The dataset encompasses a comprehensive record of Formula 1 race winners spanning from the inaugural season in 1950 to the latest available data in 2024. It meticulously documents the triumphant drivers, their respective teams, and the circuits where they clinched victory, offering a rich historical perspective on the evolution of this prestigious motorsport. This extensive compilation not only serves as a testament to the skill and determination of the drivers who graced the podium over the decades but also provides invaluable insights into the competitive dynamics and technological advancements that have shaped the sport's narrative throughout its illustrious history. Whether for statistical analysis, historical research, or pure enthusiast curiosity, this dataset stands as a definitive resource for exploring the captivating saga of Formula 1 racing.

Graph and download economic data for Expenditures: Food by Race: White and All Other Races, Not Including Black or African American (CXUFOODTOTLLB0903M) from 2003 to 2023 about white, expenditures, food, and USA.

This dataset provides a report on pre term delivery among women across all races and demographics

About this dataset

Context

This list ranks the 51 states in the United States by Some Other Race (SOR) population, as estimated by the United States Census Bureau. It also highlights population changes in each states over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Other Race Population: This column displays the rank of states in the United States by their Some Other Race (SOR) population, using the most recent ACS data available.
• states: The states for which the rank is shown in the previous column.
• Other Race Population: The Other Race population of the states is shown in this column.
• % of Total states Population: This shows what percentage of the total states population identifies as Other Race. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total U.S. Other Race Population: This tells us how much of the entire United States Other Race population lives in that states. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

In 2024, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the overall poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States The poverty threshold for a single person in the United States was measured at an annual income of ****** U.S. dollars in 2023. Among families of four, the poverty line increases to ****** U.S. dollars a year. Women and children are more likely to suffer from poverty. This is due to the fact that women are more likely than men to stay at home, to care for children. Furthermore, the gender-based wage gap impacts women's earning potential. Poverty data Despite being one of the wealthiest nations in the world, the United States has some of the highest poverty rates among OECD countries. While, the United States poverty rate has fluctuated since 1990, it has trended downwards since 2014. Similarly, the average median household income in the U.S. has mostly increased over the past decade, except for the covid-19 pandemic period. Among U.S. states, Louisiana had the highest poverty rate, which stood at some ** percent in 2024.

Context

The FIA Formula 2 Championship is a second-tier single-seater championship organized by the Fédération Internationale de l'Automobile (FIA). The championship was introduced in 2017, following the rebranding of the long-term Formula One feeder series GP2.

In addition to being the championship that awards the most points for the FIA Superlicence, it has been the previous step for many F1 drivers such as Charles Leclerc, George Russell, Lando Norris, Yuki Tsunoda, Guanyu Zhou and others.

Content

The information is divided into 5 files that correspond to each event. Not all events take place every week (for example the sprint race). The columns are:

• LAPS: Total laps traveled by the pilot
• TIME: Total event time
• GAP: Distance from pilot to race leader (seconds)
• INT: Distance to the next pilot (seconds)
• KPH: Speed (mean)
• BEST: Best time lap
• LAP: Lap for the best time
• POS: Final position of the event
• CAR: Car number
• PILOT NAME: Pilot name
• TEAM: Constructor
• CIRCUIT: Circuit name
• TYPE: Type of the event
• ROUND: Round (from 1 to total races per season)
• DATE: Date of the feature race
• LAP SET ON: Hot lap (fastest) for the Quali
• QUALI TYPE: multi-session indicator of quali

Collection Methodology

You can visit the source code of the data pipeline in GitHub

About this dataset

Context

The dataset tabulates the population of Mississippi County by race. It includes the population of Mississippi County across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Mississippi County across relevant racial categories.

Key observations

The percent distribution of Mississippi County population by race (across all racial categories recognized by the U.S. Census Bureau): 58.30% are white, 34.78% are Black or African American, 0.15% are American Indian and Alaska Native, 0.41% are Asian, 0.05% are Native Hawaiian and other Pacific Islander, 1.41% are some other race and 4.91% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Mississippi County
• Population: The population of the racial category (excluding ethnicity) in the Mississippi County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Mississippi County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Mississippi County Population by Race & Ethnicity. You can refer the same here

Description

This dataset consists of race odds, race information and prior race performance statistics for six greyhounds in each of 2,000 greyhound races run at Crayford in the United Kingdom. All races were run at the standard 380 metres distance.

2,000 races, each with six runners comprises 12,000 data-points in total.

Each data-point/greyhound has 27 predictor variables, plus two potential target variables 'finished' for finish position, first to sixth, and 'Winner', for race win-lose. These are explained below.

The data was constructed from the Racing Post Greyhound Portal and a Betfair API. Many of the predictor variables such as 'Wins_380' were constructed by aggregating prior performance data for each greyhound.

Some features are highly correlated such as the different odds/betting data - 'BSP' (Betfair Starting Price) and 'Odds' (The starting price quoted in The Racing Post).

Features/Predictors and the Target of Finish Position

27 features were considered as predictors of race finish position. A description of each feature is provided below. Features are divided into relevant groups.

Race identifier

• 'Race_ID' --- Unique identifier for each race. Use in machine learning questionable.

Odds related

• 'Odds'--- Bookmaker odds found in the racing post.
• 'BSP'--- Betfair odds, normally higher than the bookmaker odds.
• 'Public_Estimate'--- 1 to 6 reflecting the expected finish position based on BSP.

All previous races run by the greyhound

• 'Races_All'--- Total number of races run by the greyhound prior to the race in question.
• 'Distance_All'--- Average distance for all races run by a greyhound.
• 'Finish_All'--- Average finish position for all races run by a greyhound. Normalised to 6 runner races.
• 'Distance_Places_All'---The average distance of races were the greyhound finished 1st or 2nd. This statistic should indicate a distance preference for each greyhound. For example, if a greyhound has a statistic of 474.0 metres, he appears to perform well at longer distances than the 380.0 metres of the race to be tested.
• 'Wide'--- Percentage of races in which the greyhound ran wide, thereby hampering their win chances. Wide runs are not very common.

Recent races (seven most recent)

• 'Distance_Recent'--- Average distance run in seven most recent races.
• 'Finish_Recent'--- Average finish position in seven most recent races. Statistic corrected for trial races.
• 'Odds_Recent'--- Average bookmaker odds seven most recent races.
• 'Early_Recent'--- Average early position in seven most recent races. The recorded position of the greyhound roughly 20 percent of the way into a race.

Crayford 380 metre races only

• 'Races_380'--- Total races of this type in form. This is the race type it is now racing in and for which we want to predict an expected finish position.
• 'Wins_380'--- Win percentage for all races at Crayford 380 metres.
• 'Finish_380'--- Average finish position in seven most recent races at Crayford 380 metres. Corrected for trial races.
• 'Odds_380'--- Average bookmaker odds in the seven most recent races at Crayford 380 metres.

• 'Early_380'--- Average relative early position in seven most recent Crayford 380m races.

• 'Grade_380'--- Average race grade in the seven most recent races at Crayford 380 metres.

• 'Stay_380'--- Average finish position minus early position for seven most recent Crayford 380m races. A measure of the greyhound’s stamina at the distance. For example, a statistic of -2.5 indicates that a greyhound starts relatively well but then fails back towards the end.

• 'Time_380'--- Average race completion time for races at Crayford 380 metres. Seven most recent.

• 'Early_Time_380'--- Average time to first bend (20 percent into the race) for races at Crayford 380 metres. Seven most recent.

• 'Wide_380'--- Average number of wide ‘W’ remarks in races at Crayford 380 metres. Seven most recent.

• 'Dist_By'--- Average distance in metres that a greyhound finished to the race winner. Calculated from seven most recent Crayford 380m races.

Other features

• 'Trap'--- Trap number from 1 to 6 for each greyhound’s starting trap/stall.
• 'Last_Run'--- Number of days since the greyhound’s last race.
• 'Favourite'--- Trap number of the greyhound who is race f...

Dataset Description: F2 Driver Stats vs F1 Graduation

The F2 Driver Stats vs F1 Graduation dataset aggregates per-driver performance metrics from the FIA Formula 2 Championship (2018–2019) and labels whether each driver eventually reached Formula 1.

Data Source: This dataset was created by processing and aggregating data originally sourced from the Formula 2 Dataset (2018-2019) by alarchemn on Kaggle. Modifications include calculating aggregate statistics per driver and adding the REACHED_F1 and cluster columns.

Each row represents one driver and includes the following columns:

• PILOT NAME: Driver’s name
• LAPS: Total laps completed across all races during the specified period.
• AVG_GAP: The average time gap (in seconds) between the driver and the race winner across all races they finished.
• AVG_KPH: The driver's average race speed (in kilometers per hour) calculated over all completed race laps.
• AVG_POS: The driver's average finishing position across all races they completed.
• AVG_TIME_seconds: The driver's mean total race time (in seconds) for completed races.
• AVG_BEST_seconds: The driver's mean best lap time (in seconds) across all races where a best lap was recorded.
• REACHED_F1: A binary indicator where 1 signifies the driver competed in at least one official Formula 1 Grand Prix race after their F2 stint, and 0 signifies they did not.
• cluster: An identifier for the cluster assigned to the driver based on an unsupervised K-Means clustering algorithm (using a custom NumPy implementation) applied to their performance metrics. The dataset contains 3 distinct clusters.

Potential Uses:

You can use this dataset to:

• Develop and evaluate supervised machine learning models to predict whether an F2 driver will graduate to Formula 1 based on their F2 performance statistics.
• Analyze the characteristics of the different driver clusters identified by the K-Means algorithm and explore potential correlations between cluster membership and F1 progression.
• Conduct statistical analyses and create visualizations to investigate the relationships between specific on-track performance metrics (like average position, speed, or gap to winner) and a driver's likelihood of reaching F1.
• Compare the performance profiles of drivers who successfully transitioned to F1 versus those who did not.

Hourly wages in the United States are broken into different percentiles to show the hourly earnings of White, Black, and Latino renters in the different percentiles. White workers in all earning percentiles had a higher wage than Black or Latino people. Considering that the housing wages for one- and two-bedroom housing were 28.17 and 33.63 U.S. dollars, respectively, not all earners in the 70th percentile and lower could afford housing. In fact, only white renters in the 60th could afford a one-bedroom apartment that year. Moreover, while only Black renters in the 70th percentile could afford one-bedroom housing, white renters were able to afford both. However, for a Latino worker making a wage at the 70th percentile, even a one-bedroom unit was not affordable.

United States - Income Gini for Households by Race of Householder, All Races was 0.48800 Ratio in January of 2024, according to the United States Federal Reserve. Historically, United States - Income Gini for Households by Race of Householder, All Races reached a record high of 0.49400 in January of 2021 and a record low of 0.38600 in January of 1968. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Income Gini for Households by Race of Householder, All Races - last updated from the United States Federal Reserve on November of 2025.

Measure reports the percent of the State of Iowa's population that is classified as American Indian and Alaska Native Alone, Native Hawaiian and Other Pacific Islander Alone, or Some Other Race Alone based data collected over a 60 month period. Data is from the American Community Survey, Five Year Estimates, Table B02001.

Graph and download economic data for Expenditures: Vehicle Maintenance and Repairs by Race: White and All Other Races, Not Including Black or African American (CXUCAREPAIRLB0903M) from 2003 to 2023 about repair, maintenance, white, vehicles, expenditures, and USA.

Graph and download economic data for Expenditures: Total Average Annual Expenditures by Race: White, Asian, and All Other Races, Not Including Black or African American (CXUTOTALEXPLB0902M) from 1984 to 2023 about asian, white, average, expenditures, and USA.

Graph and download economic data for Income Gini Ratio for Households by Race of Householder, All Races (GINIALLRH) from 1967 to 2024 about gini, households, income, and USA.

About this dataset

Context

The dataset tabulates the population of Blue Ridge by race. It includes the population of Blue Ridge across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Blue Ridge across relevant racial categories.

Key observations

The percent distribution of Blue Ridge population by race (across all racial categories recognized by the U.S. Census Bureau): 70.66% are white, 0.44% are Black or African American, 0.26% are Asian, 11.45% are some other race and 17.18% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Blue Ridge
• Population: The population of the racial category (excluding ethnicity) in the Blue Ridge is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Blue Ridge total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Blue Ridge Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Early by race. It includes the population of Early across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Early across relevant racial categories.

Key observations

The percent distribution of Early population by race (across all racial categories recognized by the U.S. Census Bureau): 75.91% are white, 3.48% are Black or African American, 1.23% are Asian, 0.60% are some other race and 18.77% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Early
• Population: The population of the racial category (excluding ethnicity) in the Early is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Early total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Early Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Hampden township by race. It includes the population of Hampden township across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Hampden township across relevant racial categories.

Key observations

The percent distribution of Hampden township population by race (across all racial categories recognized by the U.S. Census Bureau): 75.77% are white, 2.12% are Black or African American, 0.01% are American Indian and Alaska Native, 14.13% are Asian, 2.04% are some other race and 5.93% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Hampden township
• Population: The population of the racial category (excluding ethnicity) in the Hampden township is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Hampden township total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Hampden township Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Lake City by race. It includes the population of Lake City across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Lake City across relevant racial categories.

Key observations

The percent distribution of Lake City population by race (across all racial categories recognized by the U.S. Census Bureau): 58.50% are white, 31.76% are Black or African American, 0.06% are American Indian and Alaska Native, 1.23% are Asian, 2.81% are some other race and 5.64% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Lake City
• Population: The population of the racial category (excluding ethnicity) in the Lake City is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Lake City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Lake City Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Overland Park by race. It includes the population of Overland Park across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Overland Park across relevant racial categories.

Key observations

The percent distribution of Overland Park population by race (across all racial categories recognized by the U.S. Census Bureau): 76.78% are white, 5.39% are Black or African American, 0.27% are American Indian and Alaska Native, 8.73% are Asian, 0.10% are Native Hawaiian and other Pacific Islander, 1.81% are some other race and 6.93% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Overland Park
• Population: The population of the racial category (excluding ethnicity) in the Overland Park is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Overland Park total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Overland Park Population by Race & Ethnicity. You can refer the same here