About this dataset

Context

The dataset tabulates the Gay population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Gay across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Gay was 116, a 4.13% decrease year-by-year from 2022. Previously, in 2022, Gay population was 121, an increase of 5.22% compared to a population of 115 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Gay decreased by 37. In this period, the peak population was 153 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Gay is shown in this column.
• Year on Year Change: This column displays the change in Gay population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Year. You can refer the same here

LGBT+ rights are human rights that all lesbian, gay, bisexual, transgender and other people outside traditional sexuality and gender categories have. But in practice, these rights are often not protected to the same extent as the rights of straight and cisgender people.

Among others, LGBT+ rights include: physical integrity rights, such as not being executed for their sexuality or gender and not being subjected to conversion therapies; social rights, such as changing their legal gender, being sexually intimate, marrying, and adopting children with people of the same sex; economic rights such as not being discriminated at work; and political rights, such as being able to advocate for themselves and their communities publicly.

The protection of these rights allows LGBT+ people to live the lives they want and to thrive in them.

On this page, you can find data and visualizations on how the protection of LGBT+ rights has changed over time, and how it differs across countries.

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. 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. 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.

About this dataset

Context

The dataset tabulates the Gay population by race and ethnicity. The dataset can be utilized to understand the racial distribution of Gay.

Content

The dataset will have the following datasets when applicable

Please note that in case when either of Hispanic or Non-Hispanic population doesnt exist, the respective dataset will not be available (as there will not be a population subset applicable for the same)

• Gay, GA Population Breakdown by Race
• Gay, GA Non-Hispanic Population Breakdown by Race
• Gay, GA Hispanic or Latino Population Distribution by Their Ancestries

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/.

Context Corner

While looking for a Capstone Project for the Google Data Analytics Program I came across a dataset compiled by Bradd Carey (LGBTQ Characters in Youth Cartoons). This dataset was specific to data parsed from an Insider.com article published on 06/2021. I decided I wanted to expand this dataset to include characters from any animated show regardless of target audience.

Modifications to the Dataset

I initially scraped information regarding LGBTQ characters from the following wikipedia pages: https://en.wikipedia.org/wiki/List_of_animated_series_with_LGBT_characters#1990s https://en.wikipedia.org/wiki/List_of_animated_series_with_LGBT_characters:_1990%E2%80%931994 https://en.wikipedia.org/wiki/List_of_animated_series_with_LGBT_characters:_1995%E2%80%931999 https://en.wikipedia.org/wiki/List_of_animated_series_with_LGBT_characters:_2000%E2%80%932004 https://en.wikipedia.org/wiki/List_of_animated_series_with_LGBT_characters:_2005%E2%80%932009 https://en.wikipedia.org/wiki/List_of_animated_series_with_LGBT_characters:_2010%E2%80%932014 https://en.wikipedia.org/wiki/List_of_animated_series_with_LGBT_characters:_2015%E2%80%932019#2018 https://en.wikipedia.org/wiki/List_of_animated_series_with_LGBT_characters:_2020%E2%80%93present

I removed data on disability representation (for now) to narrow my project as that information was not included on the wikipedia pages and the Insider dataset was specific to youth cartoons.

I removed studio information.

If there was a difference between IMDB and Wikipedia for seasons, number of episodes, or start and end dates I went with what was on IMDB.

Removed shows that did not have an IMDB or Wikipedia page.

Removed characters that appeared in spin-off shows and only included them on the first show they appeared.

Split data into two separate datasets for ease of queries: general show information and specific character information

Acknowledgements

Bradd Carey for the dataset he created from the Insider database

Original Insider Article: Abbey White and Kalai Chik -- Reporting Joi-Marie McKenzie, Brea Cubit, Emma LeGault, and Megan Willett-Wei -- Editing Sawyer Click, Skye Gould, Taylor Tyson, and Joanna Lin Su -- Design and Development Chris Snyder, Jess Chou, A.C. Fowler, Kyle Desiderio, and Kuwilileni Hauwanga -- Video

Inspiration

I wanted to complete a Capstone that was personal to me

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

About this dataset

Context

The dataset tabulates the Gay population by age. The dataset can be utilized to understand the age distribution and demographics of Gay.

Content

The dataset constitues the following three datasets

• Gay, GA Age Group Population Dataset: A complete breakdown of Gay age demographics from 0 to 85 years, distributed across 18 age groups
• Gay, GA Age Cohorts Dataset: Children, Working Adults, and Seniors in Gay - Population and Percentage Analysis
• Gay, GA Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

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/.

Context

I uploaded from https://www.kaggle.com/gauravdahiya/lgbt-community-in-london

Content

I simply downloaded the Dataset from Kaggle. There is no mention about sources in his Dataset . https://www.kaggle.com/gauravdahiya/lgbt-community-in-london Kaggle Dataset Title: LGBT Community in London.

Acknowledgements

Photo by Mikael Frivold on Unsplash

https://www.kaggle.com/gauravdahiya/lgbt-community-in-london

Inspiration

In times we experience many followers, likes, influencers. What are the real proportion and quality of that kind of relationship? Just looking at the column's titles we can evidence that friendship links aren't very consistent.

About this dataset

Context

The dataset tabulates the population of Gay by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Gay across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 54.84% of total population being female. 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.

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. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Gay is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender 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

Siterip of articles from the LGBT literature digital archive nifty.org (excluding any beastiality). Datasets is currently Work-In-Progress.

The Northern Ireland Life and Times Survey (NILT) series began in 1998, and was the successor to the previous Northern Ireland Social Attitudes series, which was discontinued in 1996.
The main aims of the NILT series are:

• to monitor public attitudes towards social policy and political issues in Northern Ireland;
• to provide a time series on attitudes to key social policy areas;
• to facilitate academic social policy analysis;
• to provide a freely available resource on public attitudes for the wider community of users in Northern Ireland.

NILT originally had a companion series which also began in 1998, the Young Life and Times Survey (YLT), although the YLT methodology changed in 2003 and it is conducted separately each year. The Kids' Life and Times (KLT) survey of P7 children (10-11 year olds) is also part of the same suite of surveys as YLT and NILT.

NILT also forms part of the International Social Survey Programme (ISSP), although it does not do so every year. Unfortunately, NILT did not run in 2011 due to funding issues, though YLT ran as normal that year; NILT resumed in 2012 (SN 7408). In addition, several open access teaching datasets were created by ARK (Access Research Knowledge) from various years of NILT, covering different topics such as Lesbian, Gay, Bisexual and Transgender (LGBT) issues, politics and community relations, attitudes to ageing and ageism, and dementia.

Further information about the series may be found on the ARK http://www.ark.ac.uk/nilt/" target="_blank" rel="noopener">NILT webpage.

The Northern Ireland Life and Times Survey, 2012: Lesbian, Gay, Bisexual and Transgender Issues Teaching Dataset is part of a suite of teaching and learning resources created as part of a Higher Education Academy (HEA) strategic project focusing on teaching research methods. The project Learning by numbers: new open educational resources for teaching quantitative methods involved the creation of new teaching datasets from two major surveys focusing on Northern Ireland, with accompanying 'student-friendly' documentation and teaching guidelines. Specifically, two teaching datasets were created using NILT 2012 (see also SN 7547, which covers politics and good relations) as well as a time-series teaching dataset drawing on the 2003-2012 Young Life and Times (YLT) surveys (see SN 7548). Documentation combining an edited technical report and codebook accompanies the teaching datasets. This documentation includes details of all the variables included in the teaching datasets as well as a summary technical report, with the main issues outlines in accessible language, for example, research design, sampling and response rates. Teaching guidelines drawing upon the particular variables included in the datasets are also available.

This dataset focuses on public attitudes to Lesbian, Gay, Bisexual and Transgender (LGBT) issues in Northern Ireland. It is based on NILT 2012, and adapted for the purposes of this project. Some variables have been constructed and/or simplified for this teaching dataset – notes are provided in the codebook. While the teaching datasets contains the same total number of respondents, they are intended for teaching purposes only; it is advisable to use the original NILT 2012 study (see SN 7408) for research. Further information about the teaching datasets may be found on the ARK Teaching datasets: Learning by numbers webpage.

20,000 Dear Abby Questions

1985-2017 Questions on Sex, LGBTQ Issues, Religion and Relationships

By Kelly Garrett [source]

About this dataset

This dataset contains 20,000+ questions submitted to the iconic advice columnist and agony aunt, Dear Abby. Drawing from articles published around the United States between 1985 to 2017, this data reveals a wealth of information about the anxieties and concerns of ordinary citizens during that time period.

The topics covered can be grouped into three main categories: sex education and relationships; LGBTQ issues; and religious queries. Questions regarding parents, children, friends, bosses, etc fill out the rest of our corpus. This data offers an interesting glimpse into the collective experiences of people living in America during these decades - showing how different issues have risen up over time or evolved in their representation - as well as offering insights on how people resorted to Dear Abby for advice on problems both mundane and extraordinary during these years.

If you're looking for meaningful datasets with conversations on human connection or seeking a better understanding of American history beyond current headlines - then this is your one-stop source! Explore all that this data has to offer by discovering trends within it. License information can be found here: LICENSE. Enjoy!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Start by Familiarizing Yourself with The Dataset: Before diving too deep into the data it's important to take some time familiarize yourself with the columns available and their corresponding data. This includes year, month day and url which provide information about when and where the particular question was published; as well as title which provides overlap about when/where a question was published; and lastly question_only which includes only the text of each individual submitted question itself.

• Consider Your Topic: Think broadly about what you are most interested in exploring through this data as it gives us direct insight into topics that Americans have been struggling with over time (such as LGBTQ issues or religion). Some ideas for questions might include “how has anxiety around a certain topic changed over time?” or “what are people most struggling with today?” Also consider specific keywords related to your topic – Dear Abby discussions sex may include words such as sexual harassment or infidelity while keywords exploring LGBTQ issues may include transgender or same-sex marriage – since you’ll likely use them in your analysis moving forward!

• Perform Your Analysis: Now that you know what columns matter most & what questions you want answered; it’s time to start performing your analysis! In order to get meaningful results out of your findings be sure make sure measure several points (e.g., mean values) along larger trends over different timelines throughout history such as 5 year intervals or looking at specific decades on their own). You can also manually group questions into categories when appropriate so that navigating complex topics becomes easier & more streamlined from an end user perspective (or further pursue techniques like t-SNE).

• Visualize Your Findings: Use any form of visualization tools (such trends graphs/charts) – e specially those available through Kaggle – for easier consumption of whatever insights its pulled out form these 20k submissions! As mentioned before these visualizations will allow you understand macro (& micro!) trends between a given topic an countless other social ones without having read even 1 entire single letter yourself .

5 . Share Your Insights : It isn't just enough knowing how Americans felt during certain

Research Ideas

• Social and Cultural Trends Analysis: This data can be used to analyze the changes in social and cultural trends over the 30-year period, such as identifying topics that were more prominent at certain times.
• Characterization of Reader Demographics: By analysing this dataset, we can gain insight into the demographics of Dear Abby's readers (e.g., age groupings, gender, location).
• Building a Natural Language Understanding Model: The data can be used to create a natural language understanding model that could accurately predict answers based on questions submitted by readers

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the d...

This table presents a socio-demographic and socio-economic statistical profile of the population aged 15 and older by sexual orientation, geographic region, sex and age group. The characteristics included are: marital status, presence of children under 12 in the household, education, employment, household income, Indigenous identity, belonging to a population group designated as a visible minority, language(s) spoken at home, and place of residence (urban/rural). These estimates are obtained from Canadian Community Health Survey, 2015 to 2018 pooled data.

Cities are in constant competition for residents, businesses, and employees, and inclusiveness is a crucial factor that attracts all three. The Municipal Equality Index (MEI) specifically measures laws and policies of municipalities to examine how inclusive cities are of LGBTQ (Lesbian, Gay, Bisexual, Transgender, and Questioning) people.Administered by the Human Rights Campaign, the MEI scorecard criteria annually evaluate a municipality on six categories, with bonus points available: Non-Discrimination Laws: This category evaluates whether discrimination based on sexual orientation and gender identity is prohibited by city, county, or state in areas of employment m housing, and public accommodations.Relationship Recognition: Marriage, civil unions, and comprehensive domestic partnerships are matters of state policy; cities and counties have only the power to create domestic partner registries.Municipality as Employer: By offering equivalent benefits and protections to LGBTQ employees, and by awarding contracts to fair-minded businesses, municipalities commit themselves to treating LGBTQ employees equally.Municipal Services: The section assesses the efforts of the city to ensure LGBTQ constituents are included in city services and programs.Law Enforcement: Fair enforcement of the law includes responsible reporting of hate crimes and engaging with the LGBTQ community in a thoughtful and respectful way.Relationship with the LGBTQ Community: This category measures the city leadership’s commitment to fully include the LGBTQ community and to advocate for full equality. Additional information available at hrc.org/meiThis page provides data for the Municipality Equality Index performance measure. The performance measure dashboard is available at 3.12 Municipal Equality Index. Additional Information Source: Contact: Wydale HolmesContact E-Mail: wydale_holmes@tempe.govData Source Type: ExcelPreparation Method: Publish Frequency: Annually, OctoberPublish Method: ManualData Dictionary

Examining LGBTQ+-related Concepts in the Semantic Web

Introduction

Welcome to the project. We study the links between LGBTQ+ ontologies and structured vocabularies. More specifically, we focus on GSSO, Homosaurus, QLIT, and Wikidata. The code is free for use with the license CC-BY 4.0. You can resue/extend the code for free as long as you give credits to us in your publication/data. Citation information will be added after the corresponding paper gets accepted. The paper is under submission and will be included soon.

If you would like to extend this work, you may want to contact the experts in the acknowledgement before releasing your data/code about legal and ethical issues. The DOI for this version is 10.5281/zenodo.12684870. The latest code can be found at https://github.com/Multilingual-LGBTQIA-Vocabularies/Examing_LGBTQ_Concepts.

To reproduce the results or extend our work, you need to take the following steps.

Step 1: Preparing the data

In this project, the following datasets were used:

QLIT: version 1.0

Homosaurus: version 3.5 and version 2.3

Wikidata: retrieved from the SPARQL Endpoint (https://query.wikidata.org/sparql) and processed between 5th May and 8th May, 2024.

GSSO: we used gsso.owl (version 2.0.10) obtained from its Github (https://github.com/Superraptor/GSSO).

LCSH was obtained from the official website: https://id.loc.gov/authorities/subjects.html on 9th May, 2024. The LCSH data was converted to its HDT format.

Please put the corresponding files in the following folders (and change its names where necessary) to make sure that the Python scripts can find your code.

./data/GSSO/gsso.owl

./data/Homosaurus/v2.ttl and ./data/Homosaurus/v3.ttl

./data/LCSH/lcsh.hdt (we used its HDT format for fast query and analysis). The original file is also attached: subjects.skosrdf.nt.

./data/QLIT/Qlit-v1.ttl

The case of Wikidata is more complicated. The following scripts were used for the retrival of data. These scripts are all in the folder ./data/wikidata/

We used the Wikidata SPARQL endpoint: https://query.wikidata.org/

The following relations from Wikidata were used while extracting triples.

Wikidata - GSSO: http://www.wikidata.org/prop/direct/P9827

Wikidata - Homosaurus 2: http://www.wikidata.org/prop/direct/P6417

Wikidata - Homosaurus 3: http://www.wikidata.org/prop/direct/P10192

Wikidata - LCSH: http://www.wikidata.org/prop/direct/P244

The generated files are:

'wikidata-homosaurus-v2-links.nt'

'wikidata-homosaurus-v3-links.nt'

'wikidata-gsso-links.nt'

'wikidata-qlit-links.nt'

'wikidata-lcsh-links-all.nt'

Please note that the case of Wikdiata-LCSH is more complicated: there are so many links that are nothing to do with the entities in our scope. We restrict it to only entities in the scope of this paper. See below for more details.

You can find all the scripts in the corresponding folder in the data folder.

All the SPARQL queries used can be found in the folder ./SPARQL/

Note! For GSSO, the following two mistakes were corrected while preprocessing:

https://www.wikidata.org/wiki/Q1823134 should not be used as a relation. We have replaced it with http://www.wikidata.org/prop/direct/P244.

Instead of referring to the page, we refer to the entity. We use http://www.wikidata.org/entity/* instead of https://www.wikidata.org/wiki/*

The redirection test was conducted on 30th April, 2024, between 6PM and 8PM. The files can be found in the folder of ./data/Homosaurus/redirect/.

Integrating the data

In the folder ./integrated_data/, you can find all the scripts related to the integrated data. Unfortunately, due to the CC-BY-NC-ND license of GSSO and Homosaurus, the integrated data will not be made available. But you can generate it with the instructions above and by using the following scripts.

The script ./integrated_data/integrate.py takes advantage of the data generated. It first integrates a list of files of links. Then we go through the links between Wikidata and LCSH. Only those that are in the scope of the study are included.

If your steps are correct and using the same version as we did, you should be able to get four files:

a) the integrated file as integrated.nt

b) the links that are relevant for this study: wikidata-lcsh-links-selected.nt.

c) a plot of the distribution of the size of WCCs

d) a mapping of entities and their corresponding ID of WCCs.

Weakly Connected Components

The weakly connected components (WCCs) were computed for the following three purposes:

a) Discovering missing links. See the section below for details.

b) The WCCs can be used for manual examination. These are entities that form clusters about related concepts. The intuition is that the larger they are, the more likely there is concept drift/change, ambiguity, and mistakes.

c) Multilingual information reuse. Smaller WCCs with exactly one entity from each dataset (e.g. Homosaurus and Wikidata) can then be used to suggest labels for the one with fewer labels for some given languages. See below for more details.

As mentioned above, the distribution has been plotted. You can find this plot here: ./integrated_data/frequency.png

In the folder ./integrated_data/weakly_connected_components/, you can find all the WCCs and their links.

Two examples were given in the folder. The largest WCC about sex, gender, fucking, etc. The other is about BDSM and fetish.

Discovering missing and outdated links

Taking advantage of WCCs, we can further find missing and outdated links. The scripts are in the folder ./discover_missing_links.

Three examples were given. The first two is about discovering missing links. The last one is about finding outdated links.

The script ./discover_missing_links/discover_H3_LCSH.py and ./discover_missing_links/discover_QLIT_LCSH.py are scripts that outputs links that could be missing in Homosaurus and QLIT respectively. This was computed by looking at the WCCs. If two entities are both involved in the same WCC, there could be a link between them. The csv files in the same folder are the corresponding links found.

The script ./discover_missing_links/find_qlit_outdated_links/ is used to discover the outdated links between QLIT and Homosaurus v3. There was only one link found.

The 105 potentially missing links were taken for further review by Swedish-speaking experts from the QLIT team, which showed that 78 (72.38%) suggested links should be included: 38 (36.19%) can be included using skos:exactMatch and another 38 (36.19%) using skos:closeMatch. 28 (26.67%) suggested links are incorrect. The manual annotation are included in the file ./discover_missing_links/Annotated_found_new_links_qlit-lcsh.xlsx.

Multilingual Information Reuse

You can find two attempts in the folders about the use of GSSO and Wikidata for Homosaurus respectively.

./WCC-based-gsso-multilingual_info_reuse/

./WCC-based-wikidata-multilingual_info_reuse/

Additionally, we provide also some code for the reuse of Wikidata multilingual info for QLIT. It's in the folder

./WCC-based-QLIT-info-reuse-from-Wikidata/

They follow very similar steps:

Compute the one-to-one mapping using the WCCs. The script is named compute-one-to-one-mapping.py

Extract the multilingual labels from sources. The corresponding file is extract_multilingual_labels_from_one_to_one_mappings.py

Provide the extracted multilingual as suggestions for targeting entities. The name of the corresponding files are like "*suggesting-labels.py", where the * is replaced by the actual source/target.

For GSSO, we use the following relations:

http://www.w3.org/2000/01/rdf-schema#label

http://www.geneontology.org/formats/oboInOwl#hasRelatedSynonym

http://www.geneontology.org/formats/oboInOwl#hasSynonym

http://www.geneontology.org/formats/oboInOwl#hasExactSynonym

http://purl.org/dc/terms/replaces

https://www.wikidata.org/wiki/Property:P5191

https://www.wikidata.org/wiki/Property:P1813

https://schema.org/alternateName

http://www.w3.org/2002/07/owl#annotatedTarget

Additioinally, we found the relation to be studied in the future: http://www.geneontology.org/formats/oboInOwl#hasNarrowSynonym

For Wikidata, there are only two:

http://www.w3.org/2000/01/rdf-schema#label

http://www.w3.org/2004/02/skos/core#altLabel

Additional analysis

Additionally, we perform an analysis using only redirection and replacement for GSSO and Homosaurus. The scripts are in the folder ./additional_test_gsso_multilingual_info_reuse. We consider also Homosaurus v2. This additional analysis shows the following:

For the Turkish language, in total there are 103 triples about labels about 23 entities. The average suggested labels per entity is 3.0.

For the Spanish language, in total there are 205 triples about labels about 43 entities. The average suggested labels per entity is 2.12.

For the French language, in total there are 277 triples about labels about 47 entities. The average suggested labels per entity is 2.19.

For the Danish language, in total there are 115 triples about labels about 47 entities. The average suggested labels per entity is 2.70.

Some analysis about the replacement relations of Homosaurus is in the folder ./data/Homosaurus/replace_relations_homosaurus/.

Finally, some additional analysis is included in the folder ./analysis_integrated_graph. Currently, there is only one that is about outdated entities in Homosaurus v3. Some more analysis will be added in the future.

Acknowledgement

The authors appreciate the help of the following researchers:

Siska Humlesjö, QLIT, Göteborgs Universitet (siska.humlesjo@lir.gu.se)

Olov Kriström, former member of QLIT

Jack van der Wel, IHLIA (jack@ihlia.nl)

Clair Kronk, GSSO (clair.kronk@mountsinai.org)

If you would like to extend this work, you may want to contact them before releasing your data/code about legal and ethical issues.

Contact

Shuai Wang, Vrije Universiteit Amsterdam

Bills in US State Legislatures with that target negative impacts specifically to folks in the LGBTQ community. I have not done any of the data gathering for this, data set is sourced from the ACLU https://www.aclu.org/legislative-attacks-on-lgbtq-rights?state=

The dataset mainly focuses on community perception and relationship forms among gay, bi- sexual and queer men, but also offers other insights into their living conditions and values.

The data is accompanied by the following documents: - A codebook in Excel format showing all the questions, variables and answering options - A technical report by the Data Steward Service at the ZHAW regarding how the dataset was cleaned prior to publication - A technical report on the survey and characterisation of the sample (German)

An induction of disgust can lead to more negative attitudes toward an entire social group: Participants who were exposed to a noxious ambient odor reported less warmth toward gay men. This effect of disgust was equally strong for political liberals and conservatives, and was specific to attitudes toward gay men—there was only a weak effect of disgust on people's warmth toward lesbians, and no consistent effect on attitudes toward African Americans, the elderly, or a range of political issues.

Students of sexual and gender minority (SGM) identities have long been underserved in higher education, and the limited research thus far has focused on undergraduates. There is a large gap in understanding the outcomes and experiences of LGBTQ+ graduate students, particularly in STEM. We undertook the first scoping review to examine the available literature on LGBTQ+ student experiences in research-focused doctoral programs. A scoping review methodology was utilized to compile a broad set of publications for a narrative review of emergent themes. A comprehensive search of 5 bibliographic databases yielded 1,971 unique studies, which were screened by two independent reviewers for data on LGBTQ+ doctoral students in non-clinical fields. Eighty-two publications were included in the analysis, over half of which were published in the past 5 years. Thirteen themes emerged from analyzing the included publications. LGBTQ+ ientities can continue evolving during graduate school, and some students incorporated SGM identities in their research (“mesearch”). Though students expected academia to be welcoming, many encountered repeated anti-LGBTQ+ bias that impacted their perceived safety for coming out. Nearly half of the studies mentioned intersectionality with other marginalized identities, including race/ethnicity, religion, disability, and others. Based on the information presented, we outline recommendations for practitioners to improve doctoral education, such as preparing teaching assistants to manage discriminatory classroom conduct.

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): 87.70% are white, 11.48% are some other race and 0.82% are multiracial.

Content

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