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zenodo48/100

EO-based area monitoring markers computed over the Lithuanian pilot region (2021)

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. <ol> <li> Mowing marker: used to detect mowing events on meadow/grass like Features Of Interest (FOI) </li> <li> Mean-NDVI marker: used to detect erroneous claims with no vegetation </li> <li> Homogeneity marker: used to determine if a parcel geometry really consists of a single crop or if there are multiple things growing on the parcel </li> <li> Bare soil marker: used to detect observation where bare soil is present on the feature of interest. This indicates agricultural activity on the FOI (plowing, harvest) </li> <li> Similarity and distance markers: used to give additional context to the crop classification and to detect erroneous claims </li> <li> Land marker: used to detect the land type and non-productive EFAs of the FOI </li> <li> Crop-type marker: used to detect the specific crop growing on the FOI </li> </ol> This dataset is comprised of the two subsequent geopackage files, (i) the "test_area_markers_summary.gpkg" and (ii) "crop_marker_summary_full_country.gpkg", which were computed for the Lithuanian pilot region.  Descriptions are given below. Test area markers dataset: 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. <table> <caption>Description of the information contained in the corresponding "test area markers" dataset</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 χ2 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> Crop marker full country dataset: 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. <table> <caption>Description of the information contained in the corresponding "crop marker full country" dataset</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>

opencc-by-4.0Sep 2022View details →

zenodo48/100

EO-based area monitoring markers computed over the Cypriotic pilot region (2021)

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. <ol> <li> Mowing marker: used to detect mowing events on meadow/grass like Features Of Interest (FOI) </li> <li> Mean-NDVI marker: used to detect erroneous claims with no vegetation </li> <li> Homogeneity marker: used to determine if a parcel geometry really consists of a single crop or if there are multiple things growing on the parcel </li> <li> Bare soil marker: used to detect observation where bare soil is present on the feature of interest. This indicates agricultural activity on the FOI (plowing, harvest) </li> <li> Similarity and distance markers: used to give additional context to the crop classification and to detect erroneous claims </li> <li> Land marker: used to detect the land type and non-productive EFAs of the FOI </li> <li> Crop-type marker: used to detect the specific crop growing on the FOI </li> </ol> This dataset is comprised of the geopackage file "markers_summary.gpkg", which was computed for the Cypriotic pilot region. Descriptions are given below. Markers summary dataset <table> <caption>Description of the information contained in the corresponding "markers summary" dataset</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> Land use types: 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) </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 χ2 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>

opencc-by-4.0Sep 2022View details →

zenodo48/100

Super-resolution EO-based area monitoring markers computed over the Lithuanian pilot region (2022)

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. The goal was to enhance the outputs of the area monitoring markers and especially in the monitoring of small (i.e. 100m2), narrow and elongated parcels.  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.  <ol> <li> Mowing marker: used to detect mowing events on meadow/grass like Features Of Interest (FOI) </li> <li> Similarity and distance markers: used to give additional context to the crop classification and to detect erroneous claims </li> <li> Crop-type marker: used to detect the specific crop growing on the FOI </li> </ol> This dataset is comprised of one geopackage file, the "S2SR-study-geopackage.gpkg", which was computed for the Lithuanian pilot region. Descriptions are given below. Super-resolution Markers dataset: Markers were computed for 16872 FOIs that contain less than 1 Sentinel-2 pixel, using signals from 2022-01-01 until 2022-08-01.  <table> <caption>Description of the information contained in the corresponding "Super-Resolution markers" dataset</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>

opencc-by-4.0Oct 2022View details →

zenodo48/100

EO-based area monitoring markers computed over the Cypriotic pilot region (2022)

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. <ol> <li> Mowing marker: used to detect mowing events on meadow/grass like Features Of Interest (FOI) </li> <li> Mean-NDVI marker: used to detect erroneous claims with no vegetation </li> <li> Homogeneity marker: used to determine if a parcel geometry consists of a single crop or if multiple things are growing on the parcel </li> <li> Bare soil marker: used to detect observation where bare soil is present on the feature of interest. This indicates agricultural activity on the FOI (plowing, harvest) </li> <li> Similarity and distance markers: used to give additional context to the crop classification and to detect erroneous claims </li> <li> Land marker: used to detect the land type and non-productive EFAs of the FOI </li> <li> Crop-type marker: used to detect the specific crop growing on the FOI </li> </ol> This dataset is comprised of the geopackage file "markers_summary.gpkg", which was computed for the Cypriotic pilot region. Descriptions are given below. Markers summary dataset <table> <caption>Description of the information contained in the corresponding "markers summary" dataset</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>

opencc-by-4.0Oct 2022View details →

zenodo48/100

EO-based area monitoring markers computed over the Lithuanian pilot region (2022)

opencc-by-4.0Oct 2022View details →

zenodo48/100

The potential of low-cost UAVs and open-source photogrammetry software for high-resolution monitoring of alpine glaciers: A case study from the Kanderfirn (Swiss Alps)

This dataset contains high-resolution orthophotos (5 x 5 cm) and digital surface models (25 x 25 cm) of the Kandernfirn Glacier located in the Swiss Alps. Aerial images were aquired with a self-developed fixed-wing Unmanned Aerial Vehicle during ten surveys on five different days in 2017 and 2018. The open-source photogrammetry software OpenDroneMap (version 0.4.1) was used for image processing. The orthophotos and digital surface models were validated through dGNSS point measurements of ground control points. Please refer to the corresponding paper for information on the horizontal and vertical accuracy of the files.

opencc-by-4.0May 2019View details →

zenodo48/100

Regional Datasets for Air Quality Monitoring in European Cities

The primary environmental health threat in the WHO European Region is air pollution, impacting the daily health and well-being of its citizens significantly. To effectively understand the impact, and dynamics of air quality a detailed investigation of different environmental, weather, and land cover indices is appropriate. To this end, this paper introduces three European cities’ spatiotemporal datasets, customized for air pollution monitoring at a regional level. The datasets are composed of major air quality, weather measurements and land use information. The duration is approximately from 2020 to 2023 with an hourly temporal resolution and a spatial resolution of 0.005◦. The temporal and spatiotemporal datasets are publicly released aiming to provide a solid foundation for researchers, analysts, and practitioners to conduct in-depth analyses of air pollution dynamics.

opencc-by-4.0Jan 2024View details →

zenodo48/100

MUDDAT: A SENTINEL-2 IMAGE-BASED MUDDY WATER BENCHMARK DATASET FOR ENVIRONMENTAL MONITORING.

This is a dataset for mapping muddy waters based on Sentinel-2 (L2A products) satellite imagery. The image data are saved as GeoTIFF files and metadata files are provided in json format. There are 19 images in total, based on 16 distinct European Areas of Interest (AOIs), covering a total of 9 countries such as: <ul> <li>Greece</li> <li>Italy</li> <li>France</li> <li>Spain</li> <li>Belgium</li> <li>UK</li> <li>Sweden</li> <li>Finland and</li> <li>Serbia</li> </ul> From the Sentinel-2 L2A products were extracted 10 spectral bands and then resampled to a 10m spatial resolution. All spectral bands used can be found in the Metadata/Source files. The annotated images comprise 3 classes, "Non-muddy", "Muddy" and "Ambiguous". More details about the annotation methodology can be found on the accepted abstract (file: <a href="../api/records/11220437/draft/files/Accepted_Abstract_03_15_2024.pdf/content" target="_blank" rel="noopener noreferrer">Accepted_Abstract_03_15_2024.pdf</a>) or the published paper, that you can find here: <a href="https://doi.org/10.1109/IGARSS53475.2024.10642051" target="_blank" rel="noopener">10.1109/IGARSS53475.2024.10642051</a>.

opencc-by-4.0May 2024View details →

zenodo48/100

Sentinel-2 Satellite Imagery Based Forest Fire Monitoring

Forest Fire in Villages near Berlin - Normalized Burn Ratio (NBR) Villages in Treuenbrietzen (Frohnsdorf, Klausdorf and Tiefenbrunnen) around 50 km southwest of Berlin have been severely affected by recent unpredicted wildfire and the size of the burned area is about of 400 hectares, which started to spread on 23rd of August, 2018. More than 500 people had to leave their homes as a result of the fire in Treuenbrietzen and the burning fire with dense smoke continued for days. This year Europe has faced a long hot dry summer with almost no rain and as a consequence some European countries like Germany are on high alert regarding possible forest fires.

opencc-by-4.0Feb 2019View details →

zenodo48/100

Outdoor monitoring data of an Insolight B-series module - CPV sub-module

Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. It was the basis of its power rating at CSTC and has informed several scientific and technical articles like the oral presentation by Gaël Nardin et al. "Towards Industrialization of Planar Microtracking Photovoltaic Panels" at the 15th International Conference on Concentrator Photovoltaics (CPV-15), held between 25 and 27 March of 2019 in Fes, Morocco.  Monitoring campaign: <ul> <li>Location: 40.453°N, -3.727°E. <a href="https://www.google.com/maps/place/40%C2%B027'11.6%22N+3%C2%B043'37.3%22W/@40.453215,-3.7275722,142m/data=!3m2!1e3!4b1!4m13!1m6!3m5!1s0x0:0xc636231f90c3bbeb!2sInstituto+de+Energ%C3%ADa+Solar!8m2!3d40.4531766!4d-3.7269107!3m5!1s0x0:0x0!7e2!8m2!3d40.4532142!4d-3.7270248">Instituto de Energía Solar</a>, Universidad Politécnica de Madrid. 28040 Madrid, Spain.</li> <li>Starting date: 21 November 2018</li> <li>End date: 14 December 2018</li> </ul> Description of data files: <ul> <li>Data files format: tab-separated text file; headers in first row.</li> <li>I-V parameters: single file "Insolight Preseries outdoor monitoring - IES rooftop - UPM.txt". Headers: <ul> <li>Date (DD/MM/YYYY)    </li> <li>Time (HH:MM:SS): time in CET / UTC+1</li> <li>Pmp (W): peak power</li> <li>Vmp (V): voltage at maximum power point</li> <li>Imp (A): current at maximum power point</li> <li>Isc (A): short-circuit current</li> <li>Voc (V): open-circuit voltage</li> <li>FF: fill factor</li> <li>Tair (°C): ambient temperature    </li> <li>Tlens (°C): temperature at the aperture plane</li> <li>GNI (W/m2): global normal irradiance at the aperture plane as measured with a pyranometer. Spectral range: 305 – 2800 nm.</li> </ul> </li> <li>Weather data: daily files "geonica2018_**_**.txt". Headers: <ul> <li>yyyy/mm/dd: date</li> <li>hh:mm: time in GMT/UTC</li> <li>V_Viento (m/s): wind speed</li> <li>D_Viento (°N): wind direction</li> <li>Temp_Air(°C): ambient temperature</li> <li>Rad_Dir(W/m2): direct normal irradiance as measured by a Normal Incidence Pyrheliometer from Eppley. Spectral Range: 250-3000 nm. Field of view: 5°</li> <li>Ele_Sol (°): solar tilt</li> <li>Ori_Sol (°N): solar position azimuth</li> <li>Top (W/m2): direct normal irradiance as measured by a top component cell of a lattice-matched III-V triple-junction cell in the  <a href="http://solaraddedvalue.com/en/category/products/spectro-heliometer/">ICU-3J35</a> spectroheliometer from Solar Added Value. Spectral range: 300 - 680 nm. Field of view: 5.7º</li> <li>Mid (W/m2): direct normal irradiance as measured by a middle component cell of a lattice-matched III-V triple-junction cell in the  <a href="http://solaraddedvalue.com/en/category/products/spectro-heliometer/">ICU-3J35</a> spectroheliometer from Solar Added Value. Spectral range: 680 - 900 nm. Field of view: 5.7º</li> <li>Bot (W/m2): direct normal irradiance as measured by a bottom component cell of a lattice-matched III-V triple-junction cell in the  <a href="http://solaraddedvalue.com/en/category/products/spectro-heliometer/">ICU-3J35</a> spectroheliometer from Solar Added Value. Spectral range: 900 - 1800 nm. Field of view: 5.7º</li> <li>Cal_Top: n/a</li> <li>Cal_Mid: n/a</li> <li>Cal_Bot: n/a</li> <li>Pres_Aire: n/a</li> </ul> </li> </ul>

opencc-by-4.0May 2019View details →

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GRTS master sample for habitat monitoring in Flanders

Spatially balanced sample for the whole of Flanders and the Brussels Capital Region based on the Generalized Random-Tessellation Stratified (GRTS) method (Stevens and Olsen, 2004). The sample consists of a grid of 32 meter x 32 meter cells, each having a unique ranking number. This so-called master sample is used as a basis to draw samples for different Natura 2000 habitat types in Flanders. A sample with sample size n for a certain habitat type is selected as follows: (1) select all grid cells of the master sample that overlap with the sampling frame of the target habitat type and (2) select the n grid cells with the lowest ranking number.

opencc-zeroMar 2019View details →

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Voltage and current data for IEC 62600-30 power quality monitoring from the Mutriku Wave Power Plant and Lir National Ocean Test Facility electrical laboratory

This Technical Note describes the electrical data collected from the Mutriku Wave Power Plant (MWPP) and the Lir National Ocean Test Facility (NOTF) electrical laboratory at the MaREI Centre in the Environmental Research Institute, at University College Cork. In summary, the electrical data collect is for the purpose of analysing the power quality output of a Wave Energy Converter (WEC). The data includes voltage and current signals from the output of a WEC sampled at 15 kHz from the MWPP and a WEC emulator sampled at 20 kHz from the Lir NOTF electrical laboratory. There are 24 datasets from the MWPP taken at various sea state conditions, and there are 56 datasets from the Lir NOTF which are taken with at various sea state conditions, with different control laws, and grid connections. This data is published for purpose of power quality analysis and comparison for future tests. For OPERA, power quality analysis was performed as part of WP5 T5.2 and T5.5, and presented in depth in Deliverables D5.2 and D5.4. See accompanying technical note for more Information.

opencc-by-4.0Jul 2019View details →

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Monitoring Technical Debt in an Industrial Setting

The dataset includes the answers on a survey with 60 software engineers (i.e., architects, developers, etc.) working for 11 software development companies located in 9 countries, to understand their needs for Technical Debt Management. The questionnaire is organized into an introductory section and three main ones. It begins with some demographic information (Name of Company and Role in the Company). Next, participants  are asked to rate in a Likert scale (1) a group of questions based on the usefulness of Technical Debt (TD) principal indicators, and (2) a group of questions based on the usefulness of TD interest indicators, and then, they are asked (3) to consider the optimal strategy for mitigating TD. In the beginning of each Section some basic TD definitions have been provided, so as to establish a common understanding and terminology among participants.

opencc-by-4.0Aug 2019View details →

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Meiofauna higher taxa abundance data from a monitoring study of sandy beach meiofauna before and after sand nourishment (Ahrenshoop, Baltic Sea)

We provide abundance data for meiofauna taxa determined from sediment samples collected on the sandy-beach water line of Ahrenshoop (Baltic Sea). Five sampling stations lay within the zone impacted by the sand nourishment between the boundary of the nature reserve in the north east and a site just north of the breakwater (AH01–AH05). An unaffected reference station was located south of Ahrenshoop (close to Niehagen) at the end of the road Pappelallee (PAP). Samples were collected at four dates. The first sampling was carried out before the sand nourishment took place (T0: 14 September 2021). Three samplings were realised after the impact: T1 (23 March 2022), T2 (27 September 2022), and T3 (28 March 2023). Latitude and longitude of each sampling location per station were recorded at each sampling date using a hand-held GPS application on a mobile phone. At the stations sampling locations varied over time. Prior to the sand nourishment the beach was narrow due to sand erosion in previous years. After the nourishment the additional extent of the beach was approximately 40 m at sampling date T1. Subsequently, progressive sand erosion forced the sampling locations (situated at the water line) further inland at T2 and T3. Samples were taken from the beach-water interface (water line) in the middle of the area between two groynes. Plexiglass cores (inner core diameter 5.4 cm) were inserted vertically into the sediment down to 15 cm depth. Each core was sliced in 5 cm-layers (0–5, 5–10 and 10–15 cm). Sediment horizons were preserved in 96–99% ethanol. The organisms were extracted by decantation over a 32-μm sieve. The total number of individuals per taxon was counted and is presented as individuals per 10 cm2. In the framework of our monitoring, samples were primarily taken for a large-scale metabarcoding study on meiofauna communities. One core per station and sampling date was reserved for morphology-based community analyses. Here we present the results for the stations AH01, AH03, AH05, and PAP. We selected these stations because of their location at both ends and in the center of the impacted zone (AH01, AH03, AH05) and at the control site (PAP). The meiofauna (32–1000 µm) was mostly represented by Copepoda, Nematoda, Platyhelminthes, Gastrotricha, and some Annelida. We counted 27445 individuals in total, encompassing 10 higher taxa. We counted copepod nauplii separately due to their small body size. We defined the combined group “Plathyhelminthes+Diurodrilus sp.” because members of the annelid genus Diurodrilus sp. are not distinguishable from Platyhelminthes under the stereomicroscope. Here we present a Table on meiofauna higher taxa counts per 10cm2 (as xlsx and tab-delimited file; including metadata for each sample: event; date; latitude; longitude; station, core and sample ID; sediment depth). The meiofauna abundance data are part of a larger ecological study on the influence of sand nourishment on meiofauna communities, which included grain-size and metabarcoding analyses (see “related works”). Comment: Our study is related to but not funded by the project ECAS Baltic: Strategies of ecosystem-friendly coastal protection and ecosystem-supporting coastal adaptation for the German Baltic Sea Coast <a href="https://deutsche-kuestenforschung.de/ecas-baltic.html">https://deutsche-kuestenforschung.de/ecas-baltic.html</a>

opencc-by-4.0Aug 2024View details →

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Metabarcoding data (number of reads per operational taxonomic unit) from a monitoring study of sandy beach meiofauna before and after sand nourishment (Ahrenshoop, Baltic Sea)

We provide metabarcoding data (number of reads per operational taxonomic unit, OTU) determined from sediment samples collected on the sandy-beach water line of Ahrenshoop (Baltic Sea). Five sampling stations lay within the zone impacted by the sand nourishment between the boundary of the nature reserve in the north east and a site just north of the breakwater (AH01–AH05). An unaffected reference station was located south of Ahrenshoop (close to Niehagen) at the end of the road Pappelallee (PAP). Samples were collected at four dates. The first sampling was carried out before the sand nourishment took place (T0: 14 and 16 September 2021). Three samplings were realised after the impact: T1 (23 March 2022), T2 (27 September 2022), and T3 (28 March 2023). Latitude and longitude of each sampling location per station were recorded at each sampling date using a hand-held GPS application on a mobile phone. At the stations sampling locations varied over time. Prior to the sand nourishment the beach was narrow due to sand erosion in previous years. After the nourishment the additional extent of the beach was approximately 40 m at sampling date T1. Subsequently, progressive sand erosion forced the sampling locations (situated at the water line) further inland at T2 and T3. Samples were taken from the beach-water interface (water line) in the middle of the area between two groynes. Plexiglass cores (inner core diameter 5.4 cm) were inserted vertically into the sediment down to 15 cm depth. Each core was sliced in 5 cm-layers (0–5, 5–10 and 10–15 cm). Sediment horizons were preserved in 96–99% ethanol. Three cores (2 cores at T0) per sampling date were taken for metabarcoding analyses. The organisms were extracted by decantation over a 32-μm sieve. Genomic DNA was extracted from the filters using the DNeasy PowerSoil pro kit (Qiagen). Realtime-PCR was performed to amplify V1&V2, two hypervariable regions of 18S rDNA gene. The sequencing run was performed using the MiSeq Reagent Nanokit v2 (250 cycles paired end) on an Illumina MiSeq platform at the DZMB Metabarcoding lab in Wilhelmshaven, Germany. High-resolution amplicon sequence variants (ASVs) were obtained and compared to the NCBI database to assign taxonomic information to each ASV. The target meiofauna ASVs were further classified into operational taxonomic units (OTUs) with a 3% cut-off threshold using the statistical software R. Here, we present two Tables (as xlsx and tab-delimited files): (1) the taxonomic description of the 843 OTUs and their assigned ID number; (2) the number of reads per OTU per sample (including metadata for each sample: event; date; latitude; longitude; station, core and sample ID; sediment depth). The metabarcoding data are part of a larger ecological study on the influence of sand nourishment on meiofauna communities, which included grain-size and meiofauna abundances (see “related works”). Comment: Our study is related to but not funded by the project ECAS Baltic: Strategies of ecosystem-friendly coastal protection and ecosystem-supporting coastal adaptation for the German Baltic Sea Coast <a href="https://deutsche-kuestenforschung.de/ecas-baltic.html">https://deutsche-kuestenforschung.de/ecas-baltic.html</a>

opencc-by-4.0Jul 2024View details →

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Marceddi monitoring stations

The shape file represents the position and characteristics of the three water monitoring stations and the weather station foreseen within TransformAr project.  Here is the list of the monitoring instruments:  a)       monitoring station Marceddì lagoon composed by: -Weather station (Timing, Humidity, Wind speed, Wind direction, Rainfall intensity, Rainfall height, Temperature) -Hydrometer (Piezometric level sensor with Immersion) (internal) -Hydrometer (Piezometric level sensor with Immersion) (external) -Dedicated data logger b)      monitoring station San Giovanni Pond (SMART GATE area): -N.2 - multiparameter sensor (Dissolved oxygen level, Conductivity, pH, ORP, Temperature, turbidity) -Hydrometer (Piezometric level sensor with Immersion) -Dedicated data logger c)       monitoring station San Giovanni Pond (mouth area): -N.1 multiparameter sensor (Dissolved oxygen level, Conductivity, pH, ORP, Temperature, turbidity) -Hydrometer (Piezometric level sensor with Immersion) -Dedicated data logger

opencc-by-sa-4.0Jul 2024View details →

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STAR4BBS D3.2 Report on additional indicators of monitoring system_Appendix 6.2 System Level Matrix dataset

This dataset contains the final set of indicators selected for the system level of the new monitoring system. The data is part of the D3.2 "Report on additional indicators of monitoring system". The indicators are organised by category, principles, criteria, requirements and references.

opencc-by-4.0Jul 2024View details →

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SFEM Dataset for Structural Health Monitoring

The data is generated through Spectral Finite Element Methods (SFEM) solver for Ultrasonic Guided Wave based Structural Health Monitoring. The data is used in repository "https://github.com/mahindrautela/DINS-SHM" and the paper "Ultrasonic guided wave based structural damage detection and localization using model assisted convolutional and recurrent neural networks".

opencc-by-4.0Sep 2024View details →

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STAR4BBS D3.1 Report on sustainability indicators for the monitoring system based on LCA_Appendix C1

This appendix presents the full set of LCA indicators identified in D3.1 for the environmental pillar. These 56 indicators are the result of a search limited to highly relevant sources in the field (specified in D3.1) and were used for the final selection according to pre-established criteria.

opencc-by-4.0Oct 2024View details →

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STAR4BBS D3.1 Report on sustainability indicators for the monitoring system based on LCA_Appendix C3

This appendix presents the full set of LCA indicators identified in D3.1 for the social pillar. These 571 indicators are the result of the analysis of 43 relevant articles and were used for the final selection according to pre-established criteria.

opencc-by-4.0Oct 2024View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record