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708 results for “Global dataset”
Datasets for "Reconciling global terrestrial evapotranspiration estimates from multi-product intercomparison and evaluation"
<p>Datasets for "Reconciling global terrestrial evapotranspiration estimates from multi-product intercomparison and evaluation"</p>
Dataset and Software code _ Localized and global representation of prior value, sensory evidence, and choice in male mouse cerebral cortex
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GloSoFarID: Global multispectral dataset for Solar Farm IDentification in satellite imagery
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Feldman et al. Global One Degree Datasets
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GLaD4CD: Global Landslide Dataset for Change Detection
<p>This dataset constitutes one of the outcomes of the Master's Thesis titled 'Landslide identification using deep learning-based change detection and the DeepESDL collaborative cloud platform,' authored by Julia Anna Leonardi, conducted under the supervision of Prof. Maria Antonia Brovelli and Dr. Vasil Yordanov at Politecnico di Milano. The authors extracted the image patches through the DeepESDL platform, which the ESA NoR sponsorship provided access to. </p> <p>The resource contains the .csv database containing landslide information of 174 events, such as the date, location, source, and the dates of the Sentinel-2 images from before and after the event. The sources of the data included in this inventory are:</p> <ul> <li><a href="progettoiffi.isprambiente.it" target="_blank" rel="noopener">Inventario dei Fenomeni Franosi in Italia (IFFI) compiled by ISPRA</a></li> <li>NASA <a href="https://gpm.nasa.gov/landslides/projects.html#GLC" target="_blank" rel="noopener">Global Landslide Catalog (GLC)</a> [1],[2].</li> <li><a href="https://emergency.copernicus.eu/">Copernicus Emergency Management Service</a> (© European Union, 2012-2024) [3]</li> <li>Geological Survey Ireland | <a href="https://www.gsi.ie/en-ie/data-and-maps/Pages/Geohazards.aspx">Geohazards</a> | <a href="https://www.gsi.ie/en-ie/publications/Pages/National-Landslide-Susceptibility-Map.aspx" target="_blank" rel="noopener">National Landslide Susceptibility Map</a></li> <li><a href="../records/7970874" target="_blank" rel="noopener">Monsoon triggered landslides in Nepal timed with Sentinel-1 for 2015, 2017, 2018, and 2019</a>, courtesy of Katy Burrows [4] </li> <li><a href="https://maps.disasters.nasa.gov/download/gis_products/event_specific/2023/turkiye_earthquakes_202302/landslides/">Landslides after 2023 earthquakes in Turkey</a> compiled by the NASA GSFC team</li> <li><a href="https://www.itc.nl/about-itc/centres-of-expertise/centre-for-disaster-resilience/what-we-do/education-training/Landslides-Course-21-25-November/" target="_blank" rel="noopener">University of Twente online landslide course</a></li> <li>Manually extracted sources from [5], [6], [7], [8].</li> </ul> <p>The .zip file contains the TRAIN dataset with the pre-event Sentinel-2 image patches in folder PRE and the post-event Sentinel-2 image patches in folder POST, and the TEST set with 17 bi-temporal pairs in the PRE and POST folders following the above convention and the labels in the form of change maps in the CM folder. In this updated version the full 13-band Sentinel-2 images are published. (In the previous version only 5 bands (B02, B03, B04, B08, and CLM) were available). All the image patches and the ground truth annotations are in the GeoTIFF format. </p> <p>The authors developed the dataset for change detection workflows.</p> <p>Project supported by the ESA Network of Resources Initiative.</p> <p>This work is funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001</p> <p> </p> <p>[1] Kirschbaum, D.B., Stanley, T., & Zhou, Y. (2015). Spatial and temporal analysis of a global landslide catalog. Geomorphology, 249, 4-15. doi:<a href="https://doi.org/10.1016/j.geomorph.2015.03.016">10.1016/j.geomorph.2015.03.016</a></p> <p>[2] Kirschbaum, D.B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52, 561-575. doi:<a href="https://doi.org/10.1007/s11069-009-9401-4">10.1007/s11069-009-9401-4</a></p> <p>[3] Copernicus Emergency Management Service. (n.d.). Retrieved March 12, 2024 from <a href="https://emergency.copernicus.eu/">https://emergency.copernicus.eu/</a></p> <p>[4] K. Burrows, O. Marcand C. Andermann, “Monsoon triggered landslides in Nepal timed with Sentinel-1 for 2015, 2017, 2018 and 2019”. Zenodo, May 25, 2023. doi: <a href="10.5281/zenodo.7970874.">10.5281/zenodo.7970874.</a></p> <p>[5] F. Wang et al., ‘Coseismic landslides triggered by the 2018 Hokkaido, Japan (Mw 6.6), earthquake: spatial distribution, controlling factors, and possible failure mechanism’, Landslides, vol. 16, no. 8, pp. 1551–1566, Aug. 2019, doi: <a href="10.1007/s10346-019-01187-7">10.1007/s10346-019-01187-7</a>.</p> <p>[6] P. Amatya, D. Kirschbaum, and T. Stanley, ‘Rainfall-induced landslide inventories for Lower Mekong based on Planet imagery and a semi-automatic mapping method’, Geoscience Data Journal, vol. 9, no. 2, pp. 315–327, 2022, doi: <a href="10.1002/gdj3.145">10.1002/gdj3.145</a>.</p> <p>[7] ]‘Vietnam – At Least 12 Killed in Flash Floods and Landslides in North – FloodList’. Accessed: Feb. 18, 2024. [Online]. Available: <a href="https://floodlist.com/asia/vietnam-floods-landslides-yen-baison-la-august-2017" target="_blank" rel="noopener">https://floodlist.com/asia/vietnam-floods-landslides-yen-baison-la-august-2017</a></p> <p>[8] L. Colombo, ‘Crollata, causa frana, la volta di una galleria lungo la SP72 a Fiumelatte a Varenna - GLI AGGIORNAMENTI’, Lecco Notizie. Accessed: Feb. 18, 2024. [Online]. Available: <a href="https://lecconotizie.com/cronaca/crollata-la-volta-di-una-galleria-lungo-la-sp72-a-fiumelatte-avarenna/" target="_blank" rel="noopener">https://lecconotizie.com/cronaca/crollata-la-volta-di-una-galleria-lungo-la-sp72-a-fiumelatte-avarenna/</a></p>
A global seafood methylmercury concentration dataset during 1995-2022
<p>The database provides detailed records of global seafood methylmercury (MeHg) concentrations from 1995 to 2022. The database is classified based on two scales: marine area and nation. All data files are stored in XLSX and CSV formats, and the related codes used to construct this database are saved as Python files (.py ).</p> <div> <div> <div> </div> </div> </div> <div> <div> <div> <div> </div> </div> </div> </div>
Datasets of "The Onset of a Globally Ice-covered State for a Land Planet"
<p>This dataset contains results to create figures in the paper "The Onset of a Globally Ice-covered State for a Land Planet" by T. Kodama et al.</p> <p>It contains data from our GCM calculations to create figures. We used Gtool3-dcl5 which is developed by the GFD-DENNOU Club for analysis (<a href="https://www.gfd-dennou.org/">https://www.gfd-dennou.org</a>).</p>
Global Transportation Demand Dataset using the Shared Socioeconomic Pathways (SSPs) Scenario Framework
<p>We use historical data for the land-based passenger (in passenger-kilometers (km)) across 38 countries and freight transport (in tonne-km) for 43 countries between 1990 and 2018 from the Transport Outlook of the International Transport Forum (ITF) transport database, to investigate the key drivers of transport energy demand <em><strong>source</strong>: ITF. (2019). ITF Transport Outlook 2019. ITF Transport Outlook 2019. <a href="https://www.oecd-ilibrary.org/transport/itf-transport-outlook-2019_transp_outlook-en-2019-en">https://www.oecd-ilibrary.org/transport/itf-transport-outlook-2019_transp_outlook-en-2019-en</a></em></p> <p>We collect the historical socioeconomic variables from the World Bank’s global open data bank <em><strong>source</strong>: World Bank. (2020). Data Bank: World Development Indicators. <a href="https://databank.worldbank.org/source/world-development-indicators">https://databank.worldbank.org/source/world-development-indicators</a></em></p> <p>For this scenario analysis, we rely on the shared socioeconomic pathways (SSPs) from the IIASA database (Riahi et al., 2017). <em><strong>source: </strong>Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., … Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153–168. <a href="https://doi.org/10.1016/j.gloenvcha.2016.05.009">https://doi.org/10.1016/j.gloenvcha.2016.05.009</a> Available Online: <a href="https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about">https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about</a></em></p> <p>The lack of data disaggregated by country and end-use sector in countries of interest was a significant drawback in the data collection process. We make a crucial assumption in this modeling exercise that historical demand profiles in developing countries track the global average per capita transport trends. Therefore, the resulting estimates are indicative and must be interpreted within this analysis's scope given the future is unknown and highly uncertain.</p> <p> </p> <p> </p> <p> </p> <p> </p>
LegacyPollen 1.0: A taxonomically harmonized global Late Quaternary pollen dataset of 2831 records with standardized chronologies
<p>This repository consists of code for downloading pollen data from the Neotoma Paleoecology Database, the harmonization of pollen taxa, and the assignment of age-depth data so that datasets for customized harmonization levels can be easily established. The input data includes a harmonization table and example data, stored in machine-readable data format (.CSV).</p>
Drivers of global variation in land ownership - dataset
<p><span>Land ownership shapes natural resource management and social–ecological resilience, but the factors determining ownership norms in human societies remain unclear. Here we conduct a global empirical test of long‐standing theories from ecology, economics and anthropology regarding potential drivers of land ownership and territoriality. Prior theory suggests that resource defensibility, subsistence strategies, population pressure, political complexity and cultural transmission mechanisms may all influence land ownership. We applied multi‐model inference procedures based on logistic regression to cultural and environmental data from 102 societies, 71 with some form of land ownership and 31 with no land ownership. We found an increased probability of land ownership in mountainous environments, where patchy resources may be more cost effective to defend via ownership. We also uncovered support for the role of population pressure, with a greater probability of land ownership in societies living at higher population densities. Our results also show more land ownership when neighboring societies also practiced ownership. We found less support for variables associated with subsistence strategies and political complexity.</span></p>
The datasets used in the manuscript named "Fidelity of Global Tropical Cyclone Activity in a High-Resolution Reanalysis Dataset CRA40 in Comparison with Multiple Other Reanalysis Datasets"
<p>The datasets after tracking the TC events in five reanalyses: ERA5, JRA55, CFSR, MERRA2, CRA40. </p>
Dataset of "A hierarchy of global ocean models coupled to CESM1"
<p><strong>Data associated with the following publication:</strong></p> <p>Hsu, T. Y., Primeau, F. W., & Magnusdottir, G. (2022). A Hierarchy of Global Ocean Models Coupled to CESM1.</p> <p><strong>Paper Abstract:</strong></p> <p>We develop a hierarchy of simplified ocean models for coupled ocean, atmosphere, and sea ice climate simulations using the Community Earth System Model version 1 (CESM1). The hierarchy has four members: a slab ocean model, a mixed-layer model with entrainment and detrainment, an Ekman mixed-layer model, and an ocean general circulation model (OGCM). Flux corrections of heat and salt are applied to the simplified models ensuring that all hierarchy members have the same climatology. We diagnose the needed flux corrections from auxiliary simulations in which we restore the temperature and salinity to the daily climatology obtained from a target CESM1 simulation. The resulting 3-dimensional corrections contain the interannual variability fluxes that maintain the correct vertical gradients of temperature and salinity in the tropics. We find that the inclusion of mixed-layer entrainment and Ekman flow produces sea surface temperature and surface air temperature fields whose means and variances are progressively more similar to those produced by the target CESM1 simulation.</p> <p>We illustrate the application of the hierarchy to the problem of understanding the response of the climate system to the loss of Arctic sea ice. We find that the shifts in the positions of the mid-latitude westerly jet and of the Inter-tropical Convergence Zone (ITCZ) in response to sea-ice loss depend critically on upper ocean processes. Specifically, heat uptake associated with the mixed-layer entrainment influences the shift in the westerly jet and ITCZ. Moreover, the shift of ITCZ is sensitive to the form of Ekman flow parameterization.</p> <p> </p> <p>Methods</p> <p><strong>Description of methods used for generation of data: </strong><br> The data is generated with EMOM, a hierarchy of ocean models that are applied in CESM1. The detailed description of the model is in the paper the dataset is presented in (i.e. A Hierarchy of Global Ocean Models Coupled to CESM1).<br> <br> <strong>Methods for processing the data:</strong></p> <p>This dataset consists of a set of atmospheric and oceanic fields produced by the NCAR CESM1 climate model. The data is in NETCDF format and has been post-processed and formatted using the NCO command language (see http://nco.sourceforge.net/ for more details).</p> <p><strong>Software-specific information needed to interpret the data:</strong><br> The data is in NetCDF format.</p> <p>Usage Notes</p> <p>This README file was generated on 20200416 by Tien-Yiao Hsu</p> <p><strong>Dataset of the paper</strong></p> <p>A Hierarchy of Global Ocean Models Coupled to CESM1</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> Email: tienyiah@uci.edu</p> <p> OrcID: 0000-0002-8121-1525</p> <p> </p> <p> Associate Contact Information</p> <p> Name: Francois Primeau</p> <p> Institution: University of California, Irvine</p> <p> Institutions ROR: [UCI = https://ror.org/04gyf1771]</p> <p> Address: </p> <p> </p> <p> Department of Earth System Science</p> <p> Croul Hall</p> <p> Irvine, CA 92697-3100</p> <p> </p> <p> Email: fprimeau@uci.edu</p> <p> </p> <p> Associate Contact Information</p> <p> Name: Gudrun Magnusdottir</p> <p> Institution: University of California, Irvine</p> <p> Institutions ROR: [UCI = https://ror.org/04gyf1771]</p> <p> Address: </p> <p> </p> <p> Department of Earth System Science</p> <p> Croul Hall</p> <p> Irvine, CA 92697-3100</p> <p> </p> <p> Email: gudrun@uci.edu</p> <p> </p> <p>3. Date of data organized : 20220201</p> <p> </p> <p>4. Information about funding sources that supported the collection of the data:</p> <p> Funder name: Department of Energy</p> <p> Funder uri: https://www.energy.gov/</p> <p> </p> <p>5. Contextual description of the data:</p> <p> </p> <p> The data used to produce the figures in the paper.</p> <p> </p> <p>--------------------------</p> <p>SHARING/ACCESS INFORMATION</p> <p>-------------------------- </p> <p> </p> <p> </p> <p>Licenses/restrictions placed on the data: </p> <p> </p> <p> CREATIVE COMMONS ATTRIBUTION 4.0 INTERNATIONAL CC-BY</p> <p> </p> <p>---------------------</p> <p>DATA & FILE OVERVIEW</p> <p>---------------------</p> <p> </p> <p>We separate sets of data in terms of folders. </p> <p> </p> <p>1. AMOC</p> <p> </p> <p> This directory contains the AMOC streamfunction output from </p> <p> simulations OGCM_CTL and OGCM_EXP.</p> <p> </p> <p>2. hierarchy_statistics</p> <p> </p> <p> This directory contains the statistics (mean, variability, ...)</p> <p> and diagnosed quantities (ex: EOF, heat transport) of the hierarchy</p> <p> output.</p> <p> </p> <p> The output of CTL run of year 21 to 120 is in CTL_21-120.</p> <p> The output of EXP run of year 81 to 180 is in EXP_81-180.</p> <p> </p> <p>3. hierarchy_average</p> <p> </p> <p> This directory is similar to is similar to hierarchy_statistics, </p> <p> containing CTL and EXP. The difference is that it is the raw, unprocessed</p> <p> mean data that contains the complete output variables.</p> <p> </p> <p> </p> <p>--------------------------</p> <p>METHODOLOGICAL INFORMATION</p> <p>--------------------------</p> <p> </p> <p> </p> <p>1. Description of methods used for generation of data: </p> <p> </p> <p> The data is generated with EMOM, a hierarchy of ocean models that is</p> <p> applied in CESM1. The detail description of the model is in the paper</p> <p> the dataset is preseted in (i.e. A Hierarchy of Global Ocean Models </p> <p> Coupled to CESM1).</p> <p> </p> <p>2. Methods for processing the data:</p> <p> </p> <p> The output data is mostly the mean and variance of the climate variables.</p> <p> </p> <p>3. Software-specific information needed to interpret the data:</p> <p> </p> <p> The data is in NetCDF format.</p> <p> </p> <p>---------------------------------------------</p> <p>DATA-SPECIFIC INFORMATION FOR DIRECTORY: AMOC</p> <p>---------------------------------------------</p> <p> </p> <p># Filename: MOC_[CTL|EXP].nc</p> <p> </p> <p>Variable list:</p> <p> </p> <p> 1. MOC</p> <p> </p> <p> Unit: Sv</p> <p> </p> <p> The monthly mean value of streamfunction of the meridional overturning</p> <p> circulation in ocean basins.</p> <p> </p> <p> </p> <p># Filename MOC_[CTL|EXP]_timeseries.nc</p> <p> </p> <p>Variable list:</p> <p> </p> <p> 1. AMOC_max</p> <p> </p> <p> Unit: Sv</p> <p> </p> <p> The annual maximum value of the Atlantic Meridional Overturning Circulation.</p> <p> </p> <p> 2. AMOC_max_lat</p> <p> </p> <p> Unit: degree north</p> <p> </p> <p> The latitude of the location where AMOC_max occurs.</p> <p> </p> <p> 3. AMOC_max_z</p> <p> </p> <p> Unit: m</p> <p> </p> <p> The depth of the location where AMOC_max occurs.</p> <p> </p> <p>---------------------------------------------</p> <p>DATA-SPECIFIC INFORMATION FOR DIRECTORY: hierarchy_average</p> <p>---------------------------------------------</p> <p> </p> <p>In this directory, each sub-directory is of the form [MODEL_NAME]_[CTL|EXP]</p> <p>where MODEL_NAME can be SOM, MLM, EMOM or POP2. A sub-directory has three</p> <p>files: atm.nc, ocn.nc and ocn_regrid.nc. </p> <p> </p> <p>atm.nc is the averaged data of atmosphere model output of year 21-121 of each model run on f09 grid.</p> <p>ocn.nc is the averaged data of ocean model output of year 21-121 of each model run on g16 grid.</p> <p>ocn_regrid.nc is the regrided version ocn.nc from grid g16 onto f09.</p> <p> </p> <p>Details of the atm.nc variables can be found in CAM4 documentation</p> <p>https://www.cesm.ucar.edu/models/cesm1.0/cam/docs/ug5_1/hist_flds_fv_cam4_trop_bam.html</p> <p> </p> <p>Details of the ocn.nc variables can be found in POP2 documentation</p> <p>https://ncar.github.io/POP/doc/build/html/users_guide/model-diagnostics-and-output.html</p> <p> </p> <p>---------------------------------------------</p> <p>DATA-SPECIFIC INFORMATION FOR DIRECTORY: hierarchy_statistics</p> <p>---------------------------------------------</p> <p> </p> <p>This directory conatins CTL and EXP runs folder where the statistics time</p> <p>is 21-121 for CTL and 81-180 for EXP.</p> <p> </p> <p>Each experiment folder contains sub-directories of the form [MODEL_NAME]_[CTL|EXP]</p> <p>where MODEL_NAME can be SOM, MLM, EMOM or POP2. Each of these directories has</p> <p>the same analysis listed below.</p> <p> </p> <p># Filename: atm_analysis_[AAO|AO|ENSO|NAO|PDO].nc</p> <p> </p> <p> Description: This file contains the derived climate variability patterns (i.e. </p> <p> AAO, AO, ENSO, NAO, PDO). </p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. PCAs(modes, Ny, Nx)</p> <p> </p> <p> Unit: None</p> <p> </p> <p> The normalized PCAs. Different modes of the PCAs are separated according</p> <p> to the first dimension.</p> <p> </p> <p> 2. PCAs_ts(time, modes)</p> <p> </p> <p> Unit: None</p> <p> </p> <p> The timeseries of projected PCAs onto the anomalies (i.e. the inner product of PCAs and anomalous fields).</p> <p> </p> <p># Filename: atm_analysis_mean_anomaly_[VARNAME].nc</p> <p> </p> <p> Description: This file contains the mean, standard deviation of the denoted field.</p> <p> VARNAME = [ICEFRAC|TAUX|TAUY|SST]</p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. [VARNAME]_[TIMESCALE]M</p> <p> </p> <p> Unit: ICEFRAC = None</p> <p> TAUX = N / m^2</p> <p> TAUY = N / m^2</p> <p> SST = K</p> <p> </p> <p> The mean values of each grid point. TIMESCALE = [M|S|A] where M stands for monthly,</p> <p> S for seaonal (MAM, JJA, SON, and DJF), A for annnual. </p> <p> </p> <p> </p> <p> 2. [VARNAME]_[TIMESCALE]A</p> <p> </p> <p> Unit: ICEFRAC = None</p> <p> TAUX = N / m^2</p> <p> TAUY = N / m^2</p> <p> SST = K</p> <p> </p> <p> The anomalous values of each grid point. TIMESCALE = [M|S|A] where M stands for monthly,</p> <p> S for seaonal (MAM, JJA, SON, and DJF), A for annnual. </p> <p> </p> <p> </p> <p> 3. [VARNAME]_[TIMESCALE]ASTD</p> <p> </p> <p> Unit: ICEFRAC = None</p> <p> TAUX = N / m^2</p> <p> TAUY = N / m^2</p> <p> SST = K</p> <p> </p> <p> The standard deviation of the anomalous values of each grid point. TIMESCALE = [M|S|A] </p> <p> where M stands for monthly, S for seaonal (MAM, JJA, SON, and DJF), A for annnual. </p> <p> </p> <p> 4. [VARNAME]_[TIMESCALE]ASTD</p> <p> </p> <p> Unit: ICEFRAC = None</p> <p> TAUX = (N / m^2)^2</p> <p> TAUY = (N / m^2)^2</p> <p> SST = K^2</p> <p> </p> <p> The variance of the anomalous values of each grid point. TIMESCALE = [M|S|A] </p> <p> where M stands for monthly, S for seaonal (MAM, JJA, SON, and DJF), A for annnual. </p> <p> </p> <p># Filename: atm_analysis_mean_var_[T|U].nc</p> <p> </p> <p> Description: This file contains the mean, standard deviation of the denoted field.</p> <p> VARNAME = [T|U]</p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. [VARNAME]_[TIMESCALE]M</p> <p> </p> <p> Unit: T = K</p> <p> U = m / s</p> <p> </p> <p> The mean values of each grid point. TIMESCALE = [M|A] where M stands for monthly, A for annnual. </p> <p> </p> <p> 2. [VARNAME]_[TIMESCALE]ASTD</p> <p> </p> <p> Unit: T = K</p> <p> U = m / s</p> <p> </p> <p> The standard deviation of the anomalous values of each grid point. TIMESCALE = [M|A] </p> <p> where M stands for monthly, A for annnual. </p> <p> </p> <p> 3. [VARNAME]_[TIMESCALE]AVAR</p> <p> </p> <p> Unit: T = K</p> <p> U = m / s</p> <p> </p> <p> The variance of the anomalous values of each grid point. TIMESCALE = [M|A] </p> <p> where M stands for monthly, A for annnual. </p> <p> </p> <p># Filename: atm_analysis_SST_CORR.nc</p> <p> </p> <p> Description: This file contains the year-to-year correlation of monthly anomalous SST.</p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. CORR(months, Ny, Nx)</p> <p> </p> <p> Unit: None</p> <p> </p> <p> The year-to-year correlation of monthly anomalous SST.</p> <p> </p> <p># Filename: ice_analysis_mean_anomaly_[aice|vice].nc</p> <p> </p> <p> Description: This file contains the mean, standard deviation of the denoted field.</p> <p> VARNAME = [aice|vice].</p> <p> </p> <p> The variables are exactly of the same structure as described in </p> <p> atm_analysis_mean_anomaly_[VARNAME].nc</p> <p> </p> <p> The unit for aice = None</p> <p> The unit for vice = m</p> <p> </p> <p># Filename: ocn_analysis_mean_anomaly_STRAT.nc</p> <p> </p> <p> Description: This file contains the mean, standard deviation of the denoted field.</p> <p> STRAT is the difference of mean ocean temperatures T_top - T_bot.</p> <p> T_top is the mean temperature of the top 50m of the ocean where as</p> <p> T_bot is the mean temperature of the ocean between depth 50m to 503.7m.</p> <p> </p> <p> The variables are exactly of the same structure as described in </p> <p> atm_analysis_mean_anomaly_[VARNAME].nc</p> <p> </p> <p> The unit for STRAT = K</p> <p> </p> <p># Filename: atm_analysis_AHT_OHT.nc</p> <p> </p> <p> Description: This file contains the indirectly derived atmosphere heat transport.</p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. AHT(time, lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The monthly atmospheric heat transport.</p> <p> </p> <p> 2. AHT_AM(year, lat_bnd) </p> <p> </p> <p> Unit: W</p> <p> </p> <p> The annual atmospheric heat transport.</p> <p> </p> <p> 3. AHT_MEAN(lat_bnd) </p> <p> </p> <p> Unit: W</p> <p> </p> <p> The time-averaged atmospheric heat transport.</p> <p> </p> <p> 4. AHT_TFLX_CONV(time, lat) </p> <p> </p> <p> Unit: W / m</p> <p> </p> <p> The monthly-zonally-averaged atmospheric heat convergence.</p> <p> </p> <p> 5. AHT_TFLX_CONV_MEAN(lat) </p> <p> </p> <p> </p> <p> Unit: W</p> <p> </p> <p> The time-zonally-averaged atmospheric heat convergence.</p> <p> </p> <p># Filename: ocn_analysis_OHT.nc</p> <p> </p> <p> Description: This file contains the derived ocean heat transport.</p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. ADVT(time, lat)</p> <p> </p> <p> Unit: K / s / m^3</p> <p> </p> <p> The monthly vertically-integrated temperature tendency due to advection and horizontal diffusion.</p> <p> </p> <p> 2. ADVT_MEAN(lat)</p> <p> </p> <p> Unit: K / s / m^3</p> <p> </p> <p> The time-averaged ADVT.</p> <p> </p> <p> 3. OHT(time, lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The total monthly ocean heat transport.</p> <p> </p> <p> 4. OHT_MEAN(lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The time-average of OHT.</p> <p> </p> <p> 5. OHT_ADVT(time, lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The monthly ocean heat transport due to advection and horizontal diffusion.</p> <p> </p> <p> 6. OHT_ADVT_MEAN(time, lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The time-averaged of OHT_ADVT.</p> <p> </p> <p> 7. OHT_ADVT(time, lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The monthly ocean heat transport due to advection and horizontal diffusion.</p> <p> </p> <p> 8. OHT_ADVT_MEAN(lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The time-averaged of OHT_ADVT.</p> <p> </p> <p> </p> <p> 9. OHT_WKRSTT(time, lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The monthly ocean heat transport due to weak-restoring.</p> <p> </p> <p> 10. OHT_WKRSTT_MEAN(lat_bnd)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The time-averaged of OHT_WKRSTT.</p> <p> </p> <p> 11. SHF(time, lat)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The monthly surface heat flux.</p> <p> </p> <p> 12. SHF_MEAN(lat)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The time-average of SHF.</p> <p> </p> <p> 13. WKRSTT(time, lat)</p> <p> </p> <p> Unit: K / s / m^2</p> <p> </p> <p> The vertically integrated monthly weak-restoring.</p> <p> </p> <p> 14. WKRSTT_MEAN(lat)</p> <p> </p> <p> Unit: W</p> <p> </p> <p> The time-average of WKRSTT.</p> <p> </p> <p> </p> <p>---------------------------------------------</p> <p>DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/importance_of_KH</p> <p>---------------------------------------------</p> <p> </p> <p>This directory conatins the average of CAM4 and EMOM output of field during year 21-30.</p> <p> </p> <p>The meaning of the variable can be found in official website</p> <p>https://www.cesm.ucar.edu/models/cesm1.0/cam/docs/ug5_1/hist_flds_fv_cam4_trop_bam.html</p> <p> </p> <p> </p> <p>---------------------------------------------</p> <p>DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/ocean_mean_temp</p> <p>---------------------------------------------</p> <p> </p> <p>This directory conatins the annual average of ocean mean temperature of the top 33 layers (507.33m) in</p> <p>the EXP run (sea-ice loss run)</p> <p> </p> <p># Filename: paper2021_[MODEL_NAME]_EXP.ocn_mean_T.nc</p> <p> </p> <p> Description: This file contains the annual average of ocean mean temperature of the top 33 layers (507.33m).</p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. TEMP(time, Nz)</p> <p> </p> <p> Unit: degC</p> <p> </p> <p> Ocean temperature.</p> <p> </p> <p> </p> <p> 2. SALT(time, Nz)</p> <p> </p> <p> Unit: kg / m^3 (PSU)</p> <p> </p> <p> Ocean salinity.</p> <p> </p> <p> </p> <p>---------------------------------------------</p> <p>DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/ocean_heat_content_trend</p> <p>---------------------------------------------</p> <p> </p> <p>This directory conatins the difference of the ocean state between the year 181 and year 81 of the EXP run.</p> <p> </p> <p># Filename: OHC_diff_[MODEL_NAME].nc</p> <p> </p> <p> Description: The difference of the ocean state between the year 181 and year 81 of the EXP run.</p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. TEMP(time, Nz)</p> <p> </p> <p> Unit: degC</p> <p> </p> <p> Ocean temperature.</p> <p> </p> <p>---------------------------------------------</p> <p>DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/vice_target_file</p> <p>---------------------------------------------</p> <p> </p> <p>This directory conatins the sea-ice forcing used to derive Q-flux (CTL) and the</p> <p>forcing applied in EXP run. </p> <p> </p> <p># Filename: forcing.vice.[GRID].paper2021_[RUN]_POP2.nc</p> <p> </p> <p> Description: This the sea-ice forcing used in the [RUN] in the grid of [GRID].</p> <p> GRID = [f09|gx1v6]</p> <p> [RUN] = [CTL|EXP]</p> <p> </p> <p> Variable list:</p> <p> </p> <p> 1. vice_target(time, nlat, nlon)</p> <p> </p> <p> Unit: m^3 / m^2 (volume density)</p> <p> </p> <p> Total ice volume.</p>
Dataset for "Sensitivity of subregional distribution of socioeconomic conditions to the global assessment of water scarcity"
<p>This dataset contains the data related to the final analysis for "Sensitivity of subregional distribution of socioeconomic conditions to the global assessment of water scarcity".</p>
An Ordovician to Silurian graptolite specimen image dataset for global correlation and shale gas exploration
<p>A unique high-resolution image dataset consists of key graptolite species used for dating rocks, global correlation, and “gold caliper” for locating shale gas favourable exploration beds (FEBs) in China.</p> <p>All images were taken from 1,550 carefully curated graptolite specimens, taxonomically belong to 113 graptolite species or subspecies. These specimens were collected from 154 representative geological sections of the Ordovician to Silurian sediments of China and published in 1958-2020. All specimens are housed at the Nanjing Institute of Geology and Palaeontology (NIGP), Chinese Academy of Sciences (CAS). Detailed scientific information of every piece of fossil specimen is given in the attached spreadsheet file.</p> <p>My working group spent over two years to complete photographing every specimen using a single-lens reflex camera Nikon D800E with Nikkor 60 mm macro-lens and Leica M125 and M205C microscopes equipped with Leica cameras. Every image is well focused and better shows the morphology of graptolite bodies.</p> <p>In total, we took 40,597 images, including 20,644 camera photos (each with a resolution of 4,912 × 7,360) and 19,953 microscope photos (each with a resolution of 2,720 × 2,048). Photos of low contrast or bad focus were removed from the whole collection. We only kept and selected the photos that show the visual morphology of every specimen and the diagnostic character of each graptolite species that the specimens represent. We selected one image for each specimen as the present final dataset, uploaded to and stored in our cloud server.</p> <p>We incorporated revision suggestions from distinguished palaeontologists to generate the ground-truth labels, providing a taxonomical authority of the dataset. The dataset potentially contributes to a range of scientific activities and provides 1) easy access to high-resolution images of 2951 specimens of 113 graptolite species for teaching and training in palaeontology and geologic survey; 2) Global bio-stratigraphic correlation using graptolites, especially with those bio-zone species; 3) A standard fossil specimen image dataset used in shale gas industry to improve exploration efficiency, and 4) The potential aid of developing image-based automated classification model.</p> <p>Every specimen has two photos, one is original, another shows specimen with a scale bar. Occasionally in some large image the scale bar is embedded and beside the fossil specimen. Example: The file name: ‘9721Cardiograptus_amplus_S.jpg’, ‘9721’ is the specimens number, ‘Cardiograptus_amplus’ means species name is ‘Cardiograptus amplus’, with ‘_S’ means it is a photo with scale bar. In all scale bar, the minimum unit is millimeter.</p> <p>Author and contact:</p> <p>Hong-He Xu</p> <p>Nanjing Institute of Geology and Palaeontology, Chinese Academy of Sciences</p> <p>39 East Beijing Road, Nanjing, 210008</p> <p>China</p> <p>E-mail: hhxu@nigpas.ac.cn</p>
Datasets from: The distribution of covert natural enemies of a globally invasive crop pest, the fall armyworm, in Africa; enemy-release and spillover events
<p>These datasets are for the analyses carried out in paper in Journal of Animal Ecology titled 'The distribution of covert natural enemies of a globally invasive crop pest, the fall armyworm, in Africa; enemy-release and spillover events.' The authors of the paper are Amy J. Withers, Annabel Rice, Jolanda de Boer, Philip Donkersley, Aislinn J. Pearson, Gilson Chipabika, Patrick Karangwa, Bellancile Uzayisenga, Benjamin A. Mensah, Samuel Adjei Mensah, Phillip Obed Yobe Nkunika, Donald Kachigamba, Judith A. Smith, Christopher M. Jones and Kenneth Wilson<span>.</span></p> <p><span><span> </span></span><span>Invasive species pose a significant threat to biodiversity and agriculture worldwide, and here we investigated the prevalence of natural enemies in fall armyworm</span><span> (</span><em>Spodoptera frugiperda</em><span>) </span><span>in Africa</span><span>. </span><span>This study aimed to identify which microbial pathogens are present in invasive fall armyworm, and determine the geographical, meteorological, and temporal variables that influence prevalence. </span><span>Larval samples were screened from Malawi, Rwanda, Kenya, Zambia, Sudan, and Ghana for the presence of four different microbial natural enemies; two nucleopolyhedroviruses, Spodoptera frugiperda NPV (SfMNPV) and Spodoptera exempta NPV (SpexNPV); the fungal pathogen </span><em>Metarhizium rileyi</em><span>;</span><span> and the bacterium </span><em>Wolbachia</em><span>. One dataset (</span>ALL_diseaseprevalence_year_season<span>) includes the results of this screening for all four microbial nartural enemies and sampling information, the other dataset (</span>SfMNPVprevalence_weather_topographic_temporal_variables<span>) includes the results for SfMNPV and sampling information alongside variables relating to temperature, rainfall, elevation, growing season and time since the fall armyworm first arrived in each country. These variables were used to investigate whether SfMNPV prevalence was affected by</span><span> </span><span>geographical, meteorological or temporal variables.</span></p>
Dataset presented in the recently submitted AGU manuscript "A multi-resolution finite-element approach for global electromagnetic induction modeling with application to southeast China coastal geomagnetic observatory studies"
<p>Dataset presented in the recently submitted AGU paper "A multi-resolution finite-element approach for global electromagnetic induction modeling with application to southeast China coastal geomagnetic observatory studies"</p>
[Dataset] Determining the origin of tidal oscillations in the ionospheric transition region with EISCAT radar and global simulation data
<p>Preprocessed data</p>
Long-term (2003-2020) hourly 0.25° global PM2.5 dataset (DeepCAMS) Part-2: 2012-2020
<p>This is part II (2012-2020) of our DeepCAMS.</p> <p>Part I (2003-2011) can be found at: https://doi.org/10.5281/zenodo.6967082</p> <p>Usage: The raw data -- (scaling factor: 0.1) --> the true PM2.5 concentration</p> <p>Paper title: Generating a Long-term (2003-2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS)</p> <p>Paper doi: <a href="https://doi.org/10.1016/j.scitotenv.2022.157747">https://doi.org/10.1016/j.scitotenv.2022.157747</a></p> <pre>If you find our work helpful, please cite it. Thank you very much!</pre>
The global land fAOD dataset (2001–2020) using DLFE-Satellite
<p>The fAOD is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Global fAOD can be obtained from point-scale Aerosol Robotic Network (AERONET) measurements, but the spatial coverage is limited. To obtain spatially-continuous global fAODs, we applied the DLFE-Satellite method with Moderate Resolution Imaging Spectroradiometer (MODIS) data, ERA5 climate reanalyses, and AERONET data from 2001 to 2020 as inputs.</p> <p>For independent validation, we used fAOD retrievals obtained from six Surface Radiation budget network (SURFRAD) sites in the USA. Note that SURFRAD sites are in the vicinity of AERONET sites and were not used to train the model. Data from these sites could thus be used for the independent validation of fAOD estimates. The linear relation between DLFE-Satellite and AERONET retrievals had an R value of 0.93 and RMSE of 0.09 μg/m<sup>3 </sup>; for validation with SURFRAD retrievals , R = 0.77 and RMSE = 0.14 μg/m<sup>3</sup>.</p>
Dataset for global sensitivities of reactive N and S gas and particle concentrations and deposition to precursor emissions reductions
<p>This data set contains model outputs and python scripts for the analysis presented at the paper: Global sensitivities of reactive N and S gas and particle concentrations and deposition to precursor emissions reductions.</p>
ScienceDex guides
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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.