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670 results for “gridded data”
Example data for microclimf: Fast above, below or within canopy gridded microclimate modelling with R
<p>runmicrobig.zip - example data required to run function runmicro_big in package vignette</p> <p>Package available: https://github.com/ilyamaclean/microclimf</p>
Technology pathways could help drive the U.S. West Coast grid's exposure to hydrometeorological uncertainty (Figure Data)
<p>Data used to create figures for:</p> <p>Wessel, J., Kern, J.D., Voisin, N., Oikonomou, K., Haas, J. (2021). “Technology pathways could drive the U.S. West Coast grid's exposure to hydrometeorological uncertainty”.</p> <p>California and West Coast Power System (CAPOW) model is Python based. The model was built to simulate the operations of the major markets comprising the West Coast bulk electric power system: the Mid-Columbia (Mid-C) market, and the California Independent System Operator (CAISO). This version adds future technology pathways, EV adoption, and battery storage.</p> <p>See https://github.com/jawessel/CAPOW_pathways (v1.0 release) for version of CAPOW model used.</p>
Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities"
<p>Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities", Applied Energy, 2019</p>
Fatiando a Terra Data: Earth - Geoid height grid at 10 arc-minute resolution
<p>Global 10 arc-minute resolution grids of geoid height with respect to the WGS84 ellipsoid.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Convert the grid from the ASCII format of ICGEM to CF-compliant netCDF. Add relevant metadata, including names, units, datum, etc. Fix grid coordinates to be generated by <code>numpy.linspace</code> instead <code>numpy.arange</code> (or the equivalent used by ICGEM internally) to guarantee equal spacing to a higher accuracy. Export to compressed netCDF.</p> <p><strong>Source: </strong><a href="https://doi.org/10.5880/icgem.2015.1">EIGEN-6C4</a> spherical harmonic model (generated by the <a href="http://icgem.gfz-potsdam.de/home">ICGEM calculation service</a>)</p> <p><strong>Source license: </strong><a href="https://doi.org/10.5880/icgem.2015.1">CC-BY</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/earth-geoid-10arcmin">https://github.com/fatiando-data/earth-geoid-10arcmin</a></p>
Fatiando a Terra Data: Earth - Topography grid at 10 arc-minute resolution
<p>Global 10 arc-minute resolution grids of topography (ETOPO1 ice-surface) referenced to mean sea-level (which we will consider to be the geoid).</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Convert the grid from the ASCII format of ICGEM to CF-compliant netCDF. Add relevant metadata, including names, units, datum, etc. Fix grid coordinates to be generated by <code>numpy.linspace</code> instead <code>numpy.arange</code> (or the equivalent used by ICGEM internally) to guarantee equal spacing to a higher accuracy. Export to compressed netCDF.</p> <p><strong>Source: </strong><a href="https://doi.org/10.7289/V5C8276M">ETOPO1</a> (grid generated by the <a href="http://icgem.gfz-potsdam.de/home">ICGEM calculation service</a>)</p> <p><strong>Source license: </strong><a href="https://ngdc.noaa.gov/mgg/global/dem_faq.html#sec-2.4">public domain</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/earth-topography-10arcmin">https://github.com/fatiando-data/earth-topography-10arcmin</a></p>
Fatiando a Terra Data: Earth - Gravity grid at 10 arc-minute resolution
<p>Global 10 arc-minute resolution grids of gravity acceleration (gravitational and centrifugal) at 10 km geometric height.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Convert the grid from the ASCII format of ICGEM to CF-compliant netCDF. Add relevant metadata, including names, units, datum, etc. Fix grid coordinates to be generated by <code>numpy.linspace</code> instead <code>numpy.arange</code> (or the equivalent used by ICGEM internally) to guarantee equal spacing to a higher accuracy. Export to compressed netCDF.</p> <p><strong>Source: </strong><a href="https://doi.org/10.5880/icgem.2015.1">EIGEN-6C4</a> spherical harmonic model (generated by the <a href="http://icgem.gfz-potsdam.de/home">ICGEM calculation service</a>)</p> <p><strong>Source license: </strong><a href="https://doi.org/10.5880/icgem.2015.1">CC-BY</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/earth-gravity-10arcmin">https://github.com/fatiando-data/earth-gravity-10arcmin</a></p>
Hybrid gridded demographic data for the world, 1950-2020 0.25˚ resolution
<p>This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.25 degree grid resolution.</p> <p>This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data. Demographic fractions are given for the time period covered by the UN WPP model (1950-2050) while demographic totals are given for the time period covered by the combination of GPWv4 and Histsoc (1950-2020). More detailed can be found on the page of <a href="https://doi.org/10.5281/zenodo.3768003">the original version</a> (https://doi.org/10.5281/zenodo.3768003).</p> <p>This release increases the resolution to 0.25˚ and is explicitly designed to match with the grid definition of the ERA5 climate reanalysis dataset. For pre-2000 population data, the ISIMIP Histsoc data was upscaled from it's native 0.5˚ resolution.</p>
Fatiando a Terra Data: Lightning Creek Sill Complex, Australia - Airborne total-field magnetic anomaly grid
<p>Regular grid of total-field magnetic anomaly data from the Lightning Creek Sill Complex, featuring a text-book dipolar anomaly. This is a gridded version of the Lightning Creek anomaly from <a href="https://github.com/fatiando-data/osborne-magnetic">our Osborne Mine dataset</a>.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Slice to area of interest. Project data to UTM. Interpolate to a regular 50 m grid at 500 m height. Add CF-compliant metadata to the grid. Export to compressed netCDF 4.</p> <p><strong>Source: </strong>Geophysical Acquisition & Processing Section 2019. MIM Data from Mt Isa Inlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia, Canberra. <a href="http://pid.geoscience.gov.au/dataset/ga/142419">http://pid.geoscience.gov.au/dataset/ga/142419</a></p> <p><strong>Source license: </strong><a href="http://pid.geoscience.gov.au/dataset/ga/142419">CC-BY</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/lightning-creek-magnetic-grid">https://github.com/fatiando-data/lightning-creek-magnetic-grid</a></p>
2017 August 21 WL7C Solar Eclipse Reverse Beacon Raw Data from Grid EM66HN
<p><strong>Craig Daniel Fry WL7C, EM66HN</strong></p> <p><strong>Lat/Lon: 36.561667, -87.376667</strong></p> <p><strong>Antennae Type: Fan Dipole</strong></p> <p><strong>Antenna Pointing Direction: Near Vertical Incidence Skywave (NVIS), Dipole listening NE-SW; approx 20ft in height, 80 ft loaded dipole</strong></p> <p><strong>Receiver Type:SDRPlay SP2 pro</strong></p> <p><strong>Additional Notes: </strong></p> <p><strong>(1)The CSV fi</strong><strong>le is a diary of the spots reported to the Reverse Beacon Network and pertinent timing, narrow frequency bands, and dx call signs</strong></p> <p><strong>(2) Software used: HDSDR, CWSkimmer</strong></p>
Downscaled ERA-Interim gridded historical climate data over China (1980-2010)
<p><strong>Gridded historical climate data over China, spanning 1981 to 2010. Dynamically downscaled to 25km resolution using the PRECIS2.0 (HadRM3P) Met Office regional climate model, driven by ERA-Interim reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF).</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. Data created as part of the Met Office Climate Science for Service Partnership China (<a href="https://www.metoffice.gov.uk/research/collaboration/cssp-china">CSSP China</a>), work package 1 output, supported by the Newton Fund and the Department for Business, Energy & Industrial Strategy (BEIS) <a href="https://www.gov.uk/government/publications/newton-fund-building-science-and-innovation-capacity-in-developing-countries/newton-fund-building-science-and-innovation-capacity-in-developing-countries">UK-China Research Innovation Partnership Fund</a>.</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 20CRv2c data set: <a href="https://zenodo.org/record/2558135#.XJj2uaD7RWE">Downscaled 20CRv2c (#37) gridded historical climate data over China (1851-2010)</a> doi: 1</em>0.5281/zenodo.2558135</p>
Determinant Quantum Monte Carlo data for the Hubbard model on the half filled square lattice, on a (U,B)-grid
<p>Data generated with QUEST 1.4.9. For documentation see these two homepages:<br> Original homepage: http://quest.ucdavis.edu/<br> Newest version available at: https://code.google.com/archive/p/quest-qmc/</p> <p>The simulations are done at half filling on a square lattice, with the following parameters:</p> <ul> <li>Lattice sizes: 4x4, 6x6, 8x8, 10x10, 12x12, periodic boundary conditions</li> <li>Trotter discretizations: 0.1 and 0.2</li> <li>Inverse temperature beta = 10.0</li> <li>48 values for the on-site interaction U from 0.0 to 10.0</li> <li>48 values for the magnetic field (in z-direction) B from 0.0 to 4.0</li> <li>10000 warmup sweeps, 30000 measurement sweeps</li> </ul> <p>The following data from equal time measurements are available:</p> <ul> <li>Charge-Charge Correlation (next neighbors)</li> <li>Greens Function (n.n.)</li> <li>Magnetization</li> <li>Double Occupancy</li> <li>Kinetic Energy</li> <li>Total Energy</li> <li>Spin-Spin Correlation (n.n.)</li> <li>Spin-Spin Correlation (only ZZ) (n.n.)</li> <li>Ferromagnetic Structure Factor (ZZ)</li> <li>Antiferromagnetic Structure Factor (ZZ)</li> </ul> <p>The data are available as a hdf5 archive. The python script 'extract.py' illustrates the access with h5py. Relevant QUEST input parameters are provided in the group 'parameters' within the archive.</p> <p>All calculated quantities are averaged over multiple consecutive simulations, which is why the data is not presented in the usual QUEST output. This was necessary due to limited walltime on the used supercomputer.</p> <p>The authors acknowledge the North-German Supercomputing Alliance (HLRN) for providing computing resources via project number hbp00046 that have contributed to these results.</p>
Average daily air temperature, precipitation and relative sunshine duration for Vallon de Nant catchment, extracted from gridded MeteoSwiss data (1961-2020)
<p>This excel file contains time series of daily temperature, precipitation and relative sunshine duration obtained as the spatial average of gridded data sets. The underlying original gridded data sets produced by MeteoSwiss are known as RhiresD, TabsD and SrelD. All meta data are included in the excel file.</p> <p>The data can e.g. be used for hydrological modelling. Comparison to local station data is not included.</p> <p><strong>This data set as well as the original data set should be cited</strong>.</p>
EUPPBench postprocessing benchmark dataset - gridded data - Part III
<p>The EUMETNET EUPPBench postprocessing benchmark gridded data is an analysis-ready dataset to perform benchmarks of different postprocessing methods on a common dataset.</p> <p>This dataset is using the <a href="https://zarr.dev/">Zarr</a> format. Please look at the <a href="https://zarr.readthedocs.io/en/stable/">Zarr documentation</a> to see how to load and access the data.</p> <p>The documentation of the dataset is available on <a href="https://eupp-benchmark.github.io/EUPPBench-doc/">https://eupp-benchmark.github.io/EUPPBench-doc/</a> .</p> <p>The official way to download the dataset is through the <a href="https://github.com/ecmwf/climetlab">climetlab</a> <a href="https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark">EUMETNET postprocessing benchmark plugin</a>.</p> <p>This Zenodo repository aims to preserve the dataset by providing long-term storage.</p> <p>Please read the LICENSE file for more information on the data licenses.</p> <p><strong>Installation procedure</strong></p> <p>Download the 3 parts of the dataset</p> <ol> <li> <a href="https://doi.org/10.5281/zenodo.7429236">EUPPBench-gridded.part.z01</a></li> <li> <a href="http://Remark You might also be interested by the gridded data part of this dataset also available on Zenodo here: https://doi.org/10.5281/zenodo.7428239">EUPPBench-gridded.part.z02</a></li> <li> <a href="https://doi.org/10.5281/zenodo.7429917">EUPPBench-gridded.part.zip</a></li> </ol> <p>in a given folder, and on a Linux (or mac OS) terminal, and still in this folder, enter the following commands</p> <pre><code class="language-bash">zip -FF EUPPBench-gridded.part.zip --out EUPPBench-gridded.zip rm EUPPBench-gridded.part.* unzip EUPPBench-gridded.zip </code></pre> <p>This will unpack the dataset. You need at least 250Gb of free space on your disk to perform this operation.</p> <p><strong>Citation</strong></p> <p>If you use this dataset for a publication, please cite the dataset article:</p> <ul> <li>Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A., Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., Jobst, D., Merše, J., Mlakar, P., Möller, A., Mestre, O., Taillardat, M., and Vannitsem, S.: The EUPPBench postprocessing benchmark dataset v1.0, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2022-465">https://doi.org/10.5194/essd-2022-465</a>, in review, 2023.</li> </ul> <p><strong>Remark</strong></p> <p>You might also be interested by the station data part of this dataset also available on Zenodo here: <a href="https://doi.org/10.5281/zenodo.7708362">https://doi.org/10.5281/zenodo.7708362</a>.</p>
EUPPBench postprocessing benchmark dataset - gridded data - Part II
<p>The EUMETNET EUPPBench postprocessing benchmark gridded data is an analysis-ready dataset to perform benchmarks of different postprocessing methods on a common dataset.</p> <p>This dataset is using the <a href="https://zarr.dev/">Zarr</a> format. Please look at the <a href="https://zarr.readthedocs.io/en/stable/">Zarr documentation</a> to see how to load and access the data.</p> <p>The documentation of the dataset is available on <a href="https://eupp-benchmark.github.io/EUPPBench-doc/">https://eupp-benchmark.github.io/EUPPBench-doc/</a> .</p> <p>The official way to download the dataset is through the <a href="https://github.com/ecmwf/climetlab">climetlab</a> <a href="https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark">EUMETNET postprocessing benchmark plugin</a>.</p> <p>This Zenodo repository aims to preserve the dataset by providing long-term storage.</p> <p>Please read the LICENSE file for more information on the data licenses.</p> <p><strong>Installation procedure</strong></p> <p>Download the 3 parts of the dataset</p> <ol> <li> <a href="https://doi.org/10.5281/zenodo.7429236">EUPPBench-gridded.part.z01</a></li> <li> <a href="https://doi.org/10.5281/zenodo.7429420">EUPPBench-gridded.part.z02</a></li> <li> <a href="https://doi.org/10.5281/zenodo.7429917">EUPPBench-gridded.part.zip</a></li> </ol> <p>in a given folder, and on a Linux (or mac OS) terminal, and still in this folder, enter the following commands</p> <pre><code class="language-bash">zip -FF EUPPBench-gridded.part.zip --out EUPPBench-gridded.zip rm EUPPBench-gridded.part.* unzip EUPPBench-gridded.zip </code></pre> <p>This will unpack the dataset. You need at least 250Gb of free space on your disk to perform this operation.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for a publication, please cite the dataset article:</p> <ul> <li>Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A., Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., Jobst, D., Merše, J., Mlakar, P., Möller, A., Mestre, O., Taillardat, M., and Vannitsem, S.: The EUPPBench postprocessing benchmark dataset v1.0, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2022-465">https://doi.org/10.5194/essd-2022-465</a>, in review, 2023.</li> </ul> <p><strong>Remark</strong></p> <p>You might also be interested by the station data part of this dataset also available on Zenodo here: <a href="https://doi.org/10.5281/zenodo.7708362">https://doi.org/10.5281/zenodo.7708362</a>.</p>
EUPPBench postprocessing benchmark dataset - gridded data - Part I
<p>The EUMETNET EUPPBench postprocessing benchmark gridded data is an analysis-ready dataset to perform benchmarks of different postprocessing methods on a common dataset.</p> <p>This dataset is using the <a href="https://zarr.dev/">Zarr</a> format. Please look at the <a href="https://zarr.readthedocs.io/en/stable/">Zarr documentation</a> to see how to load and access the data.</p> <p>The documentation of the dataset is available on <a href="https://eupp-benchmark.github.io/EUPPBench-doc/">https://eupp-benchmark.github.io/EUPPBench-doc/</a> .</p> <p>The official way to download the dataset is through the <a href="https://github.com/ecmwf/climetlab">climetlab</a> <a href="https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark">EUMETNET postprocessing benchmark plugin</a>.</p> <p>This Zenodo repository aims to preserve the dataset by providing long-term storage.</p> <p>Please read the LICENSE file for more information on the data licenses.</p> <p><strong>Installation procedure</strong></p> <p>Download the 3 parts of the dataset</p> <ol> <li><a href="https://doi.org/10.5281/zenodo.7429236">EUPPBench-gridded.part.z01</a></li> <li><a href="https://doi.org/10.5281/zenodo.7429420">EUPPBench-gridded.part.z02</a></li> <li><a href="https://doi.org/10.5281/zenodo.7429917">EUPPBench-gridded.part.zip</a></li> </ol> <p>in a given folder, and on a Linux (or mac OS) terminal, and still in this folder, enter the following commands</p> <pre><code class="language-bash">zip -FF EUPPBench-gridded.part.zip --out EUPPBench-gridded.zip rm EUPPBench-gridded.part.* unzip EUPPBench-gridded.zip</code></pre> <p>This will unpack the dataset. You need at least 250Gb of free space on your disk to perform this operation.</p> <p><strong>Citation</strong></p> <p>If you use this dataset for a publication, please cite the dataset article:</p> <ul> <li>Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A., Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., Jobst, D., Merše, J., Mlakar, P., Möller, A., Mestre, O., Taillardat, M., and Vannitsem, S.: The EUPPBench postprocessing benchmark dataset v1.0, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2022-465">https://doi.org/10.5194/essd-2022-465</a>, in review, 2023.</li> </ul> <p> </p> <p><strong>Remark</strong></p> <p>You might also be interested by the station data part of this dataset also available on Zenodo here: <a href="https://doi.org/10.5281/zenodo.7708362">https://doi.org/10.5281/zenodo.7708362</a>.</p>
QAMeleon Physical layer evaluation of 1x24 WSS targeting flex-grid operation simulation raw data
<p>Physical layer simulations of 1x24 WSS has been supported, targeting flex-grid operation for transmission of 16-QAM and 64-QAM at 32, 64, and 128 Gbaud </p>
SuperDARN Grid data in netCDF format (2013-Jan)
<p>2013-Jan SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN Grid data in netCDF format (2013-Feb)
<p>2013-Feb SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN Grid data in netCDF format (2013-Mar)
<p>2013-Mar SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN Grid data in netCDF format (2013-May)
<p>2013-May SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</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
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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