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138 results for “Geospatial”
Database for the Geospatial Synthesis of Biogeochemical Attributions of Porphyrins to Oil Pollution in Marine Sediments of the Gulf of México
<p>This dataset is associated with the journal article "Geospatial Synthesis of Biogeochemical Attributions of Porphyrins to Oil Pollution in Marine Sediments of the Gulf of México" by Muñoz-Arriola and Macias-Zamora (2022). The porphyrin and biogeochemical data were obtained by and analyzed at the Universidad Autónoma de Baja California's Instituto de Investigaciones Oceanológicas. The samples were collected to identify the effects of natural and human-originated oil spills in the Campeche Sound, and these efforts are part of the oceanographic campaign Xaman-Ek.</p> <p> </p> <p>Associated references are:</p> <p>Munoz-Arriola, F. and V. Macias-Zamora (2022) <em>Geospatial Synthesis of Biogeochemical Attributions of Porphyrins to Oil Pollution in Marine Sediments of the Gulf of México</em>. Geosciences. https://doi.org/10.3390/ geosciences12020077.</p> <p>Macias-Zamora, J. V., J. A. Villaescusa-Celaya; A. Munoz-Barbosa; and G. Gold-Bouchot (1999). Trace metals in sediment cores from the Campeche shelf, Gulf of Mexico. Environmental Pollution. Vol 104:69-77.</p> <p> </p>
A Geospatial Source Selector for Federated GeoSPARQL Querying - Datasets
<p>This dataset comprises of a set of individual RDF files. It contains 3 data layers that cover Austria (namely Administrative data, Snow cover data, and Crop-type data), and each layer is also divided geospatially. Thus, each RDF file contains only one thematic layer and refers to a specific area.</p> <p>This data can be used in the context of experimenting with federated query processors. Thus, if we deploy each RDF file in a separate GeoSPARQL endpoint, we will have a resulting federation of 34 GeoSPARQL source endpoints. The objective of this scenario is to evaluate the effectiveness of a source selection mechanism of a federation engine, and, in particular, if the source selector is aware of the geospatial nature of the source endpoints.</p>
Mapping 10-m global impervious surface area (GISA-10m) using multi-source geospatial data
<p>Artificial impervious surface area (ISA) documents human footprints. Accurate, timely, and detailed ISA datasets are therefore essential for global climate change and urban planning. However, due to the lack of sufficient training samples and operational mapping methods, global ISA mapping at 10-m resolution is still lacking. To this end, we proposed a global ISA mapping method leveraging multi-source geospatial data. Based on the existing satellite-derived ISA maps and the crowdsourcing OpenStreetMap (OSM), 58 million training samples were extracted via a series of temporal, spatial, spectral, and geometric rules. Combined with over 2.7 million Sentinel optical and radar images on the Google Earth Engine, we produced the 10 m global ISA dataset (GISA-10m). Based on the test samples that are independent to the training set, GISA-10m embraced an overall accuracy greater than 86%. In addition, the GISA-10m was comprehensively compared with the existing global ISA datasets, and the superiority of GISA-10m was demonstrated. </p>
Initial relevant routes and geospatial objects for refugees and asylum seekers in MS
<p>Open geospatial dataset with an initial collection of routes, landmarks, and decision and confirmation points relevant for young refugees and asylum seekers arriving to Münster (MS), Germany. The information here collected were the results of participatory workshops done with young forced migrants in 2016.</p> <p>The information of the routes, landmarks (reference objects), points (origin, destination, decision, and confirmation points) and the relationship between points and reference objects is available in .JSON format. It has as an example, the images collected for one of the relevant routes (R2) identified by the group of young forced migrants. This route is the one from the main mall downtown (Arkaden) MS to the central train station. The pictures are available in .zip format.</p> <p> </p>
Geospatial data and 3D representation of Maungataketake, Auckland, New Zealand
<p><em>Context</em></p> <p>Maungataketake (Ellett’s Mountain) was a volcanic cone on the shore of the Manukau Harbour, Mangere, New Zealand. In the second half of the twentieth century the mountain was quarried away. Maungataketake was a terraced Māori Pā, and archaeological excavations (only now in the process of being published) were undertaken there between 1972 and 1975, and in 1982, prior to its complete destruction. There is aerial imagery of the mountain available that depicts the mountain prior to quarrying. With these data a 3D model of the site was made using photogrammetry, which was also used to create a digital surface model (DSM) and contour map of the mountain. The resulting data is provided here and is aimed for further geospatial applications. In addition, the resolution of the provided DSM has analogues for the wider region and therefore could be incorporated to represent the landscape pre-destruction. Further to this a representation of the 3D model may be found on <a href="https://sketchfab.com/3d-models/maungataketake-9a58745853154b88ac9bde1a74025cc4">SketchFab</a>.</p> <p> </p> <p><em>Method</em></p> <p>The photogrammetry model was created in Agisoft Metashape version 1.5.4. Ten aerial images were used of Maungataketake and the surrounding area, captured on 19<sup>th</sup> August 1960. These images were downloaded from http://retrolens.co.nz and are licensed by LINZ CC-BY 3.0. The model was aligned and the spare point cloud filtered by gradual selection with the following parameters: projection error = 0.2; reconstruction uncertainty = 10; projection accuracy = 2.5. The dense cloud was processed with depth maps of ultra high quality and aggressive filtering. The resulting points cloud was edited to remove outlying points and processed into a 3D model.</p> <p>The resulting 3D model was manually edited to remove faces representing trees on Maungataketake only, but not the surrounding area. This was done as to obtain representative surface contours of the mountain. The model was georeferenced by the identification of points on the landscape present on the 1960 composite image and contemporary satellite imagery. A 0.5 m DSM and contours at 1 m resolution were calculated of Maungataketake.</p> <p> </p> <p><em>Contents of dataset</em></p> <ul> <li>A geodatabase with: <ul> <li>Control points used for georectification</li> <li>1 m contours without elevation of Maungataketake</li> <li>1 m contours with elevation of Maungataketake</li> <li>0.5 m composite aerial image</li> <li>0.5 m DSM of area covering control points</li> <li>0.5 m DSM of Maungataketake</li> </ul> </li> <li>Aerial photographs Crown_583-1924_22-26, Crown_583_1925_22-26</li> <li>Licence for aerial photographs from http://retrolens.co.nz</li> <li>Attributes of aerial photographs</li> </ul>
Extended 1.0 Dataset of "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary"
<p><strong>Introduction</strong></p> <p>We are enclosing the database used in our research titled "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary", along with our statistical calculations. For the sake of reproducibility, further information can be found in the file <em>Short_Description_of_Data_Analysis.pdf </em>and <em>Statistical_formulas.pdf </em></p> <p>The sharing of data is part of our aim to strengthen the base of our scientific research. As of March 7, 2024, the detailed submission and analysis of our research findings to a scientific journal has not yet been completed.</p> <p><em>The dataset was expanded on <strong>23rd September 2024</strong> to include SPSS statistical analysis data, a heatmap, and buffer zone analysis around the Health Development Offices (HDOs) created in QGIS software.</em></p> <p><strong>Short Description of Data Analysis and Attached Files (datasets):</strong></p> <p>Our research utilised data from 2022, serving as the basis for statistical standardisation. The 2022 Hungarian census provided an objective basis for our analysis, with age group data available at the county level from the Hungarian Central Statistical Office (KSH) website. The 2022 demographic data provided an accurate picture compared to the data available from the 2023 microcensus. The used calculation is based on our standardisation of the 2022 data. For xlsx files, we used MS Excel 2019 (version: 1808, build: 10406.20006) with the SOLVER add-in.</p> <p>Hungarian Central Statistical Office served as the data source for population by age group, county, and regions: <a href="https://www.ksh.hu/stadat_files/nep/hu/nep0035.html">https://www.ksh.hu/stadat_files/nep/hu/nep0035.html</a>, (accessed 04 Jan. 2024.) with data recorded in MS Excel in the <em>Data_of_demography.xlsx</em> file.</p> <p>In 2022, 108 Health Development Offices (HDOs) were operational, and it's noteworthy that no developments have occurred in this area since 2022. The availability of these offices and the demographic data from the Central Statistical Office in Hungary are considered public interest data, freely usable for research purposes without requiring permission.</p> <p>The contact details for the Health Development Offices were sourced from the following page (Hungarian National Population Centre (NNK)): <a href="https://www.nnk.gov.hu/index.php/efi">https://www.nnk.gov.hu/index.php/efi</a> (n=107). The Semmelweis University Health Development Centre was not listed by NNK, hence it was separately recorded as the 108th HDO. More information about the office can be found here: <a href="https://semmelweis.hu/egeszsegfejlesztes/en/">https://semmelweis.hu/egeszsegfejlesztes/en/</a> (n=1). (accessed 05 Dec. 2023.)</p> <p>Geocoordinates were determined using Google Maps (N=108): <a href="https://www.google.com/maps">https://www.google.com/maps</a>. (accessed 02 Jan. 2024.) Recording of geocoordinates (latitude and longitude according to WGS 84 standard), address data (postal code, town name, street, and house number), and the name of each HDO was carried out in the: <em>Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv</em> file.</p> <p>The foundational software for geospatial modelling and display (QGIS 3.34), an open-source software, can be downloaded from:</p> <p><a href="https://qgis.org/en/site/forusers/download.html">https://qgis.org/en/site/forusers/download.html</a>. (accessed 04 Jan. 2024.)</p> <p>The HDOs_GeoCoordinates.gpkg QGIS project file contains Hungary's administrative map and the recorded addresses of the HDOs from the</p> <p><em>Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv</em> file,</p> <p>imported via .csv file.</p> <p>The OpenStreetMap tileset is directly accessible from <a href="http://www.openstreetmap.org">www.openstreetmap.org</a> in QGIS. (accessed 04 Jan. 2024.)</p> <p>The Hungarian county administrative boundaries were downloaded from the following website: <a href="https://data2.openstreetmap.hu/hatarok/index.php?admin=6" target="_new">https://data2.openstreetmap.hu/hatarok/index.php?admin=6</a> (accessed 04 Jan. 2024.)</p> <p>HDO_Buffers.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding buffer zones with a radius of 7.5 km.</p> <p>Heatmap.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding heatmap (Kernel Density Estimation).</p> <p>A brief description of the statistical formulas applied is included in the <em>Statistical_formulas.pdf.</em></p> <p>Recording of our base data for statistical concentration and diversification measurement was done using MS Excel 2019 (version: 1808, build: 10406.20006) in .xlsx format.</p> <ul> <li>Aggregated number of HDOs by county: <em>Number_of_HDOs.xlsx</em></li> <li>Standardised data (Number of HDOs per 100,000 residents): <em>Standardized_data.xlsx</em></li> <li>Calculation of the Lorenz curve: <em>Lorenz_curve.xlsx</em></li> <li>Calculation of the Gini index: <em>Gini_Index.xlsx</em></li> <li>Calculation of the LQ index: <em>LQ_Index.xlsx</em></li> <li>Calculation of the Herfindahl-Hirschman Index: <em>Herfindahl_Hirschman_Index.xlsx</em></li> <li>Calculation of the Entropy index: <em>Entropy_Index.xlsx</em></li> <li>Regression and correlation analysis calculation: <em>Regression_correlation.xlsx</em></li> </ul> <p>Using the SPSS 29.0.1.0 program, we performed the following statistical calculations with the databases Data_HDOs_population_without_outliers.sav and Data_HDOs_population.sav:</p> <ul> <li>Regression curve estimation with elderly population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_elderly_without_outlier.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county, excluding outlier values such as Budapest and Pest County: Pearson_Correlation_populations_HDOs_number_without_outliers.spv.</li> <li>Dot diagram including total population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_total_population_without_outliers.spv.</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_elderly_population_without_outliers.spv</li> <li>Regression curve estimation with total population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_without_outlier.spv</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county: Dot_HDO_elderly_population.spv</li> <li>Dot diagram including total population and number of HDOs per county: Dot_HDO_total_population.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county: Pearson_Correlation_populations_HDOs_number.spv</li> <li>Regression curve estimation with total population and number of HDOs, (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_total_population.spv</li> </ul> <p>For easier readability, the files have been provided in both SPV and PDF formats.</p> <p>The translation of these supplementary files into English was completed on 23rd Sept. 2024.</p> <p> </p> <p><em>If you have any further questions regarding the dataset, please contact the corresponding author: <a target="_new">domjan.peter@phd.semmelweis.hu</a></em></p> <p> </p>
Nationwide geospatial dataset of environmental covariates at 1km resolution in Mexico (2015-2020)
<p><strong>Package of 39 covariates, a combination of topographic, climatic, and vegetation derived variables with pixel sizes of 1000 m for the period of 2015 to 2020 from google earth engine (GEE) to assemble a nationwide geospatial dataset of Mexico.</strong><br> <strong>Datasets included WorldClim V1; a set of bioclimatic variables derived from the monthly temperature and rainfall <a href="https://www.zotero.org/google-docs/?tmZL4M">(Hijmans, 2005)</a>; time-series analysis of Landsat images from the Hansen Global Forest Change v1.8 (2000-2020) dataset <a href="https://www.zotero.org/google-docs/?jGj5Rn">(Hansen et al., 2013)</a>; 4-day composite dataset from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensors with fraction of photosynthetic active radiation and leaf area index at 500-m resolution <a href="https://www.zotero.org/google-docs/?t45qy3">(Myneni, Ranga et al., 2015)</a> and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Database (2000-2008) <a href="https://www.zotero.org/google-docs/?8bVVc9">(Hulley et al., 2009, 2012, 2015; Hulley & Hook, 2008, 2009, 2011; NASA JPL, 2014)</a>. All covariates were resampled to 1000 m. The resampling was done with conventional bilinear interpolation as implemented in GEE.</strong></p>
Geospatial Analysis of Road Conditions and Hazardous Factors in Communities on Continuous vs. Sporadic Permafrost in Greenland
<p>Road conditions and hazardous factors were surveyed in two permafrost-affected communities of West Greenland, Ilulissat (underlain by continuous ice-rich permafrost) and Sisimiut (underlain by sporadic permafrost). Pavement damages, repairs, embankment structural elements, artificial drainage systems, water accumulations and preferential snow ploughing deposits were notably mapped and georeferenced in a geographic information system to form high-resolution spatial databases. In total, respectively 66 and 76 \% of the paved road networks of Ilulissat and Sisimiut were surveyed. Manual in-situ mapping took place in September 2020 and September 2021 in Ilulissat, while Global Navigation Satellite System (GNSS) equipment was used to map road conditions in Sisimiut in September 2020. The severity of the pavement damages was assessed according to the ASTM D 6433–07, Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys, by ASTM International (2008). The drainage conditions were characterized following the recommendations in Cold Regions Pavement Engineering, by Doré, G. and Zubeck, H. K. (2009).</p> <p>This dataset comprises the geospatial layers of the road damage and hazard inventories created for the settlements of Ilulissat and Sisimiut. Each settlement’s inventory is provided in a ZIP-folder, containing the geospatial layers as geopackages and sorted following a thematic structure. Further information about each layer and its attributes can be found in the metadata PDF document.</p>
Assessment of acetochlor use areas in the Sahel region of Western Africa using geospatial methods
Open the record for dataset details and reuse information.
Geospatial dataset for Cumulative Impact assessment, Sea Use conflict analysis and Marine Ecosystem Services assessment in the Adriatic Ionian Region
<p>Geospatial dataset for Cumulative Impact assessment, Sea Use conflict analysis and Marine Ecosystem Services assessment in the Adriatic Ionian Region (reference year 2014).</p> <p>The datasets are derived from ADRIPLAN Portal (http://data.adriplan.eu/).</p>
RAM Legacy Stock Assessment Database Geospatial Regions
<p>This data archive describes region definitions for the RAM Legacy Stock Assessment Database. Within the RAM Legacy database, stock assessments are associated with named areas. We approximate coordinates and bounding boxes for each of these areas, using country EEZs and fishing area shapefiles when appropriate. In addition, we develop a simple language to encode the GIS shapes of the areas, along with an interpreter to translate these codes into polygons. The syntax supports using political entities, shapefile regions, circles and rectangles, clipped versions of these, and combinations of these.</p> <p>The archive contains the following contents:</p> <p> - syntax.pdf: This document describes the geocoding syntax, and lists all of the geocoding descriptions for the assessment regions.</p> <p> - results: This folder contains a shapefile of assessment regions (ram.shp) and a summary file of each region's centroid and size.</p> <p> - sources: This folder contains shapefiles for FAO regions and New Zealand fishing regions, used by the syntax system, and latlon.csv which contains the region descriptions for each assessment region.</p> <p> - code: load_areas.R contains functions that interpret the geocoding syntax and genshape.R generates the ram.shp shapefile.</p>
Geospatial and time series dataset for hydrologic analyses within South Asia (GHSA)
<p>Summary of changes:</p> <ul> <li>v2504, GHSA <ul> <li>1,702 stations from 5 countries in South Asia (Bhutan, China, India, Nepal and Pakistan);</li> <li>districts and basin states from 7 countries in South Asia (Afghanistan, Bangladesh, Bhutan, China, India, Nepal and Pakistan)</li> <li>land use change, vegetation index (NDVI, NDVI-crop), precipitation, evaporation (E-total, E-crop and E-irrigation), streamflow, surface and root zone soil moisture, terrestrial water storage change, snow water equivalent and snow cover fraction; 1950-2023</li> <li>publication:</li> </ul> </li> <li>v2409 <ul> <li>test version</li> </ul> </li> <li>v2301, GHI <ul> <li>645 stations, limited to Peninsular India</li> <li>precipitation, evaporation and streamflow; 1950-2020</li> <li>publication: Goteti (2023), Earth Syst. Sci. Data, https://doi.org/10.5194/essd-15-4389-2023</li> </ul> </li> </ul> <p> </p>
Processed Sentinel 1, Sentinel 2 and Copernicus Emergency Management Service data for fine tuning and predicting flood extent with IBM's granite-geospatial-uki-flood-detection model
<p>This dataset contains processed Sentinel 1 Sentinel 2 imagery together with flood event labels extracted from the Copernicus Emergency Management Service. It has been assembled to demonstrate fine tuning and inference of flood event segmentation using granite geospatial foundation models developed by IBM Research. Please see <a href="https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection">https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection</a> for more information on models and use.</p> <p>Sentinel-1</p> <p>The European Space Agency. 2014. Sentinel-1 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-1">https://sentinel.esa.int/web/sentinel/missions/sentinel1</a>. Accessed: 2024-11-25.</p> <p>Sentinel-2</p> <p>The European Space Agency. 2015. Sentinel-2 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-2">https://sentinel.esa.int/web/sentinel/missions/sentinel2</a>. Accessed: 2024-11-25.</p> <p>Copernicus Emergency Management Service</p> <p><a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid">https://emergency.copernicus.eu/mapping/list-of-activations-rapid</a>. Accessed: 2024-11-25. </p> <p><strong>Attribution</strong></p> <p>Contains modified Copernicus Sentinel data [2019-2024]</p> <p>Contains modified Copernicus Service information [2019-2023]</p>
Dataset for "Geospatial analysis of toponyms in geotagged social media posts"
<h1>Geotagged Twitter posts dataset</h1> <p>Dataset used for the research presented in the following paper: Takayuki Hiraoka, Takashi Kirimura, Naoya Fujiwara (2024) "Geospatial analysis of toponyms in geo-tagged social media posts".</p> <p>We collected georeferenced Twitter posts tagged to coordinates inside the bounding box of Japan between 2012-2018. The present dataset represents the spatial distributions of all geotagged posts as well as posts containing in the text each of 24 domestic toponyms, 12 common nouns, and 6 foreign toponyms. The code used to analyze the data is available on <a href="https://github.com/takayukihir/geotagged-tweets" target="_blank" rel="noopener">GitHub</a>.</p> <h2>Data description</h2> <ul> <li><code>preprocessed_mcntlt7_selected/</code>: Number of geotagged twitter posts in each grid cell. Each csv file under this directory associates each grid cell (spanning 30 seconds of latitude and 45 secoonds of longitude, which is approximately a 1km x 1km square, specified by an 8 digit code <code>m3code</code>) with the number of geotagged tweets tagged to the coordinates inside that cell (<code>tweetcount</code>). <code>file_names.json</code> relates each of the toponyms studied in this work to the corresponding datafile (<code>all</code> denotes the full data). Note that these data files are modified from the v1.0.0 to exclude posts that contain seven or more mentions.</li> <li><code>population/population_center_2020.xlsx</code>: Center of population of each municipality based on the 2020 census. Derived from data published by the Statistics Bureau of Japan on <a href="https://www.stat.go.jp/data/kokusei/topics/topi135.html" target="_blank" rel="noopener">their website</a> (Japanese)</li> <li><code>population/census2015mesh3_totalpop_setai_area.csv</code>: Resident population in each grid cell based on the 2015 census. Derived from data published by the Statistics Bureau of Japan on <a href="https://www.e-stat.go.jp/gis/statmap-search?page=1&type=1&toukeiCode=00200521&toukeiYear=2015&aggregateUnit=S&serveyId=S002005112015&statsId=T000846" target="_blank" rel="noopener">e-stat</a> (Japanese)</li> <li><code>population/economiccensus2016mesh3_jigyosyo_jugyosya_area.csv</code>: Employed population in each grid cell based on the 2016 Economic Census. Derived from data published by the Statistics Bureau of Japan on <a href="https://www.e-stat.go.jp/gis/statmap-search?page=1&type=1&toukeiCode=00200553&toukeiYear=2016&aggregateUnit=S&serveyId=S002005112016&statsId=T000917" target="_blank" rel="noopener">e-stat</a> (Japanese)</li> <li><code>japan_MetropolitanEmploymentArea2015map/</code>: Shape file for the boundaries of Metropolitan Employment Areas (MEA) in Japan. See <a href="https://www.csis.u-tokyo.ac.jp/UEA/index_e.htm" target="_blank" rel="noopener">this website</a> for details of MEA.</li> <li><code>ward_shapefiles/</code>: Shape files for the boundaries of wards in large cities, published by the Statistics Bureau of Japan on <a href="https://www.e-stat.go.jp/gis/statmap-search?page=1&type=2&aggregateUnitForBoundary=A&toukeiCode=00200553&toukeiYear=2016&serveyId=A002005532016&coordsys=1&format=shape&datum=2011" target="_blank" rel="noopener">e-stat</a></li> </ul>
Modern China Geospatial Database - PRC Dataset
<p><strong>MCGD_PRC</strong> is a list of cities in today's People’s Republic of China. It includes 2,525 locations, with with the following variables: name in Chinese (both traditional and simplified Chinese), name in pinyin, name of the province in Chinese and in pinyin; latitude and longitude.</p>
Digitizing Los Millares (Santa Fe de Mondujar, Almería, Spain) through geospatial technologies: preserving and disseminating the archaeological heritage
<p>Data originated from the research project "Digitizing Los Millares (Santa Fe de Mondujar, Almería, Spain) through geospatial technologies: preserving and disseminating the archaeological heritage"</p>
Sample ERA5 Climate Reanalysis Data for UW Geospatial Data Analysis Course
<p>Used for Module 09: https://uwgda-jupyterbook.readthedocs.io/en/latest/modules/09_NDarrays_xarray_ERA5/</p> <p>Generated using Copernicus Climate Change Service information [2022]<br> Original license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</p>
Geospatial data used in "Estimation of river water surface elevation using UAV photogrammetry and machine learning"
<p>Geospatial data used in article "Estimation of river water surface elevation using UAV photogrammetry and machine learning" by Radosław Szostak, Marcin Pietroń, Przemysław Wachniew, Mirosław Zimnoch and Paweł Ćwiąkała (AGH UST).</p> <p>Each zip archive contains the following files:</p> <ul> <li>dsm.tif - raster of digital surface model,</li> <li>ortho.tif - raster of orthophoto,</li> <li>gnss_wse.json - geojson multipoint shape containing RTN GNSS measurements of water surface elevation,</li> <li>grid.json - geojson multipolygon shape containing square areas of samples used in deep learning solution.</li> <li>centerline.json - geojson multipoint shape containing values sampled from DSM along centerline,</li> <li>wateredge.json - geojson multipoint shape containing values sampled from DSM along "water-edge".</li> </ul> <p>Data in AMO18.zip archive was collected by Bandini et. al (https://doi.org/10.5281/zenodo.3519888).</p> <p>Preprocessed machine learning dataset and source codes are available in github repository at: https://github.com/radekszostak/river-wse-uav-ml</p>
Askja caldera 1945-2023 geospatial dataset
<p>Here, we present a geospatial dataset covering the 1945-2023 period of morphodynamics at Askja caldera, Iceland. The dataset consists of three parts: (1) digital elevation models (DEMs) generated from the 1945 and 1987 archive aerial photographs, 2013 and 2022 Pléiades satellite imagery, and 2019, 2022, and 2023 drone images; (2) orthophotographs generated from the same source data; and (3) set of shapefiles, which represents the identified morphological features at the SE wall of Askja caldera.</p> <p>The initial data was processed using Agisoft Metashape Professional v. 1.8.3 photogrammetric software. In addition, the 2022 and 2023 drone datasets contained infrared images that were pre-processed in ThermoViewer v. 3.0.4 and DJI Thermal Analysis Tool v. 3.1.0. The features in the shapefiles were extracted based on the DEMs and orthophotos using ArcGIS desktop v. 10.8.2 tools.</p> <p>Data was used to perform 2D and 3D analysis of the long-term geomorphological processes and slope instability at the caldera wall, to reveal precursors of preparing hazardous events, and to calculate volumes of the 2014 landslide. Repeated morphological analysis and feature tracking starting from 1945 revealed that changes are persistent over the observation period, locally accumulating in the area, which was later affected by the 2014 landslide. The results are relevant for understanding the factors of slope instability at Askja caldera and for possible hazard assessment.</p> <p>We acknowledge the National Land Survey of Iceland (LMI) for providing the 1987 and 1945 aerial photographs, the National Center for Space Studies (CNES) for providing Plèiades data at a preferential (institutional) price, the German Research Center for Geosciences (GFZ) for financing the fieldwork.</p> <p>This dataset corresponds to an article: Shevchenko, A.V., Walter, T.R., Gudmundsson, M.T. et al. Morphological changes of the south-eastern wall of Askja caldera, Iceland over the past 80 years. Commun Earth Environ 5, 441 (2024). https://doi.org/10.1038/s43247-024-01616-z</p>
Geospatial dataset on human uses, environmental components and MSFD pressure for the Adriatic Sea
<p>Geospatial dataset on human uses, environmental components and MSFD pressure for the Adriatic Sea (reference year 2014-2015). The datasets are derived from ADRIPLAN Portal (http://data.adriplan.eu/) and is suitable for cumulative effects assessment (CEA), maritime use conflict (MUC) analysis and CEA-based marine ecosystem service threat analysis (MES-Threat).</p> <p> </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.