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5,155 results for “resolution”
2m resolution DEM based stereoscopy Pleiades acquisitions in Telangana, South-India
<p>Four steroscopy pairs of Pléiades images (Pléiades © CNES 2021 Distribution AIRBUS DS) were acquired over Telangana state in 01 and 14 of June 2016 and the 16 of June 2019 and licensed to CESBIO by Airbus. The Pléiades Digital Elevation Model is a derivative product subject to the CC-BY-NC 4.0 license preventing commercial use. The four .tif files correspond to these DEM at 2m resolution. The dates were selected because the Rainwater Harvesting System small reservoirs of the areas covered were empty. Elevations within small reservoirs are thus equivalent to a bathymetry. We verified that 2 meter resolution is very adapted to retrieve the geometries of narrow and steep dikes that dam each small reservoirs.</p>
A Missing Piece of the E-Region Puzzle: High-Resolution Photoionization Cross Sections and Solar Irradiances in Models
<p>Dataset corresponding to the associated publication, "A Missing Piece of the E-Region Puzzle: High-Resolution Photoionization Cross Sections and Solar Irradiances in Models." The dataset includes high-resolution photoionization and photoabsorption cross section for O and N<sub>2</sub> as well as high-resolution solar spectrum. Photoionization rates from model runs obtained from AURIC and the Meier photoionization code are also included. Please refer to the readme for information on the data structure.</p> <p><strong>***Please note that the paper is under review and has not been accepted yet.***</strong></p>
A Danish high-resolution dataset for six office rooms with occupancy, indoor environment , heating, ventilation, lighting and room control monitoring
<p>A dataset containing measurement data for six office rooms in Aalborg Denmark.<br>All the measurements have been resampled to 5 minute resolution<br>The measurements consists of:</p> <ul> <li>BMS data for the rooms</li> <li>Occupancy for the rooms (from cameras)</li> <li>BMS data for the AHU supplying the rooms</li> <li>BMS data for the Heating system supplying the rooms</li> </ul> <p>Changes from v2<br>It was found that the pressure difference measurements across the exhaust fan was faulty and the following variables have therefore been removed:</p> <ul> <li>Ventilation:Fan__air_flow__exhaust</li> <li>Ventilation:Fan__pressure_difference__exhaust</li> </ul> <p>More data has been added, now increasing the dataset to span the rest of 2023. To better handle the changes between standard time and daylight-saving time the column named "timestamp" has been adjusted so the datetime format now follows the ISO 8601 format YYYY-MM-DDThh:mm:ss+hhmm. the +hhmm changes between 0100 (Danish standard time) and 0200 (Danish daylight-saving time).</p> <p> </p>
High-resolution images from a low-cost imaging device for hyphae in soil
<p>This dataset contains high-resolution images produced by a low-cost imaging device for hyphae in soil called <em>Hyphascope</em>. Using a digital microscope camera (DMC; 600× magnification),<em> </em>the device takes detailed images (0.83 × 0.62 mm imaged area) of a soil profile from evenly spaced camera positions within a user-defined volume. Repeated imaging of a soil profile with <em>Hyphascope</em> enables researchers to observe and quantify changes in the amount, distribution, and morphology of hyphae.</p> <p>Individual images were combined using the <em>Grid/Collection stitching</em> plugin of the <em>Fiji</em> distribution of <em>imageJ</em> (Preibisch et al. 2009). All images are supplied in the JPG format to limit their file size. For more details on the assembly and application of <em>Hyphascope</em>, see <a href="https://doi.org/10.17504/protocols.io.bp2l6xo3zlqe/v1">this protocol on protocols.io</a>. For information on the development, limitations, and expected outcomes of the protocol, see <a href="https://doi.org/10.1371/journal.pone.0318083">this article</a> published in PLOS ONE. </p> <p> </p> <div> <h2>Image set 1: 10 × 10 mm soil profile area at 20 - 30 mm soil depth</h2> <p>Imaged at 0.65 μm px<sup>-1</sup> (39200 dpi)* in a <em>Quercus serrata</em> grove on 2023/05/25 during a period of high hyphal density in the soil.</p> <h3>Individual images (18 rows × 14 images each)</h3> <ul> <li> <p><em>set1_foc00000.zip</em> (focus depth 0 mm)</p> </li> </ul> <h3>Combined images</h3> <ul> <li> <p><em>set1_foc00000_combined.jpg</em> (focus depth 0 mm)</p> </li> </ul> <h2>Image set 2: 5 × 5 mm soil profile area at 100 - 105 mm soil depth</h2> <p>Imaged at 0.52 μm px<sup>-1</sup> (49000 dpi) in a <em>Quercus serrata</em> grove on 2023/09/25.</p> <h3>Individual images (9 rows × 7 images each)</h3> <ul> <li> <p><em>set1_foc00000.zip</em> (focus depth 0 mm)<em><br></em></p> </li> <li> <p><em>set1_foc00025zip</em> (focus depth 0.025 mm)</p> </li> <li><em>set1_foc00050.zip</em> (focus depth 0.05 mm)</li> </ul> <h3>Combined images</h3> <ul> <li> <p><em>set1_foc00000_combined.jpg</em> (focus depth 0 mm)</p> </li> <li> <p><em>set1_foc00025_combined.jpg</em> (focus depth 0.025 mm)</p> </li> <li><em>set1_foc00050_combined.jpg</em> (focus depth 0.05 mm)</li> </ul> <h2>Image set 3: 5 × 5 mm soil profile area at soil surface level</h2> <p>Imaged at 0.52 μm px<sup>-1</sup> (49000 dpi) in a <em>Quercus serrata</em> grove on 2023/10/14 during a rain event.</p> <h3>Individual images (9 rows × 7 images each)</h3> <ul> <li> <p><em>set2_foc00000.zip</em> (focus depth 0 mm)<em><br></em></p> </li> <li> <p><em>set2_foc00025zip</em> (focus depth 0.025 mm)</p> </li> <li><em>set2_foc00050.zip</em> (focus depth 0.05 mm)</li> </ul> <h3>Combined images</h3> <ul> <li> <p><em>set2_foc00000_combined.jpg</em> (focus depth 0 mm)</p> </li> <li> <p><em>set2_foc00025_combined.jpg</em> (focus depth 0.025 mm)</p> </li> <li><em>set2_foc00050_combined.jpg</em> (focus depth 0.05 mm)</li> </ul> <p> </p> <p> </p> <p><em>*Units of imaging resolution: </em></p> <ol> <li><em>pixel width (μm px-1), i.e. the horizontal or vertical distance on the imaged surface covered by a single pixel; </em></li> <li><em>dots per inch (dpi), i.e. the number of pixels along a horizontal or vertical distance of 25.4 mm on the imaged surface.</em></li> </ol> </div>
Global Phytoplankton Phenological Indices - 25km resolution
<p>Bloom phenology metrics calculated from OC-CCI v6.0 data at a spatial resolution of 25km. </p> <p>Metrics calculated using three approaches:</p> <ol> <li>Threshold method</li> <li>Cumulative Sum method</li> <li>Rate of Change method</li> </ol> <p> </p> <p><strong>Version 1.0:</strong></p> <ul> <li>Data from 1997 to 2022.</li> </ul> <p><strong>Version 1.1:</strong></p> <ul> <li>Data from 1997 to 2023.</li> </ul> <p> </p>
Crocus-ERA-Interim daily snow product over the Northern Hemisphere at 0.5° resolution
<p>The Crocus-ERA-Interim daily snow product is derived from the complex snow scheme Crocus coupled to the ISBA (Interactions between Soil–Biosphere–Atmosphere) land surface model (<a href="http://dx.doi.org/10.1175/JHM-D-12-012.1">Brun et al., 2013</a>) and embedded into the SURFEX numerical platform (<a href="https://www.umr-cnrm.fr/surfex/">https://www.umr-cnrm.fr/surfex/</a>). The model is driven by a meteorological forcing (temperature, precipitation, humidity, winds, etc) derived from the ERA-Interim global atmospheric reanalysis (<a href="https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-interim">https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-interim</a>). This product only concerns open field snowpack, i.e. only low vegetation is modeled (no forest). It covers the entire Northern Hemisphere at 0.5° resolution over the 1979-07-01 to 2019-06-30 period. Snow depth and snow water equivalent (i.e. the snow mass) are available at a dailly frequency. The simulated snow depth, snow water equivalent, and density over open fields were validated against local observations from over 1000 monitoring sites in Northern Eurasian, available either once a day or three times per month (<a href="http://dx.doi.org/10.1175/JHM-D-12-012.1">Brun et al., 2013</a>). This product was used by the NOAA <a href="https://arctic.noaa.gov/report-card/">Artic Report Card</a> from 2017 to 2020 for the annual survey of the Terrestrial Snow Cover anomalies over the Northern Hemisphere. This product was also used in several research studies (e.g. <a href="https://doi.org/10.1175/JCLI-D-15-0229.1">Mudryk et al., 2015</a>; <a href="https://doi.org/10.5194/tc-14-1579-2020">Mortimer et al., 2020</a>; <a href="https://doi.org/10.5194/tc-17-5007-2023">Kouki et al., 2023</a>). The successor of this product based on ERA5 at 0.25° resolution over 1950 to 2023 can be found in <a href="https://doi.org/10.5281/zenodo.10943718">Decharme (2024b)</a>.</p>
Crocus-ERA5 daily snow product over the Northern Hemisphere at 0.25° resolution
<p>The Crocus-ERA5 daily snow product is derived from the complex snow scheme Crocus coupled to the ISBA (Interactions between Soil–Biosphere–Atmosphere) land surface model (<a href="http://dx.doi.org/10.1175/JHM-D-12-012.1">Brun et al., 2013</a>) and embedded into the SURFEX numerical platform (<a href="https://www.umr-cnrm.fr/surfex/">https://www.umr-cnrm.fr/surfex/</a>). The model is driven by a meteorological forcing (temperature, precipitation, humidity, winds, etc) derived from the ERA5 global atmospheric reanalysis (<a href="https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5">https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</a>). This product only concerns open field snowpack, i.e. only low vegetation is modeled (no forest). It covers the entire Northern Hemisphere at 0.25° resolution over the 1950-07-01 to 2023-06-30 period. All snow characteristics (see later) are available at a dailly frequency. This product is the successor of the Crocus-ERA-Interim daily snow product (<a href="../records/10911538">Decharme, 2024</a>). It is used by the NOAA <a href="https://arctic.noaa.gov/report-card/">Artic Report Card</a> from 2021 to present for the annual survey of the Terrestrial Snow Cover anomalies over the Northern Hemisphere. An evaluation of the snow water equivalent product can be found in <a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2023-3014/">Mudryk et al. (2024)</a> where it is compared to observations and to about twenty alternative datasets.</p>
Multi-Resolution Basemap of Northwest Aeolis Mons, Gale Crater, Mars
<p>This multi-resolution, multi-spectral basemap comprises HiRISE, CTX, MOC-NA, and CRISM imagery over the northwest of Aeolis Mons in Gale crater, Mars, to complement the HiRISE and CTX data over Sakarya Vallis (Persaud 2021, https://doi.org/10.5281/zenodo.5808371; Persaud et al. 2021, https://doi.org/10.5281/zenodo.5808357). All of the products have been georeferenced to the CTX ORI mosaic (Persaud et al. 2021) and/or each other using QGIS and GDAL; their IDs, resolutions, and sources prior to this alignment are listed below.</p> <p>The greyimage ORIs produced in this work were generated using the Ames Stereo Pipeline. The other HiRISE ORI were selected based on the data used by the USGS to make the MSL orthophoto mosaic (Calef III and Parker 2016, https://astrogeology.usgs.gov/search/map/Mars/MarsScienceLaboratory/Mosaics/MSL_Gale_Orthophoto_Mosaic_10m_v3).</p> <p>The four CRISM images were processed using the CRISM Analysis Tool (CAT) in ENVI (Morgan et al. 2009) following the workflow described in Campbell and Muller (2017), adapted from Seelos (2009). Four RGB products were assembled from one CRISM image over Sakarya Vallis using the spectral library from Viviano-Beck et al. (2014); the band assignments are listed below.</p> <p>Format: GeoTiff<br> Projection: Equidistant cylindrical<br> Datum: Spheroid (r = 3396.190 km)</p> <p>HiRISE (0.25 m/px)</p> <ul> <li>ESP_012340_1750_RED, this work</li> <li>PSP_009149_1750_RED, this work</li> <li>ESP_018854_1755_RED, this work</li> <li>ESP_019698_1750_RED, U. Arizona</li> <li>ESP_012551_1750_RED, U. Arizona</li> <li>ESP_024300_1755_RED, U. Arizona</li> <li>PSP_001488_1750_RED, U. Arizona</li> <li>PSP_009650_1755_RED, U. Arizona</li> </ul> <p>HiRISE RGB (0.50 m/px):</p> <ul> <li>ESP_061750_1750_RGB, U. Arizona (mosaicked in this work, "Mont Mercou")</li> <li>ESP_069031_1750_MRGB, U. Arizona (mosaicked in this work, "Mont Mercou")</li> <li>PSP_009149_1750_MRGB, U. Arizona (mosaicked in this work, "Mont Mercou")</li> <li>ESP_029034_1750_MRGB, U. Arizona</li> </ul> <p>MOC-NA (NASA/JPL/MSSS):</p> <ul> <li>S2200845 (2.2 m/px)</li> <li>R1100953 (6.75 m/px)</li> </ul> <p>CRISM, FRT000095ee (18 m/px):</p> <ul> <li>Hydrated mineralogy <ul> <li>Red: SINDEX2</li> <li>Green: BD2100_2</li> <li>Blue: BD1900_2</li> </ul> </li> <li>Hydrated sulphates (1) <ul> <li>Red: SINDEX2</li> <li>Green: BD1900R2</li> <li>Blue: HCPINDEX3</li> </ul> </li> <li>Mafics <ul> <li>Red: LCPINDEX3</li> <li>Green: HCPINDEX3</li> <li>Blue: OLINDEX3</li> </ul> </li> <li>Hydrated sulphates (2): SINDEX2</li> </ul> <p> </p>
Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets
<p><strong>Sydney morphology and land surface dataset</strong></p> <p>This dataset for Sydney, Australia, represents land cover, building morphology, vegetation morphology and other parameters appropriate for input into local or mesoscale urban climate models.</p> <p>The dataset is provided in netCDF4 and GeoTiff formats.</p> <p>Associated manuscript:</p> <blockquote> <p><a href="https://doi.org/10.3389/fenvs.2022.866398">A transformation in city-descriptive input data for urban climate models</a></p> </blockquote> <p>Citation for the open dataset:<br> - Lipson, M., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets (v1.01), <a href="https://doi.org/10.5281/zenodo.6579061">https://doi.org/10.5281/zenodo.6579061</a>, 2022.</p> <p>Citation for the associated manuscript:<br> - Lipson, M. J., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: A Transformation in City-Descriptive Input Data for Urban Climate Models, Frontiers in Environmental Science, 10, <a href="https://doi.org/10.3389/fenvs.2022.866398">https://doi.org/10.3389/fenvs.2022.866398</a>, 2022.</p> <p>Location of associated processing code:<br> - <a href="https://github.com/matlipson/geoscape_processing_public.git">https://github.com/matlipson/geoscape_processing_public.git</a></p> <p><strong>Acknowledgments</strong></p> <p>We gratefully acknowledge the Australian Urban Research Infrastructure Network (AURIN) and Geoscape Australia for <br> providing the datasets necessary for this study, drawing on Geoscape Buildings, Surface Cover and Trees datasets, <br> © Geoscape Australia, 2020: https://geoscape.com.au/legal/data-copyright-and-disclaimer/. <br> This research was supported by the Australian Research Council (ARC) Centre of Excellence for Climate System Science <br> (grant CE110001028), the ARC Centre of Excellence for Climate Extremes (grant CE170100023). </p> <p> </p>
Soil bulk density [10x kg/m3] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: db_od = bulk density over dry [kg/m3 ⨉ 10];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>The bulk density maps are also provided in 10 kg / m-cubic to reduce total data size; to convert values to kg / m-cubic multiply by 10 e.g. 120 = 1200 kg / m-cubic = 1.2 t / m-cubic.</p>
Soil pH in H2O [-] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: soil pH in H2O;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Soil organic carbon content [g/kg] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: log organic carbon [g/kg] to back-transform use exp(x/10)-1;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>To back-transform the log.oc maps use formula: exp(x/10)-1. These are examples of back-transformed values:</p> <p> log.oc = 15 → 0.3% SOC;<br> log.oc = 20 → 0.6% SOC;<br> log.oc = 25 → 1.1% SOC;<br> log.oc = 30 → 1.9% SOC;<br> log.oc = 35 → 3.2% SOC;<br> log.oc = 40 → 5.3% SOC;<br> log.oc = 50 → 14.8% SOC;</p>
Soil clay content [%] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: clay.tot = clay content [percent];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Soil sand content [%] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: sand.tot = sand content [percent];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Ground ice content predictions for the Northern Hemisphere permafrost region at 1-km resolution, version 1.1
<p>Ground ice content is one of the least known characteristics of the permafrost-affected soils in the Northern Hemisphere. At the same time, ground ice content exerts a crucial effect on the thermal response of permafrost to changing climate and environmental conditions, and dictates the permafrost degradation-related geomorphic, hydrologic, and ecological processes, including thermokarst. This dataset presents numerical estimates of volumetric ice content over the permafrost region at a 1-km spatial resolution. The predictions are representative of pore and segregated ice contents in the topmost five meters of permafrost. We use compilations of field measurements of ground ice contents from across the permafrost region to train statistical models and to predict volumetric ice content with the aid of high-resolution geospatial data on climatic, soil and topography conditions. The dataset facilitates assessments of conditions of changing permafrost landscapes at an improved spatial and thematic resolution.</p>
Super-resolution EO-based area monitoring markers computed over the Lithuanian pilot region (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), the following EO-based monitoring marker maps were released over defined pilot areas over the Lithuanian pilot country, enhanced by features raised from the Super-resolution models. It involved the implementation of matching marking and data fusion deep learning algorithms, which attempt to support the extraction of useful information from highly variable inputs. This will further allow the distinction of landscape features, which would otherwise not be available in initially acquired Sentinel-2 data. The use of VHR data (Copernicus Contributing Missions) in combination with drone imagery will enhance the super-resolution modelling capabilities, enabling the augmentation of the training dataset (spatio-temporal scale) and subsequently leading to increased model performance.<br> The goal was to enhance the outputs of the area monitoring markers and especially in the monitoring of small (i.e. 100m<sup>2</sup>), narrow and elongated parcels. </p> <p>For the needs of DIONE, the aforementioned data were explored and the following area-based monitoring markers were calculated from 01-01-2022 until 01-08-2022 providing tailored information for the needs of the National Paying Agency of Lithuania. </p> <ol> <li> <p><strong>Mowing marker:</strong> used to detect mowing events on meadow/grass like Features Of Interest (FOI)</p> </li> <li> <p><strong>Similarity and distance markers:</strong> used to give additional context to the crop classification and to detect erroneous claims</p> </li> <li> <p><strong>Crop-type marker:</strong> used to detect the specific crop growing on the FOI</p> </li> </ol> <p>This dataset is comprised of one geopackage file, the "S2SR-study-geopackage.gpkg"<em>, which</em> was computed for the Lithuanian pilot region<em>. </em>Descriptions are given below.</p> <p><strong>Super-resolution Markers dataset: </strong>Markers were computed for 16872 FOIs that contain less than 1 Sentinel-2 pixel, using signals from 2022-01-01 until 2022-08-01. </p> <table> <caption><strong>Description of the information contained in the corresponding "Super-Resolution markers" dataset</strong></caption> <thead> <tr> <th scope="col">Attribute name </th> <th scope="col">Description </th> </tr> </thead> <tbody> <tr> <td>CROP_LABEL</td> <td>Reference ID of the polygon </td> </tr> <tr> <td>POLY_ID </td> <td>Declared crop group</td> </tr> <tr> <td>crop_group_prediction_1_classification</td> <td>The FOI label as predicted by the crop group (v2) model</td> </tr> <tr> <td>crop_group_prediction_1_classification_score</td> <td>The pseudoprobability of the crop-group (v1) prediction. A score close to 1 indicates that the model is very confident in the prediction</td> </tr> <tr> <td>crop_group_prediction_2_classification</td> <td>The FOI label as predicted by the crop group (v2) model</td> </tr> <tr> <td>crop_group_prediction_2_classification_score</td> <td>The pseudoprobability of the crop-group (v2) prediction. A score close to 1 indicates that the model is very confident in the prediction</td> </tr> <tr> <td>distance_classification</td> <td>Most similar crops according to the distance marker</td> </tr> <tr> <td>distance_classification_score</td> <td>Distance marker score of a FOI when compared to nearby FOIs with the same claim. A value close to 100 indicates that a FOI is not similar to other FOIs with the same claim.</td> </tr> <tr> <td>mowing_event_count</td> <td>Number of detected mowing events in the observation period</td> </tr> <tr> <td>similarity_classification</td> <td>Most similar crops according to similarity marker</td> </tr> <tr> <td>similarity_classification_score</td> <td>Similarity marker score of a FOI when compared to nearby FOIs with the same claim. A value close to 100 indicates that a FOI is not similar to other FOIs with the same claim</td> </tr> </tbody> </table>
deGeco genomic compartments model fit results on whole genome at resolution of 50kb
<p>These files contain the fitted parameters for the <a href="https://www.biorxiv.org/content/10.1101/2022.10.01.510432v1.article-info">deGeco</a> model for genomic compartments. Fits were done at 50kb on four cell lines: GM12878 (from Rao, et al., 2014), H1, HFF (both from Krietenstein, et al., 2020) and mESC (from Bonev, et al., 2017). Each Hi-C file was zoomified again using cooler, to prevent duplicate entries in the pixel table.</p> <p>The file format is NumPy's npz object that has two main keys:</p> <ol> <li>Metadata - an object containing various information on the run: command line parameters, duration of run, etc</li> <li>Parameters - an object containing the actual fitted parameters: <ol> <li>state_probabilities - an NxS matrix of state probabilities, where N is the number of bins and S the number of states the model was run with</li> <li>cis_weights - an SxS matrix of cis state affinities</li> <li>trans_weights - an SxS matrix of trans state affinities</li> <li>cis_dd_power - the exponent of the power law decay of interaction intensity in cis (also denoted as alpha)</li> <li>trans_dd - the constant background level of trans interaction (also denoted as beta)</li> <li>cis_lengths - Number of bins for each chromosome. Sum of cis_lengths is N, the total number of bins.</li> </ol> </li> </ol> <p>To read using numpy:</p> <pre><code class="language-python">import numpy as np fit = np.load(filename, allow_pickle=True) metadata = fit['metadata'][()] parameters = fit['parameters'][()]</code></pre> <p>or use the gc_datafile module from the deGeco <a href="https://github.com/KaplanLab/deGeco">repository</a>:</p> <pre><code class="language-python">import gc_datafile parameters = gc_datafile.load_params(filename)</code></pre> <p> </p>
The pan-genome of Aspergillus fumigatus provides a high-resolution view of its population structure revealing high-levels of lineage-specific diversity driven by recombination
<p><em>Aspergillus fumigatus </em>is a deadly agent of human fungal disease, where virulence heterogeneity is thought to be at least partially structured by genetic variation between strains. While population genomic analyses based on reference genome alignments offer valuable insights into how gene variants are distributed across populations, these approaches fail to capture intraspecific variation in genes absent from the reference genome. Pan-genomic analyses based on <em>de novo</em> assemblies offer a promising alternative to reference-based genomics, with the potential to address the full genetic repertoire of a species. Here, we use a combination of population genomics, phylogenomics, and pan-genomics to assess population structure and recombination frequency, phylogenetically structured gene presence-absence variation, evidence for metabolic specificity, and the distribution of putative antifungal resistance genes in <em>A. fumigatus</em>. We provide evidence for three distinct populations of <em>A. fumigatus</em>, structured by both gene variation (SNPs and indels) and distinct gene presence-absence variation with unique suites of accessory genes present exclusively in each clade. Accessory genes displayed functional enrichment for nitrogen and carbohydrate metabolism, hinting that populations may be stratified by environmental niche specialization. Similarly, the distribution of antifungal resistance genes and resistance alleles were often structured by phylogeny. Despite low levels of outcrossing, <em>A. fumigatus</em> demonstrated a large pan-genome including many genes unrepresented in the Af293 reference genome. These results highlight the inadequacy of relying on a single-reference based approach for evaluating intraspecific variation, and the power of combined genomic approaches to elucidate population structure, genetic diversity, and the putative ecological drivers of clinically relevant fungi.</p> <p>Accompanying manuscript is available as preprint at <a href="https://dx.doi.org/10.1101/2021.12.12.472145">https://dx.doi.org/10.1101/2021.12.12.472145</a> </p> <p>Lotus A. Lofgren, Brandon S. Ross, Robert A. Cramer, Jason E. Stajich. Combined Pan-, Population-, and Phylo-Genomic Analysis of <em>Aspergillus fumigatus</em> Reveals Population Structure and Lineage-Specific Diversity bioRxiv 2021.12.12.472145; doi: https://doi.org/10.1101/2021.12.12.472145</p>
Soil water content (volumetric %) for 33kPa and 1500kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Training points are based on a global compilation of soil profiles (<a href="https://ncsslabdatamart.sc.egov.usda.gov/">USDA NCSS</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, <a href="https://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/a351682c-330a-4995-a5a1-57ad160e621c">ISRIC WISE</a>, <a href="http://egrpr.esoil.ru/">EGRPR</a>, <a href="https://esdac.jrc.ec.europa.eu/content/soil-profile-analytical-database-2">SPADE</a>, <a href="https://open.canada.ca/data/en/dataset/6457fad6-b6f5-47a3-9bd1-ad14aea4b9e0">CanNPDB</a>, <a href="https://data.nal.usda.gov/dataset/unsoda-20-unsaturated-soil-hydraulic-database-database-and-program-indirect-methods-estimating-unsaturated-hydraulic-properties">UNSODA</a>, <a href="https://doi.pangaea.de/10.1594/PANGAEA.885492">SWIG</a>, <a href="http://www.cprm.gov.br/en/Hydrology/Research-and-Innovation/HYBRAS-4208.html">HYBRAS</a> and <a href="http://dx.doi.org/10.4228/ZALF.2003.273">HydroS</a>). Data import steps are available <a href="https://gitlab.com/openlandmap/compiled-ess-point-data-sets/-/tree/master/themes/sol/SoilHydroDB"><strong>here</strong></a>. Spatial prediction steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/soil_water">here</a></strong>. Note: these are actually measured and mapped soil content values; no Pedo-Transfer-Functions have been used (except to fill-in the missing NCSS bulk densities). Available water capacity in mm (derived as a difference between field capacity and wilting point multiplied by layer thickness) per layer is available <strong><a href="https://doi.org/10.5281/zenodo.2629148">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize some of the maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>watercontent.33kPa = water content (volumetric percent) under field capacity (33 kPa suction),</li> <li>usda.4b1c = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
The potential of low-cost UAVs and open-source photogrammetry software for high-resolution monitoring of alpine glaciers: A case study from the Kanderfirn (Swiss Alps)
<p>This dataset contains high-resolution orthophotos (5 x 5 cm) and digital surface models (25 x 25 cm) of the Kandernfirn Glacier located in the Swiss Alps. Aerial images were aquired with a self-developed fixed-wing Unmanned Aerial Vehicle during ten surveys on five different days in 2017 and 2018. The open-source photogrammetry software OpenDroneMap (version 0.4.1) was used for image processing.</p> <p>The orthophotos and digital surface models were validated through dGNSS point measurements of ground control points. Please refer to the corresponding paper for information on the horizontal and vertical accuracy of the files.</p>
ScienceDex guides
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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.