Diversity index information by neighborhoods in Johns Creek, GA.Neighborhood boundaries are created and maintained by Johns Creek, GA.Demographics data is from Esri GeoEnrichment Services.

About this dataset

Context

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

Key observations

Of the Non-Hispanic population in Tolland town, the largest racial group is White alone with a population of 12,748 (92.55% of the total Non-Hispanic population).

https://i.neilsberg.com/ch/tolland-ct-population-by-race-and-ethnicity.jpeg" alt="Tolland town Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Tolland town
• Population: The population of the racial category (for Non-Hispanic) in the Tolland town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Tolland town 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 Tolland town Population by Race & Ethnicity. You can refer the same here

Diversity in Tech Statistics: In today's tech-driven world, discussions about diversity in the technology sector have gained significant traction. Recent statistics shed light on the disparities and opportunities within this industry. According to data from various sources, including reports from leading tech companies and diversity advocacy groups, the lack of diversity remains a prominent issue. For example, studies reveal that only 25% of computing jobs in the United States are held by women, while Black and Hispanic individuals make up just 9% of the tech workforce combined. Additionally, research indicates that LGBTQ+ individuals are underrepresented in tech, with only 2.3% of tech workers identifying as LGBTQ+. Despite these challenges, there are promising signs of progress. Companies are increasingly recognizing the importance of diversity and inclusion initiatives, with some allocating significant resources to address these issues. For instance, tech giants like Google and Microsoft have committed millions of USD to diversity programs aimed at recruiting and retaining underrepresented talent. As discussions surrounding diversity in tech continue to evolve, understanding the statistical landscape is crucial in fostering meaningful change and creating a more inclusive industry for all. Editor’s Choice In 2021, 7.9% of the US labor force was employed in technology. Women hold only 26.7% of tech employment, while men hold 73.3% of these positions. White Americans hold 62.5% of the positions in the US tech sector. Asian Americans account for 20% of jobs, Latinx Americans 8%, and Black Americans 7%. 83.3% of tech executives in the US are white. Black Americans comprised 14% of the population in 2019 but held only 7% of tech employment. For the same position, at the same business, and with the same experience, women in tech are typically paid 3% less than men. The high-tech sector employs more men (64% against 52%), Asian Americans (14% compared to 5.8%), and white people (68.5% versus 63.5%) compared to other industries. The tech industry is urged to prioritize inclusion when hiring, mentoring, and retaining employees to bridge the digital skills gap. Black professionals only account for 4% of all tech workers despite being 13% of the US workforce. Hispanic professionals hold just 8% of all STEM jobs despite being 17% of the national workforce. Only 22% of workers in tech are ethnic minorities. Gender diversity in tech is low, with just 26% of jobs in computer-related sectors occupied by women. Companies with diverse teams have higher profitability, with those in the top quartile for gender diversity being 25% more likely to have above-average profitability. Every month, the tech industry adds about 9,600 jobs to the U.S. economy. Between May 2009 and May 2015, over 800,000 net STEM jobs were added to the U.S. economy. STEM jobs are expected to grow by another 8.9% between 2015 and 2024. The percentage of black and Hispanic employees at major tech companies is very low, making up just one to three percent of the tech workforce. Tech hiring relies heavily on poaching and incentives, creating an unsustainable ecosystem ripe for disruption. Recruiters have a significant role in disrupting the hiring process to support diversity and inclusion. You May Also Like To Read Outsourcing Statistics Digital Transformation Statistics Internet of Things Statistics Computer Vision Statistics

In 2023, about 3.79 million people in Colorado were white. Furthermore, there were about 1.33 million Hispanic or Latino people and 281,430 people of two or more races living in Colorado in that year.

Sacramento River Fall-run ProductionThis data set contains yearly production values (estimated escapement abundances plus in-river and ocean harvests) of Sacramento River Fall-run Chinook salmon for 1952-2010. The Sacramento River Fall-run Chinook is an aggregate stock consisting of five populations associated with different tributaries of the Sacramento River: Battle Creek, the Sacramento River mainstem, Feather River, Yuba River, and American River. Data were previously available as part of the Central Valley ChinookProd data set, maintained by the US Fish and Wildlife Service Anadromous Fish Restoration Program (https://www.fws.gov/lodi/anadromous_fish_restoration/afrp_index.htm). These specific data are no longer available online, but are presented here in the format used for analyses in the manuscript. Note that analyzed data includes years 1957-2010.Sacramento_Fall_Production_1952_2010.csv

About this dataset

Context

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

Key observations

Of the Non-Hispanic population in Pitt County, the largest racial group is White alone with a population of 91,116 (57.16% of the total Non-Hispanic population).

https://i.neilsberg.com/ch/pitt-county-nc-population-by-race-and-ethnicity.jpeg" alt="Pitt County Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Pitt County
• Population: The population of the racial category (for Non-Hispanic) in the Pitt County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Pitt County 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 Pitt County Population by Race & Ethnicity. You can refer the same here

Hair samples were collected in discrete areas during radio-collar studies in Vermont under the auspices of University of Vermont IACUC protocol #17-035 (n=106), New Hampshire (n=34), and Maine (n=57). Hair and tissue samples were opportunistically collected from animals that were harvested, died in vehicle collisions, or translocated throughout Vermont (n = 105), Quebec (n = 198), Massachusetts (n = 5), and New York (n = 24). Of the 317 previously identified autosomal moose SNPs, 136 loci were utilized to develop a MALDI-TOF MS genotyping assay. After filtering problematic loci and individuals, genotypes from 112 of 136 SNPs (82%) were obtained for 507 individuals and all loci met Hardy-Weinberg expectations in the nine geographic regions samples.

About this dataset

Context

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

Key observations

Of the Non-Hispanic population in Ellington town, the largest racial group is White alone with a population of 13,691 (87.45% of the total Non-Hispanic population).

https://i.neilsberg.com/ch/ellington-ct-population-by-race-and-ethnicity.jpeg" alt="Ellington town Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Ellington town
• Population: The population of the racial category (for Non-Hispanic) in the Ellington town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Ellington town 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 Ellington town Population by Race & Ethnicity. You can refer the same here

This web map is provides the data and maps used in the story map Population density and diversity in New Zealand, created by Stats NZ. It uses Statistical Area 1 (SA1) data collected and published as part of the 2018 Census. The web map uses a mapping technique called multi-variate dot density mapping. The data used in the map can be found at this web service - 2018 Census Individual part 1 data by SA1.For questions or comments on the data or maps, please contact info@stats.govt.nz Census Data Quality Notes:We combined data from the census forms with administrative data to create the 2018 Census dataset, which meets Stats NZ’s quality criteria for population structure information.We added real data about real people to the dataset where we were confident the people should be counted but hadn’t completed a census form. We also used data from the 2013 Census and administrative sources and statistical imputation methods to fill in some missing characteristics of people and dwellings.Data quality for 2018 Census provides more information on the quality of the 2018 Census data.An independent panel of experts has assessed the quality of the 2018 Census dataset. The panel has endorsed Stats NZ’s overall methods and concluded that the use of government administrative records has improved the coverage of key variables such as age, sex, ethnicity, and place. The panel’s Initial Report of the 2018 Census External Data Quality Panel (September 2019), assessed the methodologies used by Stats NZ to produce the final dataset, as well as the quality of some of the key variables. Its second report 2018 Census External Data Quality Panel: Assessment of variables (December 2019) assessed an additional 31 variables. In its third report, Final report of the 2018 Census External Data Quality Panel (February 2020), the panel made 24 recommendations, several relating to preparations for the 2023 Census. Along with this report, the panel, supported by Stats NZ, produced a series of graphs summarising the sources of data for key 2018 Census individual variables, 2018 Census External Data Quality Panel: Data sources for key 2018 Census individual variables.The Quick guide to the 2018 Census outlines the key changes we introduced as we prepared for the 2018 Census, and the changes we made once collection was complete.The geographic boundaries are as at 1 January 2018. See Statistical standard for geographic areas 2018.2018 Census – DataInfo+ provides information about methods, and related metadata.Data quality ratings for 2018 Census variables provides information on data quality ratings.

1 Above the diagonal is average nucleotide diversity (π) in each combined pair of populations; along the diagonal is π within each single population; below the diagonal is average FST between the two populations. Population abbreviations are as in Table 1.

Globally destructive crop pathogens often emerge by migrating out of their native ranges. These pathogens are often diverse at their center of origin, and may exhibit adaptive variation in the invaded range via multiple introductions from different source populations. However, source populations are generally unidentified or poorly studied compared to invasive populations. Phytophthora infestans, the causal agent of late blight, is one of the most costly pathogens of potato and tomato worldwide. Mexico is the center of origin and diversity of P. infestans and migration events out of Mexico have enormously impacted disease dynamics in North America and Europe. The debate over the origin of the pathogen, and population studies of P. infestans in Mexico, have focused on the Toluca Valley, whereas neighboring regions have been little studied. We examined the population structure of P. infestans across central Mexico, including samples from Michoacán, Tlaxcala, and Toluca. We found high levels of diversity consistent with sexual reproduction in Michoacán and Tlaxcala, and population subdivision that was strongly associated with geographical region. We determined that population structure in Central Mexico has contributed to diversity in introduced populations based on relatedness of U.S. clonal lineages to Mexican isolates from different regions. Our results suggest that P. infestans exists as a metapopulation in Central Mexico, and this population structure could be contributing to the repeated re-emergence of P. infestans in the U.S. and elsewhere.

Genetic diversity and temperature increases associated with global climate change are known to independently influence population growth and extinction risk. Whether increasing temperature may influence the effect of genetic diversity on population growth, however, is not known. We address this issue in the model protist system Tetrahymena thermophila. We test the hypothesis that at temperatures closer to the species’ thermal optimum (i.e., the temperature at which population growth is maximal, or Topt), genetic diversity should have a weaker effect on population growth compared to temperatures away from the thermal optimum. To do so, we grew populations of T. thermophila with varying levels of genetic diversity at increasingly warmer temperatures and quantified their intrinsic population growth rate, r. We found that genetic diversity increases population growth at cooler temperatures, but that as temperature increases, this effect weakens. We also show that a combination of changes in...

1. Population and life-history diversity can buffer species from environmental variability and contribute to long-term stability through differing responses to varying conditions akin to the stabilizing effect of asset diversity on financial portfolios. While it is well known that many salmon populations have declined in abundance over the last century, we understand less about how different dimensions of diversity may have shifted. Specifically, how has diminished wild abundance and increased artificial production (i.e., enhancement) changed portfolios of salmon populations, and how might such change influence fisheries and ecosystems? 2. We apply modern genetic tools to century-old sockeye salmon (Oncorhynchus nerka) scales from Canada’s Skeena River watershed to (i) reconstruct historical abundance and age-trait data for 1913–1947 to compare with recent information, (ii) quantify changes in population and life-history diversity and the role of enhancement in population dynamics, and (iii) quantify the risk to fisheries and local ecosystems resulting from observed changes in diversity and enhancement. 3. The total number of wild sockeye returning to the Skeena River during the modern era is 69% lower than during the historical era; all wild populations have declined, several by more than 90%. However, enhancement of a single population has offset declines in wild populations such that aggregate abundances now are similar to historical levels. 4. Population diversity has declined by 70%, and life-history diversity has shifted: populations are migrating from freshwater at an earlier age, and spending more time in the ocean. There also has been a contraction in abundance throughout the watershed, which likely has decreased the spatial extent of salmon provisions to Indigenous fisheries and local ecosystems. Despite the erosion of portfolio strength that this salmon complex hosted a century ago, total returns now are no more variable than they were historically perhaps in part due to the stabilizing effect of artificial production. 5. Policy implications. Our study provides a rare example of the extent of erosion of within-species biodiversity over the last century of human influence. Rebuilding a diversity of abundant wild populations – that is, maintaining functioning portfolios - may help ensure that watershed complexes like the Skeena are robust to global change. Generated from fish scales collected in commercial fisheries during 1913–1947 and the skeena tyee test fishery 1973–2016, genetically assigned to population.

Microsatellite Data from Experimental PoolsIncluded in this file is the Microsatellite data for all individuals genotyped from the experimental pools for all three years of the field experiment.Holmesetal_MicrosatelliteData.xlsx

Population size is a main indicator of conservation potential, thought to predict both current and long-term population viability. However, few studies have directly examined the links between the size and the genetic and demographic properties of populations, using metrics that integrate effects across the whole life cycle. In this study, we combined six years of demographic data with SNP-based estimates of genetic diversity from 18 Swedish populations of the orchid Gymnadenia conopsea. We assessed whether stochastic growth rate increases with population size and genetic diversity, and used stochastic LTRE analysis to evaluate how underlying vital rates contribute to among-population variation in growth rate. For each population, we also estimated the probability of quasi-extinction (shrinking below a threshold size) and of a severe (90%) decline in population size, within the next 30 years. Estimates of stochastic growth rate indicated that ten populations are declining, seven increasing, and one population is approximately stable. SLTRE decomposition showed that low mean adult survival and growth characterized strongly declining populations, whereas high mean fecundity characterized strongly increasing populations. Stochastic growth rate increased with population size, mainly due to higher survival in larger populations, but was not related to genetic diversity. One third of the populations were predicted to go extinct and eight populations to undergo a 90% decrease in population size in the coming 30 years. Low survival in small populations most likely reflects a positive association between local environmental conditions and population size. Synthesis: The association between G. conopsea population size and viability was driven by variation in survival, and there was no sign that ongoing declines are due to genetic erosion. This suggests that large populations occur in favourable habitats that buffer effects of climatic variation. The results also illustrate that demographic metrics can be more informative than genetic metrics, regarding conservation priority. Methods The dataset contains six years of demographic data (2017-2022) from each of 18 populations of Gymnadenia conopsea on the island of Öland in Sweden, and the code to run integral projection models in R.

Population expansion parameters based on Mismatch Distribution analysis results. Tau = time since expansion expressed in units of mutational time (Rogers, 1995), Fu’s FS = Fu’s FS index, P(FS) = P value for Fu’s FS index, SSD = Sum of Squared Deviations, P(SSD) = P value for SSD, HRI = Harpending’s Raggedness Index, P(HRI) = P value for Harpending’s Raggedness Index. (XLSX)

Dataset comprises genotypes of 335 individuals of J. sabina.

The estimated population of the U.S. was approximately 334.9 million in 2023, and the largest age group was adults aged 30 to 34. There were 11.88 million males in this age category and around 11.64 million females. Which U.S. state has the largest population? The population of the United States continues to increase, and the country is the third most populous in the world behind China and India. The gender distribution has remained consistent for many years, with the number of females narrowly outnumbering males. In terms of where the residents are located, California was the state with the highest population in 2023. The U.S. population by race and ethnicity The United States is well known the world over for having a diverse population. In 2023, the number of Black or African American individuals was estimated to be 45.76 million, which represented an increase of over four million since the 2010 census. The number of Asian residents has increased at a similar rate during the same time period and the Hispanic population in the U.S. has also continued to grow.

Papua New Guinea is the most linguistically diverse country in the world. As of 2025, it was home to 840 different languages. Indonesia ranked second with 709 languages spoken. In the United States, 335 languages were spoken in that same year.

This dataset presents the community structure of groundfish and invertebrate in the West Coast since 1977. The community structure indices include the richness and Simpson’s evenness. The raw count data is from West Coast Groundfish Bottom Trawl survey conducted by the Northwest Fisheries Science Center. The spatial coverage of this dataset is between Pt Conception, California and north of U.S.-Mexico border.