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46 results for “surface cover”
FIGURE. Puccinia caulophylli on Caulophyllum robustum (A–G) and Milium effusum (H–N). A. Plants producing spermogonia and aecia on leaf surface in the field. B. Vertical section of a spermogonium. C. Vertical section of an aecia surrounded with peridia. D, E. Aecia on the leaf surface observed by SEM. F. Aeciospores with verrucose surface observed by SEM. G. Aeciospores. H. Telia on the lower leaf surface. I. Uredinia on the leaf surface observed by SEM. J. Uredinia and telia on the leaf surface. K. Vertical section of an uredinium with paraphyses. L. Urediniospore observed by SEM. M. Vertical section of telia covered by host epidermis. N. Vertical section of a telium covered by host epidermis observed by SEM. Scale bars: B, G, N = 30 μm, C, E, I = 100 μm, F, L = 10 μm, K, M = 20 μm. in Phylogenetic approach for identification and life cycles of Puccinia (Pucciniaceae) species on Poaceae from northeastern China
FIGURE. Puccinia caulophylli on Caulophyllum robustum (A–G) and Milium effusum (H–N). A. Plants producing spermogonia and aecia on leaf surface in the field. B. Vertical section of a spermogonium. C. Vertical section of an aecia surrounded with peridia. D, E. Aecia on the leaf surface observed by SEM. F. Aeciospores with verrucose surface observed by SEM. G. Aeciospores. H. Telia on the lower leaf surface. I. Uredinia on the leaf surface observed by SEM. J. Uredinia and telia on the leaf surface. K. Vertical section of an uredinium with paraphyses. L. Urediniospore observed by SEM. M. Vertical section of telia covered by host epidermis. N. Vertical section of a telium covered by host epidermis observed by SEM. Scale bars: B, G, N = 30 μm, C, E, I = 100 μm, F, L = 10 μm, K, M = 20 μm.
IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Orthorectified Images V001
This data set contains reprocessed, orthorectified images depicting labels that indicate the sea ice surface category, created by processing <a href="https://nsidc.org/data/iodms0">IceBridge DMS L0 Raw Imagery</a> with the Open Source Sea-ice Processing Algorithm. Orthorectification was done using digital elevation models from the <a href="https://nsidc.org/data/iodem3">IceBridge DMS L3 Ames Stereo Pipeline Photogrammetric DEM</a> collection. The standard (non-orthorectified) images are available as <a href="https://nsidc.org/data/rdsisc4">IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Images</a>.
IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Images V001
This data set contains reprocessed images depicting labels that indicate the sea ice surface category, created by processing <a href="https://nsidc.org/data/iodms0">IceBridge DMS L0 Raw Imagery</a> with the Open Source Sea-ice Processing Algorithm. An orthorectified version of this data set is available as <a href="https://nsidc.org/data/rdsisco4">IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Orthorectified Images</a>.
High Mountain Asia 2 m DEM, Surface Velocity, and Lagrangian Surface Mass Balance for Select Debris Covered Glaciers V001
This High Mountain Asia data set contains 2 m resolution digital elevation models (DEMs), surface velocities, surface mass balance (SMB) rates, and SMB uncertainties for six debris-covered glaciers in Nepal. SMB rate is estimated by applying a Lagrangian specification to DEMs derived from very-high-resolution optical stereo imagery acquired by Maxar Technologies satellites WorldView-1, WorldView-2, WorldView-3, and GeoEye-1. This data set was granted permission for public release on 1 March 2024 under the National Reconnaissance Office (NRO) Electro-Optical Commercial Layer (EOCL) program.
Data to Support 'Persistent cloud cover over mega-cities linked to surface heat release'
<p>"Persistent cloud cover over mega-cities linked to surface heat release<br> by Theeuwes et al., 2019 NPJ Climate and atmospheric science</p> <p>HRV_london_landuse.txt</p> <ul> <li>HRV processed time series of cloud fractions [-] over each land use type (pixels have to be 100% of one land use class) <ul> <li>"high" - forest/high vegetation </li> <li>"low" - low vegetation</li> <li>"urban" - urban areas </li> <li>"water" - water bodies for London domain.</li> </ul> </li> </ul> <p>HRV_paris_landuse.txt -</p> <ul> <li>HRV processed time series of cloud fractions over each land use type (pixels have to be 100% of one land use class) <ul> <li>"high" - forest/high vegetation,</li> <li>"low" - low vegetation,</li> <li>"urban" - urban areas for Paris domain.</li> </ul> </li> </ul> <p>"london_CBH.csv" -</p> <ul> <li>Cloud base heights ("CBH_L") [m] from CL31 ceilometer at London site (Marylebone road) and logical array whether it is clear or clouds are observed in the 15-min period "clear_L, cloudy_L"</li> </ul> <p>"chilbolton_CBH.csv" -</p> <ul> <li>Cloud base heights ("CBH_C") [m] from CL75K ceilometer at Chilbolton site and logical array whether it is clear or clouds are observed in the 15-min period "clear_C, cloudy_C"</li> </ul> <p>raw data: doi:10.5285/1aa2df5a-798b-46c7-b74a-421f9ca0aa82</p> <p>"london_met.csv" -</p> <ul> <li>Data from meteorological measurements at London (Kings College London site): <ul> <li>"DIP_L" - dew point depression (air temperature - dew point temperature) [K], </li> <li>"RR_L" - precipitation [mm],</li> <li>"T_L" - absolute air temperature [K],</li> <li>"Td_L" - dew point temperature [K].</li> </ul> </li> </ul> <p>"chilbolton_met.csv" -</p> <ul> <li>Data from meteorological measurements at Chilbolton: <ul> <li>"DIP_C" - dew point depression (air temperature - dew point temperature) [K],</li> <li>"T_C" - absolute air temperature [K],</li> <li>"Td_C" - dew point temperature [K].</li> </ul> </li> </ul> <p>"london_fluxes.csv"</p> <ul> <li>- Kinematic sensible ("wth_L" [K m s-1]) and latent heat ("wq_L" [kg kg-1 m s-1]) fluxes at London (Kings College London site).</li> </ul> <p>"chilbolton_fluxes.csv"</p> <ul> <li>- Kinematic sensible ("wth_C" [K m s-1]) and latent heat ("wq_C" [kg kg-1 m s-1]) fluxes at Chilbolton.</li> </ul> <p><br> "2011_chil_lidarwstats.nc"</p> <ul> <li>- Hourly statistics of the LiDAR stare data for Chilbolton site. Vertical velocity variance corrected.</li> </ul> <p>Note: all Chilbolton raw data: http://catalogue.ceda.ac.uk/uuid/7cbc3fc19bfa037a48ba4cba4b93544d</p>
Surface rock cover soil maps of the Upper Colorado River Basin
<p>The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:</p> <p>Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release,<a href="http://https//doi.org/10.5066/P9SK0DO2"> https://doi.org/10.5066/P9SK0DO2</a>.</p> <p>Associated publication:</p> <p>Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194. <a href="https://doi.org/10.1002/saj2.20080">https://doi.org/10.1002/saj2.20080</a>.</p> <p>Version 2: Unfortunately, errors were found in the original training data preparation in version 1. This version corrects those errors and has resulted in cross validation accuracy increases (R<sup>2</sup>) from ~0.4 to ~0.55 for both rock cover and representative rock size.</p> <p>Repository includes maps of surface rock cover (sfragcov) and dominant surface rock size (sfragsize) as defined by United States soil survey program. </p> <p>These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.</p> <p>The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.</p> <p>Nauman, T. W., Duniway, M. C., In Press. A hybrid approach for predictive soil property mapping using conventional soil survey data. Soil Science Society of America Journal.</p> <p>File Name Details:</p> <p>ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (NRCS field pedons plus NRCS laboratory pedons; file ending _CV_plots.tif) and for just the CV results at laboratory pedons (file ending _CV_SCD_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).</p> <p>Elements are separated by underscore (_) in the following sequence:</p> <p>property_r_model_additional_elements.extension</p> <p>Example: sfragsize_r_2D_QRF.tif</p> <p>Indicates dominant surface fragment size (sfragsize) using a 2D model employing a quantile regression forest (QRF). This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions. If the first name is sfragcov, it indicates that the layer is for surface fragment cover.</p> <p>The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).</p> <p>_95PI_h: Indicates the layer is the upper 95% prediction interval value.</p> <p>_95PI_l: Indicates the layer is the lower 95% prediction interval value.</p> <p>_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (2019) for more details on RPI.</p> <p>References</p> <p> Nauman, T. W., and Duniway, M. C.,2019, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma</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)
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DANDI Archive for NWB datasets
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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.