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6,170 results for “european”
European eel immune response against Vibrio vulnificus challenge
GEO Series GSE45163. Anguilla anguilla. 24 samples. Type: Expression profiling by array.
Effects of the total replacement of fish meal and fish oil with plant protein and oil sources on the hepatic transcriptome of two European sea bass (Dicentrarchus labrax) sub-families exhibiting diffe
GEO Series GSE27649. Dicentrarchus labrax. 24 samples. Type: Expression profiling by array.
Distinct early-life stage gene expression effects of hybridization among European and North American farmed and wild Atlantic salmon populations
GEO Series GSE184425. Salmo salar. 36 samples. Type: Expression profiling by array.
Genomic profiles vary with race and subtype in young African American and European American breast cancer (SNP)
GEO Series GSE26232. Homo sapiens. 49 samples. Type: SNP genotyping by SNP array; Genome variation profiling by SNP array.
Hepatic transcription profile study on European eels (Anguilla anguilla) exposed to Hg and b-sitosterol
GEO Series GSE53344. Anguilla anguilla. 66 samples. Type: Expression profiling by array.
FIGURE 12 in A further review of European Magelonidae (Annelida), including redescriptions of Magelona equilamellae and Magelona filiformis
FIGURE 12. Octomagelona bizkaiensis, holotype (MNCN 16.01/6887). Anterior (dorsal view).
Hydrological and Isotopic data for Central European catchments
<p>-) monthly isotope values in precipitation and runoff for major Central European catchments</p> <p>-) monthly volumes of precipitation and runoff for major Central European catchments</p> <p>-) calculation results and uncertainties of annual fraction of young water estimates for Central European catchments</p> <p>-) the North-Atlantic Oscillation index, annual and monthly</p>
FIGURE 8 in European Lepidocyrtus lignorum-group, new findings and redescritpion of Lepidocyrtus pulchellus Denis, 1926 (Collembola, Entomobryidae)
FIGURE 8. Lepidocyrtus pulchellus, dorsal head chaetotaxy (left side).
Data for "Exploring the Monthly Contribution of Drivers on European Summer Wildfires with Explainable Artificial Intelligence (XAI)"
<h3>Abstract</h3> <div> <p>We applied an XAI method to analyze the monthly contribution of wildfire drivers on summer fires in European forest, shrub and herbaceous vegetation areas from 2014 to 2023. Using burn area data and 18 features including meteorology, vegetation, topography, and anthropogenic activity, we developed a reliable wildfire occurrence model using the LSTM method.</p> </div> <h3>Methods</h3> <div> <p>The fire point data is provided by the European Forest Fire Information System (EFFIS). A total of 18 features were selected for modeling. Among these features, the four meteorological variables (Prep, LST, SM, and SR), along with the corresponding four condition indexes (RCI, TCI, SMCI, SRCI) derived from them, the Wind Speed (WS), and the two vegetation variables (Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI)), were used as monthly time series. The Lightning Frequency dataset is monthly but represents an average of data from 2012 to 2021, remaining constant across different years. The remaining six feature datasets do not vary over time. Data download and analysis were conducted using Google Earth Engine, QGIS, and Python.</p> </div> <h3>Subject keywords</h3> <p><span>Deep learning</span>, <span>Europe</span>, <span>Earth and related environmental sciences</span>, <span>vegetation</span>, <span>wildfire</span></p> <h3>Description of the data and file structure</h3> <ul> <li><strong><code>train_data_no_2023.npy</code></strong> and <strong><code>train_Y_data_no_2023.npy</code></strong>: These contain the features and labels for the training set (excluding data from 2023).</li> <li><strong><code>TEST_X_2023.npy</code></strong> and <strong><code>TEST_Y_2023.npy</code></strong>: These represent the features and labels for the test set, specifically for the year 2023.</li> <li><strong><code>model.h5</code></strong>: This is the final trained model.</li> <li><strong><code>shap_values_train_data.npy</code></strong>: This file contains the SHAP values for the training set, used to explain model predictions.</li> </ul> <p>The order of features:</p> <p>Label = ['Prep', 'LST', 'SM', 'SR', 'RCI', 'TCI', 'SMCI', 'SRCI', 'WS', 'NDVI', 'LAI', 'LF', 'CH', 'Elevation', 'Slope', 'Aspect', 'DR', 'DS']</p>
AgriLink - Data Set on Suppliers of farm advice in 7 European countries
<p>This Data Set is derived from the WP4 of the AgriLink project.</p> <p>It is part of task T4.4 of Work Package (WP) 4 of the H2020 AgriLink project. AgriLink [Agricultural Knowledge: linking farmers, advisors and researchers to boost innovation] aims at better understanding the role of advisory services in farmers’ decision making and at boosting their contribution to innovation for sustainable development of agriculture. WP4 addresses more specifically the governance of farm advisory services. The objective of the research presented in this report is to understand the institutions that influence how farm advisory services function on the ground, and to discuss implications for the support for sustainable development innovation.</p> <p><strong>Data were collected in seven European countries: the Czech Republic, France, Greece, Poland, Portugal, Spain and the UK.</strong></p> <p><strong>Data were collected for a diversity of types of innovation: Market, Technological, Process, and Social Innovation.</strong></p> <p>The Data set was built based on interviews with farm advisory suppliers.</p> <p>In total 170 farm advisory suppliers were interviewed.</p> <p>The table below provides the distribution of interviews according to countries.</p> <table> <tbody> <tr> <td> <p><strong>Country</strong></p> </td> <td> <p><strong>Market innovation (NCRO & RETRO)</strong></p> </td> <td> <p><strong>Technological innovation (TECH)</strong></p> </td> <td> <p><strong>Process innovation (BIOP & SOIL)</strong></p> </td> <td> <p><strong>Social innovation </strong><strong>(LABO & COMM)</strong></p> </td> <td> <p><strong>TOTAL</strong></p> </td> </tr> <tr> <td> <p><strong>Czech Republic</strong></p> </td> <td> <p> </p> </td> <td> <p>4</p> </td> <td> <p>16</p> </td> <td> <p> </p> </td> <td> <p><strong>20</strong></p> </td> </tr> <tr> <td> <p><strong>France</strong></p> </td> <td> <p>14</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>11</p> </td> <td> <p><strong>25</strong></p> </td> </tr> <tr> <td> <p><strong>Greece</strong></p> </td> <td> <p>11</p> </td> <td> <p> </p> </td> <td> <p>10</p> </td> <td> <p> </p> </td> <td> <p><strong>21</strong></p> </td> </tr> <tr> <td> <p><strong>Poland</strong></p> </td> <td> <p> </p> </td> <td> <p>6</p> </td> <td> <p> </p> </td> <td> <p>18</p> </td> <td> <p><strong>24</strong></p> </td> </tr> <tr> <td> <p><strong>Portugal</strong></p> </td> <td> <p> </p> </td> <td> <p>11</p> </td> <td> <p>20</p> </td> <td> <p> </p> </td> <td> <p><strong>31</strong></p> </td> </tr> <tr> <td> <p><strong>Spain</strong></p> </td> <td> <p>9</p> </td> <td> <p> </p> </td> <td> <p>29</p> </td> <td> <p> </p> </td> <td> <p><strong>38</strong></p> </td> </tr> <tr> <td> <p><strong>UK</strong></p> </td> <td> <p> </p> </td> <td> <p>7</p> </td> <td> <p> </p> </td> <td> <p>4</p> </td> <td> <p><strong>11</strong></p> </td> </tr> <tr> <td> <p><strong>TOTAL</strong></p> </td> <td> <p><strong>34</strong></p> </td> <td> <p><strong>28</strong></p> </td> <td> <p><strong>75</strong></p> </td> <td> <p><strong>33</strong></p> </td> <td> <p><strong>170</strong></p> </td> </tr> </tbody> </table> <p>The data has two aims.</p> <p><strong>First, to characterise farm advisory suppliers, in terms of (table below):</strong></p> <ul> <li><strong>what do they provide?</strong></li> <li><strong>Who is in control of the supplier?</strong></li> </ul> <table> <tbody> <tr> <td><strong>What do they provide</strong></td> <td><strong>Farmers</strong></td> <td><strong>NGO</strong></td> <td><strong>Private</strong></td> <td><strong>Public</strong></td> <td><strong>semi-public</strong></td> <td><strong>Total</strong></td> </tr> <tr> <td><strong>Advice and Bookkeeping</strong></td> <td>8</td> <td> </td> <td>4</td> <td>1</td> <td> </td> <td>13</td> </tr> <tr> <td><strong>Advice and Digital tech</strong></td> <td>1</td> <td> </td> <td>3</td> <td> </td> <td> </td> <td>4</td> </tr> <tr> <td><strong>Advice and Education</strong></td> <td>2</td> <td>4</td> <td>3</td> <td>5</td> <td> </td> <td>14</td> </tr> <tr> <td><strong>Advice and Health services</strong></td> <td> </td> <td> </td> <td>1</td> <td>2</td> <td> </td> <td>3</td> </tr> <tr> <td><strong>Advice and Inputs</strong></td> <td>1</td> <td> </td> <td>14</td> <td> </td> <td> </td> <td>15</td> </tr> <tr> <td><strong>Advice and Inputs and Outputs</strong></td> <td>15</td> <td> </td> <td>5</td> <td> </td> <td> </td> <td>20</td> </tr> <tr> <td><strong>Advice and Machinery</strong></td> <td> </td> <td> </td> <td>7</td> <td> </td> <td> </td> <td>7</td> </tr> <tr> <td><strong>Advice and Outputs</strong></td> <td>8</td> <td>1</td> <td>8</td> <td>2</td> <td> </td> <td>19</td> </tr> <tr> <td><strong>Advice and Research</strong></td> <td>2</td> <td>2</td> <td>5</td> <td>12</td> <td>1</td> <td>22</td> </tr> <tr> <td><strong>Only advice and training</strong></td> <td>16</td> <td> </td> <td>26</td> <td>10</td> <td>1</td> <td>53</td> </tr> <tr> <td><strong>Total</strong></td> <td>53</td> <td>7</td> <td>76</td> <td>32</td> <td>2</td> <td>170</td> </tr> </tbody> </table> <p><strong>Second, we have set a series of variables to characterise the services they provide. The main variables are:</strong></p> <ul> <li><strong>Number of advisors of the organisation</strong></li> </ul> <table> <tbody> <tr> <td><strong>Number of advisors</strong></td> <td><strong>Number of organisations in that group</strong></td> </tr> <tr> <td><strong>[0:5]</strong></td> <td>96</td> </tr> <tr> <td><strong>]10:50]</strong></td> <td>35</td> </tr> <tr> <td><strong>]5:10]</strong></td> <td>16</td> </tr> <tr> <td><strong>>50</strong></td> <td>19</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>4</td> </tr> <tr> <td><strong>Total</strong></td> <td>170</td> </tr> </tbody> </table> <ul> <li><strong>Percentage of advisors in the staff of the organisation</strong></li> </ul> <table> <tbody> <tr> <td><strong>% of advisors</strong></td> <td><strong>Number of organisations</strong></td> </tr> <tr> <td><strong>[0:25[</strong></td> <td>43</td> </tr> <tr> <td><strong>[25:50[</strong></td> <td>17</td> </tr> <tr> <td><strong>[50:75[</strong></td> <td>30</td> </tr> <tr> <td><strong>[75:100]</strong></td> <td>70</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>10</td> </tr> <tr> <td><strong>Total</strong></td> <td>170</td> </tr> </tbody> </table> <ul> <li><strong>Share of back-office activities in the staff of the organisation</strong></li> </ul> <table> <tbody> <tr> <td><strong>Share of back-office (%)</strong></td> <td><strong>Number of organisations</strong></td> </tr> <tr> <td><strong>[0:25[</strong></td> <td>41</td> </tr> <tr> <td><strong>[25:50[</strong></td> <td>26</td> </tr> <tr> <td><strong>[50:75[</strong></td> <td>66</td> </tr> <tr> <td><strong>[75:100]</strong></td> <td>24</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>13</td> </tr> <tr> <td><strong>Total </strong></td> <td>170</td> </tr> </tbody> </table> <ul> <li><strong>Number of farmers client of the supplier per advisor</strong></li> </ul> <table> <tbody> <tr> <td><strong>Number of clients per organisation</strong></td> <td><strong>Number of organisation</strong></td> </tr> <tr> <td><strong>[0:25[</strong></td> <td>31</td> </tr> <tr> <td><strong>[25:75[</strong></td> <td>43</td> </tr> <tr> <td><strong>[50:75[</strong></td> <td>3</td> </tr> <tr> <td><strong>[75:175[</strong></td> <td>28</td> </tr> <tr> <td><strong>>175</strong></td> <td>36</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>29</td> </tr> <tr> <td><strong>Total </strong></td> <td>170</td> </tr> </tbody> </table> <ul> <li><strong>Main advisory method</strong></li> </ul> <table> <tbody> <tr> <td><strong>Main Advisory method</strong></td> <td><strong>Number of organisations</strong></td> </tr> <tr> <td><strong>Group Advice</strong></td> <td>19</td> </tr> <tr> <td><strong>IT tool (app, software…)</strong></td> <td>2</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>1</td> </tr> <tr> <td><strong>One to One Advice</strong></td> <td>129</td> </tr> <tr> <td><strong>Phone or web helpdesk</strong></td> <td>15</td> </tr> <tr> <td><strong>Publications</strong></td> <td>4</td> </tr> <tr> <td><strong>Total</strong></td> <td>170</td> </tr> </tbody> </table> <ul> <li>Main funding source</li> </ul> <table> <tbody> <tr> <td><strong>Main funding source</strong></td> <td><strong>Number of organisations</strong></td> </tr> <tr> <td><strong>EU funds</strong></td> <td>15</td> </tr> <tr> <td><strong>Fee-for-advice</strong></td> <td>46</td> </tr> <tr> <td><strong>Joint trade</strong></td> <td>42</td> </tr> <tr> <td><strong>Membership</strong></td> <td>11</td> </tr> <tr> <td><strong>Membership fee</strong></td> <td>6</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>16</td> </tr> <tr> <td><strong>Public funding</strong></td> <td>3</td> </tr> <tr> <td><strong>Public funds</strong></td> <td>4</td> </tr> <tr> <td><strong>State budget</strong></td> <td>27</td> </tr> <tr> <td><strong>Total</strong></td> <td>170</td> </tr> </tbody> </table> <p>More detailed information about the variables collected can be found in the questionnaire that is available in the appendix of the deliverable D4.2 of AgriLink</p> <p> </p>
Made in China, bought in Europe? Exploring European consumer perceptions and purchase intentions toward food products
<p>In light of the constantly increasing scope of mandatory country of origin labelling in the EU which has taken place against a backdrop of numerous Chinese food scandals and a renewed commitment to further strengthen trade and business ties between the EU and China, this dataset explores EU consumer perceptions and purchase intentions toward food products made in China. Data was collected (2018) by means of an online survey in six European countries (France, Germany, Italy, Netherlands, Spain, United Kingdom) to examine the interrelationships between EU consumer (n=3024) demographics and perceptions on purchase intentions towards one of two categories of food products made in China (processed meat products (PMP) and processed fruit and vegetable products (PFVP)).</p>
FIGURE 4 in The Apertochrysa prasina group (Neuroptera: Chrysopidae), with a key to the European species
FIGURE 4. Shapes of interantennal spot in the European species of the prasina group.
The updated check-list of alien plant species in the Republic of Mordovia, European Russia
<p>This dataset, “THE UPDATED CHECK-LIST OF ALIEN PLANT SPECIES IN THE REPUBLIC OF MORDOVIA, EUROPEAN RUSSIA”, includes information on the composition of the alien flora of the Republic of Mordovia (European Russia) relevant by 05 May 2022.</p> <p>The dataset contains information on 439 alien plant species belonging to 264 genera and 68 families. Lists of alien flora of both Republic of Mordovia and each municipal district are presented on separate sheets of *XLSX files in Russian [Dataset(alien plants_Mordovia)RUS.xlsx] and English [Dataset(alien plants_Mordovia)ENG.xlsx]. For each species, the Latin name of the species and family is given according to Silaeva et al. (2010) and the Latin name of each taxon according to POWO (2022). The dataset provides information about the distribution of each species in districts of the Republic of Mordovia, as well as groups according to the time of introduction, the way of introduction, and the level of naturalisation.</p> <p>-----------------------</p> <p>Этот набор данных, “ОБНОВЛЕННЫЙ СПИСОК ЧУЖЕЗЕМНЫХ ВИДОВ РАСТЕНИЙ РЕСПУБЛИКИ МОРДОВИЯ (ЕВРОПЕЙСКАЯ РОССИЯ)”, включает актуальные на 05 мая 2022 г. сведения о составе чужеземной флоры Республики Мордовия (Европейская Россия).</p> <p>Набор данных содержит информацию о 439 видах чужеземной флоры, относящихся к 264 родам и 68 семействам. Списки чужеземной флоры Республики Мордовия и каждого муниципального района представлены на отдельных листах файлов *XLSX на русском [Dataset(alien plants_Mordovia)RUS.xlsx] и английском [Dataset(alien plants_Mordovia)ENG.xlsx]. Для каждого вида приведены латинское название вида и семейства согласно публикации Силаевой и др. (2010) и латинское название таксона, согласно POWO (2022). Приведены сведения о распределении каждого вида в районах Республики Мордовия, а также группы по времени интродукции, способу интродукции, степени натурализации.</p>
FIGURE 2 in Passiflora mistratensis, a new species of Passiflora (Passifloraceae) from Colombia, commonly known from European cultivation
FIGURE 2. Map of western Colombia showing distribution of Passiflora mistratensis sp. nov.
European news outlets on TikTok (2019 and 2022)
<p>This dataset contains <span>26,473 TikTok videos posted by 91 European news outlets between 2019 and 2022. The following media brands are included:<br><br></span></p> <p>alijatovolim</p> <p>sky news</p> <p>brut.</p> <p>daily mail</p> <p>rtve noticias</p> <p>the sun</p> <p>bfmtv</p> <p>itvnews</p> <p>20minuten</p> <p>tagesschau</p> <p>yahoo uk</p> <p>večernji list</p> <p>lillaaktuellt</p> <p>nu.nl</p> <p>franceinfo</p> <p>nos stories</p> <p>somos un periódico</p> <p>karrewiet</p> <p>watson actu</p> <p>onet</p> <p>daily mirror</p> <p>atvhu</p> <p>le parisien</p> <p>lemondefr</p> <p>rté news</p> <p>expressen</p> <p>vgnett</p> <p>gb news</p> <p>zeit im bild - zib</p> <p>hürriyet</p> <p>refresher</p> <p>kleine zeitung</p> <p>hln.be</p> <p>derstandard</p> <p>die zeit und zeit online</p> <p>tf1 info</p> <p>m6info</p> <p>channel 4 news</p> <p>cnnprima</p> <p>la.repubblica</p> <p>aftonbladet</p> <p>cnnportugal</p> <p>the telegraph</p> <p>blick</p> <p>tn.cz</p> <p>ilta-sanomat</p> <p>bildnews</p> <p>eldiario.es oficial</p> <p>rtsinfo</p> <p>het belang van limburg</p> <p>kronen zeitung</p> <p>yahoo france</p> <p>newstalk</p> <p>iltalehti</p> <p>dagbladet</p> <p>protv</p> <p>le figaro</p> <p>bors</p> <p>20minutos</p> <p>el país</p> <p>the independent</p> <p>bbc news</p> <p>telex.hu</p> <p>kurier</p> <p>helsingin sanomat</p> <p>buzzfeed news</p> <p>aktuality_sk</p> <p>news247gr</p> <p>antena 3 noticias</p> <p>cnews</p> <p>il sole 24 ore</p> <p>ouestfrance</p> <p>net.hr</p> <p>heute.at</p> <p>spiegel tv</p> <p>nos jeugdjournaal</p> <p>srf news</p> <p>la vanguardia</p> <p>idnes.cz</p> <p>pravda.sk</p> <p>blue news</p> <p>de telegraaf</p> <p>habertürk</p> <p>tagesanzeiger</p> <p>hirado.hu</p> <p>irish mirror</p> <p>index video</p> <p>napi.hu</p> <p>blikk.hu</p> <p>zetland</p> <p>deník</p> <p><span> </span></p>
MALDI-TOF spectra and bone images of archaeological and modern European Cyprinids
<p>Images from all 46 archaeological test bones associated with the archaeological test set MALDI-TOF data.</p> <p>MALDI-TOF data from both reference and test sets. Reference sets are labeled with their species identification. Each set consists of .tex files. Files in the same folder are technical replicates of the same extract.</p>
Fig. 1 in Revealing the diversity of the green Eulalia (Annelida, Phyllodocidae) species complex along the European coast, with description of three new species
Fig. 1 Sampling sites. Abbreviations as in Table 1
Emissions scenario database of the European Scientific Advisory Board on Climate Change, hosted by IIASA
<p>This scenario ensemble collects emissions pathways quantitative, model-based scenarios related to the mitigation of climate change.</p> <p>The ensemble was compiled from the energy and integrated-assessment modelling community in response to a call by the European Scientific Advisory Board on Climate Change, see <a href="https://www.eea.europa.eu/about-us/climate-advisory-board/call-for-scenario-data-contributions">https://www.eea.europa.eu/about-us/climate-advisory-board/call-for-scenario-data-contributions</a>.</p> <p>The scenario ensemble can be accessed via the <strong>EU Climate Advisory Board Scenario Explorer</strong> hosted by IIASA at <a href="https://data.ece.iiasa.ac.at/eu-climate-advisory-board">https://data.ece.iiasa.ac.at/eu-climate-advisory-board</a>. The data can be downloaded and re-used for analysis and data visualization, but re-publication of a substantial portion is prohibited. </p> <p>The reason for the restriction is that we anticipate updates/extensions and (possibly) error corrections of this scenario ensemble.<br> We want to avoid a situation where multiple inconsistent versions of the scenario database are in wide circulation, which can lead to confusion for users. Therefore, please refer to the IIASA Scenario Explorer for the most-up-to-date version of the database.</p> <p>Further guidance and the full license text is available at <a href="https://data.ece.iiasa.ac.at/eu-climate-advisory-board/#/license">https://data.ece.iiasa.ac.at/eu-climate-advisory-board/#/license</a>.</p>
Genetics of Periodontal Diseases in European Caucasians
ClinicalTrials.gov study NCT01691638. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Calibration of a custom designed microarray for European Fire Salamander
GEO Series GSE70055. Salamandra salamandra. 8 samples. Type: Expression profiling by array.
ScienceDex guides
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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.