About this dataset

Context

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

Key observations

Of the Non-Hispanic population in Gays, the largest racial group is White alone with a population of 162 (94.74% of the total Non-Hispanic population).

Content

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

Racial categories include:

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

Variables / Data Columns

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

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

Objective. Support for gay rights has increased in the publics of many countries over recent decades, but the scholarship on the topic has been hindered by the limited available data on these trends in public opinion. The goal of the Support for Gay Rights (SGR) dataset is to overcome this problem. Method. The SGR dataset is constructed by combining a comprehensive collection of survey data with a latent-variable model to provide annual time-series estimates of public support for gay rights across 118 countries and over as many as 51 years that are comparable across space and time. Results. We show these data perform well in validation tests and demonstrate their potential by replicating the influential but recently questioned finding of Andersen and Fetner (2008) that more income inequality yields less tolerant and supportive attitudes toward gay people. Conclusion. We anticipate that the SGR data will become a crucial source for cross-national, cross-regional, and longitudinal research that improves our understanding of the sources and consequences of public support for gay rights.

The survey asked lesbian, gay, bisexual and transgender (LGBT) people whether they had experienced discrimination, violence, verbal abuse or hate speech on the grounds of their sexual orientation or gender identity. The results reflect the experiences of more than 93,000 individuals who completed the online survey across Europe.

The EU Lesbian, Gay, Bisexual and Transgender Survey (EU LGBT) was conducted by the European Union Agency for Fundamental Rights (FRA). It is the first ever EU-wide online survey to establish an overview concerning the lives of lesbian, gay, bisexual and trans people (18 years or older) and their experiences with regard to fundamental rights in the 28 EU Member States. Up until now, there has been very little comparable data collected across the EU about the everyday experiences of LGBT people with respect to discrimination, and lot of the available information is limited to occasional news reports and court judgements. As the first EU-wide survey of its kind, the results support the development of equal treatment policies for LGBT people in the European Union and set the agenda for years to come. Based on the survey results, national and European policy makers, as well as non-governmental organisations, are able to better target their advocacy strategies and activities to support LGBT communities to live and express themselves freely in a non-discriminatory environment. The survey was completely anonymous (no additional data on the participants and their sessions were logged in any way). The survey data collection operated by Gallup Europe, a professional survey and consultancy firm. In order to ensure that the survey delivers evidence needed for policy making, the EU LGBT Survey counted on the participation of a large and diverse group of lesbian, gay, bisexual and trans people from each country. Hence, it was vitally important for the success of the survey that FRA and Gallup worked together with civil society organisations to reach a wide audience and raise awareness concerning the survey, including participants emailing the survey, sharing it through social media or simply inviting LGBT friends to take part. More information can be found on the FRA website The data represents a self-selected sample and not a random sample. Please see technical report for details on data collection and dissemination of survey to potential respondents. Web-based survey

About this dataset

Context

The dataset tabulates the population of Gays by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Gays. The dataset can be utilized to understand the population distribution of Gays by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Gays. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Gays.

Key observations

Largest age group (population): Male # 45-49 years (11) | Female # 40-44 years (14). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Gays population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Gays is shown in the following column.
• Population (Female): The female population in the Gays is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Gays for each age group.

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 Gays Population by Gender. You can refer the same here

Sexual orientation in the UK by region, sex, age, legal partnership status, and ethnic group. These are official statistics in development.

This study explores how religious identity interplays with other forms and contexts of identity, specifically those related to sexuality. It investigates at what points - and why - religious and sexual identities are rendered more or less salient in the everyday experiences, choices and identities of queer youth (16-24 yrs) involved in the Metropolitan Community Church (MCC). This is situated as a specific case-study exploration of Christianity and sexuality in young people's lives asking: how does participation shape identifications, how is marginalisation or discrimination managed and how might religion and sexuality serve as a vehicle for various forms of belonging, identification and political expression? It adopts an intersectional lens, both theoretically and methodologically, uncovering the salience of other social divisions and identities in young people's lives. Using such a model is relevant and ambitious, where theorisation has yet to be fully embedded within empirical study. Methodologically the project is innovative and sensitive to 'blended' identities and their sophisticated enactment. Qualitative methodology will be deployed informed by a mix of techniques (interviews, maps and diaries) in order to collect rich insights into the everyday lives, practices and identities of MCC youth. Interviews (n=38) were conducted across three sites: Newcastle, Manchester and London. The choice of locality built upon pre-established and ongoing contacts with key gatekeepers. Leaflets were distributed to congregations and groups, and links to our project website (http://queerreligiousyouth.wordpress.com/) and closed Facebook group ‘Queer Religious Youth’ were disseminated through mailing lists, posted to their websites and social media. This included postings to and dialogues with other inclusive churches, university LGBT societies, LGBT youth groups, support services, and publications (e.g. Diva). There were two main phases of data collection: 1) Individual interviews and social identities mapping exercises–individual interviews on the everyday experiences of queer-identifying religious young people. Key themes explored include: the location of religion in their lives; changes in religiosity over time; management of religious and sexual identities; religious identities and family life; participation in ‘community’ spaces; biographies, transitions and materialities. The interview process was also supported through the social identities mapping exercise where participants constructed A3 maps that represent important sites in their everyday lives and the ways in which their identities change across these spaces and times. This exercise is characteristic of the work within participatory research where participants are open to shape agendas (Kindon et al., 2008; Taylor and Addison, 2012). 2) Personal diaries–participants were asked to keep a diary over the period of a month in order to reflect upon the multi-intersections of their religious and sexual identities, the ways that these are mediated by space and time and the strategies they adopt in the management of their identities. Since the ‘social identities mapping’ and diary exercises were intended to be participatory an overly prescriptive approach was avoided. These methods offered insight into identities in a format alternative to the interviews and to ‘triangulate’; participants were free to use this approach in an open-ended and creative way in order to represent various aspects of the multiple places and identities of their everyday lives. Some of the approaches included various forms of ‘mind-maps,’ participatory diagrams, evaluation wheels and body maps (Chambers, 2002). Personal diaries were structured around three main themes: key moments; space and time; and management strategies. Briefly, the ‘space and time’ section, for example, offered the opportunity for young people to record places/occasions when they felt a heightened sense of their religious and/or sexual identities (such as walking down the street, during a meeting, at a family meal etc.). The data collected offers a valuable contribution to the project alongside the social identities maps and interviews.

The LGBTQI+ Dataset 2020-2022_es is a collection of 410,015 original tweets extracted from the social network Twitter between January 1, 2020, and December 31, 2022. To ensure data quality and relevance, retweets, replies, and other duplicate content were excluded, retaining only original tweets. The tweets were collected by Jacinto Mata (University of Huelva, I2C/CITES) with the support of the Python programming language and using the twarc2 tool and the Academic API v2 of Twitter. Tbis data collection is part of the project “Conspiracy Theories and Hate Speech Online: Comparison of patterns in narratives and social networks about COVID-19, immigrants and refugees and LGBTI people [NON-CONSPIRA-HATE!]”, PID2021-123983OB-I00, funded by MCIN/AEI/10.13039/501100011033/ by FEDER/EU.

The search criteria (words and hashtags) used for the data collection followed the objectives of the aforementioned project and were defined by Estrella Gualda, Francisco Javier Santos Fernández and Jacinto Mata (University of Huelva, Spain). Terms and hashtags used for the search and extraction of tweets were: #orgullogay, #orgullotrans, #OrgulloLGTB, #OrgulloLGTBI, #Díadelorgullo, #TRANSFOBIA, #transexuales, #LGTB, #LGTBI, #LGTBIQ, #LGTBQ, #LGTBQ+, anti-gay, "anti gay", anti-trans, "anti trans", "Ley Anti-LGTB", "ley trans", "anti-ley trans".

This dataset collected in the frame of the NON-CONSPIRA-HATE! project had the aim of identifying and mapping online hate speech narratives and conspiracy theories towards LGBTIQ+ people and community. Additionally, the dataset is intended to compare communication patterns in social media (rhetoric, language, micro-discourses, semantic networks, emotions, etc.) deployed in different datasets collected in this project. This dataset also contributes to mapping the actors, communities, and networks that spread hate messages and conspiracy theories, aiming to understand the patterns and strategies implemented by extremist sectors on social media. he dataset includes messages that address a wide range of topics related to the LGBTQI+ community, such as rights, visibility, the fight against discrimination and transphobia, as well as debates surrounding the Trans Law and other related issues. It includes expressions of support and celebration of Pride as well as hate speech and opposition to LGBTQI+ rights, along with debates and controversies surrounding these issues.

This dataset offers a wide range of possibilities for research in various disciplines, as the following examples express:

Social Sciences & Digital Humanities:
- Analysis of opinions, attitudes, and trends toward the LGBTIQ+ people and community.
- Studies on the evolution of public discourse and polarization around issues such as transphobia, hate speech, disinformation, LGBTIQ+ rights and pride, and others.
- Analysis on social and political actors, leaders or organizations disseminating diverse narratives on LGBTIQ+
- Research on the impact of specific events (e.g., Pride Day) on social media conversations.
- Investigations on social and semantic networks around LGBTIQ+ people and community.
- Analysis of narratives, discourses and rethoric around gender identity and sexual diversity.
- Comparative studies on the representation of the LGBTIQ+ people and community in different cultural or geographic contexts.

Computer Science and Artificial Intelligence:
- Development of algorithms for the automatic detection of hate speech, discriminatory language, or offensive content.
- Training natural language processing (NLP) models to analyze sentiments and emotions in texts related to the LGBTIQ+ people and community.

For more information on other technical details of the dataset and the structure of the .jsonl data, see the “Readme.txt” file.

About this dataset

Context

The dataset tabulates the population of Fort Gay by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Fort Gay. The dataset can be utilized to understand the population distribution of Fort Gay by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Fort Gay. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Fort Gay.

Key observations

Largest age group (population): Male # 50-54 years (33) | Female # 70-74 years (30). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Fort Gay population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Fort Gay is shown in the following column.
• Population (Female): The female population in the Fort Gay is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Fort Gay for each age group.

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 Fort Gay Population by Gender. You can refer the same here

LGBT HateSpeech Comments Dataset (Korean)

Background

Hate speech becomes a major social issue, breaking our relationships and threating our society members. However, in most Korean datasets about hate speech, there're very few samples related to LGBT(and other minorities, too).

Content

This dataset contains the contents column and label column, 1 labeled as "hate speech" and 0 as negative samples(NOT-Hatespeech comments).

Data gathering & Tagging

Train Data

• Hate Speech (label 1): Augmented dataset with user information - comments in target news, who spoke the hate speech previously(in relevant news articles)
• Non-hate speech (label 0): BEEP Paper, non-biased, non-hate speech in trainset ( from https://www.kaggle.com/c/korean-hate-speech-detection/overview )

Test Data

• Hate Speech (label 1): HateSpeech tagged by researcher
• Non-Hate speech (label 0): Non-hate speech or pro-LGBT texts tagged by researcher + BEEP Paper, non-biased, non-hate speech in dev set ( from https://www.kaggle.com/c/korean-hate-speech-detection/overview )

Acknowledgements

This dataset has NOT cross-validated by several researchers. This dataset will be updated with another validation.

Analysis of ‘What Do Men Think It Means To Be A Man?’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/masculinity-surveye on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This directory contains data behind the story What Do Men Think It Means To Be A Man?.

masculinity-survey.csv contains the results of a survey of 1,615 adult men conducted by SurveyMonkey in partnership with FiveThirtyEight from May 10-22, 2018. The modeled error estimate for this survey is plus or minus 2.5 percentage points. The percentages have been weighted for age, race, education, and geography using the Census Bureau’s American Community Survey to reflect the demographic composition of the United States age 18 and over. Crosstabs with less than 100 respondents have been left blank because responses would not be statistically significant.

The data is available under the Creative Commons Attribution 4.0 International License and the code is available under the MIT License. If you do find it useful, please let us know.

Source: https://github.com/fivethirtyeight/data

This dataset was created by FiveThirtyEight and contains around 200 samples along with Adult Men, No Children, technical information and other features such as: - Age 35 64 - Race White - and more.

How to use this dataset

• Analyze Sexual Orientation Gay/ Bisexual in relation to Has Children
• Study the influence of Race Non White on Age 18 34
• More datasets

Acknowledgements

If you use this dataset in your research, please credit FiveThirtyEight

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

This study describes factors associated with methamphetamine initiation in a racially diverse sample of methamphetamine-using, HIV-positive gay and bisexual men. A factor analysis was conducted on reasons for initiation, and four factors were identified: to party, to cope, for energy, and to improve self-esteem. Methamphetamine to party accounted for more than one-third of the variance in the factor analysis. Methamphetamine to cope captured almost 9% of the variance, methamphetamine for energy accounted for approximately 8% of the variance, and methamphetamine for self esteem accounted for approximately 7% of the variance. Regression analyses revealed differential associations between methamphetamine initiation factors and HIV risk behaviours. Methamphetamine for self esteem predicted binge methamphetamine use, while methamphetamine to cope was associated with injecting methamphetamine. Using methamphetamine for energy was associated with number of illicit drugs used and using methamphetamine to party was associated with having a greater number of STIs. These findings suggest that methamphetamine initiation among gay and bisexual men is multifaceted, which could have implications for intervention development.

Socioeconomic characteristics of the population aged 15 and older whose reported sexual orientation is lesbian or gay, bisexual or pansexual, or another sexual orientation that is not heterosexual (LGB+), by gender, age group and geographic region. Marital status, presence of children under age 12 in the household, education, employment, personal income, Indigenous identity, the visible minority population, immigrant status, language(s) spoken most often at home, place of residence (population centre/rural), self-rated general health, and self-rated mental health. Estimates are obtained from combined cycles of the Canadian Community Health Survey, 2019 to 2021.

The lived experiences of lesbian, gay, bi, trans, intersex and queer (LGBTIQ) Roma, sometimes referred to as Romany Gypsies, remain invisible to most people, who are not aware that LGBTIQ Roma exist. The visual and textual data provide a unique opportunity to gain insight into the lives of LGBTIQ Roma through their lived experiences and visual self-representations, thus increasing the visibility of LGBTIQ Roma as well as demonstrating ways in which LGBTIQ Roma wish to be represented. These self-representations pose a direct challenge to the tropes, dominant representations, stereotypes and misconceptions that exist in the popular imagination about both Roma and LGBTIQ people.Romanticising or vilifying stereotypical representations of Roma often imposed by non-Roma have been instrumental in generating and maintaining negative perceptions of Roma. Roma, sometimes also referred to as 'Gypsies' or 'Travellers', are subsequently often seen as a risk to societal and national security (Van Baar 2015). Even in Romani Studies, the academic area of study, Roma have often been essentialised as 'eternal Gypsy nomads' that does not reflect their lived realities (Tremlett 2009, Willems 1997). Focusing on romanticised or villified notions of a shared identity has led to Roma being conflated with a stigmatised group identity (Tremlett and McGarry 2013). This collective conception of ethnic identity has led to all members of an assumed 'group' being reduced to possessing the same set of assumed characteristics and values. In the political landscape, neoliberal nation states have over-visibilised and used Roma to generate solidarity, belonging and identity among non-Roma (McGarry 2017); yet, at the same time, actual Roma lives remain 'hard to see' (Stewart 2010) or 'invisible' (Okely 2010). With this backdrop, LGBTIQ Roma have barely existed in this research, leading to the needs of Roma remaining largely invisible to governments, institutions and service providers (Fremlova 2017). The candidate's doctoral research (2017), which addressed this empirical gap, found that LGBTIQ Roma experience many types of discrimination and multiple objectification from non-Roma and Roma. LGBTIQ Roma are associated with stigmatising conceptions of ethnicity/race, sexuality and gender identity, and their voices, their narratives and life experiences, are barely registered in public discourses. Simultaneously, being heard and visibility is key to acceptance and belonging (Fremlova 2017). This indicates that there is tension between the (in)visibilities and representations of Roma. According to Tremlett (2014), accurate understanding of Roma requires a conscious effort made by non-Roma to transcend historically constructed stereotypes about Roma whilst addressing the history of oppression that has resulted in a distorted imagery portraying Roma. Research needs to attend closely to the self-representations - narratives and self-made images - of Roma people themselves (Tremlett 2017). The question 'How can we challenge dominant representations of LGBTIQ Roma in public spaces through queer creative and discursive research-informed interventions?' will guide the proposed investigation into questioning stereotypical representations of LGBTIQ Roma through analysing existing and new photos of LGBTIQ Roma. In answer to Tremlett's (2014c, 2017) call, this project will unpack the conceptual interplay of (in)visibilities, self-representations, the everyday/ordinary, acceptance and belonging. This will be done with a view to maximising the impact of the findings of the candidate's PhD by re-engaging with some of the research participants from her PhD and engaging with new ones in a top-up research exploring the potential of visual self-representations to contextualise, critique and challenge the dominant representational canons through queer (non-normative) creative and discursive interventions. Data was generated through observation during two creative methods workshops (28-29 March and 10-15 September 2019); in four in-depth semi-structured photo elicitation interviews with key informants (17 May; 24 May; 6 June, 22 July 2019); and in a focus group (13 September 2019). The photos mentioned in the transcripts are available from the online gallery (see Related Resources).

About this dataset

Context

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

Key observations

The percent distribution of Gay population by race (across all racial categories recognized by the U.S. Census Bureau): 60.48% are white, 38.71% are some other race and 0.81% are multiracial.

Content

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

Racial categories include:

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

Variables / Data Columns

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

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

About this dataset

Context

The dataset tabulates the Gay population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Gay. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 80 (64.52% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Gay population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Gay is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Gay is shown in the following column.

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 Gay Population by Age. You can refer the same here

BackgroundStrong evidence suggests that HIV self-testing is highly acceptable to cisgender gay, bisexual and other men who have sex with men (GBMSM), trans and gender diverse people in England and Wales, and that this novel technology can make a meaningful difference to HIV testing behaviours. HIV self-testing is feasible to deliver at large scale, can increase testing uptake and frequency without negatively impacting on linkage to care or testing for sexually transmitted infections (STIs). Question remain as to how best to deliver HIV self-testing in a way that responds to entrenched health inequalities. This Implementation action framework and tool-kit has been produced to facilitate and promote HIV self-testing service delivery in England and Wales with the key goal of improving health equity.MethodsTo produce this guidance, we synthesised key studies from England and Wales using the consolidated framework for implementation research as a structure. Evidence supporting innovative HIV self-testing implementation includes a large randomised controlled trial conducted in England and Wales (SELPHI) and extensive social science research conducted through the NIHR funded PANTHEON programme grant, PANTHEON 2 programme development grant, and the wider academic literature. Community and sexual health sectoral engagement shaped and refined our recommendations.Implementation action framework and tool-kitThe implementation context in England and Wales is favourable for HIV self-testing. Those planning services, or seeking funding to do so, can harness this context by emphasising the need to continue to expand testing to meet the goal of HIV elimination by 2030. Concerns around linkage to care and surveillance can be addressed by highlighting the importance of respecting patient choice and autonomy.This guidance establishes a standard level of support that should be provided with HIV self-testing interventions. This includes an optional result reporting system, clear information on linkage to care, inclusion of a helpline as well as clinical follow-up for those who report reactive HIV self-testing results but have not linked to care. Potential intervention adaptations which can address health inequalities between groups of GBMSM, trans and gender diverse people include innovative approaches to HIV self-testing kit delivery, additional tests (e.g. for bacterial STIs) that can be provided in interventions, demand generation activities and the provision of additional support for those requiring it, including the most marginalised.Within organisations, HIV self-testing champions can highlight the importance of implementing this new technology and ensure buy-in of key organisational actors. When implementing, organisations should define the broad intervention and the components that will accompany it and engage with potential beneficiaries to optimise proposed approaches. Early, formative evaluation can help refined interventions, and summative evaluation can demonstrate outcomes to commissioners.Examples of best practice include trial infrastructure developed during the SELPHI RCT of HIV self-testing, intervention approaches from SH:24 and the Terrence Higgins Trust and advertising used during the English National HIV testing week campaign.ConclusionThis framework will be an invaluable resource for those seeking to plan and implement HIV self-testing among GBMSM, trans and gender diverse people in England and Wales. This guidance is not meant to be prescriptive, but rather provides an implementation roadmap detailing innovative approaches, and the evidence underpinning them, that can be used to improve health equity among the most marginalised.

In early February 2024, we will be retiring the Mpox Case Summary dataset. This dataset will be archived and no longer update. A historic record of this data will remain available. A. SUMMARY This dataset represents probable and confirmed mpox cases per CDC case definitions among San Francisco residents reported to the San Francisco Department of Public Health. Data are lagged by 4 days, based on the most recent date a case is received by the source system. B. HOW THE DATASET IS CREATED Case information is based on confirmed or probable positive laboratory tests reported to the San Francisco Department of Public Health. Processes to provide quality assurance and other data verification are completed before the data are reported. These data may not be immediately available for recently reported cases because of the time needed to process tests and validate cases. Data are continually updated to maximize completeness of information and reporting on San Francisco residents with confirmed or probable mpox. To ensure data privacy, we suppress data when cumulative counts are between 1-4. The exception to this rule is for the response option “Unknown” which does not provide any identifying information. If only one response option has a count <5 we also suppress the response option with the second lowest count (including “Unknown” or a response option with a count of 0). If the second lowest response option is 0, both response options will display “<5”. If the second lowest response option is a number greater than 0 both response options will display “< [sum of two lowest counts]”. Data notes for population characteristic types are listed below. Race/ethnicity * The response option "Other" is categorized by the source system, and the response option "Unknown" refers to a lack of data. Gender * The response option "Other" is categorized by the source system, and the response option "Unknown" refers to a lack of data. Sexual orientation * The response option "Other" is categorized by the source system, and the response option "Unknown/Declined" refers to a lack of data or those who did not disclose their sexual orientation. * The response option “Gay, Lesbian, or Same-Gender Loving” is a standard reporting option and cannot be split into individual categories. This is a limitation of the data from the source system. Sexual contact during incubation period * We track whether mpox cases had sexual contact during the 21 days prior to symptom(s) onset. This timeframe is the incubation period. * The response option "Unknown/Declined" refers to a lack of data or those who did not disclose their sexual contact history. For convenience, we provide the 2020 5-year American Community Survey population estimates. C. UPDATE PROCESS Updates to the data are made through automated and manual processes Monday through Friday. D. HOW TO USE THIS DATASET This dataset shows total cases (probable and confirmed) by population characteristics. Filter the “characteristic_type” column to explore a topic area. Then, the “characteristic_group” column shows each group or category within that topic area and the total count of cases identified within that population subgroup. E. CHANGE LOG 1/4/2023: Due to low case numbers, this page will no longer include cases after 12/31/2022.

Dataset accompanying the publication "Modeling local variations in intermarriage". We utilized all Spanish marriage records available at the municipality level from 2005-2007 to model spatial variations in intermarriage. We constructed a spatial regime zero inflated Poisson model and grouped-data probit model, with spatially lagged regressors, to predict the absolute and relative presence of intermarriage between Spaniards and migrants based on structural characteristics of the local marriage markets and their neighboring areas (i.e., relative group size, homogeneity of national origins, and sex ratio indicators). Our models do not assume collapsibility of the marriage market. Instead, they incorporate the local dimension of the marriage mar-ket and examine the association between intermarriage and structural variables at the spatial lo-cal level. The model also investigates intermarriage variation by size of place. The local characteristics of the marriage markets are robust indicators of both the absolute and relative importance of intermarriage, but their impact varies by size of municipality. The relative size of the migrant community positively impacts intermarriage. The homogeneity of the origins of migrants is negatively related to it. The impact of sex ratios in the migrant and native communities on intermarriage is not uniform across all municipalities and is not always related to more intermarriage. Spatial regime Zero Inflated Poisson model and Grouped-data Probit model How to cite the database (APA style): Esteve A, Chasco C, López-Gay A. (2022) Modeling Local Variations in Intermarriage [Data set & Code]. (doi: https://doi.org/10.23728/b2share.36129082d0884c039a266767dd3675a1) Source: Esteve A, Chasco C, López-Gay A. (2022) Modeling Local Variations in Intermarriage. Mathematics 10(7):1106. https://doi.org/10.3390/math10071106

The LGBT+ Networks data consists of 9 case studies of LGBT+ employee networks in the NHS (located in England, Scotland and Wales) and 118 short video clips from LGBT+ people and their allies. Qualitative data form the case studies was collected from September 2017 to October 2019. Each case study comprises transcripts from network meetings (38) and transcripts from interviews (65) with network chairs, network members, allies, EDI/HR representatives and chief executive officers in organisations. The short video clips (118) were recorded in a custom-made video booth located in 8 different organisations during LGBT+ history month in February 2020.This research aims to offer better understanding of how lesbian, gay, bisexual and transgender (LGBT+) employee networks are run and what they can do to improve relationships between colleagues, and ultimately, improve the wellbeing of LGBT+ employees. In doing so, the research focuses on LGBT+ employee networks within the NHS in nine different institutions by administering surveys, interviewing network members and taking part in network activities. To achieve our research aims, the following objectives have been set: 1) Establish baseline understanding of how LGBT+ employee networks operate; 2) Map network membership and explore ways of addressing insufficient representation of different groups with the networks; 3) Explore what support is in place to archive networks' vision and what barriers exists to realise this vision; 4) Examine ways of using LGBT+ employee networks to address negativity towards gender and sexual minorities more effectively. Case studies (9) were selected to ensure diversity in organisation type, location and performance on the Stonewall UK Workplace Equality Index. The sample included two community and mental health services trusts; two mental health trusts; two acute teaching hospital trusts; one service provider; an ambulance trust and a health board. Some of the organisations were located in large cities and others in semi-rural areas. There was also a range in geographical coverage. Three of the case study networks were from organisations that were placed relatively high on the 2017 Stonewall Index, three were placed lower, and three had not submitted an application to the index that year. Each case study comprised of interviews and records of LGBT+ networks activities/meetings. A total of 66 individuals were interviewed. Among these were 45 members, five HR representatives, eight EDI representatives and eight eight chief executives. In terms of our network members, we attempted to make our interviewee sample as close to being fully representative as possible. EDI, HR and Chief executives were selected on the basis of their roles. The data includes transcripts of 38 network meetings. In addition 118 short video messages were recorded in a Video Booth by individuals attending events during LGBT+ history month in 2020.