Examination of the extent to which the immigrant population has become more diverse, using data from Switzerland as an example. Nationality is used as the basis, and diversity is expressed using the Herfindahl index. Considers changes between 1850 and 2010.

This dataset tracks annual diversity score from 1991 to 2023 for Switzerland Of Ohio Local School District vs. Ohio

Raw data supporting the paper "Countrywide wild bee taxonomic and functional diversity reveal a spatial mismatch between alpha and beta-diversity facets across multiple ecological gradients". It contains taxonomic and functional metrics in 3343 community-plots distributed across Switzerland. The calculated metrics are: - Alpha taxonomic community metrics: species richness and Shannon diversity - Alpha functional community metrics: Functional richness (using the Trait Onion Peeling index, TOP), functional eveness (using the Trait Even Distribution index, TED) and the functional dispersion. - Community weighted means of 8 functional traits - The local community contributions on the functional and taxonomic beta diversity (LCBD). The dataset also includes the following: - The used predictors to model the spatial distribution of the community metrics (climate PCA, vegetation PCA, land-use metrics, beekeeping intensity). -The three types of protected areas, defined according to the protective measures. - The model evaluation, variable importance and partial dependece data.

This dataset tracks annual diversity score from 1989 to 2023 for Switzerland Co Senior High School vs. Indiana and Switzerland County School Corp School District

This dataset tracks annual diversity score from 1991 to 2023 for Swiss Hills Career Center vs. Ohio and Switzerland Of Ohio Local School District

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 Switzerland County. The dataset can be utilized to gain insights into gender-based income distribution within the Switzerland County population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Switzerland County, among individuals aged 15 years and older with income, there were 3,735 men and 3,254 women in the workforce. Among them, 1,940 men were engaged in full-time, year-round employment, while 1,216 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 11.49% fell within the income range of under $24,999, while 10.86% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 15.46% of men in full-time roles earned incomes exceeding $100,000, while 5.84% 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 Switzerland County median household income by race. You can refer the same here

This dataset tracks annual diversity score from 1993 to 2023 for Switzerland Point Middle School vs. Florida and St. Johns School District

About this dataset

Context

The dataset presents the median household income across different racial categories in Switzerland County. 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 Switzerland County population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 95.28% of the total residents in Switzerland County. Notably, the median household income for White households is $65,132. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $65,132.

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 Switzerland County.
• 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 Switzerland County median household income by race. You can refer the same here

This dataset is about book subjects. It has 2 rows and is filtered where the books is The policy challenge of ethnic diversity : immigrant politics in France and Switzerland. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Systematic collection of geo-referenced data on genetic diversity in natural populations of wild species in Switzerland.

All data and R code for "Die Entwicklung der lexikalischen Diversität und Elaboriertheit bei SchülerInnen mit portugiesischem Migrationshintergrund in der Schweiz" (Bonvin, Vanhove, Berthele & Lambelet) as a zipped directory.

This dataset tracks annual diversity score from 2008 to 2023 for Swiss Memorial Elementary School vs. Tennessee and Grundy County School District

This dataset tracks annual diversity score from 1997 to 2023 for Beallsville High School vs. Ohio and Switzerland Of Ohio Local School District

This dataset tracks annual diversity score from 1989 to 2023 for Sardis Elementary School vs. Ohio and Switzerland Of Ohio Local School District

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 Swiss town. The dataset can be utilized to gain insights into gender-based income distribution within the Swiss town population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Swiss town, among individuals aged 15 years and older with income, there were 300 men and 320 women in the workforce. Among them, 96 men were engaged in full-time, year-round employment, while 109 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 16.67% fell within the income range of under $24,999, while 13.76% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 23.96% of men in full-time roles earned incomes exceeding $100,000, while 6.42% 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 Swiss town median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Swiss town. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Swiss town, the median household income for the households where the householder is White increased by $11,141(22.30%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $49,970 in 2013 and $61,111 in 2023.
• Black or African American: Even though there is a population where the householder is Black or African American, there was no median household income reported by the U.S. Census Bureau for both 2013 and 2023.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

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 Swiss town.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are 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 Swiss town median household income by race. You can refer the same here

Given the limited success of past and current conservation efforts, an alternative approach is to preserve tissues and genomes of targeted organisms in cryobanks to make them accessible for future generations. Our pilot preservation project aimed to obtain, expertly identify, and permanently preserve a quarter of the known spider species diversity shared between Slovenia and Switzerland, estimated at 275 species. We here report on the faunistic part of this project, which resulted in 324 species (227 in Slovenia, 143 in Switzerland) for which identification was reasonably established. This material is now preserved in cryobanks, is being processed for DNA barcoding, and is available for genomic studies.

The Global Audience Segments dataset categorizes people in Switzerland based on their travel to relevant stores, businesses, or other points of interest - therefore exposing audience media habits, hobbies, and consumer behaviors.

This dataset is a valuable tool for marketers and researchers aiming to understand and reach diverse Swiss and global audiences with various interests and demographic profiles.

This dataset tracks annual diversity score from 1993 to 2023 for Monroe Central High School vs. Ohio and Switzerland Of Ohio Local School District

This data accompanies the report, "Communications, media and internet concentration in Switzerland, 2019-2021". The report examines media concentration in Switzerland from 2019 to 2021. It finds high concentration due to public and multinational companies, especially in telecom and internet services. Switzerland mirrors global trends with declining traditional media revenues but at a slower rate. Public service broadcasting remains influential. The unique Swiss market, with its linguistic diversity and federal structure, lacks transparency due to limited data disclosure requirements, highlighting the need for further investigation.