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5,155 results for “resolution”
High resolution microsection images for: Common juniper, the oldest living non-clonal woody species across the tundra biome and the European continent
<p>Two high resolution images of the stem section are available as .czi files. These images are from a living <em>Juniperus communis</em> L. branch from Abisko (Sweden) sampled in August 2021. These high-resolution photographs (2.89 pixel/μm) were created using Axio Scan 7, Zeiss, Germany. </p> <p>One high resolution image of the same stem section is archived as a .tif file (49835x25587 pixels). This image is a composition of the two .czi images created using Axio Scan 7, Zeiss, with a reduced resolution and edited adding the ring-count reference points and the reference scale.</p>
Data from: Possible provenance of IRD by tracing late Eocene Antarctic iceberg melting using a high-resolution ocean model
<p>This repository contains the data supplemented to <a href="https://doi.org/10.5194/cp-21-441-2025">Elbertsen et al. (2025)</a> based on Mark Elbertsen's MSc project in which he performed depth-integrated Lagrangian iceberg tracing around Antarctica during the late Eocene using high-resolution ocean model data. Using the OceanParcels framework, iceberg melting (or growth) was simulated using several kernels, including for the dominant iceberg melt terms: basal melt, buoyant convection and wave erosion. By defining kernels for five different order-of-magnitude iceberg size classes, the model was be used to determine the minimum iceberg size required for icebergs to survive the late Eocene warmth. The model output of these simulations can be found here.</p> <p> </p> <p>This research is funded by ERC Starting Grant 802835 (OceaNice) to Peter K. Bijl.</p>
Global bare soil, photosynthetic and non-photosynthetic vegetation fraction annual at 500 m resolution
<p>Annual mean and std for (1) bare soil fraction, and (2) photosynthetic and (3) non-photosynthetic vegetation annual at 500 m resolution for 2001–2023. The dataset was obtained from: <a href="https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/monthly/cover/catalog.html">https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/monthly/cover/catalog.html</a> (monthly values; 320GB in total). Mean and std was derived using terra package in R using functions "mean" and "std" from 12 monthly values; missing values were ignored during derivation.</p> <p>Note: the Global Vegetation Fractional Cover Product (GVFCP) v3.1 (<a href="https://doi.org/10.1016/j.agee.2021.107719">Hill and Guerschman, 2022</a>) is derived from spectral unmixing of all seven optical bands from the 500 m MODIS (Moderate Resolution Imaging Spectroradiometer) Nadir BRDF (Bidirectional Reflectance Distribution Function)-adjusted Reflectance Product (NBAR, MCD43A4 Collection 6). A similar dataset has been produced by <a href="https://doi.org/10.5194/essd-16-1333-2024">Sun et al., (2024)</a>, covering period 2001–2022. Below is the sample code explaining how were the mean, max and std derived.</p> <pre><code>## Download from: https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/monthly/cover/catalog.html ## wget -e robots=off -nH --cut-dirs 4 -nc -r -l5 -A '*.nc' -R 'catalog*' -I /thredds/fileServer/,/thredds/catalog/ 'https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/monthly/cover/catalog.html' ## 6857 tiles library(terra) modis.tiles = list.files("/mnt/lacus/raw/modis-fc/v310/tiles/monthly/cover/", pattern = glob2rx("*.nc")) mod.lst = unique(sapply(modis.tiles, function(i){strsplit(i, "\\.")[[1]][4]})) str(mod.lst) ## 272 ## aggregate per year per tile nc_tile <- function(i, year, dir.x="/mnt/lacus/raw/modis-fc/v310/tiles/monthly/cover/", mc.cores=parallel::detectCores()){ require(terra) if(year == 2023 | year == 2024){ in.filename = paste0(dir.x, "FC_Monthly_Medoid.v310.MCD43A4.", i, ".", year, ".061.nc") } else { in.filename = paste0(dir.x, "FC_Monthly_Medoid.v310.MCD43A4.", i, ".", year, ".006.nc") } bs.filenames = paste0("./modis-fc/bs_", c("mean", "max", "std"), "/FC_Monthly_Medoid.v310.MCD43A4.", i, ".", year, ".006.tif") dg = terra::rast(in.filename) if(any(!file.exists(bs.filenames))){ bs = dg["bare_soil"] ## 12 months dg.m = app(bs, fun=mean, na.rm=TRUE, filename=bs.filenames[1], wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT1S'), overwrite=TRUE, cores = mc.cores) dg.x = app(bs, fun=max, na.rm=TRUE, filename=bs.filenames[2], wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT1S'), overwrite=TRUE, cores = mc.cores) dg.s = app(bs, fun=sd, na.rm=TRUE, filename=bs.filenames[3], wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT1S'), overwrite=TRUE, cores = mc.cores) } } ## run in parallel for(year in 2001:2024){ x = parallel::mclapply(sample(mod.lst), function(i){try( nc_tile(i, year=year, mc.cores = 2) )}, mc.cores = 40) tmpFiles(remove=TRUE) }</code></pre>
ESA Cryo-TEMPO - Northern hemisphere land/ocean flag and distance to coast at resolution of 250 m.
<p>Land/Ocean flag nd distance to coast at high spatial resolution (250m) in the northern hemisphere. The land/ocean flag is computed from merged Open Street Map and Natural Earth land polygons. The shapefiles were rasterized and reprojected to northern hemisphere using gdal. All land mass with the exception of Greenland are based on Open Street Map. Distance to coast was computed with gdal (gdal_proximity.py). </p> <p>The file format is netCDF-4 and the datafile contains two variables (land_ocean_flag & distance_to_coast). The coordinate reference system of the variables is defined by EPSG:6931 (WGS 84 / NSIDC EASE-Grid 2.0 North) and the bounds of the data set are supplied as xc and yc variables in the data file. </p> <p>The file is used in the ESA CryoSat-2 Thematic Products (Cryo-TEMPO) Polar Ocean and Sea Ice products in the northern hemisphere. </p> <p> </p>
CLMS Water Bodies monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
<p>Water Bodies from Copernicus Land Monitoring Service (CLMS) as monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)</p> <p>Source data:<br>- CLMS: Water Bodies 2014-2020 (raster 300 m), global, 10-daily – version 1: <a href="https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m">https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m</a> <br>- CLMS: Water Bodies 2020-present (raster 300 m), global, monthly – version 2: <a href="https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m">https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m</a></p> <p>Water is fundamental to life on Earth. Water quality, including aspects like turbidity and trophic state, is vital for assessing a water body's ecological well-being and its suitability for drinking. Understanding the water's surface temperature is key for monitoring climate change and can influence weather patterns. Tracking water levels in lakes and rivers helps in flood prediction, irrigation planning, and hydroelectric power generation. The presence and extent of ice on lakes and rivers can have significant implications for regional climates, ecosystems, and human activities. Moreover, the surface extent of water bodies, whether permanent or ephemeral, informs land management across various sectors. In an era marked by environmental change, these metrics offer insights into sustainable water resource management.<br>The Water Bodies product group aims to address these critical issues by providing tailored datasets to users which are applicable across a wide array of sectors. It includes Lake Surface Water Temperature, providing real-time and historical data; Lake Water Quality in various resolutions; Water Bodies datasets for surface extent; Lake and River Water Level information; the River and Lake Ice Extent product for ice presence; and the Aggregated River and Lake Ice Extent product, showing percent ice coverage. These products support applications like food security, public health safeguarding, climate studies, and responsible water management practices.</p> <p>Processing steps:<br>To cover the complete time period from 2019 to 2023 two data products of the Water Bodies product group are processed. Up to December of 2020 the Water Bodies at 10-daily resolution have been used, from January 2021 the Water Bodies at monthly resolution have been used. Both original datasets have been downloaded for the area of Mauritania (NUTS MR) within Latitude-Longitude/WGS84 spatial reference system. Then both datasets have been downsampled to 30 arc seconds (ca. 1000 meter) using the most frequent occuring value. The 10-daily data have been aggregated to monthly resolution using the most frequent occurring value.</p> <p>File naming:<br>Until December 2020: <code>c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif</code> <br>e.g.: <code>c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_2020_12_01T00_00_00.tif</code> <br>From January 2021 on: <code>c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif</code> <br>e.g.: <code>c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_2023_12_01T00_00_00.tif</code></p> <p>The date within the filename is year and month of aggregated timestamp.<br>NOTE: data for 2023-04 are missing, since they are not available from CLMS</p> <p>Pixel values:<br>0: Sea<br>70: Water<br>255: No water</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 27:17:30N<br>south: 14:43:30N<br>west: 17:04:30W<br>east: 04:48:00W</p> <p>Temporal extent:<br>January 2019 - December 2023 (except: April 2023)</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>monthly</p> <p>Software used:<br>GRASS GIS 8.3.2</p> <p>Format: GeoTIFF</p> <p>Original dataset license:<br>Generated using European Union's Copernicus Land Monitoring Service information</p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact: <br>mundialis GmbH & Co. KG, info@mundialis.de</p> <p> </p>
MODIS NDVI, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
<p>Normalized Difference Vegetation Index (NDVI) from MODIS data for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023).</p> <p>Source data:<br>- MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid (MOD13A2 v061): <a href="https://lpdaac.usgs.gov/products/mod13a2v061/">https://lpdaac.usgs.gov/products/mod13a2v061/</a></p> <p><br>The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MOD13A2) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value.</p> <p>For the time period January 2019 - December 2023, the NDVI layer of the original data has been processed. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers and appear as "no data". These 16-Day data are then aggregated to monthly temporal resolution using the maximum and reprojected to Latitude-Longitude/WGS84.</p> <p>File naming:<br><code>ndvi_filt_YYYY_MM_01T00_00_00.tif</code><br>e.g.: <code>ndvi_filt_2023_12_01T00_00_00.tif</code></p> <p>The date within the filename is year and month of aggregated timestamp.</p> <p>Pixel values:<br>NDVI * 10000 Scaled to Integer, example: value 6473 = 0.6473</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 28N<br>south: 14N<br>west: 18W<br>east: 4W</p> <p>Temporal extent:<br>January 2019 - December 2023</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>monthly</p> <p>Software used:<br>GRASS GIS 8.3.2</p> <p>Format: GeoTIFF</p> <p>Original dataset license:<br>All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: <a href="https://lpdaac.usgs.gov/products/mod13a2v061/">https://lpdaac.usgs.gov/products/mod13a2v061/</a></p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact: <br>mundialis GmbH & Co. KG, info@mundialis.de</p> <p> </p>
Monthly time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2023) derived from ERA5-Land data
<p>Overview:<br>ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br>The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: <br>1. spatially aggregate CHELSA to the resolution of ERA5-Land <br>2. calculate difference of ERA5-Land - aggregated CHELSA <br>3. interpolate differences with a Gaussian filter to 30 arc seconds <br>4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2023.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>The resulting relative humidity has been aggregated to monthly averages.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>File naming scheme (YYYY = year; MM = month):<br><code>ERA5_land_rh2m_avg_monthly_YYYY_MM.tif</code></p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 82:00:30N<br>south: 18N<br>west: 32:00:30W<br>east: 70E</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>Monthly</p> <p>Pixel values:<br>Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br>GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2</p> <p>Original ERA5-Land dataset license:<br><a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br>Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br>Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://doi.org/10.5281/zenodo.7427021">https://doi.org/10.5281/zenodo.7427021</a></p>
High Resolution 30m Land Surface Parameters for Europe
<p><strong>General Description</strong></p> <p>The <em>High Resolution 30m Land Surface Parameters for Europe</em> dataset is derived from <a href="../records/7676373">Global Ensemble DTM</a>. Data is computed using GRASS GIS and SAGA GIS. Original DTM data is in projection EPSG:4326, and reprojects to Equi7 (EPSG:27704), computes the parameters, and eventually reprojects to EPSG:3035. High resolution layers are computed in tiles. In order to eliminate boundary effects and reprojection resampling, Below is the list of land-surface parameters. Hillshade and Slope in degree are available to download through this resporitory, others are available to access from public S3 server. All files are in COG.</p> <p><strong>slope in degree (slope): </strong>steepness at each cell</p> <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_p10_30m_s_2018_go_epsg4326_v20230221.tif" target="_blank" rel="noopener ugc nofollow">https://s3.eu-central-1.wasabisys.com/arco/slope_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> <p><strong>hillshade:</strong> visualizing of terrain determined by a light source and the slope and aspect of the elevation surface</p> <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> <p><strong>minimum curvature (minic): </strong>valleys in negative value and local convex landform in positive value</p> <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">https://s3.eu-central-1.wasabisys.com/arco/minic_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> <p><strong>maximum curvature (maxic):</strong> ridges in positive values and local concave landform in negative value</p> <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">https://s3.eu-central-1.wasabisys.com/arco/maxic_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> <p><strong>positive openness (pos.openness): </strong>the "dominance" of an elevated location over its surroundings</p> <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">https://s3.eu-central-1.wasabisys.com/arco/pos.openness_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> <p><strong>negative openness (neg.openness):</strong> the "enclosure" of a lower location by elevated surroundings</p> <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">https://s3.eu-central-1.wasabisys.com/arco/neg</a><a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">.openness</a><a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> January 2000 – December 2022</li> <li><strong>Type of data:</strong> Land surface parameters of geomorphometry</li> <li><strong>How the data was collected or derived:</strong> Derived from <a href="../records/7676373">Global Ensemble DTM</a> in 30m using GRASS GIS and SAGA GIS running in a local HPC.</li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900000 899000 7401000 5501000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216700, 153400</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">https://github.com/AI4SoilHealth/SoilHealthDataCube/issues</a></p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> slope = slope in degree</li> <li><strong>variable procedure combination:</strong> edtm = Ensemble digital terrain model</li> <li><strong>Position in the probability distribution / variable type:</strong> m = measurement</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = Europe</li> <li><strong>EPSG code:</strong> epsg.3035 = EPSG:3035</li> <li><strong>Version code:</strong> v20240528 = 2024-05-28 (creation date)</li> </ol> <p> </p>
Dakar very-high resolution land cover map
<p>This land cover map of Dakar (Senegal) was created from a Pléiades very-high resolution imagery with a spatial resolution of 0.5 meter. The methodology followed a open-source semi-automated framework [1] that rely on <a href="https://grass.osgeo.org/">GRASS GIS</a> using a local unsupervised optimization approach for the segmentation part [2-3].</p> <p>Description of the files:</p> <ul> <li>"Landcover.zip" : The direct output from the supervised classification using the Random Forest classifier.</li> <li>"Landcover_Postclassif_Level8_Splitbuildings.zip" : Post-processed version of the previous map ("Landcover"), with reduced misclassifications from the original classification (rule-based used to reclassify the errors, with a focus on built-up classes).</li> <li>"Landcover_Postclassif_Level8_modalfilter3.zip" : Smoothed version of the previous product (modal filter with window 3x3 applied on the "Landcover_Postclassif_Level8_Splitbuildings"). </li> <li>"Landcover_Postclassif_Level9_Shadowsback.zip" : Corresponds to the "level8_Splitbuildings" with shadows coming from the original classification.</li> <li>"Dakar_legend_colors.txt" : Text file providing the correspondance between the value of the pixels and the legend labels and a proposition of color to be used.</li> </ul> <p> </p> <p>References:</p> <p>[1] Grippa, Taïs, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and Eléonore Wolff. 2017. “An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.” <em>Remote Sensing</em> 9 (4): 358. <a href="https://doi.org/10.3390/rs9040358">https://doi.org/10.3390/rs9040358</a>.</p> <p>[2] Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and Eléonore Wolff. 2017. “A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.” In <em>Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II.</em>, edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. <a href="https://doi.org/10.1117/12.2278422">https://doi.org/10.1117/12.2278422</a>.</p> <p>[3] Georganos, Stefanos, Taïs Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. “SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.” In <em>Proceedings of the 2017 Conference on Big Data from Space (BiDS’17)</em>.</p> <p> </p> <p>Founding: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p>
Ouagadougou very-high resolution land cover map
<p>This land cover map of Ouagadougou (Burkina Faso) was created from a WorldView3 very-high resolution imagery with a spatial resolution of 0.5 meter. The methodology followed a open-source semi-automated framework [1] that rely on <a href="https://grass.osgeo.org/">GRASS GIS</a> using a local unsupervised optimization approach for the segmentation part [2-3].</p> <p>Description of the files:</p> <ul> <li>"Landcover.zip" : The direct output from the supervised classification using the Random Forest classifier.</li> <li>"Landcover_Postclassif_Level5_Splitbuildings.zip" : Post-processed version of the previous map ("Landcover"), with reduced misclassifications from the original classification (rule-based used to reclassify the errors, with a focus on built-up classes).</li> <li>"Landcover_Postclassif_Level5_modalfilter3.zip" : Smoothed version of the previous product (modal filter with window 3x3 applied on the "Landcover_Postclassif_Level5_Splitbuildings"). </li> <li>"Landcover_Postclassif_Level6_Shadowsback.zip" : Corresponds to the "level5_Splitbuildings" with shadows coming from the original classification.</li> <li>"Ouaga_legend_colors.txt" : Text file providing the correspondance between the value of the pixels and the legend labels and a proposition of color to be used.</li> </ul> <p> </p> <p>References:</p> <p>[1] Grippa, Taïs, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and Eléonore Wolff. 2017. “An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.” <em>Remote Sensing</em> 9 (4): 358. <a href="https://doi.org/10.3390/rs9040358">https://doi.org/10.3390/rs9040358</a>.</p> <p>[2] Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and Eléonore Wolff. 2017. “A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.” In <em>Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II.</em>, edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. <a href="https://doi.org/10.1117/12.2278422">https://doi.org/10.1117/12.2278422</a>.</p> <p>[3] Georganos, Stefanos, Taïs Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. “SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.” In <em>Proceedings of the 2017 Conference on Big Data from Space (BiDS’17)</em>.</p> <p> </p> <p>Founding: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p>
Dataset of High Resolution Mammographic Images
<p>This dataset contains 138 high resolution mamographic images. Contrast Limited Adjustment Histogram Equalization (CLAHE) was used to enhance selected raw mamographic images.available in mammographic image analysis society (MIAS) database. </p>
Global monthly discharge dataset, derived from dynamical 1-D water-energy routing model (DynWat) at 10 km spatial resolution
<pre>Global 10km spatial resolution discharge dataset at the global scale for all major rivers, lakes and reservoirs. Data are provided at a monthly temporal resolution.</pre>
Coarse fragments % (volumetric) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Coarse fragments % (volumetric) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize 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>coarsefrag.vfraction = variable: coarse fragments volumetric fraction,</li> <li>usda.3b1 = 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>v0.2 = version number: 0.2,</li> </ul>
Silt content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Silt content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize 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> </p> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>silt.wfraction = variable: silt weight fraction,</li> <li>usda.3a1a1a = 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>v0.2 = version number: 0.2,</li> </ul>
Comparison of X-ray crystal structures of a tetradecamer sequence d(CCCGGGTACCCGGG)2 at 1.7 Å resolution.
<p>We presented a comparison of three different X-ray crystal structures of DNA tetradecamer sequence d(CCCGGGTACCCGGG)2 all at about 1.7 Å resolution. The sequence was designed as an attempt to form a DNA four-way junction with A-type helical arms. However, in the presence of zinc, magnesium, and in the absence of any metal ion, it does not take up the junction structure, but forms an A-type double helix. This allowed us to study possible conformational changes in the double helix due to the presence of metal ions. Upon addition of the zinc ion, there is a change in the space group from P41212 to P41. The overall conformation of the duplex remains the same. There are small changes in the interaction of the metal ions with the DNA. In the zinc-bound structure, there are two zinc ions that show direct interaction with the N7 atoms of terminal G13 bases at either end of the molecule. There are small changes in the interhelical contacts. The consequence of these differences is to break some of the symmetry and change the space group.</p>
Crystal structure of d(CCGGGGTACCCCGG)2 at 1.4 Å resolution.
<p>The X-ray crystal structure of the DNA tetradecamer sequence d(CCGGGGTACCCCGG)<sub>2</sub> is reported at 1.4 Å resolution in the tetragonal space group P4<sub>1</sub>2<sub>1</sub>2. The sequence was designed to fold as a four-way junction. However, it forms an A-type double helix in the presence of barium chloride. The metal ion could not be identified in the electron-density map. The crystallographic asymmetric unit consists of one A-type double helix with 12 base pairs per turn, in contrast to 11 base pairs per turn for canonical A-DNA. A large number of solvent molecules have been identified in both the grooves of the duplex and around the backbone phosphate groups.</p>
Comparison of X-ray crystal structures of a tetradecamer sequence d(CCCGGGTACCCGGG)2 at 1.7 Å resolution.
<p>We presented a comparison of three different X-ray crystal structures of DNA tetradecamer sequence d(CCCGGGTACCCGGG)<sub>2 </sub>all at about 1.7 Å resolution. The sequence was designed as an attempt to form a DNA four-way junction with A-type helical arms. Crystals did not have any metal ion in the crystallization solution apart from the sodium in the buffer. However, the sequence does not take up the junction structure, but forms an A-type double helix in P4<sub>1</sub>2<sub>1</sub>2 space group.</p>
Comparison of X-ray crystal structures of a tetradecamer sequence d(CCCGGGTACCCGGG)2 at 1.7 Å resolution.
<p>We presented a comparison of three different X-ray crystal structures of DNA tetradecamer sequence d(CCCGGGTACCCGGG)<sub>2 </sub>all at about 1.7 Å resolution. The sequence was designed as an attempt to form a DNA four-way junction with A-type helical arms. However, in the presence of magnesium, it does not take up the junction structure, but forms an A-type double helix in P4<sub>1</sub>2<sub>1</sub>2 space group. This allowed us to study possible conformational changes in the double helix due to the presence of metal ions. Though the crystallization condition has magnesium chloride, it was not observed in the electron density map.</p>
Heat load maps at 100m resolution (Linz)
<p>Climate indices (e.g. mean annual number of summer days, hot days, tropical nights) for 30-year historical/future climate periods. The calculation method is based on the cuboid method, a statistical-dynamical downscaling procedure that combines high-resolution (100m) urban climate simulations with long-term climate information from monitoring data/regional climate projections.</p> <p><strong>Climate indices for historical/current periods:</strong> - Background climate information: monitoring data from the airport station Linz Hoersching (1961-2010) - Background climate information: historical (bias-corrected) EURO-CORDEX simulations (1971-2000)</p> <p><strong>Climate indices for future periods:</strong> - Background climate information: bias-corrected EURO-CORDEX model simulations for different representative concentration pathways (2021-2100)</p>
A multiple model high-resolution head-related impulse response database for aided and unaided ears (HDF5 format)
<p>The Multiple-Model High Resolution HRTF database is a collection of HRTFs measured using four different Head-and-Torso Simulators at high spatial resolution (2 degree azimuth and elevation). The data here is stored in HDF5 files, the SOFA files are published in a separate dataset <a href="https://zenodo.org/record/2582553">doi:10.5281/zenodo.2582553</a>.</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
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.