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5,023 results for “landing”
Homisland-IO: a homogeneous land cover over the small islands of the southwest Indian Ocean
<p>This dataset is a landcover product, called Homisland-IO<strong>,</strong> based on the analysis of high spatial resolution images acquired by the SPOT 5 satellite between December 2012 and July 2014 and produced at the SEAS-OI Station. We used an object-based image analysis method to identify the 11 major classes of land cover / land use of these tropical islands. This methodology together with a good knowledge of the field has enabled us to achieve an overall accuracy of 86%, making it an operational product. Homisland-IO is<strong> </strong>freely accessible through a web portal and thus available for future uses.</p>
Ouagadougou land use map at street block level
<p>This datatset contains a land use classification of Ouagadougou (Burkina Faso) at the street block level. It was created following the methodology presented in [1].</p> <p>Description of the files:</p> <ul> <li>"Ouagadougou_landuse.gpkg" : GeoPackage with two layers: (1) layer of the street blocks extracted from OpenStreetMap using [2] with classification results in the attribute table. (2) layer with manual correction made by GEORGANOS Stefanos (sgeorganos@ulb.ac.be).</li> <li>"Ouagadougou_landuse_style_QGIS.zip" : Files for style for rendering in QGIS.</li> </ul> <p>Attribute table content:</p> <ul> <li>"CAT", "GID" : ID of the street block</li> <li>"PROB_ACS" : Probability to belong to class ACS</li> <li>"PROB_BARE" : Probability to belong to class BARE</li> <li>"PROB_PLAN" : Probability to belong to class PLAN</li> <li>"PROB_UNPLA" : Probability to belong to class UNPLAN</li> <li>"PROB_VEG" : Probability to belong to class VEG</li> <li>"FIRST_LABE" : Class with the highest classification probability</li> <li>"SEC_LABEL" : Class with the second highest classification probability</li> <li>"FIRST_PROB" : Value of the highest classification probability</li> <li>"SEC_PROB" : Value of the second highest classification probability</li> <li>"UNCERTAIN" : Difference between "FIRST_PROB" and "SEC_PROB"</li> <li>"BUILT_PERC" : Percentage of the street blocks covered by built-up (from land cover map)</li> <li>"MAP_LABEL" : Final classification label with uncertainty and different density classes. Depending on the layer, the label is with or without manual corrections</li> </ul> <p>Legend classes label:</p> <ul> <li>"VEG" : Vegetation</li> <li>"BARE" : Bare soils</li> <li>"ACS" : Non-residential built-up (administrative, commercial, services, etc.)</li> <li>"PLAN" : Planned residential built-up</li> <li>"PLAN_LD" : Planned residential low density built-up</li> <li>"UNPLAN" : Unplanned residential built-up</li> <li>"UNPLAN_LD" : Unplanned residential low density built-up</li> <li>"UNCERT" : Uncertain classification</li> <li>"WET" : Wetlands</li> <li>"AGRI" : Agricultural land</li> </ul> <p>References:</p> <p>[1] Grippa, Tais, 2018, "Mapping urban land use at street block level using OpenStreetMap, remote sensing data and spatial metrics", <em>ISPRS Int. J. Geo-Inf.</em> <strong>2018</strong>, <em>7</em>(7), 246. <a href="https://doi.org/10.3390/ijgi7070246">https://doi.org/10.3390/ijgi7070246</a> </p> <p>[2] Grippa, Tais. 2018. “Osm Street Blocks Extraction.” Zenodo. <a href="https://doi.org/10.5281/zenodo.1290637">https://doi.org/10.5281/zenodo.1290637</a>.</p> <p>Funding: </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>
Copperplate land grant of Rudradāsa II, year 117 (Siddham OB00537, IN00537 and IN00538)
<p>An unpublished grant of Rudradāsa II (Valkhā). Online edition forthcoming at https://siddham.network; print edition forthcoming in</p> <p>Balogh, Dániel, and Csaba Kiss. 2019. “Valkhā Chronology Revisited: More Plates and More Questions.” <em>Bulletin of the School of Oriental and African Studies</em>.</p> <p>Photo by Dániel Balogh (February 2018), with permission of the State Museum, Bhopal. For scholarly use only.</p>
Copperplate land grant of Bhuluṇḍa, year 52 (Siddham OB00535, IN00535)
<p>An unpublished fragmentary grant of Bhuluṇḍa (Valkhā). Online edition forthcoming at https://siddham.network; print edition forthcoming in</p> <p>Balogh, Dániel, and Csaba Kiss. 2019. “Valkhā Chronology Revisited: More Plates and More Questions.” <em>Bulletin of the School of Oriental and African Studies</em>.</p> <p>Original image Copyright: National Museum, New Delhi (http://museumsofindia.gov.in/repository/record/nat_del-89-239-22217). Retouched and published strictly for scholarly use in good faith. Please do not disseminate without consent of the copyright holder.</p>
Copperplate land grant of Svāmidāsa, year 65 (Siddham OB00536, IN00536)
<p>An unpublished grant of Svāmidāsa (Valkhā). Online edition forthcoming at https://siddham.network; print edition forthcoming in</p> <p>Balogh, Dániel, and Csaba Kiss. 2019. “Valkhā Chronology Revisited: More Plates and More Questions.” <em>Bulletin of the School of Oriental and African Studies</em>.</p> <p>Original image Copyright: National Museum, New Delhi ( http://museumsofindia.gov.in/repository/record/nat_del-92-53-29127 ). Retouched and published strictly for scholarly use in good faith. Please do not disseminate without consent of the copyright holder.</p>
Murgia Alta: land cover map (2018)
<p>A land cover map in "Murgia Alta" PA, for 2018, obtained by considering 4 multi-seasonal Sentinel-2 images. A Support Vector machine (SVM) classifier was used for a 13 classes problem. The images were atmospherically corrected.</p> <p>The map was produced at 10 meters spatial resolution and projected in WGS84/UTM33N.</p> <p>The Overall Accuracy (OA) of the map was: OA=97.37%±0.09%.</p> <p>The map has 14 values related as follows in LCCS-FAO taxonomy:</p> <p>Value 0 = Unclassified</p> <p>Value 1 = A11/A7.A9 (olive grooves)</p> <p>Value 2 = A11/A1.A7.A10 (orchards)</p> <p>Value 3 = A11/A2.A7.A10 (vineyards)</p> <p>Value 4 = A11/A3 (cultivated herbaceous)</p> <p>Value 5 = A12/A1.D1.E1 (natural broadleaved evergreen)</p> <p>Value 6 = A12/A1.D1.E2 (natural broadleaved deciduous)</p> <p>Value 7 = A12/A1.D2.E1 (natural needleleaved evergreen)</p> <p>Value 8 = A12/A2.A6 (natural grasslands)</p> <p>Value 9 = B15/A1 (artificial structures, buildings/roads)</p> <p>Value 10 = B15/A2.A6 (extraction sites)</p> <p>Value 11 = B27-B28/A1.A5 (artificial-natural water)</p> <p>Value 12 =BURNED AREAS </p> <p>Value 13 =Out of PA</p>
A Dataset of Global Land Cover Validation Samples
<p>A dataset of global land cover validation samples in 2015. In order to guarantee the confidence and objective of the validation samples, several existing reference datasets such as GLCNMO2008 training dataset, VIIRS reference dataset, STEP reference dataset, Global cropland reference data and so on, high resolution imagery in the Google earth and time-series NDVI,NDSI values of each related point are integrated to derive the global validation datasets. The dataset is provided in .shp format.</p>
GLM2_modified and Results as used in Ma et al: Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2, Geosci. Model Dev., 2020
<p>Code modified GLM2, scripts and result as used in Ma et al 2019, Ma et al 2019, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-146</p>
National Checklists 2017: Territory of the French Southern and Antarctic Lands Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Territory of the French Southern and Antarctic Lands collected using effechecka and geonames polygons
National Checklists 2019: Territory of the French Southern and Antarctic Lands Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Territory of the French Southern and Antarctic Lands collected using effechecka and geonames polygons
Land-Cover Classification of Mesoamerica's Cimate Hubs
<p>This project presents land use classification maps obtained through satellite image analyses for 10 study areas in Mesoamerica. A classification algorithm was adapted specifically for the tropics and developed using the latest satellite images available. We applied Object Based Image Analysis method & Random Forest algorithm on 10m Sentinel 1 (S1) & Sentinel 2 (S2) image collections of years 2022/2023. The study area encompassed 10 landscapes spanning 8 countries across Mesoamerica. These areas cover a total of 256 971 km² and were previously defined by Osa Conservation as potential climate adaptation hubs.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1969
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1969.</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>
A Dataset with Synthetic Landing Trajectories for Zurich Airport
<p>The archive contains synthetic datasets in npy format generated using a TimeGAN-based model designed to capture a range of aircraft landing trajectories at Zurich airport across different operational scenarios and environmental conditions. Each dataset consists of multiple groups representing distinct patterns or behaviors in the trajectory data. The trajectories are segmented in clusters, and to some of them a smoothing filter was applied.</p> <p><span>The datasets incorporate a range of variables critical for modeling aircraft landing behaviors. Continuous variables such as longitude, latitude, and altitude exhibit multimodal distributions, capturing different operational phases and conditions within each cluster. The data is stored in an array format with dimensions (number of samples, sequence length, feature dimensions). Here, the number of samples corresponds to the total number of recorded flight trajectories included in the dataset, while the sequence length represents the duration or the number of time steps over which each trajectory is recorded. The feature dimensions denote the various variables (state vector) measured at each time step, consisting of longitude, latitude and altitude. Categorical variables, such as runway identifiers and cluster labels, follow distributions that reflect operational frequencies, with certain clusters or runways being more common under specific conditions. </span></p> <p>The archive contains the following files:</p> <p>- 5clust0.npy, 5clust1.npy, 5clust2.npy, 5clust3.np & 5clust4.npy (5 clusters of landing trajectories separated)<br>- ma_5clust0.npy, ma_5clust1.npy, ma_5clust2.npy, ma_5clust3.np & ma_5clust4.npy (5 clusters of landing trajectories separated, moving average filter applied)<br>- ma_3clust0.npy, ma_3clust1.npy & ma_3clust2.npy (3 clusters of landing trajectories separated, moving average filter applied)<br>- run28_syn.npy & run24_syn.npy (groups of landing trajectories per runway)<br>- ma_run28_syn.npy & ma_run24_syn.npy (groups of landing trajectories per runway, moving average filter applied)<br>- go_around_synthetic.npy (go-around landing trajectories on runway 14)</p>
Ultra-high Resolution Land Use Data Set of Typical Villages in Northeastern Tibetan Plateau
<p>This dataset was collected by a research team during a field investigation in the Hehuang Valley of Qinghai Province from July to August 2022. Using the DJI Mavic2pro equipped with a Hasselblad L1D-20c camera, 55 typical villages were selected in the Hehuang Valley and over 4600 aerial photographs were obtained using drone photogrammetry technology as raw data. Using AgisfphotoScan 1.25 software to synthesize orthophoto images with a spatial resolution of 0.05m. The vector data of human settlement boundaries in villages was extracted through visual interpretation. Based on the object-oriented human-machine interaction interpretation method, 55 typical village land use datasets in 2022 were obtained (including forests, grasslands, forest land, cultivated land, water bodies, roads, unused land, and building land, totaling 8 categories). By establishing 1050 sample points and using confusion matrix analysis, it was found that the overall accuracy of the dataset was 96.86%, with a Kappa coefficient of 0.95. It can accurately reflect the spatial form, land use composition, and surrounding environment of typical villages. Aerial photographs all have longitude, latitude, and altitude information, providing ultra-high resolution data sources for village spatial structure analysis, land use mapping, and analysis work, effectively assisting in the improvement of human housing and rural revitalization strategies.</p>
Northern Italy gap-filled MODIS Land Surface Temperature 1km daily
<p>Northern Italy Land Surface Temperature 1km daily Celsius gap-filled dataset, LST daily average, 2014 - 2018.</p> <p>The dataset is stored as a GRASS GIS project/mapset, in ZIP compressed format.</p> <ul> <li>Spatial resolution: 1 km</li> <li>Temporal resolution: 1 day</li> <li>Temporal extent: 2014-2018</li> <li>Units: Celsius</li> <li>Aggregation method: average</li> <li>Format: stored as a <a href="https://grass.osgeo.org/">GRASS GIS</a> 8+ project</li> <li>Software used: GRASS GIS 8.4.0</li> </ul> <p>Reference:<br><br>Metz, M.; Andreo, V.; Neteler, M. <em>A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data</em>. Remote Sens. 2017, 9, 1333. <a href="https://doi.org/10.3390/rs9121333">https://doi.org/10.3390/rs9121333</a></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/mod09a1v006/">https://lpdaac.usgs.gov/products/mod09a1v006/</a></p> <p>Data provided by:</p> <p>mundialis GmbH & Co. KG<br>Koelnstrasse 99<br>53111 Bonn, Germany<br><a href="https://www.mundialis.de">https://www.mundialis.de</a></p>
Photographic record of land degradation and resilience in Dogu'a Tembien after the shock of the Tigray war (northern Ethiopia)
<p><span>Following two years of combat, blockade, and power outage, the Tigray war in northern Ethiopia has had a substantial negative impact on the environment (2020–2022). This photographic dataset, part of a rare study carried out by the same research team before and after a war, compares 26-year legacy data on land degradation, with post-war observations at 56 sites in the Dogu'a Tembien district of Tigray (13°39'N, 39°30'E), at elevations ranging from 1600 to 2800 meters.</span></p> <p><span>With 30 years of environmental research experience in Tigray, we remained as a lone research team after the start of the war and collected ground data at previous research sites during the war. This culminated in international partners returning to the Dogu'a Tembien district in 2023 after they had been absent for four years due to coronavirus restrictions and the Tigray War. We visited 56 previously investigated sites—which have been documented in 45 prior publications—through transect walks, where we mostly made qualitative observations and discussions regarding the processes of land degradation and recovery. This included degradation processes like sheet and rill erosion</span><span>, gully erosion</span><span>, landslides</span><span></span><span>, deforestation</span><span>, as well as the most common rehabilitation approaches, i.e. stone bunds</span><span>, check dams</span><span>, exclosures</span><span>, improved hydrological cycle</span><span>, and integrated catchment management</span><span>. Local farmers and other village residents, along with experts who either reside in or have a good understanding of the research area, participated in the group observations.</span></p>
ERA5-Land selected indicators daily aggregates for Africa, 1964
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1964.</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>
ERA5-Land selected indicators daily aggregates for Africa, 1965
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1965.</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>
ERA5-Land selected indicators daily aggregates for Africa, 1966
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1966.</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>
ERA5-Land selected indicators daily aggregates for Africa, 1968
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1968.</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>
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.