Find research datasets worth reusing

opencc-by-4.0Sep 2024View details →

Pan-European dataset of subsurface temperature isolines at 1000 m and 2000 m depth

This dataset consists of two seperate geopackages, which are both digitisations of isotherms in the 1000 meters and 2000 meters below ground maps, displayed in plates 2 and 3 of the 2002 <a href="https://op.europa.eu/publication-detail/-/publication/9003d463-03ed-4b0e-87e8-61325a2d4456" target="_blank" rel="noopener">Atlas of geothermal resources in Europe</a>.

opencc-by-4.0Oct 2024View details →

Supplementary Material for "Advancing quantum technology workforce: industry insights into qualification and training needs" and "Extending the European Competence Framework for Quantum Technologies: new proficiency triangle and qualification profiles"

This is a file collection as supplementary material for the paper Advancing quantum technology workforce: industry insights into qualification and training needs, <a href="https://doi.org/10.1140/epjqt/s40507-024-00294-2">doi 10.1140/epjqt/s40507-024-00294-2</a>. It consists of: <ol> <li>Interview guide: questions and more as guideline for the interviews conducted for the industry needs analysis documented in the publication.</li> <li>Interview transcript extracts: anonymised phrases from the interviews that are given as quotes (in a shortened/liguistically smoothed out form) in the publication as well as further phrases that are refered in the results sections of the publication.</li> <li>Dataset of the follow-up survey</li> </ol> The results of this study were also used to update the <a href="https://doi.org/10.5281/zenodo.10976836" target="_blank" rel="noopener">European Competence Framework for Quantum Technologies Version 2.5</a>, which is documented in Extending the European Competence Framework for Quantum Technologies: new proficiency triangle and qualification profiles, <a href="https://doi.org/10.1140/epjqt/s40507-024-00302-5">doi 10.1140/epjqt/s40507-024-00302-5</a>. In an additional sheet, the three draft versions of qualification profile descriptions (v2.1, v2.2, v2.3) are provided.

opencc-by-4.0Oct 2024View details →

Projection of temperature-related mortality in 854 European cities under climate change and adaptation scenarios

This repository contains the data and results from the paper Estimating future heat-related and cold-related mortality under climate change, demographic and adaptation scenarios in 854 European cities published in Nature Medicine (<a href="https://doi.org/10.1038/s41591-024-03452-2">https://doi.org/10.1038/s41591-024-03452-2</a>). It provides projections of excess death rates and burden for the period 2015-2099 for five age groups in 854 cities across 30 countries, under three Shared Socioeconomic Pathway (SSP) scenarios, and four adaptation scenarios. The results include point estimates for five-year periods and four global warming levels, along with 95% empirical confidence intervals.  The fully reproducible analysis code using the data and producing the results included in this repository is provided in <a href="https://github.com/PierreMasselot/EUcityProj" target="_blank" rel="noopener">GitHub</a>. The results can be visualised and explored in a dedicated <a href="https://ehm-lab.shinyapps.io/vistemphip/">Shiny app</a>. <h3>Content</h3> This repository contains three zip files, each with an internal codebook: <ul> <li>data.zip: contains the input data necessary to run the analysis. It includes historical and projected daily temperature at the city level, age-group specific projections of population and survival rates at the country level, and exposure-response functions extracted from another Zenodo repository (<a href="https://doi.org/10.5281/zenodo.10288665" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10288665</a>). This file also include a script showing how each dataset was extracted for the purpose of this projection study.</li> <li>results_csv.zip: contains the full results from the health impact projections. It includes one file for each combination of geographical level (city, country, region or European wide) and scale of reporting (five year periods or global warming levels). </li> <li>results_parquet.zip: contains the same information as the results_csv.zip but in a parquet format. This allows for more efficient storage and data reading.</li> </ul> It is recommended to only download results_csv.zip for a quick exploration of the results, or only results_parquet.zip when the results are to be loaded into a software for deeper analysis.

opencc-by-4.0Oct 2022View details →

Base rates of food safety practices in European households: Summary data from the SafeConsume Household Survey

This data set contains estimates of the base rates of 550 food safety-relevant food handling practices in European households. The data are representative for the population of private households in the ten European countries in which the SafeConsume Household Survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK). Sampling design In each of the ten EU and EEA countries where the survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK), the population under study was defined as the private households in the country. Sampling was based on a stratified random design, with the NUTS2 statistical regions of Europe and the education level of the target respondent as stratum variables. The target sample size was 1000 households per country, with selection probability within each country proportional to stratum size. Fieldwork The fieldwork was conducted between December 2018 and April 2019 in ten EU and EEA countries (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, United Kingdom). The target respondent in each household was the person with main or shared responsibility for food shopping in the household. The fieldwork was sub-contracted to a professional research provider (Dynata, formerly Research Now SSI). Complete responses were obtained from altogether 9996 households. Weights In addition to the SafeConsume Household Survey data, population data from Eurostat (2019) were used to calculate weights. These were calculated with NUTS2 region as the stratification variable and assigned an influence to each observation in each stratum that was proportional to how many households in the population stratum a household in the sample stratum represented. The weights were used in the estimation of all base rates included in the data set. Transformations All survey variables were normalised to the [0,1] range before the analysis. Responses to food frequency questions were transformed into the proportion of all meals consumed during a year where the meal contained the respective food item. Responses to questions with 11-point Juster probability scales as the response format were transformed into numerical probabilities. Responses to questions with time (hours, days, weeks) or temperature (C) as response formats were discretised using supervised binning. The thresholds best separating between the bins were chosen on the basis of five-fold cross-validated decision trees. The binned versions of these variables, and all other input variables with multiple categorical response options (either with a check-all-that-apply or forced-choice response format) were transformed into sets of binary features, with a value 1 assigned if the respective response option had been checked, 0 otherwise. Treatment of missing values In many cases, a missing value on a feature logically implies that the respective data point should have a value of zero. If, for example, a participant in the SafeConsume Household Survey had indicated that a particular food was not consumed in their household, the participant was not presented with any other questions related to that food, which automatically results in missing values on all features representing the responses to the skipped questions. However, zero consumption would also imply a zero probability that the respective food is consumed undercooked. In such cases, missing values were replaced with a value of 0.

Data for Marine Ecological Niche Models, for 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species

Data for Ecological Niche Models: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species.

Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species, developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution

Native ecological niche models of 1508 European species (894 fish and 614 non fish) developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 and under RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, at 0.5° spatial resolution.

Atmosphere-cryosphere interactions during the last phase of the LGM (21 ka BP) in the European Alps

This dataset refers to: Del Gobbo, C., Colucci, R. R., Monegato, G., Žebre, M., and Giorgi, F.: Atmosphere-cryosphere interactions at 21 ka BP in the European Alps, Clim. Past Discuss. [preprint], https://doi.org/10.5194/cp-2022-43, in review, 2022.    We used the regional climate model RegCM4 to investigate the physical processes sustaining the glacier extent during the Last Glacial Maximum (LGM) and pre-industrial time (PI) over the European Alps. After a bias-correction of precipitation and temperature data, we reconstructed the environmental equilibrium line altitude (envELA) of the Alpine glaciers, which resulted consistent with geological records.  #----------------------------------------------------------   LGM in the file names referes to 21 ka BP PI refers to pre-industrial #----------------------------------------------------------   This dataset contains: NetCDF files ------------------------------------------------------------------------------------   <ul> <li>Monthly mean  TAS and PR <ul> <li>variables = <ul> <li>RegCM4 monthly mean near-surface air temperature (TAS)</li> <li>RegCM4 monthly mean precipitation (PR)</li> <li>model topography (topo)</li> </ul> </li> <li>units = TAS [°C], PR [mm/day], topo [m a.s.l.]</li> <li>model = RegCM4 (ICTP)</li> <li>method  = RCM forced with MPI-ESM-P</li> <li>remapped = no</li> <li>resolution = 12 km</li> <li>files = <ul> <li>LGM_PR_TAS_monmean.nc</li> <li>PI_PR_TAS_monmean.nc</li> </ul> </li> </ul> </li> </ul>   <ul> <li>Bias-corrected monthly mean TAS and PR <ul> <li>variables = <ul> <li>model topography (topo)</li> <li>Bias-corrected RegCM4 monthly mean precipitation (PR)</li> <li>Bias-corrected RegCM4 monthly mean near-surface air temperature (TAS)</li> </ul> </li> <li>units  = TAS [°C], PR [mm/day], topo [m a.s.l.]</li> <li>model = RegCM4 (ICTP)</li> <li>method  = bias-correction based on HISTALP (TAS) and LAPrec (PR) of RegCM4 data</li> <li>remapped = onto HISTALP grid</li> <li>resolution = 5 arcmin</li> <li>files= <ul> <li>LGM_PR_TAS_monmean_BC.nc</li> <li>PI_PR_TAS_monmean_BC.nc</li> </ul> </li> </ul> </li> </ul>   <ul> <li>ELA <ul> <li>variables = <ul> <li>ELA </li> <li>average RegCM-HISTALP-LAPrec topography</li> </ul> </li> <li>units = m a.s.l.</li> <li>data = calculated from bias-corrected RegCM4 data</li> <li>method = Zebre et al. (2020)</li> <li>remapped = on HISTALP grid</li> <li>resolution = 5 arcmin</li> <li>files = <ul> <li>LGM_ELA.nc</li> <li>PI_ELA.nc</li> </ul> </li> </ul> </li> </ul> csv files ------------------------------------------------------------------------------------ * dates refer to model dates, not real ones!!! tj_700_hpa_pr_lgm   : Tagliamento glacier daily wind and precipitation at the 21 ka BP tj_700_hpa_pr_pi       : Tagliamento glacier daily wind and precipitation at the PI db_700_hpa_pr_lgm : Dora Baltea glacier daily wind and precipitation at 21 ka BP db_700_hpa_pr_pi    : Dora Baltea glacier daily wind and precipitation at the PI r_700_hpa_pr_lgm    : Rhine glacier  daily wind and precipitation at 21 ka BP r_700_hpa_pr_pi        : Rhine glacier daily wind and precipitation at the PI ist_700_hpa_pr_lgm : Inn-Salzach-Traun glacier  daily wind and precipitation at 21 ka BP ist_700_hpa_pr_pi     : Inn-Salzach-Traun glacier  daily wind and precipitation at the PI

Replication package for "Motivation in the Dynamics of European Youth Migration"

Replication package for the paper "Motivation in the Dynamics of European Youth Migration". The package contains the data and the SPSS and Stata code for the the analyses presented in the paper. The original data from which the variables are extracted was collected within the EU Horizon 2020 project YMOBILITY (2015-2018).

opencc-by-4.0Jun 2022View details →

Pan-European exposure maps and uncertainty estimates from HANZE v2.0 model, 1870-2020

This dataset provides all output data generated in the standard settings of HANZE v2.0 model. The 100-m pan-European maps (GeoTIFF) provide gridded totals of five variables for years 1870-2020 for 42 countries. The rasters are group in five ZIP files: - CLC: land cover/use (Corine Land Cover classification; legend files are included in a separate ZIP) - Pop: population - GDP: gross domestic product (2020 euros) - FA: fixed asset value (2020 euros) - imp: imperviousness density (%) Two additional CSV files contain uncertainty estimates of population, GDP and fixed asset value per NUTS3 region and flood hazard zone. The files provide 5th, 20th, 50th, 80th and 95th percentile for all timesteps, separately for coastal and riverine floods. Two further Excel files contain subnational and national-level statistical data on population, land use and economic variables. For detailed description of the files, see the documentation provided with the code. This version replaces the airport list, which was previously incorrectly taken from HANZE v1, and adds land cover/use legend files for ArcGIS and QGIS.

Biodiversity Index, in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European Marine Species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution

Biodiversity Index in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European marine species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution. The Index counts the number of species (among the 1508) potentially present in each 0.5° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.

Ecological Niche Models of 96 European Marine Species, for 2019, developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines at 0.1° Resolution

Native ecological niche models of 96 European marine species of particular commercial and conservation interest developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 at 0.1° spatial resolution.

Ensemble Ecological Niche Models and Biodiversity Index for 2019 of 96 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, AquaMaps, and Support Vector Machines at 0.1° Resolution

Ensemble Ecological Niche Models for 2019 of 96 European marine species of particular commercial and conservation interest, based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.1° Resolution. The data report, for each 0.1° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell. A Biodiversity Index is also provided as the count of the number of species (among the 96) potentially present in each 0.1° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.

opencc-by-4.0Aug 2023View details →

QuantMig microsimulation population projection model and migration scenarios for 31 European countries

This open data deposit contains the data  and model code of QuantMig-Mic microsimulation population projection model for 31 European countries and accompanies deliverables D8.3: Model outputs for dissemination and D8.1: Microsimulation projection model. This Zenodo deposit contains datasets of model input (baseline population and immigration database) and output data (demography and components output tables) and model code with parameters of the Baseline scenario (termed Default in the model code) deposited in QuantMig_mic.zip file. To view the code the users must first install MODGEN software (or can view code files in Visual Studio). All scenarios share the same parameters except the immigrant population - to change the immigration assumptions the users can import immigration assumptions for any other scenario from the ImmigDataBase.csv and change it in the immigration module using the MODGEN user interface or using Visual Studio. The file structure and codebook for the data files is included in the cover note file "readme_quantmig_datasets.pdf" Detailed information about the QuantMig-Mic microsimulation model, its modules and parameters: Marois, G., Potančoková, M., González-Leonardo, M. (2023) QuantMig-Mic microsimulation population projection model. QuantMig Project Deliverable D8.2. International Institute for Applied Systems Analysis (IIASA), Austria. Available at:http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.2_forsubmission_21-07-2023_corr.pdf Instructions how to install MODGEN can be found in: Marois, G. and Potančoková, M. (2022) QuantMig-mic microsimulation tool. QuantMig Project Deliverable D8.1. International Institute for Applied Systems Analysis (IIASA). http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.1%20v1.1.pdf  Detailed information about the QuantMig migration scenarios can be found in: Marois, G., Potančoková, M., González-Leonardo, M. (2023) QuantMig-Mic microsimulation population projection model. QuantMig Project Deliverable D8.2. International Institute for Applied Systems Analysis (IIASA), Austria. Available at:http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.2_forsubmission_21-07-2023_corr.pdf A guide to the datasets and the codebook can be found in: readme_quantmig_datasets.pdf Countries included in the model: Austria, Belgium, Bulgaria, Croatia, Czechia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom

opencc-by-4.0Aug 2023View details →

NO2, O3, PM10 and PM2.5 concentrations - Daily geographical aggregates at NUTS3 level from CAMS European Air Quality Re-analyses.

This dataset offers daily aggregated measurements of air pollutants – NO2, O3, PM10, and PM2.5 – across distinct NUTS3 regions in continetal Europe. The temporal coverage spans from January 1, 2013, to December 31, 2022, providing a comprehensive temporal context for analyzing long-term air quality dynamics. Each daily entry comprises key statistical descriptors, encompassing mean, maximum, minimum, and standard deviation values of pollutant concentrations specific to each NUTS3 area. Additionally, for O3, the dataset includes an eight-hour rolling mean daily maximum. Spatial reference is established via shapefiles (EPSG:4326) sourced from Eurostat's official repository (<a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts</a>). These shapefiles link the air quality data to precise NUTS3 regions through unique identifiers. The concentration data spanning from 2018 to 2022 originate from the European Air Quality Reanalyses dataset of the Atmosphere Data Store (ADS), an initiative by the Copernicus Atmosphere Monitoring Service (CAMS). Accessible via <a href="https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc">https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc</a>, this dataset offers a robust foundation for assessing air quality. For the years 2013 to 2017, data were previously obtained from a former download platform for the same dataset. Important: in future all data will be migrated to the Atmosphere Data Store (ADS) platform. The native resolution of the CAMS data is 0.1° x 0.1° spatially and hourly temporally. To enhance spatial accuracy, the spatial resolution was virtually increased by a factor of 5 using bilinear interpolation, resulting in a refined grid. The daily mean concentrations were subsequently computed for this augmented grid. Aggregated statistics were derived for each NUTS3 polygon, employing all grid cells intersecting with the polygons. The computation was based on the proportion of cell area included within the respective polygons. This dataset constitutes a valuable resource for conducting ecologically designed epidemiological studies, as it facilitates the exploration of potential associations between air quality and health trends across broad geographical areas.

opencc-by-4.0Aug 2023View details →

PANDEM-2 European COVID-19 training data set

The PANDEM-2 COVID-19 European training dataset is a large collection of time series either of real or realistic synthetic (generated) data and indicators associated with the European pandemic response to the COVID-19 pandemic. It is intended to be used for training in pandemic management.   This dataset is the result of a data gathering requirement process for pandemic management involving feedback and inputs from several public health and first responder professionals as well as researchers and military personnel directly involved in the European COVID-19 pandemic response. This work is part of the PANDEM-2 project funded by the Horizon 2020 Secure Societies program. To collect this data, an open source software was developed named PANDEM-Source allowing reproducibility and customisation of this dataset.    The dataset includes indicators for cases, deaths, hospitalisation, testing and laboratory data including pathogen genomic information, vaccination, non-pharmaceutical interventions, participatory surveillance, social media, flights resources (human and material such as beds or vaccines), and contact tracing activities. When no open available data was found, realistic synthetic data and indicators were generated with the goal of producing a data set to be used for pandemic management  training.  The project received funding from the European Union’s Horizon 2020 Research and Innovation programme under the Grant Agreement No. 883285. The material presented and views expressed here are the responsibility of the author(s) only. The EU Commission takes no responsibility for any use made of the information set out. References

Prevalent trends in realized probability of occurrence of main European forest tree species for 2000–2020

High resolution maps resulting from a trend analysis conducted for the period 2000–2020 on the probability of occurrence maps prepared by <a href="https://doi.org/10.7717/peerj.13728">Bonannella et al. (2022)</a>. For this analysis we selected the realized distribution time series layers at 30m spatial resolution for 6 out of 16 species described in the mentioned publication: <ul> <li>Silver fir (Abies alba Mill.)</li> <li>European beech (Fagus sylvatica L.)</li> <li>Norway spruce (Picea abies L.)</li> <li>Black pine (Pinus nigra J. F. Arnold)</li> <li>Scots pine (Pinus sylvestris L.)</li> <li>Common oak (Quercus robur L.)</li> </ul> The trend analysis was conducted per pixel on each of these species individually. We fitted simple OLS regression models with the probability of occurrence as the dependent variable and time as the independent variable. After the model fitting, we also calculated the t-test statistics to determine the presence of an increasing (positive) or decreasing (negative) trend or no trend at all. By combining the regression slope coefficient (β) and the p-value from the t-test statistics we assigned each pixel to one of three classes: <ul> <li>positive: β > 0.25 AND p-value < 0.05</li> <li>negative: β < −0.25 AND p-value < 0.05</li> <li>no trend / stable: −0.25 ≤ β ≥ 0.25 OR p-value > 0.05</li> </ul> We then aggregated the resulting classes at 1km resolution maps to capture the prevalent trend in probability of occurrence over a certain area. Files are named according to the following naming convention, e.g.: <ul> <li>veg_abies.alba_slope_30m_0..0cm_epsg3035_v1.0</li> </ul> with the following fields: <ul> <li>theme: e.g. veg,</li> <li>species code: e.g. abies.alba,</li> <li>variable name: e.g. slope,</li> <li>resolution in meters e.g. 30m,</li> <li>reference depths (vertical dimension): e.g. 0..0cm,</li> <li>coordinate system: e.g. epsg3035,</li> <li>data set version: e.g. v1.0.</li> </ul> For each species here we provide the following layers: <ul> <li>veg_abies.alba_slope: slope coefficient (scaling factor: 10000)</li> <li>veg_abies.alba_pvalue: p-value (scaling factor: 1000)</li> <li>veg_abies.alba_pos.trends_30m: pixels classified as positive on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_pos.trends_1km: proportion of pixels of the positive class over a 1×1 km area (range 0–100)</li> <li>veg_abies.alba_neg.trends_30m: pixels classified as negative on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_neg.trends_1km: proportion of pixels of the negative class over a 1×1 km area (range 0–100)</li> <li>veg_abies.alba_no.trends_30m: (pixels classified as no trend / stable on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_no.trends_1km: proportion of pixels of the no trend / stable class over a 1×1 km area (range 0–100)</li> </ul> Files are provided as GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in QML format A publication describing, in detail, all processing steps is currently in review. See at: Bonannella, C., Parente, L., de Bruin, S. and Herold, M. (2023). Multi-decadal trend analysis and forest disturbance assessment of European tree species: concerning signs of a subtle shift, PREPRINT (Version 1) available at Research Square [<a href="https://doi.org/10.21203/rs.3.rs-3288937/v1">https://doi.org/10.21203/rs.3.rs-3288937/v1</a>]

opencc-by-4.0Mar 2023View details →

With or without you: Gut microbiota does not predict aggregation behaviour in females of the European earwig

Recent studies suggest that the gut microbiota could be one of the main driving forces behind the evolution of group living. However, these studies are mainly based on our knowledge of species in which non-social individuals are rare and abnormal, calling into question the adaptive value of the reported association between group-living and gut microbiota. In this study, we addressed this issue by testing this association in females of the European earwig, an insect showing frequent, naturaland wide inter-individual variation in the expression of group living. We video-tracked 320 field-sampled females to quantify their natural variation in aggregation and then tested whether the most and least gregarious females had different gut microbiota. We also compared the general activity, boldness, body size and body condition of these females and examined the association between each of these traits and the gut microbiota. Contrary to our predictions, we found no difference in gut microbiota between the most and least gregarious females, as well as no difference between these females in terms of general activity, boldness, body size and condition. We did show that the gut microbiota of females was overall linked to their body condition, even though it was also unrelated to the other measurements. Overall, these results demonstrate that a host's gut microbiota is not necessarily a major driver of aggregation behaviour in species with inter-individual variation in group living and call for future studies to investigate the determinants and role of gut microbiota in earwigs.

opencc-by-4.0Mar 2023View details →

A Pan-European, Quantile Machine learning (QML) based, Total, Fine-Mode and Coarse-Mode Aerosol Optical Depth dataset (QML AOD))

The V 1.1.0 product is an improved Aerosol Optical Depth (AOD) product based on Gap-filled MAIAC AOD, which provide first full-coverage, high-resolution monitoring of fine-mode and coarse-mode aerosols in Europe from 2003-20. This dataset has successfully rectified the previously identified issue of weak associations between satellite AOD and PM2.5 in Europe, which was primarily attributable to current limitations of AOD data. Our innovative approach has yielded stronger correlations with PM10, PM2.5, and PMcoarse than previous AOD product, laying a critical groundwork for improving PM10, PM2.5, and PMcoarse predictions in further epidemiological studies or environmental monitoring. We have uploaded three QML AOD datasets in Geotiff format, covering the region from -27° to 72° latitude and from -25° to 45° longitude. These datasets will be useful for researchers and policymakers to better understand the impacts of aerosols on the environment and human health.  Note: v1.0.0 product do not include MAIAC AOD in their models. Please read more details in our paper  <h1>Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments</h1> <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.scitotenv.2024.170593" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.scitotenv.2024.170593</a>

opencc-by-4.0Sep 2023View details →

European consumers ́ preference and willingness to pay for food products labelled as obtained by a circular farming system -in relation to environmental attitudes and consumption behaviours

Data was collected with questionnaire-based research carried out in Belgium, Croatia, Hungary, Italy, Poland, and Spain as part of a European project. The survey questions were designed to obtain the Willingness to pay using 2 different methodologies the discrete choice experiment and the open-end choice experiment. The survey also included questions about consumers environmental attitudes, and consumption behavior (purchase, use and recycling), to identify if them have influence on preferences towards more sustainable food products. The 3 analyzed food products were pork meat, milk and bread, all of them obtained through different agricultural production systems (circular, conventional, and organic agriculture). The sample was stratified in terms of gender and age to be representative to the average population in each country. Furthermore, respondents included in this study were those that are mainly, or in part responsible for the household food shopping. The questionnaire was translated to the languages of the countries involved in the data collection and pre-launched using a pilot sample of 50 consumers in each case study country.  Finally, a total of 5,362 validated questionnaires were obtained. Data was collected online using the Qualtrics market research company, and Net panel market company for Hungary from June 2021 to January 2022.

Characterization of investments profiles on the energy transition for european citizens

<ul> <li>Name: Characterization of investments profiles on the energy transition for european citizens</li> <li>Summary: The dataset contains: (1) surveyee consent form for the study, (2) different scenarios about the energy transition, (3) determinant factors about those scenarios, (4) socioeconomic description of the surveyee, (5) investment decisions, (6) and household characterization/description. </li> <li>License: cc-BY-SA</li> <li>Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.</li> <li>Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.</li> <li>Collection Date: 22/07/2022</li> <li>Publication Date: 15/10/2023</li> <li>DOI: 10.5281/zenodo.4455198</li> <li>Other repositories:</li> <li>Author: University of Deusto</li> <li>Objective of collection: This data was originally collected to analyze quantitatively the decisions of everyday people in relation to their energy consumption and their reactions to specific political interventions.</li> <li>Description: The dataset contains a CSV file file containing data collected from a survey about energy consumption investments. The fields that can be found for each entry are (1) Different scenarios about the energy transition and reactions to those scenarios, (money spent on energy investments, decisions about scenarios, actions taken under a blackout, etc.) (2) Determinant factors about the chosen scenarios in the previous question, which include different choices that could affect your decision about a scenario (3) socioeconomic information about the user (age, country of residence, studies), (4) estimation of the prices of various technologies related to the energy transition and (5) descriptive statistics about the household living situation (gender of user, people living in household, yearly rent, average savings per month, type of house, size of house) and also includes questions about climate change expertise. Next you can found a description of each field in the dataset <ul> <li>Section 1 - Scenarios for energy transition. <ul> <li>ID90. Rank in order of priority, from top to bottom, in which scenario you will be willing to live or to contribute/invest to make it possible. </li> <li>ID36, ID38, ID43, ID44, ID72. Percentage of money people are willing to spend/save out of their income per scenario</li> <li>ID191, ID192.. Amount of money people would spend based on an assumed case.</li> <li>ID191, ID192. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority for you and 10 stars means it is absolutely necessary for you.</li> <li>[ID325, ID326, ID327, ID328, ID329, ID330, ID331, ID332, ID333, ID334, ID335, ID336, ID337, ID338, ID339, ID340, ID341, ID133, ID242]. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.</li> <li>[ID251, ID256, ID257, ID292, ID293, ID294, ID295, ID296, ID297, ID298, ID299, ID301, ID302, ID303, ID304, ID305, ID306, ID250, ID251]. Priority service provision in case of full black-outs. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.</li> <li>[ID141, ID5, ID147]. Used for statements that best represent survey responder</li> </ul> </li> <li>Section 2 - Determinants (factors). Questions used to rate (from 0 to 100) factors that may influence the decision-making process contributing to make an ideal scenario possible. <ul> <li>ID100 Risk profile</li> <li>ID101 Added value</li> <li>ID102 Self-Satisfaction</li> <li>ID103 Technical Fit</li> <li>ID104 Own competence</li> <li>ID105 Knowledge</li> <li>ID106 Cost-Efficiency</li> <li>ID107 Safety</li> <li>ID108 Trust</li> <li>ID109 Autarky</li> <li>ID110 Legal</li> <li>ID111 Climate Protection</li> <li>ID112 Wellbeing</li> <li>ID113 Coziness</li> <li>ID114 Rights and Duties</li> <li>ID115 Peer-Pressure</li> <li>ID116 Socialising</li> <li>ID117 Support</li> <li>ID118 Agreement</li> <li>ID119 Brag</li> <li>ID120 Fun</li> <li>ID121 Novelty</li> <li>ID122 Trends</li> <li>ID123 Authority</li> <li>ID124 Own Significance</li> <li>ID125 Poseur</li> <li>ID2 Frugality</li> <li>ID3 Environmental concerns</li> <li>ID31 Adherence</li> <li>ID52 Commitment</li> <li>ID97 Profits</li> <li>ID99 Credit Score</li> </ul> </li> <li>Section 3 - “Socio-economic” description. Questions about the socio-economic information of the survey respondents for data stratification. The indentation represents the dependency of questions and whether this data was asked <ul> <li>ID164 Understanding of questions</li> <li>ID300 Country of residence</li> <li>ID137 Age</li> <li>ID178 Highest level of education</li> <li>ID136 Willingness to provide data on the investment decision (respond apply for -Investment decision section)</li> </ul> </li> <li>Section 4 - Investment decision. Questions about specific prices of potential purchases-decisions related to four scenarios (respondent's lifestyle) <ul> <li>Appliances <ul> <li>ID42 Affordable cost of a Regular refrigerator</li> <li>ID45 Energy efficient refrigerator costs</li> <li>ID50 Willingness to purchase an energy efficient refrigerator <ul> <li>ID65 Why no</li> <li>ID66 affordable cost of an energy efficient option</li> <li>ID67 Years to amortize an efficient option</li> </ul> </li> </ul> </li> <li>Insulation <ul> <li>ID47 Affordable cost of updating to a state of the art insulation on the facade</li> <li>ID56 Willingness for paying/invest <ul> <li>ID74 Why no?</li> <li>ID20 affordable cost of an energy efficient option</li> <li>ID34 Years to amortize an energy efficient option</li> </ul> </li> </ul> </li> <li>Energy Generation <ul> <li>ID68 Affordable cost of a solar photovoltaic system</li> <li>ID76 Willingness for paying/invest <ul> <li>ID84 Why no?</li> <li>ID132 Affordable cost of a photovoltaic system</li> <li>ID138 Years that amortize a  photovoltaic system</li> </ul> </li> </ul> </li> <li>Energy Storage <ul> <li>ID142 Affordable cost of an energy storage system</li> <li>ID146 Willingness for paying/invest <ul> <li>ID181 Why no? </li> <li>ID182 Affordable cost of an energy storage system </li> <li>ID183 Years that amortize an energy storage systems</li> </ul> </li> </ul> </li> <li>Heating <ul> <li>ID140 Affordable cost of a gas boiler</li> <li>ID209 Affordable cost of an energy efficient heating system</li> <li>ID217 Willingness for paying/invest <ul> <li>ID238 Why no?</li> <li>ID239 Affordable cost of a energy efficient option</li> <li>ID241 Years that amortize a heat pumps</li> </ul> </li> </ul> </li> <li>Mobility <ul> <li>ID41 Average kilometers traveled a typical day</li> <li>ID51 Usual travel option</li> <li>ID264 Affordable cost of a diesel or gasoline mid-range brand new car</li> <li>ID265 Affordable cost of a mid-range brand new electric car</li> <li>ID281 Willingness to buy an electric car <ul> <li>ID289 Why no?</li> <li>ID290 Affordable price of an electric car</li> <li>ID291 Years that amortize an electric car</li> </ul> </li> </ul> </li> </ul> </li> <li>Section 5 - Household characterization <ul> <li>ID127 Selecting an asked value</li> <li>ID189 Type of living area</li> <li>ID202 Gender identity</li> <li>ID1 Those living in the house</li> <li>ID32 Number of inhabitants</li> <li>ID220 Average neat yearly income</li> <li>ID229 Average monthly saving</li> <li>ID240 Type of housing</li> <li>ID249 Owner / co-owner</li> <li>ID255 Usable area of the property (m²)</li> <li>ID263 Insulation level</li> <li>ID270 Climate zone</li> <li>ID86 Level of self-awareness about climate change. On scale of 0-10, where 0 is “climate change does not exist” and 10 is “I am a climate change expert/activist”</li> <li>ID87 Level of awareness of climate change among your peers or relatives, On a scale of 0-10, where 0 is “climate change does not exist” and 10 is “They are climate change experts/activists”</li> <li>ID88 Level of self-awareness about  energy transition. On a scale of 0-10, where 0 is “It is the first time I hear about it” and 10 is “I am an expert or activist”</li> <li>ID89 Level of awareness of energy transition among your peers or relatives On a scale of 0-10, where 0 is “It is the first time they hear about it” and 10 is “They are experts or activists”</li> <li>ID190 feedback about survey</li> </ul> </li> </ul> </li> <li>5 star: ⭐⭐⭐</li> <li>Preprocessing steps: anonymization, data fusion, imputation of gaps.</li> <li>Reuse: NA</li> <li>Update policy: No more updates are planned</li> <li>Ethics and legal aspects: Spanish electric cooperative data contains the CUPS (Meter Point Administration Number), which is personal data. A pre-processing step has been carried out to substitute the CUPS by a random value hash.</li> <li>Technical aspects: </li> <li>Other:</li> </ul>