About this dataset

Context

The dataset presents the median household income across different racial categories in South Carolina. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of South Carolina population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 65.66% of the total residents in South Carolina. Notably, the median household income for White households is $73,516. Interestingly, despite the White population being the most populous, it is worth noting that Asian households actually reports the highest median household income, with a median income of $87,807. This reveals that, while Whites may be the most numerous in South Carolina, Asian households experience greater economic prosperity in terms of median household income.

https://i.neilsberg.com/ch/south-carolina-median-household-income-by-race.jpeg" alt="South Carolina median household income diversity across racial categories">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2022 1-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in South Carolina.
• Median household income: Median household income, adjusting for inflation, presented in 2022-inflation-adjusted dollars

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/.

Recommended for further research

This dataset is a part of the main dataset for South Carolina median household income by race. You can refer the same here

Percent population by race and Hispanic Origin North Carolina and all counties from the 2012-2016 American Community Survey.

This map service summarizes racial and ethnic diversity in the United States in 2012.

The Diversity Index shows the likelihood that two persons chosen at random from the same area, belong to different race or ethnic groups. The index ranges from 0 (no diversity) to 100 (complete diversity). Diversity in the U.S. population is increasing. The diversity score for the entire United States in 2012 is 61.

The data shown is from Esri's 2012 Updated Demographics. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data. This map shows Esri's 2012 estimates using Census 2010 geographies.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within North Carolina. The dataset can be utilized to gain insights into gender-based income distribution within the North Carolina population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within North Carolina, among individuals aged 15 years and older with income, there were 3.77 million men and 3.81 million women in the workforce. Among them, 2.10 million men were engaged in full-time, year-round employment, while 1.63 million women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 8.90% fell within the income range of under $24,999, while 12.16% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 25.04% of men in full-time roles earned incomes exceeding $100,000, while 14.93% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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/.

Recommended for further research

This dataset is a part of the main dataset for North Carolina median household income by race. You can refer the same here

U.S. Census Bureau QuickFacts statistics for South Carolina. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

Despite nearly a century of study, the diversity of marine fungi remains poorly understood. Historical surveys utilizing microscopy or culture-dependent methods suggest that marine fungi are relatively species-poor, predominantly Dikarya, and localized to coastal habitats. However, the use of high-throughput sequencing technologies to characterize microbial communities has challenged traditional concepts of fungal diversity by revealing novel phylotypes from both terrestrial and aquatic habitats. Here, I used ion semiconductor sequencing (Ion Torrent) of the ribosomal large subunit (LSU/28S) to explore fungal diversity from water and sediment samples collected from four habitats in coastal North Carolina. The dominant taxa observed were Ascomycota and Chytridiomycota, though all fungal phyla were represented. Diversity was highest in sand flats and wetland sediments, though benthic sediments harbored the highest proportion of novel sequences. Most sequences assigned to early-diverging fungal groups could not be assigned beyond phylum with statistical support, suggesting they belong to unknown lineages.

In 2023, 24.4 percent of South Carolina residents were Black or African American. A further 63.6 percent of the population were white, and 7 percent of South Carolina residents were of two or more races in that same year.

Schools in gentrifying neighborhoods often experience demographic changes in enrollment. The purpose of this qualitative holistic case study is to describe how leaders and teachers in a diversifying elementary school in a gentrifying neighborhood perceive and experience diversity. Drawing on Turner’s (2017) value of diversity framework, we use inductive coding to analyze interviews and also use documents to inform our findings. Although Greenleaf was striving to be intentionally diverse, consensus did not exist about the meaning of “diversity” or the desired form of diversity. Challenges associated with decentering Whiteness and resisting upholding the racial contract existed as educators worked to establish a shared mission, ensure diverse staff voice and representation with a White leader, and navigate complications of power and privilege among White families. Educators highlighted the value of diversity for developing students’ multicultural capital and global cosmopolitanism as well as the collective benefit of reducing divisiveness for our nation.

This dataset resulted from a project 'Growth dynamics of methanogens and sulfate reducers in natural marine sediments'.

This dataset includes counts of birds from surveys conducted in the French Broad River Basin in western North Carolina, USA. This basin is in the Southern Appalachian Mountains. Data were collected to examine the spatial and seasonal supply of biodiversity-based cultural ecosystem services (CES), in this case, nature study through birdwatching. The data includes bird species observed at 69 sites on public and private lands during the period 2014-04-01 to 2014-08-08. Bird species were categorized with respect to migration status, level of conservation concern (both based on literature), and relative abundance in the study region (based on eBird data). Environmental data for 56 sites are provided: elevation, early season precipitation, mean summer temperature, land cover diversity, tree cover, vegetation structural diversity, vegetation annual productivity, and building density at local and landscape scales. Graves et al. (2019, doi:10.1007/s13280-018-1068-1) used these data to analyze seasonal shifts in birdwatching supply and how those shifts impacted public access to projected birdwatching hotspots. Landscape patterns of CES supply differed substantially among five CES indicators (total bird species richness, and richness of migratory, infrequent, synanthrope, and resident species). For example, total species richness hotspots seldom overlapped with hotspots of migratory or infrequent species. Public access to CES hotspots varied seasonally. This study suggests that simple, static biodiversity metrics may overlook spatial dynamics important to CES users.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Huntersville by race. It includes the distribution of the Non-Hispanic population of Huntersville across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Huntersville across relevant racial categories.

Key observations

Of the Non-Hispanic population in Huntersville, the largest racial group is White alone with a population of 42,958 (74.28% of the total Non-Hispanic population).

Content

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:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Huntersville
• Population: The population of the racial category (for Non-Hispanic) in the Huntersville is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Huntersville total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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/.

Recommended for further research

This dataset is a part of the main dataset for Huntersville Population by Race & Ethnicity. You can refer the same here

Resource subsidies and biodiversity are essential for maintaining community structure and ecosystem functioning, but the relative importance of consumer diversity and resource characteristics to decomposition remains unclear. Forested headwater streams are detritus-based systems, dependent on leaf litter inputs from adjacent riparian ecosystems, and decomposition of these resources is an important ecosystem function. Here, we examined the effects of consumer community diversity on leaf decomposition in a reciprocal transplant experiment. We asked: (1) if stream consumer communities are adapted to local resources, and (2) how functional trait diversity among communities affects the leaf decomposition process. We did not find evidence that communities were adapted to locally-derived resource subsidies. Instead, we found that consumer biomass and functional trait diversity as well as resource characteristics were the primary biotic drivers of decomposition. Consumer biomass was stimulated by specific resource subsidies, leading to direct and indirect effects of resource subsidies on ecosystem functioning. Contrary to current theory, we show that decomposition was higher with decreased detritivore functional diversity, suggesting dominant traits encompassing a specific niche increased decomposition. We also show that top-down, consumer diversity effects can be equal in magnitude to the bottom-up effects of resource characteristics during the decomposition process. Our research illustrates the importance of considering multiple biotic and abiotic drivers interacting via multiple pathways to affect a crucial ecosystem function.

Institutions of higher education (IHE) throughout the United States have a long history of acting out various levels of commitment to diversity advancement, equity, and inclusion (DEI). Despite decades of DEI “efforts,” the academy is fraught with legacies of racism that uphold white supremacy and prevent marginalized populations from full participation. Furthermore, politicians have not only weaponized education but passed legislation to actively ban DEI programs and censor general education curricula (https://tinyurl.com/antiDEI). Ironically, systems of oppression are particularly apparent in the fields of Ecology, Evolution, and Conservation Biology (EECB)–which recognize biological diversity as essential for ecological integrity and resilience. Yet, amongst EECB faculty, people who do not identify as cis-heterosexual, non-disabled, affluent white males are poorly represented. Furthermore, IHE lack metrics to quantify DEI as a priority. Here we show that only 30.3% of US-faculty positions advertised in EECB from Jan 2019-May 2020 required a diversity statement; diversity statement requirements did not correspond with state-level diversity metrics. Though many announcements “encourage women and minorities to apply,” empirical evidence demonstrates that hiring committees at most institutions did not prioritize an applicant’s DEI advancement potential. We suggest a model for change and call on administrators and faculty to implement SMART (i.e., Specific, Measurable, Achievable, Realistic, and Timely) strategies for DEI advancement across IHE throughout the United States. We anticipate our quantification of diversity statement requirements relative to other application materials will motivate institutional change in both policy and practice when evaluating a candidate’s potential “fit”. IHE must embrace a leadership role to not only shift the academic culture to one that upholds DEI, but to educate and include people who represent the full diversity of our society. In the current context of political censure of education including book banning and backlash aimed at Critical Race Theory, which further reinforce systemic white supremacy, academic integrity and justice are more critical than ever. Methods Here we investigated the (lack of) process in faculty searches at IHE for evaluating candidates’ ability to advance DEI objectives. We quantified the prevalence of required diversity statements relative to research and/or teaching statements for all faculty positions posted to the Eco-Evo Jobs Board (http://ecoevojobs.net) from January 2019 - May 2020 as a proxy for institutional DEI prioritization (Supplement). We also mapped the job posts that required diversity statements geographically to gauge whether and where diversity is valued in higher education across the US. Data analysis We pulled all faculty jobs posted on Eco-Evo jobs board (http://ecoevojobs.net) from Jan 1, 2019, to May 31, 2020. For each position, we recorded the Location (i.e., state), Subject Area, Closing Date, Rank, whether or not the position is Tenure Track, and individual application materials (i.e., Research statement, Teaching statement, combined Teaching and Research statement, Diversity statement, Mentorship statement). Of the 543 faculty positions posted during this time, we eliminated 299 posts because the web links were broken or application information was no longer available (i.e., “NA”), leaving 244 faculty job posts. For each of the retained posts, we coded the requirement of teaching, research, diversity, and/or mentorship statements as follows:

"Yes” = statement required “No” = statement not required “Other” = application materials did not explicitly require a Diversity Statement (i.e., option or suggested that applicants include a statement on diversity and inclusion as a component of their teaching and/or research statement or in their cover letter)

Data visualization We created a Sankey diagram using Sankey Flow Show (THORTEC Software GmbH: www.sankeyflowshow.com) to compare diversity and representation from the general population, through (Science, Technology, Engineering, and Mathematics) STEM academia (a career hierarchy often referred to as the “leaky pipeline”). We procured population data from the US Census Bureau (US Department of Commerce: https://www.census.gov/quickfacts/fact/table/US/PST045219) and quantified the diversity/representation in Conservation Biology (https://datausa.io/profile/cip/ecology-evolution-systematics-population-biology#demographics) and Ecology (https://datausa.io/profile/cip/conservation-biology) using Data USA (developed by Deloitte Touche Tohmatsu Limited and Datawheel). We used the 2015 Diversity Index (produced by PolicyLink and the USC Program for Environmental and Regional Equity: https://nationalequityatlas.org/indicators/Diversity_index/Ranking:33271/United_States/false/Year(s):2015/) to quantify relative ethnic diversity per state, and graphed Figure 2B using the tidyverse, rgdal, broom, and rgeos packages in R (see Base code used to produce Figure 2 in R, below). The Diversity index measures the representation of White, Black, Latino, Asian/Pacific Islander, Native American, and Mixed/other race in a given population. A maximum possible diversity score (1.79) would indicate even representation of all ethnic/racial groups. We checked all figures using the Color Blindness Simulator (ColBlindor: https://www.color-blindness.com/coblis-color-blindness-simulator/) to maintain inclusivity.

Chaetognaths (Phylum: Chaetognatha) are one of the most abundant phyla of zooplankton worldwide and play an important role in marine trophic interactions. Although the role of chaetognaths in global ecosystems is well understood, the spatial variation and environmental drivers of estuarine chaetognath populations is poorly understood. To provide the first known record of chaetognath species composition in a coastal estuary in the south-eastern USA, chaetognaths were identified and quantified from zooplankton samples collected on a monthly basis in 2019 and 2020 from North Inlet Estuary in South Carolina. Parasagitta tenuis was the most abundant species of the five found, making up 33% of total abundance. The egg presence of these chaetognaths was further analyzed to gauge reproductive cycles. Abundance and egg presence were compared with surface and bottom measurements of temperature, salinity, and dissolved oxygen levels to determine the driving abiotic factors behind chaetognath’s sea...

This bar chart depicts PERM case filings at University of South Carolina-Lancaster sorted by the citizenship of the graduates. The filter by major feature provides a deeper understanding of the international diversity of graduates who are being sponsored by employers in the U.S.

Collection of occurrence data obtained from the literature archived in the Marine Biology Laboratory of Universidad Simón Bolívar.

About this dataset

Context

The dataset presents the median household income across different racial categories in North Carolina. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of North Carolina population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 63.26% of the total residents in North Carolina. Notably, the median household income for White households is $77,601. Interestingly, despite the White population being the most populous, it is worth noting that Asian households actually reports the highest median household income, with a median income of $113,412. This reveals that, while Whites may be the most numerous in North Carolina, Asian households experience greater economic prosperity in terms of median household income.

Content

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:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in North Carolina.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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/.

Recommended for further research

This dataset is a part of the main dataset for North Carolina median household income by race. You can refer the same here

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.

This study will examine spatial patterns of ant diversity, body size, and community composition along the elevational gradient at Coweeta. The data will be part of a larger study that will examine several gradients in the US and abroad to assess whether there are general mechanisms that shape these diversity gradients. Patterns of ant species diversity are well documented and yet the mechanisms promoting species coexistence among communities are often elusive. Two emerging hypotheses that account for coexistence in ant communities are the discovery-dominance tradeoff and the dominance-thermal tolerance tradeoff. Here we used behavioural assays and community-level sampling from ant assemblages in the southern Appalachians, USA to test for the discovery-dominance and dominance-thermal tolerance tradeoffs. The investigators involved were Nathan Sanders, Robert Dunn, JP Lessard, and Melissa Geraghty.

About this dataset

Context

The dataset tabulates the population of Huntersville by race. It includes the population of Huntersville across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Huntersville across relevant racial categories.

Key observations

The percent distribution of Huntersville population by race (across all racial categories recognized by the U.S. Census Bureau): 70.90% are white, 13.65% are Black or African American, 0.07% are American Indian and Alaska Native, 5.52% are Asian, 0.04% are Native Hawaiian and other Pacific Islander, 2.64% are some other race and 7.18% are multiracial.

Content

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:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Huntersville
• Population: The population of the racial category (excluding ethnicity) in the Huntersville is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Huntersville total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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/.

Recommended for further research

This dataset is a part of the main dataset for Huntersville Population by Race & Ethnicity. You can refer the same here