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6,170 results for “european”
European Investment Bank Projects in ACP, OCT, Africa, Asia, and Latin America (1957-2024)
<p>This dataset offers a comprehensive analysis of European Investment Bank (EIB) projects in Africa, the Caribbean, and the Pacific (ACP) regions, Overseas Countries and Territories (OCT), Asia, and Latin America, spanning from 1975 to 2023. The dataset includes information on 2,558 projects; each entry in the dataset includes key project details such as the project’s sector, date of signature, and financial commitments. All numbers are in 2015 euros.</p>
TOMCAT model data & IASI/GOME-2B satellite data of European ozone between 2008 - 2023
<p>Daily mean data of ozone (O3) from the TOMCAT 3D chemical transport model (Chipperfield, 2006) and two satellite products, the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A & B satellites and the Global Ozone Monitoring Experiment-2 (GOME-2) on the MetOp-B satellite. The satellite observations are retrieved using schemes developed by the Rutherford Appleton Laboratory (RAL) see Miles et al. (2015) and Pope et al. (2021). The TOMCAT model data is available for 2017 - 2021, the IASI data is available for 2008 - 2023 and the GOME-2 data for 2015 - 2020. </p>
High resolution microsection images for: Common juniper, the oldest living non-clonal woody species across the tundra biome and the European continent
<p>Two high resolution images of the stem section are available as .czi files. These images are from a living <em>Juniperus communis</em> L. branch from Abisko (Sweden) sampled in August 2021. These high-resolution photographs (2.89 pixel/μm) were created using Axio Scan 7, Zeiss, Germany. </p> <p>One high resolution image of the same stem section is archived as a .tif file (49835x25587 pixels). This image is a composition of the two .czi images created using Axio Scan 7, Zeiss, with a reduced resolution and edited adding the ring-count reference points and the reference scale.</p>
Derived Data from "Expanding European protected areas through rewilding"
<p>We present the major derived data obtained through the study "Expanding European protected areas through rewilding" published in Current Biology.</p> <p>Data refer to three shapefiles and it is structured as: </p> <p>1) "Rewilding Patches" folder - presenting European rewilding patches (human footprint <=5), classified by area</p> <p>2) "Marxan Solutions" folder - presenting optimized solutions to expand current European protected areas through rewilding such to achieve ,in each country, 30% area with protected areas ("PA_all" sub-folder) and 10% area with strict protected areas ("PA_strict" sub-folder)</p> <p>For detail on data, users are adviced to read the "Readme" files in each folder.</p>
Structural, ecological and biogeographical attributes of European vegetation alliances
<p>This is a database of structural, ecological and biogeographical attributes of 1115 European phytosociological alliances. The original version was published by Preislerová et al. (2024). This article also contained definitions and descriptions of individual attributes.</p> <p>Version 2 of this dataset has been updated to match Version 3 of EuroVegChecklist (Mucina et al. 2016) published on https://floraveg.eu/download/. This version contains syntaxonomic changes in the vegetation of coastal dunes (classes <em>Ammophiletea arundinaceae</em>, <em>Helichryso-Crucianelletea maritimae</em> and <em>Honckenyo peploidis-Leymetea arenarii</em>), Mediterranean pine forests (order <em>Pinetalia halepensis</em>) and bogs (class <em>Oxycocco-Sphagnetea</em>) approved by the European Vegetation Classification Committee in January 2024 following the proposals published by Marcenò et al. (2018, 2024), Bonari et al. (2021) and Jiroušek et al. (2022), respectively.</p> <p>The data are provided in two files with identical contents, one in the XLSX format, and the other in the TXT format with columns separated by tabs.</p> <p><strong>References</strong></p> <div> <div> <div> <ul> <li>Bonari G., Fernández‐González F., Çoban S., Monteiro‐Henriques T., Bergmeier E., Didukh Ya. P. … Chytrý, M. (2021). Classification of the Mediterranean lowland to submontane pine forest vegetation. <em>Applied Vegetation Science</em>, 24, e12544. <a href="https://doi.org/10.1111/avsc.12544">https://doi.org/10.1111/avsc.12544</a></li> <li>Jiroušek, M., Peterka, T., Chytrý, M., Jiménez-Alfaro, B., Kuznetsov, O.L., Pérez-Haase, A. … Hájek, M. (2022). Classification of European bog vegetation of the Oxycocco-Sphagnetea class. <em>Applied Vegetation Science</em>, 25, e12646. <a href="https://doi.org/10.1111/avsc.12646">https://doi.org/10.1111/avsc.12646</a></li> <li>Marcenò, C., Guarino, R., Loidi, J., Herrera, M., Isermann, M., Knollová, I. … Chytrý, M. (2018). Classification of European and Mediterranean coastal dune vegetation. <em>Applied Vegetation Science</em>, 21, 533–559. <a href="https://doi.org/10.1111/avsc.12379">https://doi.org/10.1111/avsc.12379</a></li> <li>Marcenò, C., Danihelka, J., Dziuba, T., Willner, W. & Chytrý, M. (2024). Nomenclatural revision of the syntaxa of European coastal dune vegetation. <em>Vegetation Classification and Survey</em>, 5, 27–37. <a href="https://doi.org/10.3897/VCS.108560">https://doi.org/10.3897/VCS.108560</a></li> <li>Mucina, L., Bültmann, H., Dierßen, K., Theurillat, J.-P., Raus, T., Čarni, A. … Tichý, L. (2016). Vegetation of Europe: Hierarchical floristic classification system of vascular plant, bryophyte, lichen, and algal communities. <em>Applied Vegetation Science</em>, 19(Suppl. 1.), 3–264. <a href="https://doi.org/10.1111/avsc.12257">https://doi.org/10.1111/avsc.12257</a></li> <li>Preislerová Z., Marcenò C., Loidi J., Bonari G., Borovyk D., Gavilán R.G., Golub V., Terzi M., Theurillat J.-P., Argagnon O., Bioret F., Biurrun I., Campos J.A., Capelo J., Čarni A., Çoban S., Csiky J., Ćuk M., Ćušterevska R., Dengler J., Didukh Ya., Dítě D., Fanelli G., Fernández-González F., Guarino R., Hájek O., Iakushenko D., Iemelianova S., Jansen F., Jašková A., Jiroušek M., Kalníková V., Kavgacı A., Kuzemko A., Landucci F., Lososová Z., Milanović Đ., Molina J.A., Monteiro-Henriques T., Mucina L., Novák P., Nowak A., Pätsch R., Perrin G., Peterka T., Rašomavičius V., Reczyńska K., Rūsiņa S., Sánchez Mata D., Santos Guerra A., Šibík J., Škvorc Ž., Stešević D., Stupar V., Świerkosz K., Tzonev R., Vassilev K., Vynokurov D., Willner W. & Chytrý M. (2024) Structural, ecological and biogeographical attributes of European vegetation alliances. <em>Applied Vegetation Science</em>, 27, e12766. <a href="https://doi.org/10.1111/avsc.12766">https://doi.org/10.1111/avsc.12766</a></li> </ul> </div> </div> </div>
Open database on distributional information on European pollinators
<p>(abstract) This dataset was produced in the framework of the work package 1 (task 1) of the Horizon EU project Safeguard. We aimed to mobilise EU experts and data to compile and make available distributional data for bees, butterflies, moths and hoverflies. This will allow us to assess the magnitude, scale and extent of status and trends in pollinator distributions, diversity, abundance, communities and plant-pollinator networks.</p> <p>(method) Regarding distribution data for bees, UMons have been in contact with 23 bee taxonomists, 52 national champions and 5 museums. To date, we collected 52 bio-geographical databases of European bees from both restricted (i.e. databases shared under ad hoc agreement) and public (i.e. openly accessible databases) sources. Regarding distributional data for hoverflies, the starting point was the recently published in the IUCN Red List of hoverflies. To expand the number of species with precise distributional data on syrphid flies, UNSPMF further contacted taxonomists working with this species group : Gunilla Stahls from Finland; Jeroen van Steenis, Wouter van Steenis and Gerard Pennards from Netherlands; Grigory Popov from Ukraine; Santos Rojo from Spain; Axel Ssymank from Germany; Libor Mazanek from Czech Republic; Daniele Sommaggio from Italy. They provided additional data and conducted validation of the existing data, but also engaged additional experts who provided the data. For the butterflies and the moth, the data was collected by UFZ and come from an original initiative of the scientific expert on those two groups. As the publication of the row data of some databases (e.g. bees from The Netherlands) required the clustering of the spatial records to geographic grid squares (e.g. 10x10 km²), we simplified all the records in the present dataset.</p> <p>(dataset) We consider as a data, a record that includes the following information: the name of the species, the coordinates where the species was collected. Additional information were collected (e.g. collector, determinator, number of the individuals collected, sex, data owner and reference code) but were not displayed in the present dataset. The aggregation of bee databases include 4,837,731 row data for bees, 680,641 row data for hoverflies, 1,209,320 row data for butterflies and 6,862,835 row data for moths.</p>
Sources used for content analysis in Crespy, A. and Szabo, I. : Healthcare reforms and fiscal discipline in Europe: Responsibility or responsiveness? forthcoming: European Policy Analysis
<p>Sources used for content analysis in Crespy, A. and Szabo, I. : Healthcare reforms and fiscal discipline in Europe: Responsibility or responsiveness? forthcoming: European Policy Analysis. Data from the ENLIGHTEN project (H2020 #649456).</p>
Questions in European Parliament on Brain Drain
<p>This is data collated for a discourse analysis of the treatment of questions on "brain drain" in the European Parliament. Collated by Jacob A. Hasselbalch for the ENLIGHTEN project (H2020 #649456).</p>
Data and R script for 'Evaluating the cyclic ratio schedule as an assay of feeding behaviour in the European starling (Sturnus vulgaris)'
<p>Data files and R script for Dunn et al. "Evaluating the cyclic ratio schedule as an assay of feeding behaviour in the European starling (<em>Sturnus vulgaris</em>)"</p> <p>Includes a single R script that produces all the analyses in the paper. The script makes use of three different .csv data files.</p>
CATCH-EyoU: Exploiting European data and testing the integrated theory of youth active EU citizenship: PIDOP subset reanalysis
<p>This is a subset of the full PIDOP dataset. The derived subset contains cross-sectional survey results from the PIDOP questionnaire survey that were collected in 9 European countries (incl. Turkey) during a period of 16-26 year old in 2011. The data set includes 9060 individual cases. The questionnaire used in the survey is published in Barrett, M. & Zani, B. (Eds.) (2015). <em>Political and civic engagement: Multidisciplinary perspectives.</em> Hove: Routledge (p.519-534).</p>
Checklist of the moss of aquatic and riverside habitats of the Komi Republic (European North-East of Russia)
<p>Представленная информация о мхах водных и прибрежно-водных местообитаний Республики Коми является дополнением к статье Г.В. Железновой, Т.П. Шубиной, Б.Ю. Тетерюка «Анализ флоры мхов водных и прибрежно-водных местообитаний Республики Коми», принятой к публикации в журнале «Известия Коми НЦ УрО РАН» в 2019 г.</p> <p>Список включает 275 таксонов мхов из 103 родов и 37 семейств. Он составлен на основе фактического материала, хранящегося в гербарии Института биологии Коми научного центра Уральского отделения Российской академии наук (SYKO) (УНУ «Научный гербарий SYKO Института биологии Коми НЦ УрО РАН») и литературных сведений (Ruprecht, 1850; Zickendrath, 1895, 1900; Поле, 1915; Кильдюшевский, 1956; Куваев, 1970).</p> <p>Исследованиями были охвачены прибрежные и водные местообитания водотоков и озер Республики Коми. На равнинной территории сборы выполнены в пределах тундры (подзона южной тундры), лесотундры, тайги (подзоны северной и средней тайги), в горах – на Полярном, Приполярном и Северном Урале. Полевые бриологические исследования проводились с использованием маршрутного и стационарного методов.</p> <p>Объем семейств, родов и названия видов приведены в основном согласно списку мхов Восточной Европы и Северной Азии (Check-list…, 2006)</p> <p>The checklist provides information about mosses aquatic and riverside habitats of the Komi Republic. It is a supplement to the article by G. V. Zheleznova, T. P. Shubina, B. Yu. Teteryuk "Analysis of the moss flora of aquatic and riverside habitats of the Komi Republic (European North-East of Russia)", accepted for publication in the journal "Proceedings of the Komi Science Center URD RAS" in 2019.</p> <p>The checklist includes 275 moss taxa from 103 genera and 37 families. It is based on the samples preserved in the Herbarium of the Institute of Biology of the Komi Scientific Center of the Ural Branch of the Russian Academy of Sciences (SYKO) and literary data (Ruprecht, 1850; Zickendrath, 1895, 1900; Pole, 1915; Kildyushevsky, 1956; Kuvaev, 1970). The species names were given according to “Checklist of mosses of East Europe and North Asia” (2006).</p> <p>The mosses were collected in aquatic and riverside habitats of the mountains and plain territories of the Komi Republic. The research covered three parts of the Urals mountain range: the Polar Urals, the Subpolar Urals and the Northern Urals. The plain territory was covered within the southern tundra, forest tundra, northern taiga and middle taiga.</p>
MMoveT15: A Twitter Dataset for Extracting and Analysing Migration-Movement Data of the European Migration Crisis 2015
<p>In the 2015 migration crisis thousands of refugees and migrants crossed the border to Hungary, Austria and Germany. The movements of these people are reflected in social media, especially on Twitter. We present a dataset of 3275 Tweets form the months September and October 2015. These Tweets are annotated regarding their relevance to the quantitative movement of refugees/migrants into Hungary, Austria and Germany. We present this dataset for a posterior analysis of the 2015 migration crisis or as a basis for an early warning or forecasting system</p>
Photovoltaic time series for European countries and different system configurations
<p>This repository comprises 38 years-long hourly time series representing the photovoltaic (PV) capacity factors in every European country (EU-28 plus Serbia, Bosnia-Herzegovina, Norway, and Switzerland). The term capacity factor is defined as the ratio between the delivered power and the cumulative installed capacity (DC). 3 letter codes (ISO-3166-3) are used to identify the countries. Time series include years from 1979 to 2017.</p> <p>To obtain PV time series irradiance from Climate Forecast System Reanalysis (CFSR) dataset has been converted into electricity generation and aggregated at country level. The PV model used for the conversion is described in the article linked below. Prior to conversion, reanalysis irradiance is bias corrected using satellite-based SARAH dataset and a globally-applicable methodology, which is also described in the article.</p> <p>For every country, four different time series assuming alternative PV configurations, <em>i.e</em>., rooftop, optimum tilt, 2-axis tracking, and delta are provided. To obtain the PV hourly capacity factors for a country, different assumptions on the shares of the alternative configurations can be made and the weighted time series can be aggregated accordingly.</p> <p>The license for the AU REatlas photovoltaic time series dataset is: <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC BY 4.0)</a></p> <p>When using this data please make sure you include the following citation:</p> <p><em>M. Victoria and Gorm B. Andresen, Using validated reanalysis data to investigate the impact of the PV system configurations at high penetration levels in European countries, Progress in Photovoltaics: Research and Applications (2019) </em><a href="https://doi.org/10.1002/pip.3126">https://doi.org/10.1002/pip.3126</a></p> <p>More information can be requested from M. Victoria (<a href="mailto:mvp@eng.au.dk">mvp@eng.au.dk</a>) and Gorm B. Andresen (<a href="mailto:gba@eng.au.dk">gba@eng.au.dk</a>).</p> <p>Version 2 assumes tilt angle of 60º for PV panels in delta configuration (in version 1, tilt angle in delta configuration is equal to latitude). The remaining files do not change.</p> <p>Version 3 includes one additional file corresponding to country-wise time series obtained assuming 1 axis-tracker (horizontal axis oriented North-South). In addition, small corrections of the previous time series have been implemented affecting only early hours in the day.<br> </p>
Dataset associated with Schyns & Vanham (2019) "The water footprint of wood for energy consumed in the European Union"
<p>Input and output datasets related to the paper Schyns & Vanham (2019) The water footprint of wood for energy consumed in the European Union. <em>Water</em>, 11(2): 206.</p>
Effects of a Delayed Expansion of Interconnector Capacities in a High RES-E European Electricity System - Dataset
<p>Input and output data of the modelling work for the paper Effects of a Delayed Expansion of Interconnector Capacities in a High RES-E European Electricity System</p> <ul> <li>Considered scenario years: 2030, 2040 and 2050</li> <li>The profiles are based on the historical year 2016.</li> <li>Two scenarios are included: lower connectivity and high connectivity</li> <li>The data cover the ENTSO-E member countries except Iceland and Cyprus and is given in country-specific resolution.</li> </ul> <p><strong>Input:</strong></p> <ul> <li>Demand as hourly profile in MWh</li> <li>Variable RES-E as hourly profile in MWh</li> <li>Power plant fleet as capacities in MW</li> <li>NTCs as capacities in MW</li> </ul> <p><strong>Output:</strong></p> <ul> <li>CO2 emissions as annual data in Mt</li> <li>Variable electricity generation costs as annual data in MEur</li> <li>Variable electricity generation costs per generation as annual data in Euro/MWh</li> <li>Electricity generation as annual data in TWh</li> <li>Electricity export as annual data in TWh</li> <li>Electricity import as annual data in TWh</li> <li>Transit flows as annual data in TWh</li> </ul> <p>The sources are described in the corresponding paper under the following link: <a href="https://www.mdpi.com/1996-1073/12/16/3098">https://www.mdpi.com/1996-1073/12/16/3098</a></p>
Corresponding spreadsheet to the Paper 'Variability in the assessment of childcare in 30 European countries'
<p>The spreadsheet provides the list of indicators reported by the national experts to assess the quality of child care in the relevant countries along with those gathered from official documents provided by the experts. It has been adopted to the Paper 'Variability in the assessment of childcare in 30 European countries'. </p>
Dataset for "Gross primary productivity of four European ecosystems constrained by joint CO2 and COS flux measurements"
<p>Data of measurements and model output of the publication "Gross primary productivity of four European ecosystems constrained by joint CO<sub>2</sub> and COS flux measurements".</p> <p>Data consists of micrometeorological data, COS and CO<sub>2</sub> flux measurements for 4 sites including filters for the fluxes.</p> <p>The sites include: a managed temperate mountain grassland in Austria (18.06.-21.08.2015), a Mediterranean savanna ecosystem in Spain(29.04.-24.05.2016)), a Temperate beach forest in Denmark(07.06.-03.07.2016) and an agricultural soy bean field in Italy(03.07.-01.08.2017).</p> <p>Version 2: param2950** are now correct (were filled with the same values for all field sites) </p> <p>For additional information please contact: <a href="mailto:Georg.Wohlfahrt@uibk.ac.at">Georg.Wohlfahrt@uibk.ac.at</a></p>
CLDF dataset derived from Heggarty, Paul & Anderson, Cormac & Scarborough, Matthew's "Indo-European Cognate Relationships database" ([IE-CoR version 1.0](https://github.com/lexibank/iecor/releases/tag/v1.0)) from 2019
<p>Cite the source of the dataset as:</p> <blockquote> <p>Heggarty, Paul & Anderson, Cormac & Scarborough, Matthew 2024. Indo-European Cognate Relationships database (IE-CoR version 1.1). Leipzig: Max Planck Institute for Evolutionary Anthropology</p> </blockquote>
JOINT WEBINAR: SUSTAFUELS, Three European Solutions Working on Algal & Renewable Fuels
<p>On May 21, 2024, an informative webinar titled “SUSTAFUELS, Three European Solutions Working on Algal & Renewable Fuels” was held from 12:00 to 13:00 CET. This online event was a collaborative effort among three key projects—ALFAFUELS, COCPIT, and FUELGAE—aimed at advancing renewable fuel technologies. Attendees were introduced to the main concepts, ambitions, and methodologies behind these innovative European initiatives. The event was structured in six parts, including presentations on non-biological algal renewable fuels, detailed discussions on each project, and a Q&A session.</p> <p>The webinar was moderated by Pablo Morales Moya from Sustainable Innovations (SIE), and featured a presentation from Javier Sánchez López of CINEA, who discussed the agency’s role in supporting climate, infrastructure, and environmental initiatives. Following the introductory segments, the spotlight shifted to the project coordinators. Charis Xiros from RISE Research Institutes of Sweden presented the ALFAFUELS project, Sary Awad from IMT Atlantique showcased the COCPIT project, and Silvia Morales de la Rosa from CSIC presented the FUELGAE project. Each coordinator provided insights into their project’s objectives, impacts, and collaborative efforts.</p> <p>Participants had the opportunity to learn about groundbreaking renewable fuel solutions and their potential for carbon capture. The event underscored the importance of European collaboration in tackling environmental challenges through innovative research and development. Recordings of the session will be used for dissemination purposes, ensuring that the knowledge shared continues to benefit a wider audience interested in sustainable fuel technologies.</p>
European Forest Disturbance Atlas
<p><strong>Description</strong></p> <p>This repository holds maps of annual forest disturbances across 38 European countries derived from Landsat satellite data. The European Forest Disturbance Atlas currently covers the period 1985-2023 and consists of a set of maps:</p> <ul> <li>The <em>year of disturbance</em> layers contain the year of the most recent disturbance event in the time-series, the greatest disturbance in terms of spectral change and stack of annual disturbances indicating undisturbed (0) and disturbed (1).</li> <li>The <em>number of disturbances</em> layer shows the number of disturbance events detected within the time-series.</li> <li>The <em>disturbance severity</em> layer indicates the spectral change in NBR relative to pre-disturbance.</li> <li>The <em>disturbance agent</em> layer summarises the attribution of agents over the full time series. The causal agents assigned are wind/bark beetle complex (1), fire (2), harvest (3) and mixed agents (4, where more than one agent occurred). The stack of disturbance agents provides annual information on causal agent assigned.</li> </ul> <p>The maps are available per country as GeoTIFF. The spatial reference system is EPSG 3035 (ETRS89 / LAEA Europe). The most current version is 2.1.1. The maps will be updated regularly. <a href="https://albaviana.users.earthengine.app/view/european-forest-disturbance-map">The maps can also be explored online</a>.</p> <p><strong>Version history</strong></p> <ul> <li>2.0.0 - Initial version, covering 1985-2021.</li> <li>2.1.0 - Maps updated until 2023. Added forest land use layer and annual stacks of disturbances (including annual disturbance probabilities and disturbance agents).</li> <li>2.1.1 - Improvements to the disturbance maps introduced and disturbance severity layer added.</li> </ul> <p><strong>Known issues</strong></p> <ul> <li>Some SLC-off artefacts (both version 2.0.0 and 2.1.0).</li> <li>Known errors related to the forest mask of Belarus, Moldova and Ukraine (now corrected in 2.1.0)</li> <li>Known some false disturbances mapped north of 67°N in Norway, Sweeden and Finland.</li> </ul>
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.