US Naturalizations Statistics

Trends and statistics on US naturalizations from 1999 to 2017

By Throwback Thursday [source]

About this dataset

The dataset US Naturalizations 1999-2017 provides information on the naturalization process of immigrants in the United States during the period from 1999 to 2017. The dataset includes various features or columns, capturing valuable insights into trends and statistics related to immigrants becoming US citizens.

Firstly, there is a column that specifies the year in which each naturalization case occurred, allowing for analysis and comparison over time. Additionally, there is a column indicating the country of birth of each individual who went through the naturalization process. This information allows for an exploration of patterns and trends based on country of origin.

The dataset also includes columns providing details about gender and age groups. By examining the distribution of naturalized individuals across different genders and age ranges, one can gain insights into demographic patterns and changes in immigration over time.

Furthermore, this dataset features columns related to occupation and educational attainment. These variables contribute to understanding the socio-economic characteristics of immigrants who became US citizens. By analyzing occupational trends or educational levels among naturalized individuals, researchers can gain valuable knowledge regarding immigrant integration within various industries or sectors.

Moreover, this dataset contains data on whether an applicant had previous experience as a lawful permanent resident (LPR) before being granted US citizenship. This variable sheds light on pathways to citizenship among those who have already obtained legal status in the United States.

Finally, there are columns providing information about processing times for naturalized cases as well as any special exemptions granted under certain circumstances. These details offer insights into administrative aspects related to applicants' journeys towards acquiring US citizenship.

In summary, this comprehensive dataset offers a wide range of variables that capture important characteristics related to immigrants becoming US citizens between 1999 and 2017. Researchers can use this data to analyze trends based on year, country of origin, gender/age groups, occupation/education levels,and pathways to citizenship such as previous LPR status or special circumstances exemptions

How to use the dataset

• Understand the columns: Familiarize yourself with the different columns available in this dataset to comprehend the information it offers. The columns included are:

  • Year: The year of naturalization.
  • United States: The number of individuals naturalized within the United States.
  • Continents:
    • Africa: Number of individuals born in African countries who were naturalized.
    • Asia: Number of individuals born in Asian countries who were naturalized.
    • Europe: Number of individuals born in European countries who were naturalized.
    • North America (excluding Caribbean): Number of individuals born in North American countries (excluding Caribbean nations) who were naturalized.
    • Oceania: Number of individuals born in Oceanian countries who were naturalized, including Australia and New Zealand.
    • South America: Number of individuals born in South American countries who were naturalized.

• Overview by year: Analyze the total number of people being granted US citizenship over time by examining the United States column. Use statistical methods like mean, median, or mode to understand trends or identify any outliers or significant changes across specific years.

• Continent-specific analysis:

  a) Identify patterns among continents over time by examining each continent's respective column (Africa, Asia, Europe, etc.). Compare growth rates and determine any regions experiencing higher or lower rates compared to others.

  b) Determine which continent contributes most significantly to overall US immigration by calculating continent-wise percentages based on total immigrants for each year.

• Identify region-specific trends:

  a) Analyze immigration patterns within individual continents by dividing them further into specific regions or countries. For example, within Asia, you can examine trends for East Asia (China, Japan, South Korea), Southeast Asia (Vietnam, Philippines), or South Asia (India, Bangladesh).

  b) Perform comparative analysis between regions/countries to identify variations in immigration rates or any interesting factors influencing these variances. ...

This data has been collected by the Government of the United States of America to track the number of people who change their legal status (become permanent resident) through various means. The data represents the number of people who had changed their status in the fiscal year of 2021. It also represents both males and females from a very young age such as 5 years old all the way to 75 years and even older. Furthermore, This data provides an overview of immigration to the United States, with a breakdown by region and country of birth, as well as the type of immigration (adjustments of status and new arrivals) and the various categories of immigrants.

Legal permanent residents (LPRs) are foreign nationals who have been granted the right to reside permanently in the United States. LPRs are often referred to as simply 'immigrants,' but they are also known as 'permanent resident aliens' and 'green card holders.

This national survey was designed to collect information on police departmental policies and practices pertaining to the use of physical force--both deadly and less than lethal--by law enforcement officers. A further objective was to investigate the enforcement of these policies by examining the extent to which complaints of policy violations were reviewed and violations punished. Additionally, the survey sought to determine the extent to which departments kept records on the use of force, and to collect from those agencies that recorded this information data relating to how frequently officers used force, the characteristics of officers who did and did not have complaints filed against them, and the training of recruits on the appropriate use of force. The study also provides data on citizen complaints of excessive force, the disposition of those complaints, and litigation concerning allegations of excessive force. Additional variables provide agency size, demographic characteristics, and workload.

This dataset contains different collected datasets with crime data of many large cities. Below are the descriptions for each seperate dataset. Note: Dataset properties and column may differ from each other since the information was collected by the local police in different styles and situations.

1. Los Angeles

The Los Angeles dataset has the collected data on different crimes that happened in Los Angeles from 2000 up until May 2024. The columns are as follows:

• DR_NO - Division of Records Number: Official file number made up of a 2 digit year, area ID, and 5 digits

• Date Rptd - The date when the police found out about the crime

• Date OCC - The actual date of the crime

• Time OCC - In military time

• Area - The LAPD has 21 Community Police Stations referred to as Geographic Areas within the department. These Geographic Areas are sequentially numbered from 1-21.

• Area Name - The 21 Geographic Areas or Patrol Divisions are also given a name designation that references a landmark or the surrounding community that it is responsible for. For example 77th Street Division is located at the intersection of South Broadway and 77th Street, serving neighborhoods in South Los Angeles.

• Rpt Dist No - A four-digit code that represents a sub-area within a Geographic Area. All crime records reference the "RD" that it occurred in for statistical comparisons. Find LAPD Reporting Districts on the LA City GeoHub at http://geohub.lacity.org/datasets/c4f83909b81d4786aa8ba8a74a4b4db1_4

• Crm Cd - Indicates the crime committed. (Same as Crime Code 1)

• Crm Cd Desc - Defines the Crime Code provided.

• Mocodes - Modus Operandi: Activities associated with the suspect in commission of the crime.

• Vict Age - The age of the victim

• Vict Sex - The gender of the victim. They are as follows:

  • M - Male
  • F - Female
  • X - Unknown

• Vict Descent - Descent Code:

  • A - Other Asian
  • B - Black
  • C - Chinese
  • D - Cambodian
  • F - Filipino
  • G - Guamanian
  • H - Hispanic/Latin/Mexican
  • I - American Indian/Alaskan Native
  • J - Japanese
  • K - Korean
  • L - Laotian
  • O - Other
  • P - Pacific Islander
  • S - Samoan
  • U - Hawaiian
  • V - Vietnamese
  • W - White
  • X - Unknown
  • Z - Asian Indian

• Premis Cd - The type of structure, vehicle, or location where the crime took place.

• Premis Desc - Defines the Premise Code provided.

• Weapon Used Cd - The type of weapon used in the crime.

• Status - Status of the case. (IC is the default)

• Status Desc - Defines the Status Code provided.

• Crm Cd 1 - Indicates the crime committed. Crime Code 1 is the primary and most serious one. Crime Code 2, 3, and 4 are respectively less serious offenses. Lower crime class numbers are more serious.

• Crm Cd 2 - May contain a code for an additional crime, less serious than Crime Code 1.

• Crm Cd 3 - May contain a code for an additional crime, less serious than Crime Code 1.

• Crm Cd 4 - May contain a code for an additional crime, less serious than Crime Code 1.

• Location - Street address of crime incident rounded to the nearest hundred block to maintain anonymity.

• Cross Street - Cross Street of rounded Address

• LAT - Latitude

• LON - Longitude

This dataset has 28 columns and 944K rows. I hope you will find it useful. God bless you

1. Chicago

This dataset contains crime data on Chicago, from 2001 to present. The columns are as follows:

• ID - Unique Identifier for the record

• Case Number - The Chicago Police Department RD Number (Records Division Number), which is unique to the incident.

• Date - Date when the incident occurred. this is sometimes a best estimate.

• Block - The partially redacted address where the incident occurred, placing it on the same block as the actual address.

• IUCR - The Illinois Unifrom Crime Reporting code. This is directly linked to the Primary Type and Description. See the list of IUCR codes at https://data.cityofchicago.org/d/c7ck-438e..

• Primary Type - The primary description of the IUCR code.

• Description - The secondary description of the IUCR code, a subcategory of the primary description.

• Location Description - Description of the location where the incident occurred.

• Arrest - Indicates whether an arrest was made.

• Domestic - Indicates whether the incident was domestic-related as defined by the Illinois Domestic Violence Act.

• Beat - Indicates the beat where the incident occurred. A beat is the smallest police geographic area – each beat has a dedicated police beat car. Three to five beats make up a police sector, and three sectors make up a police district. The Chicago Police Department has 22 police districts. See the beats at https://data.cityofchicago.org/d/aerh-rz74.

• Distric...

About

Data from the State of California. From website:

Access raw State data files, databases, geographic data, and other data sources. Raw State data files can be reused by citizens and organizations for their own web applications and mashups.

Openness

Open. Effectively in the public domain. Terms of use page says:

In general, information presented on this web site, unless otherwise indicated, is considered in the public domain. It may be distributed or copied as permitted by law. However, the State does make use of copyrighted data (e.g., photographs) which may require additional permissions prior to your use. In order to use any information on this web site not owned or created by the State, you must seek permission directly from the owning (or holding) sources. The State shall have the unlimited right to use for any purpose, free of any charge, all information submitted via this site except those submissions made under separate legal contract. The State shall be free to use, for any purpose, any ideas, concepts, or techniques contained in information provided through this site.

ABSTRACT

Statements about building walls, deportation and denying services to undocumented immigrants made by the US president may induce fear in Latino populations and create barriers to their health care access. To assess the impact of these statements on undocumented Latino immigrants' (UDLI) and Latino legal residents/citizens' (LLRC) perceptions of safety and their presentations for emergency care, we conducted surveys of adult patients at three county emergency departments (EDs) in California from June 2017 to December 2018. Of 1,684 patients approached, 1,337 (79.4%) agreed to participate: 34.3% UDLI, 36.9% LLRC, and 29.8% non-Latino legal residents/citizens (NLRC). The vast majority of UDLI (95%), LLRC (94%), and NLRC (85%) had heard statements about immigrants by President Trump. Most UDLI (89%), LLRC (88%), and NLRC (87%) either thought that these measures were being enacted now or will be enacted in the future. Most UDLI and LLRC reported that these statements made them feel unsafe living in the US, 75% (95% CI 70 to 80%) and 51% (95% CI 47 to 56%), respectively. More UDLI reported that these statements made them afraid to come to the ED (24% 95% CI 20 to 28%) vs LLRC (4.4% [95% CI 3 to 7%]) and NLRC (3.5% [95% CI 2 to 6%]); 55% of UDLI with this fear stated it caused them to delay coming to the ED (median delay 2-3 days). The vast majority of patients in our California EDs have heard statements about immigrants by the US president, which have induced worry and safety concerns in both UDLI and LLRC patients. These statements may also act as a barrier to some UDLI's access of emergency care. Given California's sanctuary state status, these safety concerns and ED access fears may be greater in a nationwide population of Latinos.

Residence permits data collection refers to residence permits as any authorisation issued by the authorities of a Member State allowing a third-country national (non-EU citizen) to stay legally on its territory. These statistics cover also some specific cases in which the third-country nationals have the right to be move to and stay in other EU Member States.

Data is based on administrative sources¹, provided mainly by the Ministries of Interior or related Immigration Agencies. Data are generally disseminated in July in the year following the reference year, subject to data availability and data quality.

Residence permits statistics is based on http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32007R0862" target="_blank">Council Regulation (CE) No 862 of 11 July 2007 (Migration Statistics Regulation) as amended by the Regulation 2020/851 and it covers the following topics:

• the main RESPER data collection based on Article 6 of the Migration Statistics Regulation
  • First residence permits;
  • Residence permits issued on the occasion of changing the immigration status or reason to stay;
  • Residence permits valid at the end of the year;
  • Long-term residence permits valid at the end of the year;
  • Long-term permits issued during the year.

• Statistics collected on voluntary basis
  • Residence permits issued for family reunification with beneficiaries of protection status.

The definitions used for residence permits and other concepts (e.g. first permit) are presented in the section 3.4. Statistical concepts and definitions. The detailed data collection methodology is presented in Annex 9 of this metadata file.

Temporary protection status is considered of different administrative nature then the residence permits reported in RESPER data collection. Therefore, persons benefitting from temporary protection are not included in any of the Residence permits statistics. These persons are subject of another data collection on Temporary Protection (TP).

LEGAL FRAMEWORK

Residence data contain statistical information based on Article 6 of http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32007R0862" target="_blank">Council Regulation (CE) No 862 of 11 July 2007. This legal framework refers to the initial residence permits data collection with 2008 first reference period (e.g. first residence permits; change of immigration status or reason to stay; all valid residence permits in the end of the year and long-term residence permits valid in the end of the year) and it provides also a general framework for newer data collections based on specific European legal acts (e.g. statistics on EU Blue Cards and statistics on single permits) or provided on voluntary basis (e.g. residence permits issued for family reunification with beneficiaries of protection status).

Regulation 2020/851 amending Council Regulation (CE) No 862 of 11 July 2007 was recently implemented. The amendment introduced several changes to the statistics on Asylum and Managed Migration. Some data collections become mandatory starting with the 2021 reference period, while new statistics are subject to pilot studies for further assessing the feasibility of collecting these statistics.

RECENT DEVELOPMENTS

Starting with the 2021 reference period, there were several improvements in the data collection, including the methodological aspects. These changes were introduced through the implementation of Regulation 2020/851 amending Council Regulation (CE) No 862 of 11 July 2007. More details are available in the Annex 9.

Starting from 2025, the residence permits and EU directives data collection now includes six metadata files in total. Countries are required to submit six distinct files. For those that have not yet provided the updated six files, the previous metadata format, included in the annex of this metadata file (Annex 10), remains available as a reference.

INDICATORS

The indicators presented in the table 'Long-term residents among all non-EU citizens holding residence permits by citizenship on 31 December (%)' are produced within the framework of the pilot study related to the integration of migrants in the Member States, following the Zaragoza Declaration.

The Zaragoza Declaration, adopted in April 2010 by EU Ministers responsible for immigrant integration issues, and approved at the Justice and Home Affairs Council on 3-4 June 2010, called upon the Commission to undertake a pilot study to examine proposals for common integration indicators and to report on the availability and quality of the data from agreed harmonised sources necessary for the calculation of these indicators. In June 2010 the ministers agreed "to promote the launching of a pilot project with a view to the evaluation of integration policies, including examining the indicators and analysing the significance of the defined indicators taking into account the national contexts, the background of diverse migrant populations and different migration and integration policies of the Member States, and reporting on the availability and quality of the data from agreed harmonised sources necessary for the calculation of these indicators".

These indicators are produced on the basis of residence permit statistics collected by Eurostat on the basis of Article 6 of the Migration Statistics Regulation 862/2007. As a denominator data on the stock of all valid permits to stay at the end of each reporting year are used. As a numerator data on the stock of long-term residents are used. Two types of long term residents are distinguished in accordance with the residence permit statistics: EU long-term resident status (as regulated by the Council Directive 2003/109/EC) and the National long-term resident status (as regulated by the national legislation in the Member States).

DATA CONSISTENCY

The data providers should use the same methodological specifications provided by Eurostat and some collected tables from should be cross-consistent according to this methodology. However, consistency issues between tables exist due to some technical limitations (e.g. different data sources) or different methodology applied to each table (see the quality information from below or the national metadata files) or different point in time of producing each tables.

¹There are few exceptions referring to the situation in which the administrative registers cannot provide the required information and some estimations are made. For example, the statistics for the United Kingdom (2008-2019) use different data sources to those used in EU Member States and EFTA countries. For that reason, the statistics on residence permits published by Eurostat for UK may not be fully comparable with the statistics reported by other countries. Statistics for the United Kingdom are not based on records of residence permits issued (as the United Kingdom does not operate a system of residence permits), but instead relate to the numbers of arriving non-EU citizens permitted to enter the country under selected immigration categories. According to the United Kingdom authorities, data are estimated from a combination of information due to be published in the Home Office Statistical Bulletin 'Control of Immigration: Statistics, United Kingdom' and unpublished management information. The 'Other reasons' category includes: diplomat, consular officer treated as exempt from control; retired persons of independent means; all other passengers given limited leave to enter who are not included in any other category; non-asylum discretionary permissions. Another example is the data on stock of all valid residence permits for Denmark, see Annex 8 (Data quality of valid residence permits in Denmark).

Graph and download economic data for Infra-Annual Labor Statistics: Working-Age Population Total: From 15 to 64 Years for United States (LFWA64TTUSM647S) from Jan 1977 to Aug 2025 about working-age, 15 to 64 years, population, and USA.

The estimated population of unauthorized immigrants in the U.S. stands at around ** million people. Although the number has stabilized, the United States has seen a spike in migrant encounters in the last few years, with over * million cases registered by the U.S. Border Patrol in 2023. This is a slight decrease from the previous year, when there were over *** million cases registered. Due to its proximity and shared border, Mexico remains the leading country of origin for most undocumented immigrants in the U.S., with California and Texas being home to the majority.

Immigration and political division

Despite the majority of the population having immigrant roots, the topic of immigration in the U.S. remains one of the country’s longest-standing political debates. Support among Republicans for restrictive immigration has grown alongside Democratic support for open immigration. This growing divide has deepened the polarization between the two major political parties, stifling constructive dialogue and impeding meaningful reform efforts and as a result, has led to dissatisfaction from all sides. In addition to general immigration policy, feelings toward illegal immigration in the U.S. also vary widely. For some, it's seen as a significant threat to national security, cultural identity, and economic stability. This perspective often aligns with support for stringent measures like Trump's proposed border wall and increased enforcement efforts. On the other hand, there are those who are more sympathetic toward undocumented immigrants, as demonstrated by support for the Deferred Action for Childhood Arrivals (DACA) program.

This web map displays data from the voter registration database as the percent of registered voters by census tract in King County, Washington. The data for this web map is compiled from King County Elections voter registration data for the years 2013-2019. The total number of registered voters is based on the geo-location of the voter's registered address at the time of the general election for each year. The eligible voting population, age 18 and over, is based on the estimated population increase from the US Census Bureau and the Washington Office of Financial Management and was calculated as a projected 6 percent population increase for the years 2010-2013, 7 percent population increase for the years 2010-2014, 9 percent population increase for the years 2010-2015, 11 percent population increase for the years 2010-2016 & 2017, 14 percent population increase for the years 2010-2018 and 17 percent population increase for the years 2010-2019. The total population 18 and over in 2010 was 1,517,747 in King County, Washington. The percentage of registered voters represents the number of people who are registered to vote as compared to the eligible voting population, age 18 and over. The voter registration data by census tract was grouped into six percentage range estimates: 50% or below, 51-60%, 61-70%, 71-80%, 81-90% and 91% or above with an overall 84 percent registration rate. In the map the lighter colors represent a relatively low percentage range of voter registration and the darker colors represent a relatively high percentage range of voter registration. PDF maps of these data can be viewed at King County Elections downloadable voter registration maps. The 2019 General Election Voter Turnout layer is voter turnout data by historical precinct boundaries for the corresponding year. The data is grouped into six percentage ranges: 0-30%, 31-40%, 41-50% 51-60%, 61-70%, and 71-100%. The lighter colors represent lower turnout and the darker colors represent higher turnout. The King County Demographics Layer is census data for language, income, poverty, race and ethnicity at the census tract level and is based on the 2010-2014 American Community Survey 5 year Average provided by the United States Census Bureau. Since the data is based on a survey, they are considered to be estimates and should be used with that understanding. The demographic data sets were developed and are maintained by King County Staff to support the King County Equity and Social Justice program. Other data for this map is located in the King County GIS Spatial Data Catalog, where data is managed by the King County GIS Center, a multi-department enterprise GIS in King County, Washington. King County has nearly 1.3 million registered voters and is the largest jurisdiction in the United States to conduct all elections by mail. In the map you can view the percent of registered voters by census tract, compare registration within political districts, compare registration and demographic data, verify your voter registration or register to vote through a link to the VoteWA, Washington State Online Voter Registration web page.

This study was designed to assess the effects of the activities of the Guardian Angels on citizens' fear of crime, incidence of crime, and police officers' perceptions of the Guardian Angels. The data, which were collected in several large American cities, provide information useful for evaluating the activities of the Guardian Angels from the perspectives of transit riders, residents, merchants, and police officers. Respondents who were transit riders were asked to provide information on their knowledge of and contacts with the Angels, attitudes toward the group, feelings of safety on public transit, victimization experience, and demographic characteristics. Police officers were asked about their knowledge of the Angels, attitudes toward the group, opinions regarding the benefits and effectiveness of the group, and law enforcement experiences. Data for residents and merchants include demographic characteristics, general problems in the neighborhood, opinions regarding crime problems, crime prevention activities, fear of crime, knowledge of the Angels, attitudes toward the group, and victimization experiences.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined because of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard Census Bureau geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous.

This dataset contains information about NYC Business Solutions service, a service offered by the Department of Small Business Services (SBS) aimed at giving New Yorkers free services to start, operate and grow their businesses. Each row in the dataset represents the number of public housing residents on a Borough-level who receive or utilize this service.

For datasets related to other services provided to NYCHA residents, view the data collection “Services available to NYCHA Residents - Local Law 163”.

While we witness historic changes taking place in the conception and practice of citizenship, we know little about the political consequences it may bring. What are the effects of citizenship, as a status and a process, on political engagement? To gain leverage in addressing this question, we draw on citizenship categories that combine birthplace and the number of citizenship held. We compare US-born dual citizens to both naturalized-dual citizens and US-born mono citizens, which allows us to distinguish between the potential effects of socialization and the additional legal status. The study analyzes two large nationally representative samples, presenting the first look at dual citizens in the United States. Results indicate that among dual citizens, those born in the US tend to participate more in politics than immigrants who naturalized. Among US-born citizens, the political participation of dual and mono citizens varies depending on the type of political activity. The study contributes to theoretical discussions on the relationship between an evolving citizenry and democratic participation.

Success.ai offers a comprehensive, enterprise-ready B2B leads data solution, ideal for businesses seeking access to over 150 million verified employee profiles and 170 million work emails. Our data empowers organizations across industries to target key decision-makers, optimize recruitment, and fuel B2B marketing efforts. Whether you're looking for UK B2B data, B2B marketing data, or global B2B contact data, Success.ai provides the insights you need with pinpoint accuracy.

Tailored for B2B Sales, Marketing, Recruitment and more: Our B2B contact data and B2B email data solutions are designed to enhance your lead generation, sales, and recruitment efforts. Build hyper-targeted lists based on job title, industry, seniority, and geographic location. Whether you’re reaching mid-level professionals or C-suite executives, Success.ai delivers the data you need to connect with the right people.

API Features:

• Real-Time Updates: Our APIs deliver real-time updates, ensuring that the contact data your business relies on is always current and accurate.
• High Volume Handling: Designed to support up to 860k API calls per day, our system is built for scalability and responsiveness, catering to enterprises of all sizes.
• Flexible Integration: Easily integrate with CRM systems, marketing automation tools, and other enterprise applications to streamline your workflows and enhance productivity.

Key Categories Served: B2B sales leads – Identify decision-makers in key industries, B2B marketing data – Target professionals for your marketing campaigns, Recruitment data – Source top talent efficiently and reduce hiring times, CRM enrichment – Update and enhance your CRM with verified, updated data, Global reach – Coverage across 195 countries, including the United States, United Kingdom, Germany, India, Singapore, and more.

Global Coverage with Real-Time Accuracy: Success.ai’s dataset spans a wide range of industries such as technology, finance, healthcare, and manufacturing. With continuous real-time updates, your team can rely on the most accurate data available: 150M+ Employee Profiles: Access professional profiles worldwide with insights including full name, job title, seniority, and industry. 170M Verified Work Emails: Reach decision-makers directly with verified work emails, available across industries and geographies, including Singapore and UK B2B data. GDPR-Compliant: Our data is fully compliant with GDPR and other global privacy regulations, ensuring safe and legal use of B2B marketing data.

Key Data Points for Every Employee Profile: Every profile in Success.ai’s database includes over 20 critical data points, providing the information needed to power B2B sales and marketing campaigns: Full Name, Job Title, Company, Work Email, Location, Phone Number, LinkedIn Profile, Experience, Education, Technographic Data, Languages, Certifications, Industry, Publications & Awards.

Use Cases Across Industries: Success.ai’s B2B data solution is incredibly versatile and can support various enterprise use cases, including: B2B Marketing Campaigns: Reach high-value professionals in industries such as technology, finance, and healthcare. Enterprise Sales Outreach: Build targeted B2B contact lists to improve sales efforts and increase conversions. Talent Acquisition: Accelerate hiring by sourcing top talent with accurate and updated employee data, filtered by job title, industry, and location. Market Research: Gain insights into employment trends and company profiles to enrich market research. CRM Data Enrichment: Ensure your CRM stays accurate by integrating updated B2B contact data. Event Targeting: Create lists for webinars, conferences, and product launches by targeting professionals in key industries.

Use Cases for Success.ai's Contact Data - Targeted B2B Marketing: Create precise campaigns by targeting key professionals in industries like tech and finance. - Sales Outreach: Build focused sales lists of decision-makers and C-suite executives for faster deal cycles. - Recruiting Top Talent: Easily find and hire qualified professionals with updated employee profiles. - CRM Enrichment: Keep your CRM current with verified, accurate employee data. - Event Targeting: Create attendee lists for events by targeting relevant professionals in key sectors. - Market Research: Gain insights into employment trends and company profiles for better business decisions. - Executive Search: Source senior executives and leaders for headhunting and recruitment. - Partnership Building: Find the right companies and key people to develop strategic partnerships.

Why Choose Success.ai’s Employee Data? Success.ai is the top choice for enterprises looking for comprehensive and affordable B2B data solutions. Here’s why: Unmatched Accuracy: Our AI-powered validation process ensures 99% accuracy across all data points, resulting in higher engagement and fewer bounces. Global Scale: With 150M+ employee profiles and 170M veri...

Although the secret ballot has been secured as a legal matter in the United States, formal secrecy protections are not equivalent to convincing citizens that they may vote privately and without fear of reprisal. We present survey evidence that those who have not previously voted are particularly likely to voice doubts about the secrecy of the voting process. We then report results from a field experiment where we mailed information about protections of ballot secrecy to registered voters prior to the 2010 general election. Consistent with our survey data, we find that these letters increased turnout for registered citizens without records of previous turnout, but did not appear to influence the behavior of citizens who had previously voted. The increase in turnout of more than three percentage points (20%) for those without previous records of voting is notably larger than the effect of a standard get-out-the-vote mailing for this group. Overall, these results suggest that although the secret ballot is a long-standing institution in the United States, beliefs about this institution may not match the legal reality. Providing basic information about ballot secrecy can cause former non-voters to participate.

This dataset provides the locations and associated information for amateur radio operators in the United States and territories as registered in the FCC's Universal Licensing System (ULS). It includes call signs, operator class, region codes, and historical data on previous call signs and operator classes, excluding personal and sensitive information.

Out of 1,612,849 total ULS entity records, 1,589,052 were succesfuly geocoded (98.5%). Of these, 5,486 were geocoded at either the zip code or city level (0.35%). The average Tiger geocoder rating was 16.35, indicating a fairly reasonable accuracy.

Credits Federal Communications Commission (FCC)

Summary The dataset includes the following fields:

• unique_system_identifier: An identifier for the record. This column is joinable to the other tables in the FCC ULS downloads you can get from https://www.fcc.gov/uls/transactions/daily-weekly#weekly-files
• call_sign: The assigned call sign of the radio operator.
• operator_class: The license class of the operator.
• region_codee: The FCC region code the operator is associated with.
• previous_call_sign: Previous call sign, if applicable.
• previous_operator_class: Previous license class, if applicable.
• license_status: The current status of the license. A denotes active, C denotes cancelled, E denotes expired, T denoted terminated.
• effective_date: The date the current license status took effect.
• rating: A geocoding accuracy rating from the PostgreSQL TIGER geocoder, where 0 is the highest accuracy and higher values indicate less accuracy. All geocoding results are included. If the rating is 5000 level, it was geocoded at the zip code level by the Nominatum service. Otherwise, it was geocoded at the address level by the PostGIS Tiger geocoder.
• city: The city of the operator's registered address.
• state: The state of the operator's registered address.
• zip_code: The postal zip code of the operator's registered address.
• is_po_box: A boolean value indicating whether the address was a post office box or not.

Usage Notes Users are advised to verify data accuracy and relevance for their specific purposes.

Spatial Reference Coordinate Reference System: WGS 84 (EPSG: 4326)

Content

Congress Age

This folder contains the data behind the story Both Republicans And Democrats Have an Age Problem

congress-terms.csv has an entry for every member of congress who served at any point during a particular congress between January 1947 and Februrary 2014.

House membership data is from the @unitedstates project, with Congress meeting numbers added using code from GovTrack:

• https://github.com/unitedstates/congress-legislators
• https://github.com/govtrack/govtrack.us-web/blob/master/us.py#L93

Senate membership data is mostly from the New York Times Congress API combined with birthdays from @unitedstates. (It does not include the birthday of Sen. Elmer Thomas.)

In addition, we added an incumbent field to each record.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

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

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

Cover photo by Paolo Nicolello on Unsplash
Unsplash Images are distributed under a unique Unsplash License.