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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Context
The dataset tabulates the population of South Carolina by race. It includes the population of South Carolina across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of South Carolina across relevant racial categories.
Key observations
The percent distribution of South Carolina population by race (across all racial categories recognized by the U.S. Census Bureau): 64.06% are white, 25.30% are Black or African American, 0.32% are American Indian and Alaska Native, 1.72% are Asian, 0.07% are Native Hawaiian and other Pacific Islander, 2.74% are some other race and 5.79% are multiracial.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Racial categories include:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for South Carolina Population by Race & Ethnicity. You can refer the same here
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TwitterIn 2022, around 48 percent of all reported legal abortions in South Carolina were performed on non-Hispanic Black women. This statistic depicts the distribution of reported legal abortions in South Carolina in 2022, by the race/ethnicity of the women who obtained abortions.
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TwitterThe American alligator (Alligator mississippiensis) is a species of ecological and economic importance in the southeastern United States. Within South Carolina, alligators are subject to private and public harvest programs, as well as nuisance removal. These management activities can have different impacts across alligator size classes that may not be apparent through widely-used monitoring techniques such as nightlight surveys. We synthesized multiple datasets within an integrated population model (IPM) to estimate size class-specific survival and abundance estimates, that would not be estimable through separate, non-integrated modeling frameworks. The IPM framework included a multistate mark-recapture-recovery model that used mark-recapture-recovery data from the Tom Yawkey Wildlife Center and growth transition probabilities that were estimated outside of the IPM framework. The IPM also included a state-space count model, which used nightlight survey counts of alligtaors from two survey routes: 1) Great Pee Dee and Waccamaw Rivers; and 2) South Santee Rivers. The IPM modeling framework also used mean clutch size data from the Tom Yawkey Wildlife Center and public and private harvest data within the state model. Lastly, we evaluated the effects of capture effort on capture probability, as well as the effects of water temperature and relative water level on count detection probability, and provide all covariate datasets. Our IPM framework determined that size class-specific survival rates were relatively high for all non-hatchling size classes, and abundance trends differed between the two nightlight survey sites.
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TwitterComprehensive demographic dataset for South Carolina, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.
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TwitterAccording to exit polls for the 2020 South Carolina Democratic primary, former Vice President Joe Biden dominated the vote among African-Americans, receiving ** percent of the vote. Among White voters, the vote was more evenly split, with Joe Biden receiving ** percent of the vote, and Vermont Senator Bernie Sanders receiving ** percent of the vote.
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Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Two or More Races, Two Races Including Some Other Race (5-year estimate) in Florence County, SC (B03002010E045041) from 2009 to 2023 about Florence County, SC; Florence; SC; non-hispanic; estimate; 5-year; persons; population; and USA.
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TwitterIn 2023, around ** percent of all Black adults in South Carolina were obese. The desire for fast and convenient foodstuffs can hinder people from eating healthy diets, especially with the lack of nutrition in many processed foods. This statistic depicts the obesity rates for adults in South Carolina in 2023, by race/ethnicity.
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TwitterComprehensive demographic dataset for Ruffin, SC, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Historical Dataset of Cyber Academy Of South Carolina is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2019-2023),Total Classroom Teachers Trends Over Years (2019-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2019-2023),Asian Student Percentage Comparison Over Years (2019-2020),Hispanic Student Percentage Comparison Over Years (2019-2023),Black Student Percentage Comparison Over Years (2019-2023),White Student Percentage Comparison Over Years (2019-2023),Two or More Races Student Percentage Comparison Over Years (2019-2023),Diversity Score Comparison Over Years (2019-2023),Free Lunch Eligibility Comparison Over Years (2019-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2019-2023),Reading and Language Arts Proficiency Comparison Over Years (2019-2022),Math Proficiency Comparison Over Years (2019-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2019-2023),Graduation Rate Comparison Over Years (2019-2023)
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the South Carolina median household income by race. The dataset can be utilized to understand the racial distribution of South Carolina income.
The dataset will have the following datasets when applicable
Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).
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.
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/.
Explore our comprehensive data analysis and visual representations for a deeper understanding of South Carolina median household income by race. You can refer the same here
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TwitterComprehensive demographic dataset for Blair, SC, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Historical Dataset of South Carolina Whitmore School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2019-2023),Total Classroom Teachers Trends Over Years (2019-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2019-2023),Asian Student Percentage Comparison Over Years (2019-2023),Hispanic Student Percentage Comparison Over Years (2019-2023),Black Student Percentage Comparison Over Years (2019-2023),White Student Percentage Comparison Over Years (2019-2023),Diversity Score Comparison Over Years (2019-2023),Free Lunch Eligibility Comparison Over Years (2019-2023)
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TwitterCC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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Understanding how diverse species communities develop and how the species within them coexist is one of the central questions in community ecology. The temporary wetland system occurring on the Savannah River Site near Aiken, South Carolina is home to the most species rich temporary wetland zooplankton assemblage known in the world. While previous research has documented this remarkable diversity, there has been little study directed at understanding how diversity is distributed at the landscape and local scales or on investigating potential mechanisms of what has led to the high richness of this system. The collection of studies presented here examine diversity patterns in the zooplankton community, links these patterns to spatial and temporal variation, experimentally tests the effects of two important environmental factors on diversity, and describes two new species. Results indicate that long hydroperiod lengths were associated with high species richness. Wetlands with similar species assemblages were generally closer together, suggesting the importance of dispersal. Over the course of a year, diversity increased during the spring and summer months and declined toward the fall, these changes were associated with low pH, low conductivity, and high water temperature. Vegetated areas within wetlands had greater diversity than did unvegetated areas, and diversity was particularly low in areas of decaying vegetation. Temporal comparisons provide evidence for distinct seasonal communities that arise every year. Experimental tests of the impact of hydroperiod length on diversity found that shorter hydroperiods resulted in reduced species richness, and communities dominated by just a few species. Predation was found to have no effect on diversity or community composition. During investigation of the diversity of these wetlands, two new species of the genus Chydorus were discovered and described. These two species differ from congeners both in morphology and phylogenetically. Together these studies describe how environmental variation can impact the diversity of the zooplankton communities within temporary wetlands and show how hydroperiod limits the richness of these systems. The results presented here provide insight into the forces that may lead to diverse communities in temporary wetlands, providing direction for future research into these dynamic ecosystems.
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TwitterComprehensive demographic dataset for Salters, SC, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.
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Graph and download economic data for White to Non-White Racial Dissimilarity (5-year estimate) Index for Union County, SC (RACEDISPARITY045087) from 2009 to 2023 about Union County, SC; racial dissimilarity; non-white; white; SC; 5-year; and USA.
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TwitterComprehensive demographic dataset for Saint Helena Island, SC, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.
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Twitterhttps://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain
Graph and download economic data for White to Non-White Racial Dissimilarity (5-year estimate) Index for Greenville County, SC (RACEDISPARITY045045) from 2009 to 2023 about Greenville County, SC; Greenville; racial dissimilarity; non-white; white; SC; 5-year; and USA.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Historical Dataset of South Carolina Virtual Charter School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2019-2023),Total Classroom Teachers Trends Over Years (2019-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2019-2023),Asian Student Percentage Comparison Over Years (2019-2023),Hispanic Student Percentage Comparison Over Years (2019-2023),Black Student Percentage Comparison Over Years (2019-2023),White Student Percentage Comparison Over Years (2019-2023),Two or More Races Student Percentage Comparison Over Years (2021-2023),Diversity Score Comparison Over Years (2019-2023),Free Lunch Eligibility Comparison Over Years (2019-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2019-2023),Reading and Language Arts Proficiency Comparison Over Years (2019-2022),Math Proficiency Comparison Over Years (2019-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2019-2023),Graduation Rate Comparison Over Years (2019-2023)
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Historical Dataset of Virtus Academy Of South Carolina is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2019-2023),Total Classroom Teachers Trends Over Years (2019-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2019-2023),Asian Student Percentage Comparison Over Years (2019-2023),Hispanic Student Percentage Comparison Over Years (2019-2023),Black Student Percentage Comparison Over Years (2019-2023),White Student Percentage Comparison Over Years (2019-2023),Two or More Races Student Percentage Comparison Over Years (2019-2023),Diversity Score Comparison Over Years (2019-2023),Free Lunch Eligibility Comparison Over Years (2019-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2019-2023),Reading and Language Arts Proficiency Comparison Over Years (2019-2022),Math Proficiency Comparison Over Years (2019-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2019-2023)
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TwitterFor this study, convenience store robbery victims and offenders in five states (Georgia, Massachusetts, Maryland, Michigan, and South Carolina) were interviewed. Robbery victims were identified by canvassing convenience stores in high-crime areas, while a sample of unrelated offenders was obtained from state prison rolls. The aims of the survey were to address questions of injury, to examine store characteristics that might influence the rate of robbery and injury, to compare how both victims and offenders perceived the robbery event (including their assessment of what could be done to prevent convenience store robberies in the future), and to identify ways in which the number of convenience store robberies might be reduced. Variables unique to Part 1, the Victim Data file, provide information on how the victim was injured, whether hospitalization was required for the injury, if the victim used any type of self-protection, and whether the victim had been trained to handle a robbery. Part 2, the Offender Data file, presents variables describing offenders' history of prior convenience store robberies, whether there had been an accomplice, motive for robbing the store, and whether various factors mattered in choosing the store to rob (e.g., cashier location, exit locations, lighting conditions, parking lot size, the number of clerks working, weather conditions, the time of day, and the number of customers in the store). Found in both files are variables detailing whether a victim injury occurred, use of a weapon, how each participant behaved, perceptions of why the store was targeted, what could have been done to prevent the robbery, and ratings by the researchers on the completeness, honesty, and cooperativeness of each participant during the interview. Demographic variables found in both the victim and offender files include age, gender, race, and ethnicity.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of South Carolina by race. It includes the population of South Carolina across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of South Carolina across relevant racial categories.
Key observations
The percent distribution of South Carolina population by race (across all racial categories recognized by the U.S. Census Bureau): 64.06% are white, 25.30% are Black or African American, 0.32% are American Indian and Alaska Native, 1.72% are Asian, 0.07% are Native Hawaiian and other Pacific Islander, 2.74% are some other race and 5.79% are multiracial.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Racial categories include:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for South Carolina Population by Race & Ethnicity. You can refer the same here