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5,155 results for “resolution”
Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI) [raw datasets]
<p>Raw datasets accompanying the analysis in "Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI)"</p> <p>The datasets contain raw fluorescence microscopy images aimed to be processed in a SOFI analysis. They are acquired with different camera technologies, allowing for direct comparison of an industry-grade CMOS detector with both a scientific-grade sCMOS and emCCD detector.</p>
Quality-checked, one-minute resolution cruise track of the Antarctic Circumnavigation Expedition (ACE) undertaken during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>The Antarctic Circumnavigation Expedition (ACE), undertaken in the austral summer of 2016/2017 recorded the cruise track using two independent geo-location instruments: one using GLobal NAvigation Satellite Systems (GLONASS; hereafter referred to as GLONASS) and another primarily using the Global Positioning System (GPS; hereafter referred to as the Trimble GPS). Daily log files were recorded in real-time from both instruments during the expedition and added to MySQL database tables. Following the expedition, quality-checking work has been undertaken to provide a one-second resolution set of positions for the cruise track. Here we present the final quality-checked dataset aggregated to a resolution of one minute. This is of use for understanding the position of the vessel to a lower precision.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_cruise_track_1min_YYYY-MM.csv, data file, comma-separated values</li> <li>README.txt, metadata, text file</li> <li>data_file_header.txt, metadata, text file</li> </ul> <p><strong>Dataset license</strong></p> <p>This quality-checked cruise track dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
A high-resolution 4D geospatial laser scan dataset of the beach at Mariakerke Bad, Belgium
<p>This dataset contains a high resolution (in both time and space) laser scan data set of a 1-year measurement campaign in 2017 and 2018 in the seaside resort of Mariakerke Bad in Belgium. The measurements consist of 8417 hourly laserscans of a 400 meter stretch of beach. The measurement campained was performed to study variations in shoreward sand transport at urbanized beaches. </p> <p>Laserscan data is stored in local coordinates. Time dependent corrections per laserscan epoch are provided next to a global transformation matrix to transform the local coordinates to the Belgium Lambert 2008 coordinate system.</p> <p>This data is provided as is and is licensed under the Creative Commons Attribution 4.0 International (CC-BY-4.0). See the provided PDF on more information about the CC-BY-4.0.</p> <p>Version 1 contained an error in the global transformation matrix. Version 2 corrects this.</p>
Terrasar measurement data of "Sar Super-Resolution Using Physics-Aware Adaptive Compressed Sensing"
<p>This data set was used to test of the method described in "Sar Super-Resolution Using Physics-Aware Adaptive Compressed Sensing". It consists of the related Terrasar data and a MATLAB file to import the data into MATLAB.</p>
Images and Crater Data for "Crater Detection Dependence on Resolution, Incidence Angle, Emission Angle, and Phase Angle"
<p>Images are from the LROC-NAC and have been cartographically controlled to each other and the <em>Apollo 11</em> landing site as described in Supporting Information Text S1. Images are cropped so that the cover ±0.025° from the landing site when coordinates have three significant figures. The images are provided as .png files with .pgw ("PNG World"). The images are at 1 mpp (contain a "1mpp" string in the file name), 2.5 mpp (contain a 2p5mpp" string in the file name), and 6.25 mpp (contain a "6p25mpp" string in the file name). Additionally, the three <em>e</em> > 10° images are included as unprojected .cub files; these files omit the "l2" (map projected, Level-2 data) string and any "l4" (mosaicked) string from the file name, but they instead include "trim" to indicate the image has been trimmed from its full extent to the area of interest.</p> <p>Crater data are formatted as .csv (comma-separated values) files and are one file per image per researcher. File names have the exact same name as the image file that was used to map crater data, with two differences: The initials of the author are appended, and the file extension is "csv" instead of "png". The files do not have headers, but they are formatted such that the first column is latitude (decimal degrees north), second column is longitude (decimal degrees east), and diameter (kilometers). Crater data are entirely in one .zip file.</p>
Global peatland, bare rock and bare sand extent at 100 m to 1 km spatial resolution based on multisource data
<p>Ensemble estimate of the global distribution of <a href="https://en.wikipedia.org/wiki/Peatland">peatlands</a> / extent (<strong>peatland.extent_wri.gfw.peatgrids_p</strong>). This is a simple average from three (3) sources of data:</p> <ol> <li><a href="https://data.globalforestwatch.org/datasets/gfw::global-peatlands/about">WRI Global Peatlands extent map</a> at 30-m (250-m effective);</li> <li><a href="https://doi.org/10.5281/zenodo.12559238">PEATGRIDS</a> at 1-km;</li> <li><a href="https://globalpeatlands.org/new-online-global-peatland-map-asian-peatlands-story-map-presenting-best-peatlands-mapping">Global Peatlands Map 2.0</a> produced by the Global Peatlands Initiative;</li> </ol> <p>The average between the three sources is an extent map with value 0–100%. The refence period is 2000–2020, although probably most of data is based on pre 2010. For more details about the source data please refer to the cited references below.</p> <p>Bare rock and bare sand estimates are based on the following two sources of data:</p> <ol> <li><a href="https://land.copernicus.eu/en/products/global-dynamic-land-cover">Copernicus GLC land cover</a> at 100-m for 2015 and 2019;</li> <li><a href="https://lcz-generator.rub.de/global-lcz-map">Local Climate zones</a> map at 100-m for 2018;</li> </ol> <p>Two classes are considered: (1) probability of occurrence of bare rock (<strong>bare.rock_glc.gfz_p</strong>), (2) probability of occurrence of bare sand i.e. shifting sand (<strong>bare.soil.sand_glc.gfz_p</strong>). We recommend using only the 1-km data for spatial modeling.</p> <p>The time-series of bare areas (<strong>bare.areas_esa.cci_p</strong>) are based on the <a href="https://climate.esa.int/en/odp/#/project/land-cover">ESA CCI Land Cover time-series</a> (2000–2022) 300-m resolution data; also available at 1-km resolution based on "average" resampling. </p>
Navigating deep learning strategies for large-area land cover mapping using very-high-resolution imagery in Senegal: Validation Data
<p><span><span>R</span><span>apid</span><span> advances in deep learning</span><span> for</span> <span>land cover </span><span>classification of </span><span>trees, shrubs and </span><span>very small</span> <span>agricultur</span><span>al</span> <span>fields</span> <span>using</span> <span>very high</span><span>-</span><span>resolution satellite </span><span>data </span><span>(< 2 m</span><span>)</span><span>,</span><span> has tremendous potential</span> <span>for resolving </span><span>current</span><span> challenges </span><span>in </span><span>quantifying</span> <span>land cover </span><span>change </span><span>in</span> <span>sub-</span><span>Saharan</span> <span>African (SSA</span><span>)</span><span>,</span> <span>due to</span> <span>growing </span><span>demand for food resources</span><span>.</span> <span>We</span> <span>conducted experiments </span><span>with</span><span> different training strategies for scaling up </span><span>UNet</span> <span>convolutional neural network </span><span>models for regional land cover mapping with multispectral </span><span>WorldView</span><span> (WV</span><span>)</span><span>-2 and –3,</span><span> imagery</span><span> in</span><span> three distinct regions of Senegal </span><span>which</span> <span>has</span><span> complex </span><span>seasonal wet/dry conditions and </span><span>cropland-savanna mosaics. </span></span></p> <p>The validation exercise of this research consisted in validating more than 70,000 km<sup>2</sup> across Senegal. The infrastructure was setup in the NASA SMCE system with a total of twelve George Mason University (GMU) students participating as operators. These operators validated more than 59 WV-2 and -3 images, each consisting of 200 stratified points in 5,000 x 5,000-pixel images. This effort resulted in a total of ~35,000 aggregated observations that are available through the eo-validation API for public consumption. Each validation point from this dataset has three individual observations.</p>
Dataset: Mapping saltmarsh communities in South Portugal using high spatiotemporal resolution satellite imagery
<div> <div> <div> <p>This repository containts the datasets from the article "Mapping saltmarsh communities in South Portugal using high spatiotemporal resolution satellite imagery" (Submitted). The dataset was used in a workflow used to create saltmarsh maps for the Algarve region (South Portugal), focused on the 4 main costal systems of the region: Alvor, Arade, Ria Formosa and Guadiana.</p> <p> </p> <p>For a description of the methodology see the article [link] and Github repo [link].</p> <p> </p> <h1>Repository content</h1> <h2>1. system-masks.zip</h2> <p>Contains 4 <code>geojson</code>files with a polygon which delimits the areas included in the study. The files are named after the respective systems that they delimit. Any region outside of these polygons were not used in the analysis.</p> <p><strong>CRS</strong> - EPSG:4326</p> <h2>2. manual-clean-up-masks.gpkg</h2> <p>Polygons which were manually created to mask out (exclude) pixels which were classified as saltmarsh, but are clearly not.</p> <p>File contains a single layer with 52 polygons and one variable.</p> <p><strong>Variables:</strong></p> <ul> <li>system [<em>string</em>] - Which system the polygon delimits</li> </ul> <h2>3. saltmarsh-training-data.gpkg</h2> <p>Data used for supervised model training. Each row represents one quadrat, and each column contains either quadrat identifiers, target classes, or predictor classes.</p> <p>File contains a single layer with 2448 points and 18 variables.</p> <p><strong>Variables:</strong></p> <ul> <li>water_system [<em>string</em>] - Study system in which the quadrat was sampled</li> <li>transect [<em>string</em>] - Name of transect in which the quadrat was sampled</li> <li>quad_id [<em>integer</em>] - Unique identifier per quadrat</li> <li>cluster [<em>integer</em>] - Vegetation cluster identified via hierarchical clustering. They are nested within <code>water_system</code>, and the same number within different systems will not correspond to the same vegetation type.</li> <li>marsh_type [<em>string</em>] - Functional groupings of saltmarsh vegetation (low, middle or high), created by grouping <code>cluster</code> based on niche of the defined clusters.</li> <li>train [<em>boolean</em>] - Was quadrat used in the train (TRUE) or test (FALSE) stage of model training?</li> <li>ndvi [<em>numerical</em>] - Normalized Difference Vegetation Index, calculated from the satellite image mosaic as (nir – red) / (nir + red).</li> <li>ndwi_high [<em>numerical</em>] - Normalized Difference Water Index estimated from images at high tide, calculated as (green – nir) / (green + nir)</li> <li>ndwi_low [<em>numerical</em>] - Normalized Difference Water Index estimated from images at low tide, calculated as (green – nir) / (green + nir)</li> <li>subtime [<em>numerical</em>] - Fraction of time that a cell is estimated to be submerged in water over one year.</li> <li>coastal_blue [<em>numerical</em>] - Surface reflectance values at 443 nm.</li> <li>blue [<em>numerical</em>] - Surface reflectance values at 490 nm.</li> <li>green_i [<em>numerical</em>] - Surface reflectance values at 531 nm.</li> <li>green [<em>numerical</em>] - Surface reflectance values at 565 nm.</li> <li>yellow [<em>numerical</em>] - Surface reflectance values at 610 nm.</li> <li>red [<em>numerical</em>] - Surface reflectance values at 665 nm.</li> <li>rededge [<em>numerical</em>] - Surface reflectance values at 705 nm.</li> <li>nir [<em>numerical</em>] - Surface reflectance values at 865 nm.</li> </ul> <h2>4. saltmarsh-transect-metadata.csv</h2> <p>Comma-delimited file with information about vegetation sampling transects. Each row represents one transect.</p> <p>File contains 6 variables.</p> <p><strong>Variables:</strong></p> <ul> <li>water_system [<em>string</em>] - Study system in which the transect was sampled</li> <li>transect_set [<em>string</em>] - Which set of transects was this transect sampled in? Set A was performed in 2019, set B in 2023.</li> <li>site [<em>string</em>] - Name of the site within the study system. This was used exclusively to plan transects.</li> <li>transect [<em>string</em>] - Name of transect in which the quadrat was sampled</li> <li>date [<em>date yyyy-mm-dd</em>] - Date of transect sampling.</li> <li>notes [<em>string</em>] - Notes taken during transect sampling and which might be relevant to understand data issues.</li> </ul> <h2>5. saltmarsh-vegetation-quadrats.gpkg</h2> <p>Data used for to create vegetation clusters (<code>cluster</code>) and saltmarsh community types (<code>marsh_type</code>). The later was used as the target class in the supervised model training. Each row represents one quadrat, and each column contains either quadrat identifiers, or presence/absence of species.</p> <p>File contains a single layer with 2448 points and 32 variables.</p> <p><strong>Variables:</strong></p> <ul> <li>water_system [<em>string</em>] - Study system in which the transect was sampled</li> <li>transect [<em>string</em>] - Name of transect in which the quadrat was sampled</li> <li>transect_set [<em>string</em>] - Which set of transects was this transect sampled in? Set A was performed in 2019, set B in 2023.</li> <li>quad_id [<em>integer</em>] - Unique identifier per quadrat</li> <li>distance_from_water <em>[integer]</em> - Distance from start of quadrat, which was the point closes to the water where saltmarsh was found for that transect.</li> <li>cluster [<em>integer</em>] - Vegetation cluster identified via hierarchical clustering. They are nested within <code>water_system</code>, and the same number within different systems will not correspond to the same vegetation type.</li> <li>marsh_type [<em>string</em>] - Functional groupings of saltmarsh vegetation (low, middle or high), created by grouping <code>cluster</code> based on niche of the defined clusters.</li> <li>Arthrocaulon.macrostachyum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Tripolium.pannonicum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Atriplex.halimus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Cistanche.phelypaea [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Atriplex.portulacoides [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limbarda.crithmoides [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Juncus.effusus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limoniastrum.monopetalum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Myriolimon.ferulaceum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limonium.vulgare [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Phragmites.australis [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Polygonum.maritimum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Puccinellia.maritima [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.procumbens [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.europaea [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Caroxylon.vermiculatum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.fruticosa [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.perennis [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Bolboschoenus.maritimus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Sporobolus.maritimus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Spergularia.bocconei [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Suaeda.vera [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Triglochin.maritima [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Sporobolus.montevidensis [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> </ul> <h2>6. predicted-map.tif</h2> <p>Geotiff file with a single layer for predicted saltmarsh community. Values are:<br> - <em>no data</em> - Not saltmarsh<br> - <em>1</em> - Low saltmarsh<br> - <em>2</em> - Middle saltmarsh<br> - <em>3</em> - High saltmarsh</p> <p><strong>CRS</strong> - EPSG:32629</p> </div> </div> </div>
Protected planet (protected areas), forests and intact forest landscapes at 100 m, 250 m to 1 km resolution
<p><a href="https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA">Protected planet</a> (protected areas; version Oct 2024) and <a href="https://intactforests.org/data.ifl.html">intact forest landscapes</a> (2000, 2013, 2016 and 2020) rasterized to 100 m, 250 m and 1 km resolutions. The aggregated map contains all pixels that are either protected or intacts. To use these resources please refer to original data producers:</p> <ul> <li>Defourny, P., Lamarche, C., Bontemps, S., De Maet, T., Van Bogaert, E., Moreau, I., Brockmann, C., Boettcher, M., Kirches, G., Wevers, J., Santoro, M., Ramoino, F., & Arino, O. (2017). Land Cover Climate Change Initiative - Product User Guide v2. Issue 2.0. <a href="http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf">http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf</a></li> <li>Olsson, E., Albrecht, R., & Golden Kroner, R.E. (2021). PADDDtracker Data Release Version 2.1: Technical Notes. Conservation International, Arlington, VA. DOI: 10.5281/zenodo.4749615.</li> <li>Potapov, P., Hansen, M. C., Laestadius L., Turubanova S., Yaroshenko A., Thies C., Smith W., Zhuravleva I., Komarova A., Minnemeyer S., Esipova E. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. <a href="http://advances.sciencemag.org/content/3/1/e1600821">Science Advances, 2017; 3:e1600821</a></li> <li>UNEP-WCMC and IUCN (2024), Protected Planet: The World Database on Protected Areas (WDPA) [Online], October 2024, Cambridge, UK: UNEP-WCMC and IUCN. Available at: <a title="Visit Protected Planet" href="http://protectedplanet.net/" target="_blank" rel="noopener">www.protectedplanet.net</a>.</li> </ul> <p>The time-series of forest areas (<strong>forest.areas_esa.cci_p</strong>) are based on the <a href="https://climate.esa.int/en/odp/#/project/land-cover">ESA CCI Land Cover time-series</a> (2000–2022) 300-m resolution data; also available at 1-km resolution based on "average" resampling. Two maps (<strong>forest.cover.sum_esa.cci_p_250m</strong> and <strong>forest.cover.diff_esa.cci_p_250m</strong>) show long term cumulative forest cover and difference in forest cover for 2022 vs 2000.</p> <p>The protected planet areas and intact forest landscapes were rasterized using:</p> <pre><code>## https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA for(j in 0:2){ system(paste0('gdal_rasterize -ot Byte -a_nodata 0 -burn 100 -where "IUCN_CAT LIKE \'I%\'" /data/CCI_LandCover/WDPA_Oct2024_Public_shp_', j, '/WDPA_Oct2024_Public_shp-polygons.shp WDPA_Oct2024_Public_shp_', j, '_1km.tif -tr 0.008333333 0.008333333 -te -180 -65.00208 180 87.37 -co COMPRESS=DEFLATE -a_srs EPSG:4326')) } s = sds(rast("WDPA_Oct2024_Public_shp_ALL_0_1km.tif"), rast("WDPA_Oct2024_Public_shp_ALL_1_1km.tif"), rast("WDPA_Oct2024_Public_shp_ALL_2_1km.tif")) dg.x = app(s, fun=max, na.rm=TRUE, cores = 32) dg.x0 = terra::ifel(is.na(dg.x), 0, dg.x, filename="protected.areas_wdpa.all_p_1km_s_2023_2024_go_epsg4326_v20241025.tif", wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT2S'), overwrite=TRUE) ## https://intactforests.org/data.ifl.html for(j in c(2000,2013,2016,2020)){ system(paste0('gdal_rasterize -ot Byte -a_nodata 0 -burn 100 -l \"ifl_', j, '\" /mnt/lacus/raw/protectedplanet/ifl_', j, '.shp intact.forest_gfw_p_1km_s_', j, '0101_', j, '1231_go_epsg4326_v20241025.tif -tr 0.008333333 0.008333333 -te -180 -65.00208 180 87.37 -co COMPRESS=DEFLATE -a_srs EPSG:4326')) } ## Combination IFL & WPDA b = sds(rast("protected.areas_wdpa.all_p_1km_s_2023_2024_go_epsg4326_v20241025.tif"), rast("intact.forest_gfw_p_1km_s_20200101_20201231_go_epsg4326_v20241025.tif")) bg.x = app(b, fun=max, na.rm=TRUE, cores = 32) bg.x0 = terra::ifel(is.na(bg.x), 0, bg.x, filename="protected.intact.areas_wdpa.ifl_p_1km_s_2020_2024_go_epsg4326_v20241025.tif", wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT2S'), overwrite=TRUE)</code></pre>
Simplified Object Detection for Manufacturing: Introducing a Low-Resolution Dataset
<p>This dataset was published with the dataset descriptor "Simplified Object Detection for Manufacturing: Introducing a Low-Resolution Dataset".</p> <p>ACKNOWLEDGEMENTS</p> <p>The project ”ZUKIPRO” is funded as part of the ”Future Centers” program by the Federal<br>Ministry of Labour and Social Affairs and the European Union through the European Social<br>Fund Plus (ESF Plus).Roles and Contributions.</p>
Dataset for: Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning
<p>Dataset for: Importance of satellite observations for high-resolution mapping of near-surface NO<sub>2 </sub>by machine learning</p> <p>This dataset is uploaded as a part of the article by Kim et al. (2021). The dataset is the hourly maps of near-surface nitrogen dioxide (NO<sub>2</sub>) concentrations at 100 m resolution for an Alpine domain (Switzerland and northern Italy, 6-12 °E, 42-48 °N). The dataset is provided per day (24 hours) in a netcdf (*.nc ~550MB). In this work, we have generated NO<sub>2 </sub>hourly maps for Feb. 2019 to May 2020 and, here, we upload for March 2019 only (~16 GB). If you need data for another period of time, please contact Gerrit Kuhlmann (gerrit.kuhlmann@empa.ch) or Minsu Kim (minsu.kim@empa.ch). </p>
High resolution land cover 2015 Aarhus, Denmark
<p><strong>Description</strong></p> <p>This dataset provides land-cover and land-use information at a 20cm resolution for the municipality of Aarhus, Denmark. It depicts the status for the year 2015, containing 23 thematic classes.</p> <p><strong>Spatial reference</strong><br> All data is projected in ETRS 1989 UTM Zone 32N (EPSG:25832)</p> <p><strong>Related publication</strong><br> J. M. Knopp, G. Levin and E. Banzhaf, "Aerial Data Analysis for Integration Into a Green Cadastre—An Example From Aarhus, Denmark," in <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</em>, vol. 16, pp. 6545-6555, 2023, doi: <a href="https://ieeexplore.ieee.org/document/10168752">10.1109/JSTARS.2023.3289218</a>.</p> <p><strong>Class Codec</strong></p> <table> <tbody> <tr> <td> <p><strong>Class</strong></p> </td> <td> <p><strong>Vector </strong></p> <p><strong>NumCodec</strong></p> <p><strong>(16bit)</strong></p> </td> <td> <p><strong>Raster</strong></p> <p><strong>NumCodec</strong></p> <p><strong>(8bit)</strong></p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Building</p> </td> <td> <p>100</p> </td> <td> <p>10</p> </td> </tr> <tr> <td> <p>0 Lowest rise building</p> </td> <td> <p>110</p> </td> <td> <p>11</p> </td> </tr> <tr> <td> <p>1 Low rise building</p> </td> <td> <p>120</p> </td> <td> <p>12</p> </td> </tr> <tr> <td> <p>2 Mid rise building</p> </td> <td> <p>130</p> </td> <td> <p>13</p> </td> </tr> <tr> <td> <p>3 High rise building</p> </td> <td> <p>140</p> </td> <td> <p>14</p> </td> </tr> <tr> <td> <p>4 Highest rise building</p> </td> <td> <p>150</p> </td> <td> <p>15</p> </td> </tr> <tr> <td> <p>Mineral surface</p> </td> <td> <p>210</p> </td> <td> <p>21</p> </td> </tr> <tr> <td> <p>Bare soil</p> </td> <td> <p>220</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>Artificial grass</p> </td> <td> <p>230</p> </td> <td> <p>23</p> </td> </tr> <tr> <td> <p>Grass</p> </td> <td> <p>310</p> </td> <td> <p>31</p> </td> </tr> <tr> <td> <p>Shrub round</p> </td> <td> <p>410</p> </td> <td> <p>41</p> </td> </tr> <tr> <td> <p>Shrub linear</p> </td> <td> <p>420</p> </td> <td> <p>42</p> </td> </tr> <tr> <td> <p>Evergreen</p> </td> <td> <p>510</p> </td> <td> <p>51</p> </td> </tr> <tr> <td> <p>Deciduous</p> </td> <td> <p>520</p> </td> <td> <p>52</p> </td> </tr> <tr> <td> <p>Lake</p> </td> <td> <p>610</p> </td> <td> <p>61</p> </td> </tr> <tr> <td> <p>River</p> </td> <td> <p>620</p> </td> <td> <p>62</p> </td> </tr> <tr> <td> <p>Sea</p> </td> <td> <p>630</p> </td> <td> <p>63</p> </td> </tr> <tr> <td> <p>Undergrowth</p> </td> <td> <p>710</p> </td> <td> <p>71</p> </td> </tr> <tr> <td> <p>Agriculture, intensive temporary crops</p> </td> <td> <p>810</p> </td> <td> <p>81</p> </td> </tr> <tr> <td> <p>Agriculture, intensive permanent crops</p> </td> <td> <p>820</p> </td> <td> <p>82</p> </td> </tr> <tr> <td> <p>Agriculture, extensive</p> </td> <td> <p>830</p> </td> <td> <p>83</p> </td> </tr> <tr> <td> <p>unclassified</p> </td> <td> <p>999</p> </td> <td> <p>99</p> </td> </tr> <tr> <td> <p>NonAOI</p> </td> <td> <p>999</p> </td> <td> <p>99</p> </td> </tr> </tbody> </table>
Data for Marine Ecological Niche Models, for 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species
<p>Data for Ecological Niche Models: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species.</p>
Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species, developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Native ecological niche models of 1508 European species (894 fish and 614 non fish) developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 and under RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, at 0.5° spatial resolution.</p>
Virtual Reality Dataset used for Proof of Concept in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)
<p>This dataset contains the <strong>dataset </strong>used in the Virtual Reality POC for the validation of the Conflict Detection and Resolution (CD&R) use case.</p> <p>This dataset represent a extract of different (using K-means) candidate solution, either good or bad ones.</p>
Solutions and Genetic algorithm dataset of the Scenarios used for the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION )
<p>This dataset contains the <strong>solution </strong>of the scenarios used for one of the validation of the ARTIMATION project: Conflict Detection and Resolution (CD&R) use case (link).</p> <p>The solution are computed by a Genetic Algorithm developped by Nicolas Durand.<br> <br> Inside, one can find:</p> <p>-One archive, "GA_Scenario_Solution_Dataset.zip", containing 10 couple of files (so 20 files). Each couple of file "sol_X_1.csv" and "sols_X_1.csv" are reciprocally the solutino given by the Genetic Algorithm to scenario X, and all the candidate solution explroed by the GA while solving scenario X. This archive also contain other versions of the solutions made by the GA with other parameters.<br> <br> -One archive, "GA_Toy_Dataset.zip" , containing solution to random scenarios, used to develop the first interfaces.</p> <p>Those solutions are used to developp the heatmatrix and heatmaps of the project (link), and visualisations for the validation (link).</p>
Heatmatrix and Heatmap Layers and Alternatives used in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)
<p>This dataset contains the <strong>visualisations </strong>of the solutions of Conflict Detection and Resolution (CD&R) use case.</p> <p>The solution are computed by a Genetic Algorithm developped by Nicolas Durand.<br> <br> Inside, one can find:</p> <p>-One archive, "Heatmatrix.zip" , containing the heatmatrix creating using the solutions dataset.</p> <p>-One archive, "Heatmaps_Layer_Alternatives.zip", containing all the layers created and used to created the heatmaps, the heatmaps, and alternative heatmaps (with other candidate solutions).</p> <p>Those layers and heatmaps are used to develop other visualisation used in the validation.</p>
Validation Videos and eXplainable levels used in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)
<p>This dataset contains the <strong>explaination levels and the video </strong>used in the validation of the Conflict Detection and Resolution (CD&R) use case.</p> <p>The solution are computed by a Genetic Algorithm developped by Nicolas Durand.<br> <br> Inside the dataset, one can find:</p> <p>-One archive, " Validation_Videos_Traffic.zip ", containing the video of traffic of every scenario.</p> <p>-One archive, " Validation_XAI_levels.zip", containing the Blackbox, Heatmap, and Storyboard eXplainable levels for each scenario.</p> <p>The videos and XAI levels are used in the validation exercice.</p>
Displacement measurements of the open-hardware sandbox using the AS5311 high-resolution magnetic sensor
<p>This dataset includes the experimental data from the AS5311 sensor for measuring the displacement of the Open-Hardware Geological Sandbox.</p> <p>These experiments are explained in the journal article: <a href="https://doi.org/10.1109/ACCESS.2023.3262617">Designing low-cost open-hardware electromechanical scientific equipment: A geological analogue modeling sandbox</a></p> <p>To understand this dataset, go to the Tectonic Open Hardware (TectOH) Sandbox project: <a href="https://github.com/URJCMakerGroup/TectOH">https://github.com/URJCMakerGroup/TectOH</a>. Then go to the <a href="https://github.com/URJCMakerGroup/TectOH/tree/main/optional">optional</a> folder and to the <a href="https://github.com/URJCMakerGroup/TectOH/tree/main/optional/as5311_magn_sens">magnetic sensor</a> folder.</p> <p>This data set contains two kind of files:</p> <ul> <li><strong>bin</strong>: raw binary files received from the AS5311 high resolution sensor. Although this sensor sends 12 bit data, we have truncated the most significant bits and receive only 8 bits (one byte). Therefore, each byte of these binary files is a measurement of the distance. Each distance increment corresponds to ~0.488nm (2mm/2048)</li> <li><strong>csv</strong>: csv files that can be opened with any spreadsheet app, such as Libreoffice Calc or Microsoft Excel, or even with a text editor. This file contains the processed data from the binary files. These files have been generated with the proc_magn_sensor.py Python script located in the <a href="https://github.com/URJCMakerGroup/TectOH">project repository</a>. There are some columns, which are: <ul> <li>index: measurement number</li> <li>time in milliseconds: each measurement is taken every 250 us</li> <li>median2: in micrometers, since the sensor may jitter, we have applied the median filter twice. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>median1: in micrometers, median filter only applied once. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>mean: in micrometers, mean filter. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>mean int: in micrometers, mean filter rounded to an integer value. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>orig_base: this is not in micrometers, but in the units of the sensor (~0.488nm). The only processing done is that when there is an overflow of 255 to 0, or from 0 to 255, it adds the overflow to continue the trend. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>original: this is the data received from the sensor with no processing, each value is ~0.488nm</li> <li>mean2: in micrometers, mean filter applied twice. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> </ul> </li> </ul> <p>There are two set of experiments:</p> <ul> <li><strong>Experiments with no load</strong>. These files start with <em>noload_</em><br> In these experiments the gantry is moved 1 mm alternatively to the front and then reversing direction. Moving in this alternate way a few times. There are five experiments each of them with a different speed: v= 10 mm/h; 25 mm/h; 50 mm/h; 82 mm/h and 100 mm/h. The name of the file indicates the speed: <ol> <li>noload_100mmh_1mm: FBFBF: 1mm forth, 1mm back, 1mm forth, 1mm back, 1 mm forth</li> <li>noload_25mmh_1mm: FBFFBBFB</li> <li>noload_50mmh_1mm: FBFBFB</li> <li>noload_82mmh_1mm: FBFBFB</li> <li>noload_100mmh_1mm: FBFBFB</li> </ol> </li> <li><strong>Experiments pushing a 5kg sand load</strong>. These files start with <em>load5kg_</em> <ol> <li>load5kg_25mmh_5mm: moving 5kg at 25mm/h a distance of 5mm</li> <li>load5kg_25mmh_10mm: moving 5kg at 25mm/h a distance of 10mm</li> <li>load5kg_25mmh_20mm: moving 5kg at 25mm/h a distance of 20mm</li> <li>load5kg_75mmh_20mm: moving 5kg at 75mm/h a distance of 20mm</li> <li>load5kg_75mmh_50mm: moving 5kg at 75mm/h a distance of 50mm</li> <li>load5kg_100mmh_25mm: moving 5kg at 100mm/h a distance of 20mm</li> <li>load5kg_100mmh_50mm: moving 5kg at 100mm/h a distance of 50mm</li> </ol> </li> </ul> <p> </p> <p> </p> <p> </p>
Problem discovery and resolution activities in the Apache HTTP Server Project (March 2001- March 2013).
<p>This is a dynamic visualization of problem discovery and resolution activities observed in the in the development of the Apache HTTP Server Project during the period March 2001- March 2013. The nodes in the network represent problems (software bugs). Anthropomorphic icons represent participants (software developers). The network edges connect participants to problems. Numerical labels record the internal identification numbers or participants and problems. The visible clusters represent the software modules. The central node is the project core module. Participants move closer to problems that attract their attention. When a participant allocates attention to a problem, an edge emerges connecting the two. The edge is green when a participant opens a bug report (i.e., when he discovers a new problem), red when the participant closes the bug report (i.e., when she solves an existing problem), and yellow when any other action is recorded. Problems (white nodes) are green when they first appear. They turn red immediately before being closed, and are yellow when the corresponding bug report is being modified. The animation advances by 0.05 seconds every day of historical time.</p> <p>The animation is produced using the Gource server control visualization tool developed by Andrew Caldwell (<a href="https://gource.io/">https://gource.io/</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.