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6,170 results for “european”
Figure 1 in Taxonomy of European Damaeidae X. Description of Coronabelba unicornis n. gen., n. sp. (Acari, Oribatida, Damaeidae) from Abkhazia, with comments on genusMetabelba Grandjean, 1936
Figure 1 Coronabelba unicornisn. sp., adult: A – dorsal view (legs not shown); B – ventral view (gnathosoma and legs not shown), C – lateral view (gnathosoma and legs not shown), D – cerotegument from sejugal area.
Figure 4 Coronabelba unicornis n in Taxonomy of European Damaeidae X. Description of Coronabelba unicornis n. gen., n. sp. (Acari, Oribatida, Damaeidae) from Abkhazia, with comments on genusMetabelba Grandjean, 1936
Figure 4 Coronabelba unicornis n. sp., adult: A – larval exuvium, dorsal view; B – protonymphal exuvium, dorsal view; C – deutonymphal exuvium, dorsal view; D – notogaster with tritonymphal exuvium, lateral view; E – notogastral setalm.
Figure 3 Coronabelba unicornis n in Taxonomy of European Damaeidae X. Description of Coronabelba unicornis n. gen., n. sp. (Acari, Oribatida, Damaeidae) from Abkhazia, with comments on genusMetabelba Grandjean, 1936
Figure 3 Coronabelba unicornis n. sp., adult: A – subcapitulum, ventral view; B – palp, left, antiaxial view; C – chelicera, right, paraxial
Text-fig. 1. Bivariate diagram of length and width of the upper dentition of selected European small and middle size Amphicyoninae compared with the Tuchořice specimens. a: P4; b: M1; c: M2; d: m1; e: m2. Data from Schlosser (1899, 1904), Thenius (1949), Dehm (1950), Heizmann (1973), Ginsburg (1977, 1989), Peigné (2012). Dotted line – ranges of maximum and minimum values from the average of teeth of Cynelos lemanensis from Ulm (Peigné and Heizmann 2003). Abbreviations: L – length; W – width. in The Amphicyoninae (Amphicyonidae, Carnivora, Mammalia) Of The Early Miocene From Tuchořice, The Czech Republic
Text-fig. 1. Bivariate diagram of length and width of the upper dentition of selected European small and middle size Amphicyoninae compared with the Tuchořice specimens. a: P4; b: M1; c: M2; d: m1; e: m2. Data from Schlosser (1899, 1904), Thenius (1949), Dehm (1950), Heizmann (1973), Ginsburg (1977, 1989), Peigné (2012). Dotted line – ranges of maximum and minimum values from the average of teeth of Cynelos lemanensis from Ulm (Peigné and Heizmann 2003). Abbreviations: L – length; W – width.
Fig. 4 in Spatial Heterogeneity Of Steppe Bird Community In The Azov-Black Sea Enclave Of The European Dry-Steppe Zone (Southern Ukraine)
Fig. 4. Distribution of steppe dominants, co-dominants and rare steppe species (Black Book of Ukraine, 2009) by subregions of the dry-steppe enclave: * the largest areas in most count squares are covered by large bodies of water (seas and their bays, limans, the Dnipro floodplain).
Fig. 1 in Spatial Heterogeneity Of Steppe Bird Community In The Azov-Black Sea Enclave Of The European Dry-Steppe Zone (Southern Ukraine)
Fig. 1. Division of the Azov-Black Sea dry-steppe enclave into count squares of 10x10 km and subregions: 1 — RB Prychornomoria, 2 — Lower Dnipro, 3 — LB Prychornomoria, 4 — N Prysyvashshia, 5 — NW Pryazovia, 6 — Syvash, 7 — Western Crimea, 8 — Central Crimea, 9 —Kerch Peninsula, 10 — Foothills.
Fig. 2 in Spatial Heterogeneity Of Steppe Bird Community In The Azov-Black Sea Enclave Of The European Dry-Steppe Zone (Southern Ukraine)
Fig. 2. Similarity of subregions of the dry-steppe enclave in the number of all steppe bird species: 1 — RB Prychornomoria, 2 — Lower Dnipro, 3 — LB Prychornomoria, 4 — N Prysyvashshia, 5 — NW Pryazovia, 6 — Syvash, 7 — Western Crimea, 8 — Central Crimea, 9 —Kerch Peninsula, 10 — Foothills.
Fig. 3 in Spatial Heterogeneity Of Steppe Bird Community In The Azov-Black Sea Enclave Of The European Dry-Steppe Zone (Southern Ukraine)
Fig. 3. Similarity of subregions of the dry-steppe enclave in the number of rare steppe bird species: 1 — RB Prychornomoria, 2 — Lower Dnipro, 3 — LB Prychornomoria, 4 — N Prysyvashshia, 5 — NW Pryazovia, 6 — Syvash, 7 — Western Crimea, 8 — Central Crimea, 9 —Kerch Peninsula, 10 — Foothills.
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021). in Floristic, Vegetation And Climate Assessment Of The Early/Middle Miocene Parschlug Flora Indicates A Distinctly Seasonal Climate
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021).
Journal data for European scholarly journals
<p>This relates to the following study: https://doi.org/10.5281/zenodo.5909512</p> <p>The methodology is described in the linked manuscript.</p> <p> </p>
Assessing the value of monitoring to biological inference and expected management performance for a European goose population
<p>1. Informed conservation and management of wildlife require sufficient monitoring to understand population dynamics and to direct conservation actions. Because resources available for monitoring are limited, conservation practitioners must strive to make monitoring as cost-effective as possible.</p> <p>2. Our focus was on assessing the value of monitoring to the adaptive harvest management (AHM) program for pink-footed geese (Anser brachyrhynchus). We conducted a retrospective analysis to assess the costs and benefits of a capture-mark-resight (CMR) program, a productivity survey, and biannual population censuses. Using all available data, we fit an integrated population model (IPM) and assumed that inference derived from it represented the benchmark against which reduced monitoring was to be judged. We then fit IPMs to reduced sets of monitoring data and compared their estimates of demographic parameters and expected management performance against the benchmark IPM.</p> <p>3. Costs and the precision and accuracy of key demographic parameters decreased with the elimination of monitoring data. Eliminating the CMR program, while maintaining other monitoring instruments, resulted in the greatest cost savings, usually with small effects on inferential reliability. Productivity surveys were also expensive and some reduction in survey effort may be warranted. The biannual censuses were inexpensive and generally increased inferential reliability.</p> <p>4. The expected performance of AHM strategies was surprisingly robust to a loss of monitoring data. We attribute this result to explicit consideration of parametric uncertainty in harvest-strategy optimization and the fact that a broad range of population sizes is acceptable to stakeholders.</p> <p>5. Synthesis and applications: Our study suggests that existing or potential monitoring instruments for wildlife populations should be scrutinized as to their cost-effectiveness for improving biological inference and management performance. Using Svalbard pink-footed geese as a case study, we show that the loss of some existing monitoring instruments may not be as adverse as commonly assumed if data are jointly analyzed in an integrated population model. Finally, regardless of the monitoring data available, we suggest that conservation strategies that explicitly account for uncertainty in demography are more likely to be successful than those that do not<span>.</span></p>
ENTSO-E Pan-European Climatic Database (PECD 2021.3) in Parquet format
<p><strong>ENTSO-E Pan-European Climatic Database (PECD 2021.3) in Parquet format</strong></p> <p><strong>TL;DR</strong>: this is a tidy and friendly version of a subset of the PECD 2021.3 data by ENTSO-E: hourly capacity factors for wind onshore, offshore, solar PV, hourly electricity demand, weekly inflow for reservoir and pumping and daily generation for run-of-river. All the data is provided for >30 climatic years (1982-2019 for wind and solar, 1982-2016 for demand, 1982-2017 for hydropower) and at national and sub-national (>140 zones) level.</p> <p><strong>UPDATE (19/10/2022): </strong>updated the demand files due after fixing a bug in the processing code (the file for 2030 was the same for 2025) and solving an issue caused by a malformed header in the ENTSO-E excel files. </p> <p> </p> <p> </p> <p>ENTSO-E has released with the latest European Resource Adequacy Assessment (<a href="https://www.entsoe.eu/outlooks/eraa/">ERAA 2021</a>) all the inputs used in the study.<br> Those inputs include:<br> - Demand dataset: <a href="https://eepublicdownloads.azureedge.net/clean-documents/sdc-documents/ERAA/Demand%20Dataset.7z">https://eepublicdownloads.azureedge.net/clean-documents/sdc-documents/ERAA/Demand%20Dataset.7z</a><br> - Climate data: <a href="https://eepublicdownloads.entsoe.eu/clean-documents/sdc-documents/ERAA/Climate%20Data.7z">https://eepublicdownloads.entsoe.eu/clean-documents/sdc-documents/ERAA/Climate%20Data.7z</a></p> <p>The data files and the methodology are available on the <a href="https://www.entsoe.eu/outlooks/eraa/2021/eraa-downloads/">official webpage</a>. </p> <p>As done for the previous releases (see <a href="https://zenodo.org/record/3702418#.YbmhR23MKMo">https://zenodo.org/record/3702418#.YbmhR23MKMo</a> and <a href="https://zenodo.org/record/3985078#.Ybmhem3MKMo">https://zenodo.org/record/3985078#.Ybmhem3MKMo</a>), the original data - stored in large Excel spreadsheets - have been tidied and formatted in open and friendly formats (CSV for the small tables and Parquet for the large files)</p> <p>Furthermore, we have carried out a simple country-aggregation for the original data - that uses instead >140 zones.</p> <p><strong>DISCLAIMER</strong>: <em>the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies. </em></p> <p><strong>Description</strong></p> <p>This dataset includes the following files:</p> <p>- <em>capacities-national-estimates.csv</em>: installed capacity in MW per zone, technology and the two scenarios (2025 and 2030). The files include also the total capacity for each technology per country (sum of all the zones within a country)<br> - <em>PECD-2021.3-wide-LFSolarPV-2025</em> and <em>PECD-2021.3-wide-LFSolarPV-2030</em>: tables in Parquet format storing in each row the capacity factor for solar PV for a hour of the year and all the climatic years (1982-2019) for a specific zone. The two files contain the capacity factors for the scenarios "National Estimates 2025" and "National Estimates 2030"<br> - <em>PECD-2021.3-wide-Onshore-2025</em> and<em> PECD-2021.3-wide-Onshore-2030</em>: same as above but for wind onshore<br> - <em>PECD-2021.3-wide-Offshore-2025</em> and <em>PECD-2021.3-wide-Offshore-2030</em>: same as above but for wind offshore<br> - <em>PECD-wide-demand_national_estimates-2025</em> and<em> PECD-wide-demand_national_estimates-2030</em>: hourly electricity demand for all the climatic years for a specific zone. The two files contain the load for the scenarios "National Estimates 2025" and "National Estimates 2030" <br> - <em>PECD-2021.3-country-LFSolarPV-2025</em> and <em>PECD-2021.3-country-LFSolarPV-2030</em>: tables in Parquet format storing in each row the capacity factor for country/climatic year and hour of the year. The two files contain the capacity factors for the scenarios "National Estimates 2025" and "National Estimates 2030"<br> -<em> PECD-2021.3-country-Onshore-2025</em> and<em> PECD-2021.3-country-Onshore-2030</em>: same as above but for wind onshore<br> -<em> PECD-2021.3-country-Offshore-2025</em> and <em>PECD-2021.3-country-Offshore-2030</em>: same as above but for wind offshore<br> - <em>PECD-country-demand_national_estimates-2025</em> and <em>PECD-country-demand_national_estimates-2030</em>: same as above but for electricity demand<br> - <em>PECD_EERA2021_reservoir_pumping.zip</em>: archive with four files per each scenario: 1. table.csv with generation and storage capacities per zone/technology, 2. zone weekly inflow (GWh), 3. table.csv with generation and storage per country/technology and 4. country weekly inflow (GWh)<br> - <em>PECD_EERA2021_ROR.zip</em>: as for the previous file but the inflow is daily<br> - <em>plots.zip</em>: archive with 182 png figures with the weekly climatology for all the variables (daily for the electricity demand)</p> <p><strong>Note</strong></p> <p>I would like to thank Laurens Stoop for sharing the onshore wind data for the scenario 2030, that was corrupted in the original archive.</p>
Social Network analysis on European countries involved in agroecology research
<p>All the 124 (68 European and 56 Transnational) agroecology research projects identified in the mapping activities carried out by the task 1.3 of the AE4EU project were used to perform a weighted social network analysis (SNA) having the participating countries as nodes and collaborations in projects as edges.</p> <p>This dataset contains data related to this SNA and consists of two sheets:</p> <ul> <li><strong>Indexes</strong> where values of some measures for each identified country in the social network analysis are reported (number of European agroecological research projects coordinated by the country; number of transnational agroecological research projects coordinated by the country; Degree Centrality; Closeness Centrality)</li> <li><strong>Edge_weights</strong> where the weights for each edge between two countries are provided according to the times two countries cooperated together for a European or a transnational project.</li> </ul>
Supplementary material 1 from: Gruber B, Evans D, Henle K, Bauch B, Schmeller D, Dziock F, Henry P, Lengyel S, Margules C, Dormann C (2012) "Mind the gap!" – How well does Natura 2000 cover species of European interest? Nature Conservation 3: 45-62. https://doi.org/10.3897/natureconservation.3.3732
The table shows the 54 gap species ordered by species group. Please note: The comment column is based on expert opinion of the European Topic Centre, which has the latest version of the Natura2000 data base and also knowledge on confidential sites, which are deleted from the public version of the data base to protect rare species.
Universal Concepts List of 17 Indo-European Languages
<p>Universal Concepts List (100-words, Swadesh 1971=final) with supposed Proto-Indo-European cognate stems, and their primary, “unmarked” translations in 17 representative old and recent IE languages<br> (This is the summary list of work in progress at detailed lists, and not thoroughly formatted)<br> Important note: This summary is exclusively aimed at glottochronological purposes. This, above all requires finding the most common, popular, everyday, “unmarked” translation for the given test meanings/concepts, which often will neither be the term scientists would choose, nor must be similar with the assumed meaning of the root. Only then the root has to be found, only in order to decide, which translations in the above sense are cognate or not. In most cases this aim does not necessarily require detailed notions of the grammar. Note further that pure loan pronunciations are glottochronologically not counted as replacements.</p>
Data and R script for 'Early-life begging effort reduces adult body mass but strengthens behavioural defence of the rate of energy intake in European starlings (Sturnus vulgaris)'
<p>Data files and R script for Dunn et al. "Early-life begging effort reduces adult body mass but strengthens behavioural defence of the rate of energy intake in European starlings (<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>
STARS4ALL European Photometer Network Nov 2017
<p>Monthly dataset with the measurements taken by the TESS Network (Nov 2017)</p>
Data and script for Andrews et al. 'A marker of biological ageing predicts risk preference in European starlings, Sturnus vulgaris'
<p>Data and script for Andrews et al. 'A marker of biological ageing predicts risk preference in European starlings, Sturnus vulgaris'. Consists: One R script and three data .csv files.</p>
Supplementary Data: Full Results: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the full output data from each of the scenarios considered in the above publication. They also include the post-processed input data, which might be useful if you want to rerun the scenarios with only small changes to the input data.</p> <p>The scripts to build the model, input data and result summaries can be found in a <a href="https://zenodo.org/record/1146665">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p>For each scenario, there is a <a href="https://github.com/PyPSA/PyPSA">PyPSA</a> network file in <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5 format</a> and a CSV of shadow prices.</p> <p>To read in a network file do:</p> <pre><code class="language-python">import pypsa network = pypsa.Network("network_file_name.h5")</code></pre> <p>All data is released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0).</p>
European winter windstorm days from ERA5 and CMIP6 models
European winter storm hazard days generated from ERA5 (1980–2010) and CMIP6 models (1980–2010 and 2070–2100) for the historical and Shared Socioeconomic Pathways SSP126, SSP245, SSP370, and SSP585 experiments, based on Severino et al., 2023.<br> For each storm day, the specific ensemble member of the climate model which has been used to model the data can be read in the "event_name" property (e.g. 'event_name': 'IPSL-CM6A-LR_ssp585_mem0' corresponds to a storm day which has been generated by the ensemble member 0 of the climate model IPSL-CM6A-LR for the ssp585 experiment).<br> <br> <a href="https://doi.org/10.5194/egusphere-2023-205">Severino, L. G., Kropf, C. M., Afargan-Gerstman, H., Fairless, C., de Vries, A. J., Domeisen, D. I. V., and Bresch, D. N.: Projections and uncertainties of future winter windstorm damage in Europe, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-205, 2023.</a>
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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.