The data (name, year of birth, sex, and number) are from a 100 percent sample of Social Security card applications for 1880 onward.

Analysis of ‘NYC Most Popular Baby Names Over the Years’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/most-popular-baby-names-in-nyce on 13 February 2022.

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

About this dataset

Popular Baby Name Data In NYC from 2011-2014

Rows: 13962; Columns: 6

The data include items, such as:

• BRTH_YR: birth year the baby
• GNDR: gender
• ETHCTY: mother's ethnicity
• NM: baby's name
• CNT: count of the name
• RNK: ranking of the name

Source: NYC Open Data

https://data.cityofnewyork.us/Health/Most-Popular-Baby-Names-by-Sex-and-Mother-s-Ethnic/25th-nujf

This dataset was created by Data Society and contains around 10000 samples along with Nm, Rnk, technical information and other features such as: - Gndr - Ethcty - and more.

How to use this dataset

• Analyze Brth Yr in relation to Cnt
• Study the influence of Nm on Rnk
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Data Society

Start A New Notebook!

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

United States Baby Names Count

United States Baby Names Dataset

By Amber Thomas [source]

About this dataset

The data is based on a complete sample of records on Social Security card applications as of March 2021 and is presented in three main files: baby-names-national.csv, baby-names-state.csv, and baby-names-territories.csv. These files contain detailed information about names given to babies at the national level (50 states and District of Columbia), state level (individual states), and territory level (including American Samoa, Guam, Northern Mariana Islands Puerto Rico and U.S. Virgin Islands) respectively.

Each entry in the dataset includes several key attributes such as state_abb or territory_code representing the abbreviation or code indicating the specific state or territory where the baby was born. The sex attribute denotes the gender of each baby – either male or female – while year represents the specific birth year when each baby was born.

Another important attribute is name which indicates given name selected for each individual newborn.The count attribute provides numerical data about how many babies received a particular name within a specific state/territory, gender combination for a given year.

It's also worth noting that all names included have at least two characters in length to ensure high data quality standards.

How to use the dataset

- Understanding the Columns

The dataset consists of multiple columns with specific information about each baby name entry. Here are the key columns in this dataset:

• state_abb: The abbreviation of the state or territory where the baby was born.
• sex: The gender of the baby.
• year: The year in which the baby was born.
• name: The given name of the baby.

• count: The number of babies with a specific name born in a certain state, gender, and year.

- Exploring National Data

To analyze national trends or overall popularity across all states and years: a) Focus on baby-names-national.csv. b) Use columns like name, sex, year, and count to study trends over time.

- Analyzing State-Level Data

To examine specific states' data: a) Utilize baby-names-state.csv file. b) Filter data by desired states using state_abb column values. c) Combine analysis with other relevant attributes like gender, year, etc., for detailed insights.

- Understanding Territory Data

For insights into United States territories (American Samoa, Guam, Northern Mariana Islands, Puerto Rico, U.S Virgin Islands): a) Access informative data from baby-names-territories.csv. b) Analyze based on similar principles as state-level data but considering unique territory factors.

- Gender-Specific Analysis

You can study names' popularity specifically among males or females by filtering the data using the sex column. This will allow you to explore gender-specific naming trends and preferences.

- Identifying Regional Patterns

To identify naming patterns in specific regions: a) Analyze state-level or territory-level data. b) Look for variations in name popularity across different states or territories.

- Analyzing Name Popularity over Time

Track the popularity of specific names over time using the name, year, and count columns. This can help uncover trends, fluctuations, and changes in names' usage and popularity.

- Comparing Names and Variations

Use this

Research Ideas

• Tracking Popularity Trends: This dataset can be used to analyze the popularity of baby names over time. By examining the count of babies with a specific name born in different years, trends and shifts in naming preferences can be identified.
• Gender Analysis: The dataset includes information on the gender of each baby. It can be used to study gender patterns and differences in naming choices. For example, it would be possible to compare the frequency and popularity of certain names among males and females.
• Regional Variations: With state abbreviations provided, it is possible to explore regional variations in baby naming trends within the United States. Researchers could examine how certain names are more popular or unique to specific states or territories, highlighting cultural or geographical factors that influence naming choices

Acknowledgements

If you use this dataset in your research, please credit the original a...

Popular Baby Names by Sex and Ethnic Group Data were collected through civil birth registration. Each record represents the ranking of a baby name in the order of frequency. Data can be used to represent the popularity of a name. Caution should be used when assessing the rank of a baby name if the frequency count is close to 10; the ranking may vary year to year.

Official Street Names in the City of Los Angeles created and maintained by the Bureau of Engineering.

The most popular baby names by sex and mother's ethnicity in New York City.

About this Dataset

For sale are domain names that were registered between Feb 24, 2018 and Feb 28, 2018 by registrants in United States. Domains which obfuscate registrant, administrative, and other WHO IS contact details have been omitted from this dataset. The following information is availble for download in this dataset: - Domain name, Created Date, Updated Date, Expiration Date, Registrar Name- Registrant Company, Name, Address, City, State/Province/Other, Postal Code, Country, Email, Phone #, Fax #- Administrative Company, Name, Address, City, State/Province/Other, Postal Code, Country, Email, Phone #, Fax #- Technical Company, Name, Address, City, State/Province/Other, Postal Code, Country, Email, Phone #, Fax #- Billing Company, Name, Address, City, State/Province/Other, Postal Code, Country, Email, Phone #, Fax #- NameServer1, NameServer2, NameServer3, NameServer4, - DomainStatus1, DomainStatus2, DomainStatus3, DomainStatus4 Still unsure about purchasing this dataset? View and Download a free sample dataset of global domain name registrations - https://www.dataandsons.com/categories/lead_generation/domain-names-registered-between-feb-12-2018-to-feb-18-2018. Are you interested in a more targeted domain name registration dataset? Select the "Ask Seller a Question" link, send me a message, and I'll get back to you as soon as I can.

Category

Lead Generation

Keywords

USA,United-States,newly-registered-domain-names,recently-registered-domain-names,who-is-data

Row Count

84127

Price

$10.00

This API returns the geographies specified by a geography name (e.g., Washington) of a specific geography type (e.g., congressional district) within the entire United States.

This dataset includes the taxon and species name data as part of the ITEX experiment at the US Barrow site from 1995-2000.

French First Names from Death Records (1970-2024)

This dataset contains French first names extracted from death records provided by INSEE (French National Institute of Statistics and Economic Studies) covering the period from 1970 to September 2024.

  Dataset Description





  Data Source

The data is sourced from INSEE's death records database. It includes first names of deceased individuals in France, providing valuable insights into naming patterns across different… See the full description on the dataset page: https://huggingface.co/datasets/eltorio/french_first_names_insee_2024.

This data set includes cities in the United States, Puerto Rico and the U.S. Virgin Islands. These cities were collected from the 1970 National Atlas of the United States. Where applicable, U.S. Census Bureau codes for named populated places were associated with each name to allow additional information to be attached. The Geographic Names Information System (GNIS) was also used as a source for additional information. This is a revised version of the December, 2003, data set.

This layer is sourced from maps.bts.dot.gov.

This horizontal bar chart displays rural population (people) by country full name using the aggregation sum in Central America. The data is about countries.

Abstract

The Integrated Public Use Microdata Series (IPUMS) Complete Count Data include more than 650 million individual-level and 7.5 million household-level records. The microdata are the result of collaboration between IPUMS and the nation’s two largest genealogical organizations—Ancestry.com and FamilySearch—and provides the largest and richest source of individual level and household data.

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

phsdatacore@stanford.edu for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

Documentation

Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier.

In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.

The historic US 1920 census data was collected in January 1920. Enumerators collected data traveling to households and counting the residents who regularly slept at the household. Individuals lacking permanent housing were counted as residents of the place where they were when the data was collected. Household members absent on the day of data collected were either listed to the household with the help of other household members or were scheduled for the last census subdivision.

Notes

• We provide household and person data separately so that it is convenient to explore the descriptive statistics on each level. In order to obtain a full dataset, merge the household and person on the variables SERIAL and SERIALP. In order to create a longitudinal dataset, merge datasets on the variable HISTID.

• Households with more than 60 people in the original data were broken up for processing purposes. Every person in the large households are considered to be in their own household. The original large households can be identified using the variable SPLIT, reconstructed using the variable SPLITHID, and the original count is found in the variable SPLITNUM.

• Coded variables derived from string variables are still in progress. These variables include: occupation and industry.

• Missing observations have been allocated and some inconsistencies have been edited for the following variables: SPEAKENG, YRIMMIG, CITIZEN, AGE, BPL, MBPL, FBPL, LIT, SCHOOL, OWNERSHP, MORTGAGE, FARM, CLASSWKR, OCC1950, IND1950, MARST, RACE, SEX, RELATE, MTONGUE. The flag variables indicating an allocated observation for the associated variables can be included in your extract by clicking the ‘Select data quality flags’ box on the extract summary page.

• Most inconsistent information was not edited for this release, thus there are observations outside of the universe for some variables. In particular, the variables GQ, and GQTYPE have known inconsistencies and will be improved with the next release.

%3C!-- --%3E

Section 2

This dataset was created on 2020-01-10 18:46:34.647 by merging multiple datasets together. The source datasets for this version were:

IPUMS 1920 households: This dataset includes all households from the 1920 US census.

IPUMS 1920 persons: This dataset includes all individuals from the 1920 US census.

IPUMS 1920 Lookup: This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1920 datasets.

Have you ever wondered where a place got its name from? This page is the beginning of a much larger project that will attempt to geospatially document as much as we are able to regarding Tennessee's history of place names. The data as it currently exists represents many years of work by my friend, the geographer and author, Allen Coggins. He provided me with an Access database with 11,720 records of places with information including: name origin, place description, notes, and the starting and ending dates of any associated post offices. This is the work that he managed to digitize from a card catalog (now in my possession). I estimate that the records in the database represent about 1/3rd of the card catalog's records. The data I present here relates to 2,349 records which I was able to easily (enough) match to the GNIS dataset (which has 77,746 Tennessee records in the version that I accessed 2/28/2025).The sheer volume of this project means that this will take years to develop. Index cards will need to be scanned and digitized. Records will need to be matched and georeferenced. All this will only be to catch up to where Allen got us. Surely we will learn more about origins of place names as the project progresses. We will need to accurately document our work along the way.

Piotr was the most popular male first name in Poland as of January 2023, registered with over 692 thousand persons. Krzysztof and Andrzej were next, with respectively 645.67 thousand and 542.39 thousand registrations.

The Geographic Names Information System (GNIS) is the Federal standard for geographic nomenclature. The U.S. Geological Survey developed the GNIS for the U.S. Board on Geographic Names, a Federal inter-agency body chartered by public law to maintain uniform feature name usage throughout the Government and to promulgate standard names to the public. The GNIS is the official repository of domestic geographic names data; the official vehicle for geographic names use by all departments of the Federal Government; and the source for applying geographic names to Federal electronic and printed products of all types. See https://www.usgs.gov/core-science-systems/ngp/board-on-geographic-names for additional information.

About this Dataset

For sale are domain names with WHO IS information that were registered between Mar 01, 2018 and Mar 15, 2018 by registrants in United States. Domains which obfuscate registrant, administrative, and other WHO IS contact details have been omitted from this dataset. The following information is availble for download in this dataset: - Domain name, Created Date, Updated Date, Expiration Date, Registrar Name- Registrant Company, Name, Address, City, State/Province/Other, Postal Code, Country, Email, Phone #, Fax #- Administrative Company, Name, Address, City, State/Province/Other, Postal Code, Country, Email, Phone #, Fax #- Technical Company, Name, Address, City, State/Province/Other, Postal Code, Country, Email, Phone #, Fax #- Billing Company, Name, Address, City, State/Province/Other, Postal Code, Country, Email, Phone #, Fax #- NameServer1, NameServer2, NameServer3, NameServer4, - DomainStatus1, DomainStatus2, DomainStatus3, DomainStatus4 Still unsure about purchasing this dataset? View and Download a free sample dataset of global domain name registrations - https://www.dataandsons.com/categories/lead_generation/domain-names-registered-between-feb-12-2018-to-feb-18-2018. Are you interested in a more targeted domain name registration dataset? Select the "Ask Seller a Question" link, send me a message, and I'll get back to you as soon as I can.

Category

Lead Generation

Keywords

usa,united-states,newly-registered-domains,who-is-data

Row Count

208388

Price

$20.00

The U.S. Board on Geographic Names (BGN) is a Federal body created in 1890 and established in its present form by Public Law in 1947 to maintain uniform geographic name usage throughout the Federal Government. The BGN comprises representatives of Federal agencies concerned with geographic information, population, ecology, and management of public lands. Sharing its responsibilities with the Secretary of the Interior, the BGN promulgates official geographic feature names with locative attributes as well as principles, policies, and procedures governing the use of domestic names, foreign names, Antarctic names, and undersea feature names.The original program of names standardization addressed the complex issues of domestic geographic feature names during the surge of exploration, mining, and settlement of western territories after the American Civil War. Inconsistencies and contradictions among many names, spellings, and applications became a serious problem to surveyors, map makers, and scientists who required uniform, non-conflicting geographic nomenclature. President Benjamin Harrison signed an Executive Order establishing the BGN and giving it authority to resolve unsettled geographic names questions. Decisions of the BGN were accepted as binding by all departments and agencies of the Federal Government.The BGN gradually expanded its interests to include foreign names and other areas of interest to the United States, a process that accelerated during World War II. In 1947, the BGN was recreated by Congress in Public_Law_80-242. The Bylaws of the BGN have been in place since 1948 and have been revised when needed. The usefulness of standardizing (not regulating) geographic names has been proven time and again, and today more than 50 nations have some type of national names authority. The United Nations stated that "the best method to achieve international standardization is through strong programs of national standardization." Numerous nations established policies relevant to toponomy (the study of names) in their respective countries.In this age of geographic information systems, the Internet, and homeland defense, geographic names data are even more important and more challenging. Applying the latest technology, the BGN continues its mission. It serves the Federal Government and the public as a central authority to which name problems, name inquiries, name changes, and new name proposals can be directed. In partnership with Federal, State, and local agencies, the BGN provides a conduit through which uniform geographic name usage is applied and current names data are promulgated. The U.S. Geological Survey's National Geospatial Program and the National Geospatial-Intelligence Agency provide secretariat support to the Domestic Names Committee and Foreign Names Committee, respectively.For geographic feature names policies applying to the United States, or to the use of foreign geographic names, Antarctica names, and undersea feature names by the United States, see the respective items in the main menu on the left. Any person or organization, public or private, may make inquiries or request the BGN to render formal decisions on proposed new names, proposed name changes, or names that are in conflict. Meetings are open to the public and are held according to schedule. Minutes of the BGN's meetings are available.

This list is a work-in-progress and will be updated at least quarterly. This version updates column names and corrects spellings of several streets in order to alleviate confusion and simplify street name research. It represents an inventory of official street name spellings in the City of New Orleans. Several sources contain various spellings and formats of street names. This list represents street name spellings and formats researched by the City of New Orleans GIS and City Planning Commission.Note: This list may not represent what is currently displayed on street signs. City of New Orleans official street list is derived from New Orleans street centerline file, 9-1-1 centerline file, and CPC plat maps. Fields include the full street name and the parsed elements along with abbreviations using US Postal Standards. We invite your input to as we work toward one enterprise street name list.Status: Current: Currently a known used street name in New Orleans Other: Currently a known used street name on a planned but not developed street. May be a retired street name.