A broad and generalized selection of 2014-2018 US Census Bureau 2018 5-year American Community Survey race, ethnicity and citizenship data estimates, obtained via Census API and joined to the appropriate geometry (in this case, New Mexico counties). The selection, while not comprehensive, provides a first-level characterization of the race and/or ethnicity of populations in New Mexico, along with citizenship status and nativity. The determination of which estimates to include was based upon level of interest and providing a manageable dataset for users. The U.S. Census Bureau's American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. The ACS collects long-form-type information throughout the decade rather than only once every 10 years. As in the decennial census, strict confidentiality laws protect all information that could be used to identify individuals or households.The ACS combines population or other data from multiple years to produce reliable numbers for small counties, neighborhoods, and other local areas. To provide information for communities each year, the ACS provides 1-, 3-, and 5-year estimates. ACS 5-year estimates (multiyear estimates) are “period” estimates that represent data collected over a 60-month period of time (as opposed to “point-in-time” estimates, such as the decennial census, that approximate the characteristics of an area on a specific date). ACS data are released in the year immediately following the year in which they are collected. ACS estimates based on data collected from 2009–2014 should not be called “2009” or “2014” estimates. Multiyear estimates should be labeled to indicate clearly the full period of time. The primary advantage of using multiyear estimates is the increased statistical reliability of the data for less populated areas and small population subgroups. Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. While each full Data Profile contains margin of error (MOE) information, this dataset does not. Those individuals requiring more complete data are directed to download the more detailed datasets from the ACS American FactFinder website. This dataset is organized by New Mexico county boundaries, based on TIGER/Line Files: shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database.

Estimating differences between racial/ethnic groups often requires merging demographic variables from one dataset to variables of interest in another. A common method merges Home Mortgage Disclosure Act data to property databases. One alternative is to acquire this information from voter registration files; another is to predict race with a name-based algorithm. Compared to Census data, which method is more representative varies by location and group. We explore the practical implications of each method by using the matched samples in two empirical applications. Researchers can arrive at different conclusions about racial/ethnic disparities depending on the method selected.

A broad and generalized selection of 2011-2015 US Census Bureau 2015 5-year American Community Survey race, ethnicity and citizenship data estimates, obtained via Census API and joined to the appropriate geometry (in this case, New Mexico counties). The selection, while not comprehensive, provides a first-level characterization of the race and/or ethnicity of populations in New Mexico, along with citizenship status and nativity. The determination of which estimates to include was based upon level of interest and providing a manageable dataset for users. The U.S. Census Bureau's American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. The ACS collects long-form-type information throughout the decade rather than only once every 10 years. As in the decennial census, strict confidentiality laws protect all information that could be used to identify individuals or households.The ACS combines population or other data from multiple years to produce reliable numbers for small counties, neighborhoods, and other local areas. To provide information for communities each year, the ACS provides 1-, 3-, and 5-year estimates. ACS 5-year estimates (multiyear estimates) are “period” estimates that represent data collected over a 60-month period of time (as opposed to “point-in-time” estimates, such as the decennial census, that approximate the characteristics of an area on a specific date). ACS data are released in the year immediately following the year in which they are collected. ACS estimates based on data collected from 2009–2014 should not be called “2009” or “2014” estimates. Multiyear estimates should be labeled to indicate clearly the full period of time. The primary advantage of using multiyear estimates is the increased statistical reliability of the data for less populated areas and small population subgroups. Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. While each full Data Profile contains margin of error (MOE) information, this dataset does not. Those individuals requiring more complete data are directed to download the more detailed datasets from the ACS American FactFinder website. This dataset is organized by New Mexico county boundaries, based on TIGER/Line Files: shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database.

The data is prepared using AmeriCorps members who began service on any day in fiscal year (FY) 2017. The members may have served 1 to 365 days during their term. Members who are in never served, disqualified, pre-service, or deferred statuses were excluded from this analysis. AmeriCorps VISTA and AmeriCorps NCCC race and ethnicity data come from the member application to serve. The code to extract the data between the two programs is the same. The ASN race and ethnicity data comes from the enrollment form. The enrollment form may exist multiple times if the member enrolled in more than one term. It is not uncommon for each enrollment form to have conflicting information about the member’s race and ethnicity. The member may have enrollment form data for terms served outside of the timeframe of the dataset. For example, if we are reporting on members who began service in FY17, then a member who also served in FY16 may have race and ethnicity information in the FY16 enrollment form and no race or ethnicity information or conflicting information in the FY17 enrollment form. In the case of conflicting information, this analysis assumes each instance of race designation is correct. If a member reports themselves as “Asian or Asian American” in one enrollment form and “White” in another enrollment form, then the analysis categorizes this person as someone who identifies with multiple race selections vs. one or the other. In the case of ethnicity, if a member indicates that they are not Hispanic or Latino/a in one form, but that they are in another, this analysis assumes the affirmative—and they will be categorized as Hispanic or Latino/a. Lastly, the totals include the total results from the query plus the difference between the query and the raw count of members who started service in that fiscal year. The members who did not have a record in the invite table and enrollment table were added to the non-response category. Senior Corps Figures come from the Annual Progress Report Supplement as of April 11, 2018. Percentages are calculated from totals of the subcategories, excluding the non-response categories.

A broad and generalized selection of 2013-2017 US Census Bureau 2017 5-year American Community Survey race, ethnicity and citizenship data estimates, obtained via Census API and joined to the appropriate geometry (in this case, New Mexico counties). The selection, while not comprehensive, provides a first-level characterization of the race and/or ethnicity of populations in New Mexico, along with citizenship status and nativity. The determination of which estimates to include was based upon level of interest and providing a manageable dataset for users. The U.S. Census Bureau's American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. The ACS collects long-form-type information throughout the decade rather than only once every 10 years. As in the decennial census, strict confidentiality laws protect all information that could be used to identify individuals or households.The ACS combines population or other data from multiple years to produce reliable numbers for small counties, neighborhoods, and other local areas. To provide information for communities each year, the ACS provides 1-, 3-, and 5-year estimates. ACS 5-year estimates (multiyear estimates) are “period” estimates that represent data collected over a 60-month period of time (as opposed to “point-in-time” estimates, such as the decennial census, that approximate the characteristics of an area on a specific date). ACS data are released in the year immediately following the year in which they are collected. ACS estimates based on data collected from 2009–2014 should not be called “2009” or “2014” estimates. Multiyear estimates should be labeled to indicate clearly the full period of time. The primary advantage of using multiyear estimates is the increased statistical reliability of the data for less populated areas and small population subgroups. Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. While each full Data Profile contains margin of error (MOE) information, this dataset does not. Those individuals requiring more complete data are directed to download the more detailed datasets from the ACS American FactFinder website. This dataset is organized by New Mexico county boundaries, based on TIGER/Line Files: shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database.

This data set shows the number of individuals in the Pennsylvania child care workforce by race category selected by the individual by the STAR level of the facility the individual indicated at which they were employed. This data is determined by the race category selection(s) entered within the Professional Development (PD) Registry. Data is included only for individuals working in family child care, group child care, and center child care. Data is current as of the last day of the quarter prior to the posted report. This report will be updated twice a year. To protect the confidentiality of participants in OCDEL’s programs, it is necessary to limit the amount of data that is available, even in aggregate form. Specifically, counts of 10 or less have been suppressed to protect the confidentiality of individuals (Number is not displayed when count of individuals is less than 11.). Additional counts have also been suppressed to prevent the calculation of suppressed values (Number is greater than 11 but is not displayed.) DISCLAIMER: OCDEL is not representing that this information is current or accurate beyond the day it was posted. OCDEL shall not be held liable for any improper or incorrect use of the information described and/or contained herein and assumes no responsibility for anyone's use of the information.

Pursuant to Local Laws 126, 127, and 128 of 2016, certain demographic data is collected voluntarily and anonymously by persons voluntarily seeking social services. This data can be used by agencies and the public to better understand the demographic makeup of client populations and to better understand and serve residents of all backgrounds and identities. The data presented here has been collected through either electronic form or paper surveys offered at the point of application for services. These surveys are anonymous. Each record represents an anonymized demographic profile of an individual applicant for social services, disaggregated by response option, agency, and program. Response options include information regarding ancestry, race, primary and secondary languages, English proficiency, gender identity, and sexual orientation. Idiosyncrasies or Limitations: Note that while the dataset contains the total number of individuals who have identified their ancestry or languages spoke, because such data is collected anonymously, there may be instances of a single individual completing multiple voluntary surveys. Additionally, the survey being both voluntary and anonymous has advantages as well as disadvantages: it increases the likelihood of full and honest answers, but since it is not connected to the individual case, it does not directly inform delivery of services to the applicant. The paper and online versions of the survey ask the same questions but free-form text is handled differently. Free-form text fields are expected to be entered in English although the form is available in several languages. Surveys are presented in 11 languages. Paper Surveys 1. Are optional 2. Survey taker is expected to specify agency that provides service 2. Survey taker can skip or elect not to answer questions 3. Invalid/unreadable data may be entered for survey date or date may be skipped 4. OCRing of free-form tet fields may fail. 5. Analytical value of free-form text answers is unclear Online Survey 1. Are optional 2. Agency is defaulted based on the URL 3. Some questions must be answered 4. Date of survey is automated

This data set shows the number of individuals in the Pennsylvania child care workforce by race category selected by the individual by county. This data is determined by the race category selection(s) entered within the Professional Development (PD) Registry. Data is included only for individuals working in family child care, group child care, and center child care. Data is current as of the last day of the quarter prior to the posted report. This report will be updated twice a year. To protect the confidentiality of participants in OCDEL’s programs, it is necessary to limit the amount of data that is available, even in aggregate form. Specifically, counts of 50 or less have been suppressed to protect the confidentiality of individuals (Number is not displayed when count of individuals is less than 51.). DISCLAIMER: OCDEL is not representing that this information is current or accurate beyond the day it was posted. OCDEL shall not be held liable for any improper or incorrect use of the information described and/or contained herein and assumes no responsibility for anyone's use of the information.

Abstract copyright UK Data Service and data collection copyright owner.

The Evidence for Equality National Survey (EVENS) is a national survey that documents the experiences and attitudes of ethnic and religious minorities in Britain. EVENS was developed by the Centre on the Dynamics of Ethnicity (CoDE) in response to the disproportionate impacts of COVID-19 and is the largest and most comprehensive survey of the lives of ethnic and religious minorities in Britain for more than 25 years. EVENS used pioneering, robust survey methods to collect data in 2021 from 14,200 participants of whom 9,700 identify as from an ethnic or religious minority. The EVENS main dataset, which is available from the UK Data Service under SN 9116, covers a large number of topics including racism and discrimination, education, employment, housing and community, health, ethnic and religious identity, and social and political participation.

The EVENS Teaching Dataset provides a selection of variables in an accessible form to support the use of EVENS in teaching across a range of subjects and levels of study. The dataset includes demographic data and variables to support the analysis of:

• racism and belonging
• health and well-being during COVID-19
• political attitudes and trust.

Main Topics:

Racism, belonging, impact of COVID-19, health, well-being, financial position, political attitudes and trust.

The ReAding Comprehension dataset from Examinations (RACE) dataset is a machine reading comprehension dataset consisting of 27,933 passages and 97,867 questions from English exams, targeting Chinese students aged 12-18. RACE consists of two subsets, RACE-M and RACE-H, from middle school and high school exams, respectively. RACE-M has 28,293 questions and RACE-H has 69,574. Each question is associated with 4 candidate answers, one of which is correct. The data generation process of RACE differs from most machine reading comprehension datasets - instead of generating questions and answers by heuristics or crowd-sourcing, questions in RACE are specifically designed for testing human reading skills, and are created by domain experts.

Abstract (en): This study explores attitudes and perceptions related to urban problems and race relations in 15 northern cities of the United States (Baltimore, Boston, Brooklyn, Chicago, Cincinnati, Cleveland, Detroit, Gary, Milwaukee, Newark, Philadelphia, Pittsburgh, St. Louis, San Francisco, and Washington, DC). More specifically, it seeks to define the social and psychological characteristics and aspirations of the Black and White urban populations. Samples of Blacks and Whites were selected in each of the cities in early 1968. The study employed two questionnaire forms, one for Whites and one for Blacks, and two corresponding data files were generated. Attitudinal questions asked of the White and Black respondents measured their satisfaction with community services, their feelings about the effectiveness of government in solving urban problems, and their experience with police abuse. Additional questions about the respondent's familiarity with and participation in antipoverty programs were included. Other questions centered on the respondent's opinions about the 1967 riots: the main causes, the purpose, the major participating classes, and the effect of the riots on the Black cause. Respondents' interracial relationships, their attitudes toward integration, and their perceptions of the hostility between the races were also investigated. White respondents were asked about their opinions on the use of governmental intervention as a solution for various problems of the Blacks, such as substandard schools, unemployment, and unfair housing practices. Respondent's reactions to nonviolent and violent protests by Blacks, their acceptance of counter-rioting by Whites and their ideas concerning possible governmental action to prevent further rioting were elicited. Inquiries were made as to whether or not the respondent had given money to support or hinder the Black cause. Other items investigated respondents' perceptions of racial discrimination in jobs, education, and housing, and their reactions to working under or living next door to a Black person. Black respondents were asked about their perceptions of discrimination in hiring, promotion, and housing, and general attitudes toward themselves and towards Blacks in general. The survey also investigated respondents' past participation in civil rights organizations and in nonviolent and/or violent protests, their sympathy with rioters, and the likelihood of personal participation in a future riot. Other questions probed respondents' attitudes toward various civil rights leaders along with their concurrence with statements concerning the meaning of "Black power." Demographic variables include sex and age of the respondent, and the age and relationship to the respondent of each person in the household, as well as information about the number of persons in the household, their race, and the type of structure in which they lived. Additional demographic topics include the occupational and educational background of the respondent, of the respondent's family head, and of the respondent's father. The respondent's family income and the amount of that income earned by the head of the family were obtained, and it was determined if any of the family income came from welfare, Social Security, or veteran's benefits. This study also ascertained the place of birth of the respondent and respondent's mother and father, in order to measure the degree of southern influence. Other questions investigated the respondent's military background, religious preference, marital status, and family composition. Individuals between the ages of 16 and 69, living in private households within the 1960 corporate limits of the cities sampled (Baltimore, Boston, Brooklyn, Chicago, Cincinnati, Cleveland, Detroit, Gary, Milwaukee, Newark, Philadelphia, Pittsburgh, St. Louis, San Francisco, and Washington, DC). Persons with no place of residence, the institutional population, and persons in group quarters had no chance of selection for the study. Samples of Blacks and whites were selected in each of the cities, and approximately 175 respondents of each race were interviewed in early 1968. There were three stages of sampling. First, city blocks were selected within each city. Then, dwellings were selected within each city block. Finally, individuals were selected within each dwelling. In the first stage, city blocks were sampled with probabilities proportional to the number of dwellings after stratification by r...

39.8% of workers from the Indian ethnic group were in 'professional' jobs in 2021 – the highest percentage out of all ethnic groups in this role.

Publicly available data and analysis files for Choosing Schools, Choosing Safety Project. School choice programs have grown substantially over the past thirty years, enabling families to make school selections unbounded by their residential locations. While studies document families’ stated preferences for school safety, few quantitatively and comprehensively examine which safety components associate with families’ actual school choices. Leveraging NYC high school applications, I find that families factor multiple dimensions of safety into their school choices. Independent of schools’ academic, demographic, and geographic characteristics, families screen out schools with higher neighborhood and school violence and disorder; and metal detectors in the initial elimination phase of their decisions, and prefer schools in lower violence neighborhoods in the subsequent, more detailed decision-making phase. Families’ choices suggest variation in safety priorities by race and academic background. White, Asian, and higher-achieving students prioritize protection from neighborhood and school violence; Latine and Black students particularly prioritize lower school disorder; and White students ranked schools with metal detectors lower.

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau practices "data suppression", filtering some block groups from demographic publication because they do not meet a population threshold. This practice...

The aim of our study is to assess the drivers of discriminatory behaviors of real-estate agents and private landlords toward prospective Roma tenants, relying on qualitative data from Hungary. Though there is a broad literature on the forms and frequency of discrimination, we know much less about the question of why people discriminate. Previous research suggests that discrimination on the basis of ethnicity is widespread in Hungary. To understand the drivers of discrimination, we analyzed: (a) the sources and justifications of discrimination of Roma people on the rental housing market among real-estate agents and private landlords, the actors making decisions about tenants (b) mapped the social embeddedness of discrimination, and (c) assessed the resilience of discriminatory intentions by analyzing the reactions to a 3-min advocacy video showing discrimination of Roma people on the rental housing market. We conducted and analyzed five online group discussions with 18 real estate agents and landlords advertising properties for rent in different regions of the country. Our qualitative study revealed that discrimination of Roma people is understood to be a widespread and socially acceptable practice driven by the need to avoid risks attributed to Roma tenants based on widely held stereotypes about them. We identified certain specificities in the justification and argumentation strategies of real-estate agents in comparison to private landlords. By providing counter-information presenting the perspective of Roma tenants, negative views could be challenged on the emotional level and also by shifting the group dynamics, strengthening the viewpoint of those without prejudice. We discuss our findings with regards to the possibilities of interventions against discrimination in societies in which neither social norms nor state institutions expect the equal treatment of the members of ethnic minority groups.

Inflatable Race Arches Market Outlook

The global inflatable race arches market size was estimated to be valued at USD 200 million in 2023 and is projected to reach USD 350 million by 2032, growing at a compound annual growth rate (CAGR) of 6.5% during the forecast period. The growth of this market can be attributed to the increasing popularity of outdoor sports events and marathons, along with the rising trend of experiential marketing and advertising which utilize these arches for branding and promotional activities.

One of the primary growth factors driving the inflatable race arches market is the surge in the number of sports events and competitions worldwide. With the growing interest in health and fitness, more people are participating in marathons, triathlons, and cycling events. These events often require durable and visually appealing inflatable arches to mark start and finish lines, sponsor zones, and checkpoints. The demand for customizable and aesthetically pleasing arches has been increasing significantly, as organizers aim to enhance the overall experience for participants and spectators.

Additionally, the market is benefiting from advancements in materials and technology used in the production of inflatable race arches. Manufacturers are investing in high-quality materials such as PVC, nylon, and polyester, which offer improved durability, weather resistance, and ease of installation. These advancements have made inflatable arches more attractive to event organizers, contributing to their widespread adoption. Moreover, the ability to create custom designs and branding on these arches has further fueled their popularity in the market.

The rise of experiential marketing has also played a crucial role in the growth of the inflatable race arches market. Brands and companies are increasingly using inflatable arches as part of their marketing strategies to create memorable experiences for their target audience. These arches serve as effective advertising tools at events, providing high visibility and brand exposure. The growing trend of using inflatable structures for promotional activities has opened new revenue streams for manufacturers and distributors in the market.

In addition to race arches, another innovative product gaining traction in the market is the Inflatable Dome. These domes are versatile structures used for various applications, including event hosting, exhibitions, and temporary shelters. Their lightweight and portable nature make them ideal for quick setups and dismantling, providing a flexible solution for event organizers. The ability to customize the design and branding of inflatable domes further enhances their appeal, making them a popular choice for promotional activities and experiential marketing events. As the demand for unique and engaging event experiences continues to rise, inflatable domes offer an attractive option for creating immersive environments that captivate audiences.

Regionally, North America holds a significant share of the inflatable race arches market, driven by the high number of sports events and marathons organized in the region. Europe follows closely, with countries like the UK, Germany, and France witnessing substantial growth in the market. The Asia Pacific region is expected to exhibit the highest CAGR during the forecast period, owing to the increasing popularity of outdoor sporting activities and the growing awareness of health and fitness in countries like China and India.

Product Type Analysis

The inflatable race arches market is segmented by product type into standard inflatable arches and custom inflatable arches. Standard inflatable arches are pre-designed structures that come in various shapes and sizes. These arches are typically used for general purposes and are readily available for quick deployment at events. The demand for standard inflatable arches is driven by their cost-effectiveness and ease of use, making them a popular choice for smaller events and organizers with limited budgets. The versatility of these arches allows them to be used in a wide range of applications, including sports events, community gatherings, and promotional activities.

Custom inflatable arches, on the other hand, are designed and manufactured according to specific requirements and branding needs of clients. These arches offer a higher level of customization, allowing event organizers and sponsors to incorporate unique designs, logos, and messages. The gr

Release Date: 2016-09-23..Table Name. . Statistics for U.S. Employer Firms That Totally or Partly Paid Employee Benefits by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2014. ..Release Schedule. . This file was released in September 2016.. ..Key Table Information. . These data are related to all other 2014 ASE files.. Refer to the Methodology section of the Annual Survey of Entrepreneurs website for additional information.. ..Universe. . The universe for the 2014 Annual Survey of Entrepreneurs (ASE) includes all U.S. firms with paid employees operating during 2014 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. In this file, "respondent firms" refers to all firms that reported gender, ethnicity, race, or veteran status for at least one owner or returned a survey form with at least one item completed and were publicly held or not classifiable by gender, ethnicity, race, and veteran status.. ..Geographic Coverage. . The data are shown for:. . United States. States and the District of Columbia. The top fifty most populous metropolitan areas. . ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for U.S. Employer Firms That Totally or Partly Paid Employee Benefits by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2014 contains data on:. . Number of firms with paid employees. Sales and receipts for firms with paid employees. Number of employees for firms with paid employees. Annual payroll for firms with paid employees. Percent of respondent firms with paid employees. Percent of sales and receipts of respondent firms with paid employees. Percent of number of employees of respondent firms with paid employees. Percent of annual payroll of respondent firms with paid employees. . The data are shown for:. . Gender, ethnicity, race and veteran status of respondent firms. . All firms. Female-owned. Male-owned. Equally male-/female-owned. Hispanic. Equally Hispanic/non-Hispanic. Non-Hispanic. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Some other race. Minority. Equally minority/nonminority. Nonminority. Veteran-owned. Equally veteran-/nonveteran-owned. Nonveteran-owned. All firms classifiable by gender, ethnicity, race, and veteran status. Publicly held and other firms not classifiable by gender, ethnicity, race, and veteran status. . . Years in business. . All firms. Firms less than 2 years in business. Firms with 2 to 3 years in business. Firms with 4 to 5 years in business. Firms with 6 to 10 years in business. Firms with 11 to 15 years in business. Firms with 16 or more years in business. . . Employee benefits that were totally or partly paid by the business in 2014. . All firms. Health insurance. Contributions to retirement plans, including 401(k), Keogh, etc.. Profit sharing and/or stock options. Paid holidays, vacation, and/or sick leave. Tuition assistance and/or reimbursement. None of the above. Total reporting. Item not reported. . . . ..Sort Order. . Data are presented in ascending levels by:. . Geography (GEO_ID). NAICS code (NAICS2012). Gender, ethnicity, race, and veteran status (ASECB). Years in business (YIBSZFI). Employee benefits that were totally or partly paid by the business in 2014 (BENEFITS). . The data are sorted on underlying control field values, so control fields may not appear in alphabetical order.. ..FTP Download. . Download the entire SE1400CSCB21 table at: https://www2.census.gov/programs-surveys/ase/data/2014/SE1400CSCB21.zip. ..Contact Information. . To contact the Annual Survey of Entrepreneurs staff:. . Visit the website at https://www.census.gov/programs-surveys/ase.html.. Email general, nonsecure, and unencrypted...

Description

The research employed a mixed methods online survey to understand better the meaning, use, and development of academic research software at the University of Illinois Urbana-Champaign. Other objectives include understanding academic research software support and training needs to make projects successful at Illinois, as well as investigating the use of generative AI tools in using and creating research software.

At the beginning of the survey, all participants gave informed consent. The University of Illinois Urbana-Champaign Institutional Review Board (IRB Protocol no.: Project IRB24-0989) reviewed the study and gave it an exempt determination.

Data collection took place from August 2024 to October 2024. Prior to data analysis, identifiable respondent details were removed during the data cleaning process. Not Applicable and Unsure style responses were used for descriptive statistics, but these responses were excluded for inferential statistics.

Survey design

At the beginning of the online survey, a consent form was provided based on guidelines from the University of Illinois Institutional Review Board to the respondents stating the aims of the study, its benefits and risks, ethical guidelines, being a voluntary survey for participation and withdrawal, privacy and confidentiality, data security, estimated time for survey completion, and contact information of researchers for asking questions. Respondents clicked to indicate their consent. Survey questions were divided into four parts: demographic information, using software for research, creating software for research, and the protocol of citing software for research. The survey had to stop points, whereby not all questions applied to respondents, which led to different sample sizes at the stop points. At the opening of the survey, the number of respondents was 251 with the funding demographic question being answered by all respondents, while other demographic questions had between 225 and 228 respondents answering them. For the first stop question, using research software in their research, the total respondents was 212, and at the last stop question, respondents considering themselves to be research developers, the total number of respondents was 74. The last question of the survey was answered by 71 respondents. Respondents may also have left the survey for other reasons. The questions were primarily closed-type questions with single choice, multiple choice, or Likert scale, as well as a few open-ended questions. Likert scale responses were created utilizing validated scales from Vagias' (2006) Likert Type Scale Response Anchors.

Sampling

Survey Respondents’ Demographics

While most respondents were Tenure Track Faculty (34.7%, f=227), other key categories included Principal Investigator (22.4%, f=227) and Research Scientist (12.1%, f=227). Computer Science, Information Science, Mathematics, and Engineering fields combined for 16% (f=228) of the respondents surveyed, but it should be noted the remaining respondents were from various academic fields across campus from various arts, humanities, and social science fields (25%, f=228) to agriculture (10%, f=228), education (5%, f=228), economics (3%, f=228), medical sciences (4%, f=228), and politics and policy/law (1%, f=228). Most respondents were likely to receive funding from various government agencies. A more detailed breakdown of the demographic information can be found in the supplemental figures. Of the 74 respondents who answered whether they were a research software developer, most respondents did not consider themselves a research software developer, with respondents stating Not at All (39%, n=74) and Slightly (22%, n=74). In addition, open-ended questions asked for further detail about research software titles used in research, research software developer challenges, how generative AI assisted in creating research software, and how research software is preserved (e.g., reproducibility).

Table 1: Survey Respondents’ Demographics

Characteristics

Respondent (%)

Age

 18-24

 25-34

 35-44

 45-54

 55-64

 Over 64

 Preferred Not Answer

3%

14%

33%

27%

14%

7%

2%

Gender

 Woman

 Man

 Non-binary / non-conforming

 Prefer not to answer

49%

44%

2%

4%

Race

 Asian

 Black or African American

 Hispanic or Latino

 Middle Eastern or North African (MENA; new)

 White

 Prefer not to answer

 Other

12%

5%

6%

1%

67%

8%

1%

Highest Degree

 Bachelors

 Masters

 Professional degree (e.g., J.D.)

 Doctorate

6%

19%

5%

70%

Professional Title

 Tenure Track Faculty

 Principal Investigator

 Research Scientist

 Staff

 Research Faculty

 Other

 Teaching Faculty

 Postdoc

 Research Assistant

 Research Software Engineer

35%

22%

12%

8%

7%

4%

4%

4%

2%

2%

Academic Field

 Biological Sciences

 Other

 Agriculture

 Engineering

 Psychology

 Earth Sciences

 Physical Sciences

 Education

 Medical & Health Sciences

 Computer Science

 Library

 Chemical Sciences

 Human Society

 Economics

 Information Science

 Environment

 Veterinary

 Mathematical Sciences

 History

 Architecture

 Politics and Policy

 Law

18%

10%

10%

9%

8%

6%

6%

5%

4%3%

3%

3%

3%

3%

2%

2%

2%

2%

1%

1%

1%

0%

Years Since Last Degree

 Less than 1 Year

 1-2 Years

 3-5 Years

 6-9 Years

 10-15 Years

 More than 15 Years

4%

8%

11%

14%

24%

40%

Receive Funding

 Yes

 No

73%

27%

Funders for Research

 Other

 National Science Foundation (NSF)

 United States Department of Agriculture (USDA)

 National Institute of Health (NIH)

 Department of Energy (DOE)

 Department of Defense (DOD)

 Environmental Protection Agency (EPA)

 National Aeronautics and Space Administration (NASA)

Bill and Melinda Gates Foundation

Advanced Research Projects Agency - Energy (ARPA-E)

Institute of Education Sciences

Alfred P. Sloan Foundation

W.M. Keck Foundation

Simons Foundation

Gordon and Betty Moore Foundation

Department of Justice (DOJ)

National Endowment for the Humanities (NEH)

Congressionally Directed Medical Research Programs (CDMRP)

Andrew W. Mellon Foundation

22%

18%

18%

11%

9%

5%

4%

4%

2%

2%

1%

1%

1%

1%

1%

1%

0%

0%

0%

Table 2: Survey Codebook

QuestionID

Variable

Variable Label

Survey Item

Response Options

1

age

Respondent’s Age

Section Header:

Demographics Thank you for your participation in this survey today! Before you begin to answer questions about academic research software, please answer a few demographic questions to better contextualize your responses to other survey questions.

What is your age?

Select one choice.

Years

1-Under 18

2-18-24

3-25-34

4-35-44

5-45-54

6-55-64

7-Over 64

8-Prefer not to answer

2

gender

Respondent’s Gender

What is your gender?

Select one choice.

1-Female

2-Male

3-Transgender

4-Non-binary / non-conforming

5-Prefer not to answer

6-Other:

3

race

Respondent’s Race

What is your race?

Select one choice.

1-American Indian or Alaska Native

2-Asian

3-Black or African American

4-Hispanic or Latino

5-Middle Eastern or North African (MENA; new)

6-Native Hawaiian or Pacific Islander

7-White

8-Prefer not to answer

9-Other:

4

highest_degree

Respondent’s Highest Degree

What is the highest degree you have completed?

Select one choice.

1-None

2-High school

3-Associate

4-Bachelor's

5-Master's

6-Professional degree (e.g., J.D.)

7-Doctorate

8-Other:

5

professional_title

Respondent’s Professional Title

What is your professional title?

Select all that apply.

1-professional_title_1

Principal Investigator

2-professional_title_2

Tenure Track Faculty

3-professional_title_3

Teaching Faculty

4-professional_title_4

Research Faculty

5-professional_title_5

Research Scientist

6-professional_title_6

Research Software Engineer

7-professional_title_7

Staff

8-professional_title_8

Postdoc

9-professional_title_9

Research Assistant

10-professional_title_10

Other:

6

academic_field

Respondent’s most strongly identified Academic Field

What is the academic field or discipline you most strongly identify with (e.g., Psychology, Computer Science)?

Select one choice.

1-Chemical sciences

2-Biological sciences

3-Medical & health sciences

4-Physical sciences

5-Mathematical sciences

6-Earth sciences

7-Agriculture

8-Veterinary

9-Environment

10-Psychology

11-Law

12-Philosophy

13-Economics

14-Human society

15-Journalism

16-Library

17-Education

18-Art & Design Management

19-Engineering

20-Language

21-History

22-Politics and policy

23-Architecture

24-Computer Science

25-Information science

26-Other:

7

years_since_last_degree

Number of years since last respondent’s last degree

How many years since the award of your last completed degree?

Select one choice.

1-Less than 1 year

2-1-2 years

3-3-5 years

4-6-9 years

5-10-15 years

6-More than 15 years

8

receive_funding_for_research

Whether respondent received funding for research

Do you receive funding for your research?

1-Yes

0-No

9

funders_for_research

Respondent’s funding sources if they answered yes in Question 8

Who funds your research or work (e.g., NIH, Gates Foundation)?

Select all that apply.

1-funders_for_research_1

United States Department of Agriculture (USDA)

2-funders_for_research_2

Department of Energy (DOE)

3-funders_for_research_3

National Science

Race Torque Converter Market Outlook

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Product Type Analysis

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A broad and generalized selection of 2014-2018 US Census Bureau 2018 5-year American Community Survey race, ethnicity and citizenship data estimates, obtained via Census API and joined to the appropriate geometry (in this case, New Mexico Census tracts). The selection is not comprehensive, but allows a first-level characterization of the household income, median household income by race and by age group, Social Security income, the GINI Index, per capita income, median family income, and median household earnings by age, and by education level, in New Mexico. The determination of which estimates to include was based upon level of interest and providing a manageable dataset for users.The U.S. Census Bureau's American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. The ACS collects long-form-type information throughout the decade rather than only once every 10 years. The ACS combines population or housing data from multiple years to produce reliable numbers for small counties, neighborhoods, and other local areas. To provide information for communities each year, the ACS provides 1-, 3-, and 5-year estimates. ACS 5-year estimates (multiyear estimates) are “period” estimates that represent data collected over a 60-month period of time (as opposed to “point-in-time” estimates, such as the decennial census, that approximate the characteristics of an area on a specific date). ACS data are released in the year immediately following the year in which they are collected. ACS estimates based on data collected from 2009–2014 should not be called “2009” or “2014” estimates. Multiyear estimates should be labeled to indicate clearly the full period of time. While the ACS contains margin of error (MOE) information, this dataset does not. Those individuals requiring more complete data are directed to download the more detailed datasets from the ACS American FactFinder website. This dataset is organized by Census tract boundaries in New Mexico. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area. NOTE: A '-666666666' entry indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.