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3,592 results for “Grid”
Accessibility Indicators to services at EU scale - 1km grid indicators
<p>This archive makes available <strong>accessibility indicators at EU scale from populated 1km EU grid to towns and cities at EU scale</strong> (512 million travel time by car calculated between origins and destinations). It follows a reproducible, transparent and updatable framework. It uses <strong>only open source and free routing engines (OSRM)</strong>, based on OpenStreetMap (OSM) network. This routing engine makes possible the creation of travel time indicators for a large set of origins and destinations.</p> <p>The EU towns and cities layer has been recently made available and named by the European Commission. This layer is based on a <a href="https://ec.europa.eu/regional_policy/information-sources/maps/urban-centres-towns_en">common methodology</a> for all Europe. Within GRANULAR activities, we consider the towns and cities layer as <strong>a proxy</strong> to discuss on little and medium commercial centralities in Europe.</p> <p>This methodological framework, <strong>implemented with open source solutions (data and code) only and documented in a reproducible way in R notebooks</strong>, could be easily extended to other origins and destinations, if a relevant layer will be identified in the future.</p> <p>Based on travel time matrix, it is possible to compute a large set of indicators. This archive (see readme at the root folder) <strong>describes the input data used, summarises the data processing and provide information and metadata on output indicators created at 1km grid cells.</strong></p> <p>All the output data is also available. </p> <p> </p>
Gridded active layer thickness across northern permafrost regions from 2003 to 2020
<ol><li>This dataset provides the annual gridded active layer thickness (ALT) across northern permafrost regions (NPR) at 1 km resolution for the period 2003–2020. The ALT in permafrost regions refers to the upper layer of soil that thaws and refreezes annually as a result of seasonal temperature variations. It is a critical parameter in permafrost studies because it determines the depth to which plant roots can penetrate and influences various ecological and engineering processes. This dataset was produced based on the relationship between available ALT site measurements (2966 site-years) and satellite observations of annual gridded predictors, including vegetation, temperature, soil, and topography, using a Random Forest (RF) approach. The annual ALT map for the NPR was generated using the ensemble mean of ALT from the ten best RF predictions. Extensive uncertainty analysis was also conducted. </li><li>The dataset can be viewed at https://liuzh833.users.earthengine.app/view/altv1</li></ol>
Thermally switchable, bifunctional, scalable, mid-infrared metasurfaces with VO2 grids capable of versatile polarization manipulation and asymmetric transmission
<p>The data generated by CST Studio Suite that are used to plot a part of the figures, and sample CST scripts. </p> <p>Research supported by Narodowe Centrum Nauki, project no UMO-2020/39/I/ST3/02413. </p>
Plant Atlas 2020 — British and Irish vascular plant and charophyte 2 x 2 km grid square locations up to 2019
<p><span>This resource provides the data behind the 2 × 2 km grid square (tetrad) British and Irish distribution maps, for 3,431 taxa, presented in both the Plant Atlas 2020 book and website (</span><a href="http://www.plantatlas2020.org"><span><span>www.plantatlas2020.org</span></span></a><span><span>), up to 2019. These are presence-only data, indicating where a taxon was reported from a tetrad</span></span><span>. These 2 km square presences are based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s.</span></p>
Plant Atlas 2020 — British and Irish vascular plant and charophyte 10 x 10 km grid square locations, subdivided by survey period, up to 2019
<p><span>This resource provides the data behind the 10 × 10 km grid square (hectad) British and Irish distribution maps, for 3,497 taxa, presented in both the Plant Atlas 2020 book and website (</span><a href="http://www.plantatlas2020.org"><span><span>www.plantatlas2020.org</span></span></a><span><span>), subdivided by time period<a><span>.</span></a> These are presence-only data, indicating where a taxon was reported from a hectad, within a given</span><span><span></span></span></span><span> multi-year period, up to 2019. These time periods cover the 20<sup>th</sup> Century, but also extend back to the earliest botanical records known for Britain and Ireland in the first period (pre-1930). These 10 km square presences are based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s.</span></p>
Reference grids (vector) and their centroids for harmonization of analysis
<p>This dataset contains reference grids (vector) and their centroids for harmonized analysis. All the files are <em>geoparquet</em>, they are described below. Production procedure is available at projects GitHub repository (https://github.com/aavotins/HiQBioDiv/blob/main/Templates/TemplateGrids_Vector.R):</p> <ul> <li>"tikls100_sauzeme.parquet" contains terrestrial territory of Latvia divided in 100-by-100 m polygon grid cells. Contains fields: <ul> <li>"id" feature ID;</li> <li>"yes" a fields with values "1";</li> <li>"tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"rinda300" with ID's matching file "tikls300_sauszeme.parquet";</li> <li>"ID1km" with ID's matching file "tikls1km_sauszeme.parquet";</li> <li>"rinda500" with ID's matching file "tikls500_sauszeme.parquet";</li> <li>"geom" a {sf} geometry definition field.</li> </ul> </li> <li>"tikls300_sauzeme.parquet" contains terrestrial territory of Latvia divided in 300-by-300 m polygon grid cells. Contains fields: <ul> <li>"rinda300" feature ID;</li> <li>"x" a {sf} geometry definition field.</li> </ul> </li> <li>"tikls500_sauzeme.parquet" contains terrestrial territory of Latvia divided in 500-by-500 m polygon grid cells. Contains fields: <ul> <li>"rinda500" feature ID;</li> <li>"tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"x" a {sf} geometry definition field.</li> </ul> </li> <li>"tikls1km_sauzeme.parquet" contains terrestrial territory of Latvia divided in 1000-by-1000 m polygon grid cells. Contains fields: <ul> <li>"ID1km" feature ID;</li> <li>"yes" a fields with values "1";</li> <li>"tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"geometry" a {sf} geometry definition field.</li> </ul> </li> <li>"pts100_sauszeme.parquet" contains centroids of file "tikls100_sauszeme.parquet" with their attribute fields;</li> <li>"pts300_sauszeme.parquet" contains centroids of file "tikls300_sauszeme.parquet" with their attribute fields and additionally "tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"pts500_sauszeme.parquet" contains centroids of file "tikls500_sauszeme.parquet" with their attribute fields;</li> <li>"pts1000_sauszeme.parquet" contains centroids of file "tikls1km_sauszeme.parquet" with their attribute fields;</li> <li>"tks93_50km.parquet" contains topographic map of Latvia pages (TKS-93 M:50000). Contains fields: <ul> <li>"NOSAUKUMS" with a page name;</li> <li>"NUMURS" with a page number;</li> <li>"Shape_Length" an attribute from ESRI File Geodatabase;</li> <li>"Shape_Area" an attribute from ESRI File Geodatabase;</li> <li>"Shape" a {sf} geometry definition field;</li> </ul> </li> <li>All the above mentioned files are stored also as layers in geopakage file "vector_grids.gpkg" having the same names and attributes.</li> </ul>
Modelled gridded population estimates for the Kasaï-Oriental Province in the Democratic Republic of Congo (2024) version 4.2
<h2><strong>Content</strong></h2> <p>This repository contains the input data and scripts used to create the modeled gridded population estimates for Kasaï-Oriental Province in the Democratic Republic of Congo. It also includes the grid-cell posterior distributions and scripts to aggregate them within user-defined geographic boundaries.</p> <p> In particular, this repository contains two compressed files (.zip):</p> <p><strong>1. <code>population_estimates.zip</code></strong></p> <ul> <li>Includes raster files (<code>.tif</code>) with summaries of population count posterior predictions at the grid-cell level, specifically the mean, median, lower credible interval, and upper credible interval.</li> <li>Includes spatial files (<code>.gpkg</code>) with summaries of population count posterior predictions at the health-area and health-zone levels, specifically the mean, median, lower credible interval, and upper credible interval.</li> </ul> <p><strong>2. <code>population_model.zip</code></strong></p> <p>This directory comprises five subdirectories with scripts, input data, and output data necessary to replicate the population model:</p> <ul> <li><code><strong>01_model_stan</strong></code>: Contains the Stan model, input data, and an R script (<code>01_model_stan.R</code>) with a function to run the model.</li> <li><code><strong>02_model_run</strong></code>: Includes an R script (<code>02_model_run.R</code>) for running the model, along with output data.</li> <li><code><strong>03_model_evaluate</strong></code>: Features a Quarto report template (<code>03_model_evaluate.qmd</code>) and model evaluation summary files(.pdf).</li> <li><code><strong>04_predict_posterior</strong></code>: Provides R scripts (<code>04_predict_posterior.R</code> and <code>04_predict_run.R</code>) for generating predictions, along with input and output data, namely the posterior predictions files (.rds).</li> <li><code><strong>05_aggregate_posterior</strong></code>: Contains R scripts (<code>05_aggregate_posterior.R</code> and <code>05_aggregate_run.R</code>) and associated input and output data, namely the population count posterior summaries as presented in the file <code>population_estimates.zip</code> .</li> </ul> <p>The work was carried out in <code>R</code> (version 4.4.0), with the packages <code>tidyverse</code> (version 2.0.0), <code>terra</code> (version 1.7-78), <code>sf</code> (version 1.0-16), <code>furrr</code> (version 0.3.1), <code>doParallel</code> (version 1.0.17), <code>foreach</code> (version 1.5.2), <code>rstudioapi</code> (version 0.16.0), and <code>rstan</code> (version 2.32.6), on macOS Sequoia (version 15.1.1). While the scripts are designed to be portable, minor adjustments may be required for compatibility with other operating systems.</p> <h2><strong>Important</strong></h2> <p>This version includes changes in the STAN model <code>10h_survey_survey_covariate_building_random_effect_hierarchy_building_covariate_density_fixed_effect_hierarchy_density.stan</code>. Consequentely, all the files generated in the previous versions are now changed.</p> <p> </p> <p>For inquiries regarding the model and the data, please contact Gianluca Boo at gianluca.boo@soton.ac.uk.</p>
500-meter grid of Derived Soil Profiles (DSP) for Italy - SuoliCella500
<p>National database of Italian Soil Typological Units (STU) and corresponding Derived Soil Profiles (DSP) obtained on a 500 meters grid (1,109,672 points) by neural network. The most probable WRB Reference Soil Group (RSG), WRB Qualifiers, and USDA textural soil types were mapped on the 500 meters grid, by neural network. 18,707 Observed soil profiles and the respective 33,014 Soil Horizons were grouped into 4,472 STUs based on the combinations of Soil Region, WRB Reference Soil Group (RSG), WRB Qualifiers, and USDA textural soil types obtained on the 500 meters grid. Statistics were calculated (Mean Value, Standard Deviation Value, and Numerosity) for soil rooting depth and for the most common analytical parameters of the soil horizons (Coarse fragment content fraction; pH in water; Carbon (C) - organic; Carbonate (CO3--) - Total; Clay, Sand, and Silt fraction; Granulometry; Textural soil types). The 500 meters grid adopts EPSG 23032 (ED50 UTM-32). A reference scale of 1:250.000 may be attributed to the 500-meters grid map, on the base of the numerosity of DSP produced for the whole italian territory.</p>
A Phanerozoic gridded dataset for palaeogeographic reconstructions
<p>This repository provides access to five pre-computed reconstruction files as well as the static polygons and rotation files used to generate them. This set of palaeogeographic reconstruction files provide palaeocoordinates for three global grids at H3 resolutions 2, 3, and 4, which have an average cell spacing of ~316 km, ~119 km, and ~45 km, respectively. Grids were reconstructed at a temporal resolution of one million years throughout the entire Phanerozoic (540–0 Ma). The reconstruction files are stored as comma-separated-value (CSV) files which can be easily read by almost any spreadsheet program (e.g. Microsoft Excel and Google Sheets) or programming language (e.g. Python, Julia, and R). In addition, R Data Serialization (RDS) files—a common format for saving R objects—are also provided as lighter (and compressed) alternatives to the CSV files. The structure of the reconstruction files follows a wide-form data frame structure to ease indexing. Each file consists of three initial index columns relating to the H3 cell index (i.e. the 'H3 address'), present-day longitude of the cell centroid, and the present-day latitude of the cell centroid. The subsequent columns provide the reconstructed longitudinal and latitudinal coordinate pairs for their respective age of reconstruction in ascending order, indicated by a numerical suffix. Each row contains a unique spatial point on the Earth's continental surface reconstructed through time. NA values within the reconstruction files indicate points which are not defined in deeper time (i.e. either the static polygon does not exist at that time, or it is outside the temporal coverage as defined by the rotation file).</p> <p>The following five Global Plate Models are provided (abbreviation, temporal coverage, reference) within the GPMs folder:</p> <ul> <li>WR13, 0–550 Ma, (Wright et al., 2013)</li> <li>MA16, 0–410 Ma, (Matthews et al., 2016)</li> <li>TC16, 0–540 Ma, (Torsvik and Cocks, 2016)</li> <li>SC16, 0–1100 Ma, (Scotese, 2016)</li> <li>ME21, 0–1000 Ma, (Merdith et al., 2021)</li> </ul> <p>In addition, the H3 grids for resolutions 2, 3, and 4 are provided within the grids folder. Finally, we also provide two scripts (python and R) within the code folder which can be used to generate reconstructed coordinates for user data from the reconstruction files.</p> <p>For access to the code used to generate these files:</p> <p><a href="https://github.com/LewisAJones/PhanGrids">https://github.com/LewisAJones/PhanGrids</a></p> <p>For more information, please refer to the article describing the data:</p> <p>Jones, L.A. and Domeier, M.M. 2024. A Phanerozoic gridded dataset for palaeogeographic reconstructions. (2024).</p> <p>For any additional queries, contact: </p> <p>Lewis A. Jones (lewisa.jones@outlook.com) or Mathew M. Domeier (mathewd@uio.no)</p> <p>If you use these files, please cite: </p> <p>Jones, L.A. and Domeier, M.M. 2024. A Phanerozoic gridded dataset for palaeogeographic reconstructions. DOI: <a href="../doi/10.5281/zenodo.10069221">10.5281/zenodo.10069221</a></p> <p><strong>References </strong></p> <ol> <li>Matthews, K. J., Maloney, K. T., Zahirovic, S., Williams, S. E., Seton, M., & Müller, R. D. (2016). Global plate boundary evolution and kinematics since the late Paleozoic. <em>Global and Planetary Change</em>, 146, 226–250. <a href="https://doi.org/10.1016/j.gloplacha.2016.10.002">https://doi.org/10.1016/j.gloplacha.2016.10.002</a>.</li> <li>Merdith, A. S., Williams, S. E., Collins, A. S., Tetley, M. G., Mulder, J. A., Blades, M. L., Young, A., Armistead, S. E., Cannon, J., Zahirovic, S., & Müller, R. D. (2021). Extending full-plate tectonic models into deep time: Linking the Neoproterozoic and the Phanerozoic. <em>Earth-Science Reviews</em>, 214, 103477. <a href="https://doi.org/10.1016/j.earscirev.2020.103477">https://doi.org/10.1016/j.earscirev.2020.103477</a>.</li> <li>Scotese, C. R. (2016). Tutorial: PALEOMAP paleoAtlas for GPlates and the paleoData plotter program: PALEOMAP Project, Technical Report.</li> <li>Torsvik, T. H., & Cocks, L. R. M. (2017). Earth history and palaeogeography. <em>Cambridge University Press</em>. <a href="https://doi.org/10.1017/9781316225523">https://doi.org/10.1017/9781316225523</a>.</li> <li>Wright, N., Zahirovic, S., Müller, R. D., & Seton, M. (2013). Towards community-driven paleogeographic reconstructions: Integrating open-access paleogeographic and paleobiology data with plate tectonics. <em>Biogeosciences</em>, 10, 1529–1541. <a href="https://doi.org/10.5194/bg-10-1529-2013">https://doi.org/10.5194/bg-10-1529-2013</a>.</li> </ol>
Elevating Cybersecurity for Smart Grid Systems—A Container-Based Approach Enhanced by Machine Learning
<p>README<br>Title<br>Elevating Cybersecurity for Smart Grid Systems—A Container-Based Approach Enhanced by Machine Learning</p> <p>Authors<br>Mays Abukeshek, School of Computer Science, Faculty of Technology, University of Sunderland, University of Huddersfield, UK<br>Email: mays.abukeshek@sunderland.ac.uk, Mays.abukeshek@hud.ac.uk<br>Basel Barakat, School of Computer Science, Faculty of Technology, University of Sunderland, UK<br>Email: basel.barakat@sunderland.ac.uk<br>Bamidele Ajayi, School of Computer Science, Faculty of Technology, University of Sunderland, UK<br>Email: bamidele.ajayi@research.sunderland.ac.uk<br>Abstract<br>This dataset supports the paper "Elevating Cybersecurity for Smart Grid Systems—A Container-Based Approach Enhanced by Machine Learning," which presents a comprehensive implementation of a cybersecurity solution for smart grid network containers. The methodology utilizes:</p> <p>Qualys API-based vulnerability scanning and reporting system for vulnerability identification<br>Docker deployment for security and isolation<br>Advanced load balancing techniques for resource optimization<br>Machine learning-powered anomaly detection for threat identification and vulnerability prioritization.<br>The dataset contains details of several simulated attacks enabling effective training and evaluation of a robust machine-learning model.</p> <p>Data Description<br>The dataset includes logs from conducted attacks on containerized nodes, generated to reflect real-world scenarios. The simulated attacks include:</p> <p>Denial of Service (DoS)<br>Remote-to-Local (R2L)<br>User-to-Root (U2R)<br>Probes<br>Contents<br>Csv_file.csv: This file contains the dataset used for training and evaluating the machine learning models. The columns in the dataset represent various features and results of the simulated attacks.<br>Data Columns and Rows<br>Timestamp:</p> <p>Description: The exact date and time when the data was recorded.<br>time: 2023-06-01 12:00:00</p> <p>Attack_Type:</p> <p>Description: The type of cyber-attack conducted.<br>Possible Values: DoS, R2L, U2R, Probe<br>Example: DoS<br>Notes: Categorizes the type of attack, crucial for training classification models.<br>CPU_Utilization (%):</p> <p>Description: The percentage of CPU resources used during the attack.<br>Example: 52.3<br>Notes: Indicates the load on the CPU during the attack, useful for assessing the impact of attacks on system performance.<br>Memory_Utilization (%):</p> <p>Description: The percentage of memory resources used during the attack.<br>Example: 63.4<br>Notes: Shows memory usage which can be a critical factor in understanding system performance under attack conditions.<br>Network_Bandwidth (Mbps):</p> <p>Description: The bandwidth of the network in Megabits per second.<br>Example: 100<br>Notes: Reflects the network load and is essential for analyzing the impact on network performance.<br>Vulnerabilities_Detected:</p> <p>Description: The number of vulnerabilities detected during the attack.<br>Example: 289<br>Notes: Indicates the effectiveness of the vulnerability scanning process and the system's exposure to threats.<br>Mean_Response_Time (ms):</p> <p>Description: The average response time in milliseconds during the attack.<br>Example: 87<br>Notes: Important for evaluating the responsiveness of the system under attack conditions.<br>Throughput (requests/second):</p> <p>Description: The number of requests the system can handle per second during the attack.<br>Example: 1068<br>Notes: Measures the capacity and efficiency of the system under load.<br>Example Row<br>Timestamp Attack_Type CPU_Utilization (%) Memory_Utilization (%) Network_Bandwidth (Mbps) Vulnerabilities_Detected Mean_Response_Time (ms) Throughput (requests/second)<br>2023-06-01 12:00:00 DoS 52.3 63.4 100 289 87 1068<br>Usage<br>This dataset can be used to:</p> <p>Train and evaluate machine learning models for cybersecurity applications in smart grid systems.<br>Analyze the performance of different machine learning models in detecting and prioritizing vulnerabilities.<br>Understand the impact of various types of cyber-attacks on containerized environments.<br>Methodology<br>The dataset was created using a combination of Qualys API-based vulnerability scanning and Docker containerization. Multiple container clusters were subjected to various simulated attacks, and the performance of machine learning models was evaluated based on accuracy, precision, recall, and F1-scores.</p> <p>Acknowledgments<br>This research was supported by the University of Sunderland and the University of Huddersfield.</p> <p>References<br>Please refer to the full paper for detailed methodology, implementation, and analysis:<br>IEEE</p>
Magnetic Anomaly Map of Paraná State - Final gridded data
<p>This gridded data is part of the article entitled: "THE MAGNETIC ANOMALY MAP OF PARANÁ STATE: AN<br>INTEGRATION OF AIRBORNE SURVEYS PERFORMED OVER THE YEARS", which was submitted in December 2023 to the Brazilian Journal of Geophysics. The article is still under review. </p> <p>These files include airborne magnetic data integrated at 1800m altitude, and the upwarded data to 2700m. Details of the integration and general interpretations are described in the related article.</p>
Grids
<p>Grid and grid cells mapping the geographic extent of the EAMENA database. Extended to Afghanistan</p>
ForestAge-Constrained Eddy-Covariance Gridded NEP Product
<p><strong>Description</strong></p> <p>This repository holds global spatial estimates of the Net Ecosystem Productivity of forests (NEP), circa 2010, for a grid spacing of 0.5° by 0.5º pixel size. Three different approaches were used to create the maps.</p> <ol> <li> <p><strong>Model M1 (Regional Age–NEP Relationships Per Biome)</strong>: This model scales site-level NEP observations to a global gridded field using biome-specific NEP-age curves and site-level anomalies. The random forest model (RF1) is trained on forest age, GPP, temperature, and nitrogen deposition, producing NEP anomalies that reflect site-specific deviations from biome-wide trends. Gridded predictor fields of forest age, GPP, temperature (MAT), and nitrogen deposition are used to create 0.5° by 0.5° NEP grids, with uncertainties estimated using an ensemble of 180 members. The data from Model M1 can be investigated from the ForestAge_EC_NEP_M1_v1.0.nc file.</p> </li> <li> <p><strong>Model M2 (Global Age–NEP Relationship)</strong>: This model uses a random forest algorithm (RF2) to upscale NEP observations but applies a global NEP-age relationship across all sites. It uses the same gridded predictor fields as M1—forest age, GPP, MAT, and nitrogen deposition—but the age–NEP relationship is determined globally. Uncertainty is calculated similarly to M1, using ensembles of model parameters and predictor fields. The data from Model M2 can be investigated from the ForestAge_EC_NEP_M2_v1.0.nc file.</p> </li> <li> <p><strong>Model M3 (Without Age Consideration)</strong>: This model predicts NEP solely based on GPP, MAT, and nitrogen deposition without accounting for forest age. It follows a similar approach to RF3 models from previous work and uses the same gridded predictors and uncertainty estimation methods as M1 and M2. The data from Model M3 can be investigated from the ForestAge_EC_NEP_M3_v1.0.nc file.</p> </li> </ol> <p>The variation across each model's members can assess the uncertainty in each model, which represents uncertainty caused by input variables and the k-fold cross-validation approach. </p> <p>More details about the methodologies behind the three approaches can be found in Ciais, P., Yao, Y. Besnard, S. et al. (2024) (see reference below).</p> <p><strong>Data structure</strong></p> <p>The datasets are stored in <strong>NetCDF format</strong> with a structure consistent across the different models (M1, M2, M3). Each file contains multiple variables representing components of the Net Ecosystem Production (NEP) estimates, such as the mean NEP and its quantiles. The primary variables are:</p> <ul> <li><strong>NEP_MX_mean</strong>: The mean estimate of NEP for each model (M1, M2, M3), with units of grams of carbon per square meter per year (gC m⁻² year⁻¹).</li> <li><strong>NEP_MX_quantiles</strong>: Estimates of NEP at different quantiles, providing uncertainty ranges. The quantiles represented in the data are: [0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75]<br> <div> </div> </li> <li><strong>Members dimension</strong>: Each model includes a <strong>members</strong> dimension, representing several NEP estimates generated using different ensemble members. These members capture uncertainty from input variables such as GPP, temperature, nitrogen deposition, and forest age. The members dimension provides users with multiple realizations of NEP estimates, reflecting the variability these factors introduce.</li> </ul> <p>Coordinates include latitude and longitude with CRS information (EPSG:4326). Missing data values are represented by <strong>-9999</strong>.</p> <p><strong>Citation</strong></p> <p>When using the maps, please cite the dataset, including the version number and the following paper: Ciais, P., Yao, Y. Besnard, S. et al. (2024) The global carbon balance of forests based on flux towers and forest age data, <em>submitted</em>. </p> <p><strong>Version History</strong></p> <ul> <li>1.0 - Initial version, covering 2010</li> </ul>
Arctic Gridded surface cloud fraction radiative kernels (GCF-CRKs)
<p><span>These <a name="OLE_LINK1"></a>gridded surface cloud fraction radiative kernels (GCF-CRKs) are created by integrating refined downwelling surface shortwave radiation (DSSR) estimates and a high-precision cloud fraction (CF). The DSSR is corrected by a CF-dependent model, which leveraging the correlation between the top-of-atmosphere (TOA) shortwave radiative parameters and surface radiation, combined with high-precision fused CF datasets from multiple satellite sources. </span></p> <p><span><span> </span>There are five individual files. “SFC_SW_Kernel_Arc.nc” is for CRKs of all clouds, “SFC_SW_lowcloud_Kernel_Arc.nc” is for CRKs of low-level clouds, “SFC_SW_midlowcloud_Kernel_Arc.nc” is for CRKs of mid-low-level clouds, “SFC_SW_midhighcloud_Kernel_Arc.nc” is for CRKs of mid-high-level clouds, and “SFC_SW_highcloud_Kernel_Arc.nc” is for CRKs of high-level clouds. The four cloud layers are derived from four pressure layers (surface to 700 hPa, 700-500 hPa, 500-300 hPa, and 300-50 hPa, representing low, middle-low, middle-high, and high clouds, respectively) based on the CERES-SYN stratification standard.</span></p> <p><span> </span></p> <p><span>The file format is netcdf4, and was created by Matlab. To read these files, any software supporting netcdf4 can be used. These files only involved sunlit months from Apr to Sep during 2000-2020, with the longitude from -180°~180° and the latitude from 60°N~90°N.</span></p>
Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories
<p><strong>Data Access Notice</strong></p> <p>Please note that, at present, the data for a sample of years are provided in this data record due to Zenodo's 50GB data limit. Data for all years 1959-2023 can be accessed via the following link:</p> <p><a href="http://opendap.uea.ac.uk/opendap/hyrax/greenocean/GridFED/GridFEDv2024.0/contents.html">http://opendap.uea.ac.uk/opendap/hyrax/greenocean/GridFED/GridFEDv2024.0/contents.html</a></p> <p><strong>Product Description</strong></p> <p>See Jones et al. (2021) for a detailed description of this dataset and the core methods used to produce it. Key details are provided below.</p> <p>GCP-GridFED (version 2024.0) is a gridded fossil emissions dataset that is consistent with the national CO<sub>2</sub> emissions reported by the Global Carbon Project (GCP; <a href="https://www.globalcarbonproject.org/">https://www.globalcarbonproject.org/</a>) in the annual editions of its Global Carbon Budget (Friedlingstein et al., 2023).</p> <p>GCP-GridFEDv2024.0 provides monthly fossil CO<sub>2 </sub>emissions for the period 1959-2023 at a spatial resolution of 0.1° × 0.1°. The gridded emissions estimates are provided separately for fossil CO<sub>2</sub> emitted by the oxidation of oil, coal and natural gas, international bunkers, and the calcination of limestone during cement production. The dataset also includes the cement carbonation sink of CO<sub>2</sub>. Note that positive values in GridFED signify a surface-to-atmosphere CO<sub>2 </sub>flux (emissions). Negative values signify an atmosphere-to-surface flux and apply only to the cement carbonation sink.</p> <p>GCP-GridFED also includes gridded uncertainties in CO<sub>2 </sub>emission, incorporating differences in uncertainty across emissions sectors and countries, and gridded estimates of corresponding O<sub>2</sub> uptake based on oxidative ratios for oil, coal and natural gas (see Jones et al., 2021).</p> <p><strong>Core Methodology in Brief</strong></p> <p>GCP-GridFEDv2024.0 was produced by scaling monthly gridded emissions for the year 2010, from the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2; Janssens-Maenhout et al., 2019), to the national annual emissions estimates compiled as part of the 2024 global carbon budget (GCP-NAE) for the years 1959-2023 (Friedlingstein et al., 2024). </p> <p>GCP-GridFEDv2024.0 uses a preliminary release of GCP-NAE covering the years 1959-2023 (timestamp 1st August 2024; an update from Andrew and Peters [2023]). The GCP-NAE estimates for year 2023 are based on data available at the timestamp and the estimates are thus expected to differ somewhat from those that will be presented by Friedlingstein et al. (2024), which will adopt updates to GCP-NAE since the timestamp.</p> <p>For full details of the core methodology, see Jones et al. (2021).</p> <p><strong>Changes to the Seasonality of Emissions in GCP-GridFEDv2022.2 onwards</strong></p> <p>The seasonality of emissions (monthly distribution of annual emissions) for the following countries/sources is now based on the seasonality observed in the Carbon Monitor dataset (Liu et al., 2020; Dou et al., 2022): </p> <ul> <li>Austria, Belgium, Brazil, Bulgaria, China, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, India, Ireland, Italy, Japan, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Russia, Slovakia, Slovenia, Spain, Sweden, United Kingdom, United States.</li> <li>State or province-level data is used for Brazil, China, Russia, and the United States.</li> <li>This also applies for the Bunker Aviation and Bunker Shipping sectors.</li> </ul> <p>Seasonality is determined in the following ways for those countries/sources:</p> <ul> <li>The seasonality of emissions in 2019-2023 is taken from Carbon Monitor.</li> <li>The seasonality of emissions in all years prior to 2019 is assigned as the average of the seasonality from Carbon Monitor in all years excluding 2020 (due to the impact of COVID-19 on the seasonality of emissions in 2020).</li> </ul> <p>For all countries not listed above and all years 1959-2023, GCP-GridFED adopts the seasonality from EDGAR v4.3.2 (year 2010; Janssens-Maenhout et al., 2019) and applies a small correction based on heating/cooling degree days to account for inter-annual climate variability which effects emissions in some sectors (see Jones et al., 2021).</p> <p><strong>Other New Features of GCP-GridFEDv2024.0</strong></p> <ul> <li>There have been no changes to the functionality of the GridFED code in this update versus the previous update (v2023.1).</li> </ul> <p> </p>
Global taxonomic occurrence grids using GBIF data for species distribution models.
<p>To achieve large geographic coverage, species occurrence databases that are composed of ad hoc species data collections such as that provided by the Global Biodiversity Information Facility (GBIF) are often used. A drawback to using these data is their geographic sampling bias, in which some regions are more intensively sampled than others, while other areas have very little to none reported sampling effort. Uneven sampling effort can mislead conclusions about biodiversity patterns and species distributions (Gotelli & Colwell, 2001; Lobo, 2008).</p> <p>Here we provide taxonomic occurrence grids to help mitigate the effects of sampling bias in species distribution modeling. These grids can be used to exclude areas of (a custom-defined) low sampling effort from the background when sampling for pseudo-absences’ (Phillips et al., 2009; Barbet-Massin et al.,2012). The occurrence grids have a 1 degree spatial resolution using WGS 84 as the geographic coordinate system. Each 1 degree grid cell contains the number of records present in GBIF corresponding to a specific taxonomic group: plants, mammals, reptiles, amphibians, birds and molluscs.</p> <p>To construct the occurrence grids, we used the 1- by 1-degree world latitude and longitude vector grid provided by ESRI (Redlands, California). It has a custom license which permits it reuse as long as ESRI is cited. It was downloaded from : <a href="https://www.arcgis.com/home/item.html?id=f11bcdc5d484400fa926dcce68de3df7">https://www.arcgis.com/home/item.html?id=f11bcdc5d484400fa926dcce68de3df7</a></p> <p>To map spatial sampling effort, the number of georeferenced occurrences corresponding to each taxonomic group contained by each 1- by 1-degree grid cell were counted. The grids were then converted to GeoTIFFs. The raster values correspond to the number of occurrences reported for the grid cells. For the purposes of the <a href="https://osf.io/7dpgr/">TrIAS project</a>, grid cells with fewer than 5 occurrences were removed. The TrIAS taxonomic occurrence grids are used as inputs to the TrIAS risk modelling and mapping workflow: https://github.com/trias-project/risk-modelling-and-mapping. Full (with all grid cells containing at least one occurrence) taxonomic occurrence grids are also provided.</p> <p>GBIF data for each taxonomic group were downloaded using the following criteria: “Basis of Record”: Observation, Machine Observation, Human Observation, Specimen, Material sample, Literature Occurrence, Unknown evidence., "HasCoordinate is true", "HasGeospatialIssue is false", "TaxonKey is Amphibia", "Year 1975-2005".</p> <p><strong>Raster Attributes</strong></p> <table> <tbody> <tr> <td> <p>Attribute</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p>OID</p> </td> <td> <p>numeric row ID</p> </td> </tr> <tr> <td> <p>Value</p> </td> <td> <p>the number of records contained in the grid cell</p> </td> </tr> <tr> <td> <p>Count</p> </td> <td> <p>the number of times the value appears in the raster</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p>The extent of each taxonomic occurrence grid:</p> <ul> <li> <p>longitude -180.0; latitude -90.0 (southwest corner)</p> </li> <li> <p>longitude 180.0; latitude 90.0 (northeast corner)</p> </li> </ul> <p> </p> <p><strong>Files:</strong></p> <p>TrIAS taxonomic occurrence grids</p> <p>amphib_1deg_min5.tif</p> <p>birds_1deg_min5.tif</p> <p>mammals_1deg_min5.tif</p> <p>molluscs_1deg_min5.tif</p> <p>reptiles_1deg_min5.tif</p> <p> </p> <p>Raw taxonomic occurrence grids</p> <p>amphib_1deg_grid.tif</p> <p>birds_1deg_grid.tif</p> <p>mammals_1deg_grid.tif</p> <p>molluscs_1deg_grid.tif</p> <p>reptiles_1deg_grid.tif</p> <p><br> </p> <p> </p> <p> </p>
H2020 Platone German Demonstrator - Baseline Active Power Exchange at Grid Connection Point (Medium Voltage/Low Voltage)
<p>The given data are computed values for the active power exchange at the medium (MV)/low voltage grid connecting feeder (active power). The data are provided as 15-minutes mean values in kilowatt. The computed indicate the power exchange that would have been measured, in case no use case would have been applied in the field (control of batteries).</p> <p><strong>Data Description:</strong></p> <ul> <li>p_tei_c_mean = arithmetic mean of p_tei computed in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</li> <li>p_tei_c_min = the minimum value (1-minute mean) computed within the period of p_tei_mean (15-minutes)</li> <li>p_tei_c_max = the maximum value (1-minute mean) computed within the period of p_tei_mean period (15-minutes)</li> </ul> <p><strong>Field Test Setup</strong></p> <p>The field test setup of the demonstrator consists of a MV/LV substation, 89 households, 450kW of installed PV generation capacity, a large scale battery with 300 kW and 850 kWh capacity. </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300</p>
Gridded depth and accumulation products from dated airborne radar stratigraphy over West Antarctica during the mid-Holocene, v.1.0.0
<p>This dataset comprises of codes (written in MATLAB) and gridded files (exported as GeoTIFF) presented in Bodart et al. (2023; The Crysophere; <a href="https://doi.org/10.5194/tc-2022-199">https://doi.org/10.5194/tc-2022-199</a>). A summary of the key findings from this study is provided as follows:</p> <p>"Using a spatially extensive IRH over Pine Island Glacier, Thwaites Glacier, and Institute and Möller Ice Streams (covering a total of 610 000 km2 or 30% of the WAIS), and a local layer approximation model, we infer mid-Holocene accumulation rates over the slow-flowing parts of these catchments for the past ~4700 years. By comparing our results with modern climate reanalysis models (1979 – 2019) and observational syntheses (1651 – 2010), we estimate that accumulation rates over the Amundsen-Weddell-Ross divide were on average 18% higher during the mid-Holocene than modern rates. However, no significant spatial changes in the accumulation pattern were observed."</p> <p>This dataset contains a series of files (5x .m files, 10x .tif files). The numbering of the figures in the description below refers to the order of the figures in the associated paper.</p> <ul> <li><strong> 5x MATLAB files:</strong> <ul> <li><strong>Calculate_accumulation_rates.m</strong>: calculates accumulation rates for the mid-Holocene-to-present, as well as uncertainties associated with the age and model structural uncertainty;</li> <li><strong>Calculate_D_parameter.m</strong>: calculates the D parameter (and associated L_path, L_H and L_b) to assess the feasability of the LLA over our grid;</li> <li><strong>Calculate_longitudinal_strain_rates.m</strong>: calculates the longitudinal strain rates over our grid from modern ice-flow velocities;</li> <li><strong>Calculate_vertical_strain_rates.m</strong>: calculates vertical strain rates for the mid-Holocene-to-present part of the ice column from accumulation estimates;</li> <li><strong>Resample_IRH_data.m</strong>: Re-samples the along-track IRH data into evenly distributed 500-m points for speeding up the gridding and calculations of accumulation rates;<br> </li> </ul> </li> <li><strong>10x GeoTIFF files:</strong> <ul> <li><strong>Holocene_IRH_depth_Fig2a.tif: </strong>Figure 2a;</li> <li><strong>Holocene_accumulation_rates_Fig3a.tif:</strong> Figure 3a;</li> <li><strong>Difference_Holocene_accumulation_RACMO2_Fig3c.tif:</strong> Figure 3c;</li> <li><strong>Relative_difference_Holocene_accumulation_RACMO2_Fig4.tif:</strong> Figure 4;</li> <li><strong>D_parameter_FigS1d.tif:</strong> Figure S1d;</li> <li><strong>Holocene_vertical_strain_rates_FigS2a.tif: </strong>Figure S2a;</li> <li><strong>Longitudinal_strain_rates_FigS2b.tif:</strong> Figure S2b;</li> <li><strong>Holocene_accumulation_lower_uncertainty_FigS4a.tif:</strong> Figure S4a;</li> <li><strong>Holocene_accumulation_upper_uncertainty_FigS4b.tif:</strong> Figure S4b;</li> <li><strong>Holocene_accumulation_relative_uncertainty_FigS4c.tif: </strong>Figure S4c;</li> </ul> </li> </ul> <p>Please also cite the associated paper when using this dataset.</p> <p>Any questions, please direct them to the corresponding author, Julien Bodart (julien.bodart@ed.ac.uk).</p>
MeteoEurope1km - TMAX (1991–2000): daily gridded meteorological dataset for Europe at a 1-km spatial resolution for the 1991–2020 period
<p>MeteoEurope1km is the daily gridded meteorological dataset for Europe at a 1-km spatial resolution for the 1991–2020 period. The dataset consists of five daily variables:</p> <ul> <li><strong>TMAX - maximum temperature</strong> (<strong>1991–2005 period</strong>, 2006–2020 period)</li> <li>TMIN - minimum temperature (1991–2005 period, 2006–2020 period)</li> <li>TMEAN - mean temperature (1991–2005 period, 2006–2020 period)</li> <li>SLP - mean sea level pressure</li> <li>PRCP - total precipitation</li> </ul> <p>Daily gridded temperature data were interpolated using the Regression Kriging, with digital elevation model (DEM) and topographic wetness index (TWI) as covariates.<br> Daily gridded sea level pressure data were interpolated using Ordinary Kriging.<br> Daily gridded precipitation data were interpolated using Indicator and Ordinary Kriging methodology in two steps:</p> <ol> <li>Indicator Kriging - prediction of precipitation occurence</li> <li>Ordinary Kriging - prediction of total daily precipitation for locations where precipitation occurs (1. step).</li> </ol> <p>File naming convention of the MeteoEurope1km files is <em>var_day_yyyymmdd_proj.tif</em> (e.g. <em>tmax_day_20201231_3035.tif</em>), where:</p> <ul> <li><em>var</em> is a daily meteorological variable name - tmax, tmin, tmean, slp, or prcp</li> <li><em>proj</em> is a dataset projection EPSG code - 3035</li> </ul> <p>Units of the dataset values are</p> <ul> <li>temperature (Tmean, Tmax, and Tmin) - tenths of a degree in the Celsius scale (℃)</li> <li>SLP - tenths of a mbar</li> <li>PRCP - tenths of a mm</li> </ul> <p>All dataset values are stored as integers (INT32 data type) in order to reduce the size of the GeoTIFF files, i.e., temperature values should be divided by 10 to obtain degrees Celsius, and the same for SLP and PRCP to obtain millibars and millimeters.<br> All dataset files are available as Cloud-Optimized GeoTIFFs (COGs).<br> Use the R <a href="https://github.com/AleksandarSekulic/Rmeteo">meteo</a> package, <em>europe1km</em> function to make a point query and obtain the values for a specific location and a specific period.</p>
Respiration in soils collected from the REU synoptic sample grid in the Andrews Experimental Forest, 1994-1995
The 1993 (SP005) sampling grid was expanded to 183 sites providing a more comprehensive sampling grid. Because of potential edge effects problems, all sample locations were moved at least 20 meters from the closest road or clearing. Changes from the original location are given in entity 2. CO2 is measured over a period of 41 weeks in soils collected from all of the REU synoptic sample sites in the summer of 1994. Soils were weighted at the same time that respiration was assayed and moisture content was maintained at the same level throughout the series. The time period represented by this data is 15 September 1994 to 12 July 1995. In the initial stages, CO2 concentrations were assayed every week, after 15 weeks, the incubation period was extended to 2 weeks. After the incubation vessels were assayed, the headspace was flushed with lab air and the moisture was adjusted with sterile deionized water. Results show that there was a very high correlation between respiration rates during the 1st and 3rd weeks (r = 0.96) suggesting that the two week incubation period we us as an indicator of initial concentrations of labile carbon is valid. There was also a high correlation between rates observed within the first week and rates observed after 41 weeks (r = 0.85) suggesting that the initial rates provide a good relative guide to rates observed after 10 months. In addition, respiration rates after 41 weeks were 80% of those observed during the first week.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.