et al. (ESSD, under submission):</p> <p>Mounia Mostefaoui, Philippe Ciais, Matthew J. McGrath, Philippe Peylin, Prabir Patra. Greenhouse gasses emissions and their trends over the last three decades across Africa, ESSD (under submission)</p>
Global map of soil bacterial richness
<p>This repository contains global model estimates of soil bacterial richness (Fig.4) as described in:</p> <p>Bickel, Samuel, Xi Chen, Andreas Papritz, and Dani Or. “A Hierarchy of Environmental Covariates Control the Global Biogeography of Soil Bacterial Richness.” <em>Scientific Reports</em> 9, no. 1 (August 20, 2019): 1–10. <a href="https://doi.org/10.1038/s41598-019-48571-w">https://doi.org/10.1038/s41598-019-48571-w</a>.</p>
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
<p>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.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.