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Supplementary material 2: Definitions and Concepts from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390

Definitions and concepts in the context of the main paper.

Supplementary material 1: List of selected tools. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390

List of selected tools.

Figure 7. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390

Figure 7. - Mobile app for sporadic observations reporting.

Figure 2. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390

Figure 2. - The Plazi workflow (green) within EU BON.

opencc-by-4.0Apr 2024View details →

Calibrated and uncalibrated projection data from the paper "Assessing observational constraints on future European climate in an out-of-sample framework"

Individual uncalibrated and calibrated projections for each of the five methods (A-E) in the following folders: MethodA_proj/ MethodB_proj/ MethodC_proj/ MethodD_proj/ MethodE_proj/   Also included are the out-of-sample data from the "pseudo-observations" (taken from CMIP6 models) used for the verification (see paper for full details): FUTUREverif/

opencc-by-4.0Feb 2024View details →

FORSITE-Clim Europe: European-wide climate indicators for historical periods and climate projections at high resolution

<h2>Overview</h2> This meteorological data set consists of climatologies (climate indicators) on 30-year average basis for Europe and covers two historical periods as well as two periods for three selected climate scenarios with a high spatial resolution of less than 1 km. The two 30-year periods provided for the observations allow the analysis of the climate change that has already happened.  Resolution: 30x30 arcsec Projection: EPSG 4326 Extent for historical data: 10.67°W – 47.67°E, 33.68°N – 71.33°N Extent for scenario data: 10.67°W – 39.33°E, 33.68°N – 71.33°N Periods for historical data: 1961-1990 and 1991-2020 Periods for scenario data: 2036-2065 and 2071-2100 Format: GeoTIFF List of climatologies (climate indicators)   <table> <tbody> <tr> <td> # </td> <td> Short name </td> <td> Name </td> <td> Description </td> <td> Unit </td> <td> Yearly (Y) or monthly (M) </td> </tr> <tr> <td> 1 </td> <td> tasmin </td> <td> Average daily minimum temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 2 </td> <td> tasmax </td> <td> Average daily maximum temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 3 </td> <td> tas </td> <td> Average temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 4 </td> <td> tas_warmest_month </td> <td> Average temperature mean in the warmest month </td> <td> Calculation of the mean temperature over the climate period for all months and then selection of the highest value for the warmest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 5 </td> <td> tas_coldest_month </td> <td> Average temperature mean in the coldest month </td> <td> Calculation of the mean temperature over the climate period for all months and then selection of the lowest value for the coldest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 6 </td> <td> tasmin_coldest_month </td> <td> Average temperature minimum in the coldest month </td> <td> Calculation of the mean minimum temperature over the climate period for all months and then selection of the lowest value for the coldest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 7 </td> <td> tasmax_warmest_month </td> <td> Average temperature maximum in the warmest month </td> <td> Calculation of the mean maximum temperature over the climate period for all months and then selection of the highest value for the warmest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 10 </td> <td> GSL </td> <td> Average length of the growing season </td> <td> The growing season is the duration in days of the longest continuous period of days with an average temperature of at least 5°C. However, an earlier or later period of such warm days is included in the growing season if it lasts longer than the sum of all intervening cooler days </td> <td> days </td> <td> Y </td> </tr> <tr> <td> 12 </td> <td> GDD </td> <td> Average Growing Degree Days per year above 5°C </td> <td> Σ(Tmean – 5°C) per year.  </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 13 </td> <td> FD_first </td> <td> Average date of the first frost occurrence </td> <td> Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate </td> <td> day of year </td> <td> Y </td> </tr> <tr> <td> 14 </td> <td> FD_last </td> <td> Average date of the last frost occurrence </td> <td> Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate </td> <td> day of year </td> <td> Y </td> </tr> <tr> <td> 20 </td> <td> GLO_hori </td> <td> Average sum of global radiation </td> <td>   </td> <td> kWh </td> <td> Y, M </td> </tr> <tr> <td> 33 </td> <td> pr </td> <td> Average precipitation sum </td> <td>   </td> <td> mm </td> <td> Y, M </td> </tr> <tr> <td> 39 </td> <td> ET0 </td> <td> Average annual potential evapotranspiration </td> <td> Calculation according to FAO Penman-Monteith: fao.org/3/X0490E/x0490e08.htm </td> <td> mm </td> <td> Y, M </td> </tr> <tr> <td> 40 </td> <td> WBAL </td> <td> Average climatic water balance </td> <td> Precipitation minus potential evapotranspiration </td> <td> mm </td> <td> Y, M </td> </tr> </tbody> </table> <h2>Data sources</h2> The raw historical data is a combination or extension of daily CHELSA (Climatologies at high resolution for the earth’s land surface areas) with ERA5-Land to fully cover 1961-2020.  <ul> <li>CHELSA-W5E5 v1.0 (https://doi.org/10.5194/essd-15-2445-2023) for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), maximum temperature (tasmax) and minimum temperature (tasmin) for the period 1979-2016</li> <li>CHELSA V2.1 for climatological average monthly wind speed (sfcWind_01, ..., sfcWind_12) and for climatological mean vapor pressure deficit (vpd_01, ..., vpd_12)</li> <li>ERA5-Land for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), dew point (tds), and wind speed (sfcWind) for the period 1961-2020</li> <li>v2.0: WorldClim version 2.1 for climatological monthly minimum, maximum, and average temperatures.</li> </ul> Climate models from EURO-CORDEX (doi.org/10.1007/s10113-013-0499-2): <ul> <li>MPI-M-MPI-ESM-LR_rcp45_r1i1p1_CLMcom-CCLM4-8-17</li> <li>MPI-M-MPI-ESM-LR_rcp85_r1i1p1_CLMcom-CCLM4-8-17</li> <li>ICHEC-EC-EARTH_rcp85_r12i1p1_SMHI-RCA4</li> </ul>

opencc-by-4.0Dec 2021View details →

Fig. 9 in The "Spaghetti Project": the final identification guide to European Terebellidae (sensu lato) (Annelida, Terebelliformia)

Fig. 9. Type localities of European species for which a neotype is required.

opencc-by-4.0Jun 2022View details →

Electronic Supplementary Data to "Zika vector competence data reveals risks of outbreaks: the contribution of the European ZIKAlliance project"

Electronic Supplementary Data to "Zika vector competence data reveals risks of outbreaks: the contribution of the European ZIKAlliance project"

opencc-by-4.0Sep 2017View details →

Standardizing ID-Labels for seaweed samples used for chemical composition analyses and refinery processes in Nordic and European research projects

Introduction Seaweed samples can be divided into two groups: <ol> <li>Small samples (½-3 kg wet weight (ww)) often used for chemical content analyses including seasonal variation and testing different cultivation conditions or preliminary lab scale experiments on storage, extraction, separation, fermentation, etc.</li> <li>Larger samples (>3 kg ww) for lab- or pilot scale experiments on storage, extraction, separation, fermentation, etc.</li> </ol> Seaweed samples will always have the following information-tracks: <ol> <li>Sample Code: A ID containing the most important information and the sample code will follow the sampled biomass from harvest to final research results.</li> <li>Seaweed Processing Code: The sample code will be extended with 8 digits and 1 letter if processing of biomass occurs.</li> <li>Batch Number: A code describing details about the harvest and origin of the seaweed.</li> <li>Sample Overview: An Excel file describing all details about the sample: first sample code, then species, grinding, freezing/drying specifications, seeding and harvesting information, planed aim of the sample (e.g. polysaccharides), place stored, seaweed processing details, analyse results, etc. Maintained by the sample provider.</li> </ol>

openmit-licenseJun 2024View details →

EU project OCEAN:ICE Deliverable: D1.4 Gridded European circumpolar sea ice production fluxes

This deliverable is a new dataset of sea ice production (SIP) in Antarctic coastal polynyas, critical regions for sea ice formation and dense water formation. Using Earth Observation data and atmospheric reanalysis, we use a heat budget method to estimate the SIP. We use sea ice concentration (SIC) from passive microwave sensors and ECMWF ERA5 reanalysis for near-surface wind speed and surface air temperature. Comparison against previous literature shows broad consistency in spatial patterns and magnitude of ice production, with notable variations across larger polynyas. Despite simplifications and thereby increased uncertainties in the absolute ice production values, the data set provides valuable insights into the dynamics and variability of Antarctic polynya SIP for the period 1992-2023.

opencc-by-4.0Jun 2024View details →

European funded projects related to integrated robotic sensing

This repository contains the metadata of 1371 projects in tabular form (combinedProjectData.xlsx). The data was extracted from the European Union’s COmmunity Research and Development Information Service (CORDIS) repository. The dataset was used to support a review of the latest advancements in integrated robotic sensing. CORDIS was interrogated using a Boolean search, combining multiple chosen search terms using precise logical relationships, such as AND and OR. This search approach was used to obtain precise and relevant search results by specifying the relationships among the search terms, saving time and effort while minimising the likelihood of encountering irrelevant or unrelated material. The following Boolean search string was used: “(‘robot’ OR ‘robotic’ OR ‘robotically’ OR ‘roboti?ed’) AND (‘non-destructive’ OR ‘inspection’ OR ‘evaluation’ OR ‘NDT’ OR ‘NDE’ OR ‘sensing’ OR ‘sensor’)”. This resulted in searching projects whose title and short description (teaser) contained at least one of the words in the first set of brackets and at least one in the second set. Note that the “?” in ‘roboti?ed’ allowed looking for the presence of both the British English word “robotised” and the respective American English version “robotized”. Additionally, the search results were filtered according to the funding schemes. For the sake of reviewing the recent landscape, only projects funded through the HORIZON 2020 and HORIZON EUROPE schemes were considered. The described filtered search returned 1371 projects. The resulting metadata was extracted from the CORDIS repository for each of the found projects: the project start date, the end date, the total cost, the total EU contribution, the fields of science related to the project, the coordinating institution, and the participating institutions. The fields of science of each project are given as a list of strings detailing the fields of science related to the project. Each string shows a variable-depth hierarchy from the broadest classification to specific fields (e.g., “engineering and technology/materials engineering/composites”), following the hierarchical framework adopted by the European Commission. Finally, whereas each project has one and only one coordinator, it can have none, one or multiple participants. For the coordinator and each participant (if present), the following information was extracted: country of the coordinating/participating institution, amount of EU contribution received, amount of other funds available to the institution and the project outcome in terms of peer-reviewed journal papers, conference contributions and filed patents. Thus, the project metadata extracted from CORDIS was thoroughly analysed. The "fieldsOfScience_SunburstPlot.xlsx" file contains a sunburst chart that offers a lucid overview of the diverse scientific disciplines of the selected projects. The analysis of the fields of science strings has revealed a hierarchical depth going up to the seventh classification level, showing great permeance of robotic NDT and robotic sensing into numerous and specific fields.

opencc-by-4.0Jan 2020View details →

Dataset for TECHNICAL SOLUTIONS FOR INCREASING DER HOSTING CAPACITY IN DISTRIBUTION GRIDS IN THE CZECH REPUBLIC IN TERMS OF EUROPEAN PROJECT INTERFLEX

Data set for TECHNICAL SOLUTIONS FOR INCREASING DER HOSTING CAPACITY IN DISTRIBUTION GRIDS IN THE CZECH REPUBLIC IN TERMS OF EUROPEAN PROJECT INTERFLEX

opencc-by-4.0Jan 2020View details →

Dataset for Increasing DER Hosting Capacity in LV Grids in the Czech Republic in Terms of European Project InterFlex

Dataset for Increasing DER Hosting Capacity in LV Grids in the Czech Republic in Terms of European Project InterFlex

opencc-by-4.0Jan 2020View details →

Dataset for Vliv dobijeni elektromobilu na pomery v distribucni soustave from CK CIRED Tabor 2018 Analysis of Smart Technical Measures Impacts on DER and EV Hosting Capacity Increase in LV and MV Grids in the Czech Republic in Terms of European Project InterFlex from SEST2019 Analysis of Smart Technical Measures Impacts on DER and EV Hosting Capacity Increase in LV and MV Grids in the Czech Republic in Terms of European Project InterFlex from ISGT2019 Evropský projekt InterFlex from CK CIRED Tabor 2017

Dataset for Vliv dobijeni elektromobilu  na pomery v distribucni soustave from CK CIRED Tabor 2018 Analysis of Smart Technical Measures Impacts on DER and EV Hosting Capacity Increase in LV and MV Grids in the Czech Republic in Terms of European Project InterFlex from SEST2019 Analysis of Smart Technical Measures Impacts on DER and EV Hosting Capacity Increase in LV and MV Grids in the Czech Republic in Terms of European Project InterFlex from ISGT2019 Evropský projekt InterFlex from CK CIRED Tabor 2017

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Data from: Projected impacts of warming seas on commercially fished species at a biogeographic boundary of the European continental shelf

1. Projecting the future effects of climate change on marine fished populations can help prepare the fishing industry and management systems for resulting ecological, social and economic changes. Generating projections using multiple climate scenarios can provide valuable insights for fisheries stakeholders regarding uncertainty arising from future climate data. 2. Using a range of climate projections based on the Intergovernmental Panel on Climate Change A1B, RCP4.5 and RCP8.5 climate scenarios, we modelled abundance of eight commercially important bottom dwelling fish species across the Celtic Sea, English Channel and southern North Sea through the 21st century. This region spans a faunal boundary between cooler northern waters and warmer southern waters, where mean sea surface temperatures are projected to rise by 2 to 4ºC by 2098. 3. For each species, Generalised Additive Models were trained on spatially explicit abundance data from six surveys between 2001 and 2010. Annual and seasonal temperatures were key drivers of species abundance patterns. Models were used to project species abundance for each decade through to 2090. 4. Projections suggest important future changes in the availability and catchability of fish species, with projected increases in abundance of red mullet (Mullus surmuletus L.), Dover sole (Solea solea L.), John dory (Zeus faber L.) and lemon sole (Microstomus kitt L.) and decreases in abundance of Atlantic cod (Gadus morhua L.), anglerfish (Lophius piscatorius L.) and megrim (Lepidorhombus whiffiagonis L.). European plaice (Pleuronectes platessa L.) appeared less affected by projected temperature changes. Most projected abundance responses were comparable among climate projections, but uncertainty in the rate and magnitude of changes often increased substantially beyond 2040. 5. Synthesis and applications. These results indicate potential risks as well as some opportunities for demersal fisheries under climate change. These changes will challenge current management systems, with implications for decisions on target fishing mortality rates, fishing effort and allowable catches. Increasingly flexible and adaptive approaches that reduce climate impacts on species while also supporting industry adaptation are required.

opencc-zeroJul 2020View details →

opencc-by-4.0Nov 2020View details →

Scholarly publications reported by the projects funded by the European Union under the 7th Framework Programme.

N.B:  An updated dataset has been published by CORDIS on the European Union Open Data Portal  It can be found under the following link : <a href="https://data.europa.eu/data/datasets/cordisfp7projects">CORDIS - EU research projects under FP7 (2007-2013)</a> The documentation of the data quality process has been also updated  Mugabushaka, A,M, (2020). Linking Publications to Funding at Project Level: A curated dataset of publications reported by FP7 projects.  <a href="https://arxiv.org/abs/2011.07880">https://arxiv.org/abs/2011.07880</a> ------- Here we release the first version of a complete and curated dataset of scholarly publications reported by the projects funded by the European Union under the 7th Framework Programme.  For the creation of the dataset, we not only consolidated data from different reporting channels, we also undertook extensive quality assurance steps to:  <ol> <li> to validate reported records by systematically matching them to external authoritative sources and </li> <li>to assign them external identifiers, most notably digital object identifiers. </li> </ol> The process is described in the following ArXiv preprint:  Mugabushaka, A,M, (2020). Linking Publications to Funding at Project Level: A curated dataset of publications reported by FP7 projects. <a href="https://arxiv.org/abs/2011.07880">ArXiv 2011.07880 </a>   Dataset Description This version is provided as a csv file and contains 18 fields. id_project : project/grant identifier it corresponds to the project id (grant agreement number) provided in the project dataset in the European Open data portal     https://data.europa.eu/euodp/en/data/dataset/cordisfp7projects id_record : unique identifier of the records, created by the European Commission services id_dataset: initial reporting channel (please refer to the <a href="https://arxiv.org/abs/2011.07880">ArxIv pre-print </a>for explanations) the fields with the pre-fix "reported_" refer to the entries as provided by the Grant Holders in reporting systems the fields with the pre-fix "qa_" refer the links between the records and external authoritative sources . They are the results of the data quality process as described in the <a href="https://arxiv.org/abs/2011.07880">ArXiv pre-print</a>

opencc-by-4.0Nov 2023View details →

GEOLAB Project FROSPER: FROst heaving soils-Solar Panel foundations interaction in cold European Regions: an experimental study

The objective of the proposed project is to investigate the resilience of solar panels foundations in cold climates. The efficiency of solar panels is overall better in cold climates, when the direct sunlight is available, due to the lower temperature-induced dispersion. For this reason, large solar fields tend to be installed in cold regions, such as northern Europe or Canada. A solar field with e.g., 10 MW, requires a great number of solar panels (~6000) whose foundations are generally steel piles driven into the soil down to a depth of 2÷5 m below the ground surface. In the cold regions, the shallow layers of soil are periodically subjected to freezing thus to the frost-heaving phenomena. The latter can increase the risk of uplift failure mechanism of the pile foundation compromising the exercise of the entire solar panel row.This project aims to investigate the interaction between saturated soils and solar panel foundations under frozen conditions in scaled centrifuge models. The steel piles will be at first driven into the saturated soil sample, then a set of the freezing-thawing cycles will be reproduced in the centrifuge. Possible practical interventions to reduce the soil frost-heaving effects will also be explored, as the use of protective insulating mantel at the ground surface all around the head of the pile.This project will represent a breakthrough in the understanding of the solar panel foundations behaviour, allowing a deeper insight into possible uplift failure mechanisms. The motivation of the study lies in the reduction of the costs and in the resilience improvement of critical infrastructures for the generation of energy in the EU territories.

opencc-by-4.0Feb 2022View details →

Calculation of the GHG emissions of a European research project on electrified vehicles

Open the record for dataset details and reuse information.

opencc-by-4.0Dec 2024View details →

ROS2 bagfiles associated to the European project Robs4Crops

This project contains multiple datasets in the form of ROS2 bagfiles, recorded during various agricultural robotics experiments in diverse environments: vineyards and apple orchards. Each dataset includes sensor data collected from agricultural vehicles (two retrofitted tractors and a differential robot called Carob) equipped with state-of-the-art perception, localization, and navigation systems.   The datasets include:   - Camera data: RGB images, depth information, and camera calibration details. - LiDAR data: 3D point clouds from the environment. - IMU data: acceleration and angular rates.   - GNSS data: Global positioning for outdoor localization in format WGS84. - Transformations: Static and dynamic transformations between sensor frames and vehicle base frames. Each experiment contains a readme.docx file that documents the sensor configurations, frame conventions, and trajectories followed during the experiment and other relevant information to understand how to use the data in the proper dataset.