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5,155 results for “resolution”
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 8
<p>Future projections of precipitation by the BM10 model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 5
<p>Future projections of precipitation by the BM1 model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 3
<p>Future projections of 2-meter minimum temperature by the CNN models (BM1, BM10 and BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 2
<p>Future projections of 2-meter maximum temperature by the CNN models (BM1, BM10 and BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 11
<p>Future projections of 2-meter maximum, mean and minimum temperatures by the BMlinear model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 1
<p>Input data (ERA5 and the seven GCMs) used to train the CNN models (BMlinear, BM1, BM10 and BMdense) used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 9
<p>Historical projections of all predictands (2-meter maximum, mean and minimum temperatures, and precipitation) by all CNN models (BMlinear, BM1, BM10, BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia". Each CNN model architecture is available in the file "model.json" and its optimized weights for each case are available in the file "model_weights.h5".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 7
<p>Future projections of precipitation by the BMdense model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
ACCESS-OM2 1° resolution global repeat decade forcing control simulation data for 1972-2018
<p>This data set contains <strong>control </strong>simulation output from the global ocean-sea ice model ACCESS-OM2 in the 1° horizontal configuration over the period 1972-2018.</p> <p>The model was spun-up using modified repeat decade forcing for the period 1962-1971 to equilibrate the ocean to this period prior to most of the anthropogenic ocean heat uptake. This repeat decade forcing simulation was then continued up to present time and relabeled as the control simulation. The output is saved in sets of ten years (output200, output201, ...) in the ocean/ and ice/ folders as netcdf files.</p> <p>The last output folder contains the data for 2012-2018 with the last four years of this output folder (output204) are again the 1972-1975 period and should be omitted from any analysis.</p> <p>For more information on the spin-up and the model configuration, see the Methods section and Fig. 1 in Huguenin, M.F., Holmes, R.M. & England, M.H. Drivers and distribution of global ocean heat uptake over the last half century. <em>Nat Commun</em> 13, 4921 (2022). https://doi.org/10.1038/s41467-022-32540-51</p> <p> </p>
Barriers to Resolution in 1H NMR of Rotating Solids
<p><strong>The NMR raw data is in both jcamp and topspin format. The data folders are separated according to the samples.</strong></p> <p>For the 2MeIm (2-methylimidazole) spectra:</p> <p>- dilution and deuteration tests with 1D, CP, HETCOR and DQ/SQ</p> <p>For the HMB (hexamethylbenzene) spectra:</p> <p>- dilution tests with 1D, CP, HETCOR and DQ/SQ</p> <p>- variable MAS dataset for 1D, T2' and DQ/SQ</p> <p>For the ZIF-8 (zeolitic imidazolate framework) spectra:</p> <p>- dilution tests with 1D, CP, HETCOR and DQ/SQ</p> <p>- variable MAS dataset for T2' (the variable 1D spectra correspond to the first increment of the T2' experiment) and DQ/SQ</p> <p>For the L-Tyrosine.HCl spectra:</p> <p>- variable MAS dataset for 1D, T2' and DQ/SQ</p> <p>For the PS (polystyrene) spectra:</p> <p>- variable MAS dataset for 1D, T2' and DQ/SQ</p> <p><strong>Matlab scripts:</strong></p> <p>- rmseval_conv_1pe (peak fitting script to a voigt function type)</p> <p>- rmseval_gls_1pe (peak fitting script to a GLS function type)</p> <p>- conv_2d_simu_clean_2023 (simulation of 2D peaks with Gaussian, Tilted Gaussian and Lorentzian shapes)</p> <p>- fitting_hmb_antidiag_scaled_2023 (example with HMB data to extract the antidiagonal from a DQ/SQ spectrum and fit to GLS or Voigt)</p> <p> </p>
30m resolution global forest burned area products, 2014-2021
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025° (approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p><p>contacts: zhangzhaoming@aircas.ac.cn / zhangzm@radi.ac.cn</p>
Daily 500 hPa geopotential height from ERA5 for the period 1940-2022 for the North Atlantic domain - 2 degree resolution
<p>Data downloaded from the Copernicus Data Store from the `reanalysis-era5-pressure-levels` dataset. Data converted from hourly to daily and regridded using xarray. </p>
Data from: High-resolution crossover maps for each bivalent of Zea mays using recombination nodules
<p>Recombination nodules (RNs) mark sites of crossing over along pachytene synaptonemal complexes (SCs). Thus, RNs provide the highest resolution cytological marker currently available for defining the frequency and distribution of crossovers along the length of chromosomes because they are observed by electron microscopy. Using the maize inbred line KYS, we have prepared an SC karyotype in which each SC was identified by relative length and arm ratio and related to the proper linkage group using inversion heterozygotes. We mapped 4272 RNs on 2080 identified SCs to produce high-resolution maps of RN frequency and distribution on each bivalent. Average RN frequency per bivalent is closely correlated with SC length. The total length of the RN map is about two-fold shorter than most linkage maps, but there is good correspondence between the relative lengths of the different maps when individual bivalents are considered. Each bivalent has a unique distribution of crossing over, but all bivalents share a high frequency of distal RNs and a severe reduction of RNs at and near kinetochores. The frequency of RNs at knobs is either similar to or higher than the average frequency of RNs along the SCs. These RN maps represent an independent measure of crossing over along maize bivalents.</p>
Data for "The Sensitivity of an Idealized Weddell Gyre to Horizontal Resolution"
<p>This contains restart files and namelists for the paper "The Sensitivity of an Idealized Weddell Gyre to Horizontal Resolution" which is published in JGR:Oceans.</p> <p>Paper DOI: <a href="https://doi.org/10.1029/2023JC019711">https://doi.org/10.1029/2023JC019711</a></p> <p> </p>
Fragment-sequencing unveils local tissue microenvironments at single-cell resolution
<p>This dataset contains the Resolve dataset that was used in the publication. Other data set can be found in GEO with the accession number GSE216189. The code that was used during the analysis can be found on our GitHub repository: https://github.com/Moors-Code/Fragment-sequencing</p><p> </p><p> </p>
Supporting data 3 for "Does increasing horizontal resolution improve the simulation of intense tropical rainfall?"
<p>Selected variables from a Geophysical Fluid Dynamics Laboratory's (GFDL) global atmospheric model version 4.0 (AM4) for a horizontal resolution of ~25 km (c384). Details of the simulations are documented in a manuscript titled "Does increasing horizontal resolution improve the simulation of intense tropical rainfall?" to be submitted to the Geophysical Research letters.</p>
Supporting data 2 for "Does increasing horizontal resolution improve the simulation of intense tropical rainfall?"
<p>Selected variables from a Geophysical Fluid Dynamics Laboratory's (GFDL) global atmospheric model version 4.0 (AM4) for a horizontal resolution of ~50 km (c192). Details of the simulations are documented in a manuscript titled "Does increasing horizontal resolution improve the simulation of intense tropical rainfall?" to be submitted to the Geophysical Research letters.</p>
Supporting data 1 for "Does increasing horizontal resolution improve the simulation of intense tropical rainfall?"
<p>Selected variables from a Geophysical Fluid Dynamics Laboratory's (GFDL) global atmospheric model version 4.0 (AM4) for a horizontal resolution of ~100 km (c96). Details of the simulations are documented in a manuscript titled "Does increasing horizontal resolution improve the simulation of intense tropical rainfall?" to be submitted to the Geophysical Research letters.</p>
Daily-resolution pan-Arctic sea ice thickness
<p>This dataset contains daily-resolution pan-Arctic sea ice thickness estimates, created following the methodology outlined in <strong>Stroeve</strong><a href="https://doi.org/10.1029/2022GL100696"><strong> </strong></a><strong>et al. (2024): "Mapping potential timing of ice algal blooms from satellite"</strong></p> <p>Please refer to this publication for a full description of the data and cite this publication when using these data.</p> <p>Variables: Sea Ice Thickness (m), Longitude, Latitude, Day</p> <p>Dates are stated in the file names</p> <p>IS2 data is available year-round</p> <p>CS2 and CS2S3 data is available for winter seasons only, with each winter season running from 1 October - 30 April of the specified years</p> <p>Days where no data is available are filled with NaNs</p>
GRACE High-Resolution Trend Mascons - Greenland Ice Sheet (2007-2015)
<p>High-resolution mascon trend solution, computed for the Greenland Ice Sheet over the time period from January 2007 and January 2015, where each mascon regression model (including the trend) has been directly estimated from the Gravity Recovery and Climate Experiment (GRACE) Level 1B data. The GAD product has not been restored, meaning the ocean mascons are consistent with the Level 2 GSM product information.</p><p>Description of columns in the dataset:</p><ol><li>Latitude center (deg)</li><li>Longitude center (deg)</li><li>Mass change trend (cm w.e. / yr)</li><li>Mass change trend uncertainty (cm w.e. / yr)</li><li>Latitude minimum (deg)</li><li>Latitude maximum (deg)</li><li>Longitude minimum (deg)</li><li>Longitude maximum (deg)</li><li>Area of mascon (sq. km)</li><li>Label of mascon</li></ol><p>When citing this dataset, please also include this citation:</p><p>Loomis, B. D., D. Felikson, T. J. Sabaka, and B. Medley (2021). High‐spatial‐resolution mass rates from GRACE and GRACE‐FO: Global and ice sheet analyses. <i> Journal of Geophysical Research: Solid Earth, </i><a href="https://doi.org/10.1029/2021JB023024">https://doi.org/10.1029/2021JB023024</a></p>
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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.