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708 results for “Global dataset”
GPS dataset for 'Global View of Ionospheric Disturbance Impacts on Kinematic GPS Positioning Solutions during the 2015 St Patrick's Day Storm' by Zhe Yang, Y. Jade Morton, Irina Zakharenkova, Iurii Cherniak, Shuli Song, Wei Li
<p>This dataset contains observations of ionospheric plasma irregularities and GPS positioning errors for ~5500 stations reported in the paper 'Global View of Ionospheric Disturbance Impacts on Kinematic GPS Positioning Solutions during the 2015 St Patrick’s Day Storm' by Zhe Yang, Y. Jade Morton, Irina Zakharenkova, Iurii Cherniak, Shuli Song, Wei Li</p>
CoDEC Dataset - Data underlying the paper "A high-resolution global dataset of extreme sea levels, tides and storm surges including future projections "
<p>The world’s coastal areas are increasingly at risk of coastal flooding due to sea-level rise (SLR). We present a novel global dataset of extreme sea levels, the Coastal Dataset for the Evaluation of Climate Impact (CoDEC), which can be used to accurately map the impact of climate change on coastal regions around the world. The third generation Global Tide and Surge Model (GTSM), with a coastal resolution of 2.5 km (1.25 km in Europe), was used to simulate extreme sea levels for the ERA5 climate reanalysis from 1979 to 2017, as well as for future climate scenarios from 2040 to 2100. The validation against observed sea levels demonstrated a good performance, and the annual maxima had a mean bias (MB) of -0.04 m, which is 50% lower than the MB of the previous GTSR dataset. The CoDEC-ERA5 dataset is the successor of GTSR <a href="https://www.nature.com/articles/ncomms11969">(Muis et al., 2016)</a> and is based on the next generation climate and hydrodynamic models. The main improvements are summarized in Table 2 of the accompanying paper <a href="https://www.frontiersin.org/articles/10.3389/fmars.2020.00263/abstract">(Muis et al., 2020)</a>.</p> <p><br> </p>
Global track dataset of monsoon low pressure systems
<p>This dataset contains the tracks and intensities of low pressure system (LPS) in the global tropics (35ºS-35ºN), as identified in five atmospheric reanalyses (ERA5, ERA-Interim, JRA55, MERRA2, and CFSR) using the algorithm described in the paper titled <strong>Assessing historical variability of South Asian monsoon lows and depressions with an optimized tracking algorithm</strong>. Tracking of LPS was performed using an automated Lagrangian pointwise feature tracker, TempestExtremes (Ullrich & Zarzycki, 2017), with criteria chosen to best match a subjectively analyzed LPS dataset while minimizing disagreement between four atmospheric reanalyses. A full description of the algorithm and dataset is described in the preprint *(<a href="https://doi.org/10.1029/2020JD032977">https://doi.org/10.1029/2020JD032977</a>)</p> <p> </p> <p><strong>Files:</strong></p> <ul> <li><em><strong>Header.txt</strong>: contains the names of columns of the LPS dataset files</em></li> <li><em><strong>LPS_Global_ERA5.dat</strong>: LPS track file for ERA5, 1979-2019, hourly resolution</em></li> <li><em><strong>LPS_Global_ERA-Interim.dat</strong>: LPS track file for ERA-Interim, 1979-2018, six-hourly resolution</em></li> <li><em><strong>LPS_Global_JRA55.dat</strong>: LPS track file for JRA55, 1958-2019, six-hourly resolution</em></li> <li><em><strong>LPS_Global_CFSR.dat</strong>: LPS track file for CFSR, 1979-2010, six-hourly resolution</em></li> <li><em><strong>LPS_Global_MERRA2.dat</strong>: LPS track file for MERRA2, 1980-2019, three-hourly resolution</em></li> </ul> <p><strong><em>Extented Data for ERA5:</em></strong></p> <ul> <li><em>ERA5 tracks for 1940-2022 are available on <strong><a href="https://boos.berkeley.edu">https://boos.berkeley.edu</a> </strong>in the "Data & Tools" section.</em></li> </ul> <p><strong>Script:</strong></p> <ul> <li><em><strong>Run_tempest_lps.sh</strong>: TempestExtremes script to track LPS in reanalysis dataset. </em></li> </ul> <p>Additionally, four python scripts are available to subset the dataset:</p> <ol> <li><em><strong>Python_Moist_LPS_heat_low.py</strong>: Python script to create two separate files for moist LPS and heat lows</em></li> <li><em><strong>Python_Low_Depression.py</strong>: Python script to create two separate files for monsoon lows and monsoon depressions</em></li> <li><em><strong>Python_Region.py</strong>: Python script to create a separate file for a region</em></li> <li><em><strong>Python_Season.py</strong>: Python script to create a separate file for a season</em></li> </ol> <p> </p> <ul> <li> </li> </ul> <p>Note: The TempestExtremes software can be obtained from GitHub at <a href="https://github.com/ClimateGlobalChange/tempestextremes">https://github.com/ClimateGlobalChange/tempestextremes</a>.</p> <p>For further details, contact S. Vishnu (<em>vishnuedv@gmail.com</em>) or William R Boos (<em>billboos@alum.mit.edu</em>).</p>
Dataset: Global assessment of precipitation chemistry and deposition
<p>An international team of 21 scientists from 14 countries, working under the auspices of the WMO Global Atmosphere Watch Scientific Advisory Group for Precipitation Chemistry, has produced a global assessment of precipitation chemistry and deposition. This assessment appears in a Special Issue of the journal, Atmospheric Environment, Volume 93 (2014), and includes three articles:</p> <ol> <li>Preface by Guest Editors, Robert Vet (Environment Canada), Richard Artz (National Oceanic and Atmospheric Administration), and Silvina Carou (Environment Canada). <a href="http://dx.doi.org/10.1016/j.atmosenv.2013.11.013">http://dx.doi.org/10.1016/j.atmosenv.2013.11.013.</a></li> <li>Robert Vet, Richard S. Artz, Silvina Carou, Mike Shaw, Chul-Un Ro, Wenche Aas, Alex Baker, Van C. Bowersox, Frank Dentener, Corinne Galy-Lacaux, Amy Hou, Jacobus J. Pienaar, Robert Gillett, M. Cristina Forti, Sergey Gromov, Hiroshi Hara, Tamara Khodzer, Natalie M. Mahowald, Slobodan Nickovic, P.S.P. Rao, and Neville W. Reid. A global assessment of precipitation chemistry and deposition of sulfur, nitrogen, sea salt, base cations, organic acids, acidity and pH, and phosphorus. <a href="http://dx.doi.org/10.1016/j.atmosenv.2013.10.060">http://dx.doi.org/10.1016/j.atmosenv.2013.10.060.</a></li> <li>Addendum by Vet, et al. <a href="http://dx.doi.org/10.1016/j.atmosenv.2014.02.017">http://dx.doi.org/10.1016/j.atmosenv.2014.02.017.</a></li> </ol> <p>The goal of the assessment was to provide the international science and policy communities with the best available data and information on regionally-representative precipitation chemistry and atmospheric deposition. The information in this publication, together with the supporting data and maps, is an important contribution to the study of atmospheric deposition and to related scientific studies, such as the study of ecosystem impacts, human health effects, nutrient processing, climate change, global and hemispheric modeling, and biogeochemical cycling.</p> <p>Data used in the assessment included best-available estimates of precipitation concentrations and wet, dry, and total deposition of major ions, sea salt, and phosphorus in North America, South America, Europe, Africa, Asia, Oceania, and the oceans for two periods, 2000-2002 and 2005-2007. Due to the limited contemporary data for phosphorus and organic acids, it was necessary to extend the study period back to the mid-1990s for these species.</p> <p>In order to fill gaps in the geographic coverage of the measurements, 2000-2002 data were combined with 2001 ensemble-mean results from 21 global chemical transport models. The model results were produced during Phase I of the Coordinated Model Studies Activities of the Task Force on Hemispheric Transport of Air Pollution (Dentener, et al. 2006. Global Biogeochem. Cycles 20, 21. <a href="http://dx.doi.org/10.1029/2005GB002672">http://dx.doi.org/10.1029/2005GB002672</a>. Maps of major ions in precipitation and deposition were generated from the combined measurement and model results.</p> <p>A major product of the assessment was the preparation of data sets of quality-assured ion concentrations and wet deposition, dry deposition estimates, and model results.</p> <p>Use and publication of the global assessment data sets for scientific, policy-related, or educational purposes are encouraged. Please use the following citation to identify the data set and its source:</p> <p>Vet, R., R.S. Artz, S. Carou, M. Shaw, C.-U. Ro, W. Aas, A. Baker, V.C. Bowersox, F. Dentener, C. Galy-Lacaux, A. Hou, J.J. Pienaar, R. Gillett, M.C. Forti, S. Gromov, H. Hara, T. Khodzher, N.M. Mahowald, S. Nickovic, P.S.P. Rao, N.W. Reid. 2019. Data associated with the following publication: Vet et al. (2014). A global assessment of precipitation chemistry and deposition of sulfur, nitrogen, sea salt, base cations, organic acids, acidity and pH, and phosphorus. <em>Atmospheric Environment</em>, 93, 3-100, August 2014, doi.org/10.1016/j.atmosenv.2013.10.060. <strong>Enter data file name(s)</strong> accessed from the World Data Centre for Precipitation Chemistry.</p> <p>Please also include the following acknowledgment in publications: The authors gratefully acknowledge the sources of precipitation chemistry and deposition data acknowledged on page 92 of Vet et al. (2014) <em>Atmospheric Environment</em>, 93, <a href="http://dx.doi.org/10.1016/j.atmosenv.2013.10.060">http://dx.doi.org/10.1016/j.atmosenv.2013.10.060</a>.</p>
Data from: A global dataset for economic losses of extreme hydrological events during 1960-2014
A comprehensive dataset of extreme hydrological events (EHEs) – floods and droughts, consisting of 2,171 occurrences worldwide, during 1960‐2014 was compiled, and then their economic losses were normalized using a price index in U.S. dollar. The dataset showed a significant increasing trend of EHEs before 2000, while a slight post‐2000 decline. Correspondingly, the EHEs‐caused economic losses increased obviously before 2000 followed by a slight decrease; the post‐2000 decline could be partially attributed to the decreases in drought and flood‐prone area, or climate adaptation practices. Spatially, Asia experienced most EHEs (969), corresponding to the largest share of economic losses (approximately $868 billion for floods and $50 billion for droughts, respectively), while Oceania had the least EHEs (102) and the least economic losses (approximately $19 billion for floods and $45 billion for droughts). The five countries with the highest EHE‐caused economic losses were China, USA, Canada, Australia, and India. Countries that suffered the highest flood‐caused economic losses were China, USA, and Canada. This dataset provides a quantitative linkage between climate science and economic losses at a global scale; and it is beneficial for the regional climatic impact assessments and strategical development for mitigating climate change impacts.
Data from: An updated global dataset for diet preferences in terrestrial mammals: testing the validity of extrapolation
1. Diet is a key trait of an organism's life history that influences a broad spectrum of ecological and evolutionary processes. Kissling et al. (2014) compiled a species-specific dataset of diet preferences of mammals for 38% of a total of 5364 terrestrial mammalian species assessed for the International Union for Conservation of Nature's Red List, to facilitate future studies. The authors imputed dietary data for the remaining 62% by using extrapolation from phylogenetic relatives. 2. We collected dietary information for 1261 mammalian species for which data were extrapolated by Kissling et al. (2014), in order to evaluate the success with which such extrapolation can predict true diets. 3. The extrapolation method devised by Kissling et al. (2014) performed well for broad dietary categories (consumers of plants and animals). However, the method performed inconsistently, and sometimes poorly, for finer dietary categories, varying in accuracy in both dietary categories and mammalian orders. 4. The results of the extrapolation performance serve as a cautionary tale. Given the large variation in extrapolation performance, we recommend a more conservative approach for inferring mammalian diets, whereby dietary extrapolation is implemented only when there is a high degree of phylogenetic conservatism for dietary traits. Phylogenetic comparative methods can be used to detect and measure phylogenetic signal in diet. If data for species are needed, then only the broadest feeding categories should be used. This would ensure a greater level of accuracy and provide a more robust dataset for further ecological and evolutionary analysis.
Data from: MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling
Species Distribution Models (SDMs) combine information on the geographic occurrence of species with environmental layers to estimate distributional ranges and have been extensively implemented to answer a wide array of applied ecological questions. Unfortunately, most global datasets available to parameterize SDMs consist of spatially interpolated climate surfaces obtained from ground weather station data and have omitted the Antarctic continent, a landmass covering c. 20% of the Southern Hemisphere and increasingly showing biological effects of global change. Here we introduce MERRAclim, a global set of satellite-based bioclimatic variables including Antarctica for the first time. MERRAclim consists of three datasets of 19 bioclimatic variables that have been built for each of the last three decades (1980s, 1990s and 2000s) using hourly data of 2 m temperature and specific humidity. We provide MERRAclim at three spatial resolutions (10 arc-minutes, 5 arc-minutes and 2.5 arc-minutes). These reanalysed data are comparable to widely used datasets based on ground station interpolations, but allow extending their geographical reach and SDM building in previously uncovered regions of the globe.
Dataset of some transition state problems solved with a deterministic global optimization method.
<p>Data of all solutions found for each problem and data for each (iterations vs open nodes) graph.<br /> </p>
Dataset: Global hotspots and correlates of alien species richness across taxonomic groups
<p>Data-set and data sources used in analyses, including alien species richness for 8 taxonomic groups and socio-economic, climatic and geographic variables of 609 geographic regions.</p>
Global (2M) SARS-CoV-2 genomes dataset, from Viridian, processed with MAPLE0.6.11
Open the record for dataset details and reuse information.
G4D-DOC: A global four-dimensional gridded dataset of ocean dissolved oxygen concentrations retrieval from Argo profiles
<p>Based on temperature and salinity observations from Argo floats, this dataset uses machine-learning methods to reconstruct global ocean dissolved oxygen (DO) concentrations.<br><strong>This version only provides monthly-scale netCDF format data for everyone's use. If you need other time scales, please check previous versions.</strong></p> <h2>Spatiotemporal Characteristics</h2> <ul> <li> <p><strong>Time range:</strong> 2005–2022, <strong>monthly</strong> fields.</p> </li> <li> <p><strong>Geographic range:</strong> Global oceans <strong>excluding the Arctic Ocean</strong>, from 90°S to 84°N and 180°W to 180°E.</p> </li> <li> <p><strong>Horizontal resolution:</strong> 1° × 1° (regular grid).</p> </li> <li> <p><strong>Vertical levels (26):</strong> 10, 20, 30, 40, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1750, 1995dbar .</p> </li> </ul> <h2>Data Format & Conventions</h2> <ul> <li> <p><strong>Format:</strong> NetCDF4</p> </li> <li> <p><strong>Coordinate conventions:</strong></p> <ul> <li> <p><code>lat</code> (Y axis): 89.5 → −89.5 (descending)</p> </li> <li> <p><code>lon</code> (X axis): converted to <strong>−180 → 180</strong> (1° centers)</p> </li> <li> <p><code>depth</code>: ascending (matching the 26 target levels)</p> </li> </ul> </li> <li> <p><strong>Units:</strong> DO in <strong>μmol/kg</strong> (<code>umol kg-1</code>).</p> </li> </ul> <h2>Variables & Dimensions</h2> <ul> <li> <p><strong>Variables kept:</strong> <code>DO</code>, <code>depth</code>, <code>lat</code>, <code>lon</code> (with a single-valued <code>time</code> coordinate).</p> </li> <li> <p><strong>DO dimensions:</strong> <code>(time, depth, lat, lon)</code>.</p> </li> </ul> <h2>Filenames</h2> <ul> <li> <p><strong>Pattern:</strong> <code>G4D_DOC_YYYY_MM.nc</code><br><em>Example:</em> <code>G4D_DOC_2005_07.nc</code> contains the field for <strong>July 2005</strong>.</p> </li> </ul> <h2>Citation & Disclaimer</h2> <p>Please cite the dataset and relevant literature when using it in publications or products.<br>Recommended citation (example):</p> <blockquote> <p>Xue, C., & Wang, Z. (2025). <em>A global four-dimensional gridded dataset of ocean dissolved oxygen concentrations retrieval from Argo profiles</em> (Monthly NetCDF version). Zenodo. <a target="_new" rel="noopener">https://doi.org/</a>10.5281/zenodo.13920233</p> </blockquote> <p>The data producers are not responsible for any losses arising from data use. Map boundaries or masks do not imply official positions.</p> <h2>Contacts</h2> <ul> <li> <p><strong>Cunjin Xue</strong> — <a rel="noopener">xuecj@aircas.ac.cn</a></p> </li> <li> <p><strong>Zhenguo Wang</strong> — <a rel="noopener">zgwang24@m.fudan.edu.cn</a></p> </li> </ul>
Global Invasive and Alien Traits and Records (GIATAR) dataset
<p>Monitoring and managing the global spread of invasive and alien species requires accurate spatiotemporal records of species presence and information about the biological characteristics of species of interest including life cycle information, biotic and abiotic constraints and pathways of spread. The Global Invasive and Alien Traits And Records (GIATAR) dataset provides consolidated dated records of invasive and alien presence at the country-scale combined with a suite of biological information about pests of interest in a standardized, machine-readable format. We provide dated presence records for 46,666 alien taxa in 249 countries constituting 827,300 country-taxon pairs, joined with additional biological information for thousands of taxa. GIATAR is designed to be quickly updateable with future data and easy to integrate into ongoing research on global patterns of alien species movement using scripts provided to query and analyze data. </p> <p>This publication includes:</p> <ul> <li>GIATAR dataset files (dataset)</li> <li>Functions in Python and R to join tables and query data (query_functions)</li> <li>Tutorials and example queries in Python and R (tutorials)</li> </ul> <p>For more information, please refer to the publication:</p> <div> <div>Saffer, Ariel, Thom Worm, Yu Takeuchi, and Ross Meentemeyer. “GIATAR: A Spatio-Temporal Dataset of Global Invasive and Alien Species and Their Traits.” <em>Scientific Data</em> 11, no. 1 (September 11, 2024): 991. <a href="https://doi.org/10.1038/s41597-024-03824-w">https://doi.org/10.1038/s41597-024-03824-w</a>.</div> <div> </div> <div>Changes in this version (September 15, 2025):</div> <div> <ul> <li>Latest records as of September 8, 2025</li> </ul> </div> <div>For continuous updates to code, please refer to our Github repository: https://github.com/ncsu-landscape-dynamics/GIATAR-dataset</div> </div>
Dataset: Evaluating permafrost definitions for global permafrost area estimates in CMIP6 climate models
<p>This dataset corresponds to the following publication:<br>Steinert, N., J., et al. 2023: <i>Evaluating permafrost definitions for global permafrost area estimates in CMIP6 climate models</i>, Environmental Research Letters, 10.1088/1748-9326/ad10d7<br>https://iopscience.iop.org/article/10.1088/1748-9326/ad10d7</p><p>Global permafrost regions are undergoing significant changes due to global warming, whose assessments often rely on permafrost extent estimates derived from climate model simulations. These assessments employ a range of definitions for the presence of permafrost, leading to inconsistencies in the calculation of permafrost area. This dataset contains permafrost area calculations using 10 different definitions for detecting permafrost presence based on either ground thermodynamics, soil hydrology, or air-ground coupling from an ensemble of 32 Earth System Models.</p><p>This dataset includes two file archives:<br>1. 32 CMIP6 models, 10 permafrost definitions, historical period (1850-2014), annual data<br>2. 32 CMIP6 models, 10 permafrost definitions, SSP5-85 period (2015-2100), annual data</p><p>This dataset represents source data for the following publication. Please refer to this reference for a more detailed description of the definitions used in this dataset:<br>Steinert, N., J., et al. 2023: Evaluating permafrost definitions for global permafrost area estimates in CMIP6 climate models, https://iopscience.iop.org/article/10.1088/1748-9326/ad10d7</p><p>The results show that variations between permafrost-presence definitions result in substantial differences of up to 18 million km2, where any given model could both over- or underestimate the present-day permafrost area. Ground-thermodynamic-based definitions are, on average, comparable with observations but are subject to a large inter-model spread. The associated uncertainty of permafrost area estimates is reduced in definitions based on ground-air coupling. However, their representation of permafrost area strongly depends on how each model represents the ground-air coupling processes. The definition-based spread in permafrost area can affect estimates of permafrost-related impacts and feedbacks, such as quantifying permafrost carbon changes. For instance, the definition spread in permafrost area estimates can lead to differences in simulated permafrost-area soil carbon changes of up to 28%. This dataset therefore supports an emphasis on the importance of consistent and well-justified permafrost-presence definitions for robust projections and accurate assessments of permafrost from climate model outputs.</p><p>For any questions regarding the dataset, please free feel to contact Norman J. Steinert (nste@norceresearch.no, normanst@ucm.es)</p>
Dataset for Globally synchronous meteorite rain paused the Great Ordovician Biodiversification Event
<p>Supporting information includes a detailed comparison of the stratigraphy of the YW2 borehole with the well-studied Puxi River section, the U-Pb isotopic data from the bentonite, and coupled carbon isotope data from the YW2 borehole.</p>
Global seamless and high-resolution temperature dataset (GSHTD), 2001–2020
<p>Reference: </p><p>Yao et al., 2023. Global seamless and high-resolution temperature dataset (GSHTD), 2001–2020. Remote Sensing of Environment 286, 113422. </p>
Dataset on Scenarios for Urban Emissions and LUE with Increments of Global Warming
<p>This dataset supports the original research article on “<strong>Framework for comparing urban emissions and land use efficiency scenarios to avoid increments of global warming</strong>” (Kılkış, forthcoming). <br><br>A total of 28 datasheets are involved in this dataset that is organised into four main domains as follows: </p> <ol> <li>The first domain with 3 datasheets contains urban emissions data for three urban emissions scenarios (SSP1-1.9, SSP1-2.6, and SSP1-RE) on an annual basis. The data is summed to obtain cumulative urban emissions between 2020 and 2050 per urban area for 465 urban areas.</li> <li>The second domain with 9 datasheets provides the quantification of the original parameter on contributions to increments of global warming based on cumulative urban emissions data for three urban emissions scenarios (SSP1-1.9, SSP1-2.6, and SSP1-RE) between 2020 and 2050. The best estimate of the transient climate response to cumulative carbon dioxide (CO<sub>2</sub>) emissions (TCRE) and its 5-95th percentile range are considered within the quantifications of this analysis.</li> <li>The third domain with 4 datasheets has the annual CO<sub>2</sub> sequestration penalties of four land use efficiency scenarios (LUE 5%, LUE 15%, LUE Av, and LUE Best). Data is summed for cumulative CO<sub>2</sub> sequestration penalties between 2020 and 2050 per urban area for 135 urban areas.</li> <li>The fourth domain with 12 datasheets contains the quantification of the original parameter on contributions to increments of global warming based on CO<sub>2</sub> sequestration penalties for four land use efficiency scenarios (LUE 5%, LUE 15%, LUE Av, and LUE Best) between 2020 and 2050. The analysis is based on the best estimate of TCRE as well as its 5-95th percentile range.</li> </ol> <p>The data structure is described in the information sheet and the 28 datasheets contain 292,100 cells in total. The method and results that utilise the data are described in the original article submitted to <em>Energy</em>. The dataset of this research article should be cited as: Kılkış (2023), Dataset on Scenarios for Urban Emissions and LUE with Increments of Global Warming (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.10407848">https://doi.org/10.5281/zenodo.10407848</a></p>
Global forest net primary production dataset
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A coarse pixel scale ground "truth" dataset based on the global in situ site measurements from 2000 to 2021.
<p><em>In situ </em>measurements from sparsely distributed networks worldwide are a valuable source of data for validating satellite products. However, the significant differences in spatial scale between in situ and satellite measurements pose a challenge for validation. To address this challenge, we propose a new global coarse pixel-scale ground "truth" dataset which provides sequential pixel scale albedo on a 500 m resolution over the period 2000-2021. The creation of data set is done using ground measurements from 416 sparsely distributed networks worldwide and high-resolution long-term time series data corresponding to site-based data. Furthermore, we thoroughly assessed the effectiveness of the dataset at sites with different degrees of spatial representativeness. The results demonstrate that using this dataset in validation outperforms the direct comparison between satellite and <em>in situ</em> site measurements over heterogeneous surfaces when <em>in situ</em> measurement footprints are less than satellite pixel size. The dataset offers a unique collection of coarse pixel scale relative ground "truth" with the widest spatial distribution and longest time series. By merging temporal information from ground-based observations and spatial information from high-resolution data, the dataset we provided represents a valuable resource for validating and correcting worldwide surface albedo products over heterogeneous surfaces.</p>
Global Photovoltaic Solar Panel Dataset from 2019 to 2022
<p>Using Google Earth imagery and 2019-2022 Sentinel-2 datasets, we developed a two-stage classification framework to obtain the annual global dataset of solar photovoltaic panels at 20-meter resolution from 2019 to 2022.</p> <p>To classify the global solar photovoltaic panels, we applied the global zoning method of IPCC AR6 WGI to define the main-zoning, and used 4 degree × 4 degree grids to create the sub-zoning. Then, we extracted the solar photovoltaic panels in each sub-zoning, and stored the result data in TIFF format.</p> <p>The number of each file corresponds to the ID in the attribute table of the sub-zoning-ID file, and users can download and use the corresponding file based on the sub-zoning-ID. After the ID, 2019, 2020, 2021, and 2022 respectively represent four years.</p> <p>The folder of the annual global PV dataset is named after the year, and each file is named as "sub zoning ID_year".</p>
Interactive tmaps of the Global Chemicals Inventory (cheminformatic dataset)
<p>Interactive version of the tmaps described in the thesis "Cheminformatic techniques for screening chemicals on the globals market" (2024, ETH Zurich).</p> <p> </p> <p>Due to technical limitations, legends are numerical and are as follows:</p> <p>tmap - total inventories: Numbers correspond to the total number of inventories on which each chemical is registered.</p> <p>tmap - individual inventories: A number of 1 indicates that chemical is registered on the indicated inventory. A number of 0 indicates it is not.</p> <p>tmap - qsar: A number of 0 indicates the chemical is inside the applicability domain of the indicated inventory. A number of 2 indicates it is on the boundary (within 10% of the 95th percentile). A number of 6 indicates it is outside the domain.</p> <p>tmap - innovation: A number of 0 indicates the chemical is excluded from the analysis (coloured black). A number of 1 indicates it is considered existing on the global level. A number of 2 indicates it is mixed, and a number of 3 indicates it is considered new.</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.