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3,688 results for “computer”
EDICTOR 3: Interactive Tool for Computer-Assisted Language Comparison
This software offers the most recent and mostly stable version of the EDICTOR tool, also available for direct usage from <a href="https://edictor.org">edictor.org/</a>.
Super-resolving ocean dynamics from space with computer vision algorithms: training datasets
<p>We provide here the datasets used for the development of the dilated Adaptive Residual Network for the super-resolution of ocean Absolute Dynamic Topography described in <em>Buongiorno Nardelli et al.</em> (2022). The model is designed to combine satellite altimetry and thermal observations and provides super-resolved dynamic topography. The training/test datasets have been built starting from the data originally prepared for an Observing System Simulation Experiment carried out in the framework of the European Space Agency CIRCOL project [<em>Ciani et al.</em>, 2021]. They consist of one year of synthetic daily Absolute Dynamic Topography (ADT), surface geostrophic currents and sea surface temperature data obtained from Copernicus Marine Service Mediterranean Forecasting System (MFS) (Product ID: MEDSEA-ANALYSIS- FORECAST-PHY-006-013) [<em>Clementi et al. 2021</em>]. Synthetic Altimeter-derived ADT maps were obtained by first sampling the model output along the actual tracks of a synthetic constellation composed of 4 Radar Altimeters: Jason-3, Sentinel-3A, SARAL/Altika, and Cryosat-2 missions (this step is achieved by running the SWOT simulator software [<em>Gaultier et al.</em>, 2016]) and successively applying the DUACS (<em>Data Unification and Altimeter Combination System)</em> mapping method. The original input images cover the entire Mediterranean domain at 1/24° spatial resolution, leading to an individual image size of 380x1000 pixels. Here, we have randomly chosen 40 dates (~11% of the total) to be kept aside as fully independent test data, and successively re-sampled the original images extracting much smaller tiles (76x100), which are used as input to the network training. The tiles are extracted by going through a double loop on latitude and longitude, imposing a spatial overlap of 50%. Full details on data pre-processing (e.g.normalization strategies) are given in the paper:</p> <ul> <li>Buongiorno Nardelli, B.; Cavaliere, D.; Charles, E.; Ciani, D. Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms. <em>Remote Sens.</em>, <strong>2022</strong>, 14, 1159. https://doi.org/10.3390/rs14051159</li> </ul>
Dataset for the validation of a Computational Thinking test for upper primary school (grades 3-4)
<p>This dataset contains quantitative student data acquired during the administration of a new computational thinking assessment for upper primary school (grades 3 and 4). Over 1500 students (approximately half in grade 3 and half in grade 4) participated in the data collection which took place in January 2021 in the Canon Vaud in Switzerland. The data was used to validate the psychometric properties of the instrument in the referenced article. </p> <p> </p> <p>If you use any of the resources provided in this repository, please cite the following</p> <p>• The Zenodo repository, DOI: 10.5281/zenodo.5865573</p> <p>• The corresponding journal article</p> <p>• Licence : CC-BY-NC</p> <p> </p> <p>In case of inquiries, please contact laila.elhamamsy@epfl.ch</p>
Data for the article "Computational micromagnetics based on normal modes: Bridging the gap between macrospin and full spatial discretization"
<p>Data for the article "Computational micromagnetics based on normal modes: Bridging the gap between macrospin and full spatial discretization".</p> <p>Link to publisher: https://www.sciencedirect.com/science/article/abs/pii/S0304885321009197</p> <p>Link to Arxiv preprint: https://arxiv.org/abs/2105.08829</p>
3D magnetotelluric modeling using high-order tetrahedral Nédélec elementson massively parallel computing platforms
<p>Accompanying data to journal article</p> <blockquote> <p>Castillo-Reyes, O., Modesto, D., Queralt, P., Marcuello, A., Ledo, J., Amor-Martin, A., de la Puente, J., García-Castillo, L.E. (2021) 3D magnetotelluric modeling using high-order tetrahedral Nédélec elements on massively parallel computing platforms. Computers & Geosciences, vol.(160): 105030 DOI: 10.1016/j.cageo.2021.105030. ISSN 0098-3004, Elsevier.</p> </blockquote>
AutoML for Video Analytics with Edge Computing - Dataset
<p>Latency and confidence measurements obtained from an edge-assisted object recognition system.</p> <p>The records are obtained by tuning the image encoding rate and Neural Network input layer size and measuring the latency on completing each system operation, i.e. encoding the image, transmiting it wirelessly, decoding and rotating at the server, and performing object recognition with YOLO on the server's GPU. Moreover, we document the achievable frame rate as a result of the total latency, as well as the object recognition confidence and cumulative confidence for all identified objects of each image.</p>
Data-Driven Computational Intelligence Applied to Dengue Outbreak Forecasting: a case study at the scale of the city of Natal, RN-Brazil
<p><strong>The dataset comprises survey data from the following sources:dengue_incidence_data.csv: public data provided by Municipal Health Department of Natal, State of Rio Grande do Norte, Brazil; and data of Brazilian Notifiable Diseases Information System (Sinan). The objective of this paper was to analyze incidence data of dengue cases registered in each neighborhood of Natal city, weekly sampled (52 epidemiological weeks a year) between 2016 – 2019). </strong></p>
Experimental data for the study: "Naturalistic visualization of reaching movements using head-mounted displays improves movement quality and proves high usability compared to conventional computer screens"
<p>The datasets contains the motor performance metrics and the questionnaire responses for two experiments involving a motor task with a VR controller (experiment 1, healthy old participants) or a rehabilitation assistive device (experiment 2, brain-injured patients) and three visualization technologies: an immersive virtual reality (IVR) head-mounted display (HMD), an augmented reality (AR) HMD, and a computer screen (2D screen). The study was performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. All data are stored in “csv” files. The variables inside the files are explained in “DataFrameDescription.rtf”. For questions, please contact L.MarchalCrespo@tudelft.nl.</p>
Refinements for Bragg coherent X-ray diffraction imaging: Electron backscatter diffraction alignment and strain field computation
<p>Here we present the final crystal reconstructions and analysis scripts for the paper titled "Refinement for Bragg coherent X-ray diffraction imaging: Electron backscatter diffraction alignment and strain field computation" published in Journal of Applied Crystallography, 55, 2022. Please see the README file for more information.</p>
EO-based area monitoring markers computed over the Lithuanian pilot region (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), the following EO-based monitoring marker maps were released over the two pilot regions, containing the results produced from a set of image analysis and machine learning techniques. The latest explores the benefits of Copernicus's multispectral high-resolution Sentinel-2 data acquired from 01-01-2021 until 26-07-2021 and provides tailored information on the needs of European paying agencies (e.g. CAPO and NPA), expressed with the following markers.</p> <ol> <li> <p><strong>Mowing marker:</strong> used to detect mowing events on meadow/grass like Features Of Interest (FOI)</p> </li> <li> <p><strong>Mean-NDVI marker:</strong> used to detect erroneous claims with no vegetation</p> </li> <li> <p><strong>Homogeneity marker:</strong> used to determine if a parcel geometry really consists of a single crop or if there are multiple things growing on the parcel</p> </li> <li> <p><strong>Bare soil marker:</strong> used to detect observation where bare soil is present on the feature of interest. This indicates agricultural activity on the FOI (plowing, harvest)</p> </li> <li> <p><strong>Similarity and distance markers:</strong> used to give additional context to the crop classification and to detect erroneous claims</p> </li> <li> <p><strong>Land marker:</strong> used to detect the land type and non-productive EFAs of the FOI</p> </li> <li> <p><strong>Crop-type marker:</strong> used to detect the specific crop growing on the FOI</p> </li> </ol> <p>This dataset is comprised of the two subsequent geopackage files, (i) the <em>"test_area_markers_summary.gpkg"</em> and (ii) <em>"crop_marker_summary_full_country.gpkg", which</em> were computed for the Lithuanian pilot region<em>. </em> Descriptions are given below.</p> <p><strong>Test area markers dataset: </strong>There is a total of 59995 FOIs in the test areas. Out of these, markers are computed on **54809** FOIs that contain more than 1 Sentinel-2 pixel.</p> <table> <caption><em><strong>Description of the information contained in the corresponding </strong></em><strong><em>"test area markers"</em> dataset</strong></caption> <thead> <tr> <th scope="col">Attribute Table name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>POLY_ID </td> <td>Reference ID of the polygon </td> </tr> <tr> <td>LABEL</td> <td>Declared crop group</td> </tr> <tr> <td>N_VALID_OBS</td> <td>Number of valid Sentinel-2 observations used for computing the markers</td> </tr> <tr> <td>HOMOGENEITY_PROBA</td> <td>The probability that the FOI is homogeneous</td> </tr> <tr> <td>N_MW_OBS_MAY_OCT</td> <td>Number of detected mowing observations between May and October</td> </tr> <tr> <td>N_BS_OBS_APR_OCT</td> <td>Number of detected mowing observations between April and October</td> </tr> <tr> <td>SIMILARITY_SCORE</td> <td>The normalised ([0, 100]) representation of the χ<sup>2</sup> value of the NDVI time-series difference between the target FOI and the neighbouring FOIs</td> </tr> <tr> <td>DISTANCE_SCORE</td> <td>The normalised ([0, 100]) representation of the Median Euclidean distance between the NDVI time-series of the target FOI and the neighbouring FOIs having assumed crop type declared</td> </tr> <tr> <td>MEAN_NDVI</td> <td>Μean NDVI value of all valid observations of the FOI</td> </tr> <tr> <td>PREDICTED_CROP</td> <td>Predicted crop group</td> </tr> <tr> <td>CROP_PREDICTION_SCORE</td> <td>The pseudo-probability of the crop group prediction</td> </tr> </tbody> </table> <p><strong>Crop marker full country dataset: </strong> There is a total of **1127455** FOIs for the full country GSAA dataset. Out of these, markers are computed on **1042671** FOIs that contain more than 1 Sentinel-2 pixel.</p> <table> <caption><em><strong>Description of the information contained in the corresponding </strong></em><strong><em>"crop marker full country"</em> dataset</strong></caption> <thead> <tr> <th scope="col">Attribute Table name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>POLY_ID</td> <td>Reference ID of the polygon</td> </tr> <tr> <td>LABEL</td> <td>Declared crop group</td> </tr> <tr> <td>PREDICTED_CROP</td> <td>Predicted crop group</td> </tr> <tr> <td>CROP_PREDICTION_SCORE</td> <td>The pseudo-probability of the crop group prediction </td> </tr> </tbody> </table>
EO-based area monitoring markers computed over the Cypriotic pilot region (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), the following EO-based monitoring marker maps were released over the two pilot regions, containing the results produced from a set of image analysis and machine learning techniques. The latest explores the benefits of Copernicus's multispectral high-resolution Sentinel-2 data acquired from 01-08-2020 until 05-10-2021 and provides tailored information on the needs of European paying agencies (e.g. CAPO and NPA), expressed with the following markers.</p> <ol> <li> <p><strong>Mowing marker:</strong> used to detect mowing events on meadow/grass like Features Of Interest (FOI)</p> </li> <li> <p><strong>Mean-NDVI marker:</strong> used to detect erroneous claims with no vegetation</p> </li> <li> <p><strong>Homogeneity marker:</strong> used to determine if a parcel geometry really consists of a single crop or if there are multiple things growing on the parcel</p> </li> <li> <p><strong>Bare soil marker:</strong> used to detect observation where bare soil is present on the feature of interest. This indicates agricultural activity on the FOI (plowing, harvest)</p> </li> <li> <p><strong>Similarity and distance markers:</strong> used to give additional context to the crop classification and to detect erroneous claims</p> </li> <li> <p><strong>Land marker:</strong> used to detect the land type and non-productive EFAs of the FOI</p> </li> <li> <p><strong>Crop-type marker:</strong> used to detect the specific crop growing on the FOI</p> </li> </ol> <p>This dataset is comprised of the geopackage file <em>"markers_summary.gpkg", which</em> was computed for the Cypriotic pilot region. Descriptions are given below.</p> <p><strong>Markers summary dataset</strong></p> <table> <caption><em><strong>Description of the information contained in the corresponding "markers summary" dataset</strong></em></caption> <thead> <tr> <th scope="col">Attribute name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>POLY_ID</td> <td>Reference ID of the polygon </td> </tr> <tr> <td>PLOT_ID_CROP</td> <td>The reference ID of the FOI </td> </tr> <tr> <td>PLOT_NAME</td> <td>The reference name of the FOI </td> </tr> <tr> <td>APPLICANT_NUMBER</td> <td>The ID of the FOI applicant</td> </tr> <tr> <td>LABEL</td> <td>Crop identifier column, based on `CROP_CODE` column in the GSAA dataset.</td> </tr> <tr> <td>CROP_DESC</td> <td>Crop name in Greek (Original crop name) </td> </tr> <tr> <td>CROP_ENG</td> <td>The English name of the crop on the FOI</td> </tr> <tr> <td>LAND_USE</td> <td> <p><strong>Land use types:</strong> 1-ARABLE LAND; 2-PERMANENT CROPS; 3-GRASSLAND (PERMANENT PASTURE); 5-AFFORESTED LAND (NON-AGRICULTURAL); 6-GREENHOUSE); 71-NON-ELIGIBLE (ABANDONED AT LEAST 2 YEARS);72 - NON-ELIGIBLE (FOREST/LOW WILD UNWANTED VEGETATION); 73NON-ELIGIBLE (NON PLANTED/BARREN/ROCKY); 81 - BUILT-UP (BUILDING/WAREHOUSE - MAN CONSTRUCTED); 82 - BUILT-UP (PARK/HOUSE YARD)</p> </td> </tr> <tr> <td>N_VALID_OBS</td> <td>Number of valid Sentinel-2 observations used for computing the markers</td> </tr> <tr> <td>HOMOGENEITY_PROBA</td> <td>The probability that the FOI is homogeneous</td> </tr> <tr> <td>MEAN_NDVI</td> <td>Mean NDVI value of all valid observations of the FOI</td> </tr> <tr> <td> N_MW_OBS_AUG20_OCT21</td> <td>Number of detected mowing observations between August 2020 and October 2021</td> </tr> <tr> <td>N_BS_OBS_AUG20_OCT21</td> <td>Number of detected mowing observations between August 2020 and October 2021</td> </tr> <tr> <td>BS_COUNT_MAR_START_MAR_END</td> <td>Number of bare-soil observations between March 1 2021 and April 1 2021</td> </tr> <tr> <td>BS_COUNT_MAR_START_APR_MID</td> <td>Number of bare-soil observations between March 1 2021 and April 15 2021</td> </tr> <tr> <td>BS_COUNT_MAR_APR</td> <td>Number of bare-soil observations between March 1 2021 and May 1 2021</td> </tr> <tr> <td>SIMILARITY_SCORE</td> <td>The normalised ([0, 100]) representation of the χ<sup>2</sup> value of the NDVI time-series difference between the target FOI and the neighboring FOIs</td> </tr> <tr> <td>DISTANCE_SCORE</td> <td>The normalised ([0, 100]) representation of the Median Euclidean distance between the NDVI time-series of the target FOI and the neighboring FOIs having assumed crop type declared</td> </tr> <tr> <td>PREDICTED_LAND_GROUP</td> <td>The FOI label as predicted by the land group model using crop groupings based on land use.</td> </tr> <tr> <td>LAND_GROUP_PREDICTION_SCORE</td> <td>The pseudoprobability of the crop-group prediction as assigned by the land-group model using crop groupings based on land use. A score close to 1 indicates that the model is very confident in the prediction a score close to 0 indicates that the model is not confident in the prediction.</td> </tr> <tr> <td>PREDICTED_CROP</td> <td>The FOI label as predicted by the crop group model using crop groupings proposed by CAPO.</td> </tr> <tr> <td>CROP_PREDICTION_SCORE</td> <td>The pseudoprobability of the crop-group prediction as it is assigned by the crop-group model using crop groupings proposed by PA. A score close to 1 indicates that the model is very confident in the prediction a score close to 0 indicates that the model is not confident in the prediction.</td> </tr> <tr> <td>PREDICTED_CROP_V39</td> <td>The FOI label as predicted by the crop group model using crop groupings to 39 groups.</td> </tr> <tr> <td>CROP_PREDICTION_SCORE_V39</td> <td>The pseudoprobability of the crop-group prediction as assigned by the crop-group model using crop groupings to 39 groups.</td> </tr> <tr> <td>PREDICTED_CROP_V41</td> <td>The FOI label as predicted by the crop group model using crop groupings to 41 groups</td> </tr> <tr> <td>CROP_PREDICTION_SCORE_V41</td> <td>The pseudoprobability of the crop-group prediction as assigned by the crop-group model using crop groupings to 41 groups.</td> </tr> </tbody> </table> <p><br> </p>
Super-resolution EO-based area monitoring markers computed over the Lithuanian pilot region (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), the following EO-based monitoring marker maps were released over defined pilot areas over the Lithuanian pilot country, enhanced by features raised from the Super-resolution models. It involved the implementation of matching marking and data fusion deep learning algorithms, which attempt to support the extraction of useful information from highly variable inputs. This will further allow the distinction of landscape features, which would otherwise not be available in initially acquired Sentinel-2 data. The use of VHR data (Copernicus Contributing Missions) in combination with drone imagery will enhance the super-resolution modelling capabilities, enabling the augmentation of the training dataset (spatio-temporal scale) and subsequently leading to increased model performance.<br> The goal was to enhance the outputs of the area monitoring markers and especially in the monitoring of small (i.e. 100m<sup>2</sup>), narrow and elongated parcels. </p> <p>For the needs of DIONE, the aforementioned data were explored and the following area-based monitoring markers were calculated from 01-01-2022 until 01-08-2022 providing tailored information for the needs of the National Paying Agency of Lithuania. </p> <ol> <li> <p><strong>Mowing marker:</strong> used to detect mowing events on meadow/grass like Features Of Interest (FOI)</p> </li> <li> <p><strong>Similarity and distance markers:</strong> used to give additional context to the crop classification and to detect erroneous claims</p> </li> <li> <p><strong>Crop-type marker:</strong> used to detect the specific crop growing on the FOI</p> </li> </ol> <p>This dataset is comprised of one geopackage file, the "S2SR-study-geopackage.gpkg"<em>, which</em> was computed for the Lithuanian pilot region<em>. </em>Descriptions are given below.</p> <p><strong>Super-resolution Markers dataset: </strong>Markers were computed for 16872 FOIs that contain less than 1 Sentinel-2 pixel, using signals from 2022-01-01 until 2022-08-01. </p> <table> <caption><strong>Description of the information contained in the corresponding "Super-Resolution markers" dataset</strong></caption> <thead> <tr> <th scope="col">Attribute name </th> <th scope="col">Description </th> </tr> </thead> <tbody> <tr> <td>CROP_LABEL</td> <td>Reference ID of the polygon </td> </tr> <tr> <td>POLY_ID </td> <td>Declared crop group</td> </tr> <tr> <td>crop_group_prediction_1_classification</td> <td>The FOI label as predicted by the crop group (v2) model</td> </tr> <tr> <td>crop_group_prediction_1_classification_score</td> <td>The pseudoprobability of the crop-group (v1) prediction. A score close to 1 indicates that the model is very confident in the prediction</td> </tr> <tr> <td>crop_group_prediction_2_classification</td> <td>The FOI label as predicted by the crop group (v2) model</td> </tr> <tr> <td>crop_group_prediction_2_classification_score</td> <td>The pseudoprobability of the crop-group (v2) prediction. A score close to 1 indicates that the model is very confident in the prediction</td> </tr> <tr> <td>distance_classification</td> <td>Most similar crops according to the distance marker</td> </tr> <tr> <td>distance_classification_score</td> <td>Distance marker score of a FOI when compared to nearby FOIs with the same claim. A value close to 100 indicates that a FOI is not similar to other FOIs with the same claim.</td> </tr> <tr> <td>mowing_event_count</td> <td>Number of detected mowing events in the observation period</td> </tr> <tr> <td>similarity_classification</td> <td>Most similar crops according to similarity marker</td> </tr> <tr> <td>similarity_classification_score</td> <td>Similarity marker score of a FOI when compared to nearby FOIs with the same claim. A value close to 100 indicates that a FOI is not similar to other FOIs with the same claim</td> </tr> </tbody> </table>
EO-based area monitoring markers computed over the Cypriotic pilot region (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), the following EO-based monitoring marker maps were released over the two pilot regions, containing the results produced from a set of image analysis and machine learning techniques. The latest explores the benefits of Copernicus's multispectral high-resolution Sentinel-2 data acquired from 01-08-2021 until 03-09-2022 and provides tailored information on the needs of European paying agencies (e.g. CAPO and NPA), expressed with the following markers.</p> <ol> <li> <p><strong>Mowing marker:</strong> used to detect mowing events on meadow/grass like Features Of Interest (FOI)</p> </li> <li> <p><strong>Mean-NDVI marker:</strong> used to detect erroneous claims with no vegetation</p> </li> <li> <p><strong>Homogeneity marker:</strong> used to determine if a parcel geometry consists of a single crop or if multiple things are growing on the parcel</p> </li> <li> <p><strong>Bare soil marker:</strong> used to detect observation where bare soil is present on the feature of interest. This indicates agricultural activity on the FOI (plowing, harvest)</p> </li> <li> <p><strong>Similarity and distance markers:</strong> used to give additional context to the crop classification and to detect erroneous claims</p> </li> <li> <p><strong>Land marker:</strong> used to detect the land type and non-productive EFAs of the FOI</p> </li> <li> <p><strong>Crop-type marker:</strong> used to detect the specific crop growing on the FOI</p> </li> </ol> <p>This dataset is comprised of the geopackage file <em>"markers_summary.gpkg", </em>which was computed for the Cypriotic pilot region. Descriptions are given below.</p> <p><strong>Markers summary dataset</strong></p> <table> <caption><strong>Description of the information contained in the corresponding "markers summary" dataset</strong></caption> <thead> <tr> <th scope="col">Attribute name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td> POLY_ID </td> <td>Unique polygon identifier</td> </tr> <tr> <td>OBS_ALL</td> <td>Number of all available observations</td> </tr> <tr> <td>OBS_VALID</td> <td>Number of all valid observations</td> </tr> <tr> <td>CROP_CODE</td> <td>Crop identifier</td> </tr> <tr> <td>N_PIXEL</td> <td>Number of S2 pixels within FOI</td> </tr> <tr> <td>PLOTIDCROP</td> <td>PLOT_ID_CROP</td> </tr> <tr> <td>BS_APR_END</td> <td>Number of bare-soil observations between 2022-03-01 and 2022-04-30 </td> </tr> <tr> <td>BS_FEB_JUN</td> <td>Number of bare-soil observations between 2022-02-01 and 2022-06-30</td> </tr> <tr> <td>BS_JAN_APR</td> <td>Number of bare-soil observations between 2022-01-01 and 2022-04-30</td> </tr> <tr> <td>BS_JAN_MAR</td> <td>Number of bare-soil observations between 2022-01-01 and 2022-03-31</td> </tr> <tr> <td>BS_OCT_DEC</td> <td>Number of bare-soil observations between 2021-10-01 and 2021-12-31</td> </tr> <tr> <td>BS_OCT_SEP</td> <td>Number of bare-soil observations between 2021-10-01 and 2022-09-30</td> </tr> <tr> <td>P_CROP</td> <td>The FOI label as predicted by the crop group model</td> </tr> <tr> <td>CR_P_SCORE</td> <td>The pseudoprobability of the crop-group prediction. A score close to 1 indicates that the model is very confident in the prediction</td> </tr> <tr> <td>D_GROUP</td> <td>Declared crop group</td> </tr> <tr> <td>DIST_CROP</td> <td>Most similar crop according to the distance marker</td> </tr> <tr> <td>DIST_SC00</td> <td>Distance marker score of a most similar crop</td> </tr> <tr> <td>DIST_SCORE</td> <td>Distance marker score of a FOI when compared to nearby FOIs with the same claim. A value close to 100 indicates that a FOI is not similar to other FOIs with the same claim.</td> </tr> <tr> <td>HOM_CLASS</td> <td>Homogeneous/Heterogeneous</td> </tr> <tr> <td>HOM_SCORE</td> <td>Homogeneity probability assigned by the homogeneity marker</td> </tr> <tr> <td>P_LAND_GR</td> <td>The FOI label as predicted by the land group model using crop groupings based on land use</td> </tr> <tr> <td>LGRP_SCORE</td> <td>The pseudoprobability of the crop-group prediction. A score close to 1 indicates that the model is very confident in the prediction.</td> </tr> <tr> <td>D_LAND</td> <td>Declared land group</td> </tr> <tr> <td>M_NDVI_A_J</td> <td>Mean NDVI value in interval 2022-04-01 till 2022-07-31</td> </tr> <tr> <td>M_NDVI_F_M</td> <td>Mean NDVI value in interval 2022-02-01 till 2022-03-31</td> </tr> <tr> <td>M_NDVI_J_A</td> <td>Mean NDVI value in interval 2022-01-01 till 2022-04-30 </td> </tr> <tr> <td>MW_FEB_JUN</td> <td>Number of mowing events between 2022-02-01 and 2022-06-30</td> </tr> <tr> <td>MW_OCT_SEP</td> <td>Number of mowing events between 2021-10-01 and 2022-09-30</td> </tr> <tr> <td>MW_ALL</td> <td>Number of mowing events in the observation period</td> </tr> <tr> <td>SIM_CROP</td> <td>Most similar crop according to similarity marker</td> </tr> <tr> <td>SIM_SC00</td> <td>Similarity marker score of a most similar crop</td> </tr> <tr> <td>SIM_SCORE</td> <td>Similarity marker score of a FOI when compared to nearby FOIs with the same claim. A value close to 100 indicates that a FOI is not similar to other FOIs with the same claim.</td> </tr> </tbody> </table> <p> </p>
EO-based area monitoring markers computed over the Lithuanian pilot region (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), the following EO-based monitoring marker maps were released over the two pilot regions, containing the results produced from a set of image analysis and machine learning techniques. The latest explores the benefits of Copernicus's multispectral high-resolution Sentinel-2 data acquired from 01-01-2022 until 04-07-2021 and provides tailored information for the needs of European paying agencies (e.g. CAPO and NPA), expressed with the following markers.</p> <ol> <li> <p><strong>Mowing marker:</strong> used to detect mowing events on meadow/grass like Features Of Interest (FOI)</p> </li> <li> <p><strong>Mean-NDVI marker:</strong> used to detect erroneous claims with no vegetation</p> </li> <li> <p><strong>Homogeneity marker:</strong> used to determine if a parcel geometry consists of a single crop or if multiple things are growing on the parcel</p> </li> <li> <p><strong>Bare soil marker:</strong> used to detect observation where bare soil is present on the feature of interest. This indicates agricultural activity on the FOI (plowing, harvest)</p> </li> <li> <p><strong>Similarity and distance markers:</strong> used to give additional context to the crop classification and to detect erroneous claims</p> </li> <li> <p><strong>Land marker:</strong> used to detect the land type and non-productive EFAs of the FOI</p> </li> <li> <p><strong>Crop-type marker:</strong> used to detect the specific crop growing on the FOI</p> </li> </ol> <p>This dataset is comprised of one geopackage file, the <em>"markers_summary.gpkg", which</em> was computed for the Lithuanian pilot region<em>. </em>Descriptions are given below.</p> <p><strong>Markers summary dataset: </strong>There is a total of **1074460** FOIs for the complete country GSAA dataset. Out of these, markers are computed on **996028** FOIs that contain more than 1 Sentinel-2 pixel.</p> <table> <caption><strong>Description of the information contained in the corresponding "markers summary" dataset</strong></caption> <thead> <tr> <th scope="col">Attribute name </th> <th scope="col">Description </th> </tr> </thead> <tbody> <tr> <td>POLY_ID </td> <td>Reference ID of the polygon</td> </tr> <tr> <td>CROP_LABEL</td> <td>Declared crop group</td> </tr> <tr> <td>S2_all_observations_count</td> <td>Count of all observations </td> </tr> <tr> <td>S2_valid_observations_count</td> <td>Count of all valid observations</td> </tr> <tr> <td>S2_pixel_count</td> <td>Number of S2 pixels within FOI</td> </tr> <tr> <td>declared_as</td> <td>Declared crop group (v1)</td> </tr> <tr> <td>classification_score</td> <td>The pseudoprobability of the crop-group (v1) prediction. A score close to 1 indicates that the model is very confident in the prediction</td> </tr> <tr> <td>classification </td> <td>The FOI label as predicted by the crop group (v1) model</td> </tr> <tr> <td>crop_declared_as_2</td> <td>Declared crop group (v2)</td> </tr> <tr> <td>crop_classification_score_2</td> <td>The pseudoprobability of the crop-group (v2) prediction. A score close to 1 indicates that the model is very confident in the prediction</td> </tr> <tr> <td>crop_classification_2</td> <td>The FOI label as predicted by the crop group (v2) model</td> </tr> <tr> <td>mowing_event_count</td> <td>Number of mowing events detected</td> </tr> </tbody> </table>
Dataset for the publication "The TACS Model: Understanding Teachers' Adoption of Computer Science Pedagogical Content in Primary School"
<p>This dataset contains the quantitative teacher data used to analyse an in service teacher training program for Computer Science that took place from September 2019 to March 2020 in the Canton Vaud in Switzerland. Approximately 180 teachers from the the 5th and 6th grade in primary school (ages 9-11) participated in 3 days of training sessions. At the end of each training session, teachers were asked to fill in a web-based questionnaire providing information relating to their perception of the training sessions and adoption of the computer science activities. The surveys were analysed from three perspectives which are detailed in the corresponding article (the professional development program's perspective, the activities' perspective, the teacher's perspective). The present repository thus contains three csv files, one per analysis. A README is included and provides additional information regarding :</p> <p>- the requirements for re-use. </p> <p>- the survey instrument used</p> <p>- the specific content of the 3 csv files</p>
Cross-sectional images from x-ray computed tomography (XCT) of conserved archaeological samples
<p>The repository contains cross-sections of 83 wood samples derived from X-ray computed tomography (CT) data. The samples are a part of the LEIZA reference collection, which were created within the framework of the project "Mass Finds in Archaeological Collections", which was funded by the "Kulturstiftung des Bundes" and the "Kulturstiftung der Länder" from 15.04.2008 to 31.12.2011 as part of the "Program for the Conservation and Restoration of Mobile Cultural Property" (KUR, see www.rgzm.de/kur).</p> <p>Around 10 years later, during the CuTAWAY project (ConservaTion And Wod AnalYses), the wood samples were digitized using an in-house laboratory X-ray CT system (Diondo d2, Germany) at HSLU with a nominal voxel size between 27 and 44 μm in order to analyse the structure of the interior. You can download the cross-sectional images of the data here. The 3D data acquisition was carried out during November 2019 - April 2021.</p> <p>The CuTAWAY project was funded by the German Research Association (DFG) and the Swiss National Science Foundation (SNSF) from 2019 to 2023 (CuTAWAY - Conservation and Wood Analyses, DFG - 416877131 and SNSF - 200021E_183684).</p>
Data of the article Analysis of the self-archiving policies of journals in the highest rank category of the Finnish journal classification system within computer science, physics and electronic engineering
<p>The publication forum level three journals representing the three fields of science of computer science, computer science and electrical engineering were identified by utilizing the MinEdu field search filter while searching for the top-ranked journals from the publication channel search (https://www.tsv.fi/julkaisufoorumi/haku.php?lang=en), which is based on Field of Science, Statistics Finland classification (https://www.stat.fi/meta/luokitukset/tieteenala/001-2010/index_en.html). The data were extracted during august 2017 consists of total of 127 individual journals. It is worth noting that circa 30 journals were classified into more than one fields of sciences under scrutiny. First, the journals were divided into representing gold and hybrid model journals. Second, green open access policies of the identified hybrid journals were analyzed using Laakso’s (2014) publisher policy coding framework. Also publishers of the individual journals were identified and subsequently added to the data.</p> <p>NOTE! The data includes the shortest embargo to either institutional or subject repositories. For example, Elsevier had no embargo to opening accepted manuscripts from arXiv subject repository and thus no embargoes to Elsevier's journals are included within this datasheet.</p> <p>Data is in CSV. format</p> <p> </p> <p> </p>
Zinc Doped Zeolite 13X DIAD X-Ray Computed Tomography - 0.54 micron pixel size RAW
<p>This repository contains raw data for the zinc-doped zeolite 13X sample imaged on the DIAD beamline at Diamond Light Source. Data is stored as a .nxs file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 0.54 microns. A script containing the savu process list and code used to perform the 3D reconstruction is provided.</p> <p>A detailed data descriptor pre-print can be found at https://arxiv.org/abs/2409.07322#</p> <p> </p> <p>The size of the .h5 file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 43334_raw.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 1.625 micron pixel size
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 1.625 microns.</p> <p>This data is one of four resolutions obtained.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p> <p>The size of the .h5 file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 169067_recon.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Zinc Doped Zeolite 13X DIAD X-Ray Computed Tomography - 0.54 micron pixel size
<p>This repository contains processed data for the zinc-doped zeolite 13X sample imaged on the DIAD beamline at Diamond Light Source. Data is stored as a .nxs file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 0.54 microns. A script containing the savu process list and code used to perform the 3D reconstruction is provided.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p> <p>The size of the .h5 file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 43334_recon.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</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.