Dataset underlying the analysis in: Nina Schwarz, Urban form revisited—Selecting indicators for characterising European cities, Landscape and Urban Planning, Volume 96, Issue 1, 15 May 2010, Pages 29-47, ISSN 0169-2046, http://dx.doi.org/10.1016/j.landurbplan.2010.01.007. It is a combination of two data sources for 231 European cities:- CORINE land cover for computing city size based on land use and landscape metrics for urban form.- Urban Audit for socio-economic indicators describing urban form.Copyrights for the underlying datasets:CORINE: ©EEA, Copenhagen, 2007Urban Audit: Eurostat

The source for regional typology statistics are regional indicators at NUTS level 3 or LAU2 level published on the Eurostat website or existing in the Eurostat production database.

The structure of this domain is as follows:

• https://ec.europa.eu/eurostat/web/metropolitan-regions/information-data" target="_self">Metropolitan regions (met)
• https://ec.europa.eu/eurostat/web/rural-development/information-data" target="_self">Other typologies (urt) includes data by urban-rural typology including remoteness, coastal typology, border typology, island typology and mountain typology
• https://ec.europa.eu/eurostat/web/coastal-island-outermost-regions/database" target="_self">Tourism statistics by coastal areas (mare)

The total population In the Euro Area was estimated at 351.4 million people in 2025, according to the latest census figures and projections from Trading Economics. This dataset provides the latest reported value for - Euro Area Population - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Land Cover Map of Europe 2017 is a product resulting from the Phase 2 of the S2GLC project. The final map has been produced on the CREODIAS platform with algorithms and software developed by CBK PAN. Classification of over 15 000 Sentinel-2 images required high level of automation that was assured by the developed software.

The legend of the resulting Land Cover Map of Europe 2017 consists of 13 land cover classes. The pixel size of the map equals 10 m, which corresponds to the highest spatial resolution of Sentinel-2 imagery. Its overall accuracy was estimated to be at the level of 86% using approximately 52 000 validation samples distributed across Europe.

Related publication: https://doi.org/10.3390/rs12213523

Russia is the largest country in the world by far, with a total area of just over 17 million square kilometers. After Antarctica, the next three countries are Canada, the U.S., and China; all between 9.5 and 10 million square kilometers. The figures given include internal water surface area (such as lakes or rivers) - if the figures were for land surface only then China would be the second largest country in the world, the U.S. third, and Canada (the country with more lakes than the rest of the world combined) fourth. Russia Russia has a population of around 145 million people, putting it in the top ten most populous countries in the world, and making it the most populous in Europe. However, it's vast size gives it a very low population density, ranked among the bottom 20 countries. Most of Russia's population is concentrated in the west, with around 75 percent of the population living in the European part, while around 75 percent of Russia's territory is in Asia; the Ural Mountains are considered the continental border. Elsewhere in the world Beyond Russia, the world's largest countries all have distinctive topographies and climates setting them apart. The United States, for example, has climates ranging from tundra in Alaska to tropical forests in Florida, with various mountain ranges, deserts, plains, and forests in between. Populations in these countries are often concentrated in urban areas, and are not evenly distributed across the country. For example, around 85 percent of Canada's population lives within 100 miles of the U.S. border; around 95 percent of China lives east of the Heihe–Tengchong Line that splits the country; and the majority of populations in large countries such as Australia or Brazil live near the coast.

The report covers Oilfield Services Companies in Europe and the market is segmented by Service Type (Drilling Services, Completion Services, Production and Intervention Services, and Other Service Types), Location of Deployment (Onshore and Offshore), and Geography (Russia, UK, Norway, and the Rest of Europe).

Based on the annual EGR final frame, Eurostat releases aggregated data on multinational enterprise groups as experimental statistics in the following tables:

• Multinational enterprise groups in EU-EFTA countries by controlling country (egr_mne).

Number of multinational enterprise groups having at least one legal unit located in the EU-EFTA by controlling country/area. The controlling area is defined by the location of the Ultimate controlling institutional unit (UCI).

• Persons employed in multinational enterprise groups by size class (egr_emp).

Persons employed (employees and self-employed persons) by multinational enterprise groups in the EU-EFTA countries by controling country/area, main activity and worldwide size class of multinational enterprise groups, and country/area of work.

The percentage of total employment refers to Structural business statistical figures. From 2018-2020 the total employment considers persons working in the non-financial business economy (B to N excluding K). From 2021 onwards, with the SBS extended scope, the total employment considers the person working in the activity B to S excluding O and S94. This change explains the break in the time series.

• Concentration of multinational enterprise groups by NACE Rev. 2 activity (egr_conc).

Share of total employment in EU of the largest MNE groups by NACE activity of the enterprises belonging to the groups. The total employment used Structural business statistical figures.

• Multinational enterprise groups in EU-EFTA countries by controlling country, size class and NACE Rev. 2 activity.

Number of multinational enterprise groups having at least one legal unit located in the EU-EFTA by controlling country/area, main activity and worldwide size class of multinational enterprise groups (egr_mne_n2sc).

• Multinational enterprise groups employment by controlling country group size class NACE Rev. 2 and country of work (egr_mne_empw).

Persons employed (employees and self-employed persons) by multinational enterprise groups by controlling country/area, main activity and worldwide size class of multinational enterprise groups, and country/area of work.

The EGR covers multinational enterprise groups having at least one Legal unit located in the EU Member States or EFTA countries. The EGR includes micro data about the control structures of multinational enterprise groups, their constituent Legal units and corresponding Enterprises. The core variables for multinational enterprise groups, enterprises and legal units, are:

• identification variables,
• demographic events,
• control and ownership,
• stratification parameters, and
• economic variables (e.g., employment, turnover, economic activity code, etc.).

The EGR statistical frame has the purpose to improve the consistency of national statistics on cross border phenomena and thus better measure the economic globalization in the European Union. The EGR offers to the statistical users a tool for coordinating their frame population, for deriving consistent statistical output with improved quality, and for creating new statistical output and breakdowns. This is achieved by allowing microdata linking to many business statistics and providing insights in measuring global activities of European enterprises part of multinational enterprise groups.

The EGR is a statistical business register and can be used for statistical use only by users of the National Statistical Institutes, National Central Banks and the European Central Bank.

Aim: Macroecology and species distribution modelling often assume links between fine-grain (local) abundance and coarse-grain geographic distribution and/or climatic niche. However, we lack empirical knowledge of how spatial patterns of local abundance are related to coarse-grain distribution. Here we investigate this cross-scale transferability assumption by testing for three macroecological relationships: (1) the abundance-range-size relationship, (2) the abundance-centre-relationship, and (3) the abundance-suitability relationship. Location: Europe Taxon: Vascular plants Methods: Distribution range maps of 517 vascular plant species from the Chorological Database Halle were used to capture Europe-wide geographic ranges and derive climatic niches of these species. We used data from 744,513 vegetation plots from the European Vegetation Archive where local species abundance was measured as plant cover per plot. Centrality was calculated per species for each plot in geographic and climatic space. Climatic suitability at plot locations was predicted from coarse-grain species distribution models (SDMs). The general relationship between centrality and climatic suitability with abundance was tested with linear models. Quantile regression was used to examine the relationships of centrality and climatic suitability to upper limits of abundance. We summarized the overall trend across species slopes from linear models and quantile regression using a meta-analytical approach. Results: We did not find support for a positive relationship between mean local abundance and coarse-grain range or niche size. Significant positive correlations of abundance with centrality and climatic suitability in both the geographic and climatic space were as common as significant correlations in the opposite direction. Main conclusions: Finding no unequivocal support for any of the tested relationships, our results show that coarse-grain distribution properties might be of limited use for predicting local abundance. We conclude that environmental factors influencing individual performance measured as local cover-abundance likely differ from drivers of coarse-grain occurrence patterns. Current SDM approaches seem to be not directly transferable to local plant species abundance and performance.The here provided excel-file contains all information to reproduce the results. Detailed description on the data is given in the metadata.

Retirement Notice: This item is in mature support as of June 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.WorldCover provides a new baseline global land cover product at 10 m resolution for 2020 based on Sentinel-1 and 2 data. It was developed and validated in almost near-real time and at the same time maximizes the impact and uptake for the end users.Variable mapped: 11 land cover classesData Projection: WGS84 (WKID 4326)Mosaic Projection: Web Mercator (WKID3857)Extent: WorldCell Size: 8.33333333333333E-05 degrees (10m)Source Type: 8 bit unsignedVisible Scale: All scales are visibleSource: ESA (European Space Agency)Publication Date: October 20, 2021More Details from the WorldCover consortium: https://esa-worldcover.org/en Classes 10. Tree CoverThis class includes any geographic area dominated by trees with a cover of 10% or more. Other land cover classes (shrubs and/or herbs in the understorey, built-up, permanent water bodies, …) can be present below the canopy, even with a density higher than trees. Areas planted with trees for afforestation purposes and plantations (e.g. oil palm, olive trees) are included in this class. This class also includes tree covered areas seasonally or permanently flooded with fresh water except for mangroves.20. ShrublandThis class includes any geographic area dominated by natural shrubs having a cover of 10% or more. Shrubs are defined as woody perennial plants with persistent and woody stems and without any defined main stem being less than 5 m tall. Trees can be present in scattered form if their cover is less than 10%. Herbaceous plants can also be present at any density. The shrub foliage can be either evergreen or deciduous.30. GrasslandThis class includes any geographic area dominated by natural herbaceous plants (Plants without persistent stem or shoots above ground and lacking definite firm structure): (grasslands, prairies, steppes, savannahs, pastures) with a cover of 10% or more, irrespective of different human and/or animal activities, such as: grazing, selective fire management etc. Woody plants (trees and/or shrubs) can be present assuming their cover is less than 10%. It may also contain uncultivated cropland areas (without harvest/ bare soil period) in the reference year.40. CroplandLand covered with annual cropland that is sowed/planted and harvestable at least once within the 12 months after thesowing/planting date. The annual cropland produces an herbaceous cover and is sometimes combined with some tree or woody vegetation. Note that perennial woody crops will be classified as the appropriate tree cover or shrub land cover type. Greenhouses are considered as built-up.50. Built-upLand covered by buildings, roads and other man-made structures such as railroads. Buildings include both residential and industrial building. Urban green (parks, sport facilities) is not included in this class. Waste dump deposits and extraction sites are considered as bare.60. Bare or sparse vegetationLands with exposed soil, sand, or rocks and never has more than 10 % vegetated cover during any time of the year.70. Snow and IceThis class includes any geographic area covered by snow or glaciers persistently.80. Permanent water bodiesThis class includes any geographic area covered for most of the year (more than 9 months) by water bodies: lakes, reservoirs, and rivers. Can be either fresh or salt-water bodies. In some cases the water can be frozen for part of the year (less than 9 months).90. Herbaceous wetlandLand dominated by natural herbaceous vegetation (cover of 10% or more) that is permanently or regularly flooded by fresh, brackish or salt water. It excludes unvegetated sediment (see 60), swamp forests (classified as tree cover) and mangroves see 95).95. MangrovesTaxonomically diverse, salt-tolerant tree and other plant species which thrive in intertidal zones of sheltered tropical shores, "overwash" islands, and estuaries.100. Moss and lichenLand covered with lichens and/or mosses. Lichens are composite organisms formed from the symbiotic association of fungi and algae. Mosses contain photo-autotrophic land plants without true leaves, stems, roots but with leaf-and stemlike organs.

The distylous plant Primula veris has long served as a model species for studying heterostyly, i.e., the occurrence of multiple floral morphs within a population to ensure outcrossing. Habitat loss, reduced plant population sizes, and climate change have raised concerns about the impact of these factors on morph ratios and the related consequences on fitness of heterostylous species. We studied the deviation of floral morphs of P. veris from isoplethy (i.e., equal frequency) in response to plant population size, landscape context and climatic factors, based on a pan-European citizen science campaign involving observations from 28 countries. In addition, we examined the relative frequency of morphs to determine whether landscape and climatic factors disrupt morph frequencies or whether a specific morph has an advantage over the other. Theory predicts equal frequencies of short-styled S-morphs and long-styled L-morphs in populations at equilibrium. However, data from > 3000 populations showed a substantial morph deviation from isoplethy and a significant excess (9%) of S-morphs compared to L-morphs. Deviation of morph frequency from equilibrium was substantially stronger in smaller populations and was not affected by morph identity. Higher summer precipitation and land use intensity were associated with an increased prevalence of S-morphs. Five populations containing individuals exhibiting short homostyle phenotypes (with the style and anthers in low positions) were found. Genotyping of the individuals at CYP734A50 gene of the S locus, which determines the length of the style and the position of anthers of P. veris, revealed no mutations in this region. Our results based on an unprecedented geographic sampling suggest that changes in land use and climate may be responsible for non-equilibrium morph frequencies. This large-scale citizen science initiative sets foundations for future studies to clarify whether the unexpected excess of S-morphs is due to partial intra-morph compatibility, disruption of heterostyly or survival advantage of S-morphs. Synthesis. Human-induced environmental change may affect biodiversity indirectly through altering reproductive traits, which can also lead to reduced fitness and genetic diversity. Further research should consider the possible role of pollinators in mediating the ecological and evolutionary consequences of recent landscape and climatic shifts on plant reproductive traits. Methods Data on the morph identity of Primula veris The dataset of heterostyly (i.e., whether the observed plant was of L- or S-morph; Barrett 2019) was collected by volunteer observers within the frames of the pan-European citizen science campaign "Looking for Cowslips" that took place in 2021 and 2022 (Looking for Cowslips). Citizens from the following countries contributed data: Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Netherlands, North Macedonia, Norway, Poland, Romania, Slovakia, Slovenia, Sweden, Switzerland, Ukraine and the United Kingdom. Campaign participants were asked to provide information about the approximate size of the observed population (Small: some plants, up to 100 individuals; Medium: about 100-200 individuals; Large: more than 200 to thousands) and the morph identity of 100 randomly chosen cowslip individuals (fewer in case of small populations) occurring at least 0.5 meters apart from each other. Data collected by citizen scientists was filtered during several steps: (1) exclusion of populations where wrongly identified species were detected based on submitted digital photographic material (about 80 % of observations included photos of the study species), (2) revision and correction of any mistakes in geographical coordinates of the observations performed, (3) omission of empty observations or observations with too few observed plant individuals (< 10), (4) exclusion of observations with unrealistic number of observed plant individuals and low-quality observations, and (5) retaining only one of spatially adjacent observations (closer than 100 m) in case of those populations, which were submitted together with spatial coordinates. References: Barrett, S.C.H. (2019) ‘A most complex marriage arrangement’: recent advances on heterostyly and unresolved questions. New Phytologist, 224, 1051-1067.

There were estimated to be approximately **** million small and medium-sized enterprises (SMEs) in the European Union in 2025, with the vast majority of these enterprises being micro-sized firms that employed fewer than nine people. A further **** million enterprises were small firms with between ** and ** employees, while ******* were medium-sized firms that had ** to *** employees. The contribution of SMEs to the European Economy Small and medium-sized enterprises (SMEs) form the backbone of the European economy. These companies comprise around **** percent of all active businesses in Europe, while producing almost ** percent of total value added in the EU. These companies are not just economically important to the continent, however, as they also form an important part of the cultural fabric of European communities, with SMEs being particularly important for rural regions and smaller towns.

Almost ** million employed by SMEs In 2025, SME’s in the European Union employed more than ** million people. In Europe’s biggest economy, Germany, SMEs employed **** million people, with over *** million people employed by small-sized enterprises alone.

Europe had the largest call center market in 2017, generating around ** billion U.S. dollars in revenue, followed by North America, with ** billion U.S. dollars. Latin America had the smallest market in that year, with ** billion U.S. dollars in revenue. Call center market The call center market includes the section of an organization that provides assistance to customers by phone. This can be for existing customers, for example by answering queries about the product or service they purchased, or for sales-based activities to obtain new customers. Given the broad nature of these services, virtually every industry is represented in the call center market, making it a prime candidate for outsourcing. Outsourcing can achieve lower costs through locating call center infrastructure in countries with lower costs, such as India and the Philippines, and significantly reduce the capital expenditure required to set up a call center. This has led to a growing outsourced call center market that is expected to reach **** billion U.S. dollars by 2020. Overall market growth Some analysts expect the overall call center market to experience strong growth in coming years, predicting it will more than double in size by 2022. However, other analysts expect growth to be more limited and unevenly spread. For example, some predict the European market to shrink in size by 2025, while the United States will grow to be the largest market. Data from the last few years seems to support the hypothesis that the U.S. market will overtake Europe, with many more new call centers opening there between 2016 and 2018.

The report covers European Coal Companies and the market is segmented by type (anthracite, bituminous, sub-bituminous, and lignite), application (electricity, steel, cement, and others), and geography (Russia, Germany, Poland, and the Rest of Europe). The market size and forecasts are provided in terms of revenue (USD) for all the above segments.

The Europe Poultry Feed Market report segments the industry into Animal Type (Layer, Broiler, Turkey, Other Animal Types), Ingredient (Cereals, Oilseed Meals, Molasses, Fish Oils and Fish Meals, Supplements, Other Ingredients), and Geography (United Kingdom, Germany, Italy, France, Spain, Russia, Rest of Europe). Get five years of historical data alongside five-year market forecasts.

The Europe Continuous Glucose Monitoring Market Report is Segmented by Component (Sensors, Transmitters, Receivers), End User (Hospitals/Clinics, Home/Personal), Demography (Adult, Paediatric), and Geography (Germany, United Kingdom, France, Italy, Spain, Russia, Rest of Europe). The Market Forecasts are Provided in Terms of Value (USD).

The Europe Beauty and Personal Care Market Report is Segmented by Product Type (Personal Care, Cosmetics/Makeup Products), Category (Premium Products, Mass Products), Ingredient Type (Natural and Organic, Conventional/Synthetic), Distribution Channel (Specialty Stores, Supermarkets/Hypermarkets, and More), and Geography (Germany, United Kingdom, France, Italy, and More). The Market Forecasts are Provided in Terms of Value (USD).

The Europe Hair Care Market Report is Segmented by Product Type (Shampoo, Conditioners, Hair Colorants, and More), Category (Organic and Conventional), Price Range (Mass and Luxury/Premium), Distribution Channel (Supermarkets/Hypermarkets, Convenience/Grocery Stores, and More), and Geography (Germany, United Kingdom, Italy, and More). The Market Forecasts are Provided in Terms of Value (USD).

The Europe Pet Food Market Report is Segmented by Pet Food Product (Food, Pet Nutraceuticals/Supplements, Pet Treats, and More), Pets (Cats, Dogs, and Other Pets), Distribution Channel (Convenience Stores, Online Channel, and More), and Geography (France, Germany, Italy, Netherlands, and More). The Market Forecasts are Provided in Terms of Value (USD) and Volume (Metric Tons).