The data (name, year of birth, sex, state, and number) are from a 100 percent sample of Social Security card applications starting with 1910. National data is in another dataset.

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

In order to facilitate the anonymisation of data, this list of first names and surnames was extracted from the SIRENE database of INSEE.

For each first name and surname, the number of appearances is indicated.

ATTENTION: No content check is done, and these lists may contain anomalies present in the original database!

Context

Cultural diversity in the U.S. has led to great variations in names and naming traditions and names have been used to express creativity, personality, cultural identity, and values. Source: https://en.wikipedia.org/wiki/Naming_in_the_United_States

Content

This public dataset was created by the Social Security Administration and contains all names from Social Security card applications for births that occurred in the United States after 1879. Note that many people born before 1937 never applied for a Social Security card, so their names are not included in this data. For others who did apply, records may not show the place of birth, and again their names are not included in the data.

All data are from a 100% sample of records on Social Security card applications as of the end of February 2015. To safeguard privacy, the Social Security Administration restricts names to those with at least 5 occurrences.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:usa_names

https://cloud.google.com/bigquery/public-data/usa-names

Dataset Source: Data.gov. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @dcp from Unplash.

Inspiration

What are the most common names?

What are the most common female names?

Are there more female or male names?

Female names by a wide margin?

This dataset was came from of my curiosity about baby names, being a new father myself. Luckily, the SSA has data for this. If you're naming your baby, this might come in handy! And if you're looking to explore a relatively simple dataset, this may intrigue you. Enjoy!

https://www.ssa.gov/oact/babynames/limits.html

The link above is where the dataset is sourced from. Please read through it to check the limitations and restrictions of the dataset.

The dataset is already processed. The original data was found in multiple ".txt" files and had to be parsed to form it into a single dataset.

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.

Abstract

Motivation: creating challenging dataset for testing Named-Entity

Linking. The Namesakes dataset consists of three closely related datasets: Entities, News and Backlinks. Entities were collected as Wikipedia text chunks corresponding to highly ambiguous entity names. The News were collected as random news text chunks, containing mentions that either belong to the Entities dataset or can be easily confused with them. Backlinks were obtained from Wikipedia dump data with intention to have mentions linked to the entities of the Entity dataset. The Entities and News are human-labeled, resolving the mentions of the entities.Methods

Entities were collected as Wikipedia 

text chunks corresponding to highly ambiguous entity names: the most popular people names, the most popular locations, and organizations with name ambiguity. In each Entities text chunk, the named entities with the name similar to the chunk Wikipedia page name are labeled. For labeling, these entities were suggested to human annotators (odetta.ai) to tag as "Same" (same as the page entity) or "Other". The labeling was done by 6 experienced annotators that passed through a preliminary trial task. The only accepted tags are the tags assigned in agreement by not less than 5 annotators, and then passed through reconciliation with an experienced reconciliator.

The News were collected as random news text chunks, containing mentions which either belong to the Entities dataset or can be easily confused with them. In each News text chunk one mention was selected for labeling, and 3-10 Wikipedia pages from Entities were suggested as the labels for an annotator to choose from. The labeling was done by 3 experienced annotators (odetta.ai), after the annotators passed a preliminary trial task. The results were reconciled by an experienced reconciliator. All the labeling was done using Lighttag (lighttag.io).

Backlinks were obtained from Wikipedia dump data (dumps.wikimedia.org/enwiki/20210701) with intention to have mentions linked to the entities of the Entity dataset. The backlinks were filtered to leave only mentions in a good quality text; each text was cut 1000 characters after the last mention.

Usage NotesEntities:

File: Namesakes_entities.jsonl The Entities dataset consists of 4148 Wikipedia text chunks containing human-tagged mentions of entities. Each mention is tagged either as "Same" (meaning that the mention is of this Wikipedia page entity), or "Other" (meaning that the mention is of some other entity, just having the same or similar name). The Entities dataset is a jsonl list, each item is a dictionary with the following keys and values: Key: ‘pagename’: page name of the Wikipedia page. Key ‘pageid’: page id of the Wikipedia page. Key ‘title’: title of the Wikipedia page. Key ‘url’: URL of the Wikipedia page. Key ‘text’: The text chunk from the Wikipedia page. Key ‘entities’: list of the mentions in the page text, each entity is represented by a dictionary with the keys: Key 'text': the mention as a string from the page text. Key ‘start’: start character position of the entity in the text. Key ‘end’: end (one-past-last) character position of the entity in the text. Key ‘tag’: annotation tag given as a string - either ‘Same’ or ‘Other’.

News: File: Namesakes_news.jsonl The News dataset consists of 1000 news text chunks, each one with a single annotated entity mention. The annotation either points to the corresponding entity from the Entities dataset (if the mention is of that entity), or indicates that the mentioned entity does not belong to the Entities dataset. The News dataset is a jsonl list, each item is a dictionary with the following keys and values: Key ‘id_text’: Id of the sample. Key ‘text’: The text chunk. Key ‘urls’: List of URLs of wikipedia entities suggested to labelers for identification of the entity mentioned in the text. Key ‘entity’: a dictionary describing the annotated entity mention in the text: Key 'text': the mention as a string found by an NER model in the text. Key ‘start’: start character position of the mention in the text. Key ‘end’: end (one-past-last) character position of the mention in the text. Key 'tag': This key exists only if the mentioned entity is annotated as belonging to the Entities dataset - if so, the value is a dictionary identifying the Wikipedia page assigned by annotators to the mentioned entity: Key ‘pageid’: Wikipedia page id. Key ‘pagetitle’: page title. Key 'url': page URL.

Backlinks dataset: The Backlinks dataset consists of two parts: dictionary Entity-to-Backlinks and Backlinks documents. The dictionary points to backlinks for each entity of the Entity dataset (if any backlinks exist for the entity). The Backlinks documents are the backlinks Wikipedia text chunks with identified mentions of the entities from the Entities dataset.

Each mention is identified by surrounded double square brackets, e.g. "Muir built a small cabin along [[Yosemite Creek]].". However, if the mention differs from the exact entity name, the double square brackets wrap both the exact name and, separated by '|', the mention string to the right, for example: "Muir also spent time with photographer [[Carleton E. Watkins | Carleton Watkins]] and studied his photographs of Yosemite.".

The Entity-to-Backlinks is a jsonl with 1527 items. File: Namesakes_backlinks_entities.jsonl Each item is a tuple: Entity name. Entity Wikipedia page id. Backlinks ids: a list of pageids of backlink documents.

The Backlinks documents is a jsonl with 26903 items. File: Namesakes_backlinks_texts.jsonl Each item is a dictionary: Key ‘pageid’: Id of the Wikipedia page. Key ‘title’: Title of the Wikipedia page. Key 'content': Text chunk from the Wikipedia page, with all mentions in the double brackets; the text is cut 1000 characters after the last mention, the cut is denoted as '...[CUT]'. Key 'mentions': List of the mentions from the text, for convenience. Each mention is a tuple: Entity name. Entity Wikipedia page id. Sorted list of all character indexes at which the mention occurrences start in the text.

Search for a business by name. You can obtain business information and then proceed to purchase a certificate of good standing or other documents. The purpose of this search is simply to determine whether a company/entity exists and to provide basic information on the company/entity.

The Geographic Names Information System (GNIS) was developed by the United States Geological Survey (USGS) to meet major national needs regarding geographic names and their standardization and dissemination. This dataset consists of standard report files written from the National Geographic Names Data Base, one of five data bases maintained in the GNIS. A standard format data file containing Michigan place names and geographic features such as towns, schools, reservoirs, parks, streams, valleys, springs and ridges is accompanied by a file that provides a Cross-Reference to USGS 7.5 x 7.5 minute quadrangle maps for each feature. The records in the data files are organized alphabetically by place or feature name. The other variables available in the dataset include: Federal Information Processing Standard (FIPS) state/county codes, Geographic Coordinates -- latitude and longitude to degrees, minutes, and seconds followed by a single digit alpha directional character, and a GNIS Map Code that can be used with the Cross-Reference file to provide the name of the 7.5 x 7.5 minute quadrangle map that contains that geographic feature.

People of different countries, have different names in different languages. This dataset, consists a collection of names of people in different languages. I downloaded this data from PyTorch's tutorial on Generating Character Level RNN. And made a notebook elaborating everything I Learnt about RNN's.

The first names file contains data on the first names attributed to children born in France since 1900. These data are available at the level of France and by department. The files available for download list births and not living people in a given year. They are available in two formats (DBASE and CSV). To use these large files, it is recommended to use a database manager or statistical software. The file at the national level can be opened from some spreadsheets. The file at the departmental level is however too large (3.8 million lines) to be consulted with a spreadsheet, so it is proposed in a lighter version with births since 2000 only. The data can be accessed in: - a national data file containing the first names attributed to children born in France between 1900 and 2022 (data before 2012 relate only to France outside Mayotte) and the numbers by sex associated with each first name; - a departmental data file containing the same information at the department of birth level; - a lighter data file that contains information at the department level of birth since the year 2000.

This database is part of the ArabLEX set of data which consists of the Database of Arabic General Vocabulary (DAG), Database of Arabic Place Names (DAP), Database of Foreign Names in Arabic (DAF) and Database of Arab Names (DAN) available from ELRA under references, respectively, ELRA-L0131, ELRA-M0105, ELRA-M0106 and ELRA-M0107.With over 218 million forms based on 100,000 lemmas, this full-form database covers Arab personal names (both given names and surnames) in both Arabic and English and contains a rich set of romanized name variants for each name with a variety of supplementary information such as gender, name type and frequency statistics. This comprehensive lexicon (over 6.4 million variants) contains precise phonemic transcriptions and vocalized Arabic for all inflected and cliticized forms for each name.This database is provided with three options: 1) proclitics, 2) phonetic information (CARS) and 3) orthographic variants. Subsets excluding some of the three proposed options may be provided upon demand. CARS is an accurate phonemic transcription. Optionally, phonetic transcriptions, IPA and/or SAMPA, can be provided, fine tuned to a customer's specifications.Quantity and size: 218,215,875 lines / 32,659 MB (31.9 GB)File format: flat TSV text filesSamples and a specifications document available upon request.

Dataset

This dataset was created by medahmed krichen

Contents

Database of Irish Place Names --> --> External Link--> --> -->

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.

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

Baby Names from Social Security Card Applications-National Level Data. The data (name, year of birth, sex and number) are from a 100 percent sample of Social Security card applications after 1879.

This Dataset was queried through Bigquery using SQL from a much larger database as listed below. I managed to extract the Generation "Gen z" which is categorized usually from 1995 to 2012. It can be used for many types of cool visualization projects. Hope you all enjoy exploring this dataset The table contains the number of applicants for a Social Security card by year of birth and sex. The number of such applicants is restricted to U.S. births where the year of birth, sex, and State of birth (50 States and the District of Columbia) are known, and where the given name is at least 2 characters long.

source: http://www.ssa.gov/OACT/babynames/limits.html