This data set lists the sex and number of birth registrations for each first name, from 1900 onward. Years are grouped by the date of the birth registration, not by the date of birth. Some birth registrations are not included, such as registrations with a sex other than Male or Female (i.e. indeterminate or not recorded), or where the birth registration date is not recorded. These excluded records are so few their exclusion is unlikely to have any significant impact on the data. Where a name has less than 10 instances in a particular year, the name will not be included in the data for that year. Due to this, total volumes will be less than the total birth registrations in that year. As first and middle names are recorded in our system together, the first name has been split off from the middle names. Due to the size of the data set, this was done with an automated system, generally looking for the first space in the name. This means there may be names not correctly added. Also, certain symbols in names may not carry through to the data correctly. Please let us know using the contact email address if you find any errors in the data.

This dataset contains statistics on names (first names of women, first names of men, and last names) by country of birth. In total, there are 231,505 names by 202 countries. The data comes from Statistics Sweden's population statistics (name register) and refers to persons registered in Sweden on December 31st, 2020. However, some names are excluded due to confidentiality, such as names with fewer than five carriers. The data is licensed with Creative Commons Attribution 4.0 International (CC BY 4.0) and may be used as long as Statistics Sweden is stated as the source. In this dataset, you will also find (in addition to the original data from Statistics Sweden) tidied data where the ISO code for each country has been added, as well as data in so-called wide format and long format to facilitate easier data processing.

Please see the Swedish version of the post and the README file for more information about the data.

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.

This statistic shows the most frequent combinations of first name and last name in the United States, as of 2013. According to this ranking, the name "James Smith" occurs most often and is most popular in the United States.

Between 2017 and 2020, the most common last name registered in Mexico was Hernandez with over half a million cases, followed by Garcia and Martinez. Sofia was the most popular female name in Mexico in 2021, while Santiago was the most popular name for male newborns.

This dataset contains ranks and counts for the top 25 baby names by sex for live births that occurred in California (by occurrence) based on information entered on birth certificates.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1930 datasets.

This data base contains gridded (one degree by one degree) information on the world-wide distribution of the population for 1990 and country-specific information on the percentage of the country's population present in each grid cell (Li, 1996a). Secondly, the data base contains the percentage of a country's total area in a grid cell and the country's percentage of the grid cell that is terrestrial (Li, 1996b). Li (1996b) also developed an indicator signifying how many countries are represented in a grid cell and if a grid cell is part of the sea; this indicator is only relevant for the land, countries, and sea-partitioning information of the grid cell. Thirdly, the data base includes the latitude and longitude coordinates of each grid cell; a grid code number, which is a translation of the latitude/longitude value and is used in the Global Emission Inventory Activity (GEIA) data bases; the country or region's name; and the United Nations three-digit country code that represents that name. For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ftp/db1016/

In 2021, Giant Bikes manufactured by the Taiwanese bicycle manufacturer of the same name, was the most popular bike brand in the UK as indicated by its high monthly average monthly search volume of 43,4000. Trek bikes followed closely as the second most popular bike brand in the country with an average monthly search volume 43,300.

Queensland Top 100 Baby Names

Find out import shipments and details about The Name People Inc Import Data report along with address, suppliers, products and import shipments.

Documentation

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.

Historic data are scarce and often only exists in aggregate tables. The key advantage of the IPUMS 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 IPUMS 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 IPUMS 1900 census data was collected in June 1900. 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.

Section 2

This dataset was created on 2020-01-10 22:51:40.810 by merging multiple datasets together. The source datasets for this version were:

IPUMS 1900 households: This dataset includes all households from the 1900 US census.

IPUMS 1900 persons: This dataset includes all individuals from the 1910 US census.

IPUMS 1900 Lookup: This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1900 datasets.

Section 3

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.

Historic data are scarce and often only exists in aggregate tables. The key advantage of the IPUMS 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 IPUMS 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 IPUMS 1900 census data was collected in June 1900. 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.

This application is a tool to find School Districts in Nebraska by County or Education Service Unit (ESU). Information for each School District includes: Address, District Name and Number, ESU Number, and Student Population. Search results may be filtered by School District Name and ESU. School District and ESU information are derived from Tax Unit Group (TUG) parcel data. The parcels used herein reflect County assessor data at the end of the 2018 calendar year.

List of male and female baby names in South Australia from 1944 to 2024. The annual data for baby names is published January/February each year.

USA Census Block Groups (CBG) for Urban Search and Rescue. This layer can be used for search segment planning. Block groups generally contain between 600 and 5,000 people and the boundaries generally follow existing roads and waterways. The field segment_designation is the last 6 digits of the unique identifier and matches the field in the SARCOP Segment layer.Data download date: August 12, 2021Census tables: P1, P2, P3, P4, H1, P5, HeaderDownloaded from: Census FTP siteProcessing Notes:Data was downloaded from the U.S. Census Bureau FTP site, imported into SAS format and joined to the 2020 TIGER boundaries. Boundaries are sourced from the 2020 TIGER/Line Geodatabases. Boundaries have been projected into Web Mercator and each attribute has been given a clear descriptive alias name. No alterations have been made to the vertices of the data.Each attribute maintains it's specified name from Census, but also has a descriptive alias name and long description derived from the technical documentation provided by the Census. For a detailed list of the attributes contained in this layer, view the Data tab and select "Fields". The following alterations have been made to the tabular data:Joined all tables to create one wide attribute table:P1 - RaceP2 - Hispanic or Latino, and not Hispanic or Latino by RaceP3 - Race for the Population 18 Years and OverP4 - Hispanic or Latino, and not Hispanic or Latino by Race for the Population 18 Years and OverH1 - Occupancy Status (Housing)P5 - Group Quarters Population by Group Quarters Type (correctional institutions, juvenile facilities, nursing facilities/skilled nursing, college/university student housing, military quarters, etc.)HeaderAfter joining, dropped fields: FILEID, STUSAB, CHARITER, CIFSN, LOGRECNO, GEOVAR, GEOCOMP, LSADC, and BLOCK.GEOCOMP was renamed to GEOID and moved be the first column in the table, the original GEOID was dropped.Placeholder fields for future legislative districts have been dropped: CD118, CD119, CD120, CD121, SLDU22, SLDU24, SLDU26, SLDU28, SLDL22, SLDL24 SLDL26, SLDL28.P0020001 was dropped, as it is duplicative of P0010001. Similarly, P0040001 was dropped, as it is duplicative of P0030001.In addition to calculated fields, County_Name and State_Name were added.The following calculated fields have been added (see long field descriptions in the Data tab for formulas used): PCT_P0030001: Percent of Population 18 Years and OverPCT_P0020002: Percent Hispanic or LatinoPCT_P0020005: Percent White alone, not Hispanic or LatinoPCT_P0020006: Percent Black or African American alone, not Hispanic or LatinoPCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or LatinoPCT_P0020008: Percent Asian alone, Not Hispanic or LatinoPCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or LatinoPCT_P0020010: Percent Some Other Race alone, not Hispanic or LatinoPCT_P0020011: Percent Population of Two or More Races, not Hispanic or LatinoPCT_H0010002: Percent of Housing Units that are OccupiedPCT_H0010003: Percent of Housing Units that are VacantPlease note these percentages might look strange at the individual block group level, since this data has been protected using differential privacy.* *To protect the privacy and confidentiality of respondents, data has been protected using differential privacy techniques by the U.S. Census Bureau. This means that some individual block groups will have values that are inconsistent or improbable. However, when aggregated up, these issues become minimized. The pop-up on this layer uses Arcade to display aggregated values for the surrounding area rather than values for the block group itself.Download Census redistricting data in this layer as a file geodatabase.Additional links:U.S. Census BureauU.S. Census Bureau Decennial CensusAbout the 2020 Census2020 Census2020 Census data qualityDecennial Census P.L. 94-171 Redistricting Data Program

This dataset represents the popular last names in the United States for White.

The 1881 Canadain Census gives information about the individual's given name, sex, age, marital status, place of birth, religion, occupation and infirmities.

In the first half of 2024, Nikodem was the most common name for a newborn child in Poland, with over 3 thousand registrations. Next were Jan, Aleksander, and Anton with over two thousand registrations each.