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708 results for “Global dataset”
Track Dataset for "Impact of high temporal resolution FY-4A Geostationary Interferometric Infrared Sounder (GIIRS) radiance measurements on Typhoon forecasts: Maria (2018) case with GRAPES global 4D-Var assimilation system"
<p>The track dataset and code at 00z/06z 10 July 2018 for Typhoon Maria (2018).</p>
Figure 4 from: Pérez-Luque AJ, Zamora R, Bonet FJ, Pérez-Pérez R (2015) Dataset of MIGRAME Project (Global Change, Altitudinal Range Shift and Colonization of Degraded Habitats in Mediterranean Mountains). PhytoKeys 56: 61-81. https://doi.org/10.3897/phytokeys.56.5482
Figure 4 - Diagram of integration of the dataset within Information System of Sierra Nevada Global Change Observatory (http://obsnev.es/linaria.html). Field data were recorded with Smartphone devices (see Pérez-Pérez et al. 2013). After a validation process (see Quality Control section) the occurrence and measurement data were accommodated to Darwin Core Archive and integrated into GBIF.
Figure 3 from: Pérez-Luque AJ, Zamora R, Bonet FJ, Pérez-Pérez R (2015) Dataset of MIGRAME Project (Global Change, Altitudinal Range Shift and Colonization of Degraded Habitats in Mediterranean Mountains). PhytoKeys 56: 61-81. https://doi.org/10.3897/phytokeys.56.5482
Figure 3 - Sampling Design. a Altitudinal migration hypothesis. At each study site, from the forest edge to treeline ecotone, we sampled each 25 m of elevation b Colonization of marginal habitat hypothesis. Transects were located on three habitat types: Forests (brown circles), Forest Edges (red squares) and Inside Marginal Habitats (blue triangles).
Figure 2 from: Pérez-Luque AJ, Zamora R, Bonet FJ, Pérez-Pérez R (2015) Dataset of MIGRAME Project (Global Change, Altitudinal Range Shift and Colonization of Degraded Habitats in Mediterranean Mountains). PhytoKeys 56: 61-81. https://doi.org/10.3897/phytokeys.56.5482
Figure 2 - Distribution of Quercus pyrenaica forests in Iberian Peninsula (a). Sierra Nevada harbours eight populations of Quercus pyrenaica clustered into three groups (different colours). We selected two study sites: Robledal de Cañar (c) and Robledal San Juan (d). Colour Orthophotography of 2009 from Regional Ministry of the Environment, Regional Government of Andalusia.
Figure 1 from: Pérez-Luque AJ, Zamora R, Bonet FJ, Pérez-Pérez R (2015) Dataset of MIGRAME Project (Global Change, Altitudinal Range Shift and Colonization of Degraded Habitats in Mediterranean Mountains). PhytoKeys 56: 61-81. https://doi.org/10.3897/phytokeys.56.5482
Figure 1 - Schematic representation of the two main hypothesis of the project: altitudinal migration (a) and colonization of marginal areas (b) of Quercus pyrenaica forests.
CMLR: A mechanistic global 0.05° Gross Primary Production dataset using TROPOMI Solar-induced fluorescence observations
<p>CMLR GPP is a mechanistic global Gross Primary Production dataset using TROPOMI Solar-induced chlorophyll fluorescence observations (TROPOSIF). This dataset provided GPP estimates from May 2018 to December 2021 with a 0.05° spatial resolution at 1-day time step. We modified the mechanistic light response (MLR) model proposed by Gu et al. (2019) to apply it to the canopy-scale, and then generated this dataset. The parameterization of q<sub>L</sub> (the fraction of open photosystem II reaction centers) in the MLR framework is accomplished using a random forest model. For the continuous global mapping purpose, we further composited the original CMLR GPP using an 8-day moving window and applied a 2D Gaussian function in 3 × 3 moving windows to fill in the gaps. Pixels with filled data were marked using flag = 1 in the quality control layer.</p>
Global Wheat Head Dataset 2021 (jpg compressed)
<p>The jpg compressed version of Global Wheat Head Dataset 2021. Converting all the images from png to jpg resulted in compression of abt. 10Gb to 1Gb. File name in the csv annotation file were also converted. Conversion script is included in this zip file (png2jpg.py). This should aid the download waiting time to a certain extent, however I do not assure any data loss upon image compression. I do not claim any rights regarding this dataset.</p> <p>https://zenodo.org/record/5092309</p>
Global subnational Gini coefficient (income inequality) and gross national income (GNI) per capita PPP datasets for 1990-2023
<p>This dataset provides a gridded subnational datasets for</p> <ul> <li>Income inequality (Gini coefficient) at admin 1 level</li> <li>Gross national income (GNI) per capita PPP at admin 1 level</li> </ul> <p>The datasets are based on reported subnational admin data and spans three decades from 1990 to 2023 </p> <p>The datasets are presented in details in the following publication. <strong><em>Please cite this paper when using data. </em></strong></p> <p>Chrisendo D, Niva V, Hoffman R, Sayyar SM, Rocha J, Sandström V, Solt F, Kummu M. 2024. Income inequality has increased for over two-thirds of the global population. Preprint. doi: <a href="https://doi.org/10.21203/rs.3.rs-5548291/v1" target="_blank" rel="noopener">https://doi.org/10.21203/rs.3.rs-5548291/v1</a></p> <p><strong>Code is available</strong> at following repositories:</p> <ul> <li>Gini coefficient data creation: <a href="https://github.com/mattikummu/subnatGini" target="_blank" rel="noopener">https://github.com/mattikummu/subnatGini</a> </li> <li>GNI per capita data creation: <a href="https://github.com/mattikummu/subnatGNI" target="_blank" rel="noopener">https://github.com/mattikummu/subnatGNI</a> </li> <li>analyses for the article: <a href="https://github.com/mattikummu/gini_gni_analyses">https://github.com/mattikummu/gini_gni_analyses</a> </li> </ul> <p><strong>The following data is given (formats in brackets)</strong></p> <p>Gini coefficient:</p> <p>Please note, two distinct datasets for Gini cofficient are given. One based on SWIID national dataset, and another for WID national dataset. These are separated in file names as follows: _disp_ for SWIID (disposable income); _WID_ for WID. </p> <ul> <li>Income inequality (Gini coefficient) at admin 0 level (national) (GeoTIFF, gpkg, csv)</li> <li>Income inequality (Gini coefficient) at admin 1 level (subnational) (GeoTIFF, gpkg, csv)</li> <li>Slope for Gini coefficient at admin 1 level (GeoTIFF; slope is given also in gpk and csv files)</li> <li>Uncertainty (standard deviation for each year and admin area; uncertainty for slope) for Gini (based on SWIID) at admin 1 level (gpkg)</li> <li>Input data for the script that was used to generate the Gini coefficient (input_data_gini.zip)</li> </ul> <p>Gross national income (GNI) pear capita PPP (in 2021 USD):</p> <ul> <li>Gross national income (GNI) per capita PPP at admin 0 level (national) (GeoTIFF, gpkg, csv)</li> <li>Gross national income (GNI) per capita PPP at admin 1 level (subnational) (GeoTIFF, gpkg, csv)</li> <li>Slope for GNI per capita (log10) at admin 1 level (GeoTIFF; slope is given also in gpk and csv files)</li> <li>Input data for the script that was used to generate the GNI per capita PPP (input_data_GNI.zip)</li> </ul> <p><strong>Files are named as follows</strong><br><em>Format</em>: raster data (GeoTIFF) starts with rast_*, polygon data (gpkg) with polyg_*, and tabulated with tabulated_*. <br><em>Admin levels:</em> adm0 for admin 0 level, adm1 for admin 1 level<br><em>Product type:</em> </p> <ul> <li>_gini_disp_ for gini coefficient based on SWIID national dataset (disposable income)</li> <li>_gini_WID_ for gini coefficient based on WID national dataset</li> <li>_uncertainty_slope_gini_disp_ for slope uncertainty of SWIID based Gini</li> <li>_uncertainty_SD_gini_disp_ for standard deviation of SWIID based Gini</li> <li>_gni_perCapita_ for GNI per capita PPP</li> </ul> <p> </p> <p><strong>Metadata </strong></p> <p><em>Grids </em></p> <p>Resolution: 5 arc-min (0.083333333 degrees) </p> <p>Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax) </p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: Multiband geotiff; one band for each year over 1990-2021</p> <p>Unit: no unit for Gini coefficient and PPP USD in 2017 international dollars for GNI per capita</p> <p> </p> <p><em>Geospatial polygon (gpkg) files: </em></p> <p>Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax) </p> <p>Temporal extent: annual over 1990-2021</p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: gkpk </p> <p>Unit: no unit for Gini coefficient and PPP USD in 2017 international dollars for GNI per capita</p> <p> </p> <p><strong>Version 3 changes (24.07.2025)</strong></p> <ul> <li>both datasets updated to cover 1990-2023 (previously 1990-2021)</li> <li>extrapolation method updated</li> <li>Gini data now produced also using WID national data as a base (previously only one based on SWIID was provided)</li> <li>uncertainty analysis done for Gini (SWIID)</li> </ul>
Global dataset on nutrient removal with crop residues
Open the record for dataset details and reuse information.
IPRC/SOEST Aquarius V5.0 Optimally Interpolated Sea Surface Salinity 7-Day global Dataset
The IPRC/SOEST Aquarius OI-SSS v5 product is a level 4, near-global, 0.5 degree spatial resolution, 7-day, optimally interpolated salinity dataset based on version 5.0 of the AQUARIUS/SAC-D level 2 mission data. This is a PI led dataset produced at the International Pacific Research Center (IPRC) at the University of Hawaii (Manoa) School of Ocean and Earth Science and Technology. The optimal interpolation (OI) mapping procedure used to create this product corrects for systematic spatial biases in Aquarius SSS data with respect to near-surface in situ salinity observations and takes into account available statistical information about the signal and noise, specific to the Aquarius instrument. Bias fields are constructed by differencing in situ from Aquarius derived SSS fields obtained separately using ascending and descending satellite observations for each of the three Aquarius beams, and by removal of small-scale noise and low-pass filtering along-track using a two-dimensional Hanning window procedures prior to application of the OI algorithm. Additional enhancements for this new version of the product include: 1) The V5.0 (end-of mission) version of Aquarius Level-2 (swath) SSS data are used as input data for the OI SSS analysis. 2) The source of the first guess fields has changed from the APDRC Argo-derived SSS product to the average of four different in-situ based SSS products. 3) The bias correction algorithm has changed to adjust SSS retrievals for large-scale systematic biases on a repeat-track basis. 4) New, less restrictive thresholds are implemented to filter observations for land and ice contamination, thus improving coverage in the coastal areas and semi-enclosed seas. 5) Level-2 RFI masks for descending and ascending satellite passes are used to discard observations in specific geographic zones where excessive ascending-descending differences are observed due to contamination from undetected RFI. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath. The Aquarius polar orbit is sun synchronous at 657 km with a 6 pm, ascending node, and has a 7-Day repeat cycle.
Multi-Mission Optimally Interpolated Sea Surface Salinity Global Monthly Dataset V1
This is a level 4 product on a 0.25-degree spatial and monthly temporal grid. The product is the monthly mean of the level 4 OISSS dataset using three satellite missions: the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) using Optimal Interpolation (OI) with a 7-day decorrelation time scale. This dataset is produced by the International Pacific Research Center (IPRC) of the University of Hawaii at Manoa in collaboration with the Remote Sensing Systems (RSS), Santa Rosa, California. More details can be found in the users guide and Addendum I to the product Technical Notes.
ASTER Global Emissivity Dataset 1 kilometer Binary
The AG1kmB Version 3 dataset was decommissioned as of December 14, 2016. Users are encouraged to use the ASTER Global Emissivity Dataset 1-kilometer [AG1km](https://doi.org/10.5067/Community/ASTER_GED/AG1km.003) dataset in HDF5. The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) MOD07 atmospheric profiles and the MODerate spectral resolution TRANsmittance (MODTRAN) 5.2 radiative transfer model. This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. The HDF5 version of the data are available for distribution, please see [AG1km](https://doi.org/10.5067/Community/ASTER_GED/AG1km.003) for more information.The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product.Knonw Issues* Known issues are provided in Section 4 of the User Guide.Improvements/Changes from Previous Versions* Includes data for the entire globe.* Includes the mean climatology of all ASTER data for the specific temporal extent.
GHRSST Level 4 GAMSSA_28km Global Foundation Sea Surface Temperature Analysis v1.0 dataset (GDS2)
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology (BoM) using optimal interpolation (OI) on a global 0.25 degree grid. This Global Australian Multi-Sensor SST Analysis (GAMSSA) v1.0 system blends satellite SST observations from passive infrared and passive microwave radiometers with in situ data from ships, drifting buoys and moorings from the Global Telecommunications System (GTS). SST observations that have experienced recent surface wind speeds less than 6 m/s during the day or less than 2 m/s during night are rejected from the analysis. The processing results in daily foundation SST estimates that are largely free of nocturnal cooling and diurnal warming effects. Sea ice concentrations are supplied by the NOAA/NCEP 12.7 km sea ice analysis. In the absence of observations, the analysis relaxes to the Reynolds and Smith (1994) Monthly 1 degree SST climatology for 1961 - 1990.
Multi-Mission Optimally Interpolated Sea Surface Salinity Global Monthly Dataset V2
This is a level 4 product on a 0.25-degree spatial and monthly temporal grid. The product is the monthly mean of the level 4 OISSS dataset using three satellite missions: the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) using Optimal Interpolation (OI) with a 7-day decorrelation time scale. This dataset is produced by the Earth and Space Research (ESR), Seattle, WA and the International Pacific Research Center (IPRC) of the University of Hawaii at Manoa in collaboration with the Remote Sensing Systems (RSS), Santa Rosa, California. More details can be found in the users guide.
Multi-Mission Optimally Interpolated Sea Surface Salinity Global Dataset V1
This is a level 4 product on a 0.25-degree spatial and 4-day temporal grid. The product is derived from the level 2 swath data of three satellite missions: the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) using Optimal Interpolation (OI) with a 7-day decorrelation time scale. The product offers a continuous record from August 28, 2011 to present by concatenating the measurements from Aquarius (September 2011 - June 2015) and SMAP (April 2015 present). ESAs SMOS data was used to fill the gap in SMAP data between June and July 2019, when the SMAP satellite was in a safe mode. The two-month overlap (April - June 2015) between Aquarius and SMAP was used to ensure consistency and continuity in data record. The product covers the global ocean, including the Arctic and Antarctic in the areas free of sea ice, but does not cover internal seas such as Mediterranean and Baltic Sea. In-situ salinity from Argo floats and moored buoys are used to derive a large-scale bias correction and to ensure consistency and accuracy of the OISSS dataset. This dataset is produced by the International Pacific Research Center (IPRC) of the University of Hawaii at Manoa in collaboration with the Remote Sensing Systems (RSS), Santa Rosa, California. More details can be found in the users guide.
ASTER Global Emissivity Dataset, 1 kilometer, HDF5 V003
The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) MOD07_L2 atmospheric profiles and the MODerate Spectral resolution TRANsmittance (MODTRAN 5.2) radiative transfer model. This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product.Known Issues* Known issues are provided in Section 4 of the User Guide.Improvements/Changes from Previous Versions* Includes data for the entire globe.* Includes the mean climatology of all ASTER data for the specific temporal extent.
ASTER Global Emissivity Dataset, 100 meter, HDF5 V003
Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) MOD07_L2 atmospheric profiles and the MODerate spectral resolution TRANsmittance (MODTRAN 5.2 radiative transfer model). This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. AG100 data are available globally at spatial resolution of 100 meters.The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product. Known Issues* Known issues are provided in Section 4, starting on page 8 of the User Guide.Improvements/Changes from Previous Versions* Includes data for the entire globe.* Includes the mean climatology of all ASTER data for the specific temporal extent.
ASTER Global Emissivity Dataset 100 meter Binary
The AG100B Version 3 dataset was decommissioned as of December 14, 2016. Users are encouraged to use the ASTER Global Emissivity Dataset 100-meter [AG100 Version 3](https://doi.org/10.5067/Community/ASTER_GED/AG100.003) dataset in HDF5. The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) MOD07 atmospheric profiles and the MODerate Spectral resolution TRANsmittance (MODTRAN) 5.2 radiative transfer model. This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. The HDF5 version of the data are available for distribution, please see [AG100](https://doi.org/10.5067/Community/ASTER_GED/AG100.003) for more information.The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product. Known Issues* Known Issues are provided in Section 4 of the ASTER GED User Guide.Improvements/Changes from Previous Versions* Includes data for the entire globe.* Includes the mean climatology of all ASTER data for the specific temporal extent.
Multi-Mission Optimally Interpolated Sea Surface Salinity Global Dataset V2
This is a level 4 product on a 0.25-degree spatial and 4-day temporal grid. The product is derived from the level 2 swath data of three satellite missions: the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) using Optimal Interpolation (OI) with a 7-day decorrelation time scale. The product offers a continuous record from August 28, 2011 to present by concatenating the measurements from Aquarius (September 2011 - June 2015) and SMAP (April 2015 present). ESAs SMOS data was used to fill the gap in SMAP data between June and July 2019, when the SMAP satellite was in a safe mode. The two-month overlap (April - June 2015) between Aquarius and SMAP was used to ensure consistency and continuity in data record. The product covers the global ocean, including the Arctic and Antarctic in the areas free of sea ice, but does not cover internal seas such as Mediterranean and Baltic Sea. In-situ salinity from Argo floats and moored buoys are used to derive a large-scale bias correction and to ensure consistency and accuracy of the OISSS dataset. This dataset is produced by the Earth and Space Research (ESR), Seattle, WA and the International Pacific Research Center (IPRC) of the University of Hawaii at Manoa in collaboration with the Remote Sensing Systems (RSS), Santa Rosa, California. More details can be found in the users guide.
Filtering of RNA-seq datasets and differences between cell types in global coordination of splicing and proportion of highly expressed genes
GEO Series GSE85458. Mus musculus. 0 samples. Type: Expression profiling by high throughput sequencing; Third-party reanalysis.
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.