Explore the historical conflicts involving the United States from the 18th century to the present day with this comprehensive dataset. Derived from publicly available sources like Wikipedia, this dataset offers a detailed glimpse into the wars, conflicts, and military engagements the US has been involved in over the years.

The dataset comprises the following columns:

Conflict: Name of the conflict Century: Century in which the conflict occurred Year: Specific year of the conflict Location: Geographical location of the conflict Allies: Parties allied with the United States Opponent(s): Opposing forces faced by the United States Result for the United States and its Allies: Outcome of the conflict for the US and its allies Presidents of the United States: Presidents in office during the conflict The dataset presents a unique opportunity for historical analysis, military studies, and political research. Analysts can delve into patterns of alliances, examine the impact of conflicts on presidential legacies, and trace the evolution of US military engagements over time. Researchers interested in diplomacy, military strategy, and international relations will find this dataset invaluable for their studies.

Note: This dataset is provided for non-commercial use. Credits to Wikipedia for the primary data source.

Content

Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period.

Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010–2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011–2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates.

Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook for a more thorough clarification. https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf

Context

This is a dataset from the U.S. Census Bureau hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according the amount of data that is brought in. Explore the U.S. Census Bureau using Kaggle and all of the data sources available through the U.S. Census Bureau organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 2009-01-01

• Observation End : 2017-01-01

Acknowledgements

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

In the past four centuries, the population of the Thirteen Colonies and United States of America has grown from a recorded 350 people around the Jamestown colony in Virginia in 1610, to an estimated 346 million in 2025. While the fertility rate has now dropped well below replacement level, and the population is on track to go into a natural decline in the 2040s, projected high net immigration rates mean the population will continue growing well into the next century, crossing the 400 million mark in the 2070s. Indigenous population Early population figures for the Thirteen Colonies and United States come with certain caveats. Official records excluded the indigenous population, and they generally remained excluded until the late 1800s. In 1500, in the first decade of European colonization of the Americas, the native population living within the modern U.S. borders was believed to be around 1.9 million people. The spread of Old World diseases, such as smallpox, measles, and influenza, to biologically defenseless populations in the New World then wreaked havoc across the continent, often wiping out large portions of the population in areas that had not yet made contact with Europeans. By the time of Jamestown's founding in 1607, it is believed the native population within current U.S. borders had dropped by almost 60 percent. As the U.S. expanded, indigenous populations were largely still excluded from population figures as they were driven westward, however taxpaying Natives were included in the census from 1870 to 1890, before all were included thereafter. It should be noted that estimates for indigenous populations in the Americas vary significantly by source and time period. Migration and expansion fuels population growth The arrival of European settlers and African slaves was the key driver of population growth in North America in the 17th century. Settlers from Britain were the dominant group in the Thirteen Colonies, before settlers from elsewhere in Europe, particularly Germany and Ireland, made a large impact in the mid-19th century. By the end of the 19th century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. It is also estimated that almost 400,000 African slaves were transported directly across the Atlantic to mainland North America between 1500 and 1866 (although the importation of slaves was abolished in 1808). Blacks made up a much larger share of the population before slavery's abolition. Twentieth and twenty-first century The U.S. population has grown steadily since 1900, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. Since WWII, the U.S. has established itself as the world's foremost superpower, with the world's largest economy, and most powerful military. This growth in prosperity has been accompanied by increases in living standards, particularly through medical advances, infrastructure improvements, clean water accessibility. These have all contributed to higher infant and child survival rates, as well as an increase in life expectancy (doubling from roughly 40 to 80 years in the past 150 years), which have also played a large part in population growth. As fertility rates decline and increases in life expectancy slows, migration remains the largest factor in population growth. Since the 1960s, Latin America has now become the most common origin for migrants in the U.S., while immigration rates from Asia have also increased significantly. It remains to be seen how immigration restrictions of the current administration affect long-term population projections for the United States.

PLEASE NOTE: This is an index of a historical collection that contains words and phrases that may be offensive or harmful to individuals investigating these records. In order to preserve the objectivity and historical accuracy of the index, State Archives staff took what would today be considered archaic and offensive descriptions concerning race, ethnicity, and gender directly from the original court papers. For more information on appropriate description, please consult the Diversity Style Guide and Archives for Black Lives in Philadelphia: Anti-Racist Description Resources. The Litchfield County Court African Americans and Native Americans Collection is an artificial collection consisting of photocopies of cases involving persons of African descent and indigenous people from the Files and Papers by Subject series of Litchfield County Court records. This collection was created in order to highlight the lives and experiences of underrepresented groups in early America, and make them more easily accessible to researchers. Collection Overview The collection consists of records of 188 court cases involving either African Americans or Native Americans. A careful search of the Files for the Litchfield County Court discovered 165 on African Americans and 23 on Native Americans, about one third of the total that was found in Files for the New London County Court for the period up to the American Revolution. A couple of reasons exist for this vast difference in numbers. First, Litchfield County was organized much later than New London, one of Connecticut's four original counties. New London was the home of four of seven recognized tribes, was a trading center, and an area of much greater wealth. Second, minority population in the New London County region has been tracked and tabulated by Barbara Brown and James Rose in Black Roots of Southeastern Connecticut.1 Although this valuable work does not include all of Negro or Indian background, it provides a wonderful starting point and it has proven to be of some assistance in tracking down minorities in Litchfield County. In most instances, however, identification is based upon language in the documents and knowledge of surnames or first names.2 Neither surname nor first name provides an invariably reliable guide so it is possible that some minorities have been missed and some persons included that are erroneous. In thirteen of 188 court cases, the person of African or Native American background cannot be identified even by first name. He or she is noted as "my Negro," a slave girl, or an Indian. In twenty-three lawsuits, a person with a first name is identified as a Negro, as an Indian in two other cases, and Mulatto in one. In the remaining 151 cases, a least one African American or Native American is identified by complete name.3 Thirteen surnames recur in three or more cases.4 A total of seventy surnames, some with more than one spelling, are represented in the records. The Jacklin surname appears most frequently represented in the records. Seven different Jacklins are found in eighteen cases, two for debt and the remaining sixteen for more serious crimes like assault, breach of peace, keeping a bawdy house, and trespass.5 Ten cases concern Cuff Kingsbury of Canaan between 1808 and 1812, all involving debts against Kingsbury and the attempts of plaintiffs to secure writs of execution against him. Cyrus, Daniel, Ebenezer, Jude, Luke, Martin, Nathaniel, Pomp, Titus, and William Freeman are found in nine cases, some for debt, others for theft, and one concerning a petition to appoint a guardian for aged and incompe

There were almost 700 thousand slaves in the U.S. in 1790, which equated to approximately 18 percent of the total population, or roughly one in six people. By 1860, the final census taken before the American Civil War, there were four million slaves in the South, compared with less than 500,000 free Black Americans in all of the U.S.. Of the 4.4 million Blacks in the U.S. before the war, almost four million of these people were held as slaves; meaning that for all African Americans living in the US in 1860, there was an 89 percent* chance that they lived in slavery. A brief history Trans-Atlantic slavery began in the early 16th century, when the Portuguese and Spanish forcefully brought enslaved Africans to the New World. The British Empire introduced slavery to North America on a large scale, and the economy of the British colonies there depended on slave labor, particularly regarding cotton, sugar, and tobacco output. In the seventeenth and eighteenth century the number of slaves being brought to the Americas increased exponentially, and at the time of American independence it was legal in all thirteen colonies. Although slavery became increasingly prohibited in the north, the number of slaves remained high during this time as they were simply relocated or sold from the north to the south. It is also important to remember that the children of slaves were also viewed as property, and were overwhelmingly born into a life of slavery. Abolition and the American Civil War In the years that followed independence, the Northern States gradually prohibited slavery, it was officially abolished there by 1805, and the importation of slave labor was prohibited nationwide from 1808 (although both still existed in practice after this). Business owners in the Southern States however depended on slave labor in order to meet the demand of their rapidly expanding industries, and the issue of slavery continued to polarize American society in the decades to come. This culminated in the election of President Abraham Lincoln in 1860, who promised to prohibit slavery in the newly acquired territories to the west, leading to the American Civil War from 1861 to 1865. Although the Confederacy (south) took the upper hand in much of the early stages of the war, the strength in numbers of the northern states including many free, Black men, eventually resulted in a victory for the Union (north), and the nationwide abolishment of slavery with the Thirteenth Amendment in 1865. Legacy In total, an estimated twelve to thirteen million Africans were transported to the Americas as slaves, and this does not include the high number who did not survive the journey (which was as high as 23 percent in some years). In the 150 years since the abolition of slavery in the US, the African-American community have continuously campaigned for equal rights and opportunities that were not afforded to them along with freedom. The most prominent themes have been the Civil Rights Movement, voter suppression, mass incarceration, and the relationship between the police and the African-American community.

Live city rankings with ignore politics political preference weighting applied. Showing 1-50 of 386 cities.

The Rise of Home Schooling

Data from The Post's analysis of home-schooling enrollment across the US

This repository shares data hand-collected by The Washington Post from individual school districts and states as a whole regarding home-school enrollment from 2017-18 through 2022-23. The data is what is behind this story published on Oct. 31, 2023 by Peter Jamison, Laura Meckler, Prayag Gordy, Clara Ence Morse and Chris Alcantara.

There are two separate data files, both of which cover the same time period: - home_school_district.csv - home_school_state.csv

There is also a data dictionary explaining each file.

Methodology

To measure the growth of home schooling during the pandemic, The Washington Post collected home-school student counts from 6,738 school districts. Together with students from The Washington Post Investigative Reporting Workshop practicum at American University, reporters trawled state websites, contacted education officials in all 50 states and the District of Columbia and submitted multiple public records requests for an annual count of home-schoolers from the 2017-18 school year through 2022-23. The Post ultimately collected data for all public school districts in 29 states and D.C. In all, The Post gathered data from states representing 61% of the American school-age population.

Three states — Pennsylvania, Rhode Island and Tennessee — have not published the number of home-schoolers in 2022-23, and Maine only shared district-level data starting with the 2020-21 school year. In seven states, The Post was unable to obtain usable home-school enrollment figures: In Arizona, Nevada and Oregon, only new home-school registrations are tracked annually at the district level; in North Carolina, home-school registration rolls are not regularly purged as students age out of the system; and in West Virginia, Utah and Alabama, annual enrollment data is unavailable. Eleven additional states do not require any notice when families decide to home-school their children, so enrollment figures in those states are also unavailable. Finally, Montana, Vermont and Nebraska collect data at a county level, not a district level, so there is no district data available - only statewide figures.

The Post made every effort to capture all legal ways to home-school, which vary by state. However, data on home schools established by certain methods, such as registering one’s home-school as a private school, are tracked by some states but not others. That means The Post’s tally is almost certainly an undercount, even in the states from which it gathered data. For instance, Wisconsin and Georgia only provided The Post with tallies of home-schoolers who had submitted required forms electronically. In Kentucky, some districts incorrectly reported zero or one home-schooled students in certain years, which a state education official attributed to an unclear form. The Post excluded those enrollment figures from its analysis. In California, which does not explicitly permit home schooling, many parents operate home-based private schools. The California Department of Education characterizes private schools with five or fewer students as home schools. In Louisiana, many home schools operate as nonpublic schools not seeking accreditation; The Post counted such schools with five or fewer students as home schools as well.

The statewide numbers are not always equivalent to the sum of all district totals in a state. Some states suppress district-level counts of home schoolers below a certain threshold. In Maine, the threshold is 5; in New Mexico, 6; in Mississippi, Ohio and Tennessee, 10; in Wisconsin before 2020-21, 5; and in Wisconsin from 2021-22 on, 20. The Post marked such suppressions as NA within its data. In addition, New Hampshire collects separate data on students who enter home schooling from schools run by the state department of education or from private schools; these additional students are reflected in state data but not district data.

The Post used a variety of methods to match each school district name to an NCES district id. However, this was not always possible. In Georgia, families self-report their school district on home-schooling forms; some report programs which are not school districts, and therefore have no corresponding NCES id. In California, families were only required to report county and school district beginning in 2020-21; in addition, district mergers and name changes mean that some districts could not be matched wi...

Context

Our data set contains information like Admission rate,Financial Aid, Program Information etc for the past 20 years of all the universities of United State. This data set originally was provided to give transparency to student and their families about the colleges.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Context

I ran into the American Presidency Project and was inspired by the incredible amount of data that the founders of this project had accumulated. Further, I ran into a few key projects such as The Wordy Words of Hillary Clinton and Donald Trump and IBM Watson Compares Trump's Inauguration Speech to Obama's that used this data.

The site itself, however, simply has a PHP page that individually returns every one of the 120,000+ documents in an HTML format. My goal was to extract the almost 60,000 documents released by the offices of all of the presidents of the United States, starting with George Washington, in an effort to make this data available to anyone interested in diving into this data set for unique studies and experimentation.

Content

The data is normalized using two key properties of a document: President and Document Category. Document categories can include, but are not limited to: Oral, Written, etc.

Each document has a variety of properties:

• category - This category field is a further detailed categorial assignment, such as Address, Memo, etc.
• subcategory - Inaugural, etc.
• document_date - Format: 1861-03-04 00:00:00
• title - Title of the released document.
• pid - This value, stored as an integer, can be used to access the original document at the following URL: http://www.presidency.ucsb.edu/ws/index.php?pid={}. where {} can be replaced with the value in this field.
• content - This is the full text of the released document.

A markdown version of this JSON structure can be found on GitHub.

Acknowledgements

A HUGE thank you for the data and inspiration to the American Presidency Project.

The following describes the data files, code and programs used to produce the figures and tables displayed in the manuscript, “What Happens When You Can’t Check the Box? Categorization Threat and Public Opinion among Middle Eastern and North African Americans,” as well as in the supplemental materials. All required packages are included at the start of each script file. Abstract: Middle Eastern and North African (MENA) Americans occupy a paradoxical position: a highly politicized, highly visible group rendered institutionally invisible by the absence of an official ethnoracial category. From 1977 to 2024, the federal government categorized MENA Americans as “White.” Despite this categorization, research shows that they are neither perceived as White nor identify as such, often preferring to self-categorize as “MENA.” Yet many forms—whether issued by governments, universities, or private organizations—rarely include “MENA” as an option. What are the consequences of having one’s identity omitted on political attitudes related to that identity? I argue that denying MENA Americans the ability to self-categorize induces categorization threat, a response well-documented in social psychology but less often connected to politics. Drawing on two survey experiments and in-depth interviews, I show that MENA individuals who cannot self-categorize as “MENA” engage in identity assertion by expressing stronger opinions on MENA-related political issues. This assertion may also generalize to issues tied to broader “People of Color” (POC) identity. Identity categories are not merely bureaucratic formalities; they structure how individuals see themselves and how they respond to politics. By showing that categorization threat shapes political expression among MENA Americans, this article underscores how institutional categories can marginalize groups and affect the validity of the data used to govern them. As identities become increasingly complex and salient, understanding the consequences of category exclusion becomes vital for both empirical research and democratic inclusion.

US Presidents Dataset

US Presidents: Birth, Death, Terms, and Parties

By Throwback Thursday [source]

About this dataset

The dataset also contains essential personal information, including each president's date of birth and date of death. Additionally, it includes specific details about when each president took office and when they left office.

Furthermore, the dataset provides insight into where each president was born and where they ultimately passed away. This includes information on both the cities and states associated with their births and deaths.

With this extensive collection of data on US presidents throughout history, researchers can analyze trends related to education backgrounds, regional representation among presidents' origins and final resting places, as well as political party distributions throughout different eras in American history

How to use the dataset

• Number: The numerical order of the US Presidents

  • This column provides the sequential number assigned to each President. You can use this information to quickly identify specific presidents within the dataset.

• Colleges: The colleges or universities attended by the US Presidents

  • In this column, you can find details about which colleges or universities each President attended during their academic years.

• Birth City: The city where the US Presidents were born

  • This column lists the birth city of each President. It can be interesting to explore patterns or similarities between their places of birth.

• Birth State: The state where the US Presidents were born

  • Similar to Birth City, this column contains information about which state each President was born in.

• Birth Date: The date of birth for each President

  • Discovering famous birthdays has always been intriguing! Explore this column for insights into when these influential figures were born.

• Death City: The city where the US Presidents died

  • Uncover notable locations by exploring where each President passed away using this data column.

• Death State: The state where the US Presidents died

  • Just like Death City, you can gain insights into important locations associated with Presidential deaths through this data field.

• Death Date: The date of death for each President

  • Although it is a solemn topic, knowing when these historical figures passed away offers context within their lifetime.

• Left Office :The date when people left office

• Took Office:The date when US Presidents took office.

• Party: The political party affiliation of the US Presidents

  • Understanding the political party affiliations of each President can reveal interesting trends, patterns, and shifts in party dominance over time.

By utilizing this dataset and interpreting these columns, you can gain valuable insights into the lives and backgrounds of the US Presidents. Additionally, this information also allows for comparisons between presidents based on various factors such as birthplace or educational background.

Feel free to leverage visualizations, statistical analyses or create your research questions to dive deeper into this data!

Remember that using dates from different columns together will help you organize and analyze the

Research Ideas

• analyzing the relationship between the colleges attended by US Presidents and their political affiliations
• studying the impact of geographical factors, such as birth cities and states, on presidential careers or political ideologies
• examining trends in terms served and the length of time between taking office and leaving office for different political parties

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: ThrowbackDataThursday Week 8 - US Presidents.csv | Column name | Description | |:----------------|:--------------------------------------------------------------------------------------| | Number | The numerical order of each US President. (Numeric) | | Colleges | Information about the colleges or universities attended by each President. (Text) | | Birth City | The city where each President was born. (Text) | | Birth State | The state where each President was born. (Text) | | ...

Real-time city rankings with political preference weighting for Ignore Politics

Graph and download economic data for Homeownership Rates by Race and Ethnicity: Black Alone in the United States (BOAAAHORUSQ156N) from Q1 1994 to Q2 2025 about African-American, homeownership, rate, and USA.

School enrollment data are used to assess the socioeconomic condition of school-age children. Government agencies also require these data for funding allocations and program planning and implementation.

Data on school enrollment and grade or level attending were derived from answers to Question 10 in the 2015 American Community Survey (ACS). People were classified as enrolled in school if they were attending a public or private school or college at any time during the 3 months prior to the time of interview. The question included instructions to “include only nursery or preschool, kindergarten, elementary school, home school, and schooling which leads to a high school diploma, or a college degree.” Respondents who did not answer the enrollment question were assigned the enrollment status and type of school of a person with the same age, sex, race, and Hispanic or Latino origin whose residence was in the same or nearby area.

School enrollment is only recorded if the schooling advances a person toward an elementary school certificate, a high school diploma, or a college, university, or professional school (such as law or medicine) degree. Tutoring or correspondence schools are included if credit can be obtained from a public or private school or college. People enrolled in “vocational, technical, or business school” such as post secondary vocational, trade, hospital school, and on job training were not reported as enrolled in school. Field interviewers were instructed to classify individuals who were home schooled as enrolled in private school. The guide sent out with the mail questionnaire includes instructions for how to classify home schoolers.

Enrolled in Public and Private School – Includes people who attended school in the reference period and indicated they were enrolled by marking one of the questionnaire categories for “public school, public college,” or “private school, private college, home school.” The instruction guide defines a public school as “any school or college controlled and supported primarily by a local, county, state, or federal government.” Private schools are defined as schools supported and controlled primarily by religious organizations or other private groups. Home schools are defined as “parental-guided education outside of public or private school for grades 1-12.” Respondents who marked both the “public” and “private” boxes are edited to the first entry, “public.”

Grade in Which Enrolled – From 1999-2007, in the ACS, people reported to be enrolled in “public school, public college” or “private school, private college” were classified by grade or level according to responses to Question 10b, “What grade or level was this person attending?” Seven levels were identified: “nursery school, preschool;” “kindergarten;” elementary “grade 1 to grade 4” or “grade 5 to grade 8;” high school “grade 9 to grade 12;” “college undergraduate years (freshman to senior);” and “graduate or professional school (for example: medical, dental, or law school).”

In 2008, the school enrollment questions had several changes. “Home school” was explicitly included in the “private school, private college” category. For question 10b the categories changed to the following “Nursery school, preschool,” “Kindergarten,” “Grade 1 through grade 12,” “College undergraduate years (freshman to senior),” “Graduate or professional school beyond a bachelor’s degree (for example: MA or PhD program, or medical or law school).” The survey question allowed a write-in for the grades enrolled from 1-12.

Question/Concept History – Since 1999, the ACS enrollment status question (Question 10a) refers to “regular school or college,” while the 1996-1998 ACS did not restrict reporting to “regular” school, and contained an additional category for the “vocational, technical or business school.” The 1996-1998 ACS used the educational attainment question to estimate level of enrollment for those reported to be enrolled in school, and had a single year write-in for the attainment of grades 1 through 11. Grade levels estimated using the attainment question were not consistent with other estimates, so a new question specifically asking grade or level of enrollment was added starting with the 1999 ACS questionnaire.

Limitation of the Data – Beginning in 2006, the population universe in the ACS includes people living in group quarters. Data users may see slight differences in levels of school enrollment in any given geographic area due to the inclusion of this population. The extent of this difference, if any, depends on the type of group quarters present and whether the group quarters population makes up a large proportion of the total population. For example, in areas that are home to several colleges and universities, the percent of individuals 18 to 24 who were enrolled in college or graduate school would increase, as people living in college dormitories are now included in the universe.

Graph and download economic data for All Employees, Government (USGOVT) from Jan 1939 to Sep 2025 about establishment survey, government, employment, and USA.

Context

NYC Open Data is an opportunity to engage New Yorkers in the information that is produced and used by City government. We believe that every New Yorker can benefit from Open Data, and Open Data can benefit from every New Yorker. Source: https://opendata.cityofnewyork.us/overview/

Content

Thanks to NYC Open Data, which makes public data generated by city agencies available for public use, and Citi Bike, we've incorporated over 150 GB of data in 5 open datasets into Google BigQuery Public Datasets, including:

• Over 8 million 311 service requests from 2012-2016

• More than 1 million motor vehicle collisions 2012-present

• Citi Bike stations and 30 million Citi Bike trips 2013-present

• Over 1 billion Yellow and Green Taxi rides from 2009-present

• Over 500,000 sidewalk trees surveyed decennially in 1995, 2005, and 2015

This dataset is deprecated and not being updated.

Fork this kernel to get started with this dataset.

Acknowledgements

https://opendata.cityofnewyork.us/

https://cloud.google.com/blog/big-data/2017/01/new-york-city-public-datasets-now-available-on-google-bigquery

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - https://data.cityofnewyork.us/ - 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.

By accessing datasets and feeds available through NYC Open Data, the user agrees to all of the Terms of Use of NYC.gov as well as the Privacy Policy for NYC.gov. The user also agrees to any additional terms of use defined by the agencies, bureaus, and offices providing data. Public data sets made available on NYC Open Data are provided for informational purposes. The City does not warranty the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set made available on NYC Open Data, nor are any such warranties to be implied or inferred with respect to the public data sets furnished therein.

The City is not liable for any deficiencies in the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set, or application utilizing such data set, provided by any third party.

Banner Photo by @bicadmedia from Unplash.

Inspiration

On which New York City streets are you most likely to find a loud party?

Can you find the Virginia Pines in New York City?

Where was the only collision caused by an animal that injured a cyclist?

What’s the Citi Bike record for the Longest Distance in the Shortest Time (on a route with at least 100 rides)?

https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png" alt="enter image description here"> https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png

About 23 percent of Americans wanted to start 2022 by living healthier, which was the most popular New Year’s resolution. In addition, personal improvement or happiness was the year's resolution for 21 percent of Americans.

Resolution makers, resolution keepers?

While some might say that they do not need New Year’s Eve to finally turn their life around, making resolutions on December 31 is a common, well-liked tradition, especially in the Western world. They are usually meant to contain some kind of improvement or betterment of one’s conduct or life choices. However, these resolutions are not compulsive; only a small share of people who make them actually keep them, according to a Statista survey. They are more like a signal for a new start than an actual catalyst for change.

Traditional changes

While they signal a change of choices and behavior, New Year’s resolutions themselves hardly ever change: When comparing past resolutions for 2018 and 2019, for example, it’s obvious that people still just want to be healthy and happy, maybe broaden their horizons, and save up – in general, be a sensible, content adult. This is not only true for Americans – Italians also wish for stable finances and their own and loved ones’ health, as do South Koreans.

There were approximately 18.58 million college students in the U.S. in 2022, with around 13.49 million enrolled in public colleges and a further 5.09 million students enrolled in private colleges. The figures are projected to remain relatively constant over the next few years.

What is the most expensive college in the U.S.? The overall number of higher education institutions in the U.S. totals around 4,000, and California is the state with the most. One important factor that students – and their parents – must consider before choosing a college is cost. With annual expenses totaling almost 78,000 U.S. dollars, Harvey Mudd College in California was the most expensive college for the 2021-2022 academic year. There are three major costs of college: tuition, room, and board. The difference in on-campus and off-campus accommodation costs is often negligible, but they can change greatly depending on the college town.

The differences between public and private colleges Public colleges, also called state colleges, are mostly funded by state governments. Private colleges, on the other hand, are not funded by the government but by private donors and endowments. Typically, private institutions are  much more expensive. Public colleges tend to offer different tuition fees for students based on whether they live in-state or out-of-state, while private colleges have the same tuition cost for every student.

There were 1,126,690 international students studying in the United States in the 2023/24 academic year. This is an increase from the previous year, when 1,057,188 international students were studying in the United States.

In 2024, 27 million people in the United States had no health insurance. The share of Americans without health insurance saw a steady increase from 2015 to 2019 before starting to decline from 2020 to 2024. Factors like the implementation of Medicaid expansion in additional states and growth in private health insurance coverage led to the decline in the uninsured population, despite the economic challenges due to the pandemic in 2020. Positive impact of Affordable Care Act In the U.S. there are public and private forms of health insurance, as well as social welfare programs such as Medicaid and programs just for veterans such as CHAMPVA. The Affordable Care Act (ACA) was enacted in 2010, which dramatically reduced the share of uninsured Americans, though there’s still room for improvement. In spite of its success in providing more Americans with health insurance, ACA has had an almost equal number of proponents and opponents since its introduction, though the share of Americans in favor of it has risen since mid-2017 to the majority. Persistent disparity among ethnic groups The share of uninsured people is higher in certain demographic groups. For instance, Hispanics continue to be the ethnic group with the highest rate of uninsured people, even after ACA. Meanwhile the share of uninsured White and Asian people is lower than the national average.