Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.

Due to rounding, age categories may not sum to 100%. Estimates are based on provisional data and subject to change.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

These data contain lifetables derived from the ONS Longitudinal study dataset, and according to age, sex and individual socio-economic status measured with education, occupation or wage in England and Wales in 2011. Life table according to age, sex and individual’s education, or occupation or wage for the England & Wales population in 2011 The data contained in these files are aggregated data from the ONS Longitudinal Study (ONS LS). The ONS LS is a long-term census-based multi-cohort study. It uses four annual birthdates as random selection criteria, giving a 1% sample of the England and Wales population (10.1093/ije/dyy243). The initial sample was drawn from the 1971 Census, and study members’ census records have been linked every 10 years up to the 2011 Census. New members enter the study through birth or immigration, and existing members leave through death or emigration. Vital life events information (births, deaths and cancer registrations) are also linked to sample members’ records. File lifetab_2011_educ.csv Life table according to age, sex and education level for the England & Wales population in 2011 age x: attained age (years) from 20 to 100 sex: 2 categories: male (m) and female (f) educ: 6 categories of highest educational attainment: A: no qualifications; B: 1-4 GCSEs/O levels; C: 5+ GCSEs/O levels, D: Apprenticeships/Vocational qualifications, E: A/AS levels, F: Degree/Higher Degree mx: mortality rate for 1 person-year qx: annual probability of death ( = 1 - exp(-mx) ) ex: life-expectancy (years) File lifetab_2011_inc.csv Life table from age 20 onwards and according to age, sex and income level for the England & Wales population in 2011 age x: attained age (years) from 20 to 100 sex: 2 categories: male (m); female (f) inc: 5 categories of income: Least deprived; 4; 3; 2; Most deprived mx: mortality rate for 1 person-year qx: annual probability of death ( = 1 - exp(-mx) ) ex: life-expectancy (years) File lifetab_2011_occ.csv Life table from age 20 onwards and according to age, sex and occupation for the England & Wales population in 2011 age x: attained age (years) from 20 to 100 sex: 2 categories: male (m); female (f) occ: 3 categories of occupation: C: Technical/Routine; B: Intermediate; A: Managerial/Administrative/Professional mx: mortality rate for 1 person-year qx: annual probability of death ( = 1 - exp(-mx) ) ex: life-expectancy (years) File lifetab_2011_overall.csv Life table from age 20 onwards and according to age and sex for the England & Wales population in 2011 age x: attained age (years) from 20 to 100 sex: 2 categories: male (m); female (f) mx: mortality rate for 1 person-year qx: annual probability of death ( = 1 - exp(-mx) ) ex: life-expectancy (years) More details can be found in the following paper: Ingleby F, Woods L, Atherton I, Baker M, Elliss-Brookes L, Belot A. (2021). Describing socio-economic variation in life expectancy according to an individual's education, occupation and wage in England and Wales: An analysis of the ONS Longitudinal Study. SSM - Population Health, doi: 10.1016/j.ssmph.2021.100815

This shapefile represents habitat suitability categories (High, Moderate, Low, and Non-Habitat) derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California during spring, which is a surrogate for habitat conditions during the sage-grouse breeding and nesting period. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Spring included telemetry locations (n = 14,058) from mid-March to June, and is a surrogate for habitat conditions during the sage-grouse breeding and nesting period. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion and using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the spring season. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion boundaries described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry location sample) was split into locations falling within respective north and south regions. HSI values from the composite and relativized statewide HSI surface were then extracted to each classification dataset location within the north and south region. The distribution of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate), and 1.5 (low) standard deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into four discrete categories of habitat suitability: High, Moderate, Low, and Non-Habitat. In terms of percentiles, High habitat comprised greater than 30.9 % of the HSI values, Moderate comprised 15 – 30.9%, Low comprised 6.7 – 15%, and Non-Habitat comprised less than 6.7%.The classified north and south regions were then clipped by the boundary layer and mosaicked to create a statewide categorical surface for habitat selection. Each habitat suitability category was converted to a vector output where gaps within polygons less than 1.2 million square meters were eliminated, polygons within 500 meters of each other were connected to create corridors and polygons less than 1.2 million square meters in one category were incorporated to the adjacent category. The final step was to mask major roads that were buffered by 50m (Census, 2014), lakes (Peterson, 2008) and urban areas, and place those masked areas into the non-habitat category. The existing urban layer (Census 2010) was not sufficient for our needs because it excluded towns with a population lower than 1,500. Hence, we masked smaller towns (populations of 100 to 1500) and development with Census Block polygons (Census 2015) that had at least 50% urban development within their boundaries when viewed with reference imagery (ArcGIS World Imagery Service Layer). REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

Birth distribution per quartile for top 100 table tennis players according to age category.

In all age groups until 29 years old, there were more men than women in Russia as of January 1, 2024. After that age, the female population outnumbered the male population in each category. The most represented age group in the country was from 35 to 39 years old, with approximately *** million women and *** million men. Male-to-female ratio in Russia The number of men in Russia was historically lower than the number of women, which was a result of population losses during World War I and World War II. In 1950, in the age category from 25 to 29 years, ** men were recorded per 100 women in the Soviet Union. In today’s Russia, the female-to-male ratio in the same age group reached *** women per 1,000 men. Russia has the highest life expectancy gender gap The World Health Organization estimated the average life expectancy of women across the world at over five years longer than men. In Russia, this gap between genders exceeded 10 years. According to the study “Burden of disease in Russia, 1980-2016: A systematic analysis for the Global Burden of Disease Study 2016,” Russia had the highest gender difference in life expectancy worldwide.

Overview The Human Vital Signs Dataset is a comprehensive collection of key physiological parameters recorded from patients. This dataset is designed to support research in medical diagnostics, patient monitoring, and predictive analytics. It includes both original attributes and derived features to provide a holistic view of patient health.

Attributes Patient ID

Description: A unique identifier assigned to each patient. Type: Integer Example: 1, 2, 3, ... Heart Rate

Description: The number of heartbeats per minute. Type: Integer Range: 60-100 bpm (for this dataset) Example: 72, 85, 90 Respiratory Rate

Description: The number of breaths taken per minute. Type: Integer Range: 12-20 breaths per minute (for this dataset) Example: 16, 18, 15 Timestamp

Description: The exact time at which the vital signs were recorded. Type: Datetime Format: YYYY-MM-DD HH:MM Example: 2023-07-19 10:15:30 Body Temperature

Description: The body temperature measured in degrees Celsius. Type: Float Range: 36.0-37.5°C (for this dataset) Example: 36.7, 37.0, 36.5 Oxygen Saturation

Description: The percentage of oxygen-bound hemoglobin in the blood. Type: Float Range: 95-100% (for this dataset) Example: 98.5, 97.2, 99.1 Systolic Blood Pressure

Description: The pressure in the arteries when the heart beats (systolic pressure). Type: Integer Range: 110-140 mmHg (for this dataset) Example: 120, 130, 115 Diastolic Blood Pressure

Description: The pressure in the arteries when the heart rests between beats (diastolic pressure). Type: Integer Range: 70-90 mmHg (for this dataset) Example: 80, 75, 85 Age

Description: The age of the patient. Type: Integer Range: 18-90 years (for this dataset) Example: 25, 45, 60 Gender

Description: The gender of the patient. Type: Categorical Categories: Male, Female Example: Male, Female Weight (kg)

Description: The weight of the patient in kilograms. Type: Float Range: 50-100 kg (for this dataset) Example: 70.5, 80.3, 65.2 Height (m)

Description: The height of the patient in meters. Type: Float Range: 1.5-2.0 m (for this dataset) Example: 1.75, 1.68, 1.82 Derived Features Derived_HRV (Heart Rate Variability)

Description: A measure of the variation in time between heartbeats. Type: Float Formula: 𝐻 𝑅

𝑉

Standard Deviation of Heart Rate over a Period Mean Heart Rate over the Same Period HRV= Mean Heart Rate over the Same Period Standard Deviation of Heart Rate over a Period ​

Example: 0.10, 0.12, 0.08 Derived_Pulse_Pressure (Pulse Pressure)

Description: The difference between systolic and diastolic blood pressure. Type: Integer Formula: 𝑃

𝑃

Systolic Blood Pressure − Diastolic Blood Pressure PP=Systolic Blood Pressure−Diastolic Blood Pressure Example: 40, 45, 30 Derived_BMI (Body Mass Index)

Description: A measure of body fat based on weight and height. Type: Float Formula: 𝐵 𝑀

𝐼

Weight (kg) ( Height (m) ) 2 BMI= (Height (m)) 2

Weight (kg) ​

Example: 22.8, 25.4, 20.3 Derived_MAP (Mean Arterial Pressure)

Description: An average blood pressure in an individual during a single cardiac cycle. Type: Float Formula: 𝑀 𝐴

𝑃

Diastolic Blood Pressure + 1 3 ( Systolic Blood Pressure − Diastolic Blood Pressure ) MAP=Diastolic Blood Pressure+ 3 1 ​ (Systolic Blood Pressure−Diastolic Blood Pressure) Example: 93.3, 100.0, 88.7 Target Feature Risk Category Description: Classification of patients into "High Risk" or "Low Risk" based on their vital signs. Type: Categorical Categories: High Risk, Low Risk Criteria: High Risk: Any of the following conditions Heart Rate: > 90 bpm or < 60 bpm Respiratory Rate: > 20 breaths per minute or < 12 breaths per minute Body Temperature: > 37.5°C or < 36.0°C Oxygen Saturation: < 95% Systolic Blood Pressure: > 140 mmHg or < 110 mmHg Diastolic Blood Pressure: > 90 mmHg or < 70 mmHg BMI: > 30 or < 18.5 Low Risk: None of the above conditions Example: High Risk, Low Risk This dataset, with a total of 200,000 samples, provides a robust foundation for various machine learning and statistical analysis tasks aimed at understanding and predicting patient health outcomes based on vital signs. The inclusion of both original attributes and derived features enhances the richness and utility of the dataset.

This is an Image Data of Indian Students collaboratively made by Students themselves by taking there 20 Images in different Lighting Conditions, Poses, With and Without Spectacles, etc. This Dataset is divided into Test and Train Dataset with there Names in Individual Folders in both Categories. The Train Data has 14 Images and Test Data has 6 Images of Individuals.

Total Images = 20 Images | 100% Data Train Data = 14 Images | 70% Data Test Data = 06 Images | 30% Data

The MARS file contains modified race and age data based on the 1990 Census. Both race and age are tabulated by sex and Hispanic origin for several layers of geography. The race data were modified to make reporting categories comparable to those used by state and local agencies. The 1990 Census included 9,804,847 persons who checked the "other race" category and were therefore not included in one of the 15 racial categories listed on the Census form. "Other race" is usually not an acceptable reporting category for state and local agencies. Therefore, the Census Bureau assigned each "other race" person to the specified race reported by another person geographically close with an identical response to the Hispanic-origin question. Hispanic origin was taken into account because over 95 percent of the "other race" persons were of Hispanic origin. (Hispanic-origin persons may be of any race.) The assignment of race to Hispanic-origin persons did not affect the Hispanic-origin category that they checked (i.e, Mexican, Puerto Rican, Cuban, etc.). Age data were modified because respondents tended to report age as of the date they completed the 1990 questionnaire, instead of age as of the April 1, 1990 Census date. In addition, there may have been a tendency for respondents to round up their age if they were close to having a birthday. Age data for individuals in households were modified by adjusting the reported birth-year data by race and sex for each of the 1990 Census's 449 district offices to correspond with the national level quarterly distribution of births available from the National Center for Health Statistics. The data for persons in group quarters were adjusted similarly, but on a state basis. The age adjustment affects approximately 100 million people. In this file their adjusted age is one year different from that reported in the 1990 Census. STF-S-2B only contains data for 12 states, their counties, and Minor Civil Divisions (MCDs). (Source: ICPSR, retrieved 06/15/2011)

The dataset includes the permanent resident population in persons by district, individual age and nationality (two categories: Switzerland and abroad) from 2015. 100-year-olds and older adults are grouped into a collection category “100+”. The data come from the cantonal population survey of the Statistical Office of the Canton of Thurgau. The permanent resident population includes all persons with main residence in the canton of Thurgau. For the foreign population, persons with the residence permit (C), residence permit (B) or short-stay permit (L) of 12 months or more are taken into account. The population in persons is listed as at 31.12. The exact description of the codes used in the records is listed in the codebook under “More information”. Source: Department of Statistics Canton Thurgau, cantonal population survey.

This statistic depicts the age distribution of India from 2013 to 2023. In 2023, about 25.06 percent of the Indian population fell into the 0-14 year category, 68.02 percent into the 15-64 age group and 6.92 percent were over 65 years of age. Age distribution in India India is one of the largest countries in the world and its population is constantly increasing. India’s society is categorized into a hierarchically organized caste system, encompassing certain rights and values for each caste. Indians are born into a caste, and those belonging to a lower echelon often face discrimination and hardship. The median age (which means that one half of the population is younger and the other one is older) of India’s population has been increasing constantly after a slump in the 1970s, and is expected to increase further over the next few years. However, in international comparison, it is fairly low; in other countries the average inhabitant is about 20 years older. But India seems to be on the rise, not only is it a member of the BRIC states – an association of emerging economies, the other members being Brazil, Russia and China –, life expectancy of Indians has also increased significantly over the past decade, which is an indicator of access to better health care and nutrition. Gender equality is still non-existant in India, even though most Indians believe that the quality of life is about equal for men and women in their country. India is patriarchal and women still often face forced marriages, domestic violence, dowry killings or rape. As of late, India has come to be considered one of the least safe places for women worldwide. Additionally, infanticide and selective abortion of female fetuses attribute to the inequality of women in India. It is believed that this has led to the fact that the vast majority of Indian children aged 0 to 6 years are male.

*(Age-standardised incidence rates per 100,000 individuals per year with 95% confidence intervals. † For cells labeled as NA, 95% CIs could not be estimated as there was only 1 data point).

Upper limit of normal for DMB per age category.

The dataset includes the permanent resident population in persons by district, individual age and denomination (evangelical, catholic and the rest) from 2015. The collection category “other” includes persons who do not belong to one or another religious community. 100-year-olds and older adults are grouped into a collection category “100+”. The data come from the cantonal population survey of the Statistical Office of the Canton of Thurgau. The permanent resident population includes all persons with main residence in the canton of Thurgau. For the foreign population, persons with the residence permit (C), residence permit (B) or short-stay permit (L) of 12 months or more are taken into account. The population in persons is listed as at 31.12. The exact description of the codes used in the records is listed in the codebook under “More information”.

Post-Discharge MHealth Participant Satisfaction Responses by Age Category. Categorical variables expressed as n (%). Percentages include participants who Agreed or Strongly Agreed to survey questions.