ABSTRACTIn March 2024, the Office of Management and Budget updated guidelines for measuring race/ethnicity on federal forms in the United States (US). By March 2029, Middle Eastern and North African (MENA) Americans will have a new category. This population was previously included in the definition for the White race. It is unknown how this change will alter health estimates for other racial/ethnic groups, particularly among the aging population that has become increasingly diverse. Using cognitive difficulty as the health outcome of interest, our objectives were to 1) compare the prevalence of cognitive difficulty using 2020 and 2030 US Census racial/ethnic categories and 2) determine whether the odds of cognitive difficulty differs with and without a MENA checkbox. We used 2018-2022 American Community Survey data (ages >=65 years; n=3,351,611). We categorized race/ethnicity based on 2020 US Census categories (White, Black, AI/AN, Asian, NH/OPI, Some Other Race, Two or More Races, Hispanic/Latino) then created a separate category for older adults of MENA descent using questions on ancestry and place of birth to align with 2030 categories. Bivariate statistics and multivariable logistic regression models were calculated. Using 2020 categories, the odds of cognitive difficulty were higher among all racial/ethnic groups compared to Whites. Using 2030 categories, the odds of cognitive difficulty were 1.53 times greater (95%CI=1.43-1.62) among MENA compared to Whites. The odds of cognitive difficulty using 2020 and 2030 US Census racial/ethnic categories for other groups were not significantly different. Our results highlight the disparity in cognitive health among MENA and White older adults. Including a separate MENA checkbox on the ACS starting in 2027 is critical to provide baseline data and move forward discussions on health disparities among older adults.

This CD consists of the Race and Hispanic or Latino Summary File. It contains summary statistics. This CD contains summary population counts for two universes, total population and population 18 years and over. The data were derived from the basic questions asked on all census questionnaires. These are often called the 100-percent questions. This file contains four tables: a count of all persons by race. a count of the population 18 years and over by race. a count of Hispanic or Latino and a count of not Hispanic or Latino by race for all persons. a count of Hispanic or Latino and a count of not Hispanic or Latino by race for the population 18 years and over.

Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

The Race and Hispanic or Latino Summary File is an extract of selected geographic areas pr eviously released in the state Census 2000 Redistricting Data (Public Law 94-171) Summary Files. In addition, this file provides summaries for the United States, regions, divisions, and American Indian and Alaska Native areas that cross state boundaries. The file structure is as follows: United States Region Division American Indian Area/Alaska Native Area/Hawaiian Home Land State County Place Consolidated city American Indian Area/Alaska Native Area/Hawaiian Home Land American Indian Area/Alaska Native Area (Reservation or Statistical Entity Only)4 American Indian Area (Off-Reservation rust Land Only)/Hawaiian Home Land Alaska Native Regional Corporation

Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Statistics for Employer and Nonemployer Firms by Industry and Race for the U.S., States, and Metro Areas: 2019.Table ID.ABSNESD2019.AB00MYNESD01C.Survey/Program.Economic Surveys.Year.2019.Dataset.ECNSVY Nonemployer Statistics by Demographics Company Summary.Source.U.S. Census Bureau, 2019 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2023-05-11.Release Schedule.The Nonemployer Statistics by Demographics (NES-D) is released yearly, beginning in 2017..Sponsor.National Center for Science and Engineering Statistics, U.S. National Science Foundation.Table Universe.Data in this table combines estimates from the Annual Business Survey (employer firms) and the Nonemployer Statistics by Demographics (nonemployer firms).Includes U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series).Includes U.S. employer firms estimates of business ownership by sex, ethnicity, race, and veteran status from the 2020 Annual Business Survey (ABS) collection. The employer business dataset universe consists of employer firms that are in operation for at least some part of the reference year, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees and annual receipts of $1,000 or more, and are classified in one of nineteen in-scope sectors defined by the 2017 North American Industry Classification System (NAICS), except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered.Data are also obtained from administrative records and other economic surveys. Note: For employer data only, the collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2020 ABS collection year produces statistics for the 2019 reference year. The "Year" column in the table is the reference year..Methodology.Data Items and Other Identifying Records.Total number of employer and nonemployer firmsTotal sales, value of shipments, or revenue of employer and nonemployer firms ($1,000)Number of nonemployer firmsSales, value of shipments, or revenue of nonemployer firms ($1,000)Number of employer firmsSales, value of shipments, or revenue of employer firms ($1,000)Number of employeesAnnual payroll ($1,000)These data are aggregated by the following demographic classifications of firm for:All firms Classifiable (firms classifiable by sex, ethnicity, race, and veteran status) Race White Black or African American American Indian and Alaska Native Asian Native Hawaiian and Other Pacific Islander Minority (Firms classified as any race and ethnicity combination other than non-Hispanic and White) Equally minority/nonminority Nonminority (Firms classified as non-Hispanic and White) Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status) Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the NES-D and the ABS are companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The data are shown for the total of all sectors (00) and the 2-digit NAICS code levels for:United StatesStates and the District of ColumbiaMetropolitan Statistical AreasData are also shown for the 3-digit NAICS code for:United StatesStates and the District of ColumbiaFor information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2- through 3-digit NAICS code levels depending on geography. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sampling.NES-D nonemployer data are not conducted through sampling. Nonemployer Statistics (NES) data originate from statistical information obtained through business income tax records that the Internal Revenue Service (IRS) provides to the Census Bureau. The N...

This study explores attitudes and perceptions related to urban problems and race relations in 15 northern cities of the United States (Baltimore, Boston, Brooklyn, Chicago, Cincinnati, Cleveland, Detroit, Gary, Milwaukee, Newark, Philadelphia, Pittsburgh, St. Louis, San Francisco, and Washington, DC). More specifically, it seeks to define the social and psychological characteristics and aspirations of the Black and White urban populations. Samples of Blacks and Whites were selected in each of the cities in early 1968. The study employed two questionnaire forms, one for Whites and one for Blacks, and two corresponding data files were generated. Attitudinal questions asked of the White and Black respondents measured their satisfaction with community services, their feelings about the effectiveness of government in solving urban problems, and their experience with police abuse. Additional questions about the respondent's familiarity with and participation in antipoverty programs were included. Other questions centered on the respondent's opinions about the 1967 riots: the main causes, the purpose, the major participating classes, and the effect of the riots on the Black cause. Respondents' interracial relationships, their attitudes toward integration, and their perceptions of the hostility between the races were also investigated. White respondents were asked about their opinions on the use of governmental intervention as a solution for various problems of the Blacks, such as substandard schools, unemployment, and unfair housing practices. Respondent's reactions to nonviolent and violent protests by Blacks, their acceptance of counter-rioting by Whites and their ideas concerning possible governmental action to prevent further rioting were elicited. Inquiries were made as to whether or not the respondent had given money to support or hinder the Black cause. Other items investigated respondents' perceptions of racial discrimination in jobs, education, and housing, and their reactions to working under or living next door to a Black person. Black respondents were asked about their perceptions of discrimination in hiring, promotion, and housing, and general attitudes toward themselves and towards Blacks in general. The survey also investigated respondents' past participation in civil rights organizations and in nonviolent and/or violent protests, their sympathy with rioters, and the likelihood of personal participation in a future riot. Other questions probed respondents' attitudes toward various civil rights leaders along with their concurrence with statements concerning the meaning of 'Black power.' Demographic variables include sex and age of the respondent, and the age and relationship to the respondent of each person in the household, as well as information about the number of persons in the household, their race, and the type of structure in which they lived. Additional demographic topics include the occupational and educational background of the respondent, of the respondent's family head, and of the respondent's father. The respondent's family income and the amount of that income earned by the head of the family were obtained, and it was determined if any of the family income came from welfare, Social Security, or veteran's benefits. This study also ascertained the place of birth of the respondent and respondent's m other and father, in order to measure the degree of southern influence. Other questions investigated the respondent's military background, religious preference, marital status, and family composition.

BackgroundRacial and ethnic disparities persist in preterm birth (PTB) and gestational age (GA) at delivery in the United States. It remains unclear whether exposure to environmental chemicals contributes to these disparities.ObjectivesWe applied recent methodologies incorporating environmental mixtures as mediators in causal mediation analysis to examine whether racial and ethnic disparities in GA at delivery and PTB may be partially explained by exposures to polybrominated diphenyl ethers (PBDEs), a class of chemicals used as flame retardants in the United States.MethodsData from a multiracial/ethnic US cohort of 2008 individuals with low-risk singleton pregnancies were utilized, with plasma PBDE concentrations measured during early pregnancy. We performed mediation analyses incorporating three forms of mediators: (1) reducing all PBDEs to a weighted index, (2) selecting a PBDE congener, or (3) including all congeners simultaneously as multiple mediators, to evaluate whether PBDEs may contribute to the racial and ethnic disparities in PTB and GA at delivery, adjusted for potential confounders.ResultsAmong the 2008 participants, 552 self-identified as non-Hispanic White, 504 self-identified as non-Hispanic Black, 568 self-identified as Hispanic, and 384 self-identified as Asian/Pacific Islander. The non-Hispanic Black individuals had the highest mean ∑PBDEs, the shortest mean GA at delivery, and the highest rate of PTB. Overall, the difference in GA at delivery comparing non-Hispanic Black to non-Hispanic White women was −0.30 (95% CI: −0.54, −0.05) weeks. This disparity reduced to −0.23 (95% CI: −0.49, 0.02) and −0.18 (95% CI: −0.46, 0.10) weeks if fixing everyone's weighted index of PBDEs to the median and the 25th percentile levels, respectively. The proportion of disparity mediated by the weighted index of PBDEs was 11.8%. No statistically significant mediation was found for PTB, other forms of mediator(s), or other racial and ethnic groups.ConclusionPBDE mixtures may partially mediate the Black vs. White disparity in GA at delivery. While further validations are needed, lowering the PBDEs at the population level might help reduce this disparity.

Black Americans who are perceived as more racially phenotypical—that is, who possess more physical traits that are closely associated with their race—are more often associated with racial stereotypes. These stereotypes, including assumptions about criminality, can influence how Black Americans are treated by the legal system. However, it is unclear whether other forms of racial stereotypicality, such as a person’s way of speaking, also activate stereotypes about Black Americans. We investigated the links between speech stereotypicality and racial stereotypes (Experiment 1) and racial phenotype bias (Experiment 2). In Experiment 1, participants listened to audio recordings of Black speakers and rated how stereotypical they found the speaker, the likely race and nationality of the speaker, and indicated which adjectives the average person would likely associate with this speaker. In Experiment 2, participants listened to recordings of weakly or strongly stereotypical Black American speakers and indicated which of two faces (either weakly or strongly phenotypical) was more likely to be the speaker’s. We found that speakers whose voices were rated as more highly stereotypical for Black Americans were more likely to be associated with stereotypes about Black Americans (Experiment 1) and with more stereotypically Black faces (Experiment 2). These findings indicate that speech stereotypicality activates racial stereotypes as well as expectations about the stereotypicality of an individual’s appearance. As a result, the activation of stereotypes based on speech may lead to bias in suspect descriptions or eyewitness identifications.

Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Statistics for Employer and Nonemployer Firms by Industry and Veteran Status for the U.S., States, and Metro Areas: 2018.Table ID.ABSNESD2018.AB00MYNESD01D.Survey/Program.Economic Surveys.Year.2018.Dataset.ECNSVY Nonemployer Statistics by Demographics Company Summary.Source.U.S. Census Bureau, 2018 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2021-12-16.Release Schedule.The Nonemployer Statistics by Demographics (NES-D) is released yearly, beginning in 2017..Sponsor.National Center for Science and Engineering Statistics, U.S. National Science Foundation.Table Universe.Data in this table combines estimates from the Annual Business Survey (employer firms) and the Nonemployer Statistics by Demographics (nonemployer firms).Includes U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series).Includes U.S. employer firms estimates of business ownership by sex, ethnicity, race, and veteran status from the 2019 Annual Business Survey (ABS) collection. The employer business dataset universe consists of employer firms that are in operation for at least some part of the reference year, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees and annual receipts of $1,000 or more, and are classified in one of nineteen in-scope sectors defined by the 2017 North American Industry Classification System (NAICS), except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered.Data are also obtained from administrative records and other economic surveys. Note: For employer data only, the collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2019 ABS collection year produces statistics for the 2018 reference year. The "Year" column in the table is the reference year..Methodology.Data Items and Other Identifying Records.Total number of employer and nonemployer firmsTotal sales, value of shipments, or revenue of employer and nonemployer firms ($1,000)Number of nonemployer firmsSales, value of shipments, or revenue of nonemployer firms ($1,000)Number of employer firmsSales, value of shipments, or revenue of employer firms ($1,000)Number of employeesAnnual payroll ($1,000)These data are aggregated by the following demographic classifications of firm for:All firms Classifiable (firms classifiable by sex, ethnicity, race, and veteran status) Veteran Status (defined as having served in any branch of the U.S. Armed Forces) Veteran Equally veteran/nonveteran Nonveteran Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status) Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the NES-D and the ABS are companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.Data are shown for the total for all sectors (00) and the 2-digit NAICS levels for the U.S., states and District of Columbia, and metro areas.For information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2-digit NAICS code levels depending on geography. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Management of Companies and Enterprises (NAICS 55)Private Households (NAICS 814)Public Administration (NAICS 92)Industries Not Classified (NAICS 99)For information about NAICS, see North American Industry Classification System..Sampling.NES-D nonemployer data are not conducted through sampling. Nonemployer Statistics (NES) data originate from statistical information obtained through business income tax records that the Internal Revenue Service (IRS) provides to the Census Bureau. The NES-D adds demographic characteristics to the NES data and produces the total firm counts and the total receipts by those demographic characteristics. The NES-D utilizes various administrative records (AR) and the Census ...

Recent public and scholarly discourse suggests that partisanship informs how people feel about social groups in the United States by organizing those groups into camps of political friends and enemies. More generally, this implies that Americans’ attitudes toward social groups exhibit interdependence, a heretofore underexplored proposition. We develop a conceptual and methodological approach to investigating such interdependence and apply it to attitudes toward 17 social groups, the broadest set of measures available to date. We identify three subpopulations with distinct attitude logics: partisans, who feel warm toward groups commonly associated with their political party and cool toward those linked to the out-party; racials, distinguished by their consistently warmer or cooler feelings toward all racial groups relative to other forms of social group membership; and neutrals, who generally evaluate social groups neither warmly nor coolly. Individuals’ social positions and experiences, particularly the strength of their partisanship, their race, and their experience of racial discrimination, inform how they construe the social space. These findings shed light on contemporary political and social divisions while expanding the toolkit available for the study of attitudes toward social groups.

A public comment period outlining changes to the collection of race and ethnicity data on the US Census and other federal forms opened in January 2023. The purpose of this project is to collect and manage public comments in relation to the Office of Management and Budget (OMB) Statistical Policy Directive 15 proposals regarding the addition of a separate Middle Eastern and North African (MENA) checkbox on the US Census and other required federal forms. Public comments posted in February and March 2023 were reviewed to determine whether MENA was mentioned, whether comments supported a MENA checkbox, and whether comments mentioned support for health-related reasons. During the first review of comments, there were 3,062 comments reviewed. Most (71.49%) mentioned adding a MENA checkbox. Of those, 98.86% supported adding a MENA checkbox. Among those, 31.98% mentioned adding a MENA checkbox for health-related reasons. These results have been submitted for publication and are currently under peer review (see citation for pre-print below).Data collection and review of comments is ongoing. As of April 25, 2023, there were 3,355 comments reviewed posted from February 1 though March 6, 2023. Of those, 72.10% mention the addition of the MENA checkbox. Of those, 98.97% supported adding a MENA checkbox. Among those, 31.38% mentioned adding a MENA checkbox for health-related reasons. For this analysis, we also looked at mentions of adding the MENA checkbox for language or linguistic services. Of the comments that supported adding the checkbox, 55.25% of the comments mentioned the need for a MENA checkbox for services to be provided for language and linguistic services.The data provided is a STATA (.dta) file.

Purpose: To determine if medical students of different races/ethnicities or genders have different perceptions of bias in the United States (US). Methods: An IRB-approved, anonymous survey was sent to US medical students from November 2022 through February 2024. Students responded to statements regarding perceptions of bias toward them from attendings, patients, and classmates. Chi-square tests, or Fisher’s exact tests, when appropriate, were used to calculate if significant differences exist among genders or races/ethnicities in response to these statements. Results: 370 students responded to this survey. Most respondents were women (n=259, 70%), and nearly half were White (n=164, 44.3%). 8.5% of women agreed that they felt excluded by attendings due to their gender, compared to 2.9% of men (p=0.018). 87.5% and 73.3% of Hispanic and Black students agreed that bias due to race negatively impacted research opportunities compared to 37.2% of White students (p<0.001). 87% and 85.7% of W..., This data was collected through Google Forms, and respondents were asked to log in with their email addresses to make sure that they could only submit their responses once. Data was processed in R studio., , # Experiences of US medical students - a national survey

https://doi.org/10.5061/dryad.cz8w9gjbq

This dataset contains responses to an anonymous, IRB-approved survey sent to medical students across the country. The survey included demographic information and students' responses to various questions regarding their medical school experience.Â

Description of the data and file structure

The data is structured so that each row is an individual response. A researcher could analyze the data to see what demographic factors are related to various survey responses.Â

There are certain questions on the survey that respondents could respond "NA" to if the question did not apply to them. For example, the last question on the survey asks,

...

The purpose of this project was to examine possible defendant and victim race effects in capital decisions in the federal system. Per the terms of their grant, the researchers selected cases that were handled under the revised Death Penalty Protocol of 1995 and were processed during Attorney General Janet Reno's term in office. The researchers began the project by examining a sample of Department of Justice Capital Case Unit (CCU) case files. These files contained documents submitted by the United States Attorney's Office (USAO), a copy of the indictment, a copy of the Attorney General's Review Committee on Capital Cases (AGRC's) draft and final memorandum to the Attorney General (AG), and a copy of the AG's decision letter. Next, they created a list of the types of data that would be feasible and desirable to collect and constructed a case abstraction form and coding rules for recording data on victims, defendants, and case characteristics from the CCU's hard-copy case files. The record abstractors did not have access to information about defendant or victim gender or race. Victim and defendant race and gender data were obtained from the CCU's electronic files. Five specially trained coders used the case abstraction forms to record and enter salient information in the CCU hard-copy files into a database. Coders worked on only one case at a time. The resulting database contains 312 cases for which defendant- and victim-race data were available for the 94 federal judicial districts. These cases were received by the CCU between January 1, 1995 and July 31, 2000, and for which the AG at the time had made a decision about whether to seek the death penalty prior to December 31, 2000. The 312 cases includes a total of 652 defendants (see SAMPLING for cases not included). The AG made a seek/not-seek decision for 600 of the defendants, with the difference between the counts stemming mainly from defendants pleading guilty prior to the AG making a charging decision. The database was structured to allow researchers to examine two stages in the federal prosecution process, namely the USAO recommendation to seek or not to seek the death penalty and the final AG charging decision. Finally, dispositions (e.g., sentence imposed) were obtained for all but 12 of the defendants in the database. Variables include data about the defendants and victims such as age, gender, race/ethnicity, employment, education, marital status, and the relationship between the defendant and victim. Data are provided on the defendant's citizenship (United States citizen, not United States citizen), place of birth (United States born, foreign born), offense dates, statute code, counts for the ten most serious offenses committed, defendant histories of alcohol abuse, drug abuse, mental illness, physical or sexual abuse as a child, serious head injury, intelligence (IQ), or other claims made in the case. Information is included for up to 13 USAO assessments and 13 AGRC assessments of statutory and non-statutory aggravating factors and mitigating factors. Victim characteristics included living situation and other reported factors, such as being a good citizen, attending school, past abuse by the defendant, gross size difference between the victim and defendant, if the victim was pregnant, if the victim had a physical handicap, mental or emotional problems or developmental disability, and the victim's present or former status (e.g., police informant, prison inmate, law enforment officer). Data are also provided for up to 13 factors each regarding the place and nature of the killing, defendant motive, coperpetrators, weapons, injuries, witnesses, and forensic and other evidence.

Motorcycle Racing Market Outlook

The global motorcycle racing market size is projected to grow from USD 7.5 billion in 2023 to an estimated USD 12.3 billion by 2032, driven by a compound annual growth rate (CAGR) of 5.2%. The surge in market size is primarily attributed to factors such as increasing popularity of motorcycle racing sports, technological advancements in motorcycles, and rising sponsorship and investment in the sports industry. The growing interest among the younger generation and the increasing number of racing events globally are significant contributors to this market's expansion.

One of the major growth factors for the motorcycle racing market is the increasing global popularity of motorsports. Motorcycle racing has captivated audiences worldwide, thanks to its high-speed thrills and the prowess of riders. Events like MotoGP, World Superbike Championship, and other local and international competitions have drawn significant media attention, spectators, and sponsorships. These events not only boost ticket sales but also drive merchandise sales, broadcasting rights, and other revenue streams, contributing to the market's substantial expansion.

Technological advancements in motorcycle engineering are another critical growth driver. Innovations such as advanced aerodynamics, enhanced safety features, and the development of high-performance engines have transformed motorcycle racing. These advancements have not only improved the performance and safety of racing motorcycles but have also heightened the excitement and competitiveness of the sport. Manufacturers invest heavily in research and development to stay ahead in the race, thereby pushing the market forward.

The rising investment and sponsorship in motorcycle racing events also play a pivotal role in the market's growth. Major corporations, automotive brands, and other industries see motorcycle racing as an effective marketing and promotional platform. Sponsorship deals fund racing teams, events, and infrastructure, thus fostering the sport's growth. In addition, the increasing media coverage and the proliferation of digital platforms have made it easier for sponsors to reach a global audience, thereby amplifying their brand visibility and return on investment.

Regionally, Asia Pacific leads the motorcycle racing market, driven by the region's large population, growing middle class, and increasing disposable incomes. Countries like India, China, and Japan are key markets due to their robust motorcycling culture and the presence of numerous racing events. Europe and North America follow closely, with high levels of participation in professional and amateur races, sophisticated racing infrastructure, and a strong tradition of motorsports. Latin America and the Middle East & Africa, while currently smaller markets, are showing promising growth potential due to rising interest and investment in motorsports.

Type Analysis

The motorcycle racing market is segmented by type into road racing, off-road racing, track racing, drag racing, and others. Road racing, which includes popular events like MotoGP and the Isle of Man TT, remains the most prominent segment. This type of racing, conducted on paved tracks, attracts significant viewership and participation due to its high-speed action and the involvement of top-tier racing teams and manufacturers. The professional setup, extensive media coverage, and lucrative sponsorship deals make road racing a major contributor to the market's revenue.

Off-road racing, which includes motocross and enduro events, is another significant segment. This type of racing is conducted on rough terrains and natural obstacles, making it a favorite among adventure enthusiasts and participants who seek adrenaline-pumping experiences. The growth of off-road racing is supported by the increasing number of events and competitions globally, coupled with the rising popularity of adventure sports. The segment is also benefiting from the development of dedicated off-road motorcycles equipped with advanced suspension systems and robust engines.

Track racing, which encompasses different forms such as flat track and speedway racing, is a niche yet growing segment. Track racing is conducted on oval tracks and usually involves dirt or paved surfaces. This type of racing is particularly popular in regions like North America and parts of Europe, where it has a strong historical following. The relative simplicity and lower costs associated with track racing make it accessible to a wider audience, including amateur racers.

Taking a qualitative approach of looking for specific data within a set of organizations that were both POC and PWI, it was determined those to be the two key areas that I would be looking at. Within this, I had taken note of using non-probability sampling, as was the best way to conclusively get the most accurate data possible to format this study. In this study, a total of 40 organizations all throughout the United States. The data accumulated would be based on public records in regards to each organization’s tax form 990 were used. Each of these 990 forms have been released by the organizations into websites, such as Propublica and Guidestar, which were both used to fulfill the data entry. Within establishing a set of key attributes for this study, these are outlined by: race, region, and founding year. It was determined that it would be beneficial run multiple tests on these 40 organizations that would deal with all or any singular attribute as noted. In each test we would specifically look at how both their revenue and contributions particularly correlate to one another and whether it would provide us any further evidence to understand our overall hypothesis.

Context

A single table of the prominent data regarding all Formula E races, derived from Wikipedia race reports.

Content

This data set is merely a single CSV file, backed with all the files I used to create it. This is taken purely from Wikipedia race reports, with some R code to parse the relevant results tables and clean things up.

So while 57 files are available (as of Version 1), the main output file, as shown in the preview is the intended data set to use.

It has not been denormalized, so in it we have race, driver, team, and results information. Race: season, race number, race date, and race name Driver: name Team: car number, team name from Wikipedia, continuity-based team name Results: two forms of rank, grid start, number of laps, report time/retirement message, the points awarded, and the three categories of points

Acknowledgements

Wikipedia's race reports are consistent enough that a couple hours of cleanup was all that was needed to derive this data set. A big thanks is owed to the contributors there. Motorsports Stats information is a bit more expansive and possibly simpler to parse, but I used Wikipedia to keep licensing as simple as possible.

Inspiration

The inspiration for adding this to Kaggle was that it begs a comparison to Formula 1. @vopani has posted the ergast.com data set, and its accessibility had me able to work with the data enough to do some simple predictions. I have not found a Formula E data set that provides the results in one place. Unfortunately I don't know of a source for lap times at all. But with Formula E continually branding themselves as one of the most unpredictable championships in racing, putting this data in Kaggle seemed useful. It's my first true data set, and it's nice to give back to a community I've been part of for so long.

So I aim to add a few notebooks here soon to start this out. I also aim to manually keep it updated through the flurry of Berlin races to finish Season 6, ideally the night following each race using hand-entered results.

Data that is available that I have chosen not to use would be a deeper dive into Qualifying results, and potentially practice times. The qualifying results are already in the HTML pages I've posted here, they'd just need to be parsed. But even with that data in hand with the F1 data set, I have yet to use it other than pre-penalty grid positions. For those that don't know, Formula E's qualifying introduces a negative feedback loop, in that the top 6 of the Championship are forced to qualify in the first group, where the track is frequently very dirty/dusty and has less grip. It is rare that a driver from Group 1 makes it to super pole. And listening to the commentators, they frequently will comment on who "looked fast in practice" so if you had that information it might help predict race finish.

In Italy, the episodes of public hate speech and incitement to hatred motivated by racism have been increasing in the last years. In the first three months of 2020, ***** cases were recorded, while in the previous year, there were ** episodes. Hate speech describes every kind of writing, talking, or behavior which encourage violence towards a person or group on the basis of their identity or characteristics, such as race, religion, or sexual orientation. The data only report the cases collected by source. Thus, the actual cases of racial hate speech and incitement to hatred might be much higher.

Hate speech as political propaganda

Hate speech and incitement to ethnic or racial hatred represent a major issue in Italy. In politics, for instance, this phenomenon is quite widespread. One of the largest political parties in the country, Lega, has been making a significant number of statements based on xenophobia and incitement to hatred. Its secretary, Matteo Salvini, has been focusing a part of its campaign on anti-migration positions, cultivating feelings of hate in the country. During his two years as Minister of the Interior, he declared, among others, that Italian ports were closed to ships carrying rescued migrants.

Against all forms of racial hate

In October 2020, an Extraordinary Commission was founded in Italy to combat racism, anti-Semitism, incitement to hatred, violence, and all forms of racial hate. The decision passed in the Parliament with *** votes in favor, ** abstainers, and no vote against. The right-wing parties Lega, Forza Italia, and Fratelli d'Italia decided to abstain. The Commission was proposed by the Senator Liliana Segre, a Holocaust survivor. Indeed, the commission is also called "Segre Commission".

The application currently consists of the following forms: • Application for Employment (FR 28), which collects information to determine the qualifications of applicants for employment with the Board (such as education and training, employment record, and other information since the time the applicant left high school), • Applicant’s Voluntary Self-Identification (FR 28s), which is an optional form that collects information on the applicant’s gender, race, and ethnicity, and • Research Assistant Candidate Survey of Interests and Computer Experience (FR 28i) if the applicant is applying for a position as a Research Assistant (RA), which collects information on the RA applicant’s level of interest in various economic topics and experience in different data analytics/programs.

Giving money to candidates is an important but unequal form of political voice. Among those Americans worst represented as campaign contributors are Black women and Latinas. While inequalities in income and wealth fuel inequalities in campaign contributions, resources are an incomplete explanation. This study investigates, for Black women and Latinas, whether their views on donations to candidates differ from their views on other forms of civic and political engagement. The results, including the absence of a shared norm about giving to candidates, illuminate the challenges and opportunities of mobilizing a more representative group of campaign contributors.

Release Date: 2024-02-08.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (2020 NES-D Project No. 7504866, Disclosure Review Board (DRB) approval number: CBDRB-FY24-0051)...Key Table Information:.Includes U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series)...Data Items and Other Identifying Records:.Data include estimates on:.Number of nonemployer firms (firms without paid employees). Sales and receipts of nonemployer firms (reported in $1,000s of dollars)...These data are aggregated by the following demographic classifications of firm for:.All firms. Classifiable (firms classifiable by sex, ethnicity, race, and veteran status). . Sex. Female. Male. Equally male/female (50% / 50%). . Ethnicity. Hispanic. Equally Hispanic/non-Hispanic (50% / 50%). Non-Hispanic. . Race. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Minority (Firms classified as any race and ethnicity combination other than non-Hispanic and White). Equally minority/nonminority (50% / 50%). Nonminority (Firms classified as non-Hispanic and White). . Veteran Status (defined as having served in any branch of the U.S. Armed Forces). Veteran. Equally veteran/nonveteran (50% / 50%). Nonveteran. . . . Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status). ...The data are also shown by the following legal form of organization (LFO) categories:. S-Corporations. C-Corporations. Individual proprietorships. Partnerships...Data Notes:.. Business ownership is defined as having 51 percent or more of the stock or equity in the business. Data are provided for firms owned equally (50% / 50%) by men and women, by Hispanics and non-Hispanics, by minorities and nonminorities, and by veterans and nonveterans. Firms not classifiable by sex, ethnicity, race, and veteran status are counted and tabulated separately.. The detail may not add to the total or subtotal because a Hispanic firm may be of any race; because a firm could be tabulated in more than one racial group; or because the number of nonemployer firm's data are rounded.. For C-corporations, there is no tax form or business registry that clearly and unequivocally identifies all owners of this type of business. For this reason, the Census Bureau is unable to assign demographic characteristics for C-corporations. Data for C-corporations are included in the published tables but are not shown by the demographic characteristics of the firms....Industry and Geography Coverage:.The data are shown for the total for all sectors (00) and 2-digit NAICS code levels for:..United States. States and the District of Columbia. Metropolitan Statistical Areas...Data are also shown for the 3- and 4-digit NAICS code for:..United States...Data are excluded for the following NAICS industries:.Crop and Animal Production (NAICS 111 and 112). Rail Transportation (NAICS 482). Postal Service (NAICS 491). Monetary Authorities-Central Bank (NAICS 521). Funds, Trusts, and Other Financial Vehicles (NAICS 525). Management of Companies and Enterprises (NAICS 55). Private Households (NAICS 814). Public Administration (NAICS 92). Industries Not Classified (NAICS 99)...For more information about NAICS, see NAICS Codes & Understanding Industry Classification Systems. For information about geographies used by economic programs at the Census Bureau, see Economic Census: Economic Geographies...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/abs/data/2020/AB2000NESD03.zip...API Information:.Nonemployer Demographic Statistics data are housed in the Census Bureau API. For more information, see https://api.census.gov/data/2020/absnesd.html...Symbols:. D - Withheld to avoid disclosing data for individual companies; data are included in higher level totals. S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.. N - Not available or not comparable. X - Not applicable..The following symbols are used to identify the...

In January 2018, 798 Hispanic/Latino adults living in the United States were recruited through Qualtrics Panels to complete a survey in English or Spanish. Respondents were diverse in their nativity (e.g., 52% Mexican or Mexican American; 17% Puerto Rican; 8.5% Cuban). The survey included the following measures: -Demographic and Health Information – Demographic and Health Data Questionnaire (DHDQ). This researcher-constructed questionnaire is designed to obtain participant information such as: (a) race/ethnicity, (b) age, (c) gender, (d) sexual orientation, (e) relationship status, (f) household income, (g) generational status, (h) education level, (i) presence of chronic health conditions, (j) self-reported height and weight, (k) overall health status, (l) native language and proficient language(s), (m) number of health care visits in the past year, and (n) perceived weight. -Media and Technology Usage and Attitudes Scale (MTUAS). The Media and Technology Usage and Attitudes Scale is a 60-item scale used to measure the frequency of use from specific forms of media and attitudes toward technology (Rosen, Whaling, Carrier, Cheever, & Rokkum, 2013). The scale consists of eleven media usage subscales and four attitude subscales. For the purposes of this study, only the smartphone usage subscale will be included (9 items). Prompts assessing the frequency of technology use stated: “Please indicate how often you do each of the following…” and asked about smartphone usage habits on a scale from 1(Never) to 10 (All the time). Higher scores are indicative of more technology use. The MTUAS was found to show sufficient proof of reliability for smartphone usage subscale (α = .93). Validity has also been shown through comparisons with measures of daily media usage hours, technology-related anxiety, and the Internet Addiction Test (Rosen et al., 2013). -The Sedentary Behavior Questionnaire (SBQ). The Sedentary Behavior Questionnaire is an 18-item scale designed to assess nine different sedentary behaviors including the use of technological devices, hobbies, and sitting due to transportation and work (Rosenberg et al., 2010). The measure is designed to assess sedentary behaviors over weekdays as well as the weekend and then are multiplied to estimate the sum amounts of sedentary hours during a week/weekend. The scale consisted of nine items with answer choices ranging from 1 (None) to 9 (6 hours or more). The current study will slightly alter the SBQ as some of the items may be dated in regards to the technology. An example is “sitting listening to music on the radio, tapes, or CDs.” The examples used in the items will be reflective of sedentary forms of technology used nowadays. The SBQ has been found to be a reliable measure for sedentary behaviors as intraclass correlation coefficients found that the items were sufficient for both weekday (.64-.90) and weekends (.51-.93). Validity of the measure was also sufficient as partial correlations were used to compare the self-reported ratings of the SBQ to accelerometer measures of activity. The study also found that in comparison to the International Physical Activity Questionnaire and body mass index, there were significant correlations with both male and female samples (Rosenberg et al., 2010). -PHQ-9- English: The Patient Health Questionnaire (PHQ-9). The PHQ-9 is a 9-item instrument that measures depressive symptoms (Kroenke, Spitzer, & Williams, 2001). Instructions on the PHQ-9 are as follows: “Over the last 2 weeks, how often have you been bothered by any of the following problems?” The assessment uses a 4-point Likert-type scale with responses ranging from 0 (not at all) to 3 (nearly every day). Scores for PHQ-9 scale are determined by assigning a score to each response ranging from 0 to 3 and then summing the responses. The PHQ-9 score can range from 0 to 27. Higher scores on the measure indicate higher levels of depressive symptoms. -Health Promoting Behaviors – Health Promoting Lifestyle Profile II (HPLP-II). The HPLP-II is a 52-item inventory designed to measure engagement in behaviors that characterize a health-promoting lifestyle (Walker, Sechrist, Pender, 1995). The HPLPII is comprised of a scale and six subscales, which include Spiritual Growth, Interpersonal Relations, Nutrition, Physical Activity, Health Responsibility, and Stress Management. Only the Nutrition (9 items) and Physical Activity (8 items) subscales will be used for the current study. Instructions on the HPLP-II are to indicate level of engagement in each listed behavior using a Likert-type scale, with responses ranging from 1 (never) to 4 (routinely). Scores for the HPLP-II scale and subscale are determined by calculating means for each. Higher scores on the scale and subscales indicate higher levels of engagement in the assessed health promoti... Visit https://dataone.org/datasets/sha256%3A947312a2e719300f2006c0c8f48294d38a5b6a63ad0f31869ed48ea690048cde for complete metadata about this dataset.

This component of the Philadelphia Social History Project examines the demographic composition of city grid squares using census data from years 1850, 1860, 1870, and 1880. The collection consists of two types of data files: (1) grid tallies, and (2) grid dictionaries. The grid tally files consist of counts of individuals living in PSHP grid squares, with totals broken down by race/ethnicity, sex, and age. The grid dictionary files link lines in the census manuscripts to PSHP grid squares, allowing users to follow the movements of census-takers as they moved house-to-house on foot, adding individuals to the printed census manuscript forms. The "grid" network consists of a set of vertical and horizontal lines drawn at fixed intervals across a city map, forming the foundation for the spatial organization of the data. The grid dictionary files show when census-takers crossed from one grid square to another; each row in the grid dictionary describes a set of rows that are in a specific grid square by listing the starting page/line and the ending page/line.