This dataset includes birth rates for unmarried women by age group, race, and Hispanic origin in the United States since 1970. Methods for collecting information on marital status changed over the reporting period and have been documented in: • Ventura SJ, Bachrach CA. Nonmarital childbearing in the United States, 1940–99. National vital statistics reports; vol 48 no 16. Hyattsville, Maryland: National Center for Health Statistics. 2000. Available from: http://www.cdc.gov/nchs/data/nvsr/nvsr48/nvs48_16.pdf. • National Center for Health Statistics. User guide to the 2013 natality public use file. Hyattsville, Maryland: National Center for Health Statistics. 2014. Available from: http://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm. National data on births by Hispanics origin exclude data for Louisiana, New Hampshire, and Oklahoma in 1989; for New Hampshire and Oklahoma in 1990; for New Hampshire in 1991 and 1992. 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.) All birth data by race before 1980 are based on race of the child. Starting in 1980, birth data by race are based on race of the mother. SOURCES CDC/NCHS, National Vital Statistics System, birth data (see http://www.cdc.gov/nchs/births.htm); public-use data files (see http://www.cdc.gov/nchs/data_access/Vitalstatsonline.htm); and CDC WONDER (see http://wonder.cdc.gov/). REFERENCES Curtin SC, Ventura SJ, Martinez GM. Recent declines in nonmarital childbearing in the United States. NCHS data brief, no 162. Hyattsville, MD: National Center for Health Statistics. 2014. Available from: http://www.cdc.gov/nchs/data/databriefs/db162.pdf. 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.

Daily report of how many Single Adults and Families are served

Matchmaking Dataset

This is a starter dataset designed to help train a basic dating recommender model. Although this doesn't have many records, there is a lack of publicly available dating app datasets. This dataset can help kickstart a basic recommendation model for a dating or matchmaking app.

  Usage

from datasets import load_dataset

dataset = load_dataset("dstam/matchmaking")

To transform the data into seperate user features and actions, run import pandas as pd… See the full description on the dataset page: https://huggingface.co/datasets/dstam/matchmaking.

Housing Starts Single Family in the United States decreased to 890 Thousand units in August from 957 Thousand units in July of 2025. This dataset includes a chart with historical data for the United States Housing Starts Single Family.

Dataset Card for "my-single-image-dataset"

More Information needed

About this dataset

Context

The dataset tabulates the United States 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 United States. The dataset can be utilized to understand the population distribution of United States by age. For example, using this dataset, we can identify the largest age group in United States.

Key observations

The largest age group in United States was for the group of age 25-29 years with a population of 22,854,328 (6.93%), according to the 2021 American Community Survey. At the same time, the smallest age group in United States was the 80-84 years with a population of 5,932,196 (1.80%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 United States is shown in this column.
• % of Total Population: This column displays the population of each age group 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 Age. You can refer the same here

Every year the CDC releases the country’s most detailed report on death in the United States under the National Vital Statistics Systems. This mortality dataset is a record of every death in the country for 2005 through 2015, including detailed information about causes of death and the demographic background of the deceased.

It's been said that "statistics are human beings with the tears wiped off." This is especially true with this dataset. Each death record represents somebody's loved one, often connected with a lifetime of memories and sometimes tragically too short.

Putting the sensitive nature of the topic aside, analyzing mortality data is essential to understanding the complex circumstances of death across the country. The US Government uses this data to determine life expectancy and understand how death in the U.S. differs from the rest of the world. Whether you’re looking for macro trends or analyzing unique circumstances, we challenge you to use this dataset to find your own answers to one of life’s great mysteries.

Overview

This dataset is a collection of CSV files each containing one year's worth of data and paired JSON files containing the code mappings, plus an ICD 10 code set. The CSVs were reformatted from their original fixed-width file formats using information extracted from the CDC's PDF manuals using this script. Please note that this process may have introduced errors as the text extracted from the pdf is not a perfect match. If you have any questions or find errors in the preparation process, please leave a note in the forums. We hope to publish additional years of data using this method soon.

A more detailed overview of the data can be found here. You'll find that the fields are consistent within this time window, but some of data codes change every few years. For example, the 113_cause_recode entry 069 only covers ICD codes (I10,I12) in 2005, but by 2015 it covers (I10,I12,I15). When I post data from years prior to 2005, expect some of the fields themselves to change as well.

All data comes from the CDC’s National Vital Statistics Systems, with the exception of the Icd10Code, which are sourced from the World Health Organization.

Project ideas

• The CDC's mortality data was the basis of a widely publicized paper, by Anne Case and Nobel prize winner Angus Deaton, arguing that middle-aged whites are dying at elevated rates. One of the criticisms against the paper is that it failed to properly account for the exact ages within the broad bins available through the CDC's WONDER tool. What do these results look like with exact/not-binned age data?
• Similarly, how sensitive are the mortality trends being discussed in the news to the choice of bin-widths?
• As noted above, the data preparation process could have introduced errors. Can you find any discrepancies compared to the aggregate metrics on WONDER? If so, please let me know in the forums!
• WONDER is cited in numerous economics, sociology, and public health research papers. Can you find any papers whose conclusions would be altered if they used the exact data available here rather than binned data from Wonder?

Differences from the first version of the dataset

• This version of the dataset was prepared in a completely different many. This has allowed us to provide a much larger volume of data and ensure that codes are available for every field.
• We've replaced the batch of sql files with a single JSON per year. Kaggle's platform currently offer's better support for JSON files, and this keeps the number of files manageable.
• A tutorial kernel providing a quick introduction to the new format is available here.
• Lastly, I apologize if the transition has interrupted anyone's work! If need be, you can still download v1.

This dataset displays data from the 2005 Census of Japan. It displays data on Aged Single Persons throughout prefectures in Japan. This dataset specifically breaks up the age ranges into 6 different categories (65 and older, 65-69, 70-74, 75-79, 80-84 85 and older) for males, females, and both sexes. This data comes from Japan's Ministry of Internal Affairs and Communication's Statistics Bureau.

This dataset was created from the CDC's National Vital Statistics Reports Volume 56, Number 6. The dataset includes all data available from this report by state level and includes births by race and Hispanic origin, births to unmarried women, rates of cesarean delivery, and twin and multiple birth rates. The data are final for 2005. No value is represented by a -1. "Descriptive tabulations of data reported on the birth certificates of the 4.1 million births that occurred in 2005 are presented. Denominators for population-based rates are postcensal estimates derived from the U.S. 2000 census".

First, we would like to thank the wildland fire advisory group. Their wisdom and guidance helped us build the dataset as it currently exists. Currently, there are multiple, freely available fire datasets that identify wildfire and prescribed fire burned areas across the United States. However, these datasets are all limited in some way. Their time periods could cover only a couple of decades or they may have stopped collecting data many years ago. Their spatial footprints may be limited to a specific geographic area or agency. Their attribute data may be limited to nothing more than a polygon and a year. None of the existing datasets provides a comprehensive picture of fires that have burned throughout the last few centuries. Our dataset uses these existing layers and utilizes a series of both manual processes and ArcGIS Python (arcpy) scripts to merge these existing datasets into a single dataset that encompasses the known wildfires and prescribed fires within the United States and certain territories. Forty different fire layers were utilized in this dataset. First, these datasets were ranked by order of observed quality (Tiers). The datasets were given a common set of attribute fields and as many of these fields were populated as possible within each dataset. All fire layers were then merged together (the merged dataset) by their common attributes to created a merged dataset containing all fire polygons. Polygons were then processed in order of Tier (1-8) so that overlapping polygons in the same year and Tier were dissolved together. Overlapping polygons in subsequent Tiers were removed from the dataset. Attributes from the original datasets of all intersecting polygons in the same year across all Tiers were also merged so that all attributes from all Tiers were included, but only the polygons from the highest ranking Tier were dissolved to form the fire polygon. The resulting product (the combined dataset) has only one fire per year in a given area with one set of attributes. While it combines wildfire data from 40 wildfire layers and therefore has more complete information on wildfires than the datasets that went into it, this dataset has also has its own set of limitations. Please see the Data Quality attributes within the metadata record for additional information on this dataset's limitations. Overall, we believe this dataset is designed be to a comprehensive collection of fire boundaries within the United States and provides a more thorough and complete picture of fires across the United States when compared to the datasets that went into it.

Single Family Home Prices in the United States decreased to 422600 USD in August from 425700 USD in July of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Housing Starts in the United States decreased to 1307 Thousand units in August from 1429 Thousand units in July of 2025. This dataset provides the latest reported value for - United States Housing Starts - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Projected Deaths by Single Year of Age, Sex, Race, and Hispanic Origin for the United States: 2016-2060 // Source: U.S. Census Bureau, Population Division // There are four projection scenarios: 1. Main series, 2. High Immigration series, 3. Low Immigration series, and 4. Zero Immigration series. // Note: Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. // For detailed information about the methods used to create the population projections, see https://www2.census.gov/programs-surveys/popproj/technical-documentation/methodology/methodstatement17.pdf. // Population projections are estimates of the population for future dates. They are typically based on an estimated population consistent with the most recent decennial census and are produced using the cohort-component method. Projections illustrate possible courses of population change based on assumptions about future births, deaths, net international migration, and domestic migration. The Population Estimates and Projections Program provides additional information on its website: https://www.census.gov/programs-surveys/popproj.html.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Tennessee. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Tennessee, the median income for all workers aged 15 years and older, regardless of work hours, was $43,390 for males and $29,723 for females.

These income figures highlight a substantial gender-based income gap in Tennessee. Women, regardless of work hours, earn 69 cents for each dollar earned by men. This significant gender pay gap, approximately 31%, underscores concerning gender-based income inequality in the state of Tennessee.

- Full-time workers, aged 15 years and older: In Tennessee, among full-time, year-round workers aged 15 years and older, males earned a median income of $59,813, while females earned $48,133, leading to a 20% gender pay gap among full-time workers. This illustrates that women earn 80 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Tennessee.

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.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Tennessee median household income by race. You can refer the same here

The data is from:

https://simplemaps.com/data/world-cities

We're proud to offer a simple, accurate and up-to-date database of the world's cities and towns. We've built it from the ground up using authoritative sources such as the NGIA, US Geological Survey, US Census Bureau, and NASA.

Our database is:

• Up-to-date: It was last refreshed on May 11, 2025.
• Comprehensive: Over 4 million unique cities and towns from every country in the world (about 48 thousand in basic database).
• Accurate: Cleaned and aggregated from official sources. Includes latitude and longitude coordinates.
• Simple: A single CSV file, concise field names, only one entry per city.

Brackish groundwater (BGW), defined for this assessment as having a dissolved-solids concentration between 1,000 and 10,000 milligrams per liter is an unconventional source of water that may offer a partial solution to current (2016) and future water challenges. In support of the National Water Census, the U.S. Geological Survey has completed a BGW assessment to gain a better understanding of the occurrence and character of BGW resources of the United States as an alternative source of water. Analyses completed as part of this assessment relied on previously collected data from multiple sources, and no new data were collected. One of the most important contributions of this assessment was the creation of a database containing chemical data and aquifer information for the known quantities of BGW in the United States. Data were compiled from single publications to large datasets and from local studies to national assessments, and includes chemical data on the concentrations of disso ...

The data included in the GIS Traffic Stations Version database have been assimilated from station description files provided by FHWA for Weigh-in-Motion (WIM), and Automatic Traffic Counters (ATR). Location referencing information was derived from the National Highway Planning Network version 4.0. and State offices of Transportation The attributes on the point elements of the database have come from two primary sources, the Station Description Records and the National Highway Planning Network's Linear Referencing System. The attributes for these databases have been intentionally limited to location referencing attributes since the core station description attribute data are contained within the Station Description Tables (SDT). There is a separate Station Description Table (SDT) for each of the different station types; WIM, and ATR. The attributes in the Station Description Table correspond with the Station Description Record found in Chapter 6 of the latest Traffic Monitoring Guide. The SDT contains the most recent stations available for each state and station type. This table was derived from files provided UTCTR by FHWA. The Station Description Table can be linked to the station shapefile via the STNNKEY field . A single exception table containing records for those stations that could not be located is provided for WIM, and ATR stations. Generally, this table contains records where location descriptions were not clear enough to locate a station. It is hoped that FHWA will be able to contact the States for a more detailed description Purpose: The GIS Traffic Stations database provides structural linkages between the National Highway Planning Network Weigh In Motion (WIM), and Automatic Traffic Count Stations (ATR)

Existing Home Sales in the United States decreased to 4000 Thousand in August from 4010 Thousand in July of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The 2018 edition of Woods and Poole Complete U.S. Database provides annual historical data from 1970 (some variables begin in 1990) and annual projections to 2050 of population by race, sex, and age, employment by industry, earnings of employees by industry, personal income by source, households by income bracket and retail sales by kind of business. The Complete U.S. Database contains annual data for all economic and demographic variables for all geographic areas in the Woods & Poole database (the U.S. total, and all regions, states, counties, and CBSAs). The Complete U.S. Database has following components: Demographic & Economic Desktop Data Files: There are 122 files covering demographic and economic data. The first 31 files (WP001.csv – WP031.csv) cover demographic data. The remaining files (WP032.csv – WP122.csv) cover economic data. Demographic DDFs: Provide population data for the U.S., regions, states, Combined Statistical Areas (CSAs), Metropolitan Statistical Areas (MSAs), Micropolitan Statistical Areas (MICROs), Metropolitan Divisions (MDIVs), and counties. Each variable is in a separate .csv file. Variables: Total Population Population Age (breakdown: 0-4, 5-9, 10-15 etc. all the way to 85 & over) Median Age of Population White Population Population Native American Population Asian & Pacific Islander Population Hispanic Population, any Race Total Population Age (breakdown: 0-17, 15-17, 18-24, 65 & over) Male Population Female Population Economic DDFs: The other files (WP032.csv – WP122.csv) provide employment and income data on: Total Employment (by industry) Total Earnings of Employees (by industry) Total Personal Income (by source) Household income (by brackets) Total Retail & Food Services Sales ( by industry) Net Earnings Gross Regional Product Retail Sales per Household Economic & Demographic Flat File: A single file for total number of people by single year of age (from 0 to 85 and over), race, and gender. It covers all U.S., regions, states, CSAs, MSAs and counties. Years of coverage: 1990 - 2050 Single Year of Age by Race and Gender: Separate files for number of people by single year of age (from 0 years to 85 years and over), race (White, Black, Native American, Asian American & Pacific Islander and Hispanic) and gender. Years of coverage: 1990 through 2050. DATA AVAILABLE FOR 1970-2019; FORECASTS THROUGH 2050

Content

Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period.

Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010–2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011–2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates.

Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook for a more thorough clarification. https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf

Context

This is a dataset from the U.S. Census Bureau hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according the amount of data that is brought in. Explore the U.S. Census Bureau using Kaggle and all of the data sources available through the U.S. Census Bureau organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 2009-01-01

• Observation End : 2017-01-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.