About this dataset

Context

The dataset tabulates the Germany township population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Germany township. The dataset can be utilized to understand the population distribution of Germany township by age. For example, using this dataset, we can identify the largest age group in Germany township.

Key observations

The largest age group in Germany Township, Pennsylvania was for the group of age 50 to 54 years years with a population of 313 (10.95%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Germany Township, Pennsylvania was the 80 to 84 years years with a population of 60 (2.10%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Germany township is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Germany township total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Germany township Population by Age. You can refer the same here

Unemployment Rate in Germany remained unchanged at 6.30 percent in June. This dataset provides the latest reported value for - Germany Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Abstract

The Urban Green Raster Germany is a land cover classification for Germany that addresses in particular the urban vegetation areas. The raster dataset covers the terrestrial national territory of Germany and has a spatial resolution of 10 meters. The dataset is based on a fully automated classification of Sentinel-2 satellite data from a full 2018 vegetation period using reference data from the European LUCAS land use and land cover point dataset.
The dataset identifies eight land cover classes. These include Built-up, Built-up with significant green share, Coniferous wood, Deciduous wood, Herbaceous vegetation (low perennial vegetation), Water, Open soil, Arable land (low seasonal vegetation).
The land cover dataset provided here is offered as an integer raster in GeoTiff format. The assignment of the number coding to the corresponding land cover class is explained in the legend file.

Data acquisition

The data acquisition comprises two main processing steps: (1) Collection, processing, and automated classification of the multispectral Sentinel 2 satellite data with the “Land Cover DE method”, resulting in the raw land cover classification dataset, NDVI layer, and RF assignment frequency vector raster. (2) GIS-based postprocessing including discrimination of (densely) built-up and loosely built-up pixels according NDVI threshold, and creating water-body and arable-land masks from geo-topographical base-data (ATKIS Basic DLM) and reclassification of water and arable land pixels based on the assignment frequency.

Data collection

Satellite data were searched and downloaded from the Copernicus Open Access Hub (https://scihub.copernicus.eu/).

The LUCAS reference and validation points were loaded from the Eurostat platform (https://ec.europa.eu/eurostat/web/lucas/data/database).

The processing of the satellite data was performed at the DLR data center in Oberpfaffenhofen.

GIS-based post-processing of the automatic classification result was performed at IOER in Dresden.

Value of the data

The dataset can be used to quantify the amount of green areas within cities on a homogeneous data base [5].

Thus it is possible to compare cities of different sizes regarding their greenery and with respect to their ratio of green and built-up areas [6].

Built-up areas within cities can be discriminated regarding their built-up density (dense built-up vs. built-up with higher green share).

Data description

A Raster dataset in GeoTIFF format: The dataset is stored as an 8 bit integer raster with values ranging from 1 to 8 for the eight different land cover classes. The nomenclature of the coded values is as follows: 1 = Built-up, 2=open soil; 3=Coniferous wood, 4= Deciduous wood, 5=Arable land (low seasonal vegetation), 6=Herbaceous vegetation (low perennial vegetation), 7=Water, 8=Built-up with significant green share. Name of the file ugr2018_germany.tif. The dataset is zipped alongside with accompanying files: *.twf (geo-referencing world-file), *.ovr (Overlay file for quick data preview in GIS), *.clr (Color map file).

A text file with the integer value assignment of the land cover classes. Name of the file: Legend_LC-classes.txt.

Experimental design, materials and methods

The first essential step to create the dataset is the automatic classification of a satellite image mosaic of all available Sentinel-2 images from May to September 2018 with a maximum cloud cover of 60 percent. Points from the 2018 LUCAS (Land use and land cover survey) dataset from Eurostat [1] were used as reference and validation data. Using Random Forest (RF) classifier [2], seven land use classes (Deciduous wood, Coniferous wood, Herbaceous vegetation (low perennial vegetation), Built-up, Open soil, Water, Arable land (low seasonal vegetation)) were first derived, which is methodologically in line with the procedure used to create the dataset "Land Cover DE - Sentinel-2 - Germany, 2015" [3]. The overall accuracy of the data is 93 % [4].

Two downstream post-processing steps served to further qualify the product. The first step included the selective verification of pixels of the classes arable land and water. These are often misidentified by the classifier due to radiometric similarities with other land covers; in particular, radiometric signatures of water surfaces often resemble shadows or asphalt surfaces. Due to the heterogeneous inner-city structures, pixels are also frequently misclassified as cropland.

To mitigate these errors, all pixels classified as water and arable land were matched with another data source. This consisted of binary land cover masks for these two land cover classes originating from the Monitor of Settlement and Open Space Development (IOER Monitor). For all water and cropland pixels that were outside of their respective masks, the frequencies of class assignments from the RF classifier were checked. If the assignment frequency to water or arable land was at least twice that to the subsequent class, the classification was preserved. Otherwise, the classification strength was considered too weak and the pixel was recoded to the land cover with the second largest assignment frequency.

Furthermore, an additional land cover class "Built-up with significant vegetation share" was introduced. For this purpose, all pixels of the Built-up class were intersected with the NDVI of the satellite image mosaic and assigned to the new category if an NDVI threshold was exceeded in the pixel. The associated NDVI threshold was previously determined using highest resolution reference data of urban green structures in the cities of Dresden, Leipzig and Potsdam, which were first used to determine the true green fractions within the 10m Sentinel pixels, and based on this to determine an NDVI value that could be used as an indicator of a significant green fraction within the built-up pixel. However, due to the wide dispersion of green fraction values within the built-up areas, it is not possible to establish a universally valid green percentage value for the land cover class of Built-up with significant vegetation share. Thus, the class essentially serves to the visual differentiability of densely and loosely (i.e., vegetation-dominated) built-up areas.

Acknowledgments

This work was supported by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) [10.06.03.18.101].The provided data has been developed and created in the framework of the research project “Wie grün sind bundesdeutsche Städte?- Fernerkundliche Erfassung und stadträumlich-funktionale Differenzierung der Grünausstattung von Städten in Deutschland (Erfassung der urbanen Grünausstattung)“ (How green are German cities?- Remote sensing and urban-functional differentiation of the green infrastructure of cities in Germany (Urban Green Infrastructure Inventory)). Further persons involved in the project were: Fabian Dosch (funding administrator at BBSR), Stefan Fina (research partner, group leader at ILS Dortmund), Annett Frick, Kathrin Wagner (research partners at LUP Potsdam).

References

[1] Eurostat (2021): Land cover / land use statistics database LUCAS. URL: https://ec.europa.eu/eurostat/web/lucas/data/database

[2] L. Breiman (2001). Random forests, Mach. Learn., 45, pp. 5-32

[3] M. Weigand, M. Wurm (2020). Land Cover DE - Sentinel-2—Germany, 2015 [Data set]. German Aerospace Center (DLR). doi: 10.15489/1CCMLAP3MN39

[4] M. Weigand, J. Staab, M. Wurm, H. Taubenböck, (2020). Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data. Int J Appl Earth Obs, 88, 102065. doi: https://doi.org/10.1016/j.jag.2020.102065

[5] L. Eichler., T. Krüger, G. Meinel, G. (2020). Wie grün sind deutsche Städte? Indikatorgestützte fernerkundliche Erfassung des Stadtgrüns. AGIT Symposium 2020, 6, 306–315. doi: 10.14627/537698030

[6] H. Taubenböck, M. Reiter, F. Dosch, T. Leichtle, M. Weigand, M. Wurm (2021). Which city is the greenest? A multi-dimensional deconstruction of city rankings. Comput Environ Urban Syst, 89, 101687. doi: 10.1016/j.compenvurbsys.2021.101687

Inflation Rate in Germany decreased to 2 percent in June from 2.10 percent in May of 2025. This dataset provides the latest reported value for - Germany Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in New Germany, MN, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 30,969, while the mean income for the highest quintile (20% of households with the highest income) is 215,949. This indicates that the top earners earn 7 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 309,570, which is 143.35% higher compared to the highest quintile, and 999.61% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Germany median household income. You can refer the same here

The Gross Domestic Product (GDP) in Germany expanded 0.40 percent in the first quarter of 2025 over the previous quarter. This dataset provides the latest reported value for - Germany GDP Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Do you know further standard populations?

If you know any further standard populations worth integrating in this dataset, please let me know in the discussion part. I would be happy to integrate further data to make this dataset more useful for everybody.

German "Federal Health Monitoring System" about 'standard populations':

"Standard populations are "artificial populations" with fictitious age structures, that are used in age standardization as uniform basis for the calculation of comparable measures for the respective reference population(s).

Use: Age standardizations based on a standard population are often used at cancer registries to compare morbidity or mortality rates. If there are different age structures in populations of different regions or in a population in one region over time, the comparability of their mortality or morbidity rates is only limited. For interregional or inter-temporal comparisons, therefore, an age standardization is necessary. For this purpose the age structure of a reference population, the so-called standard population, is assumed for the study population. The age specific mortality or morbidity rates of the study population are weighted according to the age structure of the standard population. Selection of a standard population:

Which standard population is used for comparison basically, does not matter. It is important, however, that

1. the demographic structure of the standard population is not too dissimilar to that of the reference population and
2. the comparable rates refer to the same standard."

Aim of this dataset

The aim of this dataset is to provide a variety of the most commonly used 'standard populations'.

Currently, two files with 22 standard populations are provided: - standard_populations_20_age_groups.csv - 20 age groups: '0', '01-04', '05-09', '10-14', '15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80-84', '85-89', '90+' - 7 standard populations: 'Standard population Germany 2011', 'Standard population Germany 1987', 'Standard population of Europe 2013', 'Standard population Old Laender 1987', 'Standard population New Laender 1987', 'New standard population of Europe', 'World standard population' - source: German Federal Health Monitoring System

• standard_populations_19_age_groups.csv
  • 19 age groups: '0', '01-04', '05-09', '10-14', '15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80-84', '85+'
  • 15 standard populations: '1940 U.S. Std Million', '1950 U.S. Std Million', '1960 U.S. Std Million', '1970 U.S. Std Million', '1980 U.S. Std Million', '1990 U.S. Std Million', '1991 Canadian Std Million', '1996 Canadian Std Million', '2000 U.S. Std Million', '2000 U.S. Std Population (Census P25-1130)', '2011 Canadian Standard Population', 'European (EU-27 plus EFTA 2011-2030) Std Million', 'European (Scandinavian 1960) Std Million', 'World (Segi 1960) Std Million', 'World (WHO 2000-2025) Std Million'
  • source: National Institutes of Health, National Cancer Institute, Surveillance, Epidemiology, and End Results Program

Terms of use

No restrictions are known to the author. Standard populations are published by different organisations for public usage.

The Gross Domestic Product per capita in Germany was last recorded at 44108.70 US dollars in 2024. The GDP per Capita in Germany is equivalent to 349 percent of the world's average. This dataset provides the latest reported value for - Germany GDP per capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This is NOT a raw population dataset. We use our proprietary stack to combine detailed 'WorldPop' UN-adjusted, sex and age structured population data with a spatiotemporal OD matrix.

The result is a dataset where each record indicates how many people can be reached in a fixed timeframe (30 Mins in this case) from that record's location.

The dataset is broken down into sex and age bands at 5 year intervals, e.g - male 25-29 (m_25) and also contains a set of features detailing the representative percentage of the total that the count represents.

The dataset provides 76174 records, one for each sampled location. These are labelled with a h3 index at resolution 7 - this allows easy plotting and filtering in Kepler.gl / Deck.gl / Mapbox, or easy conversion to a centroid (lat/lng) or the representative geometry of the hexagonal cell for integration with your geospatial applications and analyses.

A h3 resolution of 7, is a hexagonal cell area equivalent to: - ~1.9928 sq miles - ~5.1613 sq km

Higher resolutions or alternate geographies are available on request.

More information on the h3 system is available here: https://eng.uber.com/h3/

WorldPop data provides for a population count using a grid of 1 arc second intervals and is available for every geography.

More information on the WorldPop data is available here: https://www.worldpop.org/

One of the main use cases historically has been in prospecting for site selection, comparative analysis and network validation by asset investors and logistics companies. The data structure makes it very simple to filter out areas which do not meet requirements such as: - being able to access 70% of the German population within 4 hours by Truck and show only the areas which do exhibit this characteristic.

Clients often combine different datasets either for different timeframes of interest, or to understand different populations, such as that of the unemployed, or those with particular qualifications within areas reachable as a commute.

The Gross Domestic Product (GDP) in Germany was worth 4659.93 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Germany represents 4.39 percent of the world economy. This dataset provides the latest reported value for - Germany GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Youth Unemployment Rate in Germany decreased to 6.60 percent in May from 6.70 percent in April of 2025. This dataset provides the latest reported value for - Germany Youth Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Germany DE: Tariff Rate: Most Favored Nation: Weighted Mean: Primary Products data was reported at 1.870 % in 2022. This records a decrease from the previous number of 2.840 % for 2021. Germany DE: Tariff Rate: Most Favored Nation: Weighted Mean: Primary Products data is updated yearly, averaging 2.840 % from Dec 2000 (Median) to 2022, with 23 observations. The data reached an all-time high of 5.340 % in 2001 and a record low of 1.870 % in 2022. Germany DE: Tariff Rate: Most Favored Nation: Weighted Mean: Primary Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Trade Tariffs. Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals).;World Bank staff estimates using the World Integrated Trade Solution system, based on tariff data from the United Nations Conference on Trade and Development's Trade and Development's Trade Analysis and Information System (TRAINS) database and global imports data from the United Nations Statistics Division's Comtrade database.;;The tariff data for the European Union (EU) apply to EU Member States in alignment with the EU membership for the respective countries/economies and years. In the context of the tariff data, the EU membership for a given country/economy and year is defined for the entire year during which the country/economy was a member of the EU (irrespective of the date of accession to or withdrawal from the EU within a given year). The tariff data for the EU are, thus, applicable to Belgium, France, Germany, Italy, Luxembourg, and the Netherlands (EU Member State(s) since 1958), Denmark and Ireland (EU Member State(s) since 1973), the United Kingdom (EU Member State(s) from 1973 until 2020), Greece (EU Member State(s) since 1981), Spain and Portugal (EU Member State(s) since 1986), Austria, Finland, and Sweden (EU Member State(s) since 1995), Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovakia, and Slovenia (EU Member State(s) since 2004), Romania and Bulgaria (EU Member State(s) since 2007), Croatia (EU Member State(s) since 2013). For more information, please revisit the technical note on bilateral applied tariff (https://wits.worldbank.org/Bilateral-Tariff-Technical-Note.html).

Exports in Germany decreased to 131.10 EUR Billion in April from 133.30 EUR Billion in March of 2025. This dataset provides - Germany Exports - actual values, historical data, forecast, chart, statistics, economic calendar and news.

With increasing road encroachment, habitat fragmentation by transport infrastructures has been a serious threat for European biodiversity. Areas with no roads or little traffic (“roadless and low-traffic areas”) represent relatively undisturbed natural habitats and functioning ecosystems. They provide many benefits for biodiversity and human societies (e.g., landscape connectivity, barrier against pests and invasions, ecosystem services). Roadless and low-traffic areas, with a lower level of anthropogenic disturbances, are of special relevance in Europe because of their rarity and, in the context of climate change, because of their contribution to higher resilience and buffering capacity within landscape ecosystems. An analysis of European legal instruments illustrates that, although most laws aimed at protecting targets which are inherent to fragmentation, like connectivity, ecosystem processes or integrity, roadless areas are widely neglected as a legal target. A case study in Germany underlines this finding. Although the Natura 2000 network covers a significant proportion of the country (16%), Natura 2000 sites are highly fragmented and most low-traffic areas (75%) lie unprotected outside this network. This proportion is even higher for the old Federal States (western Germany), where only 20% of the low-traffic areas are protected. We propose that the few remaining roadless and low-traffic areas in Europe should be an important focus of conservation efforts; they should be urgently inventoried, included more explicitly in the law and accounted for in transport and urban planning. Considering them as complementary conservation targets would represent a concrete step towards the strengthening and adaptation of the Natura 2000 network to climate change.

The European Business Performance database describes the performance of the largest enterprises in the twentieth century. It covers eight countries that together consistently account for above 80 per cent of western European GDP: Great Britain, Germany, France, Belgium, Italy, Spain, Sweden, and Finland. Data have been collected for five benchmark years, namely on the eve of WWI (1913), before the Great Depression (1927), at the extremes of the golden age (1954 and 1972), and in 2000.The database is comprised of two distinct datasets. The Small Sample (625 firms) includes the largest enterprises in each country across all industries (economy-wide). To avoid over-representation of certain countries and sectors, countries contribute a number of firms that is roughly proportionate to the size of the economy: 30 firms from Great Britain, 25 from Germany, 20 from France, 15 from Italy, 10 from Belgium, Spain, and Sweden, and 5 from Finland. By the same token, a cap has been set on the number of financial firms entering the sample, so that they range between up to 6 for Britain and 1 for Finland.The second dataset, or Large Sample (1,167 firms), is made up of the largest firms per industry. Here industries are so selected as to take into account long-term technological developments and the rise of entirely new products and services. Firms have been individually classified using the two-digit ISIC Rev. 3.1 codes, then grouped under a manageable number of industries. To some extent and broadly speaking, the two samples have a rather distinct focus: the Small Sample is biased in favour of sheer bigness, whereas the Large Sample emphasizes industries.As far as size and performance indicators are concerned, total assets has been picked as the main size measure in the first three benchmarks, turnover in 1972 and 2000 (financial intermediaries, though, are ranked by total assets throughout the database). Performance is gauged by means of two financial ratios, namely return on equity and shareholders’ return, i.e. the percentage year-on-year change in share price based on year-end values. In order to smooth out volatility, at each benchmark performance figures have been averaged over three consecutive years (for instance, performance in 1913 reflects average performance in 1911, 1912, and 1913).All figures were collected in national currency and converted to US dollars at current year-average exchange rates.

Home Ownership Rate in Germany decreased to 47.20 percent in 2024 from 47.60 percent in 2023. This dataset provides the latest reported value for - Germany Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Corporate Tax Rate in Germany stands at 30 percent. This dataset provides the latest reported value for - Germany Corporate Tax Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size in Germany was 1,012 individuals.

Mode of data collection

Other [oth]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

🌍 World's largest open dataset for privacy masking 🌎 The dataset is useful to train and evaluate models to remove personally identifiable and sensitive information from text, especially in the context of AI assistants and LLMs. # Dataset Analytics 📊 - ai4privacy/open-pii-masking-500k-ai4privacy ## p5y Data Analytics - Total Entries: 580,227 - Total Tokens: 19,199,982 - Average Source Text Length: 17.37 words - Total PII Labels: 5,705,973 - Number of Unique PII Classes: 20 (Open PII Labelset) - Unique Identity Values: 704,215 --- ## Language Distribution Analytics Number of Unique Languages: 8 | Language | Count | Percentage | |--------------------|----------|------------| | English (en) 🇺🇸🇬🇧🇨🇦🇮🇳 | 150,693 | 25.97% | | French (fr) 🇫🇷🇨🇭🇨🇦 | 112,136 | 19.33% | | German (de) 🇩🇪🇨🇭 | 82,384 | 14.20% | | Spanish (es) 🇪🇸 🇲🇽 | 78,013 | 13.45% | | Italian (it) 🇮🇹🇨🇭 | 68,824 | 11.86% | | Dutch (nl) 🇳🇱 | 26,628 | 4.59% | | Hindi (hi)* 🇮🇳 | 33,963 | 5.85% | | Telugu (te)* 🇮🇳 | 27,586 | 4.75% | *these languages are in experimental stages --- ## Region Distribution Analytics Number of Unique Regions: 11 | Region | Count | Percentage | |-----------------------|----------|------------| | Switzerland (CH) 🇨🇭 | 112,531 | 19.39% | | India (IN) 🇮🇳 | 99,724 | 17.19% | | Canada (CA) 🇨🇦 | 74,733 | 12.88% | | Germany (DE) 🇩🇪 | 41,604 | 7.17% | | Spain (ES) 🇪🇸 | 39,557 | 6.82% | | Mexico (MX) 🇲🇽 | 38,456 | 6.63% | | France (FR) 🇫🇷 | 37,886 | 6.53% | | Great Britain (GB) 🇬🇧 | 37,092 | 6.39% | | United States (US) 🇺🇸 | 37,008 | 6.38% | | Italy (IT) 🇮🇹 | 35,008 | 6.03% | | Netherlands (NL) 🇳🇱 | 26,628 | 4.59% | --- ## Machine Learning Task Analytics | Split | Count | Percentage | |-------------|----------|------------| | Train | 464,150 | 79.99% | | Validate| 116,077 | 20.01% | --- # Usage Option 1: Python terminal pip install datasets python from datasets import load_dataset dataset = load_dataset("ai4privacy/open-pii-masking-500k-ai4privacy") # Compatible Machine Learning Tasks: - Tokenclassification. Check out a HuggingFace's guide on token classification. - ALBERT, BERT, BigBird, BioGpt, BLOOM, BROS, CamemBERT, CANINE, ConvBERT, Data2VecText, DeBERTa, DeBERTa-v2, DistilBERT, ELECTRA, ERNIE, ErnieM, ESM, Falcon, FlauBERT, FNet, Funnel Transformer, GPT-Sw3, OpenAI GPT-2, GPTBigCode, GPT Neo, GPT NeoX, I-BERT, LayoutLM, LayoutLMv2, LayoutLMv3, LiLT, Longformer, LUKE, MarkupLM, MEGA, Megatron-BERT, MobileBERT,...

Germany DE: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: Female: % Cumulative data was reported at 25.306 % in 2022. This records an increase from the previous number of 24.831 % for 2021. Germany DE: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: Female: % Cumulative data is updated yearly, averaging 20.690 % from Dec 2010 (Median) to 2022, with 13 observations. The data reached an all-time high of 25.306 % in 2022 and a record low of 0.730 % in 2011. Germany DE: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: Female: % Cumulative data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Social: Education Statistics. The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;;