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6,170 results for “european”
cis-mQTL results in prostate benign tissue of European ancestry patients and African ancestry patients
Open the record for dataset details and reuse information.
cis-mQTL results in prostate cancer tissue of European ancestry patients and African ancestry patients
<p>cis-mQTL results of in tumor tissues</p>
Figure 2 in Parrillo v Italy: is there life in the European Court of Human Rights?
Figure 2 Species richness and density cf habitat type cf butterfly cbserved at five study sites. Species richness (number of species) and density (abundance per 1-cm transect) of habitat type (forest interior species, forest edge species, and grassland species) of butterfly observed at five study sites. Different letters above the bars indicate significant difference.
Figure 5 in Parrillo v Italy: is there life in the European Court of Human Rights?
Figure 5 CA crdinaticn cf butterfly ccmmunities. Two axes explain 97% of total variation. Singleton species which occurred one site were excluded in CA ordination.
Figure 3 in Parrillo v Italy: is there life in the European Court of Human Rights?
Figure 3 Species richness and density cf niche breadth cf butterfly cbserved at five study sites. Species richness (number of species) and density (abundance per 1-cm transect) of niche breadth (specialist species, intermediate species, and generalist species) of butterfly observed at five study sites. Different letters above the bars indicate significant difference.
Figure 4 in Parrillo v Italy: is there life in the European Court of Human Rights?
Figure 4 Estimaticn cf species richness %Cack 1) and species diversity %H') cf butterfly at five study sites. Species richness and species diversity were obtained using Estimate S (Colwell et al. 2004). Different letters above the error bars indicate significant difference. The error bars indicate one standard deviation.
Figure 1 in Parrillo v Italy: is there life in the European Court of Human Rights?
Figure 1 Map cf the study sites. NS, Namsan Parc; EW, Ewha Womans University; BD, Bucseoul Dream Forest; HF, Hongneung Forest; GF, Gwangneung Forest.
Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach (Example codes)
<p>## Introduction<br>This folder contains example code used in our paper with the title "Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach". These scripts are intended to demonstrate key functionalities and calculations described in the paper.</p> <p>## Files Description</p> <p>1. **DL_Model_test.py**<br> - Description: Loads the trained deep learning algorithms featured in the paper.<br> - Functionality: Demonstrates the use of the model with example data points.</p> <p>2. **SIC_class_contribution.py**<br> - Description: Calculates the class contribution for Sea Ice Concentration (SIC).<br> - Note: The procedure can be adapted for other fields in a similar manner.</p> <p>## Requirements<br>the requred linrary are listed in the code header</p>
Supplementary material 2 from: Csősz S, Seifert B, László M, Yusupov ZM, Herczeg G (2023) Broadly sympatric occurrence of two thief ant species Solenopsis fugax (Latreille, 1798) and S. juliae (Arakelian, 1991) in the East European Pontic-Caspian region (Hymenoptera, Formicidae) is disclosed. ZooKeys 1187: 189-222. https://doi.org/10.3897/zookeys.1187.105866
Morphometric data of 15 continuous morphometric traits of 203 individuals collected by SC is given in µm
Supplementary material 4 from: Csősz S, Seifert B, László M, Yusupov ZM, Herczeg G (2023) Broadly sympatric occurrence of two thief ant species Solenopsis fugax (Latreille, 1798) and S. juliae (Arakelian, 1991) in the East European Pontic-Caspian region (Hymenoptera, Formicidae) is disclosed. ZooKeys 1187: 189-222. https://doi.org/10.3897/zookeys.1187.105866
R script of NC clustering and method PART implementing cluster methods "hclust" and "kmeans", including "Mark dendrogram" function mapping the results of partitioning algorithm PART on the dendrogram
Supplementary material 3 from: Csősz S, Seifert B, László M, Yusupov ZM, Herczeg G (2023) Broadly sympatric occurrence of two thief ant species Solenopsis fugax (Latreille, 1798) and S. juliae (Arakelian, 1991) in the East European Pontic-Caspian region (Hymenoptera, Formicidae) is disclosed. ZooKeys 1187: 189-222. https://doi.org/10.3897/zookeys.1187.105866
Morphometric data of 18 continuous morphometric traits of 171 individuals collected by BS is given in mm
Data needed to reproduce analysis from "Frost matters: Incorporating late-spring frost in a dynamic vegetation model regulates regional productivity dynamics in European beech forests"
<p>Data to reproduce analysis from "Frost matters: Incorporating late-spring frost in a dynamic vegetation model regulates regional productivity dynamics in European beech forests".</p> <p>This includes:</p> <ol> <li>Tree ring data (meyer, bdn, principe, dittmar)</li> <li>LPJ-GUESS model output (frost_validation, frost_sensitivity, runs_22012024_revision)</li> <li>Data used for plotting</li> </ol>
Food aid in four European countries: Assessing the price and content of charitable food aid packages
<table> <tbody> <tr> <td> <table> <tbody> <tr> <td><strong>Description</strong></td> <td>The dataset is the result of a study on the content and monetary value of food aid packages distributed by local food aid organisations in four European cities: Antwerp, Barcelona, Budapest and Helsinki. Concretely, three food aid organisations per city, who fulfilled the inclusion criteria of the study, were randomly selected. In each organisation, two types of data were collected: 1) a structured interview with the head or a well-informed volunteer, and 2) four visits in which the content of the food aid packages was registered by a participating researcher. After this, food basket data was used to monetize the food aid products, and where necessary this was supplemented by available online pricing data of supermarkets in the four countries. The interviews were conducted at the location of the organisations and a few organisations also provided some of the information via e-mail or telephone. Although the questions were objective/factual about the history and functioning of the organisations only, some of the answers contain estimations of the interviewee in case when no objective information or data was available. The interview questions were set up in English and discussed with the involved researchers, but the questions were translated and the interviews were conducted in respectively Dutch, Hungarian, Finnish and Spanish. For analysis, the interview answers back to English. The registration of the content of the food aid packages included writing down relevant information about that product (such as volume, expiration date etc.) and to whom the product was given. The researchers collected this information in a harmonised way by making use of a uniformly constructed Microsoft Excel template.</td> </tr> <tr> <td><strong>Personal data yes/no</strong></td> <td>no</td> </tr> <tr> <td><strong>Type(s) of data and data format</strong></td> <td>Interview, content and prices of food aid packages data, 13 excel files</td> </tr> <tr> <td><strong>Temporal and special coverage</strong></td> <td>The data was collected between February 2022 and May 2022, in Antwerp (Belgium), Barcelona (Spain), Budapest (Hungary) and Helsinki (Finland).</td> </tr> <tr> <td><strong>Language of files</strong></td> <td>English</td> </tr> <tr> <td><strong>Subject</strong></td> <td>Food aid in 4 European countries, food aid packages, interview data</td> </tr> <tr> <td><strong>Audience</strong></td> <td>Social Sciences</td> </tr> <tr> <td><strong>Rightsholder</strong></td> <td>University of Antwerp, Karen Hermans, phd student, ORCID 0000-0001-9192-2948</td> </tr> <tr> <td><strong>Access rights</strong></td> <td>Restricted access: users may view and download the data by sending an e-mail to <a href="mailto:karen.hermans@uantwerpen.be">karen.hermans@uantwerpen.be</a> and specifying the purpose of the data use and how the data will be used. If the data is to be used for scientific purposes and the data is necessary or useful to reach the objectives, access will be granted. <div> <div> <div> </div> </div> </div> </td> </tr> </tbody> </table> <p> </p> </td> </tr> </tbody> </table>
Data for: Climatic and management-related drivers of endemic European spruce bark beetle populations in boreal forests
<p>Climate change is already reducing carbon sequestration in Central European forests dramatically through extensive droughts and bark beetle outbreaks. Further warming may threaten the enormous carbon reservoirs in the boreal forests in northern Europe, unless disturbance risks can be reduced by adaptive forest management. The European spruce bark beetle (<em>Ips typographus</em>) is a major natural disturbance agent in spruce-dominated forests and can overwhelm the defences of healthy trees through pheromone-coordinated mass-attacks.</p> <p>We used an extensive dataset of bark beetle trap counts to quantify how climatic and management-related factors influence bark beetle population sizes in boreal forests. Trap data was collected during a period without outbreaks and can thus identify mechanisms that drive populations towards outbreak thresholds.</p> <p>The most significant predictors of bark beetle population size were volume of mature spruce, extent of newly exposed clearcut edges, temperature, and soil moisture. For clearcut edge, temperature, and soil moisture, a three-year time lag produced the best model fit. We demonstrate how a model incorporating the most significant predictors, with a time lag, can be a useful management tool by allowing spatial prediction of future beetle population sizes.</p> <p><em>Synthesis and Applications</em>: Some of the population drivers identified here, i.e., spruce volume and clearcut edges, can be targeted by adaptive management measures to reduce the risk of future bark beetle outbreaks. Implementing such measures may help preserve future carbon sequestration of European boreal forests.</p>
Future electricity demand time series for European Countries from 2023 to 2100
<p>This dataset represents the future time series of electricity demand for European countries from 2023 to 2100, aligning with the findings presented in our paper 'Future Electricity Demand for Europe: Unraveling the Dynamics of the Temperature Response Function,' published in Applied Energy. To cite this dataset, please cite the published paper <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.apenergy.2024.123387" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.apenergy.2024.123387</a></p> <p>This dataset includes electricity demand data for 36 European countries, with each year being presented as a distinct .CSV file. Data for all years in each country are then compressed in a single .ZIP file. </p> <p>The column explanation is as below:</p> <ul> <li>'country_code': the country code in 2 digits</li> <li>'year': the projection year</li> <li>'month': month of the year</li> <li>'day': day of the month</li> <li>'S0_RCP26_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S0_RCP26_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S0_RCP45_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S0_RCP45_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S0_RCP85_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S0_RCP85_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S1_RCP26_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S1_RCP26_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S1_RCP45_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S1_RCP45_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S1_RCP85_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S1_RCP85_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S2_RCP26_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S2_RCP26_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S2_RCP45_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S2_RCP45_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S2_RCP85_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S2_RCP85_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S3_RCP26_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S3_RCP26_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S3_RCP45_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S3_RCP45_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S3_RCP85_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S3_RCP85_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S4_RCP26_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S4_RCP26_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S4_RCP45_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S4_RCP45_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S4_RCP85_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S4_RCP85_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> </ul>
Main MARINA-Nutrients model outputs for European river basins for the period of 2017-2020
<p>The excel file "main_MARINA-Nutrients_model_outputs_for_Europe_2017-2020" contains main results of the publication (https://doi.org/10.15302/J-FASE-2023526). The model results are presented in unit of load (kg/yr) for the period of 2017-2020 per river basin. These results are obtained by model calculations following the equations that are provided in the "Materials and Methods" of the publication and in the supporting file. "Codebook.csv" file includes more details on the data.</p>
Supplementary material 2 from: Grabowska J, Płóciennik M, Grabowski M (2024) Detailed analysis of prey taxonomic composition indicates feeding habitat partitioning amongst co-occurring invasive gobies and native European perch. NeoBiota 92: 1-23. https://doi.org/10.3897/neobiota.92.116033
Relative abundance of prey categories (%N) (number of given prey category in relation to total number of prey) identified in fish guts at sites Z, R, B in the Western Bug River in August 2007
Supplementary material 1 from: Grabowska J, Płóciennik M, Grabowski M (2024) Detailed analysis of prey taxonomic composition indicates feeding habitat partitioning amongst co-occurring invasive gobies and native European perch. NeoBiota 92: 1-23. https://doi.org/10.3897/neobiota.92.116033
Relative abundance of species (%N) in fish assemblages found at sites Z, R, B in the Western Bug River in August 2007 (Penczak et al. 2010)
FIGURES 8–16. European problem genera. Bebelothrips flavicinctus 8–10 in New generic synonyms amongst Thysanoptera Phlaeothripinae listed from Europe and the Mediterranean area
FIGURES 8–16. European problem genera. Bebelothrips flavicinctus 8–10: (8) head; (9) tergites IX–X; (10) antenna. (11) Brachythrips flavicornis holotype. (12) Brachythrips dirghavadana head and pronotum. Euryaplothrips crassus 13–15: (13) head and pronotum; (14) prosternites; (15) antenna. (16) Haplothrips angusticornis meso & metanota – arrows indicate tegula.
FIGURES 1–7. European problem genera. Aulothrips syntype female 1–4 in New generic synonyms amongst Thysanoptera Phlaeothripinae listed from Europe and the Mediterranean area
FIGURES 1–7. European problem genera. Aulothrips syntype female 1–4: (1) head; (2) pronotum; (3) epimeral seta; (4) antenna. Haplothrips timori 5–7: (5) pronotum; (6) female head and pronotum; (7) male head.
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.