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708 results for “Global dataset”
Geologic Map of Ceres [Dawn Mission] - Global dataset based on the 15 individual quadrangle maps
<p><strong>Background:</strong> Between 2011 and 2018, the NASA Dawn spacecraft visited asteroid (4) Vesta and dwarf planet (1) Ceres to investigate the surfaces of both protoplanets through optical and hyperspectral imaging and their composition through gamma-ray and neutron spectroscopy from orbit.<br> For both Vesta and Ceres, a geologic mapping investigation was realized based on optical and hyperspectral data as well as a photogrammetrically derived digital terrain model. For the global mapping investigation, mappers employed Geographic Information System (GIS) software to map 15 quadrangles. The results were published as individual map sheets alongside research papers discussing the geologic evolution. The style of collaborative mapping to produce a consistent global view represented by individual quadrangle maps is comparably new despite abundantly available mapping experiences. Ongoing data acquisition during mapping created considerable challenges for the coordination and homogenization of mapping results.</p> <p>To handle this issue simultaniously to the active mission phase as best as possible a GIS-based environment was needed in order to conduct one homogenous dataset (w.r.t. geometrical and visual character) that represents one geologically-consistent map at the end. Therefore, the mapping team was supported by an predefined mapping template which was generated in the proprietary ArcGIS environment. The template contains different layers (called feature classes) for the different object/geomoetry types and contains predefined attribute values as well as cartographic symbols. The cartographic symbols follow international standards as far as possible. The colours for the geological units refering to established colour values used in geologic maps, e.g., standardized planetary maps generated by USGS, but considering individual needs and requests within the mapping team, too.<br> <br> The <strong>data product pubished here</strong> based on the mentioned GIS-based template and represents the merged global GIS-dataset of the 15 individually conducted geological maps of Ceres within the Dawn Mission. The detailed descriptions of all those scientific interpretions are published in the papers listed within the reference section. Based on team-internal decisions the dataset is provided within the properitary format of ESRIs ArcGIS environment. However, in order to use the data product also outside this software environment, single shapefiles with additional information about the symbology are also included. All available data are available within the compressed folder and the readme-file gives some informative remarks for the useage of the data</p> <p><strong>Additional remark: </strong>The data set provided here does not represent a holistic (in term of topological and scientifical) unification of the 15 individual mapping data as primarily geometric and content-related inconsistencies at quadrangle boundaries prohibited a unified compilation. On the one side, this is due to the fact that the the aim of the mapping project was not to produce a uniform global map, but rather to gain a first impression of the geology of Ceres and publish associated scientific papers. On the other side, that the geological mapping project ran parallel to the regular mission phase, and a finalizing review process for creating a global geological dataset wasn´t scheduled in the mission planning. This deficiency cannot be remedied simply by merging topological missmatches or changing the visualisation. Rather it will require ongoing and detailed scientific discussion of the interpretation results, which could be solved within an updating version of the global map.</p>
Seaweed C:N:P Global Dataset
<p>Seaweed C:N:P ratios found in the peer-reviewed literature and previously unpublished data, current to 2022. </p> <p>This is the accompanying dataset for “SEAWEED BIOGEOCHEMISTRY: GLOBAL ASSESSMENT OF C:N AND C:P RATIOS AND IMPLICATIONS FOR OCEAN AFFORESTATION” (Sheppard et al., in press). Methods for collection of the dataset are found therein. </p> <p>For queries, please contact dataset creator and caretaker, Emily Sheppard. emily.sheppard@utas.edu.au </p>
Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022 (V1.2)
<p><strong>Brief Introduction:</strong></p> <p>The PKU GIMMS Normalized Difference Vegetation Index product (PKU GIMMS NDVI, version 1.2) provides spatiotemporally consistent global NDVI data in half-month and 1/12° from 1982 to 2022. It is created to address the major uncertainties presented in current global long-term NDVI products, i.e., the effects of NOAA satellite orbital drift and AVHRR sensor degradation.</p> <p> </p> <p>The PKU GIMMS NDVI was generated based on biome-specific BPNN models that employed GIMMS NDVI3g product and 3.6 million high-quality global Landsat NDVI samples. It was then consolidated with the MODIS NDVI (MOD13C1) to extend the temporal coverage to 2022 via a pixel-wise Random Forests fusion method.</p> <p> </p> <p>The PKU GIMMS NDVI exhibits overall high accuracy evaluated by Landsat NDVI samples. Besides, it efficiently eliminated the effects of satellite orbital drift and sensor degradation and presents a good temporal consistency with MODIS NDVI in terms of pixel value and global vegetation trend. It could potentially provide a more solid data basis for global change studies.</p> <p> </p> <p>Here we provide two versions of PKU GIMMS NDVI for download, one solely based on AVHRR data (1982−2015) and the other consolidated with the MODIS NDVI (1982−2022). <strong>We strongly recommend an adequate use of the quality control (QC) layer in the product. </strong>Please refer to the Readme file for more details. <strong>We also recommend removing sparse vegetation by a threshold (e.g., 0.1) in trend analysis (Zhou et al., 2001; Liu et al., 2016)</strong></p> <p> </p> <p><strong>Major updates:</strong></p> <p>Version 1.0 (December 15, 2022):</p> <p>· The original version of the product.</p> <p> </p> <p>Version 1.1 (June 17, 2023):</p> <p>· A pixel-wise Random Forests consolidation method is used to replace the linear one.</p> <p>· The data files have been re-organized on a decade basis.</p> <p> </p> <p>Version 1.2 (August 17, 2023):</p> <p>· The BPNN model without explanatory variables of NOAA satellite number and years since launch is used to generate NDVI values of EBF during the periods of 1982−1984 and all October to April, when the Landsat NDVI samples were relatively scarce.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <p>Spatial Coverage: 180ºW~180ºE, 63ºS~90ºN</p> <p>Projection: Geographic</p> <p>Spatial Resolution: 1/12 degree</p> <p>Temporal Resolution: Half month</p> <p>Temporal Coverage: January 1982 to December 2022</p> <p>Image Dimension: Rows-2160; Columns-4320</p> <p>Units: unitless</p> <p>Fill Value: 65535</p> <p>Data Type: uint16</p> <p>Valid Range: 0-1000</p> <p>Scale Factor: 0.001</p> <p>File Format: TIFF(.tif)</p> <p>File Size: ~8Mb each file</p> <p> </p> <p><strong>References:</strong></p> <p>Li, M., Cao, S., Zhu, Z., Wang, Z., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022, Earth Syst. Sci. Data, 15, 4181–4203, <a href="https://doi.org/10.5194/essd-15-4181-2023">https://doi.org/10.5194/essd-15-4181-2023</a>, 2023.</p> <p>Liu, Q., Fu, Y. H., Zhu, Z., Liu, Y., Liu, Z., Huang, M., Janssens, I. A., and Piao, S.: Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology, Global Change Biology, 22, 3702–3711, <a href="https://doi.org/10.1111/gcb.13311">https://doi.org/10.1111/gcb.13311</a>, 2016.</p> <p>Zhou, L., Tucker, C. J., Kaufmann, R. K., Slayback, D., Shabanov, N. V., and Myneni, R. B.: Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999, J. Geophys. Res., 106, 20069–20083, <a href="https://doi.org/10.1029/2000JD000115">https://doi.org/10.1029/2000JD000115</a>, 2001.</p> <p> </p>
Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020 (V1.2)
<p><strong>Brief Introduction:</strong></p> <p> </p> <p>The fourth generation GIMMS Leaf Area Index product (GIMMS LAI4g, version 1.2) provides spatiotemporally consistent global LAI data in half-month and 1/12° from 1982 to 2020. It is created to address two major uncertainties presented in current global long-term LAI products, i.e., (1) the effects of NOAA satellite orbital drift and AVHRR sensor degradation and (2) insufficient LAI reference data to build robust LAI model particularly before the late 1990s.</p> <p> </p> <p>The GIMMS LAI4g was generated based on biome-specific BPNN models that employed the latest PKU GIMMS NDVI product and 3.6 million high-quality global Landsat LAI samples. It was then consolidated with the Reprocess MODIS LAI to extend the temporal coverage to 2020 via a pixel-wise Random Forests fusion method.</p> <p> </p> <p>The GIMMS LAI4g exhibits overall high accuracy and low underestimation evaluated by field LAI measurements and Landsat LAI samples. It efficiently eliminated the effects of satellite orbital drift and sensor degradation and presents a good temporal consistency before and after the year 2000 and a more reasonable global vegetation trend. It could potentially facilitate mitigating the disagreements between studies of the long-term global vegetation changes and benefit the model development in Earth and environmental sciences.</p> <p> </p> <p>Here we provide two versions of GIMMS LAI4g for download, one solely based on AVHRR data (1982−2015) and the other consolidated with the Reprocess MODIS LAI (1982−2020). We strongly recommend an adequate use of the quality control (QC) layer in the product. Please refer to the Readme file for more details.</p> <p> </p> <p><strong>Major updates:</strong></p> <p>Version 1.0 (February 17, 2023):</p> <p>· The original version of the product.</p> <p> </p> <p>Version 1.1 (June 14, 2023):</p> <p>· The GIMMS LAI4g is now validated by ground LAI measurements.</p> <p>· A pixel-wise Random Forests consolidation method is used to replace the linear one.</p> <p>· Two versions of GIMMS LAI4g are now available, one solely based on AVHRR data and one consolidated with MODIS LAI.</p> <p> </p> <p>Version 1.2 (August 25, 2023):</p> <p>· The BPNN model without explanatory variables of NOAA satellite number and years since launch is used to generate LAI values during 1982−1984 for all biomes, October−April for EBF, and winters for ENF, when the Landsat NDVI samples were absent or relatively scarce.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <p>Spatial Coverage: 180ºW~180ºE, 63ºS~90ºN</p> <p>Projection: Geographic</p> <p>Spatial Resolution: 1/12 degree</p> <p>Temporal Resolution: Half month</p> <p>Temporal Coverage: January 1982 to December 2020</p> <p>Image Dimension: Rows-2160; Columns-4320</p> <p>Units: m<sup>2</sup>/m<sup>2</sup></p> <p>Fill Value: 65535</p> <p>Data Type: uint16</p> <p>Valid Range: 0-7000</p> <p>Scale Factor: 0.001</p> <p>File Format: TIFF(.tif)</p> <p>File Size: ~8Mb each file</p> <p> </p> <p><strong>References:</strong></p> <p>Cao, S., Li, M., Zhu, Z., Wang, Z., Zha, J., Zhao, W., Duanmu, Z., Chen, J., Zheng, Y., Chen, Y., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-68, in review, 2023.</p>
30 m resolution global forest burned area dataset 2018
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025<sup>°</sup>(approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p>
30 m resolution global forest burned area dataset 2016
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025<span>°</span> (approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p>
30 m resolution global forest burned area dataset 2014
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025°(approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p><p>contacts : zhangzhaoming@aircas.ac.cn / zhangzm@radi.ac.cn</p>
30 m resolution global forest burned area dataset 2020
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025°(approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p><p>contacts : zhangzhaoming@aircas.ac.cn / zhangzm@radi.ac.cn</p>
Diminishing returns on labor in the global marine food system: Dataset S1 and code for analysis
<p>Dataset on the number of marine fishers 1950-2015 accompanying the manuscript "Diminishing returns on labor in the global marine food system" by K. J. N. Scherrer, Y. Rousseau, L. C. L. Teh, U. R. Sumaila and E. D. Galbraith. Includes 1) script for data analysis, 2) processed fisheries labor data set, 3) separate data file with average socioeconomic indicators by country needed for analysis, 4) data documentation. </p>
Global tidal marshes 2020 dataset
<p>Tidal marsh ecosystems are heavily impacted by human activities, highlighting a pressing need to address gaps in our knowledge of their distribution. To better understand the global distribution and changes in tidal marsh extent, and identify opportunities for their conservation and restoration, it is critical to develop a spatial knowledge base of their global occurrence. Here, we develop a globally consistent tidal marsh distribution map for the year 2020 at 10-m resolution. To map the location of the world’s tidal marshes we applied a random forest classification model to earth observation data from the year 2020. We trained the classification model with a reference dataset developed to support distribution mapping of coastal ecosystems, and predicted the spatial distribution of tidal marshes between 60°N to 60°S. We validated the tidal marsh map using standard accuracy assessment methods, with our final map having an overall accuracy score of 0.852. We estimate the global extent of tidal marshes in 2020 to be 52,880 km<sup>2</sup> (95% CI: 32,030 to 59,780 km<sup>2</sup>) distributed across 120 countries and territories. Tidal marsh distribution is centred in temperate and Arctic regions, with nearly half of the global extent of tidal marshes occurring in the temperate Northern Atlantic (45%) region. At the national scale, over a third of the global extent (18,510 km<sup>2</sup>; CI: 11,200 – 20,900) occurs within the USA. Our analysis provides the most detailed spatial data on global tidal marsh distribution to date and shows that tidal marshes occur in more countries and across a greater proportion of the world’s coastline than previous mapping studies. Our map fills a major knowledge gap regarding the distribution of the world’s coastal ecosystems and provides the baseline needed for measuring changes in tidal marsh extent and estimating their value in terms of ecosystem services. </p> <p> </p> <p>This dataset accompanies the preprint <a href="http://doi.org/10.1101/2023.05.26.542433">https://doi.org/10.1101/2023.05.26.542433</a> </p>
Global Biotic Interactions: Elton Dataset Cache iNaturalist
<p>Global Biotic Interactions: Elton Dataset Cache iNaturalist</p><p>The intended use of this archive/cache is to allow for offline-enabled access to versions of existing species interaction datasets provided by iNaturalist. The program "Elton" (https://doi.org/10.5281/zenodo.998263) was used to populate the content of elton-datasets.tar.gz . The same program can be used to extract information from the cache archive also. Global Biotic Interactions (https://globalbioticinteractions.org, https://doi.org/10.1016/j.ecoinf.2014.08.005) also uses these archives to create derived species interaction data archives, search indexes and APIs.</p><p>To get offline-enabled access to versions of other species interactions datasets, please see Global Biotic Interactions: Elton Dataset Cache at https://doi.org/10.5281/zenodo.2007418 .</p><p> </p><p>Contents</p><p>--------</p><p> </p><p>README: the first part of this file</p><p>elton-datasets.tar.gz:versioned archive with species interaction datasets</p><p>elton-datasets.tar.sha256:content signature of elton-datasets.tar</p><p>elton-datasets.tsv:list of included datasets</p><p>elton.jar:commandline program to help access the species interaction datasets</p><p> </p><p>Usage</p><p>-----</p><p> </p><p>To install, extract elton-datasets.tar.gz into a directory of choice using:</p><p> </p><p>tar xfz elton-dataset.tar.gz</p><p> </p><p>To use, download elton.jar included this publication and execute the following to get a list of available datasets:</p><p>java -Xmx4G -jar elton.jar datasets</p><p>on a system that has java v8+ installed.</p><p>If all goes well, you should be able to regenerate the included file elton-dataset.tsv .</p><p>For more information on how to use elton.jar, execute:</p><p>java -jar elton.jar usage</p><p>or visit https://github.com/globalbioticinteractions/elton for more available commands.</p><p>Alternatively, without using Elton, you can access the data by inspecting the access.tsv files in the various directories of the datasets directory.</p><p>When using these datasets in a publication or product, please cite the *original* data providers and publications. You can find the citations in the data.</p><p>Included datasets:</p><p>globalbioticinteractions/inaturalist https://github.com/globalbioticinteractions/inaturalist/archive/db6545f3d7afd88f48064dddb9f4692603545a44.zip 2023-10-14T00:29:47.032Z application/globi</p><p>globalbioticinteractions/inaturalist https://github.com/globalbioticinteractions/inaturalist/archive/db6545f3d7afd88f48064dddb9f4692603545a44.zip 9a1342936d3abd508a039b4216a9c3b18b6d135160338c966f40a6fee3191731 2023-10-14T00:29:49.693Z</p><p>globalbioticinteractions/inaturalist https://www.inaturalist.org/observations/globi-observations-resource-relationships-dwca.zip 32642cd31854c4e4c93e40ed2e0117819be2397b736bff12bee32e5045204df2 2023-10-14T00:30:04.130Z</p><p>globalbioticinteractions/inaturalist https://github.com/globalbioticinteractions/inaturalist/archive/db6545f3d7afd88f48064dddb9f4692603545a44.zip 9a1342936d3abd508a039b4216a9c3b18b6d135160338c966f40a6fee3191731 2023-10-14T00:34:30.397Z</p><p>globalbioticinteractions/inaturalist https://www.inaturalist.org/taxa/inaturalist-taxonomy.dwca.zip b3355c65d28c3dc7a4e9b66d6e20bf603d91c68e6c392473be93ed43e680055c 2023-10-14T00:34:40.679Z</p><p>globalbioticinteractions/inaturalist https://github.com/globalbioticinteractions/inaturalist/archive/db6545f3d7afd88f48064dddb9f4692603545a44.zip 9a1342936d3abd508a039b4216a9c3b18b6d135160338c966f40a6fee3191731 2023-10-14T00:35:17.450Z</p>
PyFLEXTRKR Global MCS Tracking Dataset using GPM MergedIR Tb and IMERG precipitation data
<p>This is the global mesoscale convective system (MCS) tracking dataset developed by <a href="https://doi.org/10.1029/2020JD034202">Feng et al. (2021) JGR</a>. It contains the MCS track data (location, time, lifecycle evolution of MCS cloud and precipitation characteristics), monthly mean and 20-year climatological MCS statistics on 0.1 degree x 0.1 degree (lat x lon) grid. All data files are in netCDF format.</p><p>The periods are from June 2000 to December 2020. The geographic coverage is 180°W-180°E, 60°S-60°N. For more detailed documentations, please refer to the README "PyFLEXTRKR_MCS_Tracking_Data_Readme.pdf".</p><p>Due to the large file size of the native 1-hourly resolution pixel-level data on the 0.1 degree x 0.1 degree grid, they are not included in this dataset. Please contact Zhe Feng (<a href="mailto:zhe.feng@pnnl.gov">zhe.feng@pnnl.gov</a>) if you are interested in obtaining the pixel-level data.</p>
Supplementary dataset for "Global agricultural economic water scarcity"
<p>This repository contains supporting data for: "<strong>Global agricultural economic water scarcity"</strong></p> <p>Cite: Rosa, L., Chiarelli, D.D., Rulli, M.C., Dell’Angelo J., and D’Odorico, P. Global agricultural economic water scarcity. Science Advances. 2020<br> Email: lorenzo_rosa@berkeley.edu</p> <p>The dataset contains the number of months (#months) croplands are facing green water scarcity (GWS), blue water scarcity (BWS), and economic water scarcity (EWS). Where "0" indicates that the pixel does not face water scarcity, "1" indicates that the pixel is facing water scarcity for 1 month, "12" indicates that the pixel is facing water scarcity for 12 months. Files are uploaded in arcmap and netcdf formats. </p> <p> </p>
Lamb et al. (2020): Global Whole Lithosphere Isostasy datasets
<p>Model outputs from Lamb, S., Moore, J., Perez-Gussinye, M., Stern, T. (2020). Global whole lithosphere isostasy: implications for surface elevations, structure, strength and densities of the continental lithosphere, Geochem, Geophys, Geosyst., doi :10.1029/2020GC009150</p> <p>Data sets supplied here are the outcome of modelling described in the text. Files are given in either ASCII or GMT grd format.</p> <p>Data Set S1 (ds01.grd). Gridded crustal model of Antarctica based on whole lithosphere isostasy described in this study, and used to construct Figure 7c. Data columns are: x distance, y distance, crustal thickness. In GMT grd format with bounds in km -R-3000/3000/-3000/3000 -I5. Uses same projection as Bedmap 2 - see Fretwell et al. (2013) for details of projection. Suggested colour palette in GMT: seis -T0/60/2.5 -I</p> <p>Data Set S2 (ds02.xyz). Average elevation and crustal thickness of continental interiors calculated in this study, used to plot Figure 3c and described in text, using a standard lithospheric thickness of 100 km. Data columns are: Name, area, average elevation (m), average reduced elevation for 100 km standard lithosphere (m), average lithospheric thickness (km), average crustal thickness (km), 1 sigma uncertainty in elevation (m) or reduced elevation (m), 1 sigma uncertainty in lithospheric thickness (km), 1 sigma uncertainty in crustal thickness (km). ASCII file.</p> <p>Data Set S3 (ds03.grd). Gridded elevation anomalies (observed elevation – elevation calculated from whole lithosphere isostasy), as described in text and used to construct Figure 8. Data columns are: Longitude, Latitude, elevation anomaly (m). In GMT grd format with bounds in degrees -R-179/179/-60/80 -I1. Suggested colour palette in GMT: seis -T-2000/2000/200 -Z -I -M -D --COLOR_NAN=white</p> <p>Data Set S4 (ds04.grd). Gridded global crustal density perturbation model calculated to give zero elevation anomaly, based on elevation anomalies in Data Set 3, used to plot Fig. 9a. Data columns are: Longitude, Latitude, crustal density perturbation in kgm<sup>-3</sup>. In GMT grd format with bounds in degrees -R-179/179/-60/80 -I1. Suggested colour palette in GMT: seis -T-200/200/10 -Z -I -M -D --COLOR_NAN=white</p> <p>Data Set S5 (ds05.grd). Gridded global conductive lithosphere mantle density perturbation model calculated to give zero elevation anomaly, based on elevation anomalies in Data Set 3, used to plot Fig. 9b. Data columns are: Longitude, Latitude, mantle density perturbation in kgm<sup>-3</sup>. In GMT grd format with bounds in degrees -R-179/179/-60/80 -I1. Suggested clour palette in GMT: seis -T-50/50/5 -Z -I -M -D --COLOR_NAN=white</p> <p>Data Set S6 (ds06.grd). Gridded global crustal thickness perturbation model calculated to give zero elevation anomaly, based on elevation anomalies in Data Set 3, used to plot Fig. 9c. Data columns are: Longitude, Latitude, crustal thickness in km. In GMT grd format with bounds in degrees -R-179/179/-60/80 -I1. Suggested colour palette in GMT: seis -T-10/10/1 -Z -I -M -D --COLOR_NAN=white</p> <p>Data Set S7 (ds07.grd). Gridded global conductive lithosphere thickness perturbation model calculated to give zero elevation anomaly, based on elevation anomalies in Data Set 3, used to plot Fig. 9d. Data columns are: Longitude, Latitude, thickness in km. In GMT grd format with bounds in degrees -R-179/179/-60/80 -I1. Suggested colour palette in GMT: seis -T-100/100/5 -Z -I -M -D --COLOR_NAN=white</p> <p>Data Set S8 (ds08.grd). Compilation of gridded ratios of elastic thickness to conductive lithospheric thickness in the continents used to construct Figure 10c and d. Data columns are: Longitude, Latitude, ratio of elastic thickness to conductive lithospheric thickness from sources cited below. In GMT grd format with bounds in degrees -R-179/179/-60/80 -I1. Suggested colour palette in GMT: rainbow -T0/1/0.05 -Z -I -D --COLOR_NAN=white</p> <p><em>Data references:</em></p> <p><em>Lowry, A.R. and Pérez-Gussinyé, M., 2011. The role of crustal quartz in controlling Cordilleran deformation. Nature, 471(7338), 353-357.</em></p> <p><em>Pérez‐Gussinyé, M., Lowry, A.R., Watts, A.B. and Velicogna, I., (2004). On the recovery of effective elastic thickness using spectral methods: examples from synthetic data and from the Fennoscandian Shield. Journal of Geophysical Research: Solid Earth, 109(B10).</em></p> <p><em>Pérez-Gussinyé, M. and Watts, A.B., (2005). The long-term strength of Europe and its implications for plate-forming processes. Nature, 436(7049), 381.</em></p> <p><em>Pérez‐Gussinyé, M., Lowry, A.R. and Watts, A.B., (2007). Effective elastic thickness of South America and its implications for intracontinental deformation. Geochemistry, Geophysics, Geosystems, 8(5).</em></p> <p><em>Pérez‐Gussinyé, M., Lowry, A.R., Phipps Morgan, J. and Tassara, A., (2008). Effective elastic thickness variations along the Andean margin and their relationship to subduction geometry. Geochemistry, Geophysics, Geosystems, 9(2).</em></p> <p><em>Pérez-Gussinyé, M., Metois, M., Fernández, M., Vergés, J., Fullea, J. and Lowry, A.R., (2009). Effective elastic thickness of Africa and its relationship to other proxies for lithospheric structure and surface tectonics. Earth and Planetary Science Letters, 287(1-2), 152-167.</em></p> <p><em>Swain, C.J. and Kirby, J.F., 2006. An effective elastic thickness map of Australia from wavelet transforms of gravity and topography using Forsyth's method. Geophysical Research Letters, 33(2).</em></p>
ModIs Dust AeroSol (MIDAS): A global fine resolution dust optical depth dataset
<p>Monitoring and describing the spatiotemporal variability of dust aerosols is crucial to understand their multiple effects, related feedbacks and impacts within the Earth system. This study describes the development of the MIDAS (ModIs Dust AeroSol) dataset. MIDAS provides columnar daily dust optical depth (DOD) at 550 nm at global scale and fine spatial resolution (0.1° x 0.1°) over a 15-year period (2003-2017). This new dataset combines quality filtered satellite aerosol optical depth (AOD) retrievals from MODIS-Aqua at swath level (Collection 6.1, Level 2), along with DOD-to-AOD ratios provided by MERRA-2 reanalysis to derive DOD on the MODIS native grid. The uncertainties of MODIS AOD and MERRA-2 dust fraction with respect to AERONET and LIVAS, respectively, are taken into account for the estimation of the total DOD uncertainty. MERRA-2 dust fractions are in very good agreement with those of LIVAS across the “dust belt”, in the Tropical Atlantic Ocean and the Arabian Sea; the agreement degrades in North America and the Southern Hemisphere where dust sources are smaller. MIDAS, MERRA-2 and LIVAS DODs strongly agree when it comes to annual and seasonal spatial patterns, with collocated global DOD averages of 0.033, 0.031 and 0.029, respectively; however, deviations in dust loading are evident and regionally dependent. Overall, MIDAS is well correlated with AERONET-derived DODs (R=0.89), only showing a small positive bias (0.004 or 2.7%). Among the major dust areas of the planet, the highest R values (> 0.9) are found at sites of N. Africa, Middle East and Asia. MIDAS expands, complements and upgrades existing observational capabilities of dust aerosols and it is suitable for dust climatological studies, model evaluation and data assimilation.</p>
A new dataset of global irrigation areas from 2000 to 2015
<pre>We provide global irrigation maps README FOR GLOBAL IRRIGATION MAPS --------------------------------- Prediction Maps --------------- v3b_combined_*.tif: GeoTIFF files with model predictions, from 2001 to 2015. 0=not irrigated, 1=low-to-medium irrigated, 2=highly irrigated. Two of these are available in PNG format as well: 2001, 2015 Difference Between 2001 and 2015 -------------------------------- diff2001vs2015.tif: 0=no difference, 1=large decrease, 2=decrease, 3=no change, 4=increase, 5=large increase Also available in PNG format. Dark green=large decrease, green=decrease, grey=no change, orange=increase, red=large increase Assessment Map -------------- assessment_map.tif: FN=false negatives, FP=false positives, TP=true positives, mask=cropland mask</pre>
BOATS-Global-Fisheries-Economics-Model-Dataset-Carozza-et-al-2016
<p>Here we provide the MATLAB functions, fisheries data, model forcing data (net primary production and temperature), and model output required to generate the figures and perform calculations from Carozza et al. (2017) [Carozza DA, Bianchi D, Galbraith ED (2017) Formulation, General Features and Global Calibration of a Bioenergetically-Constrained Fishery Model. PLoS ONE 12(1): e0169763. doi:10.1371/journal.pone.0169763]. The plot script (plot_figures_PLoSONE_repository.m) is written in MATLAB version R2012a.</p>
Dataset supplementing Lichtenberg et al. (2017) A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Global Change Biology
<p>This dataset contains data and scripts that supplement the publication</p> <p>Lichtenberg et al. (2017) A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Global Change Biology. DOI: 10.1111/gcb.13714</p> <p> </p> <p>Please cite the above article if you use any of the included data or code.</p> <p> </p> <p>Files are described in README.md.</p>
Caravan - A global community dataset for large-sample hydrology
<p><strong>This is the </strong><strong>accompanying dataset to the following paper <a href="https://www.nature.com/articles/s41597-023-01975-w">https://www.nature.com/articles/s41597-023-01975-w</a></strong></p> <p><em>Caravan</em> is an open community dataset of meteorological forcing data, catchment attributes, and discharge daat for catchments around the world. Additionally, Caravan provides code to derive meteorological forcing data and catchment attributes from the same data sources in the cloud, making it easy for anyone to extend Caravan to new catchments. The vision of Caravan is to provide the foundation for a truly global open source community resource that will grow over time.</p> <p>If you use Caravan in your research, it would be appreciated to not only cite Caravan itself, but also the source datasets, to pay respect to the amount of work that was put into the creation of these datasets and that made Caravan possible in the first place.</p> <p><strong>All current development and additional community extensions can be found at <a href="https://github.com/kratzert/Caravan">https://github.com/kratzert/Caravan</a><br></strong><br><strong>IMPORTANT: Due to size limitations for individual repositories, the netCDF version and the CSV version of Caravan (since Version 1.6) are split into two different repositories. You can find the CSV version at <a href="https://zenodo.org/records/15530021">https://zenodo.org/records/15530021</a></strong></p> <p>Channel Log:</p> <ul> <li><strong>23 May 2022: Version 0.2</strong> - Resolved a bug when renaming the LamaH gauge ids from the LamaH ids to the official gauge ids provided as "govnr" in the LamaH dataset attribute files.</li> <li><strong>24 May 2022: Version 0.3</strong> - Fixed gaps in forcing data in some "camels" (US) basins.</li> <li><strong>15 June 2022: Version 0.4</strong> - Fixed replacing negative CAMELS US values with NaN (-999 in CAMELS indicates missing observation).</li> <li><strong>1 December 2022: Version 0.4 </strong>- Added 4298 basins in the US, Canada and Mexico (part of HYSETS), now totalling to 6830 basins. Fixed a bug in the computation of catchment attributes that are defined as pour point properties, where sometimes the wrong HydroATLAS polygon was picked. Restructured the attribute files and added some more meta data (station name and country).</li> <li><strong>16 January 2023: Version 1.0</strong> - Version of the official paper release. No changes in the data but added a static copy of the accompanying code of the paper. For the most up to date version, please check https://github.com/kratzert/Caravan</li> <li><strong>10 May 2023: Version 1.1</strong> - No data change, just update data description.</li> <li><strong>17 May 2023: Version 1.2</strong> - Updated a handful of attribute values that were affected by a bug in their derivation. See https://github.com/kratzert/Caravan/issues/22 for details.</li> <li><strong>16 April 2024: Version 1.4</strong> - Added 9130 gauges from the original source dataset that were initially not included because of the area thresholds (i.e. basins smaller than 100sqkm or larger than 2000sqkm). Also extended the forcing period for all gauges (including the original ones) to 1950-2023. Added two different download options that include timeseries data only as either csv files (Caravan-csv.tar.xz) or netcdf files (Caravan-nc.tar.xz). Including the large basins also required an update in the earth engine code</li> <li><strong>16 Jan 2025: Version 1.5</strong> - Added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND. Also added all PET-related climated indices derived with the Penman-Monteith PET band (suffix "_FAO_PM") and renamed the old PET-related indices accordingly (suffix "_ERA5_LAND"). </li> <li><strong>27 May 2025: Version 1.6</strong><br> <ul> <li>Updated the CAMELS-AUS data to source from CAMELS-AUS v2. This means more basins (561 compared to 222) and more recent streamflow data (2022 compared to 2014). Note that the gauge id for four basins changed between the original CAMELS-AUS version and v2. Those gauges are ['camelsaus_224213A', 'camelsaus_224214A', 'camelsaus_227225A', 'camelsaus_403213A'] that all lost their trailing "A". To stay synced with CAMELS-AUS (v2), we also adapted the new naming.</li> <li>Added VERSION file to the root directory that contains the current version number.</li> <li>Updated the code to the most recent GitHub snapshot (commit 6eab036).</li> <li>Due to the 50GB repository limit, we had to split the netCDF version and the CSV version into two separate repositories. The CSV version can be found under https://zenodo.org/records/15530021</li> </ul> </li> </ul>
Global Sand Dams Dataset
<p>The Global Sand Dams Dataset (GSDD) contains data on sand dams location, year of construction, water access, and characteristics, including dams’ crest length, throwback and stream width. </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.