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708
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ShareScore release 0.9.0
Dataset results
708 results for “Global dataset”
A global surface CO2 flux dataset (2015–2021) inferred from OCO-2 retrievals using the GONGGA inversion system
<p>Here we present a global spatially-resolved terrestrial and ocean carbon flux dataset for 2015–2021. The dataset is generated by the Global ObservatioN-based system for monitoring Greenhouse GAses (GONGGA) atmospheric inversion system through assimilating Observing Carbon Observatory 2 (OCO-2) v10r XCO<sub>2</sub> retrievals. The flux files contain NEE, ocean carbon fluxes, fossil fuel emissions, and biomass burning emissions. The NEE and ocean carbon fluxes include the prior and posterior estimates. The corresponding gridded uncertainty of NEE and ocean fluxes are also included in the flux files. The global gridded fluxes are 3-hourly with a resolution of 2° latitude × 2.5° longitude . Users can aggregate the gridded fluxes on their preferred regions. The GEOS-Chem simulated CO<sub>2</sub> concentrations driven by posterior NEE and ocean fluxes, as well as fossil fuel emissions and biomass burning emissions, are generated every 3 hours with a resolution of 2° latitude × 2.5° longitude at 47 vertical levels.</p>
Supplementary dataset for "Daytime surface park cool island magnitude across global cities"
<p>This dataset is supplementary to the paper "<strong>Daytime surface park cool island magnitude across global cities</strong>" by Agathangeldis et al. The vector file "parks.gpkg" contains the boundaries of the parks that were used in the study, with additional metadata as attributes.</p>
Global Fishing Index: a global dataset of relative biomass of fish stocks in national waters
<p>The Global Fishing Index (GFI) is an independent global assessment of the state of marine fisheries, measuring country-level progress towards achieving the United Nation’s Sustainable Development Goal Target 14.4. This stock-level dataset provides estimates of relative biomass (current biomass relative to historic ‘unfished’ biomass) and reconstructed catch for 1,438 unique marine fish stocks, spanning the national waters of 142 maritime countries. The dataset includes biomass estimates for 2018 from stock assessments published by national and regional authorities, as well as estimates made using data-limited models (the Bayesian Schaefer Model and CMSY++) and publicly available data. To ensure the integrity and reliability of stock-level data included in the GFI 2021 sustainability dataset, the data were subject to a comprehensive and rigourous quality assurance process, including both internal and external expert review. This global dataset advances scientific knowledge on the state of fisheries globally, serves as a benchmark to track progress and inform future studies, and supports improved research and management to advance global efforts towards fisheries sustainability.</p>
Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux Transfer Events on the Ionosphere"
<p>Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux1 Transfer Events on the Ionosphere"</p> <p>Author: Arghyadeep Paul, Antoine Strugarek and Bhargav Vaidya<br> Date: 21 May, 2023</p> <p><br> Figure 1 has been plotted from two data files named C0_320.vtk and c1_320.vtk using the visualisation toolkit VisIt. Visit can be freely downloaded from https://wci.llnl.gov/simulation/computer-codes/visit</p> <p>Figure 2 has been plotted using the ipython notebook named "figure_2.ipynb"</p> <p><br> Figure 3 has been plotted using the data file named "t_4964.vtk" and the visualisation toolkit VisIt.</p> <p><br> Figure 4 has been plotted using the ipython notebook named "figure_4.ipynb"</p> <p><br> Figure 5 has been plotted using the ipython notebook named "figure_5.ipynb"</p> <p><br> Figure 6 has been plotted using the ipython notebook named "figure_6.ipynb"</p> <p><br> Figure 7 has been plotted using the ipython notebook named "figure_7.ipynb"</p> <p><br> Figure 8 has been plotted using the ipython notebook named "figure_8.ipynb"</p> <p><br> Figure 9 has been plotted using the ipython notebook named "figure_9.ipynb"</p> <p><br> All the associated data files are uploaded with the ipython notebooks</p>
The SEIA dataset of global eddies from 2015-2019
<p>The SEIA dataset of global eddies from 2015-2019.</p>
ASTER Global Emissivity Dataset, Monthly, 0.05 deg, HDF5 V041
The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) is a collection of monthly files (see known issues for gaps) for each year of global emissivity. The ASTER GED data products are generated for 2000 through 2015 using the ASTER Temperature Emissivity Separation (TES) algorithm atmospheric correction method. This algorithm method uses Moderate Resolution Imaging Spectroradiometer (MODIS) Atmospheric Profiles product MOD07 and the MODerate spectral resolution TRANsmittance (MODTRAN) 5.2 radiative transfer model along with the snow cover data from the standard monthly MODIS/Terra snow cover monthly global 0.05 degree product [MOD10CM](https://doi.org/10.5067/MODIS/MOD10CM.006), and vegetation information from the MODIS monthly gridded NDVI product [MOD13C2](https://doi.org/10.5067/MODIS/MOD13C2.006). ASTER GED Monthly V041 data products are offered in Hierarchical Data Format 5 (HDF5).The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product.Known Issues* Since ASTER GED Monthly V041 uses MOD10CM as an input, there will be some months that were unable to be created due to missing data. Please see the [MODIS/Terra Data Outages Webpage](https://landweb.modaps.eosdis.nasa.gov/knownissue?as=61) for more information.Improvements/Changes from Previous Versions* The removal of the extraneous snow layer.* The optimized rows and columns for the data layers.* The updated SDS Attributes in the metadata for bounding coordinates.* The previous ASTER GED V004 product incorporated an emissivity adjustment to the V003 product. See [“The ASTER Global Emissivity Dataset (ASTER GED): Mapping Earth’s emissivity at 100 meter spatial scale”](https://doi.org/10.1002/2015GL065564) publication Section 4 for more information.
ASTER Global Emissivity Dataset, Monthly, 0.05 deg, HDF5
The AG5KMMOH Version 4 dataset was decommissioned as of December 14, 2016. Users are encouraged to use [Version 4.1 ASTER Global Emissivity Dataset, Monthly, 0.05 degree, HDF5](https://doi.org/10.5067/Community/ASTER_GED/AG5KMMOH.041).The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) is a collection of monthly files for each year of global emissivity. The ASTER GED data products are generated for 2000 through 2015 using the ASTER Temperature Emissivity Separation (TES) algorithm atmospheric correction method. This algorithm method uses Moderate Resolution Imaging Spectroradiometer (MODIS) Atmospheric Profiles product [MOD07](https://doi.org/10.5067/MODIS/MOD07_L2.006) and the MODTRAN 5.2 radiative transfer model along with the snow cover data from the standard monthly MODIS/Terra snow cover monthly global 0.05 degree product [MOD10CM](https://doi.org/10.5067/MODIS/MOD10CM.006), and vegetation information from the MODIS monthly gridded Normalized Difference Vegetation Index (NDVI) product [MOD13C2](https://doi.org/10.5067/MODIS/MOD13C2.006).The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product.Improvements/Changes from Previous Versions* This V4 product incorporates an emissivity adjustment to the ASTER GED V3 product. See “[The ASTER Global Emissivity Dataset (ASTER GED): Mapping Earth’s emissivity at 100 meter spatial scale](https://doi.org/10.1002/2015GL065564)" publication Section 4 for more information.
ASTER Global Emissivity Dataset, Monthly, 0.05 deg, netCDF4
The AG5KMMOH Version 4 dataset was decommissioned as of December 14, 2016. Users are encouraged to use [Version 4.1 of ASTER Global Emissivity Dataset, Monthly, 0.05 degree, HDF5](https://doi.org/10.5067/Community/ASTER_GED/AG5KMMOH.041).The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) is a collection of monthly files (see known issues for gaps) for each year of global emissivity. The ASTER GED data products are generated for 2000 through 2015 using the ASTER Temperature Emissivity Separation (TES) algorithm atmospheric correction method. This algorithm method uses Moderate Resolution Imaging Spectroradiometer (MODIS) Atmospheric Profiles product [MOD07](https://doi.org/10.5067/MODIS/MOD07_L2.006) and the MODerate spectral resolution TRANsmittance (MODTRAN) 5.2 radiative transfer model along with the snow cover data from the standard monthly MODIS/Terra snow cover monthly global 0.05 degree product [MOD10CM](https://doi.org/10.5067/MODIS/MOD10CM.006), and vegetation information from the MODIS monthly gridded Normalized Difference Vegetation Index (NDVI) product [MOD13C2](https://doi.org/10.5067/MODIS/MOD13C2.006).The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product.Improvements/Changes from Previous Versions* This V4 product incorporates an emissivity adjustment to the ASTER GED V003 product. See “[The ASTER Global Emissivity Dataset (ASTER GED): Mapping Earth’s emissivity at 100 meter spatial scale](https://doi.org/10.1002/2015GL065564)" publication Section 4 for more information.
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.