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5,023 results for “landing”
OpenStreetMap+ Land Use / Land Cover classes and administrative regions of Europe
<p>This dataset contains 23 30m resolution raster data of continental Europe land use / land cover classes extracted from <a href="https://www.openstreetmap.org/">OpenStreetMap</a>, as well as administrative areas, and a harmonized building dataset based on OpenStreetMap and <a href="https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness#:~:text=The%20imperviousness%20products%20capture%20the,over%20long%20periods%20of%20time.">Copernicus HRL Imperviousness</a> data.</p> <p>The land use / land cover classes are:</p> <ol> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:building%3Dcommercial">buildings.commercial</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:building%3Dindustrial">buildings.industrial</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:building%3Dresidential">buildings.residential</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dcemetery">cemetery</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dconstruction">construction.site</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dlandfill">dump.site (landfill)</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dfarmland">farmland</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dfarmyard">farmyard</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dforest">forest</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dgrass">grass</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:building%3Dgreenhouse">greenhouse</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dharbour">harbour</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dmeadow">meadow</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dmilitary">military</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dorchard">orchard</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dquarry">quarry</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:railway%3Drail">railway</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dreservoir">reservoir</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:highway%3Droad">road</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dsalt_pond">salt</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dvineyard">vineyard</a></li> </ol> <p>The land use / land cover data was generated by extracting OSM vector layers from https://download.geofabrik.de/). These were then transformed into a 30 m density raster for each feature type. This was done by first creating a 10 m raster where each pixel intersecting a vector feature was assigned the value 100. These pixels were then aggregated to 10 m resolution by calculating the average of every 9 adjacent pixels. This resulted in a 0—100 density layer for the three feature types. Although the digitized building data from OSM offers the highest level of detail, its coverage across Europe is inconsistent. To supplement the building density raster in regions where crowd-sourced OSM building data was unavailable, we combined it with Copernicus High Resolution Layers (HRL) (obtained from https://land.copernicus.eu/pan-european/ high-resolution-layers), filling the non-mapped areas in OSM with the Impervious Built-up 2018 pixel values, which was averaged to 30 m. The probability values produced by the averaged aggregation were integrated in such a way that values between 0—100 refer to OSM (lowest and highest probabilities equal to 0 and 100 respectively), and the values between 101—200 refer to Copernicus HRL (lowest and highest probability equal to 200 and 101 respectively). This resulted in a raster layer where values closer to 100 are more likely to be buildings than values closer to 0 and 200. Structuring the data in this way allows us to select the higher probability building pixels in both products by the single boolean expression: Pixel > 50 AND pixel <150.</p> <p>This dataset is part of the OpenStreetMap+ was used to pre-process the LUCAS/CORINE land use / land cover samples (https://doi.org/10.5281/zenodo.4740691) used to train machine learning models in Witjes et al., 2022 (https://doi.org/10.21203/rs.3.rs-561383/v4)</p> <p>Each layer can be viewed interactively on the Open Data Science Europe data viewer at <a href="https://maps.opendatascience.eu/?base=OpenStreetMap%20(grayscale)&layer=Copernicus-OSM%20buildings&zoom=4&eye=5000000&center=53.7139,17.0066&opacity=45">maps.opendatascience.eu</a>.</p>
Alignment used in "A phylogenomically informed five-order system for the closest relatives of land plants"
<p>Alignment that served as the basis for the phylogenomic analyses presented in "A phylogenomically informed five-order system for the closest relatives of land plants" — preprint on bioRxiv doi: https://doi.org/10.1101/2022.07.06.499032</p>
The InvL Dataset of Invasive Species from drones in a Land Environment
<p><strong>Summary</strong></p> <p>An original dataset for semantic segmentation, InvL, is introduced, which to the best of the authors' knowledge, is the first dataset of its kind with pixel-level annotations pertaining to invasive species (Himalayan balsam) in a land environment from drones with height 10-30 m (GSD 3-13 mm) using optical imaging and having diverse outdoor blur, noise and contrast.</p> <p><strong>Objectives</strong></p> <p>Automated detection and quantification of invasive species from drones would enable more efficient mapping using simple low-cost technologies. This would help to <em>remove invasive and later protect species. </em>However, it is difficult to distinguish invasive species from common ones from higher altitudes using drone images. Training a machine learning algorithm to accurately detect a given invasive species from images taken in the field requires a massive amount of human-generated training data.</p> <p>Objective is to encourage people to open share images that can be used to develop technology. We are interested expand database and add author for every 1 GB added to the new version InvL or every 200 hours spent to improve annotation quality or annotation type.</p> <p>Objective is to expand establish a reference dataset for automatic extraction using gold standard training sets to gain Intersection over Union (IoU) over 0.8 from drone height over 30 meters with least GSD using low cost drones.</p> <p><strong>Data description</strong></p> <p>This data set contains images of Himalayan balsam (Impatiens glandulifera) taken mostly with the DJI Mavic 2 PRO in Finland from heights 10, 15, 20 and 30 meters, low speed 1- 2 m/s, no filters, 90 degrees angle, still images, GSD varying from 3-13mm, image size ~15 MB, between July (usually no flowers) and August (flowers).</p> <p> </p> <p> </p> <p><strong><em>Table 1. Drone images of invasive species</em></strong></p> <table> <tbody> <tr> <td> <p><strong><em>Label</em></strong></p> </td> <td> <p><strong><em>Name of data file/set</em></strong></p> <p> </p> </td> <td> <p><strong>Dataset size (GB)</strong></p> </td> <td> <p><strong>File type (extension)</strong></p> <p> </p> </td> <td> <p><strong>Data repository and identifier</strong></p> </td> <td> <p><strong>IoU estimate </strong><strong>- model</strong></p> </td> <td> <p><strong>Comment</strong></p> </td> </tr> <tr> <td> <p><em>Himalayan_balsam</em></p> </td> <td> <p><em>InvL/Himalayan_balsam</em></p> <p><em>../ann – contains annotations</em></p> <p><em>…/ann/H10m_Lahti_Peitsikatu_H10m_10072020</em></p> <ul> <li><em>H10 = 10 m UAV heigth</em></li> <li><em>Lahti = city</em></li> <li><em>10072020 (last) = date of fligth</em></li> </ul> <p><em>…/original – orginal UAV images, folder structure same as for “ann”</em></p> </td> <td> <p>2</p> </td> <td> <p>InvL.zip (folder structure with annotations and original images)</p> <p> </p> </td> <td> <p> </p> </td> <td> <p><em>Himalayan balsam </em></p> <p><em>IoU = 0.53, FCN, 15 m with augmentation IoU=0.37, FCN, 30 m</em></p> </td> <td> <p><em>Himalayan balsam, green</em></p> <p><em>Elevation varies in area +- 5m, so actual UAV flight height varies</em></p> <p><em>Each image around 15 MB</em></p> </td> </tr> </tbody> </table> <p><strong>Limitations</strong></p> <p>Annotations do not indicate width or margins.</p> <ul> <li>There is no indication of confidence of annotations.</li> <li>Blur, noise or contrast values are not calculated.</li> <li>Mostly species are easily visible and images with bright sunlight have been removed in selection of images.</li> <li>Annotator needs good and tested instruction to annotate images. Annotator needs to do work for tens of hours with developed and tested instructions to guarantee good quality. After image annotator each image should be checked by an “expert” and reannotated if necessary (silver standard, if this is done twice it is gold standard). If expert is used each image should be annotated once (silver standard) and twice (gold standard).</li> <li>Annotation time for each 15 MB image should be around one hour.</li> <li>Image GSD should be around 5 mm and less for smaller species.</li> <li>In different field areas species color and size varies and mixed vegetation may cause problems.</li> <li>In images there are flowers and non-flowers.</li> </ul> <p><strong>Abbreviations</strong> FCN: a fully convolutional neural network.</p> <p> </p>
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : Model Ouput Data
<p>This upload includes data associated with the manuscript "Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : )" submitted to Geoscientific Model Development. The dataset includes an output file with the simulated ammonia emissions for the agricultural sector.</p> <p>The emissions (manure management and soil), manure production and soil ammonium concentrations are monthly fields from the simulation for 2007-2015.</p> <p>Additional information is given in the readme file</p>
Land Use - Crop - Climate Scenarios Oudlandpolder
<p>Regionalized time series for 2020-2100 with a day resolution based on different combinations of 4 climate RCP (Representative Concentration Pathways) and 4 Shared Social-economic Pathways (SSP) scenarios for the Oudlandpolder region in Belgium. This coastal lowland (polder) faces challenges related to climate adaptation and mitigation to be addressed by, amongst others, a proper balance of water distribution for the two main land use categories: agriculture and nature. The following variables are included:</p> <ul> <li>land use cover (agriculture and nature) in ha related to SSP</li> <li>crop fractions for 13 crops related to SSP</li> <li>crop factors Kc (dimensionless)</li> <li>precipitation in mm/day related to RCP</li> <li>potential evapotranspiration in mm/day related to RCP</li> <li>sea level in mm wrt January 1, 2020 related to RCP </li> <li>Waste Water Treatment Plant (WWTP) effluence ratio wrt to 2020</li> </ul> <p>RCP scenarios: RCP 2.6; RCP 4.5; RCP 6.0; RCP 8.5</p> <p>SSP scenarios: SSP1; SSP2; SSP4; SSP5. These SSP scenarios are based on consistent projections for land use change (agriculture vs nature) and crop schemes. </p> <p>Land use cover change was modelled with the VITO spatial-dynamic RuimteModel (see references below). </p> <p>Uncertainty in the RCP scenarios has been addressed by working with an ensemble of the following 8 climate models:</p> <ul> <li>BNU-ESM</li> <li>CSIRO-Mk3-6-0</li> <li>GFDL-ESM2G</li> <li>GFDL-ESM2M</li> <li>IPSL-CM5A-MR</li> <li>MPI-ESM-LR</li> <li>MPI-ESM-MR</li> <li>NorESM1-M</li> </ul> <p>The scenarios point at potential seasonal water shortages for certain combinations of RCP and SSP scenarios, in particular during the dry summer season. Climate adaptation actions can also benefit to a limited extent from identifying the climate robust crop schemes. </p> <p>For further information see: </p> <ul> <li>https://h2020-coastal.eu</li> <li>https://h2020-coastal.eu/publications/flipbook/171</li> <li>https://www.vlm.be/nl/projecten/Paginas/Oudlandpolder.aspx</li> <li>https://vito.be/en/spatial-model-flanders-ruimtemodel-vlaanderen</li> </ul>
NDVI from Copernicus Global Land Service over Mumbai (India)
<p>This data collection contains two datasets over the region of Mumbai (India):</p> <p>- Long-term statistics of NDVI computed over 1999-2019</p> <p>- NDVI values for 2021 (10-days)</p> <p> </p> <p>The original NDVI datasets can be downloaded from the <a href="https://land.copernicus.vgt.vito.be/PDF/portal/Application.html">Copernicus Global Land Service portal</a> (registration is mandatory but free of charge). <br> Data can be downloaded as well directyl from <a href="https://scihub.copernicus.eu/">Copernicus Open Access Hub</a> but data are not based on atmospherically and BRDF corrected data.</p>
Sentinel-3 NDVI ARD and Long Term Statistics (1999-2019) from the Copernicus Global Land Service over Lombardia
<p>Sentinel-3 NDVI Analysis Ready Data (ARD) (C_GLS_NDVI_20220101_20220701_Lombardia_S3_2.nc) product provided by the Copernicus Global Land Service [3]. The file C_GLS_NDVI_20220101_20220701_Lombardia_S3_2_masked.nc is derived from C_GLS_NDVI_20220101_20220701_Lombardia_S3_2.nc but values have been scaled (raw_value * ( 1/250) - 0.08) and values lower then -0.08 and greater than 0.92 have been removed (set to missing values).</p> <p>The original dataset can also be discovered through the OpenEO API[5] from the CGLS distributor VITO [4]. Access is free of charge but an <a href="https://aai.egi.eu/">EGI registration</a> is needed.</p> <p>The file called Italy.geojson has been created using the Global Administrative Unit Layers <a href="https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/9c35ba10-5649-41c8-bdfc-eb78e9e65654">GAUL G2015_2014</a> provided by FAO-UN (see <a href="https://data.apps.fao.org/map/catalog/srv/api/records/9c35ba10-5649-41c8-bdfc-eb78e9e65654/attachments/GAUL2015_Documentation.zip">Documentation</a>). It only contains information related to Italy.</p> <p> </p> <p>Further info about drought indexes can be found in the Integrated Drought Management Programme [5]</p> <p>[1] <a href="https://www.sciencedirect.com/science/article/abs/pii/027311779500079T">Application of vegetation index and brightness temperature for drought detection</a> [2] <a href="https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index">NDVI</a> [3] <a href="https://land.copernicus.eu/global/index.html">Copernicus Global Land Service</a> [4] <a href="https://vito.be/en">Vito</a> [5] <a href="https://openeo.org/">OpenEO</a> [5] <a href="https://www.droughtmanagement.info/indices">Integrated Drought Management</a></p>
Influence of Large-scale Land-sea Atmosphere Interaction on Ozone Pollution in Coastal Cities in the Northern Bohai Sea
<p><strong>O3_obs </strong>includes ozone observations for Qinhuangdao (QHD), Jinzhou (JZ), Yingkou (YK), Dalian (DL) from 29 August to 5 September 2017, and the information of four sites including station code, longitude and latitude. <strong>O3_sim</strong> includes ozone simulation in the four sites extracted according to location of them. <strong>Met_obs</strong> and <strong>Met_sim</strong> include the observations of 2 m temperature (℃), 2 m relative humidity (RH2) and 10 m wind speed for the 4 stations from 29 August to 5 September 2017, and the information of four stations including station code and their location. <strong>Slp_wind_9km.nc</strong> is mean sea-level pressure and wind in Phase Ⅰ and Phase Ⅱ. <strong>O3_wind_9km.nc</strong> is mean simulated surface ozone mixing ratios and wind at 10 m in 19:00-09:00 LT and 10:00-18:00 LT during Phase Ⅰ and Phase Ⅱ. <strong>Process_contribution </strong>includes mean surface O<sub>3</sub> mixing ratios and O<sub>3</sub> contribution at the bottom level in Phase Ⅰ, Phase Ⅱ, and at different heights (AGL) in Phase Ⅱ in four sites, respectively. <strong>O3_source_site</strong> includes time series of O<sub>3 </sub>source in QHD, JZ, YK, and DL. <strong>Mean_source_base_27km.nc </strong>is the mean O­<sub>3</sub> contribution in Phase Ⅰ and Phase Ⅱ from five primary exogenous source regions. <strong>Mean_source_control_27km.nc</strong> is the O<sub>3</sub> contribution in Phase Ⅱ from the BTH and NEC emissions in Phase I, in which BTH and NEC’s emissions in Phase Ⅱ are set zero. <strong>Trjectory_conc_pa</strong> includes three trajectories analyzed in this work and vertical O<sub>3</sub> and NO<sub>X</sub> mixing ratios, and the chemical generations and consumptions of O<sub>3</sub> within the air masses along the trajectories.</p>
Urban land expansion in area with decreased urban sprawl at global, national, and city scales during 2000 to 2020
<p>I used calibrated population density thresholds from the year 2000 and 2020 Worldpop population model to measure area and densities for urban and suburban density classes (≥ 250 humans per km<sup>2</sup>) at global and national scales and both broad multi-city agglomerations and fine city cores.</p>
Outputs of the Jupyter Notebook - Exploring Land Cover Data (Impact Observatory)
<p>The dataset contains the outputs of the notebook "Exploring Land Cover Data (Impact Observatory)" published in The Environmental Data Science Book.</p>
Dataset of land uses for the period 1961-2014 (Peyne watershed)
<p>The study area corresponds to the Peyne watershed, which covers approximately 76 km2 and is located in Languedoc-Roussillon in southern France (43° 35′N, 3° 19′E). The smaller enclosed watershed is called the Bourdic subwatershed and covers 7 km2. The area of the Peyne watershed is mostly covered by perennial crops (mainly vineyards), and five towns are present in the zone. In 2012, the IGN released a large, open-access database of aerial black-and-white photographs from 1937 until the present. We selected a sample of images from the IGN database covering the Peyne watershed from 1962 to 2003 with a time interval between 4 and 5 years. We completed the series by taking orthophotos from 2005 to 2012 processed by the IGN from colour photographs. The aerial photographs were orthorectified using structure-from-motion approach and corrected from vignetting effects to get orthophotos defined at pixel resolution of less than 1 m. The satellite images were also sampled at the same resolution. The raster database was then transformed into polygons with a minimal area of approximately 200 m2 using manual digitizing and classification procedures to separate field entities and their associated land use categories using QGIS software. Land uses of each homogeneous polygon were classified according to the Corine Land Cover nomenclature expanded to the fourth level of detail for vineyards to distinguish goblet vines from trellised vines, with a special case noted for undefined vines when the difference in land management was difficult to detect. The distinction between goblet and trellised vineyards was based on the presence of a clear row orientation in the pictures.</p>
Data used in 'Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements.'
<p>This repository contains the data used in:</p> <blockquote> <p>Gadal, C., Delorme, P., Narteau, C. et al. Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements. Boundary-Layer Meteorol 185, 309–332 (2022). <a href="https://doi.org/10.1007/s10546-022-00733-6">https://doi.org/10.1007/s10546-022-00733-6</a></p> </blockquote> <p>where wind data measured at 4 different places in and across the Namib Sand Sea are compared to the data from the ERA5/ERA5Land climate reanalyses.</p> <p>The use this data, one should first look at the GitHub repository <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a> and at the corresponding documentation <a href="https://cgadal.github.io/GiantDunes/">https://cgadal.github.io/GiantDunes/</a>. The description sometimes refers to scripts used in <a href="https://github.com/Cgadal/GiantDunes/tree/master/Processing">https://github.com/Cgadal/GiantDunes/tree/master/Processing</a>.</p> <p>The two folders 'raw_data' and 'processed_data' contain the input raw_data, and the output data after processing used to make the paper figures, respectively. In each of them, '.npy' files contain Python dictionaries with different variables in them. They can be loaded using the Python library <code>numpy</code> as <code>data = np.load('file.npy', allow_pickle=True).item()</code>; and the different keys (variables) can be printed with <code>data.keys()</code> or <code>data[station].keys()</code> if <code>data.keys()</code> return the different stations. Unless specified otherwise below, note that all variables are given in the International System of Units (SI), and wind direction is given anticlockwise, with the 0 being a wind blowing from the West to the East.</p> <ul> <li>raw_data: <ul> <li>DEM: contains the Digital Elevation Models of the two stations from the SRTM30, downloaded from here: https://dwtkns.com/srtm30m/</li> <li>ERA5: hourly data from the ER5 climate reanalysis, on surface (_BLH) and pressure levels (_levels). Downloaded from https://cds.climate.copernicus.eu/</li> <li>ERA5Land: hourly data from the ER5Land climate reanalysis Downloaded from https://cds.climate.copernicus.eu/</li> <li>KML_points: kml points of the measurement station. It can be opened directly in GoogleEarth.</li> <li>measured_wind_data: contains the measured in situ data. The windspeed is measured using Vector Instruments A100-LK cup anemometers, the wind direction using Vector Instruments W200-P wind vane and the time using Campbell Instruments CR10X and CR1000X dataloggers.<br> </li> </ul> </li> <li>processed_data: <ul> <li>'Data_preprocessed.npy': preprocessed_data, output of 1_data_preprocessing_plot.py</li> <li>'Data_DEM.npy': properties of the processed DEM, the output of 2_DEM_analysis_plot.py</li> <li>'Data_calib_roughness.npy': data from the calibration of the hydrodynamic roughnesses, the output of 3_roughness_calibration_plot.py</li> <li>'Data_final.npy': file containing all computed quantities</li> <li>'time_series_hydro_coeffs.npy': file containing the time series of the calculated hydrodynamic coefficients by '5_norun_hydro_coeff_time_series.npy'.</li> </ul> </li> </ul> <p> Depending on the loaded data file, main dictionary keys can be:</p> <ul> <li>'lat': latitude, in degree</li> <li>'lon': longitude, in degree</li> <li>'time': time vector, in datetime objects (https://docs.python.org/3/library/datetime.html)</li> <li>'DEM': elevation data array in [m], with dimensions matching 'lat' and 'lon' vectors</li> <li>'z_mes', 'z_insitu', 'z_ERA5LAND': height of the corresponding velocity</li> <li>'direction': measured wind direction, in [degrees]</li> <li>'velocity': measured wind velocity, in [m/s]</li> <li>'orientaion': dune pattern orientation, [deg]</li> <li>'wavelength': dune pattern wavelength, [km]</li> <li>'z0_insitu': chosen hydrodynamic roughness for the considered station.</li> <li>'U_insitu', 'Orientation_insitu': hourly averaged measured wind velocities and direction</li> <li>'U_era', 'Orientation_era': hourly 10m wind data from the ERA5Land data set</li> <li>'Boundary layer height', 'blh': boundary layer height from the hourly ERA5 dataset</li> <li>'Pressure levels', 'levels': Pressure levels from the pressure levels ERA5 dataset</li> <li>'Temperature', 't': Temperature from the pressure levels ERA5 dataset</li> <li>'Specific humidity', 'q': Specific humidity from the pressure levels ERA5 dataset</li> <li>'Geopotential', 'z': Geopotential from the pressure levels ERA5 dataset</li> <li>'Virtual_potential_temperature': Virtual potential temperature calculated from the pressure levels ERA5 dataset</li> <li>'Potential_temperature': Potential temperature calculated from the pressure levels ERA5 dataset</li> <li>'Density': Density calculated from the pressure levels ERA5 dataset</li> <li>'height': Vertical coordinates calculated from the pressure levels ERA5 dataset</li> <li>'theta_ground': Averaged virtual potential temperature within the ABL.</li> <li>'delta_theta': Virtual potential temperature at the ABL.</li> <li>'gradient_free_atm': Virtual potential temperature gradient in the FA.</li> <li>'Froude': time series of the Froude number U/((delta_theta/theta_ground)*g*BLH)</li> <li>'kH': time series of the number 'kH'</li> <li>'kLB': time series of the internal Froude number kU/N</li> </ul> <p>Other keys are not relevant and are stored for verification purposes. For more details, please contact Cyril Gadal (see authors), and look at the following GitHub repository: <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a>, where all the codes are present.<br> </p>
Shape from Shading Digital Elevation Model for Oxia Planum Candidate Landing Site
<p>This data set contains the calibrated and map-projected HiRISE image ESP_037558_1985 and the matching Shape from Shading DEM using the method described in Hess et al., (2019a), and in more detail in Hess et al. (2022). The SfS DTM was part of the EPSC abstract Hess et al., (2019b). When using the data please reference Hess et al. (2022) for the method.</p> <p>ESP_037558_1985_30cm_o.cub: Image data in radiances, ISIS cube file, Equirectangular map projection at 0.25 m/pixel resolution.</p> <p>ESP_037558_1985_30cm_DEM.cub: Digital Elevation Model (DEM) with heights in meter, ISIS cube file, Equirectangular map projection at 0.25 m/pixel resolution.</p> <p>Cube files can be converted to other data formats using gdal (https://gdal.org/) or directly loaded in, e.g., ArcGIS or QGIS.</p> <p> </p> <p>Hess, M., Wohlfarth, K., Grumpe, A., Wöhler, C., Ruesch, O., and Wu, B.: ATMOSPHERICALLY COMPENSATED SHAPE FROM SHADING ON THE MARTIAN SURFACE: TOWARDS THE PERFECT DIGITAL TERRAIN MODEL OF MARS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1405–1411, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1405-2019, 2019a.</p> <p>Hess, Marcel. "High Resolution Digital Terrain Model for the Landing Site of the Rosalind Franklin (ExoMars) Rover." Proc. European Planetary Science Congress, EPSC-DPS2019-1533-4, Geneva, Switzerland, 2019b.</p> <p>Hess, M.; Tenthoff, M.; Wohlfarth, K.; Wöhler, C. Atmospheric Correction for High-Resolution Shape from Shading on Mars. <em>J. Imaging</em> <strong>2022</strong>, <em>8</em>, 158. https://doi.org/10.3390/jimaging8060158</p>
The Identification of Extinct Megafauna in Rock art Using Geometric Morphometrics: A Genyornis newtoni Painting in Arnhem Land, Northern Australia?
<p>Raw data files used for the analysis of a contentiously identified rock-art image located in Arnhem Land, Northern Australia. The data were used to test a novel approach to quantifying species identification in rock art images to assess the extent to which an image resembles other rock art of sound identification or anatomical images of visually similar species.</p> <p>Included files are the raw coordinate data files ("[feature] PCA file", .txt format) for use in Morphologika2, and formatted files for use in CVAGen8 ("[feature]" x1y1 file for CVA", .x1y1 format; "[feature] group file", .txt format).</p> <p>Files produced using software by Rohlf (2015) and Sheets (2014)</p>
Evolutionary dynamics of mycorrhizal symbiosis in land plant diversification - phylogenetic data
<p>This submission supplements the manuscript entitled <em>Evolutionary dynamics of mycorrhizal symbiosis in land plant diversification</em> by <strong>Frida A.A. Feijen, Rutger A. Vos, Jorinde Nuytinck & Vincent S.F.T. Merckx.</strong></p> <p>The contents of this submission are dating analysis results for rootings of the land plant topology. Contains the following files:</p> <ul> <li>*.log.gz BEAST logs</li> <li>*.trees.gz BEAST trees</li> <li>*.tiff screen dumps of tracer, showing the burn-in</li> <li>*.consensus.trees produced with treeannotator</li> </ul> <p><strong>For more information</strong>: https://github.com/naturalis/mycorrhiza/tree/v1.0.0</p>
ERA5-Land selected indicators daily aggregates for Africa, 2010
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2010.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 2013
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2013.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 2015
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2015.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 2016
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2016.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 2017
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2017.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</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.