About this dataset

Context

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

Key observations

Largest age group (population): Male # 55-59 years (17) | Female # 60-64 years (9). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 State Line City population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the State Line City is shown in the following column.
• Population (Female): The female population in the State Line City 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 State Line City 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 State Line City Population by Gender. You can refer the same here

Forbes Women of the Year

Global Rankings and Designations

By Priyanka Dobhal [source]

About this dataset

This dataset contains the rankings of the 2020 Forbes list of 100 most powerful women from around the world. This dataset includes detailed insights on each woman, such as their age, country/territory, category, and designation. This comprehensive ranking celebrates female leaders that are making an impact in their field and around the world while inspiring us to continue striving for gender parity and driving positive social change. Explore this dataset to get an idea of who are some of the top female voices right now at the forefront of progress

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

Research Ideas

• Creating personalized stories of each woman to showcase their inspiring accomplishments, achievements and successes.
• Analyzing the age range of female Forbes 100 Power Women list to adjust marketing, staffing, and other outreach initiatives aimed at empowering women globally.
• Developing an interactive map with information about the country/territory of origin for each Forbes Power Woman, with an interactive feature that provides stories from successful women from these countries/territories that can serve as inspiration for other aspiring entrepreneurs

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: Forbes 100 Women List 2020.csv | Column name | Description | |:----------------------|:-------------------------------------------------------------------------------| | Name | Name of the Power Woman. (String) | | Age | Age of the Power Woman. (Integer) | | Country/Territory | Country or territory of origin of the Power Woman. (String) | | Category | Category of the Power Woman's achievements. (String) | | Designation | Designation of the Power Woman. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Priyanka Dobhal.

About this dataset

Context

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

Key observations

There is a slight majority of female population, with 50.5% of total population being female. 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 United States is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of United States 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 United States Population by Race & Ethnicity. You can refer the same here

"""

County-Level Demographic: Population, Race, Gender

Overview

This dataset provides a detailed breakdown of demographic information for counties across the United States, derived from the U.S. Census Bureau's 2023 American Community Survey (ACS). The data includes population counts by gender, race, and ethnicity, alongside unique identifiers for each county using State and County FIPS codes.

Dataset Features

The dataset includes the following columns: - County: Name of the county. - State: Name of the state the county belongs to. - State FIPS Code: Federal Information Processing Standard (FIPS) code for the state. - County FIPS Code: FIPS code for the county. - FIPS: Combined State and County FIPS codes, a unique identifier for each county. - Total Population: Total population in the county. - Male Population: Number of males in the county. - Female Population: Number of females in the county. - Total Race Responses: Total race-related responses recorded in the survey. - White Alone: Number of individuals identifying as White alone. - Black or African American Alone: Number of individuals identifying as Black or African American alone. - Hispanic or Latino: Number of individuals identifying as Hispanic or Latino.

Processing Methodology

1. Source:
   • Data was retrieved using the U.S. Census Bureau ACS API.
2. County-Level Aggregation:
   • Each county is uniquely identified using State FIPS Code and County FIPS Code.
   • These codes were concatenated to form the unified FIPS column.
3. Data Cleaning:
   • All numeric columns were converted to appropriate data types.
   • County and state names were extracted from the raw NAME field for clarity.

Why Use This Dataset?

This dataset is highly versatile and suitable for: - Demographic Analysis: - Analyze population distribution by gender, race, and ethnicity. - Geographic Studies: - Use FIPS codes to map counties geographically. - Data Visualizations: - Create visual insights into demographic trends across counties.

File Format

• The dataset is available as a CSV file with 3,000+ rows (one for each county).

Licensing

• Source: Data is sourced from the U.S. Census Bureau's 2023 American Community Survey (ACS).
• License: This dataset is in the public domain and provided under the U.S. Census Bureau’s terms of use. Attribution to the Census Bureau is appreciated.

Acknowledgments

Special thanks to the U.S. Census Bureau for making this data publicly available and to the Kaggle community for fostering a collaborative space for data analysis and exploration. """

Women in Headlines: Bias

Investigating Gendered Language, Temporal Trends, and Themes

By Amber Thomas [source]

About this dataset

This dataset contains all of the data used in the Pudding essay When Women Make Headlines published in January 2022. This dataset was created to analyze gendered language, bias and language themes in news headlines from across the world. It contains headlines from top50 news publications and news agencies from four major countries - USA, UK, India and South Africa - as published by SimilarWeb (as of 2021-06-06).

To collect this data we used RapidAPI's google news API to query headlines containing one or more of keywords selected based on existing research done by Huimin Xu & team and The Swaddle team. We analyzed words used in headlines manually curating two dictionaries — gendered words about women (words that are explicitly gendered) and words that denote societal/behavioral stereotypes about women. To calculate bias scores, we utilized technology developed through Yasmeen Hitti & team’s research on gender bias text analysis. To categorize words used into themes (violence/crime, empowerment, race/ethnicity/identity etc), we manually curated four dictionaries utilizing Natural Language Processing packages for Python like spacy & nltk for our analysis. Plus, inverting polarity scores with vaderSentiment algorithm helped us shed light on differences between women-centered/non-women centered polarity levels as well as differences between global polarity baselines of each country's most visited publications & news agencies according to SimilarWeb 2020 statistics..

This dataset enables journalists, researchers and educators researching issues related to gender equity within media outlets around the world further insights into potential disparities with just a few lines of code! Any discoveries made by using this data should provide valuable support for evidence-based argumentation . Let us advocate for greater awareness towards female representation better quality coverage!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides a comprehensive look at the portrayal of women in headlines from 2010-2020. Using this dataset, researchers and data scientists can explore a range of topics including language used to describe women, bias associated with different topics or publications, and temporal patterns in headlines about women over time.

To use this dataset effectively, it is helpful to understand the structure of the data. The columns include headline_no_site (the text of the headline without any information about which publication it is from), time (the date and time that the article was published), country (the country where it was published), bias score (calculated using Gender Bias Taxonomy V1.0) and year (the year that the article was published).

By exploring these columns individually or combining them into groups such as by publication or by topic, there are many ways to make meaningful discoveries using this data set. For example, one could explore if certain news outlets employ more gender-biased language when writing about female subjects than other outlets or investigate whether female-centric stories have higher/lower bias scores than average for a particular topic across multiple countries over time. This type of analysis helps researchers to gain insight into how our culture's dialogue has evolved over recent years as relates to women in media coverage worldwide

Research Ideas

• A comparative, cross-country study of the usage of gendered language and the prevalence of gender bias in headlines to better understand regional differences.
• Creating an interactive visualization showing the evolution of headline bias scores over time with respect to a certain topic or population group (such as women).
• Analyzing how different themes are covered in headlines featuring women compared to those without, such as crime or violence versus empowerment or race and ethnicity, to see if there’s any difference in how they are portrayed by the media

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: headlines_reduced_temporal.csv | Column name | Description | |:---------------------|:-------------------------------------------------------------------------------------...

Time series data for the statistic Population, ages 7-10, female and country United States. Indicator Definition:Population, ages 7-10, female is the total number of females age 7-10.The indicator "Population, ages 7-10, female" stands at 8.44 Million as of 12/31/2015, the highest value at least since 12/31/1991, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.8502 percent compared to the value the year prior.The 1 year change in percent is 0.8502.The 3 year change in percent is 2.67.The 5 year change in percent is 4.17.The 10 year change in percent is 5.06.The Serie's long term average value is 7.95 Million. It's latest available value, on 12/31/2015, is 6.17 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1990, to it's latest available value, on 12/31/2015, is +19.34%.The Serie's change in percent from it's maximum value, on 12/31/2015, to it's latest available value, on 12/31/2015, is 0.0%.

The depression dataset consist of factors that affect the mental state of women in India. The dataset has been collected through a survey among various women affected psychological problems across the country. These factors varies with states, culture and financial status. Depression prediction can be performed by providing the factors as input to the model. The factors included in the dataset are considered as the features for developing the proposed deep learning model. Survey has been conducted on 6150 women across all states. There are 21 features and ground truth (diagnosis) in the dataset.

**Features Range **

Academic Status Never Attended-0, Attended-1, Drop Out-2, Currently Attending-3 Academic Performance Poor-0, Average-1, Good-2 Social Media Exposure Nil-0, Rarely-1, Frequently-2 Physical Abuse No-0, Yes-1 Sexual Abuse No-0, Yes-1 Communication with Parents Difficult-0, Easy-1 Communication with Friends Difficult-0, Easy-1 Freedom to Move No-0, Yes-1 Expression of Opinion No-0, Yes-1 Confront Wrong Acts No-0, Yes-1 Engaged/Marriage Decided No-0, Yes-1 Discussion about Sexual Problems in Family No-0, Yes-1 Discussion about Relationships in Family No-0, Yes-1 Medical Symptoms Nil-0, Mild-1, Moderate-2, Severe-3 Impulsive / Aggressive Behavior No-0, Yes-1 Family Problems No-0, Yes-1 Divorce No-0, Yes-1 Partner Abuse No-0, Yes-1 Substance Abuse No-0, Yes-1 Relationship Problems No-0, Yes-1 Peer Pressure No-0, Yes-1

About this dataset

Context

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

Key observations

Largest age group (population): Male # 40-44 years (98) | Female # 50-54 years (73). 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 State Line population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the State Line is shown in the following column.
• Population (Female): The female population in the State Line 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 State Line 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 State Line Population by Gender. You can refer the same here

Demographic Trends and Health Outcomes in the U.S

Inequalities,Risk Factors and Access to Care

By Data Society [source]

About this dataset

This dataset contains key demographic, health status indicators and leading cause of death data to help us understand the current trends and health outcomes in communities across the United States. By looking at this data, it can be seen how different states, counties and populations have changed over time. With this data we can analyze levels of national health services use such as vaccination rates or mammography rates; review leading causes of death to create public policy initiatives; as well as identify risk factors for specific conditions that may be associated with certain populations or regions. The information from these files includes State FIPS Code, County FIPS Code, CHSI County Name, CHSI State Name, CHSI State Abbreviation, Influenza B (FluB) report count & expected cases rate per 100K population , Hepatitis A (HepA) Report Count & expected cases rate per 100K population , Hepatitis B (HepB) Report Count & expected cases rate per 100K population , Measles (Meas) Report Count & expected cases rate per 100K population , Pertussis(Pert) Report Count & expected case rate per 100K population , CRS report count & expected case rate per 100K population , Syphilis report count and expected case rate per 100k popuation. We also look at measures related to preventive care services such as Pap smear screen among women aged 18-64 years old check lower/upper confidence intervals seperately ; Mammogram checks among women aged 40-64 years old specified lower/upper conifence intervals separetly ; Colonosopy/ Proctoscpushy among men aged 50+ measured in lower/upper limits ; Pneumonia Vaccination amongst 65+ with loewr/upper confidence level detail Additionally we have some interesting trend indicating variables like measures of birth adn death which includes general fertility ratye ; Teen Birth Rate by Mother's age group etc Summary Measures covers mortality trend following life expectancy by sex&age categories Vressionable populations access info gives us insight into disablilty ratio + access to envtiromental issues due to poor quality housing facilities Finally Risk Factors cover speicfic hoslitic condtiions suchs asthma diagnosis prevelance cancer diabetes alcholic abuse smoking trends All these information give a good understanding on Healthy People 2020 target setings demograpihcally speaking hence will aid is generating more evience backed policies

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

What the Dataset Contains

This dataset contains valuable information about public health relevant to each county in the United States, broken down into 9 indicator domains: Demographics, Leading Causes of Death, Summary Measures of Health, Measures of Birth and Death Rates, Relative Health Importance, Vulnerable Populations and Environmental Health Conditions, Preventive Services Use Data from BRFSS Survey System Data , Risk Factors and Access to Care/Health Insurance Coverage & State Developed Types of Measurements such as CRS with Multiple Categories Identified for Each Type . The data includes indicators such as percentages or rates for influenza (FLU), hepatitis (HepA/B), measles(MEAS) pertussis(PERT), syphilis(Syphilis) , cervical cancer (CI_Min_Pap_Smear - CI_Max\Pap \Smear), breast cancer (CI\Min Mammogram - CI \Max \Mammogram ) proctoscopy (CI Min Proctoscopy - CI Max Proctoscopy ), pneumococcal vaccinations (Ci min Pneumo Vax - Ci max Pneumo Vax )and flu vaccinations (Ci min Flu Vac - Ci Max Flu Vac). Additionally , it provides information on leading causes of death at both county levels & national level including age-adjusted mortality rates due to suicide among teens aged between 15-19 yrs per 100000 population etc.. Furthermore , summary measures such as age adjusted percentage who consider their physical health fair or poor are provided; vulnerable populations related indicators like relative importance score for disabled adults ; preventive service use related ones ranging from self reported vaccination coverage among men40-64 yrs old against hepatitis B virus etc...

Getting Started With The Dataset

To get started with exploring this dataset first your need to understand what each column in the table represents: State FIPS Code identifies a unique identifier used by various US government agencies which denote states . County FIPS code denotes counties wi...

This dataset provides comprehensive information about the employment-to-population ratio and the actual population in the United States, spanning from 1979 to 2022.

The employment-to-population ratio signifies the percentage of the civilian noninstitutional population that is employed.

Interesting Task Ideas:

1. Take a closer look at how many people are employed compared to the total population, and see how it relates to different stuff like education, gender, race, and age groups.
2. Look into how employment has changed over time for specific groups of people, like women, different age groups, and different races or ethnicities.
3. See if there's any connection between the employment-to-population ratio and economic stuff like how much the country's making (GDP growth) or how many people are out of work (unemployment rates)
4. Find out if education levels have anything to do with how many people are employed in different racial and ethnic groups.
5. Dive into what happens to employment rates during tough times like a recession or when the economy is doing well.
6. Use the dataset to make predictions about what might happen with employment in the future.

If you find this dataset useful, please consider giving it an upvote! 😊💝

Checkout my other datasets

Poverty-Level Wages in the USA Dataset

Productivity and Hourly Compensation

150K TMDb TV Shows

900K TMDb Movies

Health Insurance Coverage in the USA

Column Descriptions

Context

The Gender Development Index (GDI) is a composite measure designed to assess gender disparities and inequalities in a society by considering factors related to human development. It is an extension of the Human Development Index (HDI) and focuses on three key dimensions: health, education, and income. In the GDI, these dimensions are assessed separately for males and females, allowing for a comparison of gender-based development gaps. Health indicators typically include life expectancy at birth for both genders. Education indicators encompass literacy rates and enrollment in primary, secondary, and tertiary education for both males and females. The income component typically examines income levels and workforce participation for both genders.

Content

This dataset provides comprehensive historical data on gender development indicators at a global level. It includes essential columns such as ISO3 (the ISO3 code for each country/territory), Country (the name of the country or territory), Continent (the continent where the country is located), Hemisphere (the hemisphere in which the country is situated), Human Development Groups, UNDP Developing Regions, HDI Rank (2021) representing the Human Development Index Rank for the year 2021, and Gender Development Index spanning from 1990 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Continent - Name of the Continent
• Hemisphere - Name of the Hemisphere
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• Gender Development Index from 1990 to 2021 - Gender Development Index from 1990 to 2021

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

https://i.imgur.com/NI4UY57.png" alt="">

Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: Freepik

Thumbnail by: Freepik

Women's Soccer Participation in High Schools

Women's Soccer Participation in High Schools

By Eva Murray [source]

About this dataset

This dataset contains information on participation in high school soccer in the United States from 2006 to 2014. It includes data on the number of schools participating, the number of students participating, and the gender split of participants. This dataset can be used to understand the popularity of soccer among high school students and compare participation rates between boys and girls

How to use the dataset

Research Ideas

• Analyzing the correlation between boys and girls soccer participation in high school and the level of success of each gender's national soccer team.
• Determining which states have the largest disparities between boys and girls soccer participation rates.
• Analyzing how participation rates have changed over time, both nationally and by state

Acknowledgements

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: partcipation_statistics_06_14_2020 10_22.csv | Column name | Description | |:------------------------|:---------------------------------------------------------------------| | Year | The year the data was collected. (Integer) | | State | The state the data was collected from. (String) | | Sport | The sport the data is for. (String) | | Boys School | The number of schools that offered a boys soccer program. (Integer) | | Girls School | The number of schools that offered a girls soccer program. (Integer) | | Boys Participation | The number of boys who participated in soccer. (Integer) | | Girls Participation | The number of girls who participated in soccer. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit Eva Murray.

Description

This dataset contains counts and rankings of the most common first names in the United States, sourced from comprehensive name census data. It is ideal for analyzing naming trends, demographic patterns, and cultural preferences, as well as for building statistical models to explore name popularity over time.

Dataset structure

male_first_names.csv: Male first name frequencies and rankings in the U.S.

female_first_names.csv: Female first name frequencies and rankings in the U.S.

Background: In 1986, the Congress enacted Public Laws 99-500 and 99-591, requiring a biennial report on the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). In response to these requirements, FNS developed a prototype system that allowed for the routine acquisition of information on WIC participants from WIC State Agencies. Since 1992, State Agencies have provided electronic copies of these data to FNS on a biennial basis.FNS and the National WIC Association (formerly National Association of WIC Directors) agreed on a set of data elements for the transfer of information. In addition, FNS established a minimum standard dataset for reporting participation data. For each biennial reporting cycle, each State Agency is required to submit a participant-level dataset containing standardized information on persons enrolled at local agencies for the reference month of April. The 2020 Participant and Program Characteristics (PC2020) is the 17th to be completed using the prototype PC reporting system. In April 2020, there were 89 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and 33 Indian Tribal Organizations (ITOs).Processing methods and equipment used: Specifications on formats (“Guidance for States Providing Participant Data”) were provided to all State agencies in January 2020. This guide specified 20 minimum dataset (MDS) elements and 11 supplemental dataset (SDS) elements to be reported on each WIC participant. Each State Agency was required to submit all 20 MDS items and any SDS items collected by the State agency. Study date(s) and duration The information for each participant was from the participants’ most current WIC certification as of April 2020.Study spatial scale (size of replicates and spatial scale of study area): In April 2020, there were 89 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and 33 Indian Tribal Organizations (ITOs).Level of true replication: UnknownSampling precision (within-replicate sampling or pseudoreplication):State Agency Data Submissions. PC2020 is a participant dataset consisting of 7,036,867 active records. The records, submitted to USDA by the State Agencies, comprise a census of all WIC enrollees, so there is no sampling involved in the collection of this data.PII Analytic Datasets. State agency files were combined to create a national census participant file of approximately 7 million records. The census dataset contains potentially personally identifiable information (PII) and is therefore not made available to the public.National Sample Dataset. The public use SAS analytic dataset made available to the public has been constructed from a nationally representative sample drawn from the census of WIC participants, selected by participant category. The national sample consists of 1 percent of the total number of participants, or 70,368 records. The distribution by category is 5,469 pregnant women, 6,131 breastfeeding women, 4,373 postpartum women, 16,817 infants, and 37,578 children.Level of subsampling (number and repeat or within-replicate sampling): The proportionate (or self-weighting) sample was drawn by WIC participant category: pregnant women, breastfeeding women, postpartum women, infants, and children. In this type of sample design, each WIC participant has the same probability of selection across all strata. Sampling weights are not needed when the data are analyzed. In a proportionate stratified sample, the largest stratum accounts for the highest percentage of the analytic sample.Study design (before–after, control–impacts, time series, before–after-control–impacts): None – Non-experimentalDescription of any data manipulation, modeling, or statistical analysis undertaken: Each entry in the dataset contains all MDS and SDS information submitted by the State agency on the sampled WIC participant. In addition, the file contains constructed variables used for analytic purposes. To protect individual privacy, the public use file does not include State agency, local agency, or case identification numbers.Description of any gaps in the data or other limiting factors: All State agencies provided data on a census of their WIC participants.Resources in this dataset:Resource Title: WIC PC 2020 National Sample File Public Use Codebook.; File Name: PC2020 National Sample File Public Use Codebook.docx; Resource Description: WIC PC 2020 National Sample File Public Use CodebookResource Title: WIC PC 2020 Public Use CSV Data.; File Name: wicpc2020_public_use.csv; Resource Description: WIC PC 2020 Public Use CSV DataResource Title: WIC PC 2020 Data Set SAS, R, SPSS, Stata.; File Name: PC2020 Ag Data Commons.zipResource; Description: WIC PC 2020 Data Set SAS, R, SPSS, Stata One dataset in multiple formats

This dataset includes live births, birth rates, and fertility rates by race of mother in the United States since 1960.

Data availability varies by race and ethnicity groups. All birth data by race before 1980 are based on race of the child. Since 1980, birth data by race are based on race of the mother. For race, data are available for Black and White births since 1960, and for American Indians/Alaska Native and Asian/Pacific Islander births since 1980. Data on Hispanic origin are available since 1989. Teen birth rates for specific racial and ethnic categories are also available since 1989. From 2003 through 2015, the birth data by race were based on the “bridged” race categories (5). Starting in 2016, the race categories for reporting birth data changed; the new race and Hispanic origin categories are: Non-Hispanic, Single Race White; Non-Hispanic, Single Race Black; Non-Hispanic, Single Race American Indian/Alaska Native; Non-Hispanic, Single Race Asian; and, Non-Hispanic, Single Race Native Hawaiian/Pacific Islander (5,6). Birth data by the prior, “bridged” race (and Hispanic origin) categories are included through 2018 for comparison.

SOURCES

NCHS, National Vital Statistics System, birth data (see https://www.cdc.gov/nchs/births.htm); public-use data files (see https://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm); and CDC WONDER (see http://wonder.cdc.gov/).

REFERENCES

1. National Office of Vital Statistics. Vital Statistics of the United States, 1950, Volume I. 1954. Available from: https://www.cdc.gov/nchs/data/vsus/vsus_1950_1.pdf.

2. Hetzel AM. U.S. vital statistics system: major activities and developments, 1950-95. National Center for Health Statistics. 1997. Available from: https://www.cdc.gov/nchs/data/misc/usvss.pdf.

3. National Center for Health Statistics. Vital Statistics of the United States, 1967, Volume I–Natality. 1969. Available from: https://www.cdc.gov/nchs/data/vsus/nat67_1.pdf.

4. Martin JA, Hamilton BE, Osterman MJK, et al. Births: Final data for 2015. National vital statistics reports; vol 66 no 1. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01.pdf.

5. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Drake P. Births: Final data for 2016. National Vital Statistics Reports; vol 67 no 1. Hyattsville, MD: National Center for Health Statistics. 2018. Available from: https://www.cdc.gov/nvsr/nvsr67/nvsr67_01.pdf.

6. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Births: Final data for 2018. National vital statistics reports; vol 68 no 13. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_13.pdf.

This dataset includes teen birth rates for females by age group, race, and Hispanic origin in the United States since 1960.

Data availability varies by race and ethnicity groups. All birth data by race before 1980 are based on race of the child. Since 1980, birth data by race are based on race of the mother. For race, data are available for Black and White births since 1960, and for American Indians/Alaska Native and Asian/Pacific Islander births since 1980. Data on Hispanic origin are available since 1989. Teen birth rates for specific racial and ethnic categories are also available since 1989. From 2003 through 2015, the birth data by race were based on the “bridged” race categories (5). Starting in 2016, the race categories for reporting birth data changed; the new race and Hispanic origin categories are: Non-Hispanic, Single Race White; Non-Hispanic, Single Race Black; Non-Hispanic, Single Race American Indian/Alaska Native; Non-Hispanic, Single Race Asian; and, Non-Hispanic, Single Race Native Hawaiian/Pacific Islander (5,6). Birth data by the prior, “bridged” race (and Hispanic origin) categories are included through 2018 for comparison.

National data on births by Hispanic origin exclude data for Louisiana, New Hampshire, and Oklahoma in 1989; New Hampshire and Oklahoma in 1990; and New Hampshire in 1991 and 1992. Birth and fertility rates for the Central and South American population includes other and unknown Hispanic. Information on reporting Hispanic origin is detailed in the Technical Appendix for the 1999 public-use natality data file (see ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/DVS/natality/Nat1999doc.pdf).

This data contains all the essential data in the form of % with respect to rural and urban Indian states . This dataset is highly accurate as this is taken from the Indian govt. it is updated till 2021 for all states and union territories. source of data is data.gov.in titled - ******All India and State/UT-wise Factsheets of National Family Health Survey******

it is advised to you pls search the data keywords you need by using (Ctrl+f) , as it will help to avoid time wastage. States/UTs

Different columns it contains are Area

Number of Households surveyed Number of Women age 15-49 years interviewed Number of Men age 15-54 years interviewed

Female population age 6 years and above who ever attended school (%)

Population below age 15 years (%)

Sex ratio of the total population (females per 1,000 males)

Sex ratio at birth for children born in the last five years (females per 1,000 males)

Children under age 5 years whose birth was registered with the civil authority (%)

Deaths in the last 3 years registered with the civil authority (%)

Population living in households with electricity (%)

Population living in households with an improved drinking-water source1 (%)

Population living in households that use an improved sanitation facility2 (%)

Households using clean fuel for cooking3 (%) Households using iodized salt (%)

Households with any usual member covered under a health insurance/financing scheme (%)

Children age 5 years who attended pre-primary school during the school year 2019-20 (%)

Women (age 15-49) who are literate4 (%)

Men (age 15-49) who are literate4 (%)

Women (age 15-49) with 10 or more years of schooling (%)

Men (age 15-49) with 10 or more years of schooling (%)

Women (age 15-49) who have ever used the internet (%)

Men (age 15-49) who have ever used the internet (%)

Women age 20-24 years married before age 18 years (%)

Men age 25-29 years married before age 21 years (%)

Total Fertility Rate (number of children per woman) Women age 15-19 years who were already mothers or pregnant at the time of the survey (%)

Adolescent fertility rate for women age 15-19 years5 Neonatal mortality rate (per 1000 live births)

Infant mortality rate (per 1000 live births) Under-five mortality rate (per 1000 live births)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Any method6 (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Any modern method6 (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Female sterilization (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Male sterilization (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - IUD/PPIUD (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Pill (%)  

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Condom (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Injectables (%)

Total Unmet need for Family Planning (Currently Married Women Age 15-49 years)7 (%)

Unmet need for spacing (Currently Married Women Age 15-49 years)7 (%)

Health worker ever talked to female non-users about family planning (%)

Current users ever told about side effects of current method of family planning8 (%)

Mothers who had an antenatal check-up in the first trimester (for last birth in the 5 years before the survey) (%)

Mothers who had at least 4 antenatal care visits (for last birth in the 5 years before the survey) (%)

Mothers whose last birth was protected against neonatal tetanus (for last birth in the 5 years before the survey)9 (%)

Mothers who consumed iron folic acid for 100 days or more when they were pregnant (for last birth in the 5 years before the survey) (%)

Mothers who consumed iron folic acid for 180 days or more when they were pregnant (for last birth in the 5 years before the survey} (%)

Registered pregnancies for which the mother received a Mother and Child Protection (MCP) card (for last birth in the 5 years before the survey) (%)

Mothers who received postnatal care from a doctor/nurse/LHV/ANM/midwife/other health personnel within 2 days of delivery (for last birth in the 5 years before the survey) (%)

Average out-of-pocket expenditure per delivery in a public health facility (for last birth in the 5 years before the survey) (Rs.)

Children born at home who were taken to a health facility for a check-up within 24 hours of birth (for last birth in the 5 years before the survey} (%)

Children who received postnatal care from a doctor/nurse/LHV/ANM/midwife/ other health personnel within 2 days of delivery (for last birth in the 5 years before the survey) (%)

Institutional births (in the 5...

This dataset assembles all final birth data for females aged 15–19, 15–17, and 18–19 for the United States.

Time series data for the statistic Survival Rate from Age 15-60, Female and country United States. Indicator Definition:Adult Survival Rate is calculated by subtracting the mortality rate for 15-60 year-olds from 1. Most recent estimates are used. Year of most recent estimate shown in data notes.

This dataset includes data on weight status for children aged 3 months to 4 years old from Women, Infant, and Children Participant and Program Characteristics (WIC-PC). This data is used for DNPAO's Data, Trends, and Maps database, which provides national and state specific data on obesity, nutrition, physical activity, and breastfeeding. For more information about WIC-PC visit https://www.fns.usda.gov/wic/national-survey-wic-participants.