Number of database distributions according to the age classification model.

Disclaimer: This is not my dataset, but was made available from here The file structure has been modified for easier use. The label for each image is now contained in the filename of the image and the hierarchy is flattened. The original dataset version can be found here on kaggle.

Face-Age-10K Dataset

The Face-Age-10K dataset consists of over 9,000 facial images annotated with age group labels. It is designed for training machine learning models to perform age classification from facial features.

  Dataset Details

Total Images: 9,165

Image Size: 200x200 pixels

Format: Parquet

Modality: Image

Split:

train: 9,165 images

  Labels

The dataset includes 8 age group classes: labels_list = [ 'age 01-10', 'age 11-20', 'age 21-30'… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Face-Age-10K.

The New Mexico 2000 Unified School Districts layer was derived from the TIGER Line files from the US Census Bureau. The districts are clipped to the state boundaries, and available for download from the website.

We aim to estimate the age and reddening parameters of already identified star clusters within the Small Magellanic Cloud (SMC) in a consistent way using available photometric data, classify them based on their mass and strength, and study their spatio-temporal distribution. We have used a semi-automated quantitative method, developed in the first paper of this series (Paper I), to estimate the cluster parameters using the V and I band photometric data from the Optical Gravitational Lensing Experiment (OGLE) III survey. We estimated parameters of 179 star clusters (17 are newly parameterised) and classified them into four groups. We present an online catalogue of parameters as well as cleaned and isochrone-fitted colour magnitude diagrams of 179 clusters. We compiled age information of 468 clusters by combining previous studies with our catalogue, to study their spatio-temporal distribution. Most of the clusters located in the southern part of the SMC are in the age range 600Myr-1.25Gyr, whereas, the clusters younger than 100Myr are mostly found in the northern SMC, with the central SMC showing continuous cluster formation. The peak of the cluster age distribution is identified at 130+/-35Myr, very similar to the Large Magellanic Cloud (LMC) in Paper I. We suggest that the burst of cluster formation at 130Myr is due to the most recent LMC-SMC interaction. 90% of the studied sample is found to have mass <1700M_{sun}_, suggesting that the SMC is dominated by low mass clusters. There is tentative evidence for compact clusters in the LMC when compared to those in the Galaxy and the SMC. A progressive shifting of cluster location from the south to north of the SMC is identified in last ~600Myr. The details of spatio-temporal distribution of clusters presented in two videos as part of this study can be used as a tool to constrain details of the recent LMC-SMC interactions. Cone search capability for table J/A+A/616/A187/table2 (Parameters and classification for 179 star clusters in the SMC)

About this dataset

Context

The dataset tabulates the data for the Wrangell, AK population pyramid, which represents the Wrangell population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Wrangell, AK, is 29.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Wrangell, AK, is 40.7.
• Total dependency ratio for Wrangell, AK is 70.4.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Wrangell, AK is 2.5.

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 for the Wrangell population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Wrangell for the selected age group is shown in the following column.
• Population (Female): The female population in the Wrangell for the selected age group is shown in the following column.
• Total Population: The total population of the Wrangell for the selected age group 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 Wrangell Population by Age. You can refer the same here

Data on broad age groups and gender for the population 15 years of age and older in private households of Canada, provinces and territories.

As of June 2025, 24.2 percent of Facebook users in the United States were aged between 25 and 34 years, making up Facebook’s largest audience in the country. Overall, almost 19 percent of users belonged to the 18 to 24-year age group. Does everyone in the U.S. use Facebook? In 2024, there were approximately 250 million Facebook users in the U.S., a figure which is projected to steadily increase, and reach 262.8 million by 2028. Social media users in the United States have a very high awareness of the social media giant. Expectedly, 94 percent of users had heard of the brand in 2025. Although the vast majority of U.S. social networkers knew of Facebook, the likeability of the platform was not so impressive at 68 percent. Nonetheless, usage, loyalty, and buzz around the brand remained relatively high. Facebook, Meta, and the metaverse A strategic rebranding from Facebook to Meta Platforms in late 2021 boded well for the company in Mark Zuckerberg’s attempt to be strongly linked to the metaverse, and to be considered more than just a social media company. According to a survey conducted in the U.S. in early 2022, Meta Platforms is the brand that Americans most associated with the metaverse.  

About this dataset

Context

The dataset tabulates the data for the Yuma County, AZ population pyramid, which represents the Yuma County population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Yuma County, AZ, is 34.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Yuma County, AZ, is 34.4.
• Total dependency ratio for Yuma County, AZ is 69.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Yuma County, AZ is 2.9.

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 for the Yuma County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Yuma County for the selected age group is shown in the following column.
• Population (Female): The female population in the Yuma County for the selected age group is shown in the following column.
• Total Population: The total population of the Yuma County for the selected age group 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 Yuma County Population by Age. You can refer the same here

Classification by causes, age groups and sex. National. Classification by causes in combination with age groups and sex.

This SeaWinds on QuikSCAT scatterometer-derived Arctic sea ice classification dataset is provided as a service to the ocean and sea ice research communities on behalf of Dr. David Long at Brigham Young University (BYU) and the Scatterometer Climate Record Pathfinder (SCP). This dataset provides nominal 4.45 km (pixel resolution at reference latitude 70 N) gridded fields that classify First-Year (FY) and Multi-Year (MY) sea ice using SeaWinds on QuikSCAT scatterometer observations on a daily basis from 20 June 2002 through 23 November 2009. It is unique from other sea ice classification datasets primarily due to its utilization of passive microwave AMSR-E data to provide the initial ocean/ice masking edge and the use of a seasonal threshold for FY and MY sea ice classification from only SeaWinds data.The dataset is derived from daily gridded QuikSCAT vertically polarized (V-pol) egg backscatter data from the SCP, which is made available at the PO.DAAC: http://podaac.jpl.nasa.gov/dataset/QUIKSCAT_BYU_L3_OW_SIGMA0_ENHANCED . A seasonally varying threshold was derived by Swan and Long (2012) and used to discriminate between FY and MY sea ice based on QuikSCAT V-pol egg backscatter. The sea ice mask was derived by applying threshold constraints to the SCP AMSR-E 6 GHz images, which are made available by the BYU SCP: http://www.scp.byu.edu/data/AMSRE/SIR/AMSRE_sir.html . The data is provided in a CF-compliant, netCDF version 3 format. Further details on the algorithms and validation are described in further detail by Swan and Long (2012). The dataset, software readers and user guide documentation may all be accessed in PO.DAAC Drive at https://podaac-tools.jpl.nasa.gov/drive/files/allData/quikscat/preview/L3/byu_scp/sea_ice_age/arctic/ . For more information on the QuikSCAT platform and mission, please visit http://podaac.jpl.nasa.gov/OceanWind/QuikSCAT .

Curious about age demographics of your clientele in Norway? Wondering about which generation can be most often seen flocking to your store? Dive deep into customer insights using our population by age group data of Norway. Whether your customers are down your street or across the globe, we empower you to pinpoint the ideal demographic for your marketing campaigns or projects. Our dataset offers intricate details on this country's age distribution.

This dataset contains non-seasonally adjusted California Labor Force Participation Rate by age groups, from the Current Population Survey (CPS). The age group ranges are as follows: 16-19; 20-24; 25-34; 35-44; 45-54; 55-64; 65+. This data is based on a 12-month moving average.

The dataset includes age-standardized estimates of the prevalence of diabetes mellitus (DM) and its associated risk factors. The data is derived from the Non-Communicable Disease Risk Factor Collaboration (NCD-RisC), a global network of health scientists and practitioners that aims to provide reliable and up-to-date information on the prevalence of non-communicable diseases and their risk factors.

The dataset includes information from 200 countries and territories and covers the period from 1980 to 2014. The data is presented in both male and female categories, and estimates are given for different age groups ranging from 20-79 years old. The data is standardized to account for differences in age distributions across countries and over time.

Age and gender classification for sampled E. helvum annobonensis.

Curious about age demographics of your clientele in Finland? Wondering about which generation can be most often seen flocking to your store? Dive deep into customer insights using our population by age group data of Finland. Whether your customers are down your street or across the globe, we empower you to pinpoint the ideal demographic for your marketing campaigns or projects. Our dataset offers intricate details on this country's age distribution.

As of February 2025, 37.5 percent of X’s (formerly Twitter) global audience was aged between 25 and 34 years. The second-largest age group demographic on the platform was represented by users aged between 18 and 24 years, with a share of 32.1 percent. Users aged less than 18 years accounted for two percent of users, while those aged 50 or older accounted for roughly 7.3 percent. X is a male-dominated platform As of January 2024, more than 60 percent of X users were male. Although all mainstream social media platforms tend to have a slightly more male-skewing audience, X stands out above Instagram, Snapchat, TikTok, and Facebook when it comes to user gender demographics. Overall, Pinterest is the only mainstream platform to have a higher share of female users. X Blue for you It is not uncommon for social media users to now have the chance to become subscribers of their chosen online networks for a monthly fee. X Blue is a subscription service from X that gives users special benefits and features. A blue verification mark, edit post functionality, fewer ads, priority ranking in chats, and longer video upload times are some of the perks offered.

Curious about age demographics of your clientele in France? Wondering about which generation can be most often seen flocking to your store? Dive deep into customer insights using our population by age group data of France. Whether your customers are down your street or across the globe, we empower you to pinpoint the ideal demographic for your marketing campaigns or projects. Our dataset offers intricate details on this country's age distribution.

Curious about age demographics of your clientele in United Kingdom? Wondering about which generation can be most often seen flocking to your store? Dive deep into customer insights using our population by age group data of United Kingdom. Whether your customers are down your street or across the globe, we empower you to pinpoint the ideal demographic for your marketing campaigns or projects. Our dataset offers intricate details on this country's age distribution.

Vintage 2024 Population projections by race, sex and age group for North Carolina. Includes population by race (American Indian/Alaska Native), Asian, Black, White, Other (includes persons identified as two or more races).