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46 results for “Variable resolution”
2005-2099 High resolution bioclimatic variables for the surface and bottom of the Mediterranean Sea.
<p><em><span>This dataset provides annual statistical descriptors (mean, minimum, maximum, range and standard deviation) of key biogeochemical and physical variables for the Mediterranean Sea. It covers the period 2005-2099 under the RCP8.5 scenario, with a spatial resolution of 1/24 degree (~4km²). Variables include temperature, salinity, pH, water velocity, nutrients (NO3, PO4, NH4), dissolved inorganic carbon, oxygen, and net primary production. Data are available for both surface and at bathymetry level. The original projections were generated using OGSTM-BFM and MFS16 models at daily time and 1/16 degree grid resolution. We downscaled these to 1/24 degree and applied Quantile Delta Mapping bias correction using CMEMS reanalysis products for 2005-2020. The dataset is provided in a user-friendly format, making it accessible for various ecological and environmental modelling applications.</span></em></p>
CLM/CTSM glacier input datasets used for study on evaluation variable-resolution CESM2 in High-Mountain Asia
<p><strong>General Info</strong></p> <p>This data archive contains the updated glacier-cover and glacier regions for the Community Land Model version 5 (CLM5)/Community Terrestrial Systems Model (CTSM). The updated glacier-cover and glacier regions are used for a study on the evaluation of variable-resolution (VR) CESM2 in High Mountain Asia (<a href="https://tc.copernicus.org/preprints/tc-2022-256/">https://tc.copernicus.org/preprints/tc-2022-256/</a>). The data archive also contains the model scripts and input files that have been used to create the glacier-cover dataset. The global glacier outlines used for the glacier-cover dataset were retrieved from the Randolph Glacier Inventory version 6 (RGI-Consortium, 2017). The vector data for the Greenland and Antarctic ice sheets were retrieved from the masks of Bedmachine version 4 (Morlighem et al., 2017, 2021) and version 2 (Morlighem et al., 2020; Morlighem, 2020), respectively. </p> <p><strong>Contact</strong></p> <p>René Wijngaard (<a href="mailto:r.r.wijngaard.uu@gmail.com">r.r.wijngaard.uu@gmail.com</a> / <a href="mailto:r.r.wijngaard@uu.nl">r.r.wijngaard@uu.nl</a>) </p> <p><strong>Dataset Contents </strong></p> <pre><code>mksrf_glacier_3x3min_simyr2000.c210708.nc</code></pre> <p>The updated glacier-cover dataset, encompassing three 3-minute datasets: 1) fractional land ice coverage, including both glaciers and ice sheets (PCT_GLACIER), 2) distributions of areal glacier coverage by elevation (PCT_GLC_GIC), and 3) distributions of areal ice-sheet coverage by elevation (PCT_GLC_ICESHEET).</p> <pre><code>mksrf_GlacierRegion_10x10min_nomask_c200813.nc</code></pre> <p>The updated glacier regions, encompassing five different glacier regions (0 - Other regions, 1 - Inside standard CISM grid but outside Greenland itself, 2 - Greenland, 3 - Antarctica, and 4 - High Mountain Asia (new)), used to set the ice melt and runoff behaviour in CLM5/CTSM (more detailed information can be found in the CLM5 Documentation, <a href="https://escomp.github.io/ctsm-docs/">https://escomp.github.io/ctsm-docs/</a>)</p> <pre><code>model_scripts.tar</code></pre> <p>Model scripts used for creating the glacier-cover dataset. A README file is included that lists instructions on how to make the glacier-cover dataset. </p> <pre><code>glacier_final.tar</code></pre> <p>Input files used to create the glacier-cover dataset. The following files are included: a global 30-arcsec merged BedMachine/GMTED2010 elevation dataset (gmted_bedmachine_stitched.nc) and land-sea mask (gmted2010_modis-rawdata-lonshift.nc), Antarctica land mask (BedMachineAntarcticaRotate2RotateBack_2020-07-15_v02_lonshift.map_TO_30arcsec.nc), Greenland land mask (BedMachineGreenland-2021-04-20.map_TO_30arcsec.nc), and 30-arcsec datasets encompassing glacier-cover (30arcsec_00_rgi60_World.nc) and ice-sheet cover (30arcsec_00_BM_World.nc).</p>
Global Soil Bioclimatic variables at 30 arc second resolution
<p>Soil temperature layers were calculated by adding monthly soil temperature offsets to monthly air-temperature maps from CHELSA (date range 1979-2013) (Karger et al. 2017, Sci Data). These soil temperature layers were then used to calculate annual means, temperature ranges, standard deviation, warmest and coldest months and quarters. Wettest and driest quarters were identified for each pixel based on CHELSA monthly values. A quarter is a period of three months (1/4 of the year).</p> <p>When using any of these layers, please cite: Lembrechts et al., Global maps of soil temperature (2021). Global Change Biology. DOI: <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16060">10.1111/gcb.16060</a></p> <p>For each Soil Bioclim layer, two depth intervals are available: 0 - 5 cm and 5 - 15 cm.</p> <p>We have followed the generally accepted definitions of BIO 1 - BIO 11: </p> <ul> <li>SBIO1 = Annual Mean Temperature</li> <li>SBIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp))</li> <li>SBIO3 = Isothermality (BIO2/BIO7) (×100)</li> <li>SBIO4 = Temperature Seasonality (standard deviation ×100)</li> <li>SBIO5 = Max Temperature of Warmest Month</li> <li>SBIO6 = Min Temperature of Coldest Month</li> <li>SBIO7 = Temperature Annual Range (BIO5-BIO6)</li> <li>SBIO8 = Mean Temperature of Wettest Quarter</li> <li>SBIO9 = Mean Temperature of Driest Quarter</li> <li>SBIO10 = Mean Temperature of Warmest Quarter</li> <li>SBIO11 = Mean Temperature of Coldest Quarter</li> </ul> <p>These layers are also publicly available as Google Earth Engine assets. These are acessible via:</p> <ul> <li><a href="https://code.earthengine.google.com/?asset=projects/crowtherlab/soil_bioclim/SBIO_v1_0_5cm">https://code.earthengine.google.com/?asset=projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm</a></li> <li><a href="https://code.earthengine.google.com/?asset=projects/crowtherlab/soil_bioclim/SBIO_v1_5_15cm">https://code.earthengine.google.com/?asset=projects/crowtherlab/soil_bioclim/SBIO_v2_5_15cm</a></li> </ul> <p>Also available are monthly maps of soil temperature, for two depth intervals (0-5 cm and 5-15 cm). For example, the soil temperature map for month 1 (January) at 0-5 cm is named soilT_1_0_5cm.tif'. </p> <p>To mask pixels by the proportion of extrapolation, the files 'PCA_int_ext_0_5cm.tif' and 'PCA_int_ext_5_15cm.tif' can be used. </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>
Global 32-4 km variable-resolution mesh for the MPAS-Atmosphere model
<div> <div> <div>This mesh was created by a collaboration between the Department of Energy’s Water Cycle and Climate Extremes Modeling (WACCEM) project and the</div> <div>Mesoscale and Microscale Meteorology (MMM) Laboratory at the National Science Foundation National Center for Atmospheric Research (NSF/NCAR).</div> <div> </div> <div>The mesh contains 1,830,914 horizontal grid cells. The circular refinement region has a radius of approximately 20 degrees. The 4-32km grid-spacing range aims to achieve convection-permitting resolution in the high-resolution domain and resolution sufficient for the jet stream and mid-latitude wave activities (Lu et al., 2015) in the low-resolution domain.</div> <div> </div> <div>The netcdf file "x8.1830914.grid.nc" includes the variables defining the global unstructured grid for the MPAS model as described in the <a href="https://mpas-dev.github.io/files/documents/MPAS-MeshSpec.pdf">MPAS Mesh Specification</a>. Following the other MPAS mesh data, we provide graph.info.part.* files necessary for the Message Passing Interface (MPI) parallelism. For example, a simulation using 1024 MPI ranks will use graph.info.part.1024. For the number of MPI tasks not provided in this dataset, a user needs to create a new partitioning file using the METIS tool and the graph.info file as described in the MPAS user guide (available <a href="https://mpas-dev.github.io/atmosphere/atmosphere_download.html" target="_blank" rel="noopener">here</a>).</div> <div> </div> </div> </div> <div>This data will also be available from the <a href="https://mpas-dev.github.io/atmosphere/atmosphere_meshes.html">MPAS mesh website</a>.</div> <div> </div> <div>The mesh generation is supported by the U.S. Department of Energy Office of Science Biological and Environmental Research (BER) as part of the Regional and Global Model Analysis Program Area. We acknowledge the use of computational resources of the National Energy Research Scientific Computing Center (NERSC). The Pacific Northwest National Laboratory is operated for the Department of Energy by Battelle Memorial Institute under contract DE-AC05-76RL01830.</div>
MPAS-A v7.0 variable resolution simulations over China
<p>Dynamical downscaling is conducted by MPAS-A v7.0 with a global variable resolution (MPAS-VR) and truncated regional (MPAS-RCM) configurations.</p> <p>Two sets of MPAS experiments are carried out to evaluate the simulation performance of summer precipitation in China. One is a global variable resolution run (VR) using 4 times grid refinement over the domain center (32<sup>o</sup>N, 100<sup>o</sup>E), and the grid spacing smoothly varies from ~25km to ~92km outside the refinement. The other is a regional configuration experiment (RCM) with the grid truncated according to an ellipse in the same center.</p>
Variability of OB stars from TESS southern Sectors 1-13 and high-resolution IACOB and OWN spectroscopy
<p>Typical MESA and GYRE inlists associated with <a href="https://arxiv.org/abs/2005.09658">Burssens et al. 2020</a>. MESA v. 12155, GYRE version v. 5.2.</p> <p><em>Context:</em> Lack of high-precision long-term continuous photometric data for large samples of stars has prevented the large-scale exploration of pulsational variability in the OB star regime. As a result, the candidates for in-depth asteroseismic modelling remained limited to a few tens of dwarfs. The TESS nominal space mission has surveyed the southern sky, including parts of the galactic plane, yielding continuous data of at least 27 d for hundreds of OB stars.<br> <em>Aims:</em> We aim to couple TESS data in the southern sky with ground-based spectroscopy to study the variability in two dimensions, mass and evolution. We focus mainly on the presence of coherent pulsation modes that may or may not be present in the predicted theoretical instability domains and unravel all frequency behaviour in the amplitude spectra of the TESS data.<br> <em>Methods: </em>We compose a sample of 98 OB-type stars observed by TESS in Sectors 1-13 and with available multi-epoch, high-resolution spectroscopy gathered by the IACOB and OWN surveys. We present the short-cadence 2-min light curves of dozens of OB-type stars, that have one or more spectra in the IACOB or OWN database. Based on these light curves and their Lomb-Scargle periodograms we perform variability classification and frequency analysis. We place the stars in the spectroscopic Hertzsprung-Russell diagram to interpret the variability in an evolutionary context.<br> <em>Results:</em> We deduce diverse origins of the mmag-level variability found in all of the 98 OB stars in the TESS data. We find among the sample several new variable stars, including three hybrid pulsators, three eclipsing binaries, high frequency modes in a Be star, and potential heat-driven pulsations in two Oe stars. <br> <em>Conclusions:</em> We identify stars for which future asteroseismic modelling is possible, provided mode identification is achieved. By comparing the position of the variables to theoretical instability strips we discuss the current shortcomings in non-adiabatic pulsation theory, and the distribution of pulsators in the upper Hertzsprung-Russell diagram.</p> <p> </p> <p> </p>
Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity - ACCESS-OM2 data and plotting routines
<p>This repository contains the processed data and plotting routines associated with the article</p> <p>Holmes, Groeskamp, Stewart and McDougall (2022), Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity, Journal of Advances in Modeling Earth Systems (JAMES), doi: 10.1029/2021MS002914, http://dx.doi.org/10.1029/2021MS002914</p> <p>The contents includes post-processed data output from the 1-degree ACCESS-OM2 ocean-sea-ice model simulations and the python/jupyter plotting routines required to make the plots.</p> <p>The processing script is Holmes2022JAMES_Neutral_Diffusion_ACCESS-OM2_Plotting_Script.ipynb. The data files consist of time-averages or time series of certain metrics processed using NCO tools from the raw ACCESS-OM2 simulation output.</p>
Variable-resolution CESM2 over Antarctica (ANTSI): Monthly outputs used for evaluation.
<p>Funding generously provided by NSF Award 1952199 as well as additional support from NCAR in the creation of the dataset and the use of VRCESM. </p> <p>Included are outputs from an AMIP-style (data-driven ocean and sea ice) simulation of Variable-Resolution CESM2 from 1979-2015. Resolution is 1° globally with a refined 0.25° resolution over the Southern Ocean as well over the Antarctic ice sheet. Outputs are at a monthly timescale, and include those variables relevant for evaluation.</p> <p>Each netcdf file ends with several relevant tags to indicate <source>.<output deisgnator>.<variable>.<time period>.nc. Atmospheric variables are labeled with "cam" whereas ice sheet variables are labeled with "clm2".</p> <p>clm2.h0.SNOW.198901-199812.nc</p> <p>cam.h1.Q.198901-199812.nc</p> <p>Variables are described for CESM2 (see NCAR documentation for clm and cam)</p> <p>Variables included for cam include FLDS, FLNS, CLDICE, CLDLIQ, LHFLX, PRECC, PRECL, PRECSC, PS,Q, SHFLX, U, V, Z3</p> <p>Variables included for clm2 include FIRA, FIRE, FLDS, FSDS, FSH, QICE, QRUNOFF, QSNOMELT, QSOIL, RAIN, SNOW</p> <p> </p>
High-resolution large weighing lysimeter measurements with meteorological and soil-hydrological variables from a Mediterranean Savanna
<p>Raw and processed lysimeter weighing and flux data at the instrumental site ES-LMa of six large high-precision weighing lysimeters in a Mediterranean Savanna ecosystem for the period from 2019-06-01 to 2020-05-31. Additionally, meteorological and radiometric data are provided. Additionally, code for the lysimeter processing is provided.</p> <p>Reproducible workflow of the article <strong>Paulus et al. 2022: Resolving seasonal and diel dynamics of non-rainfall water inputs in a Mediterranean ecosystem using lysimeters. HESS, https://doi.org/10.5194/hess-2021-519</strong></p> <p>Variables, units and detailed description are found in the README.html file.</p>
Dataset used for "Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia"
<p><strong>General Info</strong></p> <p>This dataset contains monthly output from two 20-year (1979-1998) variable-resolution (VR) CESM2 simulations (HMA_VR7a and HMA_VR7b). The coupled atmosphere-land simulations were run with a newly generated VR grid that has regional grid refinements up to 7 km over High Mountain Asia. The HMA_VR7b simulation was performed with an updated glacier-cover dataset (<a href="https://doi.org/10.5281/zenodo.7864689">https://doi.org/10.5281/zenodo.7864689</a>) and includes snow and glacier model modifications. Further, monthly output from a globally uniform 1-degree CESM simulation (NE30), used for evaluation of the HMA VR simulations, is also included. The monthly output have been used for analysis and discussion in the paper “<em>Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia” </em>that is currently under review in the Cryosphere Discussions, <a href="https://tc.copernicus.org/preprints/tc-2022-256/">https://tc.copernicus.org/preprints/tc-2022-256/</a>.</p> <p><strong>Contact</strong></p> <p>René Wijngaard (<a href="mailto:r.r.wijngaard.uu@gmail.com">r.r.wijngaard.uu@gmail.com</a> / <a href="mailto:r.r.wijngaard@uu.nl">r.r.wijngaard@uu.nl</a>) </p> <p><strong>Raw Data</strong></p> <p>Raw monthly and daily unstructured HMA VR model output are available on request. </p> <p><strong>Dataset Contents</strong></p> <pre><code>NE30.tar HMA_VR7a.tar HMA_VR7b.tar </code></pre> <p>These files contain atmosphere (CAM) and land (CLM) model output that are regridded to a 1-degree finite volume (0.9 x 1.25 degrees latitude/longitude) grid. The following variables are included: CLDLIQ, OMEGA, Q, STEND_CLUBB, SWCF, T, Z3, EFLX_LH_TOT, FGR, FIRE, FLDS, FSA, FSDS, FSH, FSM, FSNO, FSM, FSR, H2OSNO, PCT_LANDUNIT, QICE_MELT, QSNOFRZ, RAIN, SNOW, and TSA. </p> <pre><code>SMB_HMA_VR7a.tar SMB_HMA_VR7b.tar</code></pre> <p>These files contain unstructured SMB-related CLM model output (i.e., on the HMA VR grid). The following variables are included: PCT_LANDUNIT, QRUNOFF_ICE, QSNOFRZ_ICE, QSNOMELT_ICE, QSOIL_ICE, RAIN_ICE, and SNOW_ICE.</p>
Climate model experiments of regional-scale tree die-off replaced by shrubs (select variables at daily time resolution): Part 4
Open the record for dataset details and reuse information.
Climate model experiments of regional-scale tree die-off replaced by grass (select variables at daily time resolution): Part 2
Open the record for dataset details and reuse information.
Output files for Variable-Resolution Community Earth System Model (VR-CESM) simulations with highest resolutions over the Euro-Mediterranean
<p>Output files for Variable-Resolution Community Earth System Model (VR-CESM) simulations with highest resolutions over the Euro-Mediterranean and notebooks created for analyses and visualization.</p> <p>Configuration names:</p> <ul> <li>ne30_n</li> <li>ne30x4_n</li> <li>ne30x4_t</li> <li>ne30x8_t</li> </ul> <p>Variables at single level: PHIS,PRECC,PRECL,PS,TREFHT,LHFLX,SWCF,LWCF,TMQ,SNOWHLND</p> <p>Variables at pressure levels:U,V,OMEGA,RELHUM,Z3,Q</p>
High resolution solar irradiance variability climatology dataset part 1: direct, diffuse, and global irradiance
<p><strong>Dataset paper</strong></p> <p>See the official dataset description paper (preprint) over at <a href="https://essd.copernicus.org/preprints/essd-2022-456/">Earth System Science Data</a>.</p> <p><strong>Dataset description</strong></p> <p>High resolution surface solar irradiance observations from the Baseline Surface Radiation Network (BSRN) of Cabauw, the Netherlands. This dataset spans 10 years, <em>from 2011-02 until 2020-12-31</em>.</p> <p>This dataset is the preprocessed 1 Hz observational record of direct, diffuse, and global horizontal irradiance, which is the basis for the official BSRN 1-minute dataset published at <a href="https://doi.pangaea.de/10.1594/PANGAEA.940531">PANGAEA</a>. Please refer to the official dataset for detailed metadata, instrument information, quality control flags, the full radiation balance and more (1 minute resolution). More information about the observational site Cabauw can be found at the <a href="https://ruisdael-observatory.nl/cabauw/">Ruisdael Observatory website</a>, and more general information about BSRN is <a href="https://essd.copernicus.org/articles/10/1491/2018/">published on ESSD</a>.</p> <p><strong>Part 1 of 2</strong></p> <p>This dataset is the basis for an analysis of surface solar irradiance variability, derived variables, supplementary meteorological data, and quicklooks, available in part 2 here: <a href="https://doi.org/10.5281/zenodo.7092058">https://doi.org/10.5281/zenodo.7092058</a></p> <p><strong>Usage disclaimer</strong></p> <p>While resampling this 1-Hz data to 1-minute, with the official BSRN quality flags, should reproduce an identical dataset to the official 1-minute BSRN dataset, this has not yet been validated for this version. When you require the most reliable version of the radiation measurements, where variability at a higher resolution than 1 minute is not of concern, please refer to the official BSRN dataset at PANGAEA.</p>
Project files provided as supporting information to the manuscript "Fast, accurate, and system-specific variable-resolution modelling of proteins"
<p>Project files provided as supporting information to the manuscript "Fast, accurate, and system-specific variable-resolution modelling of proteins".</p> <p>The dataset contains the following files:</p> <p>- RMSD_adk: files containing the root-mean-square deviation computed on the C-alpha atoms/beads in the atomistic and CANVAS simulations of adk (Figure 6).</p> <p>- RMSF_adk: files containing the root-mean-square fluctuations of the C-alpha atoms/beads in the atomistic and CANVAS simulations of adk (Figure 6).</p> <p>- APBS_adk: PQR file of the system and DX file of the surface potential, for both the atomistic and CANVAS systems (Figure 7).</p> <p>- SASA_antibody: files containing the per-residue solvent accessible surface area of the atomistic region of the ake protein, in the atomistic and CANVAS simulations (Figure 8).</p> <p>- RMSF_antibody: files containing the root-mean-square fluctuations of the C-alpha atoms/beads in the atomistic and CANVAS simulations of antibody (Figure 9).</p> <p>- APBS_antibody: PQR file of the system and DX file of the surface potential, for both the atomistic and CANVAS systems (Figure 10).</p> <p>- SASA_antibody: files containing the per-residue solvent accessible surface area of the hinge region of the antibody for each conformational cluster, in the atomistic and CANVAS simulations (Figure 11).</p> <p>- RMSIP_antibody: RMSIP between the essential subspaces computed from the atomistic and CANVAS simulations (Figure S3).</p> <p>- rgyr_antibody: files containing the radii of gyration of the antibody for each conformational cluster, in the atomistic and CANVAS simulations (Figure S4).</p> <p>- ake_AA.mp4: video of the all-atom simulation of adenylate kinase</p> <p>- ake_canvas.mp4: video of the canvas simulation of adenylate kinase</p>
High resolution solar irradiance variability climatology dataset part 2: classifications, supplementary data, and statistics
<p><strong>Dataset paper</strong></p> <p>See the official dataset description paper (preprint) over at <a href="https://essd.copernicus.org/preprints/essd-2022-456/">Earth System Science Data</a></p> <p><strong>Dataset description</strong></p> <p>High resolution surface solar irradiance series classification, cloud shadow and enhancement statistics, and satellite observations for studying intra-day surface solar irradiance variability.</p> <p><strong>Part 2 of 2</strong></p> <p>This dataset is the derived from the <a href="https://doi.org/10.5281/zenodo.7093164">1 Hz observational record of direct, diffuse, and global horizontal irradiance</a> measured by the Baseline Surface Radiation Network station at Cabauw, the Netherlands. More information about the observational site Cabauw can be found at the <a href="https://ruisdael-observatory.nl/cabauw/">Ruisdael Observatory website</a>.</p> <p><strong>Methodology</strong></p> <p>An extensive dataset description is currently being written for Earth System Science Data. In the mean time, a more condensed description is available in preprint at <a href="https://arxiv.org/abs/2209.10284">Arxiv</a>.</p> <p>Processing scripts are published at this <a href="https://zenodo.org/record/7099491">Zenodo release</a>.</p> <p><strong>Dataset contents</strong></p> <p>This dataset contains daily time series with the following data, from 2011-02 until 2020-12-31:</p> <ol> <li>Cloud shadow and cloud enhancement time series classifications (see methodology)</li> <li>Overcast, clear-sky and variable time series classifications (see methodology)</li> <li><a href="https://www.soda-pro.com/web-services/radiation/cams-mcclear/">CAMS McClear</a> for clear-sky global horizontal irradiance (version 3.5)</li> <li><a href="https://www.soda-pro.com/web-services/radiation/cams-mcclear/">CAMS McClear</a> atmospheric composition input (aerosols, ozone, and total column water vapour)</li> <li>Solar elevation and azimuth angles (calculated using <a href="https://github.com/pingswept/pysolar/releases/tag/0.10">PySolar</a>)</li> <li>Quality flags: non-official 1 Hz and official 1-minute (from <a href="https://doi.pangaea.de/10.1594/PANGAEA.940531">BSRN at PANGAEA</a>)</li> <li>Cabauw observatory<a href="https://dataplatform.knmi.nl/dataset/cesar-tower-meteo-lb1-t10-v1-2"> tower wind speed and direction</a></li> </ol> <p>Additional satellite data time series from 2014-01 until 2016-12:</p> <ol> <li>MSGCPP satellite data for an area over central Netherlands (<a href="https://essd.copernicus.org/articles/9/415/2017/">CLAAS2 source</a>)</li> <li>Post processed timeseries of cloud types over Cabauw derived from this MSGCPP satellite data</li> <li>A nubiscope + satellite derived validation dataset for overcast and clear-sky classifications</li> </ol> <p>Statistics files:</p> <ol> <li>Cloud shadow and cloud enhancement event detection and event statistics based on the time series for 2011-2020</li> <li>Daily radiation statistics for 2011-2020</li> </ol> <p>And finally, for all days there are quicklooks available that visualize the irradiance time series, classification, and if available satellite data.</p> <p><strong>Version History</strong></p> <p><em>New in v1.1</em><strong> </strong></p> <ul> <li>CAMS McClear updated from v3.1 to v3.5 (2011-2020)</li> <li>Fix incorrect dominant cloud type in CLAAS2 timeseries (`claas2_processed.zip`, 2014-2016)</li> <li>Update timeseries statistics and quicklooks with new clear-sky data (2011-2020)</li> <li>Added official quality flags (2011-2020)</li> <li>Added preprocessed validation dataset (2014-2016)</li> <li>Added nubiscope to quicklooks (2014-2016)</li> </ul>
Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution (data)
<p>Data for "Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution"</p>
The Quasi-Uniform and Variable-Resolution Mesh files for GRIST
<p>This entry contains the GRIST mesh files to enable the tests in the submitted paper. The "g10.tar.gz" file contains the quasi-uniform mesh of the G10 grid, the "g8-qu.tar.gz" file contains the quasi-uniform mesh of the G8 grid, and the "g8-vr.tar.gz" file contains the variable-resolution mesh of the G8 grid.</p>
Data from: MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling
Species Distribution Models (SDMs) combine information on the geographic occurrence of species with environmental layers to estimate distributional ranges and have been extensively implemented to answer a wide array of applied ecological questions. Unfortunately, most global datasets available to parameterize SDMs consist of spatially interpolated climate surfaces obtained from ground weather station data and have omitted the Antarctic continent, a landmass covering c. 20% of the Southern Hemisphere and increasingly showing biological effects of global change. Here we introduce MERRAclim, a global set of satellite-based bioclimatic variables including Antarctica for the first time. MERRAclim consists of three datasets of 19 bioclimatic variables that have been built for each of the last three decades (1980s, 1990s and 2000s) using hourly data of 2 m temperature and specific humidity. We provide MERRAclim at three spatial resolutions (10 arc-minutes, 5 arc-minutes and 2.5 arc-minutes). These reanalysed data are comparable to widely used datasets based on ground station interpolations, but allow extending their geographical reach and SDM building in previously uncovered regions of the globe.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.