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123 results for “European region”
Diversification and Management Practices in Selected European Regions. A Data-analysis of Arable Crops Production and soil organic carbon
<p>This data set contains a data-mining performed to assess the impact of intercropping, tillage and fertilizer type on soil organic carbon and crop yield in arable crops from four selected European pedoclimatic regions and typical cropping systems in the Atlantic, Boreal, Mediterranean North, and Mediterranean South regions. A further meta-analysis was performed with these data. </p> <p>These data correspond to the open-access articles:</p> <p>- Diversified Arable Cropping Systems and Management Schemes in Selected European Regions Have Positive Effects on Soil Organic Carbon Content. Agriculture 2019, 9, 261. https://www.mdpi.com/2077-0472/9/12/261?type=check_update&version=2</p> <p>- Diversification and Management Practices in Selected European Regions. A Data-analysis of Arable Crops Production. Agronomy 2020, 10, 297; doi:10.3390/agronomy10020297. https://www.mdpi.com/2073-4395/10/2/297</p> <p>- Deficit Drip Irrigation in Processing Tomato Production in the Mediterranean Basin: A Data Analysis for Italy. Agriculture 2019, 9, 79; doi:10.3390/agriculture9040079. https://www.mdpi.com/2077-0472/9/4/79?type=check_update&version=2</p> <p>The research and publications have been funded by he European Commission Horizon 2020 project Diverfarming [grant agreement 728003]. </p>
Large-Scale Traveling Ionospheric Disturbances over the European sector during the geomagnetic storm on March 23-24, 2023: energy deposition in the source regions and the propagation characteristics
<p>IMAGE 2D Ionospheric Equivalent Currents for 23 and 24 March 2023 (https://space.fmi.fi/image/). </p> <p><em>We thank the institutes who maintain the IMAGE Magnetometer Array (<a href="https://space.fmi.fi/image/">https://space.fmi.fi/image/</a>): Tromsø Geophysical Observatory of UiT the Arctic University of Norway (Norway), Finnish Meteorological Institute (Finland), Institute of Geophysics Polish Academy of Sciences (Poland), GFZ German Research Centre for Geosciences (Germany), Geological Survey of Sweden (Sweden), Swedish Institute of Space Physics (Sweden), Sodankylä Geophysical Observatory of the University of Oulu (Finland), DTU Technical University of Denmark (Denmark), and Science Institute of the University of Iceland (Iceland). The provisioning of data from AAL, GOT, HAS, NRA, VXJ, FKP, ROE, BFE, BOR, HOV, SCO, KUL, and NAQ is supported by the ESA contracts number 4000128139/19/D/CT as well as 4000138064/22/D/KS. The authors would like to thank Dr. Liisa Juusola for providing the IMAGE 2D Ionospheric Equivalent Currents data.</em></p>
Survey and focus group results to identify European and Regional needs, opportunities and expectations to bio-based education/training model.
<p>This data was used to produce the “Report on European and regional analysis of the needs, opportunities and expectations to bio-based education/training model” in the framework of BioBEC project (101023381-H2020-BBI-JTI-2020).<br>The data provide information about the needs and expectations of the main stakeholders (Academics / Researchers / Consultants / Educational Administrators / Non profit foundations / Clusters) regarding the design, development and implementation of Bio Based Education Centres across different European Regions. The survey used asked the informants to provide their opinion regarding different statements in a scale from 1 (strongly disagree) to 5 (strongly agree). The focus group includ open questions focused on the topics described. </p>
EEAR-Clim: A high density observational dataset of daily precipitation and air temperature for the Extended European Alpine Region
<p>Data, metadata and code for paper published in Earth System Science Data:</p> <p>A high density observational dataset of daily precipitation and air temperature for the Extended Alpine Region</p> <p> </p> <p><strong>Code </strong>(working copy all written in R statistical software): scripts.zip</p> <ul> <li>to read and process data in from different sources</li> <li>to perform intra and inter-stations quality control</li> <li>to perform break detection and homogenization</li> <li>to read results of quality control and homogenization</li> </ul> <p><strong>Data</strong>:</p> <ul> <li>Daily time series of air temperature (mean, minimum and maximum) and precipitation as .zip files, grouped by data provider.</li> <li>Information on column content is provided in separate files "data_readme.txt"</li> <li>about 10000 stations from Italy, France, Switzerland, Austria, Germany, Slovenia, Croatia, Bosnia-Herzegovina, Czech Republic, Slovakia and Hungary</li> <li>Meta data (code, name, longitude, latitude, elevation, measurements availability for each variable, starting date, ending date) in "metadata.zip", including a file for each data provider</li> <li>If you <strong>use the data you agree to adhere to the respective data provider's terms</strong> as listed in "License.pdf"</li> <li>The license terms especially (and additionally to any other terms of the single data providers) include: <strong>Attribution</strong> — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. [from <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>] </li> </ul> <p> </p> <p><strong>Version history:</strong></p> <p>v1.0: initial upload</p> <p>v2.0: update of data policies; addition of France and Croatia time series</p>
Meteorological indicator for selected European NUTS 3 regions
<p>13 datasets have been concatenated into one dataset for the purpose to predict future temperature scenario from 13 European countries.</p> <p>The 13 datasets have been published by Angelova, Denitsa; Blanco, Norman (2020), “Meteorological indicator dataset for selected European NUTS 3 regions ”, Mendeley Data, V3, doi: 10.17632/sf9x4h5jfk.3</p> <p>Further details at https://data.mendeley.com/datasets/sf9x4h5jfk/3</p>
European eVALuation sites (EVAL): A European sampling for regional satellite product intercomparison
<p>For the quality assessment of satellite-based products it is necessary to perform product intercomparisons at global and continental scales. In the context of the Copernicus Land Monitoring Service High-Resolution Vegetation and Productivity Parameters (HR-VPP) project, a network of European eVALuation (EVAL) sites was defined to perform product intercomparisons over a representative sampling in terms of land cover and geographical area. EVAL is thus a spatial sampling of 3800 evaluation sites over the 39 countries of the European Economic Area (EEA39) [1]. The main selection criterium is the homogeneity, in terms of land cover, over areas of 1 km x 1 km (more than 70% of the area corresponds to the same land cover class) to be useful for the evaluation of decametric and hectometric (300 – 500 m) resolution products. </p> <p>The selection was based on surface albedo validation sites (SAVS1.0) [2], and European long-term ecosystem research (eLTER) sites [3]. Those sites were first screened to be homogeneous in land cover type, and then visually inspected using Google Earth to select homogeneous sites. Initially, only 597 sites (158 SAVS1.0 and 439 eLTER) were selected. Then, the network was complemented with Land use and land cover survey (LUCAS) samples [4] to get a better representativeness of European land cover types and geographical distribution. 10 additional LUCAS sites were selected for each 1 x 1 degree according to the same homogeneity criteria. Thereafter, random rejection of sites was conducted to keep the distribution of EVAL sites similar to that of the EEA-39 area (see distribution in <em>Figure_1</em>). In the last step, under sampled regions (e.g., Turkey or Iceland) were filled through visually identification of additional homogeneous sites.</p> <p>A total of 3800 sites were finally selected. The CORINE Land Cover 2018 (CLC2018) [5] was used to assign the land cover class to each site to allow the analysis per cover type. An aggregation of the CLC land cover classes was performed to reduce the number of classes to 8 main classes: Broad-leaved forest (BLF), Needle-leaf forest (NLF), Mixed Forest (MF), Crops, Grass, Shrubs, Agroforestry and Sparse. <em>Figure_2</em> shows the distribution of the 3800 EVAL sites per aggregated land cover type as compared to the actual distribution over the whole EEA-39. </p> <p> The xlsx file provided in this dataset contains two spreadsheets with the following information:</p> <ul> <li>‘EVAL_sites’: ‘Site label’, ‘Latitude’, ‘Longitude’, ‘CLC 2018’,’ Aggregated Land Cover’, and ‘Environmental zone’ for each location. </li> <li> ‘Legend’: information about the legends used in the dataset (CLC2018, aggregated land cover and environmental zone) and the number of EVAL samples for each class.</li> </ul> <p> </p> <p><strong><u>References:</u></strong></p> <p>[1] <a href="https://sdi.eea.europa.eu/catalogue/srv/api/records/8526ff78-b000-42e1-8360-a2fb3a51e4ac">https://sdi.eea.europa.eu/catalogue/srv/api/records/8526ff78-b000-42e1-8360-a2fb3a51e4ac</a></p> <p>[2] Loew, A., Bennartz, R., Fell, F., Lattanzio, A., Doutriaux-Boucher, M., Schulz, J., 2016. A database of global reference sites to support validation of satellite surface albedo datasets (SAVS 1.0). Earth Syst. Sci. Data 8, 425–438. https://doi.org/10.5194/essd-8-425-2016</p> <p>[3] <a href="https://www.lter-europe.net/">https://www.lter-europe.net/</a></p> <p>[4] <a href="https://ec.europa.eu/eurostat/statistics-explained/index.php?title=LUCAS_-_Land_use_and_land_cover_survey">https://ec.europa.eu/eurostat/statistics-explained/index.php?title=LUCAS_-_Land_use_and_land_cover_survey</a></p> <p>[5] <a href="https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac" target="_blank" rel="noopener">https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac</a></p> <p> </p>
Phase picker models and training data for paper "Deep learning models for regional phase detection on seismic stations in Northern Europe and the European Arctic"
<p>This ZIP file includes tensorflow models for seismic phase detection. Please see how to use these models here: https://github.com/NorwegianSeismicArray/tphasenet</p> <p>The HDF5 files includes waveforms and labels which are part of the training data set (only NORSAR event catalogue and station ARA0).</p>
Dataset of FEUTURE Online Paper No. 10 "Knowledge Cohesion in European Regions: Convergence and Cohesion with Turkey" - Data on PhD
<p>The dataset of the paper on "Flow of Knowledge" consists of the following variables: </p> <p>- Doctorate holders by region of doctoral award 2009</p> <p>- Mobility intentions of doctorate holders by intended region of destination in year 2009</p> <p>- Principal job of employed doctorate holders by occupation and field of science, 2009 </p> <p>- Doctorate holders' satisfaction level on their principal job by selected title, 2009</p> <p>- Recent doctorate recipients' satisfaction level on their principal job by selected title, 2009</p> <p>- Doctorate holders by sector of employed and sex, 2009</p> <p>- Recent doctorate recipients' average and median gross annual earnings by sector of employed and sex, 2009</p> <p>- Employed doctorate holders : perception regarding their job qualification by field of doctorate degree, 2009</p> <p>- Doctorate holders by employment situation and sex, 2009</p> <p>- Doctorate recepients's average and median gross annual earnings by sector of employed and sex, 2009</p> <p>- Recent doctorate recipients' avarage and median age at graduation and gross and net time to compilation their qualification by field of doctorate degree, 2009</p> <p>- Doctorate holders by age group and sex, 2009</p> <p>- Employed doctotare holders by employment situation, type of contract, working time in current employment and sex, 2009</p> <p>- Proportion of recent(**) doctorate recipients by field of doctorate degree and primary(*) source of funding during completion doctorate, 2009 </p> <p>- Employment situation of doctorate holders by sex and year of doctorate award, 2009</p> <p>- Recent doctorate recipients by employment situation and sex, 2009</p> <p>- Doctorate recepints' avarage and median age at graduation and gross and net time to compilation their qualification by field of doctorate degree, 2009</p> <p>- Doctorate holders who moved out Turkey for a period of at least 3 months between January 2000 and December 2009(*)</p> <p>- Principal job of employed recent doctorate recipients by occupation, 2009</p> <p>- Reasons of mobility intentions of doctorate holders in the next 12 months</p> <p>- Proportion of doctorate holders by field of doctorate degree and primary(*) source of funding during completion doctorate, 2009 </p>
Dataset of FEUTURE Online Paper No. 10 "Knowledge Cohesion in European Regions: Convergence and Cohesion with Turkey"
<p>The dataset provides network statistics based on the EU Framework Programme data from the first round (FP1, 1984-1987) till the last round (FP8 -H2020, 2013-2020) (Nodes (Vertices), Unique Edges, Edges With Duplicates, Total Edges, Self-Loops, Average Geodesic Distance, Graph Density, Average Degree, Average Betweenness, Centrality Average Closeness, Centrality Average Eigenvector, Centrality Average Clustering Coefficient).</p>
Dataset used in the article: Cunico, G., Aivazidou, E., Mollona, E. (2019), "European Cohesion Policy performance and citizens' awareness: A holistic System Dynamics framework", Investigaciones Regionales – Journal of Regional Research (In Press)
<p>In the context of the abovementioned article, interviews with experts about EU funding, as well as a workshop, were performed to find any missing factors or links in the conceptual model developed. This dataset provides detailed information about the interviews and the workshop. The related research is conducted in the context of the Horizon 2020 project PERCEIVE (www.perceiveproject.eu).</p>
Flight altitude dynamics of migrating European nightjars across regions and seasons
<p>Avian migrants may fly at a range of altitudes, but usually concentrate near strata where a combination of flight conditions is favourable. The aerial environment can have a large impact on the performance of the migrant and is usually highly dynamic, making it beneficial for the bird to regularly check the flight conditions at alternative altitudes. We recorded the migrations between northern Europe and sub-Saharan Africa of European nightjars <em>Caprimulgus europaeus</em> to explore their altitudinal space use during spring and autumn flights to test whether their climbs and descents were performed according to predictions from flight mechanical theory. The nightjars commonly operated at ascent rates below the theoretical maximum, and periods of descent were commonly undertaken by active flight, and rarely by energetically cheaper gliding flight, allowing the birds to sink at lower rates than possible during a best glide scenario. Spring migration across all regions was associated with more exploratory vertical flights involving major climbs, a higher degree of vertical displacement within flights, and less time spent in level flight, although flight altitude <i>per se</i> was only higher during the Sahara crossing. This study demonstrates a surprisingly frequent use of slow vertical displacements during migratory flights of a long-distance avian migrant and show how these behaviours vary between seasons and regions, presumably in response to different flight conditions. The results should inspire future studies on the potential costs associated with frequent altitude changes and their trade-offs against anticipated flight condition improvements for aerial migrants.</p>
Orchid-mycorrhizal fungi interactions reveal a duality in their network structure in two European regions differing in climate
<p><span>Network analysis is an effective tool to describe and quantify the ecological interactions between plants and root-associated fungi.</span><span> Mycoheterotrophic plants, such as orchids, critically rely on mycorrhizal fungi for nutrients to survive, therefore, investigating the structure of those intimate interactions brings new insights into the plant community assembly and coexistence. So far, there is little consensus on the structure of those interactions, described either as nested (generalist interactions), modular (highly specific interactions) or of both topologies. Biotic factors (e.g., mycorrhizal specificity) were shown to influence the network structure, while there is less evidence of abiotic factor effects. By</span><span> using next-generation sequencing of the orchid mycorrhizal fungal (OMF) community associated with 238 plant individuals belonging to 17 orchid species, we assessed the structure of four orchid-OMF networks in two European regions under contrasting climatic conditions (Mediterranean vs Continental).</span> <span>Each network contained four to 12 co-occurring orchid species, including up to eight species shared among the sites</span><span>. All four networks were both nested and modular, and fungal communities were different between co-occurring orchid species, despite multiple sharing of fungi across some orchids. Co-occurring orchid species growing in Mediterranean climates were associated with more dissimilar fungal communities, consistent with a greater modular structure compared to the Continental ones. The OMF diversity was comparable among orchid species since most orchids were associated with multiple rarer fungi and with only a few highly dominant ones in the roots. Our results provide useful highlights on potential factors involved in structuring plant-mycorrhizal fungi interactions in different climatic conditions.</span></p>
Spiriva Observational Study Measuring Saint George's Respiratory Questionnaire (SGRQ) in Routine Medical Practice in Central & Eastern European Region
ClinicalTrials.gov study NCT01006135. IPD Sharing: Not stated. Countries: 6. Publications: 1.
Orchid-mycorrhizal fungi interactions reveal a duality in their network structure in two European regions differing in climate
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High-quality SNPs from genic regions highlight introgression patterns among European white oaks (Quercus petraea and Q. robur)
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Regional variation in insularity effects on acorn herbivory in European oaks challenges predictions of lower herbivory on islands
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Flight altitude dynamics of migrating European nightjars across regions and seasons
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Data from: High-resolution analysis of red deer (<em>Cervus elaphus</em>) management units in a Central European region of high human population density reveals severe effects on genetic diversity and differentiation
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Multiple full-length variants of the Mitochondrial COI DNA Barcode Region are prevalent in North European Sawflies
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Data from: Small beetle, large-scale drivers: how regional and landscape factors affect outbreaks of the European spruce bark beetle
Unprecedented bark beetle outbreaks have been observed for a variety of forest ecosystems recently, and damage is expected to further intensify as a consequence of climate change. In Central Europe, the response of ecosystem management to increasing infestation risk has hitherto focused largely on the stand level, while the contingency of outbreak dynamics on large-scale drivers remains poorly understood. To investigate how factors beyond the local scale contribute to the infestation risk from Ips typographus (Col., Scol.), we analysed drivers across seven orders of magnitude in scale (from 103 to 1010 m²) over a 23-year period, focusing on the Bavarian Forest National Park. Time-discrete hazard modelling was used to account for local factors and temporal dependencies. Subsequently, beta regression was applied to determine the influence of regional and landscape factors, the latter characterized by means of graph theory. We found that in addition to stand variables, large-scale drivers also strongly influenced bark beetle infestation risk. Outbreak waves were closely related to landscape-scale connectedness of both host and beetle populations as well as to regional bark beetle infestation levels. Furthermore, regional summer drought was identified as an important trigger for infestation pulses. Large-scale synchrony and connectivity are thus key drivers of the recently observed bark beetle outbreak in the area. Synthesis and applications. Our multiscale analysis provides evidence that the risk for biotic disturbances is highly dependent on drivers beyond the control of traditional stand-scale management. This finding highlights the importance of fostering the ability to cope with and recover from disturbance. It furthermore suggests that a stronger consideration of landscape and regional processes is needed to address changing disturbance regimes in ecosystem management.
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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.