The ranking of countries by average age of the population shows at one end of the spectrum the countries with the highest average age of the population. At the other end are the countries with the youngest populations: they usually have high birth rates and not very long life expectancy.

Results of the rest area review and make final recommendations regarding future planning and programming decisions related to the rest area system. This feature layer depicts the locations of existing full service rest areas, parking-only rest areas, and weigh stations in Iowa. It also includes scores and ranking of rest areas based on several criteria including: Rest Area Usage, Rest Area Facility Age, Rest Area Facility Services, Rest Area, Spacing, Presence of 24-Hour Alternative Service Locations (ASLs), Truck Parking Availability, Truck Parking Demand, and Uniqueness.

The study investigates the preferences for urban wetlands within the Stockholm region, three natural and three constructed. The ranking of the photo image was determined by calculating the mean of the five item scores for each photo from every respondent. Each of the 243 responses was converted into rankings where 1 denoted the lowest score, while a ranking of 9 denoted the highest score. To obtain the group rank orders for each age and gender group, the median rank orders were calculated using the median ranking according to Kemeny’s axiomatic approach in the R package “ConsRank” (D’Ambrosio et al. 2017). Kemeny’s method finds a ranking that best represents the collective preferences of all individuals when each alternative in the ranking is assigned a unique position without any shared rankings. The median group rankings where analysed.

Data for the article The Role of Ecological Factors, Gender, and Age in Shaping Visual Preferences for Urban Wetlands. Journal of Environmental Studies and Sciences. https://doi.org/10.1007/s13412-025-01026-3

National Last Name Ranking and distribution by age group of 0-14 years old, 15-64 years old, and 65 years old and above. statistic_year, ranking, lastname, age, population

Image Classification Age Range Labels

This repository uses the following labels to categorize age ranges in image classification tasks:

[!Warning] labels_list = ['0-12', '13-20', '21-44', '45-64', '65+']

the values are age range in iamge classification give the README.MD for Repository

0-12: Images depicting individuals aged 0 to 12 years old. 13-20: Images depicting individuals aged 13 to 20 years old. 21-44: Images depicting individuals aged 21 to 44 years old. 45-64: Images… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Age-Classification-Set.

The average for 2023 based on 196 countries was 58.49 percent. The highest value was in Niger: 104.73 percent and the lowest value was in the United Arab Emirates: 20.6 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

Analysis of ‘Ranking of citizenship by sex, age and migration status’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://data.europa.eu/data/datasets/rgq6ucb5hgwyqvlz61fnq on 30 September 2021.

--- No further description of dataset provided by original source ---

--- Original source retains full ownership of the source dataset ---

Ranking of citizenship by sex, age and migration status

Age-specific ranking of pathogens during the entire study period.

Ranking of country of birth of first generation of immigrants by sex and age

Context

The Axial Age dataset tracks a variety of sociopolitical norms and their development across key areas in Afro-Eurasia. The specific scores for each sociopolitical norm for each date (varying time spans between 5300 BCE and 1800 CE in 100 year increments) within 10 NGAs (natural geographic area) were agreed-upon by a group of experts and compiled into the dataset.

Acknowledgements

Turchin, P., R. Brennan, T. E. Currie, K. Feeney, P. François, […] H. Whitehouse. 2015. “Seshat: The Global History Databank.” Cliodynamics 6(1): 77–107. https://doi.org/10.21237/C7clio6127917.

Mullins, D., D. Hoyer, […] P. Turchin. Preprint. “Mullins, D., D. Hoyer, […] P. Turchin. 2018. “A Systematic Assessment of ‘Axial Age’ Proposals Using Global Comparative Historical Evidence.” American Sociological Review 83(3): 596–626. https://doi.org/10.1177/0003122418772567.

Age Range Classification

## Overview

Age Range Classification is a dataset for classification tasks - it contains 1 2 3 4 5 annotations for 9,794 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

In 2024, out of 814 domestically produced movies in South Korea, a total of *** films were rated as suitable for people aged 18 years and older. The amount of movies produced by age rating declined with each step down, with only ** films being rated for general audiences and *** films rated for 12 years and above.

This dataset provides detailed information on various global companies, including ratings, reviews, employee counts, company status, company age, and locations. It offers valuable insights into the profiles and reputations of companies from different industries, making it a rich resource for analysts, researchers, and professionals. Data Fields: name: The name of the company. rating: The average rating of the company as provided by employees and users. reviews: The total number of reviews for the company. company_type: The industry or sector the company operates in. employee_count: The range of the number of employees working in the company. company_status: Indicates whether the company is public, private, or part of a notable list (e.g., Forbes Global 2000). company_age: The age of the company in years. location: The primary location of the company's headquarters along with additional locations if available.

This table contains figures on live births and first live births of the mother by age (on 31 December) of the father. In addition, relative figures per thousand of the average male population are included. It concerns live-born children belonging to the population of the Netherlands. Data available from: 1996 Status of the figures: All figures included in the table are final figures. Changes as of June 16, 2021: The figures for 2020 have been added. When will new numbers come out? In the 2nd quarter of 2023, the figures for 2021 and 2022 will be included in this table.

The average for 2021 based on 58 countries was 94.32 percent. The highest value was in Romania: 100 percent and the lowest value was in Burkina Faso: 66.65 percent. The indicator is available from 1970 to 2023. Below is a chart for all countries where data are available.

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

doi: 10.6084/m9.figshare.4956284.t005.

Top ten ranked research questions disaggregated by age category.

Context

Recently, many applications from biometrics,to entertainment use the information extracted from face images that contain information about age, gender, ethnic background, and emotional state. Automatic age estimation from facial images is one of the popular and challenging tasks that have different fields of applications such as controlling the content of the watched media depending on the customer's age. So facial feature analysis has been a topic of interest mainly due to its applicability and Deep Learning techniques are now making it possible for face analysis to be not just a dream but a reality. This simple practice dataset can get you more acquainted with application of deep learning in age detection. #

https://media.gettyimages.com/photos/facial-recognition-technology-picture-id1139859279?k=6&m=1139859279&s=612x612&w=0&h=H-i0yAM3A49I_r44424-jACD667nxiKb7bZR52ByOA=" alt="im"> #

Content

Indian Movie Face database (IMFDB) is a large unconstrained face database consisting of 34512 images of 100 Indian actors collected from more than 100 videos. All the images are manually selected and cropped from the video frames resulting in a high degree of variability interms of scale, pose, expression, illumination, age, resolution, occlusion, and makeup. IMFDB is the first face database that provides a detailed annotation of every image in terms of age, pose, gender, expression and type of occlusion that may help other face related applications.

The dataset provided a total of 19906 images.The attributes of data are as follows:

• ID – Unique ID of image
• Class – Age bin of person in image

#

https://ars.els-cdn.com/content/image/1-s2.0-S0925231215017348-gr1.jpg" alt="face"> image ref : Automatic age estimation based on CNN #

Acknowledgements

CVIT focuses on basic and advanced research in image processing, computer vision, computer graphics and machine learning. This center deals with the generation, processing, and understanding of primarily visual data as well as with the techniques and tools required doing so efficiently. The activity of this center overlaps the traditional areas of Computer Vision, Image Processing, Computer Graphics, Pattern Recognition and Machine Learning. CVIT works on both theoretical as well as practical aspects of visual information processing. Center aims to keep the right balance between the cutting edge academic research and impactful applied research.

Inspiration

The main task is to predict the age of a person from his or her facial attributes. For simplicity, the problem has been converted to a multiclass problem with classes as Young, Middle and Old.

Version 2: Faces dataset for regression added

UTKFace dataset is a large-scale face dataset with long age span (range from 0 to 116 years old). The dataset consists of over 20,000 face images with annotations of age, gender, and ethnicity. The images cover large variation in pose, facial expression, illumination, occlusion, resolution, etc. This dataset could be used on a variety of tasks, e.g., face detection, age estimation, age progression/regression, landmark localization, etc. Some sample images are shown as following: https://susanqq.github.io/UTKFace/icon/samples.png" alt="face2">

• consists of 20k+ face images in the wild (only single face in one image)
• provides the correspondingly aligned and cropped faces
• provides the corresponding landmarks (68 points)
• images are labelled by age, gender, and ethnicity

Complete Dataset: https://susanqq.github.io/UTKFace/

The labels of each face image is embedded in the file name, formated like [age]_[gender]_[race]_[date&time].jpg

• [age] is an integer from 0 to 116, indicating the age
• [gender] is either 0 (male) or 1 (female)
• [race] is an integer from 0 to 4, denoting White, Black, Asian, Indian, and Others (like Hispanic, Latino, Middle Eastern).
• [date&time] is in the format of yyyymmddHHMMSSFFF, showing the date and time an image was collected to UTKFace

*If you download and find the data useful your upvote is an explicit feedback for future works*