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20 results for “EO Data”
Training data for bathymetry estimation via EO satellite - Hel Peninsula
<p>This dataset contains satellite image from Sentiel-2A (bands B2, B3, B4, B8) and reference sonar based bathymetry measurements.</p> <p>The reference data was acquired from Polish Maritime Administration (htttp://www.um.gdy.pl) and is publicly available. File reference_data_34.csv contains in-situ measrements aquired at northen shore of Hel Peninsula. Points coordinates are expressed in UTM34N coordinate system.</p> <p>The satellite and the reference datasets were preprocessed by the authors for adjust them for Machine Learning algorithms used in the research.</p> <p> </p>
Ranking data for the eo-Delphi project
<p>The eo-Delphi project (https://osf.io/8f3aj/) created consensus for a core set of outcomes for future studies evaluating the effects of oral corticosteroid therapy in chronic obstructive pulmonary disease (COPD) patients stratified by eosinophil levels. The dataset presented here reports individual ranking scores for proposed outcomes.</p>
Use of EO data to test machine learning algorithms
<p>This data set is composed of one Sentinel-2 image of Darwin City, Australia. The objective of this work is to use EO data to compare the performance of different machine learning algorithms.</p>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types probabilities (part 1)</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types probabilities (part 2)</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types classification and relative entropy</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Phenomenological EOS Data Set
<p>The MATLAB workspace file eos_data_set.mat contains the parameters and physical properties of 1,966,225 phenomenological neutron star (NS) equations of state (EOS).</p>
EOS-DATA-2016-01: Monte Carlo Pseudo Events for the Decays Bbar -> P mu X_nubar, P = D,pi
<p>This archive EOS-DATA-2016-01 contains pseudo events of the decays</p> <p> Bbar -> P mu X_nubar</p> <p>where X_nubar is either nubar or (nubar nu nubar), and P = D or pi. The archive<br /> complements the publication EOS-2016-01. The events are stored as binary files<br /> in the HDF5 format.</p>
EOS AQUA, MODIS l1b data, HDF4, 2019-02-26
<p>Data covers western Europe, from south of Greece and Italy, up to Greenland</p> <p>Received in SMHI ground station in Norrköping, Sweden on the 26th of February 2019. Processed from raw to level 1 with Seadas</p>
FIGURE 15. Leptolalax eos, MNHN 2004.0274 in Sorting out Lalos: description of new species and additional taxonomic data on megophryid frogs from northern Indochina (genus Leptolalax, Megophryidae, Anura) 3147
FIGURE 15. Leptolalax eos, MNHN 2004.0274, holophoront, adult male. (A) Lateral view of head, ventral view of (B) right foot and of (C) right hand. Scale bar = 5 mm (3 + 2 mm).
FIGURE 14. Leptolalax eos, MNHN 2004.0274 in Sorting out Lalos: description of new species and additional taxonomic data on megophryid frogs from northern Indochina (genus Leptolalax, Megophryidae, Anura) 3147
FIGURE 14. Leptolalax eos, MNHN 2004.0274, holophoront, adult male, SVL 34.7 mm. (A) Dorsal and (B) ventral view.
Data from field campaign project TACTIC EO-Africa
<p>Field ground data from the EO-Africa project Tactic (2023) and the previous field campaigns of 2016 and 2017. Leaf area index measurements with licor2200, spectral information measurements with ASD LabSpec spectrometer. Protocols of measurement. GPS points. Vegetation characteristics.</p>
EO-1 (Earth Observing-1) Advanced Land Imager (ALI) Instrument Level 1R, Level 1Gs, Level 1Gst Data
Advanced Land Imager (ALI) provides image data from ten spectral bands (band designations). The instrument operates in a pushbroom fashion, with a spatial resolution of 30 meters for the multispectral bands and 10 meters for the panchromatic band. The standard scene width is 37 kilometers. Standard scene length is 42 kilometers, with an optional increased scene length of 185 kilometers (additional information). For Advanced Land Imager (ALI) data, the following levels of correction are available: Level 1R radiometrically corrected with no geometric correction applied. The image data are provided in 16-bit radiance values. The data are available in Hierarchical Data Format (HDF) and are distributed on CD-ROM, DVD, and File Transfer Protocol (FTP). Level 1Gs is geometrically corrected and will be provided as a single "stitched" file. The image data are provided in 16-bit radiance values. The data are available in Hierarchical Data Format (HDF) or Geographic Tagged Image-File Format (GeoTIFF) and are distributed on DVD and File Transfer Protocol (FTP). Level 1Gst is terrain corrected and will be provided as a single "stitched" file. The image data are provided in 16-bit radiance values. The data are available in Hierarchical Data Format (HDF) or Geographic Tagged Image-File Format (GeoTIFF) and are distributed on DVD and File Transfer Protocol (FTP). [Source: USGS/EDC Homepage]
SAGE III Meteor-3M L2 Monthly Cloud Presence Data (HDF-EOS) V004
A monthly data file coincident with solar event granules, that provides information about cloud presence during data capture of the granules.
Gene profiling data of CD4+ T cells doubly transduced with EOS+LEF1 or GATA1+SATB1
GEO Series GSE40277. Mus musculus. 8 samples. Type: Expression profiling by array.
SAGE III Meteor-3M L2 Monthly Cloud Presence Data (HDF-EOS) V004
A monthly data file coincident with solar event granules, that provides information about cloud presence during data capture of the granules
SAGE III Meteor-3M L1B Solar Event Transmission Data (HDF-EOS) V003
SAGE III Meteor-3M L1B Solar Event Transmission Data are Level 1B pixel group transmission profiles for a single solar event. The Stratospheric Aerosol and Gas Experiment III (SAGE III) obtains profile measurements of aerosol extinction, ozone, water vapor, nitrogen dioxide, nitrogen trioxide, chlorine dioxide, clouds, temperature and pressure in the mesosphere, stratosphere, and upper troposphere with a vertical resolution of 0.5 - 1 km resolution.SAGE III was a fourth generation, satellite-borne instrument and a crucial element in NASA's Earth Observing System (EOS) . The instrument was launched on the Russian Meteor-3M spacecraft in December 2001. The Meteor-3M mission, along with the SAGE III mission, was terminated on March 6, 2006, because of a power supply system failure resulting in loss of communication with the satellite.The newest SAGE mission, SAGE III on ISS, is scheduled to launch in 2015. Plans include sending a copy of the SAGE III instrument to the International Space Station aboard a commercial Space X flight.
Data and Script for: Olive Trees Health and Yield Prediction through EO data and Machine Learning (OLEA-PRED) / EO AFRICA – Research and Development Facility
<p>This database was collected under the "Olive Trees Health and Yield Prediction through EO data and Machine Learning" project, funded by the European Space Agency in the framework of the "EO AFRICA R&D Facility".</p> <p>- Shapefiles.rar contains shp files for Orchads boundaries and tree locations.</p> <p>- Field_data contains data collected in the field (Chllorophyl, Yield and Soil ).</p> <p>- SCRIPTS.rar contains all scripts and extracted data used for prediction for Settat orchad.</p> <p>- FBS images.rar contains UAV and M6 images for Fkih Ben Saleh orchad.</p>
SAGE III Meteor-3M L2 Monthly Cloud Presence Data (HDF-EOS) V003
A monthly data file coincident with solar event granules, that provides information about cloud presence during data capture of the granules
REPLACED: EOS-DATA-2016-01: Monte Carlo Pseudo Events for the decays Bbar -> P mu X_nubar, P = D,pi
<p>This dataset has been replaced after an error in the original analytical calculation has been uncovered.</p> <p>This archive EOS-DATA-2016-01 contains pseudo events of the decays</p> <p> Bbar -> P mu X_nubar</p> <p>where X_nubar is either nubar or (nubar nu nubar), and P = D or pi. The archive<br /> complements the publication EOS-2016-01. The events are stored as binary files<br /> in the HDF5 format.</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.