7 datasets found
  1. w

    Area and Population of Portugal and Spain by NUTS3

    • data.wu.ac.at
    Updated Oct 10, 2013
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    Economics Datasets (2013). Area and Population of Portugal and Spain by NUTS3 [Dataset]. https://data.wu.ac.at/schema/datahub_io/NGE4NGY5ODEtZGM0OS00MTY3LTg5YjUtOGY0YmMxMDYxMjQw
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    Dataset updated
    Oct 10, 2013
    Dataset provided by
    Economics Datasets
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Description

    The areas in km2 and populations of the Portuguese and Spanish NUTS3 subregions. Data compiled from wikipedia articles.

  2. Coastal dataset including exposure and vulnerability layers, Deliverable 3.1...

    • zenodo.org
    Updated Nov 25, 2023
    + more versions
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    E. Ieronymidi; D. Grigoriadis; E. Ieronymidi; D. Grigoriadis (2023). Coastal dataset including exposure and vulnerability layers, Deliverable 3.1 - ECFAS Project (GA 101004211), www.ecfas.eu [Dataset]. http://doi.org/10.5281/zenodo.7319270
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    Dataset updated
    Nov 25, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    E. Ieronymidi; D. Grigoriadis; E. Ieronymidi; D. Grigoriadis
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Description

    The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

    The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

    Description of the containing files inside the Dataset.

    The ECFAS Coastal Dataset represents a single access point to publicly available Pan-European datasets that provide key information for studying coastal areas. The publicly available datasets listed below have been clipped to the coastal area extent, quality-checked and assessed for completeness and usability in terms of coverage, accuracy, specifications and access. The dataset was divided at European country level, except for the Adriatic area which was extracted as a region and not at the country level due to the small size of the countries. The buffer zone of each data was 10km inland in order to be correlated with the new Copernicus product Coastal Zone LU/LC.

    Specifically, the dataset includes the new Coastal LU/LC product which was implemented by the EEA and became available at the end of 2020. Additional information collected in relation to the location and characteristics of transport (road and railway) and utility networks (power plants), population density and time variability. Furthermore, some of the publicly available datasets that were used in CEMS related to the above mentioned assets were gathered such as OpenStreetMap (building footprints, road and railway network infrastructures), GeoNames (populated places but also names of administrative units, rivers and lakes, forests, hills and mountains, parks and recreational areas, etc.), the Global Human Settlement Layer (GHS) and Global Human Settlement Population Grid (GHS-POP) generated by JRC. Also, the dataset contains 2 layers with statistics information regarding the population of Europe per sex and age divided in administrative units at NUTS level 3. The first layer includes information for the whole of Europe and the second layer has only the information regarding the population at the Coastal area. Finally, the dataset includes the global database of Floods protection standards. Below there are tables which present the dataset.

    * Adriatic folder contains the countries: Slovenia, Croatia, Montenegro, Albania, Bosnia and Herzegovina

    * Malta was added to the dataset

    Copernicus Land Monitoring Service:

    Coastal LU/LC

    Scale 1:10.000; A Copernicus hotspot product to monitor landscape dynamics in coastal zones

    EU-Hydro - Coastline

    Scale 1:30.000; EU-Hydro is a dataset for all European countries providing the coastline

    Natura 2000

    Scale 1: 100000; A Copernicus hotspot product to monitor important areas for nature conservation

    European Settlement Map

    Resolution 10m; A spatial raster dataset that is mapping human settlements in Europe

    Imperviousness Density

    Resolution 10m; The percentage of sealed area

    Impervious Built-up

    Resolution 10m; The part of the sealed surfaces where buildings can be found

    Grassland 2018

    Resolution 10m; A binary grassland/non-grassland product

    Tree Cover Density 2018

    Resolution 10m; Level of tree cover density in a range from 0-100%

    Joint Research Center:

    Global Human Settlement Population Grid
    GHS-POP)

    Resolution 250m; Residential population estimates for target year 2015

    GHS settlement model layer
    (GHS-SMOD)

    Resolution 1km: The GHS Settlement Model grid delineates and classify settlement typologies via a logic of population size, population and built-up area densities

    GHS-BUILT

    Resolution 10m; Built-up grid derived from Sentinel-2 global image composite for reference year 2018

    ENACT 2011 Population Grid

    (ENACT-POP R2020A)

    Resolution 1km; The ENACT is a population density for the European Union that take into account major daily and monthly population variations

    JRC Open Power Plants Database (JRC-PPDB-OPEN)

    Europe's open power plant database

    GHS functional urban areas
    (GHS-FUA R2019A)

    Resolution 1km; City and its commuting zone (area of influence of the city in terms of labour market flows)

    GHS Urban Centre Database
    (GHS-UCDB R2019A)

    Resolution 1km; Urban Centres defined by specific cut-off values on resident population and built-up surface

    Additional Data:

    Open Street Map (OSM)

    BF, Transportation Network, Utilities Network, Places of Interest

    CEMS

    Data from Rapid Mapping activations in Europe

    GeoNames

    Populated places, Adm. units, Hydrography, Forests, Hills/Mountains, Parks, etc.

    Global Administrative Areas

    Administrative areas of all countries, at all levels of sub-division

    NUTS3 Population Age/Sex Group

    Eurostat population by age and sex statistics interescted with the NUTS3 Units

    FLOPROS

    A global database of FLOod PROtection Standards, which comprises information in the form of the flood return period associated with protection measures, at different spatial scales

    Disclaimer:

    ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.

    This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211

  3. Dataset of concentrations of bisphenol A, F and S sulfates in wastewater...

    • zenodo.org
    • research.science.eus
    • +2more
    bin, csv, txt
    Updated Jul 2, 2024
    + more versions
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    Andrea Estévez Danta; Andrea Estévez Danta; Rosa Montes; Rosa Montes; Ailette Prieto; Ailette Prieto; Miguel M. Santos; Miguel M. Santos; Gorka Orive; Gorka Orive; Unax Lertxundi; Unax Lertxundi; José Benito Quintana; José Benito Quintana; Rosario Rodil; Rosario Rodil (2024). Dataset of concentrations of bisphenol A, F and S sulfates in wastewater from Spain and Portugal and back-calculation of human exposure by wastewater-based epidemiology [Dataset]. http://doi.org/10.5281/zenodo.10459047
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    csv, txt, binAvailable download formats
    Dataset updated
    Jul 2, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Andrea Estévez Danta; Andrea Estévez Danta; Rosa Montes; Rosa Montes; Ailette Prieto; Ailette Prieto; Miguel M. Santos; Miguel M. Santos; Gorka Orive; Gorka Orive; Unax Lertxundi; Unax Lertxundi; José Benito Quintana; José Benito Quintana; Rosario Rodil; Rosario Rodil
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Spain
    Description

    Dataset of concentrations of bisphenol A, F and S sulfates in wastewater from Spain and Portugal and back-calculation of human exposure by wastewater-based epidemiology.

    Data is provided in MS Excel (xlsx) and CSV formats and contains details on WWTP characteristics, concentrations of bisphenols sulfates and extrapolated population-normalizad daily loads and different extrapolations of human exposure to bisphenols.

    Further details are provided in the associated publication:

    A. Estévez-Danta, R. Montes, A. Prieto, M.M. Santos, G. Orive, U. Lertxundi, J.B. Quintana, R. Rodil. Wastewater-Based Epidemiology Methodology To Investigate Human Exposure To Bisphenol A, Bisphenol F and Bisphenol S.
    Water Research 2024, 122016. DOI: 10.1016/j.watres.2024.122016.
    https://doi.org/10.1016/j.watres.2024.122016

    The data is deposited in ZENODO:
    If you reuse the data, please cite the publication and ZENODO deposit mentioned above
  4. Z

    Base rates of food safety practices in European households: Summary data...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Nov 4, 2022
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    Scholderer, Joachim (2022). Base rates of food safety practices in European households: Summary data from the SafeConsume Household Survey [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_7264924
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    Dataset updated
    Nov 4, 2022
    Dataset authored and provided by
    Scholderer, Joachim
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  5. d

    Data from: Applying genomic approaches to identify historic population...

    • datadryad.org
    • data.niaid.nih.gov
    zip
    Updated Oct 29, 2023
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    Orly Razgour (2023). Applying genomic approaches to identify historic population declines in European forest bats [Dataset]. http://doi.org/10.5061/dryad.wstqjq2qd
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    zipAvailable download formats
    Dataset updated
    Oct 29, 2023
    Dataset provided by
    Dryad
    Authors
    Orly Razgour
    Time period covered
    2022
    Description

    Most population genomics R packages can open vcf format files.

  6. d

    Genetic variation data to explore the phylogeny of Iberian weedy rice

    • datadryad.org
    • data.niaid.nih.gov
    zip
    Updated Jul 8, 2022
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    Xiang Li; Ana Caicedo; Shulin Zhang (2022). Genetic variation data to explore the phylogeny of Iberian weedy rice [Dataset]. http://doi.org/10.5061/dryad.zkh1893cn
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    zipAvailable download formats
    Dataset updated
    Jul 8, 2022
    Dataset provided by
    Dryad
    Authors
    Xiang Li; Ana Caicedo; Shulin Zhang
    Time period covered
    2022
    Area covered
    Iberian Peninsula
    Description

    Text editor or vcftools.

  7. f

    Data file.

    • plos.figshare.com
    xlsx
    Updated Jun 23, 2023
    + more versions
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    Thaveesha Jayawardhana; Ruwan Jayathilaka; Thamasha Nimnadi; Sachini Anuththara; Ridhmi Karadanaarachchi; Kethaka Galappaththi; Thanuja Dharmasena (2023). Data file. [Dataset]. http://doi.org/10.1371/journal.pone.0287207.s001
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    xlsxAvailable download formats
    Dataset updated
    Jun 23, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Thaveesha Jayawardhana; Ruwan Jayathilaka; Thamasha Nimnadi; Sachini Anuththara; Ridhmi Karadanaarachchi; Kethaka Galappaththi; Thanuja Dharmasena
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This study explores the causal relationship between the economy and the elderly population in 15 European countries. The economy was measured by the Per Capita Gross Domestic Product growth rate, while the population aged above 65 as a percentage of the total was considered the elderly population. The data were obtained from a time series dataset published by the World Bank for six decades from 1961 to 2021. The Granger causality test was employed in the study to analyse the impact between the economy and the elderly population. An alternate approach, wavelet coherence, was used to demonstrate the changes to the relationship between the two variables in Europe over the 60 years. The findings from the Granger causality test indicate a unidirectional Granger causality from the economy to the elderly population for Luxembourg, Austria, Denmark, Spain, and Sweden, while vice versa for Greece and the United Kingdom. Furthermore, for Belgium, Finland, France, Italy, Netherlands, Norway, Portugal, and Turkey, Granger causality does not exist between the said variables. Moreover, wavelet coherence analysis depicts that for Europe, the elderly population negatively affected the economic growth in the 1960s, and vice versa in the 1980s.

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Economics Datasets (2013). Area and Population of Portugal and Spain by NUTS3 [Dataset]. https://data.wu.ac.at/schema/datahub_io/NGE4NGY5ODEtZGM0OS00MTY3LTg5YjUtOGY0YmMxMDYxMjQw

Area and Population of Portugal and Spain by NUTS3

Explore at:
Dataset updated
Oct 10, 2013
Dataset provided by
Economics Datasets
License

ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically

Description

The areas in km2 and populations of the Portuguese and Spanish NUTS3 subregions. Data compiled from wikipedia articles.

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