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225 results for “Global database”
Database For Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD Simulation
Open the record for dataset details and reuse information.
[OBSOLETE] OCTOPUS Database v.2: The CRN Denudation Global v.2 collection. Basin-averaged denudation rates from cosmogenic Be-10 and Al-26 abundances
<p>Database of published cosmogenic Be-10 and Al-26 concentrations from modern river sediment and basin-averaged denudation rates inferred from these data. Ancillary spatial data includes: sample site location (point), basin outline (polygon), digital elevation model (raster), D8 flow direction and flow accumulation grids (raster), topographic gradient (raster), atmospheric pressure (raster), and cosmogenic nuclide production scaling factor and topographic shielding grids (raster). The vector spatial data uses the WGS84/Pseudo-Mercator (EPSG: 3857) projected coordinate reference system. The raster data uses the WGS86/UTM projected coordinate reference system, UTM zones depending on the extent and location of each data package. Sample metadata is comprehensive and includes all necessary information and input files for the recalculation of denudation rates using the CAIRN model (https://github.com/LSDtopotools/LSDTopoTools_CRNBasinwide). All denudation rates were recalculated and harmonised using CAIRN. The extent of the data is global, excluding Australia.</p> <p>Accompanying publication: Codilean, A. T., Munack, H., Saktura, W. M., Cohen, T. J., Jacobs, Z., Ulm, S., Hesse, P. P., Heyman, J., Peters, K. J., Williams, A. N., Saktura, R. B. K., Rui, X., Chishiro-Dennelly, K., and Panta, A.: OCTOPUS database (v.2), Earth Syst. Sci. Data, 14, 3695–3713, <a href="https://doi.org/10.5194/essd-14-3695-2022" target="_blank" rel="noopener">https://doi.org/10.5194/essd-14-3695-2022</a>, 2022.</p>
Figure 1 from: Martínez-Morales M, Pinilla-Buitrago G, González-García F, Enríquez P, Rangel-Salazar J, Guichard Romero C, Navarro-Sigüenza A, Monterrubio-Rico T, Escalona-Segura G (2014) CracidMex1: a comprehensive database of global occurrences of cracids (Aves, Galliformes) with distribution in Mexico. ZooKeys 420: 87-115. https://doi.org/10.3897/zookeys.420.7050
Figure 1 - Distribution of the 23,896 records by species in the CracidMex1 database.
Global Utilization And Registry Database for Improved preservAtion of doNor LUNGs
ClinicalTrials.gov study NCT04930289. IPD Sharing: Not stated. Countries: 2. Publications: 0.
Global Utilization And Registry Database for Improved preservAtion of doNor Livers
ClinicalTrials.gov study NCT05082077. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Association Between Antidepressant Use and Falls in Older Adults: Analysis of the World Health Organization Global Database
ClinicalTrials.gov study NCT05628467. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Association Between Prescription of Antidepressants and Delirium in the Elderly : Analysis From the World Health Organization Global Database
ClinicalTrials.gov study NCT05356078. IPD Sharing: NO. Countries: 1. Publications: 0.
Creating a Global Research Database That Connects Genetic Information and Long-term Health Data to Improve Personalized Treatment for People With Serious Mental Illness
ClinicalTrials.gov study NCT06641726. IPD Sharing: NO. Countries: 1. Publications: 0.
Sub-global Scenarios that Extend the Global SSP Narratives: Literature Database, Version 1, 2014-2021
The Sub-global Scenarios that Extend the Global SSP Narratives: Literature Database, Version 1, 2014-2021 consists of 37 columns of bibliographic data, methodological and analytical insights, from 155 articles published from 2014 to 2021 that extended the narratives of global SSPs. Local and regional scale Shared Socioeconomic Pathways (SSPs) have grown largely in addressing Climate Change Impact, Adaptation, and Vulnerability (CCIAV) assessments at sub-global levels. Common elements of these studies, besides their focus on CCIAV, are the use of both quantitative and qualitative elements of the SSPs. To explore and learn from current literature on novel methods and insights on extending SSPs, the sub-global extended SSPs literature database is constructed in the research for analyses. The database was developed in four stages: searches; screening; data extraction; and coding. The search stage incorporated three approaches: using a search string in three academic databases (Scopus, Web of Science Core Collection, ScienceDirect); a targeted search of a specific relevant database (ICONICS); and a targeted selection in Google Scholar of all papers that cited the publication of the global SSP narratives. In the screening step, criteria were assessed for full-text papers for eligibility including relevant typologies, methodologies, and other criteria. Finally, data from eligible papers was extracted and entered in a coding framework in an Excel workbook spreadsheet. The coding framework resulted in 37 columns to systematize coding of data from the 155 papers selected along several different dimensions, including categories of papers or analysis, several subcategories for SSP Applications and SSP Extensions, specific SSPs used, specific Representative Concentration Pathways (RCPs) used, typologies of extensions of qualitative and quantitative SSPs, and the types of models and nature of the extended SSPs.
Global Reservoir and Dam Database, Version 1 (GRanDv1): Dams, Revision 01
The Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The dams were geospatially referenced and assigned to polygons depicting reservoir outlines at high spatial resolution. Dams have multiple attributes, such as name of the dam and impounded river, primary use, nearest city, height, area and volume of reservoir, and year of construction (or commissioning). While the main focus was to include all dams associated with reservoirs that have a storage capacity of more than 0.1 cubic kilometers, many smaller dams and reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data.
Combined ASTER and MODIS Emissivity database over Land (CAMEL) Uncertainty Monthly Global 0.05Deg V003
The NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty comprising three independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). Corresponding emissivity values can be found in the [CAM5K30EM](https://doi.org/10.5067/MEaSUREs/LSTE/CAM5K30EM.003) data product.Provided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information.Known Issues* The following granules are missing from the product: January, February, and August of 2000; June and July of 2001; March of 2002; December of 2003; July of 2010; and February of 2016.Improvements/Changes from Previous Version* Version 3 includes new MODIS Terra and Aqua Land Surface Temperature and 3-Band Emissivity (MYD/MOD21) input data in the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) emissivity database and the latest 6.1 version of MODIS Terra and Aqua Land Surface Temperature and Emissivity (MYD/MOD11) and MYD/MOD21 products with the most up-to-date calibration corrections. Additional information on the Version 3 updates is provided in Section 2 of the User Guide and ATBD.
Combined ASTER and MODIS Emissivity database over Land (CAMEL) Uncertainty Monthly Global 0.05Deg V002
The NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty, comprising 3 independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). Corresponding emissivity values can be found in the CAM5K30EM data product.Provided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information.Improvements/Changes from Previous Version* Version 2 includes two additional laboratory datasets to more accurately characterize snowy scenes.
Combined ASTER and MODIS Emissivity database over Land (CAMEL) Coefficient Monthly Global 0.05Deg V003
The NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly coefficients at 0.05 degree (~5 kilometer) resolution (CAM5K30CF). The CAMEL Principal Components Analysis (PCA) input coefficients utilized in the CAMEL high spectral resolution (HSR) algorithm are provided in the CAM5K30CF data product and are congruent to the temporally equivalent [CAM5K30EM](https://doi.org/10.5067/MEaSUREs/LSTE/CAM5K30EM.003) emissivity data product. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD).Provided in the CAM5K30CF product are layers for PCA coefficients, number of PCA coefficients, laboratory version, snow fraction derived from MODIS Snow Cover data (MOD10), latitude, longitude, and the CAMEL quality information. PCA coefficients are dependent on the version of lab Principal Component (PC) data and the number of PCs used.Known Issues* The following granules are missing from the product: January, February, and August of 2000; June and July of 2001; March of 2002; December of 2003; July of 2010; and February of 2016.Improvements/Changes from Previous Version* Version 3 includes new MODIS Terra and Aqua Land Surface Temperature and 3-Band Emissivity (MYD/MOD21) input data in the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) emissivity database and the latest 6.1 version of MODIS Terra and Aqua Land Surface Temperature and Emissivity (MYD/MOD11) and MYD/MOD21 products with the most up-to-date calibration corrections. Additional information on the Version 3 updates is provided in Section 2 of the User Guide and ATBD.
Global Lake and River Ice Phenology Database, Version 1
The Global Lake and River Ice Phenology Database contains freeze and thaw/breakup dates as well as other descriptive ice cover data for 865 lakes and rivers in the Northern Hemisphere. Of the 542 water bodies that have records longer than 19 years, 370 of them are in North America and 172 are in Eurasia. 249 lakes and rivers have records longer than 50 years, and 66 have records longer than 100 years. A few water bodies have data available prior to 1845. This database, with water bodies distributed around the Northern Hemisphere, allows for the analysis of broad spatial patterns as well as long-term temporal patterns.
Global Reservoir and Dam Database, Version 1 (GRanDv1): Reservoirs, Revision 01
Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The reservoirs were delineated from high spatial resolution satellite imagery and are available as polygon shape files. The only attribute for the reservoirs is the area of the reservoir. The associated dams data set includes multiple attributes such as name of the dam and the impounded river, primary use, nearest city, area, and year of construction (or commissioning). While the main focus was to include all reservoirs with a storage capacity of more than 0.1 cubic kilometers, many smaller reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data.
Global Database of Light-based Geospatial Income Inequality (LGII) Measures, Version 1
The Global Database of Light-based Geospatial Income Inequality (LGII) Measures, Version 1 data set contains Gini-coefficients of inequality for 234 countries and territories from 1992 to 2013. The measurement Unit is the Gini-Coefficient (Range: 0-1), with higher values representing higher inequality. These measures are constructed using worldwide geospatial satellite data on nighttime lights emission as a proxy for economic prosperity, matched with varying sources of data on geo-located population counts. The nighttime lights data were supplied by the National Oceanic and Atmospheric Administration (NOAA), National Centers for Environmental Information (NCEI), Earth Observation Group (EOG), and Operational Linescan System (OLS) instruments. The population data used consisted of CIESIN's Gridded Population of the World (GPW) collection, and the Oak Ridge National Laboratory (ORNL) LandScan (LSC) data set. The nighttime lights and population data were combined to produce an array of geospatially-informed Gini-coefficients, which were then weighted to optimize their correlation with a benchmark - specifically, the Standardized World Income Inequality Database (SWIID), to generate a parsimonious composite inequality metric.
Combined ASTER and MODIS Emissivity database over Land (CAMEL) Coefficient Monthly Global 0.05Deg V002
The NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects)) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly coefficients at 0.05 degree (~5 kilometer) resolution (CAM5K30CF). The CAMEL Principal Components Analysis (PCA) input coefficients utilized in the CAMEL high spectral resolution (HSR) algorithm are provided in the CAM5K30CF data product and are congruent to the temporally equivalent CAM5K30EM emissivity data product. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD).Provided in the CAM5K30CF product are layers for PCA coefficients, number of PCA coefficients, laboratory version, snow fraction derived from MODIS Snow Cover data (MOD10), latitude, longitude, and the CAMEL quality information. PCA coefficients are dependent on the version of lab PC data and number of PCs used.Improvements/Changes from Previous Version* Version 2 includes two additional laboratory datasets to more accurately characterize snowy scenes.
Database for: Meta-analysis of the impact of land use intensification on selected soil fauna in global agroecosystems
<p>The dataset is an aggregate of research works examining how intensification of land usage affects soil faunal populations worldwide in agroecosystems, particularly focused on nematodes, springtails, mites and enchytraeids. The dataset, which was taken from peer-reviewed articles, covers a number of topics, including the Köppen-Geiger climate classification system's description of climate features. Properties of soil, including type, texture, pH, and organic matter, are recorded. Furthermore, information is given about the experimental parameters, including replicates, sampling depth, and extraction techniques. The dataset also includes details on grazing, tillage techniques, pesticide, herbicide, and pesticide usage, fertilizer kind and rate, and days after tillage for faunal collection. For both the control and treatment locations, abundance, diversity, and the parameters that go along with it are noted.</p>
GEOSatDB: global civil earth observation satellite semantic database
<p>The new version at <a href="https://doi.org/10.57760/sciencedb.11805">https://doi.org/10.57760/sciencedb.11805</a></p> <p>GEOSatDB is a semantic representation of Earth observation satellites and sensors that can be used to easily discover available Earth observation resources for specific research objectives.</p> <p><strong>Relevant Papers</strong></p> <p>Ming Lin, Meng Jin, Juanzi Li & Yuqi Bai (2024) GEOSatDB: global civil earth observation satellite semantic database, Big Earth Data, DOI: <a href="https://doi.org/10.1080/20964471.2024.2331992">10.1080/20964471.2024.2331992</a></p> <p><strong>Background</strong></p> <p>The widespread availability of coordinated and publicly accessible Earth observation (EO) data empowers decision-makers worldwide to comprehend global challenges and develop more effective policies. Space-based satellite remote sensing, which serves as the primary tool for EO, provides essential information about the Earth and its environment by measuring various geophysical variables. This contributes significantly to our understanding of the fundamental Earth system and the impact of human activities.</p> <p>Over the past few decades, many countries and organizations have markedly improved their regional and global EO capabilities by deploying a variety of advanced remote sensing satellites. The rapid growth of EO satellites and advances in on-board sensors have significantly enhanced remote sensing data quality by expanding spectral bands and increasing spatio-temporal resolutions. However, users face challenges in accessing available EO resources, which are often maintained independently by various nations, organizations, or companies. As a result, a substantial portion of archived EO satellite resources remains underutilized. Enhancing the discoverability of EO satellites and sensors can effectively utilize the vast amount of EO resources that continue to accumulate at a rapid pace, thereby better supporting data for global change research.</p> <p><strong>Methodology</strong></p> <p>This study introduces GEOSatDB, a comprehensive semantic database specifically tailored for civil Earth observation satellites. The foundation of the database is an ontology model conforming to standards set by the International Organization for Standardization (ISO) and the World Wide Web Consortium (W3C). This conformity enables data integration and promotes the reuse of accumulated knowledge. Our approach advocates a novel method for integrating Earth observation satellite information from diverse sources. It notably incorporates a structured prompt strategy utilizing a large language model to derive detailed sensor information from vast volumes of unstructured text.</p> <p><strong>Dataset Information</strong></p> <p>The downloadable files in RDF Turtle format are located in the data directory and contain a total of 130,134 statements:</p> <p>- GEOSatDB_ontology.ttl: Ontology modeling of concepts, relations, and properties.</p> <p>- satellite.ttl: 2,365 Earth observation satellites and their associated entities.</p> <p>- sensor.ttl: 1,021 Earth observation sensors and their associated entities.</p> <p>- sensor2satellite.ttl: relations between Earth observation satellites and sensors.</p> <p>In addition, a user-friendly portal is under development to facilitate easy access to GEOSatDB. The portal currently offers preliminary SPARQL query functionality, enabling the execution of SPARQL query examples.</p> <p>GEOSatDB undergoes quarterly updates, involving the addition of new satellites and sensors, revisions based on expert feedback, and the implementation of additional enhancements.</p>
A global distribution of dissolved organic carbon in soil and in leaching - (database)
<p>Current global carbon (C) models are not representing the fraction of C which is displaced along the terrestrial aquatic continuum thus overestimating the land sink capacity. In order to obtain more reliable C budgets, we need to integrate the lateral transfers of C from terrestrial ecosystems through the inland water network down to the oceans, including biogeochemical transformation during transport and C exchange with the atmosphere.Representing the production and cycling of dissolved organic C (DOC) in the soil column and the leaching of DOC into the inland water network is a first major step in this development.</p> <p>In this study we used newly developed model JULES-DOCM to obtain the first global estimate of global soil DOC stock and DOC concentration, DOC concentration in runoff and DOC leaching flux.</p> <p>In this dataset model produced files are stored as netcdf files including soil DOC stocks (at Top (0-35 cm) and Total soil (0-300cm), soil DOC concentration (at Top (0-35 cm) and Bottom ( 35-300cm)), DOC leaching flux (averaged over 1980-2010) and DOC concentration in runoff (averaged over 1980-2010).</p> <p>The measured DOC collected database is enclosed as the Excel file.</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.