This dataset provides values for POPULATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

About this dataset

Context

The dataset tabulates the population of Continental by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Continental. The dataset can be utilized to understand the population distribution of Continental by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Continental. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Continental.

Key observations

Largest age group (population): Male # 10-14 years (99) | Female # 65-69 years (104). 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Continental population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Continental is shown in the following column.
• Population (Female): The female population in the Continental is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Continental for each age group.

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 Continental Population by Gender. You can refer the same here

This dataset provides values for POPULATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Currently available genetic tools effectively distinguish between different continental origins. However, North Eurasia, which constitutes one-third of the world’s largest continent, remains severely underrepresented. The dataset used in this study represents 266 populations from 12 North Eurasian countries, including most of the ethnic diversity across Russia’s vast territory. A total of 1,883 samples were genotyped using the Illumina Infinium Omni5Exome-4 v1.3 BeadChip. Three principal components were computed for the entire dataset using three iterations for outlier removal. It allowed the merging of 266 populations into larger groups while maintaining intragroup homogeneity, so 29 ethnic geographic groups were formed that were genetically distinguishable enough to trace individual ancestry. Several feature selection methods, including the random forest algorithm, were tested to estimate the number of genetic markers needed to differentiate between the groups; 5,229 ancestry-informative SNPs were selected. We tested various classifiers supporting multiple classes and output values for each class that could be interpreted as probabilities. The logistic regression was chosen as the best mathematical model for predicting ancestral populations. The machine learning algorithm for inferring an ancestral ethnic geographic group was implemented in the original software “Homeland” fitted with the interface module, the prediction module, and the cartographic module. Examples of geographic maps showing the likelihood of geographic ancestry for individuals from different regions of North Eurasia are provided. Validating methods show that the highest number of ethnic geographic group predictions with almost absolute accuracy and sensitivity was observed for South and Central Siberia, Far East, and Kamchatka. The total accuracy of prediction of one of 29 ethnic geographic groups reached 71%. The proposed method can be employed to predict ancestries from the populations of Russia and its neighbor states. It can be used for the needs of forensic science and genetic genealogy.

The burden of animal disease is widespread globally and is especially severe for developing countries dependent on livestock production. Ethiopia has the largest livestock population in Africa and the second-largest human population on the continent. Ethiopia is one of the fastest-growing economies in Africa; however, much of the population still lives in extreme poverty, and most households depend on agriculture. Animal disease negatively affects domestic livestock production and limits growth potential across the domestic agricultural supply chain. This research investigates the economic effects of livestock disease burden in Ethiopia by employing a computable general equilibrium model in tandem with animal health loss estimates from a compartmental livestock population model. Two scenarios for disease burden are simulated to understand the effects of improved animal health on domestic production, prices, trade, gross domestic product (GDP), and economic welfare in Ethiopia. Results show that improved animal health may increase Ethiopian GDP by up to 3.6%, which improves national welfare by approximately $US 2.5 billion. This research illustrates the economic effects of improved livestock health, which is critical for Ethiopian households and the national economy.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Continental by race. It includes the distribution of the Non-Hispanic population of Continental across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Continental across relevant racial categories.

Key observations

Of the Non-Hispanic population in Continental, the largest racial group is White alone with a population of 1,299 (98.86% of the total Non-Hispanic population).

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Continental
• Population: The population of the racial category (for Non-Hispanic) in the Continental is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Continental total Non-Hispanic 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 Continental Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Continental population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Continental. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 784 (56.52% of the total population). 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 cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Continental population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Continental is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Continental is shown in the following column.

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 Continental Population by Age. You can refer the same here

Variation in age structure influences population dynamics, yet we have limited understanding of the spatial scale at which its fluctuations are synchronised between populations. Using 32 great tit populations, spanning 4○W–33○E and 35–65○N involving >130,000 birds across 67 years, we quantify spatial synchrony in breeding demographic structure (subadult vs. adult breeders) and its drivers. We show that larger clutch sizes, colder winters, and larger beech crops lead to younger populations. We report distance-dependent synchrony of demographic structure, maintained at approximately 650km. Despite covariation with demographic structure, we do not find evidence for environmental variables influencing the scale of synchrony, except for beech masting. We suggest that local ecological and density-dependent dynamics impact how environmental variation interacts with demographic structure, influencing estimates of the environment’s effect on synchrony. Our analyses demonstrate the operation o..., Study systems and data collection The great tit Parus major is a passerine bird found in mixed woodlands across much of the Western Palearctic. Their reproductive lifespan ranges from 1–9, averaging 1.8 years (Bouwhuis et al. 2009; Woodman et al. 2022). Although there are some continuous changes with age (Bouwhuis et al. 2009), the main age effects on individual-level traits and population processes are captured by two age-classes: 1-year-olds (hereafter subadults) and older (hereafter adults, Gosler 1993; Harvey et al. 1979; Perrins 1979; Gamelon et al. 2016, 2019; Woodman et al. 2022). Great tits generally undertake one breeding attempt during a single annual breeding season April–June (in some parts of their range second clutches can occur, Verhulst 1998; Visser et al. 2003). Data used here are from 32 populations, the geographical range of which represents a large part of the species’ breeding range (Sullivan et al. 2009). Generally, data collection at these sites included regular v..., , # Continent-wide drivers of spatial synchrony in breeding demographic structure across wild great tit populations

Access this dataset on Dryad

Presented here is the raw data ("bred_dem_synchrony.RData") and annotated R Script ("Continent-wide drivers of spatial synchrony in breeding demographic structure across wild great tit populations.R") needed to run analyses for the project "Continent-wide drivers of spatial synchrony in breeding demographic structure across wild great tit populations".

Description of the data and file structure

"bred_dem_synchrony.RData": R-data file needed to run analyses. Descriptions of the three datasets within this file are found in the R script, and also below.

1. "bred_dem_variables" = Base data for annual breeding demographic structure variables of great tit populations. Only includes populations with annual population size equal or greater than 20 individuals and where 25% or more of individuals have be...

This statistic shows a ranking of the estimated number of Facebook users in 2020 in Africa, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

The number of LinkedIn users in Africa was forecast to continuously increase between 2024 and 2028 by in total 37 million users (+68.13 percent). After the ninth consecutive increasing year, the LinkedIn user base is estimated to reach 91.29 million users and therefore a new peak in 2028. Notably, the number of LinkedIn users of was continuously increasing over the past years.User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 number of LinkedIn users in countries like South America and Caribbean.

This statistic shows a ranking of the estimated number of Instagram users in 2020 in Africa, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

This statistic shows a ranking of the estimated average internet connection speed in 2020 in Africa, differentiated by country. The speed refers to a weighted average of fixed- and mobile broadband connection speeds. Fixed-broadband connections are weighted with the household size in the selected region to account for the fact that these connections are usually shared by multiple household members.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 more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

This statistic shows a ranking of the estimated number of Youtube users in 2020 in Africa, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.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 more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

The number of Pinterest users in Africa was forecast to continuously increase between 2024 and 2028 by in total 15 million users (+68.81 percent). After the ninth consecutive increasing year, the Pinterest user base is estimated to reach 36.81 million users and therefore a new peak in 2028. Notably, the number of Pinterest users of was continuously increasing over the past years.User figures, shown here regarding the platform pinterest, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 number of Pinterest users in countries like Europe and Australia & Oceania.

The number of snapchat users in Africa was forecast to continuously increase between 2024 and 2028 by in total 40.3 million users (+52.07 percent). After the ninth consecutive increasing year, the snapchat user base is estimated to reach 117.68 million users and therefore a new peak in 2028. Notably, the number of snapchat users of was continuously increasing over the past years.The user numbers, depicted here regarding the platform Snapchat, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 number of snapchat users in countries like Asia and Australia & Oceania.

This statistic shows a ranking of the estimated number of Pinterest users in 2020 in Africa, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

This statistic shows a ranking of the estimated number of Snapchat users in 2020 in Africa, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average daily active users over the period and count multiple accounts by persons only once.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 more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).