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3,592 results for “Grid”
Downscaled 20CRv2c (#37) gridded historical climate data over China (1851-2010)
<p><strong>Gridded historical climate </strong><strong>data over China, spanning 1851 to 2010. Dynamically downscaled to 25km resolution using the PRECIS2.0 (HadRM3P) Met Office regional climate model, driven by 20th century reanalysis (20CRv2c, NOAA/ESRL PSD 20th Century Reanalysis version 2c, ensemble member 37).</strong></p> <p>This data has been un-rotated to true latitude longitude coordinates from its original rotate pole frame of reference. For more information on the PRECIS regional climate model, visit <a href="http://www.metoffice.gov.uk/precis">www.metoffice.gov.uk/precis</a>. Data near the boundaries should be used with caution due to model configuration aspects of regional climate modelling, and the interpolation method applied.</p> <p><strong>Domain</strong>: 17N to 58.84N, 73E to 135.7E</p> <p><strong>Countries covered</strong>: China, Nepal, Bhutan, Bangladesh, Taiwan, Mongolia, North Korea, South Korea, Kyrgzstan, and northern parts of India, Myanmar, Lao PDR & Vietnam.</p> <p><strong>Variables</strong>: pr (mean precipitation flux), tm (mean surface temperature), tn (minimum surface temperature) & tx (maximum surface temperature)</p> <p><strong>Time averaging</strong>: monthly</p> <p> </p> <p><em>This data set supplements the equivalent downscaled ERA-Interim data set: <a href="https://zenodo.org/record/2600192#.XJj3uKD7RWE">Downscaled ERA-Interim gridded historical climate data over China (1980-2010)</a> doi: 10.5281/zenodo.2600192</em></p>
EMAG2: Earth Magnetic Anomaly Grid (2-arc-minute resolution) compressed for NumPy
<p>A compressed NumPy version of the <a href="https://www.ngdc.noaa.gov/geomag/emag2.html">EMAG2 (v3)</a> global Earth Magnetic anomaly grid compiled from satellite, ship, and airborne magnetic measurements. The original CSV data was imported, transformed, and saved to a compressed NumPy archive as follows:</p> <pre><code class="language-python">import numpy as np mag_data = np.loadtxt('EMAG2_V3_20170530.csv', delimiter=',', usecols=(2,3,4,5,7)) lon_mask = mag_data[:,0] > 180.0 mag_data[lon_mask,0] -= 360.0 np.savez_compressed('EMAG2_V3_20170530.npz', data=mag_data.astype(np.float32))</code></pre> <p>The NumPy archive (contained in this repository) can be efficiently loaded in Python workflows. It contains the following columns:</p> <ol> <li>Longitude - geographic longitudinal coordinates in decimal degrees (WGS84)</li> <li>Latitude - geographic latitudinal coordinates in decimal degrees (WGS84)</li> <li>SeaLevel - magnetic anomaly value at sea level (nT)</li> <li>UpCont - magnetic anomaly value at continuous 4km altitude (nT)</li> <li>Error - Error estimate (nT)</li> </ol> <p>Code 888 is assigned in certain cells on grid edges where the data source is ambiguous and assigned an error of -888 nT.<br> Code 999 is assigned in cells where no data is reported with the anomaly value assigned 99999 nT and an error of -999 nT.</p> <p><strong>Reference</strong></p> <p>Brian Meyer, Richard Saltus, Arnaud Chulliat (2017): EMAG2: Earth Magnetic Anomaly Grid (2-arc-minute resolution) Version 3. National Centers for Environmental Information, NOAA. Model. doi:10.7289/V5H70CVX</p>
CEDS_Gridded_Data_Proxies_v2019_06_18
<p>Spatial proxy data needed to produce gridded historical emission datasets using the <a href="https://github.com/JGCRI/CEDS">Community Emissions Data System</a>. The published reference for this data is <a href="https://www.geosci-model-dev.net/11/369/2018/gmd-11-369-2018.html">Hoesly et al, Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS). Geosci. Model Dev. 11, 369-408, 2018.</a></p>
Magnetic data grids of the Northern Vosges, surveys EOST2008 and GPR2015.
<p>Grid computation and details are explained in Gavazzi et al., 2019 [1] and exploited in Bertrand et al., [2].<br> This file contains non-null grid elements.</p> <p>PARAMETERS<br> Format : ASCII<br> Number of elements : 86902<br> Grid cell size : 125 m</p> <p>FIELDS<br> - LON : grid cell WGS84 longitude (°East);<br> - LAT : grid cell WGS84 latitude (°North );<br> - ALT : grid cell altitude amsl (m) - this grid was computed at a constant altitude of 1400 m above mean sea level;<br> - TMI : total magnetic intensity anomaly (nT);<br> - RTP : (double) reduction to the pole of the TMI (nT) - mean regional field direction Inclination=1.60°, Declination=64.14°;<br> - DV : vertical derivative of the RTP at order 0.5 (nT/m);<br> - DH : horizontal derivative of the RTP at order 1 (nT/m);</p> <p>[1] Gavazzi, B., Bertrand, L., Munschy, M., Mercier de Lépinay, J., Diraison, M. & Géraud, Y. (submitted). On the use of aeromagnetism for geological interpretation part I: comparison of 1 scalar and vector magnetometers for aeromagnetic surveys and an equivalent source 2 interpolator for combining, gridding and transform fixed altitude and draping 3 datasets, Journal of Geophysical Research: Solid Earth (2019).</p> <p>[2] Bertrand, L., Gavazzi, B., Mercier de Lépinay, J., Diraison, M., Géraud, Y. & Munschy, M. (submitted). On the use of aeromagnetism for geological interpretation part II: geological interpretation on outcropping basement rocks for geothermal energy prospection, Journal of Geophysical Research: Solid Earth (2019).</p>
Gridded spatial information on soil organic carbon content, density and stock in Hungary for 1992 and 2000
<p>Predictive soil organic carbon (SOC) content, density, and stock maps, along with the associated prediction uncertainty, are provided for the years 1992 and 2000, for the entire territory of Hungary. The maps refer to the topsoils (0–30 cm) with a spatial resolution of 100⨯100 m. The uncertainty associated with the SOC property maps is expressed by the lower and upper limits of the 90% prediction interval (PI), the range of values within which the true value is expected to occur 9 times out of 10. This means that there are two maps to each SOC property map, quantifying its prediction uncertainty. It should be added that all maps have been masked with open water bodies, as these areas are not relevant for soils.</p> <p><strong>For more details / to cite this dataset please use:</strong></p> <p><a href="https://doi.org/10.1038/s41597-024-04158-3">Szatmári, G., Laborczi, A., Mészáros, J., Takács, K., Benő, A., Koós, S., Bakacsi, Z., & Pásztor, L. (2024). Gridded, temporally referenced spatial information on soil organic carbon for Hungary. Scientific Data 11, 1312.</a></p> <p><strong>Custom code used for digital soil mapping and validation is available on GitHub:</strong></p> <p><a href="https://github.com/GaborSzatmari/HU-SOC-mapping" target="_blank" rel="noopener">https://github.com/GaborSzatmari/HU-SOC-mapping</a></p> <p><strong>Description of the files:</strong></p> <p>The resulting maps are shared as GeoTIFF files. The coordinate reference system is the Hungarian Unified National Projection System (HD72/EOV; EPSG: 23700) (<a href="https://epsg.io/23700" target="_blank" rel="noopener">https://epsg.io/23700</a>). The table below provides further information on the published maps. Note that the first file (00_Overview.jpg) gives an overview of the SOC property maps.</p> <table> <tbody> <tr> <td> <p><strong>SOC property maps</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Year</strong></p> </td> <td> <p><strong>Filename</strong></p> </td> </tr> <tr> <td> <p>SOC content map</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCc_0_30cm_1992_pred.tif</p> </td> </tr> <tr> <td> <p>SOC content, lower limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCc_0_30cm_1992_q05.tif</p> </td> </tr> <tr> <td> <p>SOC content, upper limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCc_0_30cm_1992_q95.tif</p> </td> </tr> <tr> <td> <p>SOC density map</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCd_0_30cm_1992_pred.tif</p> </td> </tr> <tr> <td> <p>SOC density, lower limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCd_0_30cm_1992_q05.tif</p> </td> </tr> <tr> <td> <p>SOC density, upper limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCd_0_30cm_1992_q95.tif</p> </td> </tr> <tr> <td> <p>SOC stock map</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCs_0_30cm_1992_pred.tif</p> </td> </tr> <tr> <td> <p>SOC stock, lower limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCs_0_30cm_1992_q05.tif</p> </td> </tr> <tr> <td> <p>SOC stock, upper limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCs_0_30cm_1992_q95.tif</p> </td> </tr> <tr> <td> <p>SOC content map</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCc_0_30cm_2000_pred.tif</p> </td> </tr> <tr> <td> <p>SOC content, lower limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCc_0_30cm_2000_q05.tif</p> </td> </tr> <tr> <td> <p>SOC content, upper limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCc_0_30cm_2000_q95.tif</p> </td> </tr> <tr> <td> <p>SOC density map</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCd_0_30cm_2000_pred.tif</p> </td> </tr> <tr> <td> <p>SOC density, lower limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCd_0_30cm_2000_q05.tif</p> </td> </tr> <tr> <td> <p>SOC density, upper limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCd_0_30cm_2000_q95.tif</p> </td> </tr> <tr> <td> <p>SOC stock map</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCs_0_30cm_2000_pred.tif</p> </td> </tr> <tr> <td> <p>SOC stock, lower limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCs_0_30cm_2000_q05.tif</p> </td> </tr> <tr> <td> <p>SOC stock, upper limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCs_0_30cm_2000_q95.tif</p> </td> </tr> </tbody> </table> <p> </p>
Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet
<p>This dataset can be used to reproduce the figures created in Waling et al. 2024, "Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet." Each figure has its own script which can be executed.<br><br></p>
Dataset for publication "Assessing the techno-economic benefits of LEMs for different grid topologies and prosumer shares"
<p>This dataset contains all the results and scenarios for this paper. Each region has two files in which you can find all the scenarios for the market design with and without the local energy market. The folders within these files contain the results for one scenario for a given share of PV, EVs, and heat pumps (HPs). The results folders can also be used to rerun the scenarios. To do so, place the respective scenario in the tool's scenario folder.</p> <p>USE CASE:<br>If you do not want to check all the files of the results or want to rerun the scenarios, use this version. If you want to download the relevant files for the analysis and figure creation of the paper, use the <a href="https://zenodo.org/records/13907329" target="_blank" rel="noopener">compact version</a>.</p> <p>TOOL:<br><a href="https://github.com/TUM-Doepfert/lemlab/tree/doepfert2024_lem">lemlab</a></p>
Compact version: Dataset for publication "Assessing the techno-economic benefits of LEMs for different grid topologies and prosumer shares"
<p>This is the compact version of the results. They contain only the relevant files for the analysis and figure creation of the paper.</p> <p>USE CASE:<br>If you do not want to access every single file of the results or rerun the scenarios, you should use the compact version.</p> <p>TOOL:<br><a href="https://github.com/TUM-Doepfert/lemlab/tree/doepfert2024_lem">lemlab</a></p>
Structured Power Grid Simulation Dataset for Machine Learning: Failure and Survival Events in Grid2Op's L2RPN WCCI 2022 Environment
<p>This dataset was developed for and used in the paper titled <em>"Fault Detection for Agents in Power Grid Topology Optimization: A Comprehensive Analysis"</em> by Malte Lehna, Mohamed Hassouna, Dmitry Degtyar, Sven Tomforde, and Christoph Scholz, presented at the <em>Workshop on Machine Learning for Sustainable Power Systems (ML4SPS)</em>, part of <em>ECML PKDD 2024</em>. While the paper is pending formal publication, a preprint version is available on arXiv.</p> <p>The dataset contains structured training, validation, and test data comprising failure and survival events observed in transmission power grid simulations. These were generated using Grid2Op with the WCCI 2022 L2RPN environment. Each data instance is labeled with one of four classes, representing survival or impending failure in 1, 3, and 5 timesteps. This dataset was used to train, validate and test machine learning models that predict grid agent failures in topology optimization tasks. </p>
Supplementary Material for "Probabilistic Forecasting of Regional Net-load with Conditional Extremes and Gridded NWP"
<p>Supplementary material to accompany pre-print of "Probabilistic Forecasting of Regional Net-load with Conditional Extremes and Gridded NWP" by Jethro Browell and Matteo Fasiolo available on on arXiv. This is version 3. The only changes from version 1 & 2 to forecast evaluation (significance testing and additional plots). Future releases are subject to change following revisions of this article.</p>
Benchmarking (multi)wavelet-based dynamic and static non-uniform grid solvers for flood inundation modelling (Simulation results)
<p>Simulation result data for Environment Agency benchmark test 5, Thamesmead hypothetical flood, and Carlisle 2005 case studies, using uniform DG2, adaptive MWDG2, adaptive HWFV1, non-uniform DG2, non-uniform FV1 and non-uniform ACC solvers. </p> <p>Model results are archived in 3 zip files:</p> <ul> <li>EA5.zip contains results of Environment Agency test 5 (Néelz and Pender, 2013)</li> <li>Thamesmead.zip contains results of Thamesmead hypothetical flood (Liang et al., 2008)</li> <li>Carlisle.zip contains results of Carlisle 2005 flooding (Neal et al., 2009)</li> </ul> <p>The results are stored with the following file extensions:</p> <ul> <li>".wd" for 2D flood inundation maps in ESRI ASCII format</li> <li>".stage" for water depth or water level time-series at staging points in tabulated text format</li> <li>".velocity" for velocity time-series at staging points in tabulated text format</li> </ul> <p>Model outputs are stored under directories named for each solver.</p> <p><strong>References</strong></p> <p>Néelz, S., & Pender, G. (2013). Benchmarking the latest generation of 2D hydraulic modelling packages. <em>Environment Agency: Bristol, UK</em>.</p> <p>Liang, Q., Du, G., Hall, J. W., & Borthwick, A. G. (2008). Flood Inundation Modeling with an Adaptive Quadtree Grid Shallow Water Equation Solver. <em>Journal of Hydraulic Engineering</em>, <em>134</em>(11), 1603–1610. https://doi.org/10.1061/(ASCE)0733-9429(2008)134:11(1603)</p> <p>Neal, J. C., Bates, P. D., Fewtrell, T. J., Hunter, N. M., Wilson, M. D., & Horritt, M. S. (2009). Distributed whole city water level measurements from the Carlisle 2005 urban flood event and comparison with hydraulic model simulations. <em>Journal of Hydrology</em>, <em>368</em>(1–4), 42–55. https://doi.org/10.1016/j.jhydrol.2009.01.026</p> <p> </p>
In-network convolution in grid-shaped wired sensor networks
<p>Data about the simulation of the in-network convolution in grid-shaped wired sensor networks. <br> We designed the simulation to examine the communication overhead of the technique applied on a wired sensor network at two different topologies.<br> Data include measurements of traveling time of packets and packet loss at varying of the link bitrate and kernel size. </p>
Mobility demand mesh-grid datasets
<ul> <li><strong>15m_flat_bike_count.h5</strong>: Table that contains the number of bike rides that started on every bike station in Chicago, per time interval. Time resolution is 15min, covering 2013-2020 (280512 timestamps).</li> <li><strong>15m_flat_bike_norm_abs.h5</strong>: Same as <strong>15m_flat_bike_count.h5</strong>, but normalized using the maximum number of rides recorded in that period, i.e.: 84. The normalization is calculated as x' = (x - min(X)) / (max(X) - min(X)).</li> <li><strong>15m_flat_taxi_count.h5</strong>: Table that contains the number of taxi trip counts that started on every taxi zone in Chicago, per time interval. Time resolution is 15min, covering 2013-2020 (280512 timestamps).</li> <li><strong>15m_flat_taxi_norm_abs.h5</strong>: Same as <strong>15m_flat_taxi_count.h5</strong>, but normalized using the maximum number of trips recorded in that period, i.e.: 418. The normalization is calculated as x' = (x - min(X)) / (max(X) - min(X)).</li> <li><strong>15m_map_90_60_bike_norm_abs.h5</strong>: Mobility mesh-grid for bikes, calculated from<strong> 15m_flat_bike_norm_abs.h5</strong> counting the total number of rides per element of the grid and time interval. The final shape of the dataset is 280512 x 90 x 60.</li> <li><strong>15m_map_90_60_taxi_norm_abs.h5</strong>: Mobility mesh-grid for taxis, calculated from <strong>15m_flat_taxi_norm_abs.h5</strong> using linear interpolation. The final shape of the dataset is 280512 x 90 x 60.</li> <li><strong>station-locations-bike.csv</strong>: Geolocations of the bicycle racks of Chicago.</li> <li><strong>zone-centroids-taxi.csv</strong>: Geolocations of the centroids of the taxi zones of Chicago.</li> <li><strong>grid-locations-bike.csv</strong>: Locations of the elements of a 90x60 grid overlaying Chicago that contain bicycle racks.</li> <li><strong>holidays.csv</strong>: Holidays in Chicago for the period 2013-2020.</li> </ul>
High-resolution BIOCLIM and ENVIREM grids for Europe in consecutive 100-year bins spanning the last 21,000 years
<p>Here, I provide a dataset of gridded climatic variables at a spatial resolution of 30 arc-seconds for 210 consecutive 100-year bins spanning the period from 21,000 to 0 BP. The dataset includes 19 bioclimatic and 16 ENVIREM variables (described by Title & Bemmels, 2018) commonly used in species distribution modelling. It covers the European continent and adjacent regions within the following boundaries: 32.5°W–70°E and 32.5°N–82.5°N.</p> <p>For each 100-year bin, bioclimatic and ENVIREM variables were calculated based on the downscaled and debiased monthly temperature and precipitation simulations of the Community Climate System Model version 3 (CCSM3; Collins et al., 2006) as provided by the PaleoView software (Fordham et al., 2017). The downscaling procedure was based on the delta-change method (Ramirez Villejas & Jarvis, 2010). As a baseline climatic data, I used monthly temperature and precipitation grids from the CHELSA database for 1940–1989 (Karger et al., 2017).</p> <p>A detailed description of the dataset, including the downscaling method applied, can be found in the Technical specification attached to this dataset.</p>
PSML: A Multi-scale Time-series Dataset for Machine Learning in Decarbonized Energy Grids (Dataset)
<p><strong>Abstract</strong></p> <p>The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable energy resources and electrified transportation, the reliable and secure operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML) based approaches towards reliable operation of future electric grids. The dataset is generated through a novel transmission + distribution (T+D) co-simulation designed to capture the increasingly important interactions and uncertainties of the grid dynamics, containing electric load, renewable generation, weather, voltage and current measurements at multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML baselines on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbance events; (ii) robust hierarchical forecasting of load and renewable energy with the presence of uncertainties and extreme events; and (iii) realistic synthetic generation of physical-law-constrained measurement time series. We envision that this dataset will enable advances for ML in dynamic systems, while simultaneously allowing ML researchers to contribute towards carbon-neutral electricity and mobility. </p> <p><strong>Data Navigation</strong></p> <p>Please download, unzip and put somewhere for later benchmark results reproduction and data loading and performance evaluation for proposed methods.</p> <pre><code>wget https://zenodo.org/record/5130612/files/PSML.zip?download=1 7z x 'PSML.zip?download=1' -o./ </code></pre> <p><strong>Minute-level Load and Renewable</strong></p> <ul> <li>File Name <ul> <li>ISO_zone_#.csv: `CAISO_zone_1.csv` contains minute-level load, renewable and weather data from 2018 to 2020 in the zone 1 of CAISO.</li> </ul> </li> <li>- Field Description <ul> <li>Field `<em>time</em>`: Time of minute resolution.</li> <li>Field `<em>load_power</em>`: Normalized load power.</li> <li>Field `<em>wind_power</em>`: Normalized wind turbine power.</li> <li>Field `<em>solar_power</em>`: Normalized solar PV power.</li> <li>Field `<em>DHI</em>`: Direct normal irradiance.</li> <li>Field `<em>DNI</em>`: Diffuse horizontal irradiance.</li> <li>Field `<em>GHI</em>`: Global horizontal irradiance.</li> <li>Field <em>`Dew Point</em>`: Dew point in degree Celsius.</li> <li>Field `<em>Solar Zeinth Angle</em>`: The angle between the sun's rays and the vertical direction in degree.</li> <li>Field `<em>Wind Speed</em>`: Wind speed (m/s).</li> <li>Field `<em>Relative Humidity</em>`: Relative humidity (%).</li> <li>Field `<em>Temperature</em>`: Temperature in degree Celsius.</li> </ul> </li> </ul> <p><strong>Minute-level PMU Measurements</strong></p> <ul> <li>File Name <ul> <li>case #: The `case 0` folder contains all data of scenario setting #0. <ul> <li>pf_input_#.txt: Selected load, renewable and solar generation for the simulation.</li> <li>pf_result_#.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> <li>Filed Description <ul> <li>Field <em>`time`</em>: Time of minute resolution.</li> <li>Field <em>`Vm_###`</em>: Voltage magnitude (p.u.) at the bus ### in the simulated model.</li> <li>Field <em>`Va_###`</em>: Voltage angle (rad) at the bus ### in the simulated model.</li> <li>Field <em>`P_#_#_#`</em>: `P_3_4_1` means the active power transferring in the #1 branch from the bus 3 to 4.</li> <li>Field <em>`Q_#_#_#`</em>: `Q_5_20_1` means the reactive power transferring in the #1 branch from the bus 5 to 20.</li> </ul> </li> </ul> <p><strong>Millisecond-level PMU Measurements</strong></p> <ul> <li>File Name <ul> <li>Forced Oscillation: The folder contains all forced oscillation cases. <ul> <li>row_#: The folder contains all data of the disturbance scenario #. <ul> <li>dist.csv: Three-phased voltage at nodes in the distribution system via T+D simualtion.</li> <li> info.csv: This file contains the start time, end time, location and type of the disturbance</li> <li>trans.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> <li>Natural Oscillation: The folder contains all natural oscillation cases. <ul> <li>row_#: The folder contains all data of the disturbance scenario #. <ul> <li>dist.csv: Three-phased voltage at nodes in the distribution system via T+D simualtion.</li> <li>info.csv: This file contains the start time, end time, location and type of the disturbance.</li> <li>trans.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> </ul> </li> <li>Filed Description <ul> <li>trans.csv <ul> <li> - Field <em>`Time(s)`</em>: Time of millisecond resolution.</li> <li> - Field <em>`VOLT ###`</em>: Voltage magnitude (p.u.) at the bus ### in the transmission model.</li> <li> - Field <em>`POWR ### TO ### CKT #`</em>: `POWR 151 TO 152 CKT '1 '` means the active power transferring in the #1 branch from the bus 151 to 152.</li> <li> - Field <em>`VARS ### TO ### CKT #`</em>: `VARS 151 TO 152 CKT '1 '` means the reactive power transferring in the #1 branch from the bus 151 to 152.</li> </ul> </li> <li>dist.csv <ul> <li>Field <em>`Time(s)`</em>: Time of millisecond resolution.</li> <li>Field <em>`####.###.#`</em>: `3005.633.1` means per-unit voltage magnitude of the phase A at the bus 633 of the distribution grid, the one connecting to the bus 3005 in the transmission system.</li> </ul> </li> </ul> </li> </ul>
Gridded ammonia emission inventory in mainland China
<p>We produce and provide an improved ammonia emission inventory in mainland China in 2016. The emission inventory have been developed with 1/12 by 1/12 degree spatial resolution. The unit of the emission inventory is t/grid/year. </p>
About ERIGrid 2.0 - Connecting European Smart Grid Research Infrastructures (IEA version)
<p>This video provides a brief overview of the activities and services of the <a href="https://ec.europa.eu/programmes/horizon2020/en">H2020</a> <a href="https://erigrid2.eu/">ERIGrid 2.0</a> research infrastructure project as well its links with the <a href="https://www.iea.org/">IEA</a>, especially its technology collaboration programme <a href="https://www.iea-isgan.org/">ISGAN</a> - Annex 5 <a href="https://www.iea-isgan.org/our-work/annex-5/">SIRFN</a>.</p>
Availability of information on citizen science activities, checked against the Activities & Dimensions Grid of Citizen Science on the basis of some projects
<p>The research resulting in this report aimed at answering the following questions:</p> <ul> <li> <p>Which information on citizen science activities is online available that matches the Activity & Dimension Grid of Citizen Science or goes beyond it? </p> </li> <li> <p>Is there any contradictory information?</p> </li> <li> <p>What can be the reason for the availability or non-availability of information about citizen science activities?</p> </li> <li> <p>How does/could this impact on the CS Track’s recommendations?</p> </li> </ul> <p>The corresponding dataset consists of the results of a keyword-based search in the WP2 project database. The information retrieval resulted in 3318 projects on which information is available in German or English.</p> <p>More information on this research can be found in D2.2 section 3.2.</p>
Socio-economic development of global river deltas from gridded data
<p>Crop, population, and GDP values in the world's major river deltas, derived from publicly available gridded datasets. </p> <p>v0: Dec. 2022</p> <p>v1: Jan 2023 (added Metadata)</p>
Dataset used in the publication entitled "Decomposition Problem in Process of Selective Identification and Localization of Voltage Fluctuation Sources in Power Grids" presented at 2022 20th International Conference on Harmonics and Quality of Power (ICHQP)
<p>Dataset obtained from experimental research carried out in a real power grid. Based on the dataset, the problem of decomposition in identification of sources of voltage fluctuations has been presented in the publication: Kuwałek P., Decomposition Problem in Process of Selective Identification and Localization of Voltage Fluctuation Sources in Power Grids, <em>Proceedings of the 20th International Conference on Harmonics and Quality of Power</em>, IEEE , art. no. 43, 2022, Italy, Naples. The description of the power grid model is presented in this publication. The research results are part of the work under the project entitled "Voltage fluctuation diagnostic focused on identification and localization disturbing loads in power grids" funded by the National Science Centre, Poland - 2021/41/N/ST7/00397.</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.