This table contains 2394 series, with data for years 1991 - 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).

About this dataset

Context

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

Key observations

Largest age group (population): Male # 10-14 years (738) | Female # 30-34 years (996). 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 Live Oak population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Live Oak is shown in the following column.
• Population (Female): The female population in the Live Oak 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 Live Oak 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 Live Oak Population by Gender. You can refer the same here

Life expectancy at birth and at age 65, by sex, on a three-year average basis.

Analysis of ‘LifeExpectancyData’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/just249/lifeexpectancydatacsv on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Life Expectancy Prediction Using Artificial Intelligence: Research Paper: https://docs.google.com/document/d/1Abwx7C97sMjsfow5Xk8GOCDblNaQr8T8WXh7SIJAbVo/edit?usp=sharing

Introduction: According to the survey from PwC (PricewaterhouseCoopers) report in 2016, data have shown that nearly half (47%) of 18-34 age group surveyed had changed their eating habits towards a healthier diet and further data has shown that 53% of the age 18-34 claimed that they have planned to change their eating habits to be healthier over the next year. According to research done by LiveScience, eating healthy and doing physical activity can in fact increase our life expectancy, also in one of the articles from BBC (British Broadcasting Corporation) “Do we really live longer than our ancestors? ” have stated that in 1841, a baby girl and boy was expected to live just about 40 years of age, but in 2016 a baby girl or boy was expected to live till 80 years of age. Controllable factors like eating healthy and doing exercise regularly can in fact increase our life expectancy. But can non-controllable factors like Country’s status, mortality rates, GDP, schooling, average income, government’s expenditure on health and the rate of child deaths possibly affect our life expectancy? To answer those concerns, we will input data from a Dataset called “Life Expectancy(WHO)” provided by Kumar Rajarshi in Kaggle and with the help of machine learning to process a considerable amount of data to train and analyze and make a prediction of life expectancy based on the value we feed to the algorithm.

Project Details: For this project, I have used the Dataset called “Life Expectancy(WHO)” provided by Kumar Rajarshi from Kaggle, to try to predict the total life expectancy by inputting non-controllable factors according to the data set like Country’s status, mortality rates, GDP, schooling, average income, government’s expenditure on health and the rate of child deaths to answer will non-controllable factor affect our life expectancy.

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

This dataset is about book subjects. It has 5 rows and is filtered where the books is Boys only : how to survive (almost) anything!. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This file contains original variables from the CSO regarding Males who are signing on the Live Register in 124 Social Welfare Offices in the Republic of Ireland. Data is available on a monthly basis from February 2008 to June 2014.

Analysis of ‘Males- Live Register’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/7dadefa3-33c6-44b0-8f8b-7e1fef8af264 on 15 January 2022.

--- Dataset description provided by original source is as follows ---

This file contains original variables from the CSO regarding Males who are signing on the Live Register in 124 Social Welfare Offices in the Republic of Ireland. Data is available on a monthly basis from February 2008 to June 2014.

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

Gender Detection & Classification - face recognition dataset

The dataset is created on the basis of Face Mask Detection dataset

Dataset Description:

The dataset comprises a collection of photos of people, organized into folders labeled "women" and "men." Each folder contains a significant number of images to facilitate training and testing of gender detection algorithms or models.

The dataset contains a variety of images capturing female and male individuals from diverse backgrounds, age groups, and ethnicities.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F1c4708f0b856f7889e3c0eea434fe8e2%2FFrame%2045%20(1).png?generation=1698764294000412&alt=media" alt="">

This labeled dataset can be utilized as training data for machine learning models, computer vision applications, and gender detection algorithms.

💴 For Commercial Usage: Full version of the dataset includes 376 000+ photos of people, leave a request on TrainingData to buy the dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, photo_3_extension, photo_4_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, photo_3_extension, photo_4_resolution - photo resolution in the dataset

OTHER BIOMETRIC DATASETS:

• Anti Spoofing Real Dataset
• Antispoofing Replay Dataset
• Selfies, ID Images dataset (5591 sets of 15 files)
• Selfies and video dataset (4 052 sets)
• Dataset of bald people, 5000 images

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to learn about the price and buy the dataset

Content

The dataset is split into train and test folders, each folder includes: - folders women and men - folders with images of people with the corresponding gender, - .csv file - contains information about the images and people in the dataset

File with the extension .csv

• file: link to access the file,
• gender: gender of a person in the photo (woman/man),
• split: classification on train and test

TrainingData provides high-quality data annotation tailored to your needs

keywords: biometric system, biometric system attacks, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, gender detection, supervised learning dataset, gender classification dataset, gender recognition dataset

Analysis of ‘Males 25 and Over- Live Register’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/2f0fcf13-1943-4c5a-b811-c6c9dbcc93ec on 17 January 2022.

--- Dataset description provided by original source is as follows ---

This file contains original variables from the CSO regarding Males 25 and over who are signing on the Live Register in 124 Social Welfare Offices in the Republic of Ireland. Data is available on a monthly basis from February 2008 to June 2014.

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

This file contains original variables from the CSO regarding Males 25 and over who are signing on the Live Register in 124 Social Welfare Offices in the Republic of Ireland. Data is available on a monthly basis from February 2008 to June 2014.

This dataset is about books. It has 1 row and is filtered where the book is Boys only : how to survive (almost) anything!. It features 7 columns including author, publication date, language, and book publisher.

JP: Mortality Rate: Under-5: Male: per 1000 Live Births data was reported at 2.700 Ratio in 2017. This records a decrease from the previous number of 3.200 Ratio for 2015. JP: Mortality Rate: Under-5: Male: per 1000 Live Births data is updated yearly, averaging 3.400 Ratio from Dec 1990 (Median) to 2017, with 5 observations. The data reached an all-time high of 6.900 Ratio in 1990 and a record low of 2.700 Ratio in 2017. JP: Mortality Rate: Under-5: Male: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank: Health Statistics. Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male age-specific mortality rates of the specified year.; ; Estimates Developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

By Bastian Herre, Pablo Arriagada, Esteban Ortiz-Ospina, Hannah Ritchie, Joe Hasell and Max Roser.

About dataset:

Women’s rights are human rights that all women have. But in practice, these rights are often not protected to the same extent as the rights of men.

Among others, women’s rights include: physical integrity rights, such as being free from violence and making choices over their own body; social rights, such as going to school and participating in public life; economic rights, such as owning property, working a job of their choice, and being paid equally for it; and political rights, such as voting for and holding public office.

The protection of these rights allows women to live the lives they want and to thrive in them.

On this page, you can find data on how the protection of women’s rights has changed over time, and how it differs across countries.

There are 6 dataset in here.

1- Female to male ratio of time devoted to unpaid care work. 2- Share of women in top income groups atkinson casarico voitchovsky 2018. 3- Ratio of female to male labor force participation rates ilo wdi. 4- Female to male ratio of time devoted to unpaid care work. 5- Maternal mortality 6- Gender gap in average wages ilo

In each one, there are some topics and variables that we can analysis and visualize them.

Abstract In a mix of live-action and animation, Go Make Memories is the story of Caleb’s and his family’s experience after being diagnosed with the ultra-rare neurodegenerative Niemann-Pick disease (NPD). This short film hopes to raise awareness and inform about the support offered by NPUK, a charity organization dedicated to families dealing with NPD. Details A live-action house hosts the setting of this short film, whose characters are animated. A family opens the door of their new home, full of packing boxes. The mother is carrying a baby boy, Caleb, in her arms, and a girl of about six years old, Zoey, runs around excitedly. The following scenes move around the different rooms in the house and show some happy family moments: Zoey’s first day at school, the toddler’s first steps, them playing together, and Christmas morning. One day, all of a sudden, Caleb falls on the floor while playing in the living room. Then, he starts crying, and despite the efforts of his parents, he does not stop, so they take him to the hospital. After many doctor appointments, they receive the bad news that their child suffers from a very rare neurodegenerative condition called Niemann-Pick disease, whose cure has not been found yet. The disease implies that their son’s mental and physical health would continuously decline over time, and he probably would not live more than ten years. The parents go through very rough moments accepting and understanding Caleb’s condition and its symptoms. Even though the mother is increasingly worried and experiences many emotionally difficult moments, for example, on Caleb’s birthday, she also wants to receive as much help as she can get. She suggests getting in touch with the charity organization NPUK, but her husband is in denial and refuses to ask for additional help. They are discussing this when, all of a sudden, Caleb falls again while playing in the garden, and from that moment on, his health starts to deteriorate so much that he cannot walk anymore. It is only after Caleb’s eighth birthday that the father accedes to get in touch with the NPUK and realizes that they can help them. Alicia, a social assistant from the NPUK, walks into the house and asks Caleb how he is doing. That is when the animated character in a wheelchair turns into a live-action boy. The short film closes with some captions that hope to raise awareness about the NPD and the work of NPUK and some photos to honour the memory of some children who passed away.

This table contains mortality indicators by sex for Canada and all provinces except Prince Edward Island. These indicators are derived from three-year complete life tables. Mortality indicators derived from single-year life tables are also available (table 13-10-0837). For Prince Edward Island, Yukon, the Northwest Territories and Nunavut, mortality indicators derived from three-year abridged life tables are available (table 13-10-0140).

Healthy life years (HLY) at 65 is a composite indicator that measures the number of remaining years that a person aged 65 is expected to live in a healthy condition. It is calculated separately for women and men by combining mortality data from Eurostat's demographic database with data on self-perceived activity limitations from the European Statistics of Income and Living Condition survey. A healthy conditions is defined by the absence of longstanding severe or moderate limitations in usual activities because of a health problem. Longstanding refers to a period of more than 6 months.

Abstract Aims: 1) to identify the prevalence of active commuting to school (ACS) among Brazilian regions; and 2) to determine associated factors related to ACS in this population. Methods: Cross-sectional study comprising 16,493 adolescents (mean age 14.09±2.12 years). The data comes from the National School Health Survey (2015), and the information was collected by a self-reported questionnaire. Logistic regression models were performed to identify correlates of ACS. Results: Adolescents who live in the Southeast are more prone to have ACS compared to those who live in other regions. Do not have motor vehicles been positively associated with ACS [girls: 2.04 (1.72;2.42); boys 1.85(1.63;2.10)]. Those whom self-reported white was less prone to have ACS compared to their peers from other ethnicities. Those enrolled in private schools [girls: 0.43(0.34;0.54); boys (0.45(0.39;0.53)] and schools setting in rural area [girls: 0.38(0.25;0.57); boys: 0.51(0.37;71)] are less prone to show ACS. In addition, adolescents who accumulated less active time during physical education classes [girls: 0.80(0.66;0.97)] and extracurricular shifts [boys: 0.69(0.60;0.80)] were less prone to have an ACS, compared to their most active peers. Lastly, girls who spent ≤ 2 hours presented fewer odds to have an ACS [0.75(0.63;0.90)]. Conclusions: ACS was most prevalent among those who live in the Southeast region and seems to be negativity associated with the socioeconomic level. Moreover, less active adolescents during both school and leisure time may be more prone to have passive travel go/from school.

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

About this dataset

Context

The dataset tabulates the population of Live Oak County by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Live Oak County across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 53.39% of total population being male. 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.

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. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Live Oak County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Live Oak County 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 Live Oak County Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Live Oak County, TX population pyramid, which represents the Live Oak 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 Live Oak County, TX, is 27.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Live Oak County, TX, is 32.4.
• Total dependency ratio for Live Oak County, TX is 60.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Live Oak County, TX is 3.1.

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