TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

Community Specific Profiles are grouped by race and ethnicity. We measure by race, ethnicity, and other demographics to understand the specific needs of different communities and evaluate effective service delivery and accountability. This dataset is the groupings used to combine projects with multiple levels and types of data standards. These include the minimum and comprehensive race and ethnicity categories from the City of Portland Rescue Plan Data Standards. They also include race and ethnicity categories in the HUD HMIS data standards.-- Additional Information: Category: ARPA Update Frequency: As Necessary-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=60968

In 1980, the National Institute of Justice awarded a grant to the Cornell University College of Human Ecology for the establishment of the Center for the Study of Race, Crime, and Social Policy in Oakland, California. This center mounted a long-term research project that sought to explain the wide variation in crime statistics by race and ethnicity. Using information from eight ethnic communities in Oakland, California, representing working- and middle-class Black, White, Chinese, and Hispanic groups, as well as additional data from Oakland's justice systems and local organizations, the center conducted empirical research to describe the criminalization process and to explore the relationship between race and crime. The differences in observed patterns and levels of crime were analyzed in terms of: (1) the abilities of local ethnic communities to contribute to, resist, neutralize, or otherwise affect the criminalization of its members, (2) the impacts of criminal justice policies on ethnic communities and their members, and (3) the cumulative impacts of criminal justice agency decisions on the processing of individuals in the system. Administrative records data were gathered from two sources, the Alameda County Criminal Oriented Records Production System (CORPUS) (Part 1) and the Oakland District Attorney Legal Information System (DALITE) (Part 2). In addition to collecting administrative data, the researchers also surveyed residents (Part 3), police officers (Part 4), and public defenders and district attorneys (Part 5). The eight study areas included a middle- and low-income pair of census tracts for each of the four racial/ethnic groups: white, Black, Hispanic, and Asian. Part 1, Criminal Oriented Records Production System (CORPUS) Data, contains information on offenders' most serious felony and misdemeanor arrests, dispositions, offense codes, bail arrangements, fines, jail terms, and pleas for both current and prior arrests in Alameda County. Demographic variables include age, sex, race, and marital status. Variables in Part 2, District Attorney Legal Information System (DALITE) Data, include current and prior charges, days from offense to charge, disposition, and arrest, plea agreement conditions, final results from both municipal court and superior court, sentence outcomes, date and outcome of arraignment, disposition, and sentence, number and type of enhancements, numbers of convictions, mistrials, acquittals, insanity pleas, and dismissals, and factors that determined the prison term. For Part 3, Oakland Community Crime Survey Data, researchers interviewed 1,930 Oakland residents from eight communities. Information was gathered from community residents on the quality of schools, shopping, and transportation in their neighborhoods, the neighborhood's racial composition, neighborhood problems, such as noise, abandoned buildings, and drugs, level of crime in the neighborhood, chances of being victimized, how respondents would describe certain types of criminals in terms of age, race, education, and work history, community involvement, crime prevention measures, the performance of the police, judges, and attorneys, victimization experiences, and fear of certain types of crimes. Demographic variables include age, sex, race, and family status. For Part 4, Oakland Police Department Survey Data, Oakland County police officers were asked about why they joined the police force, how they perceived their role, aspects of a good and a bad police officer, why they believed crime was down, and how they would describe certain beats in terms of drug availability, crime rates, socioeconomic status, number of juveniles, potential for violence, residential versus commercial, and degree of danger. Officers were also asked about problems particular neighborhoods were experiencing, strategies for reducing crime, difficulties in doing police work well, and work conditions. Demographic variables include age, sex, race, marital status, level of education, and years on the force. In Part 5, Public Defender/District Attorney Survey Data, public defenders and district attorneys were queried regarding which offenses were increasing most rapidly in Oakland, and they were asked to rank certain offenses in terms of seriousness. Respondents were also asked about the public's influence on criminal justice agencies and on the performance of certain criminal justice agencies. Respondents were presented with a list of crimes and asked how typical these offenses were and what factors influenced their decisions about such cases (e.g., intent, motive, evidence, behavior, prior history, injury or loss, substance abuse, emotional trauma). Other variables measured how often and under what circumstances the public defender and client and the public defender and the district attorney agreed on the case, defendant characteristics in terms of who should not be put on the stand, the effects of Proposition 8, public defender and district attorney plea guidelines, attorney discretion, and advantageous and disadvantageous characteristics of a defendant. Demographic variables include age, sex, race, marital status, religion, years of experience, and area of responsibility.

According to a survey conducted in 2021, ** percent of White Americans had a favorable opinion of private infant adoption in the United States. In comparison, ** percent of Hispanic Americans and ** percent of Black Americans shared this belief.

The most common blood type among the population in the United States is O-positive. Around 53 percent of the Latino-American population in the U.S. has blood type O-positive, while only around 37 percent of the Caucasian population has this blood type. The second most common blood type in the United States is A-positive. Around 33 percent of the Caucasian population in the United States has A-positive blood type. Blood type O-negative Those with blood type O-negative are universal donors as this type of blood can be used in transfusions for any blood type. O-negative blood type is most common in the U.S. among Caucasian adults. Around eight percent of the Caucasian population has type O-negative blood, while only around one percent of the Asian population has this blood type. Only around seven percent of all adults in the United States have O-negative blood type. Blood Donations The American Red Cross estimates that someone in the United States needs blood every two seconds. However, only around three percent of age-eligible people donate blood yearly. The percentage of adults who donated blood in the United States has not fluctuated much for the past two decades. In 2021, around 15 percent of U.S. adults donated blood, the same share reported in the year 2003.

Context

This dataset contains data of horse racings from 1990 till 2020.

Content

There are two different file types, races and horses, one pair for each year from 1990. I hope to update the current year data on a regular basis.

races_* columns description:

rid - Race id; course - Course of the race, country code in brackets, AW means All Weather, no brackets means UK; time - Time of the race in hh:mm format, London TZ; date - Date of the race; title - Title of the race; rclass - Race class; band - Band; ages - Ages allowed distance - Distance; condition - Surface condition; hurdles - Hurdles, their type and amount; prizes - Places prizes; winningTime - Best time shown; prize - Prizes total (sum of prizes column); metric - Distance in meters; countryCode - Country of the race; ncond - condition type (created from condition feature); class - class type (created from rclass feature).

horses_* columns description:

rid - Race id; horseName - Horse name; age - Horse age; saddle - Saddle # where horse starts; decimalPrice - 1/Decimal price; isFav - Was horse favorite before start? Can be more then one fav in a race; trainerName - Trainer name; jockeyName - Jockey name; position - Finishing position, 40 if horse didn't finish; positionL - how far a horse has finished from the pursued horse, horses corpses; dist - how far a horse has finished from a winner, horses corpses; weightSt - Horse weight in St; weightLb - Horse weight in Lb; overWeight - Overweight code; outHandicap - Handicap; headGear - Head gear code; RPR - RP Rating; TR - Topspeed; OR - Official Rating father - Horse's Father name; mother - Horse's Mother name; gfather - Horse's Grandfather name; runners - Runners total; margin - Sum of decimalPrices for the race; weight - Horse weight in kg; res_win - Horse won or not; res_place - Horse placed or not

forward.csv contains information collected prior a race starts. The odds are averages from from Oddschecker.com, RPRc and TRc also have current values.

Note

Please be aware, the prices provided are the SP (starting prices), and they are not available before race starts. This means prices before start may differ from SP. But usually favorites stay the same, and prices on them often higher then SP. Anyway you can't predict profit with accuracy based only on SP prices.

Inspiration

I suppose prediction of horse racing results by machine learning methods is a difficult task. There is no any highly correlated features, the outcome classes are imbalanced. I tried to make my own predictions, but with no luck. I hope to get some inspirations from your research. Please, share your experience with everyone or just with me. Thank you!

Disclaimer

The data provided has been collected from public open websites, without sign-ups, log-ins and other restrictions from sources. Please, do not use this data for any commercial purposes.

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

The Korea Racing Authority provides information on sanctions imposed by race referees for races held at racecourses in Seoul, Busan, Gyeongnam, and Jeju. (Provided information includes racecourses, race numbers, race dates, target categories, start numbers, target unique numbers, horse names, types of sanctions, specific details of sanctions, sanction start dates, sanction end dates, and fines.) ○ The types of sanctions included in the information include reprimands, ineligibility for racing, exclusion from racing, fines, suspension from riding, license suspension, changes in ranking, disqualification, suspension from training, cautions, driving evaluation, driving suspension, starting evaluation, exclusion from starting, suspension from participating, exclusion from participating, cancellation of participation, and special races.

This dataset contains the yearly statistics on the race and ethnicity of known offenders by type of offense. Major categories of offense types include crimes against persons, crimes against property and crimes against society. Here Known Offenders indicates that some aspects of the suspect are identified, thus distinguishing from an unknown offender.

An Open Context "types" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This record is part of the "Murlo" data publication.

These data examine the effects on total crime rates of changes in the demographic composition of the population and changes in criminality of specific age and race groups. The collection contains estimates from national data of annual age-by-race specific arrest rates and crime rates for murder, robbery, and burglary over the 21-year period 1965-1985. The data address the following questions: (1) Are the crime rates reported by the Uniform Crime Reports (UCR) data series valid indicators of national crime trends? (2) How much of the change between 1965 and 1985 in total crime rates for murder, robbery, and burglary is attributable to changes in the age and race composition of the population, and how much is accounted for by changes in crime rates within age-by-race specific subgroups? (3) What are the effects of age and race on subgroup crime rates for murder, robbery, and burglary? (4) What is the effect of time period on subgroup crime rates for murder, robbery, and burglary? (5) What is the effect of birth cohort, particularly the effect of the very large (baby-boom) cohorts following World War II, on subgroup crime rates for murder, robbery, and burglary? (6) What is the effect of interactions among age, race, time period, and cohort on subgroup crime rates for murder, robbery, and burglary? (7) How do patterns of age-by-race specific crime rates for murder, robbery, and burglary compare for different demographic subgroups? The variables in this study fall into four categories. The first category includes variables that define the race-age cohort of the unit of observation. The values of these variables are directly available from UCR and include year of observation (from 1965-1985), age group, and race. The second category of variables were computed using UCR data pertaining to the first category of variables. These are period, birth cohort of age group in each year, and average cohort size for each single age within each single group. The third category includes variables that describe the annual age-by-race specific arrest rates for the different crime types. These variables were estimated for race, age, group, crime type, and year using data directly available from UCR and population estimates from Census publications. The fourth category includes variables similar to the third group. Data for estimating these variables were derived from available UCR data on the total number of offenses known to the police and total arrests in combination with the age-by-race specific arrest rates for the different crime types.

** percent of Metlife's executive group and board of directors were Black or African American as of 2023. However, across all types of position - sales, non-sales, executive and board - a large majority of the company's staff members were white. The executive leadership team and sales saw the highest percentages of white employees, at ** percent and ** percent, respectively.

The use of Most or Moderately effective contraceptive (M/M) or Long-Acting Reversible Contraceptive (LARC) types by race/ethnicity, contraceptive type, age group, and year of interest, for 2014-2016. This data was compiled for the Measure CCW: Contraceptive Care – All Women Ages 15-44, as part of the Maternal and Infant Health Initiative, Contraceptive Care Quality grant.

In 2022, a survey on tobacco dependence in the U.S. found around 54.4 percent of white smokers used health professional advice to quit smoking, while 51.7 percent received health professional assistance. This statistic displays the share of U.S. adult smokers who used select types of assistance to quit smoking as of 2022, by race and ethnicity.

Faced with demographic change, many colleges are offering courses on race and ethnicity. How does taking race-centered courses affect public opinion? We theorize that while White, Latino, and Asian American students develop inclusive political attitudes through race-centered coursework, Black Americans may already enter college with a deeper understanding about racial issues. We test these expectations using two longitudinal multi-racial datasets. First, using a national panel survey of college students, we find that ethnic studies coursework is associated with increased recognition of racial discrimination among Whites, Latinos, and even Black Americans. Second, using an original panel survey from a public university, we find reduced racial resentment and increased affirmative action support - albeit varied - among Whites, Latinos, and Asian Americans after completing race-centered political science classes but not in placebo politics classes that were not focused on race. Our findings have implications for conversations about race-centered coursework in higher education.

This study analysed sprint kayak pacing profiles in order to categorise and compare an athlete’s race profile throughout their career. We used functional principal component analysis of normalised velocity data for 500m and 1000m races to quantify pacing. The first four principal components explained 90.77% of the variation over 500m and 78.80% over 1000m. These principal components were then associated with unique pacing characteristics with the first component defined as a dropoff in velocity and the second component defined as a kick. All other defined characteristics were a variation of these two, i.e., late kick. We then applied a Hidden Markov model to categorise each profile over an athlete’s career, using the PC scores, into different types of race profiles. This model included age and event type and identified a trend for a higher dropoff in development pathway athletes. Using the four different race profile types, four athletes had all their race profiles throughout their careers analysed. It was identified that an athlete’s pacing profile changes throughout their career as an athlete matures. This information provides coaches, practitioners and athletes with expectations as to how pacing profiles change across the course of an athlete’s career.

General Overview

I created this dataset originally to preserve NASCAR stage results. NASCAR's official website preserves full stage results, but as of now, only 2023-2025 results are available there. The results from earlier years were removed and links to them redirect to Racing Reference, a site which stores all NASCAR (and many other racing series) results since the beginning of the sport. However, that site only records the top 10 stage finishers for each race (points earning positions). To see full stage 1 and stage 2 finishing orders from 2017 (the first year of stage racing) through 2022, you had to use the Wayback Machine on NASCAR.com...until now.

Originality

• Only publicly available dataset (I'm aware of) that contains NASCAR results and points-per-race data all in one file, so to be useful for analysis and predictive modeling
• Only NASCAR results source containing team names (as opposed to names of owners on Racing Reference)
• Only NASCAR results source with full stage finishing orders going back to 2017 (still looking for complete stage finishing orders, beyond just top 10s, from Race 23 of 2017 through Race 36 of 2019)

Attribute overview

1) year - year in which the observation took place

2) race_num - order of races each season, with 1 being the first and 36 being the last - to include the pre-season Daytona Duel races (which count as a stage), I gave Duel 1 the value '-1' and Duel 2 the value '-2'

3) track - shortened name of the track at which the observation took place - tracks with terrain changes / multiple layouts were considered separate tracks, ex: Bristol and Bristol Dirt, Daytona and Daytona Road Course

4) track_type - 6 different track types including: short tracks (less than 1 mile), short intermediates (more than 1 mile and less than 1.5 miles), intermediates (roughly 1.5 miles / "cookie cutters"), long intermediates (longer than 1.5 miles but not superspeedways or road courses), superspeedways (drafting / pack racing tracks), road course (tracks with both left and right turns) - notably, Atlanta was the only track to change its track type in the observed years, being reconfigured from intermediate to superspeedway in 2022

5) fin - final finishing order of each race

6) start - starting order of each race (no distinction between traditional qualifying and formula / owners-points-based starting lineups)

7) car_num - the number printed on the race car, distinct from all other competitors in each race

8) driver - full name of the driver

9) manu - manufacturer of the car

10) team_name - name of the team which fielded the driver and car in a particular observation

11) laps - number of laps the driver completed in the race

12) laps_led - number of laps the driver led in the race

13) status - 'running' for drivers who finished the race; otherwise, the reason they failed to finish - 'accident' is a synonym for 'crash' (used interchangeably on different sources)

14) points - total points earned in the race - Note 1: drivers who did not declare for Cup points earned 0 points regardless of finishing position - Note 2: disqualified drivers are given last place points, and if there is more than one disqualified driver, they will be ordered by their original finishing position behind everyone else

15) stage_1 - finishing position in stage 1

16) stage-2 - finishing position in stage 2

17) stage_3_or_duel - finishing position in stage 3 (Coca Cola 600 only) or Daytona Duel race (counts as a stage)

18) stage_points - total stage points earned in the race - Note 1: drivers who did not declare for Cup points earned 0 points regardless of stage finishing position, as did disqualified drivers, and Championship 4 drivers in the season finale - Note 2: if a disqualified driver would have earned stage points, their points were given to the next finisher, and so on, so that the original 11th place finisher earns 1 stage point

Background: Ancestry is often viewed as a more objective and less objectionable population descriptor than race or ethnicity. Perhaps reflecting this, usage of the term “ancestry” is rapidly growing in genetics research, with ancestry groups referenced in many situations. The appropriate usage of population descriptors in genetics research is an ongoing source of debate. Sound normative guidance should rest on an empirical understanding of current usage; in the case of ancestry, questions about how researchers use the concept, and what they mean by it, remain unanswered.Methods: Systematic literature analysis of 205 articles at least tangentially related to human health from diverse disciplines that use the concept of ancestry, and semi-structured interviews with 44 lead authors of some of those articles.Results: Ancestry is relied on to structure research questions and key methodological approaches. Yet researchers struggle to define it, and/or offer diverse definitions. For some ancestry is a genetic concept, but for many—including geneticists—ancestry is only tangentially related to genetics. For some interviewees, ancestry is explicitly equated to ethnicity; for others it is explicitly distanced from it. Ancestry is operationalized using multiple data types (including genetic variation and self-reported identities), though for a large fraction of articles (26%) it is impossible to tell which data types were used. Across the literature and interviews there is no consistent understanding of how ancestry relates to genetic concepts (including genetic ancestry and population structure), nor how these genetic concepts relate to each other. Beyond this conceptual confusion, practices related to summarizing patterns of genetic variation often rest on uninterrogated conventions. Continental labels are by far the most common type of label applied to ancestry groups. We observed many instances of slippage between reference to ancestry groups and racial groups.Conclusion: Ancestry is in practice a highly ambiguous concept, and far from an objective counterpart to race or ethnicity. It is not uniquely a “biological” construct, and it does not represent a “safe haven” for researchers seeking to avoid evoking race or ethnicity in their work. Distinguishing genetic ancestry from ancestry more broadly will be a necessary part of providing conceptual clarity.

The statistic presents information on the favorability of selected television show genres among adults in the United States as of November 2018, broken down by ethnicity. The findings reveal that TV shows within the science and nature category were less popular with African American respondents than with other ethnic groups, with ** percent saying that they found the genre very or somewhat favorable compared to ** percent of White respondents and ** percent of Hispanics.

Growing evidence suggests that physical activity lowers the risk of several types of cancer. In 2022, Black people in the United States were the racial group with the highest incidence of physical inactivity-associated cancers, with a rate of nearly 102 cases per 100,000 people. This graph shows the rate of new physical inactivity-related cancers per 100,000 people in the United States in 2022, by race and ethnic origin.