The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolongued development arc in Sub-Saharan Africa.

Key figures on the population of the Netherlands.

The following information is available: - Population by sex; - Population by marital status; - Population by age (groups); - Population by origin; - Private households; - Persons in institutional households; - Population growth; - Population density.

CBS is in transition towards a new classification of the population by origin. Greater emphasis is now placed on where a person was born, aside from where that person’s parents were born. The term ‘migration background’ is no longer used in this regard. The main categories western/non-western are being replaced by categories based on continents and a few countries that share a specific migration history with the Netherlands. The new classification is being implemented gradually in tables and publications on population by origin.

Data available from: 1950 Figures on population by origin are only available from 2022 at this moment. The periods 1996 through 2021 will be added to the table at a later time.

Status of the figures: All the figures are final.

Changes as of 17 July 2024: Final figures with regard to population growth for 2023 and final figures of the population on 1 January 2024 have been added.

Changes as of 26 April 2023: None, this is a new table. This table succeeds the table Population; key figures; 1950-2022. See section 3. The following changes have been implemented compared to the discontinued table: - The topic folder 'Population by migration background' has been replaced by 'Population by origin'; - The underlying topic folders regarding 'first and second generation migration background' have been replaced by 'Born in the Netherlands' and 'Born abroad'; - The origin countries Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan, Turkmenistan and Turkey have been assigned to the continent of Asia (previously Europe).

When will new figures be published? In the last quarter of 2025 final figures with regard to population growth for 2024 and final figures of the population on 1 January 2025 will be added.

Use this application to view the pattern of concentrations of people by race and Hispanic or Latino ethnicity. Data are provided at the U.S. Census block group level, one of the smallest Census geographies, to provide a detailed picture of these patterns. The data is sourced from the U.S Census Bureau, 2020 Census Redistricting Data (Public Law 94-171) Summary File. Definitions: Definitions of the Census Bureau’s categories are provided below. This interactive map shows patterns for all categories except American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander. The total population countywide for these two categories is small (1,582 and 263 respectively). The Census Bureau uses the following race categories:Population by RaceWhite – A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.Black or African American – A person having origins in any of the Black racial groups of Africa.American Indian or Alaska Native – A person having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment.Asian – A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam.Native Hawaiian or Other Pacific Islander – A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.Some Other Race - this category is chosen by people who do not identify with any of the categories listed above. People can identify with more than one race. These people are included in the Two or More Races Hispanic or Latino PopulationThe Hispanic/Latino population is an ethnic group. Hispanic/Latino people may be of any race.Other layers provided in this tool included the Loudoun County Census block groups, towns and Dulles airport, and the Loudoun County 2021 aerial imagery.

PLEASE if you use or like this dataset UPVOTE 👁️

This dataset offers a detailed historical record of global life expectancy, covering data from 1960 to the present. It is meticulously curated to enable deep analysis of trends and gender disparities in life expectancy worldwide.

Dataset Structure & Key Columns:

Country Code (🔤): Unique identifier for each country.

Country Name (🌍): Official name of the country.

Region (🌐): Broad geographical area (e.g., Asia, Europe, Africa).

Sub-Region (🗺️): More specific regional classification within the broader region.

Intermediate Region (🔍): Additional granular geographical grouping when applicable.

Year (📅): The specific year to which the data pertains.

Life Expectancy for Women (👩‍⚕️): Average years a woman is expected to live in that country and year.

Life Expectancy for Men (👨‍⚕️): Average years a man is expected to live in that country and year.

Context & Use Cases:

This dataset is a rich resource for exploring long-term trends in global health and demography. By comparing life expectancy data over decades, researchers can:

Analyze Time Series Trends: Forecast future changes in life expectancy and evaluate the impact of health interventions over time.

Study Gender Disparities: Investigate the differences between life expectancy for women and men, providing insights into social, economic, and healthcare factors influencing these trends.

Regional & Sub-Regional Analysis: Compare and contrast life expectancy across various regions and sub-regions to understand geographical disparities and their underlying causes.

Support Public Policy Research: Inform policymakers by linking life expectancy trends with public health policies, socioeconomic developments, and other key indicators.

Educational & Data Science Applications: Serve as a comprehensive teaching tool for courses on public health, global development, and data analysis, as well as for Kaggle competitions and projects.

With its detailed, structured format and broad temporal coverage, this dataset is ideal for anyone looking to gain a nuanced understanding of global health trends and to drive impactful analyses in public health, social sciences, and beyond.

Feel free to ask for further customizations or additional details as needed!

The QS World University Rankings for 2025 is a list of universities from all over the world, organized to show which ones are the best in various areas. It is widely recognized as one of the most reliable ways to compare higher education institutions. This ranking helps students, researchers, and decision-makers understand how well universities perform in terms of academics, teaching, research, and global connections. Let’s break it down into simple parts so that you can understand it easily.

What’s in the Ranking? The ranking includes several key pieces of information about each university:

University Name: This is simply the name of the school. For example, Harvard University or Oxford University. Ranking Position: This tells you the university’s position on the list, like 1st, 50th, or 200th. A lower number means the university is ranked higher. Country/Region: This shows where the university is located, like the USA, the UK, or Japan. Academic Reputation Score: This score is based on surveys of professors and researchers. They give their opinions on which universities are best for studying and learning. Employer Reputation Score: Employers are asked which universities produce the most skilled graduates. This score shows how good a university is at preparing students for jobs. Faculty-Student Ratio: This measures how many students there are per teacher. A lower number means smaller classes and more personal attention for students. Citations per Faculty: This is about research. It shows how often the university’s studies are mentioned in other research papers. The more citations, the better. International Faculty & Students: This looks at how many teachers and students come from different countries, showing how global and diverse the university is. Why Is This Ranking Useful? There are many ways this ranking can help people:

For Students: It helps students decide where they might want to study. For example, if someone wants a university with a good reputation for teaching and research, they can use this ranking to find the best options. For Universities: Schools can use the rankings to see how they compare to others. If one university is ranked lower than another, it can look at the scores to find ways to improve. For Researchers: Researchers can study the ranking to learn about trends in global education. For example, they might explore why certain regions, like Asia or Europe, have universities that are improving quickly. For Policymakers: Governments and organizations can use the rankings to decide where to invest in education. They can also study which areas of education are most important for the future. What Can We Learn from It? The QS World University Rankings help us learn which universities are leading in academics and research. It also shows us how important global diversity is in education. By understanding these rankings, people can make smarter decisions about studying, teaching, or improving education systems. It’s like a guidebook for the world of universities, helping everyone find the best options and learn from the best practices.

The IMISEM dataset contains 828 indicators on the migration policies of 32 polities from Europe, South East Asia and Latin America and the Caribbean. The IMISEM project adopts a comprehensive view of migration policy that includes both its emigrant/ emigration and immigrant/ immigration sides, bridging for the first time the two sides of migration policy. Thus, the dataset includes indicators that measure emigration policies (exit policies and control of outflows), immigration policies (entry policies and control of inflows), emigrant policies (rights granted, services offered and obligations imposed on non-resident citizens), immigrant policies (mainly, rights granted to non-citizen residents) and citizenship policies (mainly, access to naturalization for immigrants and retention of citizenship by emigrants). The main sources used to complete the IMISEM questionnaires are legal sources (i.e. laws, regulations). Legal sources are complemented with secondary sources (for instance, policy reports) and interviews with experts. The IMISEM Dataset is one of the main outputs of the “The Every Immigrant is an Emigrant Project (IMISEM)” funded by the Leibniz Gemeinschaft and carried out at the GIGA German Institute for Global and Area Studies between 2017 and 2020.

Central Asia, site of the historic Silk Road trade network, has long been a conduit for the movement of people, energy, and mineral resources between Europe and Asia. Once part of the former Soviet Union, this region was and continues to be an important producer of base and precious metals, rare metals (RM), including niobium, tantalum, and beryllium, and a past producer of rare earth elements (REE). The Tien Shan and Pamir Mountains regions, encompassing parts of Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan, are of significant interest for mineral exploration as these regions are thought to host substantial undeveloped and undiscovered resources of REE and RM. Based on this legacy, and as an emerging REE and RM producing region, the Central Asian countries are implementing mining sector reforms to create a more attractive investment environment for domestic and foreign mining interests. During the most recent increase in REE prices, beginning in 2009 and cu ...

Context

The Significant Earthquake Database is a global listing of over 5,700 earthquakes from 2150 BC to the present.

Content

A significant earthquake is classified as one that meets at least one of the following criteria: caused deaths, caused moderate damage (approximately $1 million or more), magnitude 7.5 or greater, Modified Mercalli Intensity (MMI) X or greater, or the earthquake generated a tsunami. The database provides information on the date and time of occurrence, latitude and longitude, focal depth, magnitude, maximum MMI intensity, and socio-economic data such as the total number of casualties, injuries, houses destroyed, and houses damaged, and $ dollage damage estimates. References, political geography, and additional comments are also provided for each earthquake. If the earthquake was associated with a tsunami or volcanic eruption, it is flagged and linked to the related tsunami event or significant volcanic eruption.

Acknowledgements

https://catalog.data.gov/dataset/global-significant-earthquake-database-2150-bc-to-present

10 = Central, Western and S. Africa 15 = Northern Africa 20 = Antarctica 30 = East Asia 40 = Central Asia and Caucasus 50 = Kamchatka and Kuril Islands 60 = S. and SE. Asia and Indian Ocean 70 = Atlantic Ocean 80 = Bering Sea 90 = Caribbean 100 = Central America 110 = Eastern Europe 120 = Northern and Western Europe 130 = Southern Europe 140 = Middle East 150 = North America and Hawaii 160 = South America 170 = Central and South Pacific

Ms Magnitude: Valid values: 0.0 to 9.9

The Ms magnitude is the surface-wave magnitude of the earthquake.

The magnitude is a measure of seismic energy. The magnitude scale is logarithmic. An increase of one in magnitude represents a tenfold increase in the recorded wave amplitude. However, the energy release associated with an increase of one in magnitude is not tenfold, but about thirtyfold. For example, approximately 900 times more energy is released in an earthquake of magnitude 7 than in an earthquake of magnitude 5. Each increase in magnitude of one unit is equivalent to an increase of seismic energy of about 1.6 x 10,000,000,000,000 ergs.

Mw Magnitude: Valid values: 0.0 to 9.9

The Mw magnitude is based on the moment magnitude scale. Moment is a physical quantity proportional to the slip on the fault times the area of the fault surface that slips; it is related to the total energy released in the EQ. The moment can be estimated from seismograms (and also from geodetic measurements). The moment is then converted into a number similar to other earthquake magnitudes by a standard formula. The result is called the moment magnitude. The moment magnitude provides an estimate of earthquake size that is valid over the complete range of magnitudes, a characteristic that was lacking in other magnitude scales.

The magnitude is a measure of seismic energy. The magnitude scale is logarithmic. An increase of one in magnitude represents a tenfold increase in the recorded wave amplitude. However, the energy release associated with an increase of one in magnitude is not tenfold, but about thirtyfold. For example, approximately 900 times more energy is released in an earthquake of magnitude 7 than in an earthquake of magnitude 5. Each increase in magnitude of one unit is equivalent to an increase of seismic energy of about 1.6 x 10,000,000,000,000 ergs.

mb Magnitude: Valid values: 0.0 to 9.9

The Mb magnitude is the compressional body wave (P-wave) magnitude.

The magnitude is a measure of seismic energy. The magnitude scale is logarithmic. An increase of one in magnitude represents a tenfold increase in the recorded wave amplitude. However, the energy release associated with an increase of one in magnitude is not tenfold, but about thirtyfold. For example, approximately 900 times more energy is released in an earthquake of magnitude 7 than in an earthquake of magnitude 5. Each increase in magnitude of one unit is equivalent to an increase of seismic energy of about 1.6 x 10,000,000,000,000 ergs.

Bar Chart of distribution

ML Magnitude: Valid values: 0.0 to 9.9

The ML magnitude was the original magnitude relationship defined by Richter and Gutenberg for local earthquakes in 1935. It is based on the maximum amplitude of a seismogram recorded on a Wood-Anderson torsion seismograph. Although these instruments are no longer widely in use, ML values are calculated using modern instrumentation with appropriate adjustments.

The magnitude is a measure of seismic energy. The magnitude scale is logarithmic. An increase of one in magnitude represents a tenfold increase in the recorded wave amplitude. However, the energy release associated with an increase of one in magnitude is not tenfold, but about thirtyfold. For example, approximately 900 times more energy is released in an earthquake of magnitude 7 th...

Many species of migratory birds have been declining on the Palearctic-African flyways in recent decades due to human population pressure and land-use intensification. Models predict that the declining trends of migratory birds will continue into the foreseeable future across much of Africa, likely exacerbated by climate change. While sub-Saharan Africa is viewed as less important for these migrants than the Sahel, the region still receives many migrant species. We use the citizen science Southern African Bird Atlas Project data sets (SABAP1 and SABAP2) to determine relative change between atlas periods (1987–1991; 2007–2021). Firstly, we validate our metrics of population change on a dataset of 581 species that occur frequently in South Africa, Lesotho and Eswatini by examining change in relation to migratory status (Palearctic, Intra-Africa or Resident) and other species’ traits. We found greatest declines in migrants but with magnitudes not as great as expected, with largest relative decreases for Palearctic migrants, and little difference between Intra-Africa migrants and residents. Declines were best described by size independent of migratory status, even when controlling for phylogeny. For the set of Palearctic migrants, we then examine if change is related to Northern Hemisphere distribution. We found greater decreases for birds with breeding grounds in southern Asia (India and south-eastern Asia) relative to Europe. These results are useful for conservation agencies wishing to extend ties to relevant researchers and conservationists in these regions, and highlights potential challenge areas for this set of birds on their breeding grounds.

Research software is increasingly recognized as critical infrastructure in contemporary science. Research software spans a broad spectrum, including source code files, algorithms, scripts, computational workflows, and executables, all created for or during research. Research funders have developed programs, initiatives and policies to bolster research software’s role. However, there has been no empirical study of how research funders prioritize support for research software. This information is needed to clarify where current funder support is concentrated and where strategic gaps may exist. Here, we present data from a survey of research software funders (n=36) from around the world. The survey explored these funders’ priorities, finding a strong emphasis on developing skills, software sustainability, embedding open science, building community and collaboration, advancing research software funding, increasing software visibility and use, innovation and security.

Methods

This research was carried out using a survey combining qualitative and quantitative items. The survey was designed to investigate how research software funders support research software’s sustainability and impact.

The study was reviewed and given an exempt determination by the University of Illinois Urbana-Champaign Institutional Review Board (no. 24374).

Survey design

The survey designed for this study began by collecting profile information, including institutional affiliation and job title. The survey gathered information about respondents’ organization’s initiatives, policies, or programs to support research software. The range of questions yielded too much data for one article. In this article, we focus exclusively on the results generated via an open-ended question asking about the top priorities for the respondents’ organizations’ support for research software: “What are your organization's top priorities related to research software?”. Four open-response text boxes were provided for respondents to indicate and list these priorities.

Sampling

This survey was aimed at international research funders, including governmental and non-governmental (e.g., philanthropic) funders. A list of contacts to invite to participate in this survey was created based on participation in the Research Software Association (ReSA) and responsibility for research software funding known to the authors. This initial list of people was refined, with removals based on individuals having moved to unrelated professional roles or being unavailable long-term, for example, due to personal issues.

The final, refined contact list comprised 71 people. After removing individuals when a member of their organization already provided a complete answer or when the person turned out to no longer be working on a relevant topic or to be otherwise unavailable (total of n=30), 41 people remained. Five of these individuals did not complete the survey, while 36 people (representing 30 research funding organizations) did, yielding a response rate of 87.8%. Fully completed survey responses were not required for individuals to be retained in the sample, resulting in varied sample bases across survey questions.

The sample includes research funders in North and South America, Europe, Oceania and Asia, but over-represents North America and European funder representatives. Some participating funders cover a broad spectrum of disciplines, while others focus on a particular domain such as social science, health, environment, physical sciences or humanities.

The respondents represented research funders supported by governmental (n=26), philanthropic (n=6) and corporate (n=1) resources.

Respondents’ job titles span the following categories: Senior Leadership and Executive, such as a Vice President of Strategy; Program and Project Management, such as Senior Program Manager; Planning and Business Development; Scientific, Technical and IT, such as Scientific Information Lead.

Most respondents 72.7% (n=24) answered ‘Yes’ to the question, “Has your organization established any policies, initiatives or programs aimed at supporting research software?”, while 18.2% (n=6) said ‘No’ and 9.1% (n=3) ‘Unsure’.

Data collection, management and analysis

Data collection took place from December 2023 to May 2024. The mean completion time for the detailed survey was 28 minutes and 13 seconds.

The data were cleaned and prepared for analysis by removing any identifiable respondent details. The data analysis process followed a standard thematic qualitative analysis approach (e.g., Jensen & Laurie, 2016). This involved first identifying themes and organizing the data accordingly. Dimensions of each theme were identified where relevant. Then data extracts were selected from the survey responses associated with each theme and theme dimension.

Additional data: Evolving funding strategies for research software: Insights from an international survey of research funders

Data were uploaded in December 2024 to support another paper drawing on the same overall survey data. This one is entitled: 'Evolving funding strategies for research software: Insights from an international survey of research funders'. The survey data for this upload were generated using the following survey items.

"Between October 2011 and November 2012, Pew Research Center, with generous funding from The Pew Charitable Trusts and the John Templeton Foundation, conducted a public opinion survey involving more than 30,000 face-to-face interviews in 26 countries in Africa, Asia, the Middle East and Europe. The survey asked people to describe their religious beliefs and practices, and sought to gauge respondents; knowledge of and attitudes toward other faiths. It aimed to assess levels of political and economic satisfaction, concerns about crime, corruption and extremism, positions on issues such as abortion and polygamy, and views of democracy, religious law and the place of women in society.

"Although the surveys were nationally representative in most countries, the primary goal of the survey was to gauge and compare beliefs and attitudes of Muslims. The findings for Muslim respondents are summarized in the Religion & Public Life Project's reports The World's Muslims: Unity and Diversity and The World's Muslims: Religion, Politics and Society, which are available at www.pewresearch.org. [...] This dataset only contains data for Muslim respondents in the countries surveyed. Please note that this codebook is meant as a guide to the dataset, and is not the survey questionnaire." (2012 Pew Religion Worlds Muslims Codebook)

Switzerland is leading the ranking by population share with mobile internet access , recording 95.06 percent. Following closely behind is Ukraine with 95.06 percent, while Moldova is trailing the ranking with 46.83 percent, resulting in a difference of 48.23 percentage points to the ranking leader, Switzerland. The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.