This dataset contains counts of live births for California as a whole based on information entered on birth certificates. Final counts are derived from static data and include out of state births to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

This dataset contains counts of live births for California counties based on information entered on birth certificates. Final counts are derived from static data and include out of state births to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

Historical chart and dataset showing U.S. birth rate by year from 1950 to 2025.

Connecticut's Birth to Three System (B23) supports families with infants and toddlers that have developmental delays to learn new ways to make everyday activities enhance the child's development. Birth to Three is administered pursuant to Part C of the Individuals with Disabilities Education Act (IDEA). Once families with children below age 3 are referred, the child's development is evaluated for eligibility, and if eligible the family can receive supports until the child no longer has delays or until the child turns age 3. Because an infant can be referred within days of being born, a family may be enrolled for almost three full years. Connecticut's Birth to Three System publishes data annually by the fiscal and calendar year and longitudinally by birth cohort. CTData.org carries both sets of data, here and in 'Birth To Three Annual Data'. Birth cohort data looks at all children born in a particular year and tracks whether the family received B23 support. For example, the latest full year available in this dataset is for those children born in 2013 since they turned age 3 sometime in 2016. The 2013 data will tell you how many children there were whose families received support at some point during the first three years of the child's life. CTData calculates several indicators using total number of births in a town. This provides users with a general idea of the relative number of children in the community eligible for services. Using births is not perfect since families move in and out of town so it should not be used as an exact figure but as a general reference point. Below are how the indicators are calculated: % Referrals = Number referred divided by total number of births % Evaluations = Number evaluated divided by total number of births % Eligible = Number eligible divided by total number of births % Individual Family Service Plans (IFSP) = Number with IFSP divided by total number of births % Served = Number served divided by total number of births % Exited to Early Childhood Special Education = Number exited to early childhood special education divided by total number of births 'Referred that are Evaluated' represents the percent of children that were evaluated out of the total number of children referred to the Birth to Three System. 'Evaluated that are Eligible' represents the percent of children who were deemed eligible out of the total number of children that were evaluated. 'Eligible that Recieve IFSP' represents the percent of children whose family recieved an Individual Family Service Plan out of the total number of eligible children.

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...

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_6150f21b0892b3fdde546d2a1af2af82/view

The annual list of first names of newborns is a simple and popular dataset. These data, from the register of civil status, shall contain the following essential data: sex of the newborn, first name of the newborn, number of occurrences of the first name for the corresponding year, year of survey. The dataset consists of the list of first names of children born in Nancy since 2016, in CSV format, with the number of occurrences of each given name, classified by year and sex.

The first names declared below an occurrence of five are not published, with a view to protecting personal data. The standardisation of this dataset follows the recommendations of Opendata France following the work around the Common Socle des Data Locales.

Definition of headers COLL_NOM: name of the municipality COLL_INSEE: Insee code of the municipality where the first names are registered in the civil status of the place of birth. Note that the place of birth may be different from the place of residence of the parents. CHILD_SEX: Gender corresponding to first name: M or F respectively for men or women CHILD_PRENOM: first name of new born(s) recorded as first name in the civil status documents of the corresponding year. NUMBER_OCCURENCES: occurrence of first name YEAR: year of birth

Total births reported to the City of Nancy 2018 Total number of births: 5135 Total number of births of girls: 2692 Total number of births of boys: 2443

2017 Total number of births: 5483 Total number of births of girls: 2704 Total number of births of boys: 2779

2016 Total number of births: 5544 Total number of births of girls: 2692 Total number of births of boys: 2852

ANC

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.

Number and percentage of live births, by weeks of gestation and sex of the newborn, 2000 to most recent year.

This dataset includes crude birth rates and general fertility rates in the United States since 1909. The number of states in the reporting area differ historically. In 1915 (when the birth registration area was established), 10 states and the District of Columbia reported births; by 1933, 48 states and the District of Columbia were reporting births, with the last two states, Alaska and Hawaii, added to the registration area in 1959 and 1960, when these regions gained statehood. Reporting area information is detailed in references 1 and 2 below. Trend lines for 1909–1958 are based on live births adjusted for under-registration; beginning with 1959, trend lines are based on registered live births. 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 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. 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. 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. 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. 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. 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 is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/. Infant Mortality is defined as the number of deaths in infants under one year of age per 1,000 live births. Infant mortality is often used as an indicator to measure the health and well-being of a community, because factors affecting the health of entire populations can also impact the mortality rate of infants. Although California’s infant mortality rate is better than the national average, there are significant disparities, with African American babies dying at more than twice the rate of other groups. Data are from the Birth Cohort Files. The infant mortality indicator computed from the birth cohort file comprises birth certificate information on all births that occur in a calendar year (denominator) plus death certificate information linked to the birth certificate for those infants who were born in that year but subsequently died within 12 months of birth (numerator). Studies of infant mortality that are based on information from death certificates alone have been found to underestimate infant death rates for infants of all race/ethnic groups and especially for certain race/ethnic groups, due to problems such as confusion about event registration requirements, incomplete data, and transfers of newborns from one facility to another for medical care. Note there is a separate data table "Infant Mortality by Race/Ethnicity" which is based on death records only, which is more timely but less accurate than the Birth Cohort File. Single year shown to provide state-level data and county totals for the most recent year. Numerator: Infants deaths (under age 1 year). Denominator: Live births occurring to California state residents. Multiple years aggregated to allow for stratification at the county level. For this indicator, race/ethnicity is based on the birth certificate information, which records the race/ethnicity of the mother. The mother can “decline to state”; this is considered to be a valid response. These responses are not displayed on the indicator visualization.

Births rates across Lake County, Illinois by ZIP Code. Explanation of field attributes: LBW - Low birth weight is defined as a birth where the baby weighs less than 2,500 grams. This is a percent. Preterm - Preterm birth is defined as a birth that occur before 37 weeks of pregnancy. This is a percent. Teen Birth – Teen births are defined as women aged 15 to 19 years who give birth. This is a rate. Birth Rate – Birth rate is defined as the number of live births per 1,000 populations. 1st Trimester of Care – 1st Trimester of care refers to the doctor’s visits and care provided during the first 13 weeks of pregnancy. This is a percent.

US Social Security applications are a great way to track trends in how babies born in the US are named.

Data.gov releases two datasets that are helplful for this: one at the national level and another at the state level. Note that only names with at least 5 babies born in the same year (/ state) are included in this dataset for privacy.

I've taken the raw files here and combined/normalized them into two CSV files (one for each dataset) as well as a SQLite database with two equivalently-defined tables. The code that did these transformations is available here.

New to data exploration in R? Take the free, interactive DataCamp course, "Data Exploration With Kaggle Scripts," to learn the basics of visualizing data with ggplot. You'll also create your first Kaggle Scripts along the way.

This is a publication on maternity activity in English NHS hospitals. This report examines data relating to delivery and birth episodes in 2023-24, and the booking appointments for these deliveries. This annual publication covers the financial year ending March 2024. Data is included from both the Hospital Episodes Statistics (HES) data warehouse and the Maternity Services Data Set (MSDS). HES contains records of all admissions, appointments and attendances for patients admitted to NHS hospitals in England. The HES data used in this publication are called 'delivery episodes'. The MSDS collects records of each stage of the maternity service care pathway in NHS-funded maternity services, and includes information not recorded in HES. The MSDS is a maturing, national-level dataset. In April 2019, the MSDS transitioned to a new version of the dataset. This version, MSDS v2.0, is an update that introduced a new structure and content - including clinical terminology, in order to meet current clinical practice and incorporate new requirements. It is designed to meet requirements that resulted from the National Maternity Review, which led to the publication of the Better Births report in February 2016. This is the fifth publication of data from MSDS v2.0 and data from 2019-20 onwards is not directly comparable to data from previous years. This publication shows the number of HES delivery episodes during the period, with a number of breakdowns including by method of onset of labour, delivery method and place of delivery. It also shows the number of MSDS deliveries recorded during the period, with a breakdown for the mother's smoking status at the booking appointment by age group. It also provides counts of live born term babies with breakdowns for the general condition of newborns (via Apgar scores), skin-to-skin contact and baby's first feed type - all immediately after birth. There is also data available in a separate file on breastfeeding at 6 to 8 weeks. For the first time information on 'Smoking at Time of Delivery' has been presented using annual data from the MSDS. This includes national data broken down by maternal age, ethnicity and deprivation. From 2025/2026, MSDS will become the official source of 'Smoking at Time of Delivery' information and will replace the historic 'Smoking at Time of Delivery' data which is to become retired. We are currently undergoing dual collection and reporting on a quarterly basis for 2024/25 to help users compare information from the two sources. We are working with data submitters to help reconcile any discrepancies at a local level before any close down activities begin. A link to the dual reporting in the SATOD publication series can be found in the links below. Information on how all measures are constructed can be found in the HES Metadata and MSDS Metadata files provided below. In this publication we have also included an interactive Power BI dashboard to enable users to explore key NHS Maternity Statistics measures. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care. This report will also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. Any feedback on this publication or dashboard can be provided to enquiries@nhsdigital.nhs.uk, under the subject “NHS Maternity Statistics”.

This dataset contains counts of live births to California residents by ZIP Code based on information entered on birth certificates. Final counts are derived from static data and include out-of-state births to California residents. The data tables include births to residents of California by ZIP Code of residence (by residence).

Note that ZIP Codes are intended for mail delivery routing and do not represent geographic regions. ZIP Codes are subject to change over time and may not represent the same locations between different time periods. All ZIP Codes in the list of California ZIP Codes used for validation are included for all years, but this does not mean that the ZIP Code was in use at that time.

This dataset contains percent preterm and very preterm live births by race/ethnic group of mother. Preterm births are all live births less than 37 weeks of gestation. Very preterm births are all live births less than 32 weeks of gestation. Important growth and development occur throughout pregnancy, especially in the final months and weeks. There is a higher risk of serious disability or death the earlier a baby is born. Gestational age is based on obstetric estimate at delivery (OE). Data includes births with gestational age of 17-47 weeks. Note: The race and ethnic groups in this table utilize eight mutually exclusive race and ethnicity categories. These categories are Hispanic and the following Non-Hispanic categories of Multi-Race, African-American, American Indian (includes Eskimo and Aleut), Asian, Pacific Islander (includes Hawaiian), White (includes Other race) and Unknown (includes refused to state and missing).

Data should not be compared to other data where gestational age is based on the date of last normal menses (LMP) and not OE. The National Center for Health Statistics recently transitioned to using an OE-based gestational age measure due to increasing evidence of its greater validity compared with the LMP-based measure. (http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_05.pdf)

Birth to Three reports the number of Connecticut Birth to Three System children, per child's interaction with the system, by Calendar and Fiscal Year.

Number and percentage of live births, by marital status of mother, 1991 to most recent year.

This is a publication on maternity activity in English NHS hospitals. This report examines data relating to delivery and birth episodes in 2022-23, and the booking appointments for these deliveries. This annual publication covers the financial year ending March 2023. Data is included from both the Hospital Episodes Statistics (HES) data warehouse and the Maternity Services Data Set (MSDS). HES contains records of all admissions, appointments and attendances for patients admitted to NHS hospitals in England. The HES data used in this publication are called 'delivery episodes'. The MSDS collects records of each stage of the maternity service care pathway in NHS-funded maternity services, and includes information not recorded in HES. The MSDS is a maturing, national-level dataset. In April 2019 the MSDS transitioned to a new version of the dataset. This version, MSDS v2.0, is an update that introduced a new structure and content - including clinical terminology, in order to meet current clinical practice and incorporate new requirements. It is designed to meet requirements that resulted from the National Maternity Review, which led to the publication of the Better Births report in February 2016. This is the fourth publication of data from MSDS v2.0 and data from 2019-20 onwards is not directly comparable to data from previous years. This publication shows the number of HES delivery episodes during the period, with a number of breakdowns including by method of onset of labour, delivery method and place of delivery. It also shows the number of MSDS deliveries recorded during the period, with breakdowns including the baby's first feed type, birthweight, place of birth, and breastfeeding activity; and the mothers' ethnicity and age at booking. There is also data available in a separate file on breastfeeding at 6 to 8 weeks. The count of Total Babies includes both live and still births, and previous changes to how Total Babies and Total Deliveries were calculated means that comparisons between 2019-20 MSDS data and later years should be made with care. Information on how all measures are constructed can be found in the HES Metadata and MSDS Metadata files provided below. In this publication we have also included an interactive Power BI dashboard to enable users to explore key NHS Maternity Statistics measures. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care. This report will also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. Any feedback on this publication or dashboard can be provided to enquiries@nhsdigital.nhs.uk, under the subject “NHS Maternity Statistics”.