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Every year, young women from across the United States compete for the title of Miss America. The competition is open to women between the ages of 17 and 25, and includes a talent portion, an interview, and a swimsuit competition (which was removed in 2018). The winner is crowned by the previous year's titleholder and goes on to tour the nation for about 20,000 miles a month, promoting her particular platform of interest.
The Miss America dataset contains information on all Miss America titleholders from 1921 to 2022. It includes columns for the year of the pageant, the name of the crowned winner, her state or district represented, awards won, talent performed, and notes about her win
This dataset contains information on Miss America titleholders from 1921 to 2022. The data includes the name of the winner, her state or district, the city she represented, her talent, and the year she won
- Miss America could be used to study changes in American culture over time. For example, the decline in the swimsuit competition could be seen as a sign of increasing body positivity in the US.
- The dataset could be used to study the effect of winning Miss America has on a woman's career. Does winning lead to more opportunities?
- The dataset could be used to study geographical patterns inMiss America winners. For example, are there any states that have produced more winners than others?
License
License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original.
File: miss_america_titleholders.csv | Column name | Description | |:----------------------|:-----------------------------------------------------------------------| | year | The year the Miss America pageant was held. (Integer) | | crowned | The name of the Miss America titleholder. (String) | | winner | The name of the Miss America winner. (String) | | state_or_district | The state or district represented by the Miss America winner. (String) | | city | The city represented by the Miss America winner. (String) | | awards | The awards won by the Miss America winner. (String) | | talent | The talent performed by the Miss America winner. (String) | | notes | Notes about the Miss America winner. (String) |
File: eurovision_winners.csv | Column name | Description | |:--------------|:-------------------------------------------------------------------------| | Year | The year the pageant was held. (Integer) | | Date | The date the pageant was held. (Date) | | Host City | The city where the pageant was held. (String) | | Winner | The name of the pageant winner. (String) | | Song | The song performed by the pageant winner. (String) | | Performer | The name of the performer of the pageant winner's song. (String) | | Points | The number of points the pageant winner received. (Integer) | | Margin | The margin of points between the pageant winner and runner-up. (Integer) | | Runner-up | The name of the pageant runner-up. (String) |
Comprehensive dataset of 2 Cultures in Louisiana, United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.
https://www.icpsr.umich.edu/web/ICPSR/studies/36357/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/36357/terms
The Arts and Cultural Production Satellite Account (ACPSA) is produced through the partnership between the United States Bureau of Economic Analysis (BEA) and the National Endowment for the Arts (NEA). Built with the BEA's input-output (I-O) accounts, the ACPSA provides detailed statistics that illustrate the impact of arts and cultural production on the United States economy. Specifically, this account provides an assessment of the arts and cultural sector's contributions to gross domestic product (GDP). For years 1998 to 2021, the ACPSA presents annual statistics about the following items: (1) Output of detailed arts and cultural commodities and the industries producing these commodities; (2) employment and compensation within these industries; (3) arts and cultural value added by industry; and (4) commodity-flow details for arts and cultural production products. In the data tables provided, the statistics fall under two broad categories: (1) core arts and cultural production and (2) supporting arts and cultural production. The core category contains the commodities in which the output primarily contributes to arts and culture. Performing arts, museums, design services, and arts education are included in the core category. The supporting category consists of commodities that support the core category through publication, dissemination of the creative process, or other supportive functions. This category contains event promotion, printing, and broadcasting. The seven national-level data tables provided for each year from 1998 to 2021 include: Table 1. Production of Commodities by Industry Table 2. Output and Value Added by Industry Table 3. Supply and Consumption of Commodities Table 4. Employment and Compensation of Employees by Industry Table 5. Total ACPSA-related Employment by Industry Table 6. Output by ACPSA Commodity Table 7. Real Output by Commodity For years 2001-2021, a state-level value added and employment data table is included. It contains value added by industry by state, estimates for each state annually of employment and compensation by industry, and comparisons with ACPSA employment and compensation by industry the same year. It also includes the annual total of employment in each state across the arts and cultural commodities industries. In addition, estimates of real value added by industry and estimates of real gross output and prices indexes by ACPSA commodity are provided in separate Excel files. The industries and commodities presented in the data are based on the 2007 North American Industrial Classification System (NAICS). Users are encouraged to review the Table Guide as it gives important information for all data tables. Also, users should review the NEA Guide to the U.S. Arts and Cultural Production Satellite Account and the latest Arts Data Profile Series reports dedicated to the ACPSA: The U.S. Arts and Cultural Production Satellite Account (1998-2021) and State-Level Estimates of the Arts' Economic Value and Employment (2001-2021).
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This dataset is about books. It has 2 rows and is filtered where the book is The United States and the global struggle for minerals. It features 6 columns including author, publication date, book publisher, and ISBN.
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This dataset is about books. It has 7 rows and is filtered where the book subjects is Material culture-United States-History. It features 9 columns including author, publication date, language, and book publisher.
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Even people from frequently studied cultural contexts differ in how they conceptualize compassion, partly because of differences in how much they want to avoid feeling negative. To broaden this past work, we include participants from an understudied cultural context and start to examine the process through which culture shapes compassion. Based on ethnographic and empirical studies that include Ecuadorians, we predicted that Ecuadorians would want to avoid feeling negative less compared to U.S. Americans. Furthermore, we hypothesized that because of these differences in avoided negative affect, compared to U.S. Americans, for Ecuadorians, a compassionate response would contain more emotion sharing, which in turn would be associated with conceptualizing a compassionate face as one that mirrors sadness more and expresses happiness (e.g., a kind smile) less. Using a reverse correlation task, participants in the U.S. and Ecuador selected the stimuli that most resembled a compassionate face. They also reported how much they wanted to avoid feeling negative and described what a compassionate response would entail. As predicted, compared to U.S. Americans, Ecuadorians wanted to avoid feeling negative less, they conceptualized a compassionate response as one that focused more on emotion sharing, and visualized a compassionate face as one that contained more sadness and less happiness. Furthermore, exploratory analyses suggest that wanting to avoid feeling negative and conceptualizations of a compassionate response as emotion sharing partly sequentially explained the cultural differences in conceptualizations of a compassionate face. What people regard as compassionate differs across cultures, which has important implications for cross-cultural counseling.
Comprehensive dataset of 2 Cultures in West Virginia, United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.
Comprehensive dataset of 2 Cultures in Rhode Island, United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.
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This study tests the malleability of thinking styles across cultures. Participants from China, Hong Kong, and the United States were randomly assigned to one of four conditions to manipulate thinking styles (analytical, holistic, intuitive, and control). Participants first responded to a scale measuring four thinking styles (analytical, holistic, intuitive, and normative). They then read a message to induce one of these thinking styles and responded to four scenarios regarding their decision related to the scenario, the difficulty in making the decision, their confidence in their decision, and the perceived realism of the scenario. Participants then responded to the same scale measuring the four thinking styles. Results supported the expectation that people in different cultures use predominantly different thinking styles to make decisions. The manipulation of thinking styles, however, changed people’s thinking in complex rather than direct ways. Analytical thinking, which is the predominant style used by U.S. Americans, was not as malleable as the other styles, and the American participants were less changeable in their style than participants from China and Hong Kong. In other words, and in summary, some thinking styles and some cultures seem to be more malleable than others. Implications of these results for understanding culture and cognition are discussed.
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Users can obtain descriptions, maps, profiles, and ranks of U.S. metropolitan areas pertaining to quality of life, diversity, and opportunities for racial and ethnic groups in the U.S. BackgroundThe Diversity Data project operates a website for users to explore how U.S. metropolitan areas perform on evidence-based social measures affecting quality of life, diversity and opportunity for racial and ethnic groups in the United States. These indicators capture a broad definition of quality of life and health, including opportunities for good schools, housing, jobs, wages, health and social services, and safe neighborhoods. This is a useful resource for people inter ested in advocating for policy and social change regarding neighborhood integration, residential mobility, anti-discrimination in housing, urban renewal, school quality and economic opportunities. The Diversity Data project is an ongoing project of the Harvard School of Public Health (Department of Society, Human Development and Health). User FunctionalityUsers can obtain a description, profile and rank of U.S. metropolitan areas and compare ranks across metropolitan areas. Users can also generate maps which demonstrate the distribution of these measures across the United States. Demographic information is available by race/ethnicity. Data NotesData are derived from multiple sources including: the U.S. Census Bureau; National Center for Health Statistics' Vital Statistics Natality Birth Data; Natio nal Center for Education Statistics; Union CPS Utilities Data CD; National Low Income Housing Coalition; Freddie Mac Conventional Mortgage Home Price Index; Neighborhood Change Database; Joint Center for Housing Studies of Harvard University; Federal Financial Institutions Examination Council Home Mortgage Disclosure Act (HMD); Dr. Russ Lopez, Boston University School of Public Health, Department of Environmental Health; HUD State of the Cities Data Systems; Agency for Healthcare Research and Quality; and Texas Transportation Institute. Years in which the data were collected are indicated with the measure. Information is available for metropolitan areas. The website does not indicate when the data are updated.
In 2023, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the total poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States Single people in the United States making less than ****** U.S. dollars a year and families of four making less than ****** U.S. dollars a year are considered to be below the poverty line. Women and children are more likely to suffer from poverty, due to women staying home more often than men to take care of children, and women suffering from the gender wage gap. Not only are women and children more likely to be affected, racial minorities are as well due to the discrimination they face. Poverty data Despite being one of the wealthiest nations in the world, the United States had the third highest poverty rate out of all OECD countries in 2019. However, the United States' poverty rate has been fluctuating since 1990, but has been decreasing since 2014. The average median household income in the U.S. has remained somewhat consistent since 1990, but has recently increased since 2014 until a slight decrease in 2020, potentially due to the pandemic. The state that had the highest number of people living below the poverty line in 2020 was California.
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This dataset is about books. It has 336 rows and is filtered where the book subjects is Political culture-United States. It features 9 columns including author, publication date, language, and book publisher.
https://dataverse-staging.rdmc.unc.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=hdl:1902.29/D-212https://dataverse-staging.rdmc.unc.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=hdl:1902.29/D-212
These data were used in producing Robert B. Textor's A Cross- Cultural Summary and are broken down into two categories: 1) ETHNOGRAPHIC ATLAS (two decks) and 2) FINISHED CHARACTERISTICS (eleven decks)."The Ethnographic Atlas data contain a representative sample of world cultures, four-hundred in number, divided into six geographical regions, each containing a considerable number of cultural types. Of the 400 cultures, 80 are taken from Africa, 45 from the Circum-Mediterranean region, 7 0 from East Eurasia, 70 from the Insular Pacific, 70 from North America, and 65 from South America. The data are punched onto two decks (one card for each of 400 cultures) containing coded symbols for some 38 variables or ""raw characteristics"" for each culture (e.g. diet, law, work organization, level of political integration). The Finished Characteristics data are eleven decks containing codes relating each of the 536 finished characteristics or variables that are the basis of the statistical computations, and paragraphs generalizing cultural phenomena in Robert B. Textor's A Cross-Cultural Summary. The variables are dichotomous, which allows for significant correlations of classes of cultures - such as what classes of culture co-occur or overlap with other classes."
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Previous research indicates that cultural variations exist in conceptualizations of compassion, potentially attributable to the extent to which individuals in diverse cultural settings want to avoid (versus accept) feeling negative emotions and the significance they place on emotional sharing as a component of compassion. The present study investigates the conceptualization of compassion among individuals in Mexico and the United States, aiming to understand why these cultural differences occur. We hypothesized that Mexicans (1) would want to avoid feeling negative less, (2) would consequently regard emotion sharing as a more critical element of a compassionate response, and (3) would therefore conceptualize a compassionate face as one that mirrors sadness more and expresses happiness less compared to U.S. Americans. Participants from Mexico and the United States engaged in a reverse correlation task, selecting stimuli that most closely resembled a compassionate face. The selected images were aggregated and coded for the extent of sadness and happiness depicted. Additionally, participants indicated how much they wanted to avoid feeling negative and, by using an open-ended format, described what a compassionate response would entail in their view. These responses were coded for whether or not they focused on emotion sharing. Consistent with our hypotheses, Mexicans, who want to avoid feeling negative less compared to U.S. Americans, place greater importance on emotion sharing in a compassionate response. This variation is associated with Mexicans conceptualizing a compassionate face as one that portrays more sadness and less happiness compared to U.S. Americans. People in different cultural contexts have different views about what compassion might entail. Understanding and embracing these cultural differences in compassion can help us navigate our increasingly multicultural world, fostering more meaningful connections and guiding our actions with more humility and sensitivity.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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This dataset is about books. It has 13 rows and is filtered where the book subjects is Masculinity in popular culture-United States. It features 9 columns including author, publication date, language, and book publisher.
This Excel file contains all the raw data of Legionella pneumophila determined by both culture and Legiolert methods. Datasheets were prepared separately for each figure and table. This dataset is associated with the following publication: Boczek, L., M. Tang, C. Formal, D. Lytle, and H. Ryu. Comparison of two culture methods for the enumeration of Legionella pneumophila from potable water samples. JOURNAL OF WATER AND HEALTH. IWA Publishing, London, UK, 19(3): 468-477, (2021).
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 posi..., 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..., Google Sheets or Excel is required to open Lafferty et al. Data_File.xlsx Sankey Flow Show (THORTEC Software GmbH: www.sankeyflowshow.com) used to create the Sankey diagram Figure 2 produced in R
https://www.gesis.org/en/institute/data-usage-termshttps://www.gesis.org/en/institute/data-usage-terms
Political basic positions, political culture and political socialization of the population in the United States.
Topics: Except for a few items the same questionnaire as in ZA Study No. 0027 was used.
The dataset including qPCR and microcystin is used for assessment of treatment. This dataset is associated with the following publication: Struewing, I., N. Sienkiewicz, C. Zhang, N. Dugan, and J. Lu. Effective Early Treatment of Microcystis Exponential Growth and Microcystin Production with Hydrogen Peroxide and Hydroxyapatite. Toxins. MDPI, Basel, SWITZERLAND, 15(1): 3, (2023).
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The zip file contains fourteen Parquet [1] files of two kinds, for each of the seven years between 2015 and 2021 included: - region_counts: for every word found, gives how many times it appeared, regardless of capitalization ("count" column), how many times it appeared with at least one capitalized letter ("count_upper"), in how many different counties it appeared ("nr_cells"), and whether we considered it to be a proper noun ("is_proper") - raw_cell_counts: gives the count for every word by county, regardless of capitalization.
These counts were obtained from geo-tagged Tweets posted those years within the contiguous US, which were collected through the through the streaming API of Twitter, and more specifically using the “statuses/filter” end-point [2]. See the project's paper for more details on methodology, and the code repository to reproduce the analysis.
The two text files are our lists of excluded word forms.
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Every year, young women from across the United States compete for the title of Miss America. The competition is open to women between the ages of 17 and 25, and includes a talent portion, an interview, and a swimsuit competition (which was removed in 2018). The winner is crowned by the previous year's titleholder and goes on to tour the nation for about 20,000 miles a month, promoting her particular platform of interest.
The Miss America dataset contains information on all Miss America titleholders from 1921 to 2022. It includes columns for the year of the pageant, the name of the crowned winner, her state or district represented, awards won, talent performed, and notes about her win
This dataset contains information on Miss America titleholders from 1921 to 2022. The data includes the name of the winner, her state or district, the city she represented, her talent, and the year she won
- Miss America could be used to study changes in American culture over time. For example, the decline in the swimsuit competition could be seen as a sign of increasing body positivity in the US.
- The dataset could be used to study the effect of winning Miss America has on a woman's career. Does winning lead to more opportunities?
- The dataset could be used to study geographical patterns inMiss America winners. For example, are there any states that have produced more winners than others?
License
License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original.
File: miss_america_titleholders.csv | Column name | Description | |:----------------------|:-----------------------------------------------------------------------| | year | The year the Miss America pageant was held. (Integer) | | crowned | The name of the Miss America titleholder. (String) | | winner | The name of the Miss America winner. (String) | | state_or_district | The state or district represented by the Miss America winner. (String) | | city | The city represented by the Miss America winner. (String) | | awards | The awards won by the Miss America winner. (String) | | talent | The talent performed by the Miss America winner. (String) | | notes | Notes about the Miss America winner. (String) |
File: eurovision_winners.csv | Column name | Description | |:--------------|:-------------------------------------------------------------------------| | Year | The year the pageant was held. (Integer) | | Date | The date the pageant was held. (Date) | | Host City | The city where the pageant was held. (String) | | Winner | The name of the pageant winner. (String) | | Song | The song performed by the pageant winner. (String) | | Performer | The name of the performer of the pageant winner's song. (String) | | Points | The number of points the pageant winner received. (Integer) | | Margin | The margin of points between the pageant winner and runner-up. (Integer) | | Runner-up | The name of the pageant runner-up. (String) |