Incompatible reaction, indicating the race does not cause disease to the differential.*Compatible reaction, indicating the race can cause disease to the differential.

As of May 2025, there were 26,549 Hispanic candidates on the organ waiting list in the United States. Organ donation can be given through both a deceased and living donor if blood and oxygen are flowing through the organs until the time of recovery to ensure viability. There are over 100,000 people in the country waiting for an organ transplant. This statistic displays the number of candidates on organ donation waiting list in the United States, as of May 6, 2025, by race and ethnicity.

Table published by the Connecticut Department of Public Health that contains reportable disease data. Each row of data represents a case of disease in a person with their reported race/ethnicity. Information on race/ethnicity is gathered from individuals during case interviews. Reported race and ethnicity information is used create a single race/ethnicity variable. People with more than one race are classified as two or more races. People with Hispanic ethnicity are classified as Hispanic regardless of reported race(s). People with a missing ethnicity are classified as non-Hispanic. All data are preliminary; data for previous weeks are routinely updated as new reports are received, duplicate records are removed, and data errors are corrected. The following disease(s) are included in this table: MPOX (previously called Monkeypox), Influenza

Predicting Horse Races Outcomes

A dataset of horse races collected from multiple sources

About this dataset

The horse races dataset contains information on horse races, runners, and weather conditions. The data can be used to predict the winner of a race, as well as the payout odds for each horse

How to use the dataset

This dataset can be used to predict horse race outcomes. The data includes information on the horses, jockeys, trainers, and other factors that may influence the outcome of a race

Research Ideas

• Use the data to predict which horse will win a race.
• Use the data to predict which horse will place in a race.
• Use the dataset to create features that can be used to train a machine learning model to predict horse races

Acknowledgements

License

License: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for non-commercial purposes only. - Adapt - remix, transform, and build upon the material for non-commercial purposes only. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - You may not: - Use the material for commercial purposes.

Columns

File: runners.csv | Column name | Description | |:-------------------------|:----------------------------------------------------------------------------------------| | collected_at | The date and time at which the data was collected. (DateTime) | | position | The position of the horse in the race. (Integer) | | margin | The margin of the horse in the race. (Float) | | handicap_weight | The handicap weight of the horse in the race. (Float) | | number | The number of the horse in the race. (Integer) | | barrier | The barrier of the horse in the race. (Integer) | | blinkers | Whether or not the horse is wearing blinkers in the race. (Boolean) | | emergency | Whether or not the horse is an emergency in the race. (Boolean) | | form_rating_one | The form rating of the horse in the race. (Float) | | form_rating_two | The form rating of the horse in the race. (Float) | | form_rating_three | The form rating of the horse in the race. (Float) | | last_five_starts | The last five starts of the horse in the race. (List of integers) | | favourite_odds_win | The odds of the horse winning the race if it is the favourite. (Float) | | favourite_odds_place | The odds of the horse placing in the race if it is the favourite. (Float) | | favourite_pool_win | The pool of money that will be won if the favourite horse wins the race. (Float) | | favourite_pool_place | The pool of money that will be won if the favourite horse places in the race. (Float) | | tip_one_win | The odds of the horse winning the race if it is tipped by the first tipster. (Float) | | tip_one_place | The odds of the horse placing in the race if it is tipped by the first tipster. (Float) | | tip_two_win | The odds of the horse winning the race if it is tipped by the second tipster. (Float) | | tip_two_place | The odds of the horse placing in the race if it is tipped by the second tipster. ( |

File: weathers.csv | Column name | Description | |:--------------|:--------------------------------| | name | The name of the horse. (String) |

File: odds.csv | Column name | Description | |:-----------------------------|:------------------------------------------------------------------------------| | collected_at | The date and time at which the data was collected. (DateTime) | | odds_one_win | The odds of the first horse winning the race. (Float) ...

This dataset represents the popular last names in the United States for people of two or more races.

Primary election 2011 list of races

Dataset

This dataset was created by hadley laine

Contents

A large and fast-growing number of studies across the social sciences use experiments to better understand the role of race in human interactions, particularly in the American context. Researchers often use names to signal the race of individuals portrayed in these experiments. However, those names might also signal other attributes, such as socioeconomic status (e.g., education and income) and citizenship. If they do, researchers need pre-tested names with data on perceptions of these attributes. Such data would permit researchers to draw correct inferences about the causal effect of race in their experiments. In this paper, we provide the largest dataset of validated name perceptions based on three different surveys conducted in the United States. In total, our data include over 44,170 name evaluations from 4,026 respondents for 600 names. In addition to respondent perceptions of race, income, education, and citizenship from names, our data also include respondent characteristics. Our data will be broadly helpful for researchers conducting experiments on the manifold ways in which race shapes American life.

This dataset contains individual finisher information and race information for all marathons run in the United States in 2023.

I originally collected this information for a series of articles that I published on Medium, exploring alternative ways to age grade marathon performances. Ultimately, I used the data to calculate a set of tables for scoring marathon results based on percentiles (see the calculator here).

The dataset includes the individual results from 641 races. The gender, age, and finish (in seconds) for each of approximately 429,000 runners is included.

The list of races is based on results publicly available at Marathon Guide, with the addition of a couple large races that were missing from Marathon Guide. The individual results were scraped from either Marathon Guide, Athlinks, or an individual race website.

We provide datasets that that estimate the racial distributions associated with first, middle, and last names in the United States. The datasets cover five racial categories: White, Black, Hispanic, Asian, and Other. The provided data are computed from the voter files of six Southern states -- Alabama, Florida, Georgia, Louisiana, North Carolina, and South Carolina -- that collect race and ethnicity data upon registration. We include seven voter files per state, sourced between 2018 and 2021 from L2, Inc. Together, these states have approximately 36MM individuals who provide self-reported race and ethnicity. The last name datasets includes 338K surnames, while the middle name dictionaries contains 126K middle names and the first name datasets includes 136K first names. For each type of name, we provide a dataset of P(race | name) probabilities and P(name | race) probabilities. We include only names that appear at least 25 times across the 42 (= 7 voter files * 6 states) voter files in our dataset. These data are closely related to the the dataset: "Name Dictionaries for "wru" R Package", https://doi.org/10.7910/DVN/7TRYAC. These are the probabilities used in the latest iteration of the "WRU" package (Khanna et al., 2022) to make probabilistic predictions about the race of individuals, given their names and geolocations.

List of Top Disciplines of Race and Social Problems sorted by citations.

A growing body of research uses names to cue experimental subjects about race, ethnicity, and gender. However, researchers have not explored the myriad of characteristics that might be signaled by these names. In this paper, we introduce a large, publicly available database of the attributes associated with common American first and last names. For 1,000 first names and 21 last names, we provide ratings of perceived race; for 336 first names, we provide ratings on 26 social and personal characteristics. We show that the traits associated with first names vary widely, even among names associated with the same race and gender. Researchers using names to signal group memberships are thus likely cuing a number of other attributes as well. We demonstrate the importance of name selection by replicating DeSante (2013). We conclude by outlining two approaches researchers can use to choose names that successfully cue race (and gender) while minimizing potential confounds.

This layer contains a Vermont-only subset of county level 2020 Decennial Census redistricting data as reported by the U.S. Census Bureau for all states plus DC and Puerto Rico. The attributes come from the 2020 Public Law 94-171 (P.L. 94-171) tables.

Forword

Gambling is bad, m'kay.

This repository provides horse race data for the Hong Kong Jockey Club and the Singpore Turf Club. The data was obtained by scraping their respective public websites, and comes with no guarantee of correctness whatsoever.

A particularly cool thing is that we also provides historical odds for a period of time for HKJC race. Being able to predict what would be the final odds for a given horse on a given race is extremely valuable, but historical data are, as far as we know, not publicly available. We thus wrote a scraper, that ran for 2 seasons, that probed the odds at regular interval up to the race start. This allows for cool time series analysis that can't be done with historical data available on the public websites.

That dataset is provided as a set of compressed CSV files, that can easily be reloaded to a database of your choice, a pandas dataframe, or even Excel if you don't know any better. The HKJC website is just a little less crappy that the TurfClub one, in general HK data contains more information than their Singaporean counterpart.

Original Data

Dataset

horses

List of all the horses (some retired) for HKJC and SGTC that ran a race, up to 2018-07-01.

performances

Each row of this table is the result for a single horse in a single race, with their position, final odds (for first place -- more explicit dividends can be found in the all_dividends table for HK races). This is the main source of information for the statistics you want. Note that some races found in the performance table do NOT have their counterpart in the races table.

This contains historical results from 1979 up to 2018-06-27 for Hong Kong, and 2002-03-08 to 2018-04-24 for Singapore.

races

List of all the races ran between 2016-09-28 and 2018-06-27 for Hong Kong and 2016-09-25 to 2018-04-24 for Singapore. Note that some races not found in this table still have available performances in the performances table.

all_dividends

Each row of this table contains the JSON-encoded dividend results (which can be used to infer the final odds) for each race ran in Hong Kong between 2016-09-28 and 2018-06-27.

sectional_times

Each row contains the sectional times for races ran between 2008-06-05 and 2018-06-27. That's basically, for a given horse in a given race, what was their placing and time at given section of the track.

live_odds

Live odds evolution for Hong Kong race ran between 2016-09-27 and 2018-06-27. HKJC is a "pari-mutuel" system where odds for a given horse / bet evolve up to the start of the race. This dataset was collected by poking for the odds at various interval before a race (with the interval getting smaller as the race was getting closer, since that's when the odds tend to vary the most). As far as we can tell, this kind of information can not be found in historical dataset, and can only be collected in real-time.

Acknowledgement

Foto von Gene Devine auf Unsplash

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/.

About this dataset

Context

This list ranks the 1208 cities in the Texas by Multi-Racial Some Other Race (SOR) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities 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 Multi-Racial Other Race Population: This column displays the rank of cities in the Texas by their Multi-Racial Some Other Race (SOR) population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Multi-Racial Other Race Population: The Multi-Racial Other Race population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Multi-Racial Other Race. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Texas Multi-Racial Other Race Population: This tells us how much of the entire Texas Multi-Racial Other Race population lives in that cities. 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/.

List of Top Journals of Race Ethnicity and Education sorted by citations.

The list includes 4,250 first names and information on their respective count and proportions across six mutually exclusive racial and Hispanic origin groups. These six categories are consistent with the categories used in the Census Bureau's surname list.

See: https://oganm.github.io/dndstats/ Source: https://github.com/oganm/dnddata

About the data

Unique characters are acquired by grouping the characters that share the same name and class and picking the higher level version. This could have merged independent characters with tropey names like Grognak the Barbarian of Drizzt the Ranger but manual examination of the data showed no cases of characters who appear to be made by different people but still has the same name and class.

If a multi-classed character shares name with a single classed character, I assume they are duplicates if the single classed character is lower level and its class matches with one of the classes of the multi-classed character.

Any character above level 20 (there were 6) were removed.

9 Revised Rangers were merged back into the ranger class.

Most percentages are rounded to the nearest integer.

As all data, this data comes with caveats. It is a subset of all DnD players who are using a particular mobile application who also know about and use my applications and consented to let me to keep their character sheets. I don’t have reason to think that these would be enriching certain character building choices but it’s something to keep in mind.

In most parts of this document no information is provided about whether or not the differences are actually statistically significant. Sorry about that. Didn’t want to fill this place with too much math. For instance we can see that we have 24 battle masters vs 26 champions. This is not a statistically significant difference based on our sample size so we cannot state with high confidence that one is more popular than the other.

If you are interested in significance of any of these measures, you can take a peak at this article on Wikipedia where formulas needed are explained. For some of these at least you should be able to get the information you need from the article.

Data access

This dataset is present in 2 forms: in its entirety that includes duplicates of characters and filtered version that only includes unique characters.

Go here for the complete data and here for the filtered one. Click the raw button to get them in plain text. Both have the same columns as explained below. The code to generate these tables can be found here.

Below are the descriptions of the columns in the files. If you think something you’d be interested in is missing, you can let me know.

name: This column has hashes that represent character names. If the hashes are the same, that means the names are the same. Real names are removed to protect character anonymity. Yes D&D characters have rights.

race: This is the race field as it come out of the application. It is not really helpful as subrace and race information all mixed up together and unevenly available. It also includes some homebrew content. You probably want to use the processedRace column if you are interested in this.

background: Background as it comes out of the application.

date: Time & date of input. Dates before 2018-04-16 are unreliable as some has accidentally changed while moving files around.

class: Class and level. Different classes are separated by | when needed.

justClass: Class without level. Different classes are separated by | when needed.

subclass: Subclasses. Again, separated by | when needed.

level: Total character level.

feats: Feats chosen by character. Separated by | when needed.

HP: Character HP.

AC: Character AC.

Str, Dex, Con, Int, Wis, Cha: ability scores

alignment: Alignment free text field. It is a mess, don’t touch it. See processedAlignment,good and lawful instead.

skills: List of skills with proficiency. Separated by |.

weapons: List weapons. Separated by |. It is somewhat of a mess as it allows free text inputs. See processedWeapons.

spells: List of spells and their levels. Spells are separated by |s. Each spell has its level next to it separated by *s. This is a huge mess as its a free text field and some users included things like damage dice in them. See processedSpells.

day: A shortened version of date. Only includes day information.

processedAlignment: Processed version of the alignment column. Way people wrote up their alignments are manually sifted through and assigned to the matching aligmment. First character represents lawfulness (L, N, C), second one goodness (G,N,E). An empty string means alignment wasn’t written or unclear.

good, lawful: Isolated columns for goodness and lawfulness.

processedRace: I have gone through the way race column is filled by the app and asigned them to correct races. If empty, indiciates a homebrew race not natively supported by the app.

processedSpells: Formatting is same as the spells column but it is cleaned up. Using string similarity I tried to match the spells to the full list of spells avai...

"""

County-Level Demographic: Population, Race, Gender

Overview

This dataset provides a detailed breakdown of demographic information for counties across the United States, derived from the U.S. Census Bureau's 2023 American Community Survey (ACS). The data includes population counts by gender, race, and ethnicity, alongside unique identifiers for each county using State and County FIPS codes.

Dataset Features

The dataset includes the following columns: - County: Name of the county. - State: Name of the state the county belongs to. - State FIPS Code: Federal Information Processing Standard (FIPS) code for the state. - County FIPS Code: FIPS code for the county. - FIPS: Combined State and County FIPS codes, a unique identifier for each county. - Total Population: Total population in the county. - Male Population: Number of males in the county. - Female Population: Number of females in the county. - Total Race Responses: Total race-related responses recorded in the survey. - White Alone: Number of individuals identifying as White alone. - Black or African American Alone: Number of individuals identifying as Black or African American alone. - Hispanic or Latino: Number of individuals identifying as Hispanic or Latino.

Processing Methodology

1. Source:
   • Data was retrieved using the U.S. Census Bureau ACS API.
2. County-Level Aggregation:
   • Each county is uniquely identified using State FIPS Code and County FIPS Code.
   • These codes were concatenated to form the unified FIPS column.
3. Data Cleaning:
   • All numeric columns were converted to appropriate data types.
   • County and state names were extracted from the raw NAME field for clarity.

Why Use This Dataset?

This dataset is highly versatile and suitable for: - Demographic Analysis: - Analyze population distribution by gender, race, and ethnicity. - Geographic Studies: - Use FIPS codes to map counties geographically. - Data Visualizations: - Create visual insights into demographic trends across counties.

File Format

• The dataset is available as a CSV file with 3,000+ rows (one for each county).

Licensing

• Source: Data is sourced from the U.S. Census Bureau's 2023 American Community Survey (ACS).
• License: This dataset is in the public domain and provided under the U.S. Census Bureau’s terms of use. Attribution to the Census Bureau is appreciated.

Acknowledgments

Special thanks to the U.S. Census Bureau for making this data publicly available and to the Kaggle community for fostering a collaborative space for data analysis and exploration. """