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777 results for “EU”
PMa_000104_F_Eu
<u>File Name</u>: PMa_000104_F_Eu.jpg <br><u>Sublocation</u>: Château d'Eu <br><u>Location</u>: Eu <br><u>Province</u>: Normandie, Seine-Maritime <br><u>Country</u>: France <br><u>Header</u>: Oudenaards wandtapijt, "Amarilli en Mirtillo die worden gekroond na de zoenwedstrijd", eerste helft 18e eeuw, 6 kettindraden per cm, 292x286cm <br><u>Description</u>: Palace (Château d'Eu) The museum (Musée Louis-Philippe du château d'Eu) Flemish tapestry Amarilli and Mirtillo crowned after the kissing contest (Amarilli en Mirtillo die worden gekroond na de zoenwedstrijd) 292x286cm 18th century Made in Oudenaarde <br><u>Keywords</u>: Cultural heritage, Eu (Seine-Maritime), Europe, France, Museum/private collection, Normandie, Seine-Maritime, Tapestry, Techniques <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert; <br>
PMa_000107_F_Eu
<u>File Name</u>: PMa_000107_F_Eu.jpg <br><u>Sublocation</u>: Château d'Eu <br><u>Location</u>: Eu <br><u>Province</u>: Normandie, Seine-Maritime <br><u>Country</u>: France <br><u>Header</u>: Oudenaards wandtapijt, eerste helft 18e eeuw, 6 kettindraden per cm, <br><u>Description</u>: Palace (Château d'Eu) The museum (Musée Louis-Philippe du château d'Eu) Flemish tapestry 18th century <br><u>Keywords</u>: Cultural heritage, Eu (Seine-Maritime), Europe, France, Museum/private collection, Normandie, Seine-Maritime, Tapestry, Techniques <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert; <br>
PMa_000103_F_Eu
<u>File Name</u>: PMa_000103_F_Eu.jpg <br><u>Sublocation</u>: Château d'Eu <br><u>Location</u>: Eu <br><u>Province</u>: Normandie, Seine-Maritime <br><u>Country</u>: France <br><u>Header</u>: Oudenaards wandtapijt, "Amarilli en Mirtillo die begeleid door Amor, elkaar de hand reiken", eerste helft 18e eeuw, 6 kettindraden per cm, ar de hand reiken). 294x280cm <br><u>Description</u>: Palace (Château d'Eu) The museum (Musée Louis-Philippe du château d'Eu) Flemish tapestry Amarillis and Mirtillo accompanied by amor join hands (Amarilli en Mirtillo die, begeleid door Amor, elkaar de hand reiken) 294x280cm 18th century Made in Oudenaarde <br><u>Keywords</u>: Cultural heritage, Eu (Seine-Maritime), Europe, France, Museum/private collection, Normandie, Seine-Maritime, Tapestry, Techniques <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert; <br>
A Novel Crop Shortlisting Method for Sustainable Agricultural Diversification Across EU (Italy)
<p>In order to shortlist possible options from a pool of 2700 crops, a crop-climate-soil matching ex-ercise was performed across Italian territory and crops with more than 70% suitability where chosen for further analysis. In the second phase, a multicriteria ranking index was employed to assign ranks to chosen crops of 4 main types; (i) cereals and pseudocereals, (ii) legumes, (iii) starchy roots/ tubers and (iv) vegetables. In order to provide a comprehensive analysis, major crops that are grown in the region where also included in the analysis. The results of evaluation of 4 major criteria (a) calorie and nutrition demand b) functions and uses c) availability and acces-sibility to their genomic material d) possession of adaptive traits, and e) physiological traits) re-vealed the potential for teff, faba bean, cowpea, green arrow arum, Jerusalem artichoke, Fig-leaved Gourd and Watercress. </p>
Dataset of regional NRSS available for producing BBFs in the EU
<p>The data files contain information regarding the calculation of nutrient rich side stream (NRSS) quantities and their nutrient contents. The data is produced within WP1 of LEX4BIO project.</p> <p>The full description of data sources and calculations is available: https://lex4bio.eu/wp-content/uploads/2022/09/LEX4BIO_D1.1_WP1-1.pdf</p> <p>Data summaries are available: https://px.luke.fi/PxWeb/pxweb/en/maatalous/maatalous__biomassa/</p>
Life cycle inventories for the article: Circular Battery Production in the EU: Insights from integrating Life Cycle Assessment into System Dynamics Modeling on Recycled Content and Environmental Impacts
<p>This repository provides the unregionalized life cycle inventories to the paper "<span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a>".</p> <h2>Contents</h2> <p>The repository is split into 2 parts and comprises the following files:</p> <p><strong>01_production: </strong>contains the necessary life cycle inventories for battery production.</p> <ul> <li><strong>01_primary</strong>: contains the life cycle inventories for battery production from primary materials.</li> <li><strong>02_secondary</strong>: contains the life cycle inventories for battery production from secondary materials.</li> <li><strong>03_active_material</strong>: contains the life cycle inventories for the active battery materials from primary materials.</li> <li><strong>04_active_material</strong>: contains the life cycle inventories for the active battery materials from secondary materials.</li> </ul> <p> </p> <p><strong>02_recycling: </strong>contains the necessary inventories for battery recycling.</p> <ul> <li><strong>01_process</strong>: contains the life cycle inventories for battery recycling.</li> <li><strong>02_intermediate</strong>: contains the life cycle inventories for the intermediate system for battery recycling.</li> <li><strong>03_output</strong>: contains the life cycle inventories for the resulting substances from battery recycling.</li> </ul> <h2>Summary</h2> <p>These files allow to reproduce the results of our study. Each file contains the life cycle inventory of one distinct battery capacity (20, 45, 68, 85, 95, 100 kWh) with a specific cell chemistry (LFP, NCA, NMC333, NMC532, NMC622, NMC811, NMC955) for battery production (based on Knehr et al. 2022) or for battery recycling (based on Blömeke et al. 2023).</p> <h2>Related publication</h2> <p>More details on the scientific context is provided in the publication itself:</p> <p><span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a></p> <h2>Funding</h2> <p>This publication (Raphael Ginster and Steffen Blömeke) was created within the Research Training Group CircularLIB, supported by the Ministry of Science and Culture of Lower Saxony with funds from the program zukunft.niedersachsen of the Volkswagen Foundation (MWK | ZN3678).</p> <p>The publication on which this dataset is based were funded by the German Federal Ministry of Education and Research within the Competence Cluster Recycling & Green Battery (greenBatt) under the grant numbers 03XP0302A (Christian Scheller) and 03XP0331A (Jan-Linus Popien). The authors are responsible for the contents of this publication.</p>
International and EU funding in the eastern neighbourhood (2005-2022). REDEMOS Dataset 3.2
<p>International, EU and EU Member States’ funding for democracy, human rights, gender equality, the rule of law and good governance in the Eastern Neighbourhood, between 2005 and 2022.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Wave 1 Questionnaires: Czech Republic
<p>This dataset was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. Work Package 7 of this project aims to test the processes in youth’s construction of active EU citizenship on various social and psychological levels. The main file contains quantitative data from the first wave of the longitudinal survey on adolescents and young adults (age 15-26). Data collection was carried out in the Czech Republic (regions Prague, South Moravian, Moravian-Silesian, Pardubicky, Vysocina) from October to December 2016. The supplementary files contain national translations of the questionnaire for the younger (15-19) and the older (20-26) subgroups.</p>
Projected fresh water use from the European energy sector on NUTS2 level by 2050 following EU Energy Reference Scenario 2016
<p>The dataset contains projections of fresh water withdrawal and consumption from the European energy sector on NUTS2 level by 2050 following EU Energy Reference Scenario 2016.</p> <p>The energy sector in this scope includes energy production (production of coal, oil and gas) and energy transformation in oil refineries and power plants (nuclear, solid fuels, oil, gas, biomass and geothermal).</p> <p>The information in provided on NUTS 2 level following the NUTS2 2013 definition.</p> <p>The dataset is explained in more detail in the report <a href="https://ec.europa.eu/jrc/en/publication/projected-fresh-water-use-european-energy-sector">Projected fresh water use from the European energy sector</a>.</p>
DPMFA_EU_ENM_2000-2020: Dynamic Probabilistic Material Flows of Engineered Nanomaterials from 2000 to 2020 - Raw results
<p>This dataset is related to the following publication:</p> <p>Title: Dynamic probabilistic material flow analysis of engineered nanomaterials in European waste treatment systems</p> <p>Authors: Sana Rajkovic, Nikolaus A. Bornhöf<span>t</span>, Renata van der Weijden, Bernd Nowack, Véronique Adam</p> <p>Submitted to the journal Waste Management in September 2019.</p> <p>The files contain key values of probability distributions associated with the emissions of selected engineered nanomaterials to the environment.</p>
EU-27 Country Mapping of Financing Schemes to decarbonize Buildings, Heating and Cooling
<p>This dataset contains the mapping of all public and private financing instruments currently available to support the decarbonization of the building stock. The mapping is divided into two sheets: Public Schemes and Private Schemes. Each scheme is classified per country, level (European, National, Regional, Local), Name in English and in the local language, sectors (Y= directly covered, (Y)= indirectly covered, that is not explicitly mentioned, but reasonably applicable, blank= not covered), type of instrument, main and additional links, a short description and the last time the page was visited. Additional socio-economic, climate and energy indicators and a correlation matrix are provided.</p>
Financing conditions of renewable energy projects – results from an EU wide survey
<p>The dataset contains data related to financing conditions and costs of capital for onshore wind, solar PV and offshore wind within the EU. It provides data on minimum, maximum and average country and technology-specific values on costs of debt, debt service coverage ratios, loan tenors, debt size, costs of equity and WACC values. The data was collected between September 2019 and April 2020.</p> <p>The data contains values for onshore wind in Austria, Belgium, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Italy, Latvia, Lithuania, Netherlands, Poland, Portugal, Romania, Spain and Sweden. Furthermore, it contains values for solar PV in Czech Republic, Estonia, France, Greece, Hungary, Latvia, Portugal, Romania, Slovakia and Spain. Finally, it also contains values for offshore wind in Belgium, France, Germany and UK. </p> <p>The PDF files are survey questionnaires that were used for the data collection. This includes 1) a survey questionnaire used in an exploratory research phase, in which we identified the most relevant research aspects related to the impacts of auctions on costs of capital and financing 2) a survey questionnaire used for the focus-group countries (Germany, Denmark, Spain, Portugal and Greece), which includes a list of more extensive qualitative questions and 3) a survey questionnaire used for the focus-group countries (all other EU member states) and which focused only on collecting the quantitative data. </p>
i-Dreams H2020 EU Project: Sample dataset
<p>The overall objective of the <em>i</em>-DREAMS project is to setup a framework for the definition, development, testing and validation of a context-aware safety envelope for driving (‘Safety Tolerance Zone’), within a smart Driver, Vehicle & Environment Assessment and Monitoring System (<em>i</em>-DREAMS). Taking into account driver background factors and real-time risk indicators associated with the driving performance as well as the driver state and driving task complexity indicators, a continuous real-time assessment is made to monitor and determine if a driver is within acceptable boundaries of safe operation. Moreover, safety-oriented interventions were developed to inform or warn the driver real-time in an effective way as well as on an aggregated level after driving through an app- and web-based gamified coaching platform. The conceptual framework, which was tested in a simulator study and three stages of on-road trials in Belgium, Germany, Greece, Portugal and the United Kingdom on a total of 600 participants representing car, bus, and truck drivers, respectively. Specifically, the Safety Tolerance Zone (STZ) is subdivided into three phases, i.e. ‘Normal driving phase’, the ‘Danger phase’, and the ‘Avoidable accident phase’. For the real-time determination of this STZ, the monitoring module in the<em> i</em>-DREAMS platform continuously register and process data for all the variables related to the context and to the vehicle. Regarding the operator, however, continuous data registration and processing are limited to mental state and behavior. Finally, it is worth mentioning that data related to operator competence, personality, socio-demographic background, and health status, are collected via survey questionnaires. More information of the project can be seen from project website: https://idreamsproject.eu/wp/</p> <p>This dataset contains naturalistic driving data of various trips of participants recruited in i-Dreams project. Various different types of events are recorded for different intensity levels such as headway, speed, acceleration, braking, cornering, fatigue and illegal overtaking. Running headway, speed, distance, wipers use, handheld phone use, high beam use and other data is also recorded. Driver characteristics are also available but not part of this sample data. In the i-Dreams project, raw data for a particular trip was collected via CardioID gateway, Mobileye, wristband or CardioWheel. These trip data are fused using a feature-based data fusion technique, namely geolocation through synchronization and support vector machines. The system provided by CardioID integrates several data streams, generated by the different sensors that make up the inputs of the i-Dreams system. The sample dataset is fused, processed as well as aggregated to produce consistent time series data of trips for a particular time interval such as 30 secs/ 60 secs or 2- minutes intervals. More datasets can be acquired for analysis purposes by following the data acquisition process given in the data description file.</p>
Pan-EU Landmask: 10m Resolution Geospatial Land Coverage with Administrative Boundary details on country and regional level
<p><strong>Pan-EU Land Mask Summary</strong></p> <p>Considering the land mask for pan-EU, we will closely match the data coverage of <a href="https://land.copernicus.eu/pan-european">https://land.copernicus.eu/pan-european</a> i.e. the official selection of countries listed here: <a href="https://land.copernicus.eu/portal_vocabularies/geotags/eea39">https://lanEEA39d.copernicus.eu/portal_vocabularies/geotags/eea39</a>.</p> <p>There are a total of three landmask files available, each of which is aligned with the standard spatial/temporal resolution and sizes of <a href="https://ai4soilheath.eu">AI4SoilHealth</a> Data Cube specifications, which is: Xmin = 900,000, Ymin = 899,000, Xmax = 7,401,000, Ymax = 5,501,000, with Coordinate reference system of epsg:3035. Additionally, these files include a corresponding look-up table that provides explanations for the values present in the raster data. The scripts used to generate these masks can be found <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/tree/main/paneu_landmask">here</a>.</p> <p>The masks are:</p> <ol> <li> <p>Landmask</p> </li> <li> <p>ISO-code country mask</p> </li> <li> <p>NUTS3 mask</p> </li> </ol> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, the files here are named according to the standard OpenLandMap file-naming convention. The OpenLandMap file-naming convention works with 10 fields that basically define the most important properties of the data, this way users can search files, prepare data analysis etc, without even needing to access or open files. The 10 fields include:</p> <ol> <li> <p>Generic variable name: country.code</p> </li> <li> <p>Variable procedure combination i.e. method standard (standard abbreviation): iso.3166</p> </li> <li> <p>Position in the probability distribution / variable type: c</p> </li> <li> <p>Spatial support (usually horizontal block) in m or km: 30m</p> </li> <li> <p>Depth reference or depth interval e.g. below ("b"), above ("a") ground or at surface ("s"): s</p> </li> <li> <p>Time reference begin time (YYYYMMDD): 20210101</p> </li> <li> <p>Time reference end time: 20211231</p> </li> <li> <p>Bounding box (2 letters max): eu </p> </li> <li> <p>EPSG code: epsg.3035</p> </li> <li> <p>Version code i.e. creation date: v20230722</p> </li> </ol> <p>An example of a file-name based on the description above:</p> <p><em>country.code_iso.3166_c_100m_s_20210101_20211231_eu_epsg.3035_v20230722</em></p> <p><strong>Landmask</strong></p> <p>The basic principle to create the land mask is to include as much as land as possible, to avoid missing any land pixels and ensure precise differentiation between land, ocean and inland water bodies.</p> <p>Two reference datasets are used, </p> <ol> <li> <p><a href="https://esa-worldcover.org/en">WorldCover</a>, 10 m resolution.</p> </li> <li> <p><a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a>, with shapefiles of administrative boundaries, inland water bodies, ocean and landmask.</p> </li> </ol> <p>When generating the land mask, the two reference datasets in a way that:</p> <ul> <li> <p>If either of the two reference datasets identifies a pixel as land, it is considered a land pixel in our mask. </p> </li> <li> <p>Regarding ocean and inland water bodies, a pixel is classified as a water pixel only when both reference datasets confirm its identification as water.</p> </li> </ul> <p>The landmask consists of 4 values:</p> <ul> <li> <p>10: not in the pan-EU area, i.e. out of mapping scope</p> </li> <li> <p>1: land</p> </li> <li> <p>2: inland water</p> </li> <li> <p>3: ocean</p> </li> </ul> <p>This landmask is available in 10m, 30m, 100m, 250m, and 1km resolution formats respectively. The coarse resolution landmasks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “min” in GDAL. This “min” method allows taking the minimum values from the contributing pixels, to keep as much land as possible.</p> <p><strong>ISO-3166 country code mask</strong></p> <p>This ISO-3166 country code mask is created from <a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a> country shapefile. This mask is available in 10m, 30m and 100m resolution. In this raster file, each country is assigned a unique value, which allows for the interpretation and analysis of data associated with a specific country.</p> <p>The values are assigned to each country according to iso-3166 country code, which can be found in the corresponding look-up table. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.</p> <p><strong>NUTS-3 mask</strong></p> <p>The nuts-3 code mask is created from the European NUTS3 shapefile. In this raster file, each unique NUT3 level area is assigned a unique value, which allows for the interpretation and analysis of data associated with specific NUTS3 regions.</p> <p>The values of pixels and its associated meanings can be found in the corresponding look-up table. This nut-3 code mask is available in 10m, 30m and 100m resolution formats. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.</p> <p>It should be noted that the ISO-code country mask covers a more extensive area compared to the NUTS3 mask. This broader coverage includes countries like Ukraine and others beyond the NUTS3 mask, while NUTS mask shows more details about regional administrative boundaries.</p>
OEMC Hackathon 2023: EU Land Cover Classification Dataset
<p>Dataset organized by the <a href="https://earthmonitor.org/">Open-Earth-Monitor (OEMC) project</a> within the context of <a href="http://www.kaggle.com/competitions/oemc-hackathon-eu-land-cover-classification/overview">Hackathon 2023</a>.</p> <p>The dataset (both train and test) was produced by stratified sampling of the <strong>ground-truth</strong> data provided by LUCAS Survey, funded by the European Commission. The target land cover considered <strong>level-3</strong> classes from the harmonized legend, resulting in <strong>72 classes</strong> distributed over <strong>5 years </strong>(<code>2006</code>, <code>2009</code>, <code>2012</code>, <code>2015</code>, <code>2018</code>):</p> <p>All samples were overlaid with <strong>416</strong> raster spatial layers, including satellite (spectral bands and indices) and temperature images (land surface temperature), climate images (precipitation, air temperature), accessibility and distance maps (highways, water bodies, burned areas), digital terrain model (slope and elevation) and other existing maps (population count and snow covering). The result values were organized in columns, one for each spatial layers, which combined represent the feature space available for ML modeling.</p> <p><strong>Column names:</strong></p> <p>The columns are formed by six metadata fields separated by <code>_</code>:</p> <ul> <li>Example: <strong>red_landsat.glad.ard_p50_30m_jun25_sep12</strong></li> <li>Metadata fields: <ul> <li>F1 - Variable name: <strong>red</strong></li> <li>F2 - Variable procedure including product name: <strong>landsat.glad.ard</strong></li> <li>F3 - Position in the probability distribution: <strong>p50</strong></li> <li>F4 - Spatial resolution: <strong>30m</strong></li> <li>F5 - Start date: <strong>jun25</strong></li> <li>F6 - End date: <strong>sep12</strong></li> </ul> </li> </ul> <p><strong>Column description:</strong></p> <p>All the columns can be aggregated in six thematic groups according to F1 and F2:</p> <ul> <li><strong>Satellite images (spectral reflectance & vegetation indices):</strong> <ul> <li><code>blue_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat blue band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>blue_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 blue band (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>evi_mod13q1.stl.trend.ols.alpha_{..}</code>: Alpha coefficient / intercept (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>evi_mod13q1.stl.trend.ols.beta_{..}</code>: Beta coefficient / trend (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>evi_mod13q1.stl.trend_{..}</code>: Deseasonalized monthly time-series (trend component of <a href="https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL">STL</a>) for MOD13Q1 Enhanced Vegetation Index (EVI) index (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>evi_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>green_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat green band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>mir_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 mid-infrared band (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>ndvi_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 normalized vegetation index (NDVI) (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>nir_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat near-infrared band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>nir_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 near-infrared band (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>red_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat red band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>red_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 red band (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>swir1_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat short-wave infrared-1 band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>swir2_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat short-wave infrared-1 band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> </ul> </li> <li><strong>Temperature images:</strong> <ul> <li><code>lst_mod11a2.daytime_{..}</code>: Monthly time-series of MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.daytime.{month}_{..}</code>: Long-term monthly aggregation (2000—2022) for MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.daytime.trend_{..}</code>: Deseasonalized monthly time-series (trend component of <a href="https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL">STL</a>) for MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.daytime.trend.ols.alpha_{..}</code>: Alpha coefficient / intercept (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.daytime.trend.ols.beta_{..}</code>: Beta coefficient / trend (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime_{..}</code>: Monthly time-series of MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime.{month}_{..}</code>: Long-term monthly aggregation (2000—2022) for MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime.trend_{..}</code>: Deseasonalized monthly time-series (trend component of <a href="https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL">STL</a>) for MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime.trend.ols.alpha_{..}</code>: Alpha coefficient / intercept (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime.trend.ols.beta_{..}</code>: Beta coefficient / trend (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>thermal_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat thermal band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> </ul> </li> <li><strong>Climate layers:</strong> <ul> <li><code>accum.precipitation_chelsa.annual_{..}</code>: Accumulated precipitation over the entire year according to CHELSA timeseries in <code>mm</code> of water (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> <li><code>accum.precipitation_chelsa.annual.3years.dif_{..}</code>: 3-years difference considering the yearly accumulated precipitation according to CHELSA timeseries in <code>mm</code> of water (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> <li><code>accum.precipitation_chelsa.annual.log.csum_{..}</code>: Cumulative sum, in logarithmic space, consdering the yearly accumulated precipitation according to CHELSA timeseries (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> <li><code>accum.precipitation_chelsa.montlhy_{..}</code>: Accumulated precipitation for each month according to CHELSA timeseries in <code>mm</code> of water (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> <li><code>bioclim.var_chelsa.{variable_code}_{..}</code>: Bioclimatic variables derived variables from the monthly mean, max, mean temperature, and mean precipitation values. For <code>variable_code</code> descriptions see <a href="https://chelsa-climate.org/bioclim/">chelsa-climate.org</a> (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> </ul> </li> <li><strong>Accessibility & distance maps:</strong> <ul> <li><code>accessibility.to.ports_map.ox.{variable_code}_{..}</code>: Time-required to access ports of different size according to <a href="https://doi.org/10.1038/s41597-019-0265-5">Nelson et al., 2019</a></li> <li><code>burned.area.distance_global.fire.atlas_{..}</code>: Distance to burned areas mapped by <a href="https://doi.org/10.3334/ORNLDAAC/1642">Global Fire Atlas</a></li> <li><code>cost.distance.to.coast_gedi.grass.gis_{..}</code>: Cumulative cost of moving (derived by <a href="https://grass.osgeo.org/grass83/manuals/r.cost.html">r.cost</a>) to the coast</li> <li><code>road.distance_osm.highways.high.density_{..}</code>: Distance to high density of roads according to <a href="https://www.openstreetmap.org/#map=8/52.154/5.295">OpenStreetMap</a></li> <li><code>road.distance_osm.highways.low.density_{..}</code>: Distance to low density of roads according to <a href="https://www.openstreetmap.org/#map=8/52.154/5.295">OpenStreetMap</a></li> <li><code>water.distance_glad.interanual.dynamic.classes_{..}</code>: Distance to permanent / seasonal water bodies according to<br> <a href="https://doi.org/10.1016/j.rse.2020.111792">Pickens et al., 2020</a></li> </ul> </li> <li><strong>Digital terrain model (DTM):</strong> <ul> <li><code>elev.lowestmode_gedi.eml_{..}</code>: Mean estimate of the terrain elevation in <code>dm</code> filtered using <a href="https://saga-gis.sourceforge.io/saga_tool_doc/6.2.0/grid_filter_1.html">SAGA GIS Gaussian filter</a> (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>slope.percent_gedi.eml_{..}</code>: Mean slope in <code>%</code> derived from terrain elevation ([Witjes et al., 2023]</li> </ul> </li> <li><strong>Other existing maps:</strong> <ul> <li><code>pop.count_ghs.jrc_{..}</code>: Annual time-series of population count in number of people mapped by <a href="https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">Schiavina et al., 2023</a></li> <li><code>snow.duration_global.snowpack_{..}</code>: Annual duration of snow occurrence mapped by <a href="https://www.dlr.de/eoc/desktopdefault.aspx/tabid-8297/14218_read-37938/">Global SnowPack</a></li> </ul> </li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>train.csv</strong>: Training set with 42,237 rows and 420 columns, including sample id (<code>sample_id</code> - index column), land cover code (<code>land_cover</code>), land cover label (<code>land_cover_label</code>), reference year (<code>year</code>) and 416 features / covariates</li> <li><strong>test.csv</strong>: Test set with 42,271 rows and 418 columns, including sample id (<code>sample_id</code> - index column), reference year (<code>year</code>) and 416 features / covariates</li> <li><strong>sample_submission.csv</strong>: a sample submission file with 42,271 rows and 2 columns, including sample id (<code>sample_id</code> - index column) and predicted land cover code (<code>land_cover</code>)</li> </ul>
Artificial Intelligence for EU Decision-Making: Effects on Citizens Perceptions of Input, Throughput and Output Legitimacy
<p>The uploaded dataset was used for the statistical analysis of the pre-print "Artificial Intelligence for EU Decision-Making: Effects on Citizens’ Perceptions of Input, Throughput and Output Legitimacy" (Permanent identifier: <a href="https://arxiv.org/abs/2003.11320">arXiv:2003.11320</a>)</p> <p>A lack of political legitimacy undermines the ability of the European Union (EU) to resolve major crises and threatens the stability of the system as a whole. By integrating digital data into political processes, the EU seeks to base decision-making increasingly on sound empirical evidence. In particular, artificial intelligence (AI) systems have the potential to increase political legitimacy by identifying pressing societal issues, forecasting potential policy outcomes, informing the policy process, and evaluating policy effectiveness. This paper investigates how citizens’ perceptions of EU input, throughput, and output legitimacy are influenced by three distinct decision-making arrangements: (1) independent human decision-making (HDM); (2) independent algorithmic decision-making (ADM) by AI-based systems; and (3) hybrid decision-making by EU politicians and AI-based systems together. The results of a pre-registered online experiment (n = 572) suggest that existing EU decision-making arrangements are still perceived as the most democratic (input legitimacy). However, regarding the decision-making process itself (throughput legitimacy) and its policy outcomes (output legitimacy), no difference was observed between the status quo and hybrid decision-making involving both ADM and democratically elected EU institutions. Where ADM systems are the sole decision-maker, respondents tend to perceive these as illegitimate. The paper discusses the implications of these findings for (a) EU legitimacy and (b) data-driven policy-making.</p>
EU License Plates Images
<p>A collection of cropped vehicle license plates from across the EU (primarily Germany) for training automated license plate detection and extraction ML and OCR models. German plates are further sourced from a variety of states, allowing for sticker detection, extraction, and state classification models to be further developed.</p> <p>This dataset is used in the SODALITE Vehicle IoT use case for training automated license plate recognition models.</p>
QUEM SOU EU? | Savacu-de-coroa
<p>Curta-metragem, vinculado à serie "Quem sou eu?", abordando a biologia e ecologia do savacu-de-coroa (<em>Nyctanassa violacea</em>), representante Ardeidae (Pelecaniformes, Aves). Produto Fauna Brasil - UFF / Laboratório de Registro Audiovisual da Fauna Brasileira.</p>
Anthropogenic emissions of CH4, N2O, F-gases and BC from GAINS, for EU-countries plus CH, NO, UK developed under the EYE-CLIMA project - March 2025 update
<p><span>As part of the EYE-CLIMA project, GAINS emission data for CH<sub>4</sub>, N<sub>2</sub>O, BC and selected F-gases (HFC-125, HFC-134a, HFC-143a, HFC-23, HFC-32 and SF<sub>6</sub></span>) were released for all EU-27 countries plus UK, Switzerland, and Norway for the period 1990 to 2020 (with exception of F-gases, from 2005 only, and BC/CH<sub>4</sub> emissions from agricultural waste burning, from 2000). Results have been documented in EYE-CLIMA deliverable D2.8 (<a href="http://folk.nilu.no/~rthompson/eyeclima_reports/EYECLIMA_D2.8.pdf">http://folk.nilu.no/~rthompson/eyeclima_reports/EYECLIMA_D2.8.pdf</a>), and they are publicly available at the Zenodo repository under <a href="https://doi.org/10.5281/zenodo.11032177">https://doi.org/10.5281/zenodo.11032177</a>. All data is available on a 0.1°x0.1° grid and in monthly resolution. Emissions are attributed to the respective source categories according to GNFR.</p> <p>The motivation of an update resulted from the need to extending the emission data time series to 2023. With underlying statistics and national emission data currently available till 2022 only (the latter submitted to UNFCCC only by December 2024), the historical data series also could only be established for 2022. Here we use the GAINS scenario feature to extrapolate between 2022 historical data and the first scenario point, 2025 which is based on IEA’s Word Energy Outlook 2023 (https://www.iea.org/reports/world-energy-outlook-2023). Obviously, this also means that emission results for 2023 are not any more based on robust statistics but represent an extrapolation.</p> <p>Extrapolation of spatially explicit data is only possible when the spatial resolution conveys a realistic signal. For the sector “agricultural waste burning” (files with “AWB” as sector, see notation below) spatial allocation is based on actual observation from satellites. As such data products on agricultural fires have been made available until 2022 only, no spatial or temporal signal exists for 2023. The time series provided thus has to end in 2022. No recommendation can be given to modellers, other than to either use 2022 also for 2023 (understanding that the pattern will be strikingly different) or to use a five-year average (which will remove a lot of spatial specificity).</p> <p>The updated dataset covers files as follows (internally, all files now carry version number V05):</p> <p>ALL_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.csv</p> <p>BC_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>BC_FLUX_AWB_EUR_MOD_MONTH_20000101_20221231_GAINS_IIASA_V05.nc</p> <p>CH4_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>CH4_FLUX_AWB_EUR_MOD_MONTH_20000101_20221231_GAINS_IIASA_V05.nc</p> <p>HFC_FLUX_ALL_EUR_MOD_YEAR_20050101_20231231_GAINS_IIASA_V05.nc</p> <p>N2O_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>SF6_FLUX_ALL_EUR_MOD_YEAR_20050101_20231231_GAINS_IIASA_V05.nc</p> <p>This is version 2.0 of the dataset. It extends from version 1.0 by covering into the year 2023, but also benefits from a number of additional GAINS improvements. Emissions of emitted compounds are provided as kg/m²/s. File names follow the notation developed for the H-Europe project EYE-CLIMA, i.e. species _ variable-type _ sector _ region _ method (MOD=model) _ timestep _ fromTime _ toTime _ model _ institute _ version . filetype.</p> <p>This version is available at <a href="https://doi.org/10.5281/zenodo.15536170">https://doi.org/10.5281/zenodo.15536170</a>. The generic address of the dataset is <a href="https://doi.org/10.5281/zenodo.10886780">https://doi.org/10.5281/zenodo.10886780</a>, resolving to the latest update available at Zenodo. No further updates are planned in EYE-CLIMA, so this version is expected to also reflect the final update within the project.</p> <p>Compared to version 1.0, GAINS benefitted from a number of new developments such as the following:</p> <p>*) Previously, GAINS has been available in five-year timesteps only (with the aim of allowing for scenarios at that resolution). For data version 1.0, a makeshift solution was found to convert into annual data. A recent update now allows, for historic data, to store and retrieve information on an annual basis (from 1990).</p> <p>*) The energy data were obtained from IEA’s world energy balances 2024 (July version, https://www.iea.org/data-and-statistics/data-product/world-energy-balances#documentation), extending into 2022 and extrapolated towards 2025, downscaled from IEA to GAINS sectors and sub-sectors. Additionally, the annual activity of industrial production is estimated using a linear approach, based on five-year timestep data.</p> <p>*) Agricultural statistics were retrieved from Eurostat (and from FAO globally) and extended to 2022, extrapolated towards 2025.</p> <p>*) Interpretation of GAINS data was reconfirmed and updated in consultations with national experts of multiple EU countries. While the process resulted in revised emission projections to be used in the Clean Air Outlook 4 (see <a title="Protected by Check Point: https://environment.ec.europa.eu/topics/air/clean-air-outlook_en" href="https://protect.checkpoint.com/v2/r02/___https:/environment.ec.europa.eu/topics/air/clean-air-outlook_en___.YzJlOmlpYXNhOmM6bzoyYzdiNDRhNDI4Njc3ZjI5MGFjMTU1N2I2OWVmNzM2ZTo3OjE5OTM6ZTFiY2IzMDMxZGViNGE0MjI0ODRmNWQ4NzA3ZDY3Njc4M2U2NzUxNmEwNzQ0ODViNDBhODc1NmNhZmMzY2FlMjpoOkY6Tg"><span lang="EN-GB">https://environment.ec.europa.eu/topics/air/clean-air-outlook_en</span></a><span lang="EN-GB">). While the details of improvements on the individual aspects cannot be disclosed, they are useful to describe historic data most adequately, and have been integrated also in this assessment. That not only leads to changes in absolute emissions for a given year, but also affects trends that now are more plausible and confirmed through the exchange with the national experts.</span></p> <p><span lang="EN-GB">*) Technical adjustments have improved the precision of temporal allocation of emissions and the conversion of grid sizes to actual area.</span></p>
Figure: Occupational Fatality and Health Metrics within EU Consumption and Various Supply Chain Accounting Frameworks
<p><span><strong>Occupational Fatality and Health Metrics within EU Consumption and Supply Chain Contexts.</strong> Directly taken from (Koundouri et al., 2023) and reproduced with the authors' permission. </span><span>It displays in Figure A the</span><span> work-related fatal occupational injuries tied to goods finally consumed within the EU (Consumption Based Accounting -CBA- framework) and those within supply chains passing through the EU (Throughflow Based Accounting -TBA- framework) </span><span><span>(Beaufils et al., 2023) and</span></span><span> the results denote fatalities. It also displays in Figure B the D<span>isability-Adjusted Life Years</span><em><span> (</span></em></span><span>DALYs) associated with asbestos, asthmagen, and chromium-related occupational fatalities linked to European goods consumption (CBA) and traversing supply chains (TBA). Last, in Figure C, it provides</span><span> a comparative breakdown for each commodity from panels A and B, illustrating proportions by accounting framework (TBA vs. CBA).</span></p> <p><span>This figure put forward that despite potential barriers to target direct import intervention, optimized supply chain management can markedly reduce occupational fatalities related to global value chains passing through EU. As such, ILO frameworks such as Occupational Safety and Health Convention, 2006 (No. 187) </span><span><span>(International Labour Organization, 2006)</span></span><span>, and Occupational Safety and Health Convention, 1981 (No. 155) </span><span><span>(International Labour Organization, 1981)</span></span><span> offers a path to significant fatality reductions</span></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.