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European roller
ID no.: MP 007 Museum: The Krystyna and Włodzimierz Tomek Natural Science Museum in Ciężkowice https://muzea.malopolska.pl/en/objects-list/156 Digitalisation: RDW MIC, Małopolska's Virtual Museums project Source: Objaverse 1.0 / Sketchfab
THE TUBE OF THE COMMON REED FOR NESTING OF EUROPEAN ORCHARDS BEE
<p>The tube (1) of the common reed for nesting of European orchards bee, used in agriculture in field of beekeeping and subfield of rearing technology of solitary bee species such as orchard bees. The present invention (1) relates to constructing suitable, and for orchards bees acceptable nesting material in the form of a linear tube with two separate openings (3) at its ends, made from natural materials: dry common reed stem. Subject tube (1) is cutting under a sharp angle (α). The opposite ends of the tubes (1) are in a symmetrical position, facing each as object and the image in the mirror, in relation to the central node (2). The present invention (1) has an external diameter (d) at the ends, and with overall length (2L). Figure 1.</p>
DISCARDLess: Strategies for the gradual elimination of discards in European fisheries
<p>DiscardLess will help provide the knowledge, tools and technologies as well<br /> as the involvement of the stakeholders to achieve the gradual elimination of<br /> discarding. These will be integrated into Discard Mitigation Strategies (DMS)<br /> proposing cost-effective solutions at all stages of the seafood supply chain.</p> <p>The recorded presentation by Clara Ulrich, Scientific coordinator of DiscardLess Project, is part of the official programme of EC Conference <strong>The Atlantic – Our Shared Resource. Making the Vision Reality</strong> in support of the Galway Declaration, 16-17 April 2015 (link: http://ec.europa.eu/research/bioeconomy/news-events/news/20150430_1_en.htm).</p> <p>DiscardLess is funded by the European Commission’s Horizon 2020 Framework Programme (2014-2020) Call number H2020-SFS-2014-2 Topic SFS-09-2014<br /> under Grant Agreement No 633 680.</p>
Training for RD Management: Comparative European Approaches, Raw data & Tidied Data
<p>Raw and tidied data of responses to the Knowledge Exchange survey around Training for Research Data management. The data accompanies the Knowledge Exchange report 'Training for RD Management: Comparative European Approaches' DOI: 10.5281/zenodo.50068</p> <p> </p>
European Wind Atlas data disk
<p>This data set is a ZIP archive of the European Wind Atlas data disk; data set is described in Appendix D of the atlas:</p> <p>The main results of the European Wind Atlas analysis – the regionally representative wind statistics for each station – are furnished on a disk at the back of the Atlas. The disk furthermore contains the wind speed data in the form of histograms. The disk is divided into a number of subdirectories corresponding to the EC12 countries. The subdirectories are named as the country codes given below:</p> <p>B – Belgium</p> <p>DK – Denmark</p> <p>F – France</p> <p>D – Germany (FRG)</p> <p>GR – Greece</p> <p>I – Italy</p> <p>EI – Ireland</p> <p>L – Luxembourg</p> <p>NL – Netherlands</p> <p>P – Portugal</p> <p>E – Spain</p> <p>GB – United Kingdom</p> <p>Radiosonde statistics for all the countries are in a separate subdirectory with the name RS.</p> <p>The Wind Atlas data are stored as sequential ASCII files with the file name extension LIB, and contain 48 lines/records of information. The contents of a file are shown schematically in Table D.1 of the European Wind Atlas.</p> <p>The raw data are stored as sequential ASCII files with the file name extension TAB. The contents of a histogram file are shown schematically in Table D.2 of the European Wind Atlas.</p>
The raw EEG data, 4 files (EEG_A to D), in European data format (.edf)
<p>EEG data for comparison to PIR-estimated sleep in the Wellcome Open Research article:</p> <p>'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status'</p>
Supplementary material 3: List of tested and analyzed data sharing tools (non-exhaustive) from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
List of tested and analyzed data sharing tools (non-exhaustive)
Supplementary material 2: Definitions and Concepts from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Definitions and concepts in the context of the main paper.
Supplementary material 1: List of selected tools. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
List of selected tools.
Figure 7. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 7. - Mobile app for sporadic observations reporting.
Figure 2. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 2. - The Plazi workflow (green) within EU BON.
Food composition database for nutrient intake: selected vitamins and minerals in selected European countries
<p>Following a request from the European Commission for a review of European dietary reference values (DRVs), the EFSA’s Panel on Dietetic Products, Nutrition and Allergies (NDA) has prepared a number of Scientific Opinions on DRVs for micronutrients. The DATA Unit supported this activity by estimating the nutrient intake of a number of micronutrients in nine selected European countries and different age groups. In addition, the DATA Unit also provided information on average content of food sources of the respective nutrients per country based on the composition database, as well as main food group contributors to nutrient intakes and assessed the comparability of the provided data with pertinent published intake data.</p> <p>Intake estimates have been assessed using food consumption data from the EFSA Comprehensive Food Consumption Database (EFSA, 2011a) and the EFSA Nutrient composition database. Food composition data used to populate the Nutrient composition database were provided to EFSA through the EFSA procurement project ‘<em>Updated food composition database for nutrient intake’</em> (Roe at al., 2013). Data were provided following the EFSA specification for standard sample description for food and feed and were classified according to the FoodEx2 classification system of EFSA (EFSA, 2011b).</p> <p>The food composition data used in these assessments and here published cover the following vitamins and minerals: calcium (Ca); copper (Cu); cobalamin (vitamin B12); magnesium (Mg); niacin; phosphorus (P); potassium (K); riboflavin; thiamin; iron (Fe); selenium (Se); vitamin B6; vitamin K, zinc (Zn), and vitamin E<sup>1</sup>. The food composition dataset contains data from seven<sup>2</sup> countries: Finland, France, Germany, Italy, Netherlands, Sweden, and United Kingdom. This dataset version has been checked for outliers but is prior to data completion for missing foods and nutrient values.</p> <p><sup>1</sup> Vitamin E is defined as alpha-tocopherol (AT) only, however as most food composition databases in the EU contain values as alpha-tocopherol equivalents (TE), data on TE are also provided</p> <p><sup>2</sup> For the nutrient intake estimates of Ireland and Latvia present in the opinions of the EFSA Panel on Dietetic Products, Nutrition and Allergies (NDA), food composition data from UK and Germany were respectively used</p>
Supplementary Data: The Benefits of Cooperation in a Highly Renewable European Electricity Network
<p>Supplementary Data</p> <p><em>The Benefits of Cooperation in a Highly Renewable European Electricity Network</em><br> <em>doi:10.1016/j.energy.2017.06.004</em><br> <em>arXiv:1704.05492</em></p> <p>The files in this record contain the model-specific code, input data, and output data considered in the Benefits of Cooperation paper.</p> <p>You are welcome to use the provided data under the given open-source licence, and if you do please cite the paper <em>doi:10.1016/j.energy.2017.06.004</em>.<br> Please note that the derivation of the data in data/renewables/ is not open, because it uses the REatlas software [7] which has a closed source server part. (There is an free software implementation of the REatlas at https://github.com/FRESNA/atlite but it wasn't ready in time to be used for this dataset.)</p> <p>The code that is required to generate the output data consists of</p> <ul> <li>the python code opt_ws_network.py that builds and runs the PyPSA [0] model</li> <li>a SLURM script parameter_batch.py to run the model with different parameters</li> <li>a YAML file options.yml with the default parameter settings</li> </ul> <p>The code heavily relies on the python package <em>vresutils</em> which is available at https://github.com/FRESNA/vresutils</p> <p>The record also contains the input data in the data/ directory. They are described in detail in the paper, but a short summary is provided here:</p> <ul> <li><strong>costs</strong>: cost and other input parameter assumptions, see <em>Table 1</em> in the paper.</li> <li><strong>graph</strong>: the network topology is given by a list of nodes (country names) and a list of edges connecting two nodes. Based on [1,2].</li> <li><strong>hydro</strong>: hydro generation data provided by the Restore2050 project [3] <ul> <li>inflow/: contains a csv files with daily inflow data for each country</li> <li>emil_hydro_capas.csv: country-scale power and energy capacity</li> <li>ror_ENTSOe_Restore2050.csv: the share of run-of-river of the total hydro generation, from ENTSO-E [4] or if unavailable from [3]</li> </ul> </li> <li><strong>load</strong>: hourly country-scale consumption for 2011 from ENTSO-E [5]</li> <li><strong>renewables</strong>: generation potentials for the renewable technologies onshore wind, offshore wind, and solar per country based on historic weather data [6]. The jupyter-notebook europe_renewables_potentials.ipynb describes the data generation and uses the REatlas software [7] which has open-source client but closed-source server software. The used cutout can therefore not be made available here, but is solely based on data from [8]. The processed data are in: <ul> <li>store_p_nom_max/: installation potential per technology per region</li> <li>store_o_max_pu_betas/: hourly maximum generation per unit of capacity per technology per region</li> </ul> </li> </ul> <p>The output data generated by the model is in sub-folders of the results/ directory following the naming scheme [costsource]-CO[CO2costs]-T[timerange]-[technologies]-LV[linevolume]_c[crossover]_base_[costsource]_solar1_7_[formulation]-[startdate]/, where</p> <ul> <li>costsource = diw2030</li> <li>CO2costs = 0</li> <li>timerange = 1_8761</li> <li>technologies = wWsgrpHb</li> <li>linevolume = [float], None (line volume constraint of float * 5e8 TWkm, or optimised line volume)</li> <li>crossover = 0 (deactivated the cross-over phase of the Gurobi optimiser)</li> <li>formulation = angles, [blank] (power flow formulations: 'angles', or 'cycles')</li> <li>startdate = time the optimisation was started</li> </ul> <p>Footnotes</p> <p>[0] https://pypsa.org/ , https://doi.org/10.5281/zenodo.582307</p> <p>[1] S Becker, Transmission grid extensions in renewable electricity systems, PhD thesis (2015)</p> <p>[2] ENTSO-E, Indicative values for Net Transfer Capacities (NTC) in Continental Europe. European Transmission System Operators, 2011, https://www.entsoe.eu/publications/market-reports/ntc-values/ntc-matrix/Pages/default.aspx, accessed Jul 2014.</p> <p>[3] A Kies, K Chattopadhyay, L von Bremen, E Lorenz, D Heinemann, Simulation of renewable feed-in for power system studies, RESTORE 2050 project report, https://doi.org/10.5281/zenodo.804244</p> <p>[4] European Transmission System Operators, Installed Capacity per Production Type in 2015, ENTSO-E (2016), https://transparency.entsoe.eu/generation/r2/installedGenerationCapacityAggregation/show</p> <p>[5] https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package</p> <p>[6] D. Heide, M. Greiner, L. Von Bremen, C. Hoffmann, Reduced storage and balancing needs in a fully renewable European power system with excess wind and solar power generation, Renewable Energy 36 (9) (2011) 2515–2523. https://doi.org/10.1016/j.renene.2011.02.009</p> <p>[7] G. B. Andresen, A. A. Søndergaard, M. Greiner, Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis, Energy 93, Part 1 (2015) 1074 – 1088. https://doi.org/10.1016/j.energy.2015.09.071</p> <p>[8] S Saha et al., 2014: The NCEP Climate Forecast System Version 2. J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1</p>
Pig population data complementing the annual report on the epidemiological analyses of African swine fever (ASF) in the European Union - Lithuania
<p>This dataset contains swine population data.</p> <p><strong>Reporting authorities contributing to the data collection:</strong></p> <ul> <li>ASF2024_POP_EXTRACTION_LT - State Food and Veterinary Service (SFVS)</li> <li>ASF2023_POP_EXTRACTION_LT - State Food and Veterinary Service (SFVS)</li> <li>ASF2022_POP_EXTRACTION_LT - State Food and Veterinary Service (SFVS)*</li> <li>ASF2022_POP_EXTRACTION_LT - State Food and Veterinary Service (SFVS)</li> </ul> <p> </p> <p> </p> <p>*This version of the animal population data has been republished with the establishment and subnit identification code (estabId, subUnitId) columns empty due to data protection reasons</p>
Pig population data complementing the annual report on the epidemiological analyses of African swine fever (ASF) in the European Union - Slovakia
<p>This dataset contains swine population data.</p> <p><strong>Reporting authorities contributing to the data collection:</strong></p> <ul> <li>ASF2024_POP_EXTRACTION_SK - State Veterinary and Food Institute (SVPU)</li> <li>ASF2023_POP_EXTRACTION_SK - State Veterinary and Food Institute (SVPU)</li> <li>ASF2022_POP_EXTRACTION_SK - State Veterinary and Food Institute (SVPU)*</li> <li>ASF2022_POP_EXTRACTION_SK - State Veterinary and Food Institute (SVPU)</li> </ul> <p> </p> <p> </p> <p>*This version of the animal population data has been republished with the establishment and subnit identification code (estabId, subUnitId) columns empty due to data protection reasons</p>
Laboratory data complementing the annual report on the epidemiological analyses of African swine fever (ASF) in the European Union - Romania
<p>This dataset contains ASF laboratory analytical results in domestic pigs and wild boar.</p> <p><strong>Reporting authorities contributing to the data collection:</strong></p> <ul> <li>ASF2024_RO - National Sanitary Veterinary and Food Safety Authority (ANSVSA)</li> <li>ASF2023_RO - National Sanitary Veterinary and Food Safety Authority (ANSVSA)</li> <li>ASF2022_RO - National Sanitary Veterinary and Food Safety Authority (ANSVSA)*</li> <li>ASF2022_RO - National Sanitary Veterinary and Food Safety Authority (ANSVSA)</li> </ul> <p> </p> <p> </p> <p>*This version of the ASF laboratory data has been republished with the subunit identification code (sampUnitIds.subUnitId) column empty due to data protection reasons</p>
Laboratory data complementing the annual report on the epidemiological analyses of African swine fever (ASF) in the European Union - Lithuania
<p>This dataset contains ASF laboratory analytical results in domestic pigs and wild boar.</p> <p><strong>Reporting authorities contributing to the data collection:</strong></p> <ul> <li>ASF2024_LT - National Food and Veterinary Risk Assessment Institute (NMVRVI)</li> <li>ASF2023_LT - National Food and Veterinary Risk Assessment Institute (NMVRVI)</li> <li>ASF2022_LT - National Food and Veterinary Risk Assessment Institute (NMVRVI)*</li> <li>ASF2022_LT - National Food and Veterinary Risk Assessment Institute (NMVRVI)</li> </ul> <p> </p> <p> </p> <p>*This version of the ASF laboratory data has been republished with the subunit identification code (sampUnitIds.subUnitId) column empty due to data protection reasons</p>
Laboratory data complementing the annual report on the epidemiological analyses of African swine fever (ASF) in the European Union - Greece
<p>This dataset contains ASF laboratory analytical results in domestic pigs and wild boar.</p> <p><strong>Reporting authorities contributing to the data collection:</strong></p> <ul> <li>ASF2024_GR - Hellenic Ministry of Rural Development and Food (MINAGRIC)</li> <li>ASF2023_GR - Hellenic Ministry of Rural Development and Food (MINAGRIC)</li> </ul>
Exome-wide association analysis (ExWAS) of clonal haematopoiesis in 136,401 Admixed Americans and 416,118 Europeans
<p>We performed exome-wide association analysis (ExWAS) of germline genetic variants identified from whole-exome sequencing (WES) to identify novel inherited genetic determinants of clonal haematopoiesis (CH). Here, we provide the summary statistics from ExWAS CH performed on Admixed Americans recruited to the Mexico City Prospective Study (MCPS), Europeans recruited to the United Kingdom Biobank (UKB), and cross-ancestry meta-analysis of Admixed Americans and Europeans. Analyses was performed with REGENIE software (Firth's logistic regression), Fisher's exact test, and METAL software (inverse variance-weighted average method to derive effect size and <em>P</em>-value method to derive P value), respectively.</p> <p> </p> <p>In version 1 of this repository, UKB variants (*_UKB.tsv) with minor allele frequency (MAF) 1% or more were uploaded. In version 2, this is now rectified so that rare variants with MAF of 0.1% or more were uploaded. This threshold now matches the MCPS (*_MCPS.tsv) and UKB-MCPS meta-analysis summary statistics (*_MCPS-UKB_meta-analysis.tsv)</p>
Pig population data complementing the annual report on the epidemiological analyses of African swine fever (ASF) in the European Union - Latvia
<p>This dataset contains swine population data.</p> <p><strong>Reporting authorities contributing to the data collection:</strong></p> <ul> <li>ASF2024_POP_EXTRACTION_LV - Agricultural Data Centre (LDC)</li> <li>ASF2023_POP_EXTRACTION_LV - Agricultural Data Centre (LDC)</li> <li>ASF2022_POP_EXTRACTION_LV - Agricultural Data Centre (LDC)*</li> <li>ASF2022_POP_EXTRACTION_LV - Agricultural Data Centre (LDC)</li> </ul> <p> </p> <p> </p> <p>*This version of the animal population data has been republished with the establishment and subnit identification code (estabId, subUnitId) columns empty due to data protection reasons</p>
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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.