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225 results for “Global database”
Global Flood characterization (GloFlo) Database
<p>It is a large-sample dataset of multiple catchment characteristics of floods / high streamflow extremes observed worldwide. <br><br>For detailed information about this database and its development, please refer to the original research article published in the Journal of Hydrology:</p> <p>Sai Kiran Kuntla, Manabendra Saharia, & Pierre Kirstetter. (2022). Global-scale characterization of streamflow extremes. <em>Journal of Hydrology, 615</em>, 128668. <a href="https://doi.org/10.1016/j.jhydrol.2022.128668" target="_new" rel="noopener">https://doi.org/10.1016/j.jhydrol.2022.128668</a></p>
A global database of nature-based carbon offset project boundaries
<p><strong>Overview</strong></p> <p>Nature-based climate solutions (NBS) have become an important component of strategies aiming to reduce atmospheric CO2 and mitigate climate change impacts. Carbon offsets have emerged as one of the most widely implemented NBS strategies, however, these projects have also been criticized for exaggerating offsets. Verifying the efficacy of NBS-derived carbon offset is complicated by a lack of readily available geospatial boundary data. Herein, we detail methods and present a database of nature-based offset project boundaries. This database provides the locations of 575 NBS projects distributed across 55 countries. Geospatial boundaries were aggregated using a combination of scraping data from carbon project registries (<em>n</em>=433, 75.3%) as well as manual georeferencing and digitization (<em>n</em>=127, 22.1%). Database entries include three varieties of carbon projects: avoided deforestation, afforestation, reforestation and re-vegetation, and improved forest management. An accuracy assessment of the georeferencing and digitizing process indicated a high degree of accuracy (intersection over union score of 0.98 +/- 0.015).</p> <p>Details about using the database are provided in the README.txt file.</p>
Global River Widths from Landsat (GRWL) Database
<p>If you use the GRWL Database in your work, please cite: Allen and Pavelsky (2018) Global Extent of Rivers and</p> <p>Streams. <em>Science</em>. <a href="https://doi.org/10.1126/science.aat0636">https://doi.org/10.1126/science.aat0636</a></p> <p> </p> <p>This long-term repository contains three files:</p> <p>1) Simplified GRWL Vector Product: GRWL_summaryStats_V01.01.zip</p> <p>2) GRWL Mask (raster): GRWL_mask_V01.01.zip </p> <p>3) GRWL Vector Product: GRWL_vector_V01.01.zip</p> <p> </p> <p>Other data:</p> <p>- Location map of the individual GRWL tiles: <a href="https://drive.google.com/file/d/1K6x1E0mmLc0k7er4NCIeaZsTfHi2wxxI/view?usp=sharing">Shapefile download</a> </p> <p>- River and stream surface area totals by drainage basin (Fig. 4 in Allen & Pavelsky, 2018): <a title="RSSA Basins" href="https://drive.google.com/file/d/1PmoSbDFHUcQ9KXo23noQbIExAY5vJ0qb/view?usp=sharing" target="_blank" rel="noopener">Shapefile download</a></p> <p> </p> <p> </p> <p><strong>1) Documentation for the Global River Width from Landsat (GRWL) Simplified Vector Product V01.01</strong></p> <p>This zip file contains a single ESRI shapefile polyline of river centerlines. <br>Projection: Geographic WGS84 </p> <p>This file is a simplified version of the raw GRWL vector product (see #3 below). This product is a smaller and more wieldy compared to the raw GRWL vector dataset and most users of GRWL will prefer to use this simplified version. This simplified vector product reduces the number of feature vertices and attributes by simplifying the polyline geometry and by calculating summary statistics along each polyline segment. Polyline segments are roughly the line segments between each tributary junction. </p> <p>For each polyline segment, the shapefile contains the following attributes:<br>1. width_min: the minimum of river width measurements along the segment at mean discharge (meters)<br>2. width_med: the median of river width measurements along the segment at mean discharge (meters)<br>3. width_mean: the mean of river width measurements along the segment at mean discharge (meters)<br>4. width_max: the maximum of river width measurements along the segment at mean discharge (meters)<br>5. width_sd: the standard deviation of river width measurements along the segment at mean discharge (meters)<br>6. lakeflag: integer specifying if segment is located on a river (lakeflag=0), lake/reservoir (lakeflag=1), tidal river (lakeflag=2), or canal (lakeflag=3). This information is of much higher quality in the Global River Width from Landsat (GRWL) Vector Product V01.01 (product #3 below). <br>8. nSegPx: number of pixels within the segment (N pixels)<br>9. Shape_Leng: length of the segment (kilometers)</p> <p> </p> <p><strong>2) Documentation for the Global River Width from Landsat (GRWL) Mask V01.01</strong></p> <p>This zip file contains 830 GeoTIFF tiles of water masks at mean discharge. The assembly of this database is described in Allen and Pavelsky (2018) “Global Extent of Rivers and Streams” published in Science. The GRWL mask is an intermediate product in the production the GRWL vector product and thus is not explicitly validated. </p> <p>Tile coverage: 4 degrees latitude by 6 degrees longitude<br>File format: GeoTIFF (unsigned byte)<br>Projection: Geographic WGS84 <br>Resolution: 30 m</p> <p>Pixel classifications: <br>DN = 256 : No Data<br>DN = 255 : River<br>DN = 180 : Lake/reservoir <br>DN = 126 : Tidal rivers/delta <br>DN = 86 : Canal<br>DN = 0 : Land/water not connected to the GRWL river network</p> <p> </p> <p><strong>3) Documentation for the Global River Width from Landsat (GRWL) Vector Product V01.01</strong></p> <p>This zip file contains 829 ESRI shapefile polylines of river centerlines. <br>Tile coverage: 4 degrees latitude by 6 degrees longitude. <br>Projection: Geographic WGS84 <br>Resolution: 30 m</p> <p>At each GRWL measurement location, the shapefile contains the following attributes:<br>1. utm_east: UTM Easting (UTM Zone is given in tile file name; meters)<br>2. utm_north: UTM Northing (UTM Zone is given in tile file name; meters)<br>3. width_m: wetted width of river (meters)<br>note: width_m == 1 indicates NA (no width data along the centerline) <br>4. nchannels: braiding index (-)<br>5. segmentID: unique ID of river segment in each tile<br>6. segmentInd: Index of each observation in each segment. Not sorted by upstream or downstream<br>7. lakeflag: integer specifying if observation is located on a river (lakeflag=0), lake/reservoir (lakeflag=1), tidal river (lakeflag=2), or canal (lakeflag=3). <br>8. lon: Longitude (decimal degrees)<br>9. lat: Latitude (decimal degrees)<br>10. elev: Elevation (meters) – sampled from the Hydro1k DEM</p> <p> </p>
Figure 1 in Building a global database of soil microbial biomass and function: a call for collaboration
Figure 1. Sampling locations and underrepresented environmental conditions in the dataset as of 19 November, 2019. For each pixel, we measured the percentage of environmental conditions (e.g. climate, soil characteristics, topographic information, vegetation indices) captured by the current dataset. Hot deserts, tropical rainforests and sub-Arctic regions are the least represented.
HomeRange: A global database of mammalian home ranges
<p><strong>Motivation</strong>: Home range is a common measure of animal space use as it provides ecological information that is useful for conservation applications. In macroecological studies, values are typically aggregated to species means to examine general patterns of animal space use. However, this ignores the environmental context in which the home range was estimated and does not account for intraspecific variation in home range size. In addition, the focus of macroecological studies on home ranges has been historically biased toward terrestrial mammals. The use of aggregated numbers and terrestrial focus limits our ability to examine home range patterns across different environments, variation in time and between different levels of organisation. Here we introduce HomeRange, a global database with 75,611 home-range values across 960 different mammal species, including terrestrial, as well as aquatic and aerial species.</p> <p><strong>Main types of variable contained: </strong>The dataset contains mammal home-range estimates, species names, methodological information on data collection, home-range estimation method, period of data collection, study coordinates and name of location, as well as species traits derived from the studies, such as body mass, life stage, reproductive status and locomotor habit.</p> <p><strong>Spatial location and grain: </strong>The collected data is distributed globally. Across studies, the spatial accuracy varies, with the coarsest resolution being 1 degree.</p> <p><strong>Time period and grain: </strong>The data represent information published between 1939 and 2022. Across studies, the temporal accuracy varies, some studies report start and end dates specific to the day. For other studies, only the month or year is reported.</p> <p><strong>Major taxa and level of measurement: </strong>Mammal species from 24 of the 27 different taxonomic orders. Home-range estimates range from individual-level values to population-level averages. </p>
Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information
<p><strong>Citation</strong></p> <p>Zhang, Alice Tianbo, and Vincent Xinyi Gu. 2023. “Global Dam Tracker: A Database of More than 35,000 Dams with Location, Catchment, and Attribute Information.” <em>Scientific Data</em> 10 (1): 111.</p> <p><a href="https://www.nature.com/articles/s41597-023-02008-2">https://www.nature.com/articles/s41597-023-02008-2</a></p> <p> </p> <p><strong>Description</strong></p> <p>We present one of the most comprehensive geo-referenced global dam databases to date. The Global Dam Tracker (GDAT) contains 35,000 dams with cross-validated geo-coordinates, satellite-derived catchment areas, and detailed attribute information. Combining GDAT with fine-scaled satellite data spanning three decades, we demonstrate how GDAT improves upon existing databases to enable the inter-temporal analysis of the costs and benefits of dam construction on a global scale. Our findings show that over the past three decades, dams have contributed to a dramatic increase in global surface water coverage, especially in developing countries in Asia and South America. This is an important step toward a more systematic understanding of the worldwide impact of dams on local communities. By filling in the data gap, GDAT would help inform a more sustainable and equitable approach to energy access and economic development.</p>
Point sample database with spatial holdbacks for global forest edge model training
<p>Global point dataset sampling 50 biophysical parameters to train the global forest carbon edge model at https://github.com/springinnovate/carbon_edge_model/releases/tag/1.2.0</p> <p>Field schema:</p> <p>accessibility_to_cities_2015_30sec_compressed (Integer64)<br> altitude_10sec_compressed (Integer64)<br> baccini_carbon_data_2014_compressed (Integer64)<br> bio_01_30sec_compressed (Real)<br> bio_02_30sec_compressed (Real)<br> bio_03_30sec_compressed (Real)<br> bio_04_30sec_compressed (Real)<br> bio_05_30sec_compressed (Real)<br> bio_06_30sec_compressed (Real)<br> bio_07_30sec_compressed (Real)<br> bio_08_30sec_compressed (Real)<br> bio_09_30sec_compressed (Real)<br> bio_10_30sec_compressed (Real)<br> bio_11_30sec_compressed (Real)<br> bio_12_30sec_compressed (Real)<br> bio_13_30sec_compressed (Real)<br> bio_14_30sec_compressed (Real)<br> bio_15_30sec_compressed (Real)<br> bio_16_30sec_compressed (Real)<br> bio_17_30sec_compressed (Real)<br> bio_18_30sec_compressed (Real)<br> bio_19_30sec_compressed (Real)<br> cec_0-5cm_mean_compressed (Integer64)<br> cec_5-15cm_mean_compressed (Integer64)<br> cfvo_0-5cm_mean_compressed (Integer64)<br> cfvo_5-15cm_mean_compressed (Integer64)<br> clay_0-5cm_mean_compressed (Integer64)<br> clay_5-15cm_mean_compressed (Integer64)<br> fc_stack_hansen_forest_cover2014_compressed (Integer64)<br> gf_0.4_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real)<br> gf_1.45_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real)<br> gf_5.0_fc_stack_hansen_forest_cover2014_compressed (Real)<br> gf_5.0_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real)<br> hillshade_10sec_compressed (Integer64)<br> masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Integer64)<br> night_lights_10sec_compressed (Real)<br> night_lights_5min_compressed (Real)<br> nitrogen_0-5cm_mean_compressed (Integer64)<br> nitrogen_10sec_compressed (Integer64)<br> nitrogen_5-15cm_mean_compressed (Integer64)<br> phh2o_0-5cm_mean_compressed (Integer64)<br> phh2o_5-15cm_mean_compressed (Integer64)<br> sand_0-5cm_mean_compressed (Integer64)<br> silt_0-5cm_mean_compressed (Integer64)<br> silt_5-15cm_mean_compressed (Integer64)<br> slope_10sec_compressed (Real)<br> soc_0-5cm_mean_compressed (Integer64)<br> soc_5-15cm_mean_compressed (Integer64)<br> tri_10sec_compressed (Real)<br> wind_speed_10sec_compressed (Real)</p>
Data from: A global reference database in FAOSTAT of cropland nutrient budgets and nutrient use efficiency: nitrogen, phosphorus and potassium, 1961–2020
<p class="MsoNormal">Agricultural nutrient budgets help to identify an excess or an insufficiency in use of fertilizers and other nutrient sources. Nutrient budgets allow for indicators such as the nutrient balance (surplus or deficit) and nutrient use efficiency to be estimated. This can help in the monitoring of agricultural productivity and sustainability globally. The present dataset is a global database of country-level budget estimates for nitrogen (N), phosphorus (P) and potassium (K) in cropland. The database is disseminated in FAOSTAT and provides a global reference, synthesizing and continuously updating the state-of-the-art on this topic. The database covers the period 961 to 2020 for 205 countries and territories, as well as regional and global aggregates. Results indicate the wide range in nutrient use and use efficiencies across regions, nutrients, and time. This dataset introduces improvements over previous work in relation to key nutrient coefficients affecting nutrient budgets and use efficiency estimates. This is especially for nutrient removal in crop products, manure nutrient content, atmospheric deposition and crop biological N fixation rates. </p>
"Presenting Euroqcharm project outcomes to foster global database synchronization" Zoom Webinar video recording
<p>Video recording of the "Presenting Euroqcharm project outcomes to foster global database synchronization" Zoom Webinar</p>
Global Rainfall Erosivity database (GloREDa)
<p>Table with the data of annual rainfall erosivity and auxiliary information for 3,939 stations.</p> <p>Table with the data of monthly erosivity.</p> <p>Shape file with all the stations and their erosivity values.</p> <p>12 Raster (GeoTIFF) with global monthly erosivity at 1km x 1km resolution</p> <p>Relevant publication to cite for those datasets:</p> <p>Panagos, P., Hengl, T., Wheeler, I., Marcinkowski, P., Rukeza, M.B., Yu, B., Yang, J.E., Miao, C., Chattopadhyay, N., Sadeghi, S.H. and Levi, Y., et al. 2023. <a href="https://www.sciencedirect.com/science/article/pii/S2352340923005826">Global Rainfall Erosivity database (GloREDa) and monthly R-factor data at 1km spatial resolution</a>. <em>Data in Brief</em>, <strong>50</strong>, Art.no.109482. DOI: 10.1016/j.dib.2023.109482</p>
mapspamc_db: a database with global spatial datasets to support the implementation of the mapspamc R package.
<p>This repository contains the mapspamc database (mapspamc_db), a collection of global spatial datasets to support the implementation of the <a href="https://github.com/michielvandijk/mapspamc">mapspamc</a> R package. The database also includes subnational crop statistics and matching country shapefiles for several country examples. For more information on how to use the mapspamc package in combination with mapspamc_db, see the <a href="https://michielvandijk.github.io/mapspamc/">mapspamc documentation</a>. Detailed information on the contents of mapspam_db, such as the sources of information and pre-processing is described in the mapspamc_db documentation (pdf file) that is part of the repository.</p>
Global zircon U-Th-Pb geochronology database
<p>We present here a database of zircon U−Th−Pb geochronology, which samples the global continental crust and spans nearly the entire Earth's history. This database collects ~2,000,000 geochronology records from ~12,000 papers and theses over a period exceeding 50 years and is by far the largest geochronology database to our knowledge. The lithology of the host rock of the zircon samples includes sedimentary, igneous, and metamorphic rocks. The source references are from the following academic publishers: Elsevier, Cambridge, Geological Society of London, Oxford, Springer, Taylor & Francis, Wiley, and China National Knowledge Infrastructure (CNKI). Here, the zircon data compilation contains "Database" files and a "References" file. The "Database" files contain about two million zircon U−Th−Pb dating records, including four isotope ratios (206Pb/238U, 207Pb/235U, 207Pb/206Pb, and 208Pb/232Th) and errors, four corresponding ages and errors, as well as sample information, dating instrument, dating reference material, host rock lithology, sampling location, and reference number. The "References" file contains the information of the source references corresponding to zircon geochronology records, including reference number, author, year, and other publication information. For each record in the "Database" files, its reference can be found in the "References" file through the reference number. This collection not only provides us with a comprehensive platform to study zircon chronological data in deep time and space but also makes it possible to explore the underlying geodynamic mechanism and the evolution of the Earth system.</p><p> </p><p>This database is a supplement to the following article:</p><p>Wu, Y., Fang, X., & Ji, J. (2023). A global zircon U–Th–Pb geochronological database. Earth Syst. Sci. Data, 15(11), 5171-5181. https://doi.org/10.5194/essd-15-5171-2023</p>
Fig. 1 A in DarkCideS 1.0, a global database for bats in karsts and caves
Fig. 1 A schematic diagram showing the features, contents, and potential applications of the DarkCideS 1.0 database. The database is a centralised, collaborative, and open-access platform that contains information on cave-dwelling bat species and their distribution.
Fig. 2 in DarkCideS 1.0, a global database for bats in karsts and caves
Fig. 2 Percentage of species data completeness according to biogeographical realm (a) and family-level (b) between IUCN estimates (red bars) and sampled caves from DarkCideS 1.0 (black bars) species richness, the proportion of endemism, and proportion of threatened species worldwide.
Fig. 5 in DarkCideS 1.0, a global database for bats in karsts and caves
Fig. 5 Schematic diagram showing the updating workflow of the database from new data entry. The DarkCideS database aims to be a long-term biodiversity data exchange platform by including new data from fieldwork and assessments. Authors can upload their dataset containing species records, geographical information, and landscape threats on the web page. The corresponding authors will receive new data entries for validation before being merged into the database.
Fig. 3 in DarkCideS 1.0, a global database for bats in karsts and caves
Fig. 3 The geographical data turnover of the current database version: (a) geographical locations of all bat caves included in the database, (b) percent distribution of species occurrence in terms of the biogeographical realm and terrestrial biome, (c) country-level turnover.
A historic global ground-based monthly seasonal aerosol climatology based in AERONET data: a database 1993-2013
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Data from: The SCOC database – a large, open and global database with sediment community oxygen consumption rates
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Data from: A global database of butterfly species native distributions
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HomeRange: A global database of mammalian home ranges
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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.