Updated weekly on Mondays The dashboard below shows the impacts of COVID-19 on communities of color compared to whites in King County, Washington.

"race" Grouped by Article

This is a modified version of https://huggingface.co/datasets/race that returns documents grouped by article context instead of by question. Note: This dataset currently only contains that test set of the high subset of the data. The original readme is contained below.

  Dataset Card for "race"





  Dataset Summary

RACE is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/race.

Note: Starting April 27, 2023 updates change from daily to weekly. Summary The cumulative number of positive COVID-19 cases among Maryland residents by race and ethnicity: African American; White; Hispanic; Asian; Other; Unknown. Description The MD COVID-19 - Cases by Race and Ethnicity Distribution data layer is a collection of positive COVID-19 test results that have been reported each day via CRISP. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

*** The County of Santa Clara Public Health Department discontinued updates to the COVID-19 data tables effective June 30, 2025. The COVID-19 data tables will be removed from the Open Data Portal on December 30, 2025. For current information on COVID-19 in Santa Clara County, please visit the Respiratory Virus Dashboard [sccphd.org/respiratoryvirusdata]. For any questions, please contact phinternet@phd.sccgov.org ***

The dataset provides information about the demographics and characteristics of COVID-19 cases by racial/ethnic groups among Santa Clara County residents. Source: California Reportable Disease Information Exchange. Data notes: The Other category for the race/ethnicity graph includes American Indian/Alaska Native and people who identify as multi-racial.

This table is updated every Thursday.

Context

COVID-19 Cases and Deaths by Race

Content

Columns:

State Data Source Total positive cases in state Total deaths in state Percentage of Black people represented in total cases Percentage of Black people represented in total deaths Percentage of total population that identify as Black (census) Updated Notes

Acknowledgements

Data shared under an open data policy at Data for Black Lives (d4bl.org)

Banner Photo by Vince Fleming on Unsplash

As of June 14, 2023, around 24 percent of COVID-19 cases in the U.S. were among people of Hispanic or Latino origin, and 12 percent of cases were among non-Hispanic Blacks. Hispanics or Latinos account for around 18 percent of the U.S. population while non-Hispanic Blacks make up 12.5 percent. This statistic shows the distribution of coronavirus (COVID-19) cases in the United States as of June 14, 2023, by race/ethnicity.

A. SUMMARY This dataset includes San Francisco COVID-19 tests by race/ethnicity and by date. This dataset represents the daily count of tests collected, and the breakdown of test results (positive, negative, or indeterminate). Tests in this dataset include all those collected from persons who listed San Francisco as their home address at the time of testing. It also includes tests that were collected by San Francisco providers for persons who were missing a locating address. This dataset does not include tests for residents listing a locating address outside of San Francisco, even if they were tested in San Francisco.

The data were de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected). If a person tested multiple times on the same date, only one test is included from that date. When there are multiple tests on the same date, a positive result, if one exists, will always be selected as the record for the person. If a PCR and antigen test are taken on the same day, the PCR test will supersede. If a person tests multiple times on the same day and the results are all the same (e.g. all negative or all positive) then the first test done is selected as the record for the person.

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco.

When a person gets tested for COVID-19, they may be asked to report information about themselves. One piece of information that might be requested is a person's race and ethnicity. These data are often incomplete in the laboratory and provider reports of the test results sent to the health department. The data can be missing or incomplete for several possible reasons:

• The person was not asked about their race and ethnicity.
• The person was asked, but refused to answer.
• The person answered, but the testing provider did not include the person's answers in the reports.
• The testing provider reported the person's answers in a format that could not be used by the health department.

For any of these reasons, a person's race/ethnicity will be recorded in the dataset as “Unknown.”

B. NOTE ON RACE/ETHNICITY The different values for Race/Ethnicity in this dataset are "Asian;" "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" "White;" "Multi-racial;" "Other;" and “Unknown."

The Race/Ethnicity categorization increases data clarity by emulating the methodology used by the U.S. Census in the American Community Survey. Specifically, persons who identify as "Asian," "Black or African American," "American Indian or Alaska Native," "Native Hawaiian or Other Pacific Islander," "White," "Multi-racial," or "Other" do NOT include any person who identified as Hispanic/Latino at any time in their testing reports that either (1) identified them as SF residents or (2) as someone who tested without a locating address by an SF provider. All persons across all races who identify as Hispanic/Latino are recorded as “"Hispanic or Latino/a, all races." This categorization increases data accuracy by correcting the way “Other” persons were counted. Previously, when a person reported “Other” for Race/Ethnicity, they would be recorded “Unknown.” Under the new categorization, they are counted as “Other” and are distinct from “Unknown.”

If a person records their race/ethnicity as “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other” for their first COVID-19 test, then this data will not change—even if a different race/ethnicity is reported for this person for any future COVID-19 test. There are two exceptions to this rule. The first exception is if a person’s race/ethnicity value i

Research examining racial disparities in the court system has typically focused on only one of the discrete stages in the criminal process (the charging, conviction, or sentencing stages), with the majority of the literature focusing on the sentencing stage. The literature has thus largely ignored the key early decisions made by the prosecutor such as their decision to prosecute, the determination of preliminary charges, charge reductions, and plea negotiations. Further, the few studies that have examined whether racial disparities arise in prosecutorial charging decisions are rarely able to follow these cases all the way through the criminal court process. This project sought to expand the literature by using a dataset on felony cases filed in twelve Virginia counties between 2007 through 2015 whereby each criminal incident can be followed from the initial charge filing stage to the final disposition. Using each felony case as the unit of analysis, this data was used to evaluate whether African Americans and whites that are arrested for the same felony crimes have similar final case outcomes.

This data set is no longer being updated and is historical, last update 10/10/2022.Provides the percentage of COVID-19 cases by race/ethnicity in Jefferson County, KY. In addition, percentage of Jefferson county vaccine recipients broken out by race/ethnicity, excluding doses administered by Walgreens and CVS clinics. Fieldname Definition race description of race/ethnicity CensusCountPCT percentage of population make-up of Jefferson county ConfirmedCaseCountPCT percentage of confirmed cases by race/ethnicity (rounded to the whole percent) DeceasedCountPCT percentage of deceased cases by race/ethnicity (rounded to the whole percent) RecoveredCountPCT percentage of recovered cases by race/ethnicity (rounded to the whole percent) VaccinatedCountPCT percentage of Jefferson county vaccine recipients by race/ethnicity, excluding doses administered by Walgreens and CVS clinics. (rounded to the whole percent) Loaded Date the data was loaded into the system Note: This data is preliminary, routinely updated, and is subject to change

For questions about this data please contact Angela Graham (Angela.Graham@louisvilleky.gov) or YuTing Chen (YuTing.Chen@louisvilleky.gov) or call (502) 574-8279.

Note: Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This table summarizes COVID-19 case and death data submitted to CDC as case reports for the line-level dataset. Case and death counts are stratified according to sex, age, and race and ethnicity at regional and national levels. Data for US territories are included in case and death counts, but not population counts. Weekly cumulative counts with five or fewer cases or deaths are not reported to protect confidentiality of patients. Records with unknown or missing sex, age, or race and ethnicity and of multiple, non-Hispanic race and ethnicity are included in case and death totals. COVID-19 case and death data are provisional and are subject to change. Visualization of COVID-19 case and death rate trends by demographic variables may be viewed on COVID Data Tracker (https://covid.cdc.gov/covid-data-tracker/#demographicsovertime).

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

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COVID-19 Cases by Race/Ethnicity.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

In 2021, there were around ******* new cases of diagnosed diabetes among non-Hispanic white adults in the United States. In total, there were over *** million new cases of diagnosed diabetes among U.S. adults that year. This statistic shows the number of new cases of diabetes among U.S. adults in 2021, by race/ethnicity.

In 2023, the highest rates of gonorrhea in the U.S. were reported among the Black population, with men having a rate of ***** per 100,000 population and women a rate of ***** per 100,000 population. This statistic shows the rates of reported cases of gonorrhea in the United States in 2023, by race/ethnicity and gender.

In 2023, there were around 1.5 new cases of tuberculosis per 100,000 population among the Hispanic population of the United States. This statistic shows the number of new cases of tuberculosis per 100,000 population in the United States in 2023, by race/ethnicity.

Context

COVID-19 data for San Francisco from data.sfgov.org

Content

COVID-19 Hospitalizations

COVID-19 Cases Summarized by Date, Transmission and Case Disposition

COVID-19 Cases Summarized by Race and Ethnicity

COVID-19 Cases Summarized by Age Group and Gender

COVID-19 Tests

Acknowledgements

Data from https://data.sfgov.org/analytics and https://data.sfgov.org/COVID-19/COVID-19-Cases-Summarized-by-Age-Group-and-Gender/sunc-2t3k and https://data.sfgov.org/COVID-19/COVID-19-Tests/nfpa-mg4g and https://data.sfgov.org/COVID-19/COVID-19-Hospitalizations/nxjg-bhem and https://data.sfgov.org/COVID-19/COVID-19-Cases-Summarized-by-Date-Transmission-and/tvq9-ec9w and https://data.sfgov.org/COVID-19/COVID-19-Cases-Summarized-by-Race-and-Ethnicity/vqqm-nsqg

Dataset license: https://datasf.org/opendata/terms-of-use/

Banner Photo by Maarten van den Heuvel on Unsplash

The statistic shows the share of U.S. population, by race and Hispanic origin, in 2016 and a projection for 2060. As of 2016, about 17.79 percent of the U.S. population was of Hispanic origin. Race and ethnicity in the U.S. For decades, America was a melting pot of the racial and ethnical diversity of its population. The number of people of different ethnic groups in the United States has been growing steadily over the last decade, as has the population in total. For example, 35.81 million Black or African Americans were counted in the U.S. in 2000, while 43.5 million Black or African Americans were counted in 2017.

The median annual family income in the United States in 2017 earned by Black families was about 50,870 U.S. dollars, while the average family income earned by the Asian population was about 92,784 U.S. dollars. This is more than 15,000 U.S. dollars higher than the U.S. average family income, which was 75,938 U.S. dollars.

The unemployment rate varies by ethnicity as well. In 2018, about 6.5 percent of the Black or African American population in the United States were unemployed. In contrast to that, only three percent of the population with Asian origin was unemployed.

In 2019-2021, the rate of new cases of diagnosed diabetes among non-Hispanic white adults in the United States was around five per 1,000 population. This statistic shows the rate of new cases of diabetes among U.S. adults in 2019-2021, by race/ethnicity.

This data set is no longer being updated and is historical, last update 10/10/2022.Provides the percentage of COVID-19 cases by race for the last 8 weeks in Jefferson County, KY. Fieldname Description race description of race population_percent proportion of population in identified by race to total population by_race Number of confirmed cases identified by race total_confirmed number of all confirmed cases to date race_percent Proportion of confirmed cases identified by race to total number of confirmed cases to date by_race_deceased Number of deceased cased identified by race total_deceased number of all deceased cases to date deceased_percent Proportion of deceased cases identified by race to total number of deceased cases to date REPORT_BEGIN_DATE The date calculated as 8 weeks before the report end date. The beginning date of the date range of data aggregated. REPORT_END_DATE The end date of the reporting period. Loaded Date the data was loaded into the system Note: This data is preliminary, routinely updated, and is subject to change For questions about this data please contact Angela Graham (Angela.Graham@louisvilleky.gov) or YuTing Chen (YuTing.Chen@louisvilleky.gov) or call (502) 574-8279.