Population of Europe between 2017 - 2021 inclusive of (Belgium, France, Germany, Italy, Poland, Spain) for the September challenge. Please upvote if you find this useful! Thank you and happy kaggling!

I started these datasets to learn how to manipulate files in different formats with python. You can see the Github repo here https://github.com/rokelina/names-analysis

The European Copernicus Coastal Flood Awareness System (ECFAS) project will contribute to the evolution of the Copernicus Emergency Monitoring Service by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS will provide 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 will run from January 2021-December 2022. The ECFAS project is a collaboration between Istituto Universitario di Studi Superiori 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 is 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.

This project has received funding from the European Union’s Horizon 2020 programme

Description of the containing files inside the Dataset.

The dataset was divided at European country level, except the Adriatic area which was extracted as a region and not on a 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 abovementioned 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. Below there are tables which present the dataset. Finally, the dataset contains statistics information regarding the population of Europe per sex and age divided in administrative units NUTS level 3.

Copernicus Land Monitoring Service	Resolution	Comment
Coastal LU/LC	1:10.000	A Copernicus hotspot product to monitor landscape dynamics in coastal zones
EU-Hydro - Coastline	1:30.000	EU-Hydro is a dataset for all European countries providing the coastline
Natura 2000	1: 100000	A Copernicus hotspot product to monitor important areas for nature conservation
European Settlement Map	10m	A spatial raster dataset that is mapping human settlements in Europe
Imperviousness Density	10m	The percentage of sealed area
Impervious Built-up	10m	The part of the sealed surfaces where buildings can be found
Grassland 2018	10m	A binary grassland/non-grassland product
Tree Cover Density 2018	10m	Level of tree cover density in a range from 0-100%

Joint Research Center	Resolution	Comment
Global Human Settlement Population Grid GHS-POP)	250m	Residential population estimates for target year 2015
GHS settlement model layer (GHS-SMOD)	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	10m	Built-up grid derived from Sentinel-2 global image composite for reference year 2018
ENACT 2011 Population Grid (ENACT-POP R2020A)	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)	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)	1km	Urban Centres defined by specific cut-off values on resident population and built-up surface

Additional Data	Resolution	Comment
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 intersected with the NUTS3 Units

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

Explore gender statistics related to families and households, including data on both sexes, percent of total for both sexes, total live births, population, and residential information. Access valuable insights and trends for countries such as Portugal, Belgium, Spain, France, Italy, United Kingdom, United States, and many more.

Both sexes, Percent of Total for Both Sexes, Total Live Births, Population, Residential, birth

Portugal, Belgium, Spain, Bosnia and Herzegovina, France, Denmark, Italy, Uzbekistan, Bulgaria, United Kingdom, Slovenia, Czechia, Poland, Ukraine, Latvia, Sweden, Iceland, Armenia, Georgia, Canada, Montenegro, Hungary, United States, Andorra, Republic of Moldova, Croatia, Malta, San Marino, Turkmenistan, Azerbaijan, Kyrgyzstan, North Macedonia, Russian Federation, Greece, Luxembourg, Monaco, Slovakia, Norway, Tajikistan, Albania, Liechtenstein, Serbia, Switzerland, Lithuania, Estonia, Turkiye, Cyprus, Germany, Finland, Ireland, Israel, Kazakhstan, Austria, Belarus, Netherlands, RomaniaFollow data.kapsarc.org for timely data to advance energy economics research.Source: UNECE Statistical Database, compiled from national and international (Eurostat, UN Statistics Division Demographic Yearbook, WHO European health for all database and UNICEF TransMONEE) official sources.Definition: A live birth is the complete expulsion or extraction from its mother of a product of conception, irrespective of the duration of pregnancy, which after such separation breathes or shows any other evidence of life such as beating of the heart, pulsation of the umbilical cord or definite movement of voluntary muscles, whether or not the umbilical cord has been cut or the placenta is attached.General note: Data come from registers, unless otherwise specified. In years 2003 and before, the number of live births for girl child and boy child may not add up to the number for both sexes (Total) due to the rounding up of numbers.

Demographics Analysis with Consumer Edge Credit & Debit Card Transaction Data

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Transact Signal is an aggregated transaction feed that includes consumer transaction data on 100M+ credit and debit cards, including 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants and deep demographic and geographic breakouts. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

This data sample illustrates how Consumer Edge data can be used to compare demographics breakdown (age and income excluded in this free sample view) for one company vs. a competitor for a set period of time (Ex: How do demographics like wealth, ethnicity, children in the household, homeowner status, and political affiliation differ for Walmart vs. Target shopper?).

Inquire about a CE subscription to perform more complex, near real-time demographics analysis functions on public tickers and private brands like: • Analyze a demographic, like age or income, within a state for a company in 2023 • Compare all of a company’s demographics to all of that company’s competitors through most recent history

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Demographics Analysis

Problem A global retailer wants to understand company performance by age group.

Solution Consumer Edge transaction data can be used to analyze shopper transactions by age group to understand: • Overall sales growth by age group over time • Percentage sales growth by age group over time • Sales by age group vs. competitors

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on key demographic drivers of growth for company-wide reporting • Reduce investment in underperforming age groups, both online and offline • Determine retention by age group to refine campaign strategy • Understand how different age groups are performing compared to key competitors

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth • Ethnicity • Political Affiliation • Children in Household • Adults in Household • Homeowner vs. Renter • Business Owner • Retention by First-Shopped Period ...

This data set contains the population of Southern Europe.

Southern Europe countries include : Serbia Holy See Andorra Montenegro Italy Spain Malta Croatia San Marino Gibraltar Bosnia and Herzegovina Albania North Macedonia Slovenia Greece Portugal

Dataset details: Year: Year is from 1955 to 2020 Population: Count of Southern Europe country's population Yearly % Change: Percentage of yearly change in population Yearly Change: Count of yearly change in population Migrants (net): Number of Migrants per year Median Age: Median Age of the population Fertility Rate: Fertility Rate of the population Density: Population Density is in (P/Km²) Urban Pop%: percentage of Urban Population% Urban Pop: Count of Urban Population count Southern Europe's - Share of World Pop: Percentage of share of Southern Europe's the world population World Population: Count of the world population

The movement of human populations has greatly influenced the distributions of human commensals. Patterns of genetic variation in contemporary populations can shed light on their demographic history, including long-range migration events and changes in effective population size. The western house mouse, Mus musculus domesticus, is a human commensal and an outstanding model organism for studying a wide variety of traits and diseases. However, we have few genomic resources for wild mice and only a rudimentary understanding of the demographic history of house mice in Europe. Here, we sequenced 59 whole-genomes of mice collected from England, Scotland, Wales, Guernsey, northern France, Italy, Portugal, and Spain. We combined this dataset with 24 previously published sequences from southern France, Germany, and Iran, and compared patterns of population structure and inferred demographic parameters for house mice in Western Europe to patterns seen in humans. Principal component and phylogeneti..., , , # Data from: Genetic structure and demographic history of house mice in Western Europe inferred using whole genome sequences

 https://doi.org/10.5061/dryad.s1rn8pkgh

• ALL_Europe_unfiltered_merged_noAlloption.vcf.gz

Unfiltered vcf of 59 wild-caught house mice from England, Scotland, Wales, Guernsey, northern France, Italy, Portugal, and Spain collected and deposited as prepared specimens in the University of Michigan, Museum of Zoology, and 24 wild-caught mice from southern France, Germany, and Iran from a previous publication (Harr et al. 2016). Sequences were aligned to the mouse reference genome (GRCm38/mm10, RefSeq: GCF_000001635.20). See Tables S1 and S2 of the associated manuscript for sample information, including sample locations and alignment statistics.

Sharing/Access information

The raw fastq files used to generate this data is accessible from:

• NCBI SRA BioProject ID PRJNA1050608

• ALL_Europe_83samples_filtered_merged_autosomes.recode.vcf.gz

Filtered...

This dataset contains the phenotypic data of the apple REFPOP multisite trial. The apple REFPOP (reference population) is made of 534 genotypes, split into two groups: an accession group, involving 269 accessions of European and non-European origin representing the diversity in cultivated apple, and a progeny group, comprising 265 genotypes, which stemmed from 27 parental combinations. The population was planted in six locations in 2016, with two or four trees of each genotype/accession per location. The six locations were (i) Rillaar, Belgium, (ii) Angers, France, (iii) Laimburg, Italy, (iv) Skierniewice, Poland, (v) Lleida, Spain and (vi) Wädenswil, Switzerland. Thirty traits related to phenology, productivity, fruit size, outer fruit, inner fruit, and vigor were evaluated at up to six locations, during up to three seasons (2018-2020). Trunk diameter was measured in 2017 in some locations, enabling for a trunk increment calculation for 2018. Data for all traits were used in the paper by Jung et al. (2022) "Genetic architecture and genomic predictive ability of apple quantitative traits across environments" and are published in version 2 of the dataset. Data for harvest date, fruit overcolor, fruit weight and fruit number, were used in the paper by Cazenave et al. (2021) "Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple" (see Metadata for complete reference) and were made available in the first version of the dataset.

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

For a more detailed description of the dataset and the coding process, see the codebook available in the .zip-file.

Purpose:

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

Locations: 1) Marseille (France); 2) Girona (Spain); 3) Banyuls (France); 4) Portofino (Italy); 5) Elba (Italy); 6) French Catalan coast (France); 7) Apulian Adriatic coast (Italy); 8) Cap Roux Fishery Reserve and adjacent areas in Saint-Raphaël (France); 9) Menorca island (Spain).

Ploidy of flor strains from various countries (Spain, Italy, Hungary, and France) estimated from the DNA content measured in Flow cytometry.