About this dataset

Context

The dataset tabulates the Show Low population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Show Low. The dataset can be utilized to understand the population distribution of Show Low by age. For example, using this dataset, we can identify the largest age group in Show Low.

Key observations

The largest age group in Show Low, AZ was for the group of age 70 to 74 years years with a population of 1,220 (10.24%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Show Low, AZ was the 85 years and over years with a population of 97 (0.81%). 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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Show Low is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Show Low total 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 Show Low Population by Age. You can refer the same here

This statistic presents data on the share of adults who read spoilers about reality dating shows in the United States as of March 2019, sorted by age group. The findings reveal that respondents aged between 18 and 29 years old were most likely to read spoilers about reality dating shows, however the majority still said that they did not do so.

Context

The** UTKFace_Age_Groups** is a derived from the UTKFace dataset for the purpose of classification of age groups.

The images in this dataset have been through a preprocessing pipeline: 1. The images were cropped using a custom technique to avoid facial deformation 2. A random sample of the images have been selected and split into the different folders where each folder represents an age group

Content

The dataset contains 7 folders where each folder represents a different age group. The age groups are: 1-Child: 1-12 2-Teenager: 13-18 3-Young Adult:19-25 4-Adult: 26-39 5-Middle Aged: 40-60 6-Old: 61-80 7-Very old: 80-116

Acknowledgements

This dataset would not be available without the original dataset "The UTKFace dataset"; which 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. The original Dataset can be downloaded here

Social media usage in the United Kingdom reveals a diverse landscape across age groups, with the ***** bracket leading at ** percent of users in 2024. Surprisingly, the ***** age group accounted for ** percent, challenging the notion that social networks are primarily for younger users. This distribution highlights the widespread adoption of social platforms among various demographics, reflecting the evolving digital habits of UK adults. Younger users drive emerging platform adoption While established networks like Facebook maintain a strong presence, younger users are increasingly drawn to newer platforms. TikTok, for instance, has gained significant traction among the ***** age group, with over a quarter of UK smartphone users in this bracket using the app. Advertising trust varies across age groups and mediums The effectiveness of social media advertising differs across age groups, with trust playing a crucial role. Among consumers aged *****, ** percent reported not buying products promoted by influencers, indicating a potential shift in how younger audiences perceive and respond to social media marketing.

Twitter user statistics show a varying degree of how often users login to the platform. Here’s what it looks like.

About this dataset

Context

The dataset tabulates the population of Show Low by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Show Low. The dataset can be utilized to understand the population distribution of Show Low by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Show Low. 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 Show Low.

Key observations

Largest age group (population): Male # 70-74 years (601) | Female # 70-74 years (619). 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 Show Low population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Show Low is shown in the following column.
• Population (Female): The female population in the Show Low 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 Show Low 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 Show Low Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Show Low, AZ population pyramid, which represents the Show Low 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 Show Low, AZ, is 31.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Show Low, AZ, is 49.0.
• Total dependency ratio for Show Low, AZ is 80.6.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Show Low, AZ is 2.0.

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

Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences.

1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc.
2. Demographics - Gender, Age Group, Marital Status, Language etc.
3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc
4. Persona - Consumer type, Communication preferences, Family type, etc
5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc.
6. Household - Number of Children, Number of Adults, IP Address, etc.
7. Behaviours - Brand Affinity, App Usage, Web Browsing etc.
8. Firmographics - Industry, Company, Occupation, Revenue, etc
9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc.
10. Auto - Car Make, Model, Type, Year, etc.
11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases:

360-Degree Customer View:Get a comprehensive image of customers by the means of internal and external data aggregation.

Data Enrichment:Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment

Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity.

Advertising & Marketing:Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Using Factori Consumer Data graph you can solve use cases like:

Acquisition Marketing Expand your reach to new users and customers using lookalike modeling with your first party audiences to extend to other potential consumers with similar traits and attributes.

Lookalike Modeling

Build lookalike audience segments using your first party audiences as a seed to extend your reach for running marketing campaigns to acquire new users or customers

And also, CRM Data Enrichment, Consumer Data Enrichment B2B Data Enrichment B2C Data Enrichment Customer Acquisition Audience Segmentation 360-Degree Customer View Consumer Profiling Consumer Behaviour Data

Here's the schema of Consumer Data: person_id first_name last_name age gender linkedin_url twitter_url facebook_url city state address zip zip4 country delivery_point_bar_code carrier_route walk_seuqence_code fips_state_code fips_country_code country_name latitude longtiude address_type metropolitan_statistical_area core_based+statistical_area census_tract census_block_group census_block primary_address pre_address streer post_address address_suffix address_secondline address_abrev census_median_home_value home_market_value property_build+year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type year month household_id Census_median_household_income household_size marital_status length+of_residence number_of_kids pre_school_kids single_parents working_women_in_house_hold homeowner children adults generations net_worth education_level occupation education_history credit_lines credit_card_user newly_issued_credit_card_user credit_range_new
credit_cards loan_to_value mortgage_loan2_amount mortgage_loan_type
mortgage_loan2_type mortgage_lender_code
mortgage_loan2_render_code
mortgage_lender mortgage_loan2_lender
mortgage_loan2_ratetype mortgage_rate
mortgage_loan2_rate donor investor interest buyer hobby personal_email work_email devices phone employee_title employee_department employee_job_function skills recent_job_change company_id company_name company_description technologies_used office_address office_city office_country office_state office_zip5 office_zip4 office_carrier_route office_latitude office_longitude office_cbsa_code
office_census_block_group
office_census_tract office_county_code
company_phone
company_credit_score
company_csa_code
company_dpbc
company_franchiseflag
company_facebookurl company_linkedinurl company_twitterurl
company_website company_fortune_rank
company_government_type company_headquarters_branch company_home_business
company_industry
company_num_pcs_used
company_num_employees
company_firm_individual company_msa company_msa_name
company_naics_code
company_naics_description
company_naics_code2 company_naics_description2
company_sic_code2
company_sic_code2_desc...

When watching others perform an everyday activity, such as setting up a video game console, viewers spontaneously segment it into a sequence of events. Prior research has shown that brain regions associated with attentional control and eye movements are active when one perceives the start of a new event, and that older adults segment continuous actions into sub-events more idiosyncratically than young adults. We explored whether there are age-related differences in gaze similarity (i.e., the extent to which people look at the same places at the same time), and how gaze similarity changes around event boundaries. Older and young adults watched naturalistic videos of actors performing everyday activities while we tracked their eye-movements. Afterwards, they segmented the videos into sub-events. Analysis of gaze during passive-viewing indicated significantly greater clustering of gaze in young adults than older adults and more gaze similarity at event boundaries, regardless of age group. Thus, attentional selection may partially explain age-related differences in how individuals parse the continuous flow of information into events.

Organization of folders: 1) Calculate Gaze Similarity contains Matlab code for how to calculate gaze similarity 2) Calculate Perceptual Change contains python code for how we calculated perceptual change. 3) RMarkdown Files and Data contains .R code for running analyses 4) Stimuli are provided in the Videos folder.

The statistic shows the share of viewers who have used a VPN to watch a show only available in another country in the United States as of August 2017, sorted by age group. During the survey, ** percent of respondents stated that they used a VPN to watch a show only available in another country.

Introduction

Fitbit statistics: In today's health-conscious society, monitoring personal wellness metrics has become increasingly important. Fitbit, a leader in wearable technology, offers users detailed insights into their daily activities, sleep patterns, and heart health. On average, Fitbit users take between 10,000 to 18,000 steps per day, aligning with general fitness recommendations.

Sleep tracking data reveals that users typically achieve about 6.5 hours of sleep each night, accompanied by an average Sleep Score of 77. Regarding cardiovascular health, the average resting heart rate among Fitbit users is approximately 65 beats per minute, with variations influenced by factors such as age and gender. These statistics underscore Fitbit's role in providing users with actionable data to support their health and wellness goals.

Let's delve into the fascinating insights through Fitbit statistics and explore what they can tell us about the brand’s performance in 2025.

Within a cell, proteins have distinct and highly variable half-lives. As a result, molecular ages of proteins can range from seconds to years. How the age of a protein influences its environmental interactions is a largely unexplored area of biology. To investigate the age-selectivity of cellular pathways, we developed a methodology termed “proteome birthdating” that barcodes proteins based on their time of synthesis. We show that this approach provides accurate measurements of protein turnover kinetics without the requirement for multiple kinetic time points. As a first use case of the birthdated proteome, we investigated the age distribution of the human ubiquitinome. Our results indicate that the vast majority of ubiquitinated proteins in a cell consist of newly synthesized proteins and that these young proteins constitute the bulk of the degradative flux through the proteasome. Rapidly ubiquitinated proteins are enriched in cytosolic proteins and subunits of protein complexes. Conversely, proteins destined for the secretory pathway and vesicular transport have older ubiquitinated populations. Our data also identified a smaller subset of very old ubiquitinated cellular proteins that do not appear to be targeted to the proteasome for rapid degradation. Together, our data provide an age census of the human ubiquitinome and establish proteome birthdating as a methodology for investigating the protein age-selectivity of diverse cellular pathways.

Description of the user level features.

A survey conducted in the United States in 2023 revealed that most CTV viewers were between the ages of 18 and 34 years, with nearly ********** of respondents using these devices on a daily basis. Meanwhile, ** percent of people aged over 55 years watched videos via connected TVs every day.

About this dataset

Context

The dataset tabulates the Show Low 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 Show Low. 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 6,218 (52.20% 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 Show Low population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Show Low is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Show Low 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 Show Low Population by Age. You can refer the same here

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Show Low. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Show Low. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Show Low, householders within the 45 to 64 years age group have the highest median household income at $73,043, followed by those in the 25 to 44 years age group with an income of $64,071. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $62,699. Notably, householders within the under 25 years age group, had the lowest median household income at $50,192.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific 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 Show Low median household income by age. You can refer the same here

A comprehensive view of whole-brain amino acid levels holds the potential to provide valuable insights into the brain’s state, given the mutual interconnections through metabolism, food intake, and neurotransmission. We tested this concept by evaluating free amino acid levels in single Drosophila brains across 24 h and at two different ages. A large proportion of these amino acids displayed time-of-day variations, and a subset exhibited age-dependent variations. Cross-correlation analysis of the data sets confirmed broad time-of-day and age dependent interconnections between amino acids. Factor Analysis of Mixed Data revealed further data structuration along key amino acids. For example, 50% of the variance could be accounted for by an inverse coupling between gamma-aminobutyric acid and several essential amino acids during the active phase, linking food intake and sleep. This proof of concept emphasizes the value of combining multivariate analysis to whole-brain amino acid level evaluation, shedding potentially new light on sleep–wake regulation and aging.

The tree age dataset was derived for tally trees in the Forest Inventory and Analysis program (FIA) of the US Forest Service using an age-size relationship modeling framework that incorporates species-specific and environmental variables.

Associated paper: Lu, J., Huang, C., Schleeweis, K., Zou, Z., & Gong, W. (2025). Tree age estimation across the US using forest inventory and analysis database. Forest Ecology and Management, 584, 122603.

Background In order for ageing to evolve in response to a declining strength of selection with age, a genetic architecture that allows for mutations with age-specific effects on organismal performance is required. Our understanding of how selective effects of individual mutations are distributed across ages is however poor. Established evolutionary theories assume that mutations causing ageing have negative late-life effects, coupled to either positive or neutral effects early in life. New theory now suggests evolution of ageing may also result from deleterious mutations with increasing negative effects with age, a possibility that has not yet been empirically explored.

Results To directly test how the effects of deleterious mutations are distributed across ages, we separately measure age-specific effects on fecundity for each of 20 mutations in Drosophila melanogaster. We find that deleterious mutations in general have a negative effect that increases with age, and that the rate o...

Supporting data for "Ancient genomes from Bronze Age remains reveal deep diversity and recent adaptive episodes for human oral pathobionts"