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 dataset consists of 126 semi-structured (including 5 unstructured interviews) and a public survey to explore current understandings of legal sex/gender and attitudes towards its decertification. Decertification is used in this project to mean that people would no longer have a legal sex or gender (birth certificates, for instance, would no longer register a baby’s sex).

Interviews were mostly conducted in-person prior to covid. After March 2020, they were undertaken via online platforms. The transcripts include interviews with a wide range of stakeholders exploring the implications of reform to legal sex and gender certification. Interviews also addressed organisations’ current practice in relation to the use of sex and gender categories, their response to gender identities such as agender and nonbinary, the challenges that innovation in this area face, and the question of how an identity-based approach to gender could combine with one attentive to structural gender inequalities. Predominantly semi-structured interviews were conducted with 1) Members of different publics using tailored interview methods to explore continuity, change and disruption in understandings and interpretations of gender (and its relationship to sex) across social and legal contexts; 2) public bodies, service providers, NGOs, regulatory bodies, religious communities, trade unions, legislative drafters, academics, and others working in related fields.

A number of interviews were carried out for this research project that have not been archived. This is for several reasons, including in a few cases technological failure. However, one recurrent reason for non-archiving relates to the inability of organisational interviews to be sufficiently anonymised and the currently contentious nature of gender/ sex law reform discussions. In some cases, despite giving initial permission to archive when the interview was carried out, interviewees subsequently requested that their interview not be archived.

The dataset also consists of a survey which explores wider public perceptions of reforming legal sex and gender. The ‘Attitudes to Gender’ survey was conducted as part of the ‘implications for the wider public’ strand of the Future of Legal Gender research project and focused on asking questions to gain a better understanding of what legal sex and gender status means for people, and whether it matters to individuals in their everyday lives. The survey ran from October to December 2018 in partial overlap with the UK Government’s public consultation on potential reform of the Gender Recognition Act 2004 (GRA) in England and Wales. We chose to develop the survey questionnaire ourselves rather than use pre-existing measures so we could ensure the survey mapped well onto the overall aims and objectives of the project. Sampling was opportunistic. We received 3101 usable responses to the survey. Some demographic data was collected and analysed (e.g. age, class, ethnicity, sexual preference, religion) but removed from this SSPS data set for anonymity purposes.

Feminist activists and scholars have long questioned the idea that gender is anchored in natural biological distinctions, arguing instead that concepts of masculine and feminine, and what it means to be a woman or man, are socially generated. More recently, some transgender and intersex activists and scholars have developed these claims further, arguing that people's gender identities should not be restricted to the sex formally recorded at birth. As many people seek to live in ways that do not correspond to stereotypical notions of their gender or otherwise diverge from the sex and gender assigned them, law in different jurisdictions has responded. Gender-neutral laws, procedures for gender transitioning, and legal decisions recognising the possibility of nonbinary gender identities unsettle traditional legal regimes based on two, biologically fixed, socially differentiated genders. Yet, while reform initiatives internationally gain momentum, they tend to be limited in two key respects: first, they typically adopt an ad hoc or incremental approach to legal gender identity structures; second, they focus on legal accommodation of gender minorities within existing classificatory structures rather than more general reform.

Legal and policy developments, the gender activism surrounding them (with all its internal disagreements, including over the meaning of biological sex), and the rapid upsurge of wider interest and concern about how to regulate and recognise gender identity have brought a more fundamental question to the surface: should sex remain a legal status assigned at birth; and what would be the implications of reforming this? Our project addressed this question, focusing on the legal jurisdiction of England & Wales, but drawing also on developments in Scotland and overseas. Research was organised into three consecutive work packages. The first drew on international developments...

Important notice

The Office for Statistics Regulation confirmed on 12/09/2024 that the gender identity estimates from Census 2021 are no longer accredited official statistics and are classified as official statistics in development.

For further information please see: Sexual orientation and gender identity quality information for Census 2021

These datasets provide Census 2021 estimates that classify usual residents aged 16 years and over in England and Wales for gender identity by sex, gender identity by age and gender identity by sex and age.

Gender identity

Gender identity refers to a person's sense of their own gender, whether male, female or another category such as non-binary. This may or may not be the same as their sex registered at birth.

Non-binary

Someone who is non-binary does not identify with the binary categories of man and woman. In these results the category includes people who identified with the specific term "non-binary" or variants thereon. However, those who used other terms to describe an identity that was neither specifically man nor woman have been classed in "All other gender identities".

Sex

This is the sex recorded by the person completing the census. The options were "Female" and "Male".

Trans

An umbrella term used to refer to people whose gender identity is different from their sex registered at birth. This includes people who identify as a trans man, trans woman, non-binary or with another minority gender identity.

Trans man

A trans man is someone who was registered female at birth, but now identifies as a man.

Trans woman

A trans woman is someone who was registered male at birth, but now identifies as a woman.

Usual resident

A usual resident is anyone who on Census Day, 21 March 2021, was in the UK and had stayed or intended to stay in the UK for a period of 12 months or more, or had a permanent UK address and was outside the UK and intended to be outside the UK for less than 12 months.

Notes:

• To ensure that individuals cannot be identified in the data, population counts have been rounded to the nearest five and counts under 10 have been suppressed.

• Percentages have been calculated using rounded data.

Analysis of ‘COVID-19 Cases by Population Characteristics Over Time’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a3291d85-0076-43c5-a59c-df49480cdc6d on 13 February 2022.

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

Note: On January 22, 2022, system updates to improve the timeliness and accuracy of San Francisco COVID-19 cases and deaths data were implemented. You might see some fluctuations in historic data as a result of this change. Due to the changes, starting on January 22, 2022, the number of new cases reported daily will be higher than under the old system as cases that would have taken longer to process will be reported earlier.

A. SUMMARY This dataset shows San Francisco COVID-19 cases by population characteristics and by specimen collection date. Cases are included on the date the positive test was collected.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how cases have been distributed among different subgroups. This information can reveal trends and disparities among groups.

Data is lagged by five days, meaning the most recent specimen collection date included is 5 days prior to today. Tests take time to process and report, so more recent data is less reliable.

B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases and deaths are from: * Case interviews * Laboratories * Medical providers

These multiple streams of data are merged, deduplicated, and undergo data verification processes. This data may not be immediately available for recently reported cases because of the time needed to process tests and validate cases. Daily case totals on previous days may increase or decrease. Learn more.

Data are continually updated to maximize completeness of information and reporting on San Francisco residents with COVID-19.

Data notes on each population characteristic type is listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

Sexual orientation * Sexual orientation data is collected from individuals who are 18 years old or older. These individuals can choose whether to provide this information during case interviews. Learn more about our data collection guidelines. * The City began asking for this information on April 28, 2020.

Gender * The City collects information on gender identity using these guidelines.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Transmission type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation
* the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures.
These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing

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

Supplementary file 1: Data for serum/plasma laboratory tests with sex-specific reference intervals performed on patients with sexual orientation/gender identity (SOGI) field differences for legal sex/sex assigned at birth (SAAB)/gender identity (GI) in the electronic medical record (EMR) at the University of Iowa Hospitals and Clinics from January 1, 2021 to December 31, 2021. The data elements include: deidentified patient number, age (years) at time of laboratory testing, location type (outpatient, inpatient, or emergency department) at time of specimen collection, legal sex in EMR, SAAB in EMR, GI in EMR, presence of SOGI mismatch (yes/no), change of legal sex in EMR, gender-affirming gonadectomy (yes/no), GI from chart review, gender-affirming hormone and route of administration, values for tests for 17 laboratory tests, and how the values of these 17 laboratory tests were relative to age-matched reference intervals for cisgender men and women. Assays details and reference intervals are summarized in Supplemental file 3. Chart review for SOGI fields previously reported [1]. There are a total of 1,166 unique patients (all 18.0 years or older) and 7,530 laboratory tests. Supplementary file 2: Data for plasma creatinine performed for patients 18.0 years or older taking gender-affirming estradiol or testosterone at the University of Iowa Hospitals and Clinics from January 1, 2021 to December 31, 2021. The data elements include: deidentified patient number, age (years) at time of laboratory testing, location type at time of specimen collection, legal sex in EMR, SAAB in EMR, GI in EMR, GI from chart review, gender-affirming hormone and route of administration, self-declared race in EMR (African-American or not), plasma creatinine (mg/dL), estimated glomerular filtration rate (eGFR) calculation using female sex and the 2021 CKD-EPI equation without race refit, eGFR using male as sex, and chronic kidney disease (CKD) stage using either female or male as sex. eGFR equation and CKD stages are from references 2 and 3, respectively. There are a total of 620 unique patients and 1,469 plasma creatinine values. Supplementary file 3: Includes Supplemental Table 1 with details on laboratory assay and Supplemental Table 2 with reference intervals for the laboratory tests analyzed. Data tabs for Supplemental Files 1 and 2 include one for primary data and another defining abbreviations. [1] N.G. Hines et al, Patterns of gender identity data within electronic health record databases can be used as a tool for identifying and estimating the prevalence of gender-expansive people, JAMIA Open 6 (2) (2023) ooad042. DOI: 10.1093/jamiaopen/ooad042. [2] C. Delgado et al., A Unifying Approach for GFR Estimation: Recommendations of the NKF-ASN Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease, Am J Kidney Dis 79 (2) (2022) 268-288 e261. DOI: 10.1053/j.ajkd.2021.08.003. [3] Kidney Int Suppl (2011) 3 (1) (2013) 5-14.

QuoteWay Canada provides extensive Demographic Data, ideal for businesses seeking detailed audience targeting. Our Demographic Data encompasses approximately 1.3M records, specifically collected for life insurance purposes and currently resting for about 3 months without contact. This Demographic Data includes age, gender, address, and identity data, ensuring a comprehensive view of the Canadian audience.

Use Cases of Demographic Data: - Targeted Marketing Campaigns: Utilize our Demographic Data to launch highly targeted marketing campaigns with precise audience segmentation. - Customer Profiling: Create detailed customer profiles using our rich Demographic Data. - Market Research: Conduct thorough market research with reliable Demographic Data for accurate consumer insights. - Product Development: Use Demographic Data to inform product development based on the characteristics and identity of your target audience. - Geographic Analysis: Perform geographic analysis with 96% valid postal codes from our Demographic Data. - Email Campaigns: Boost email marketing success rates with 98% valid emails from our Demographic Data. - Telemarketing: Improve telemarketing outcomes using 70% valid phone numbers in our Demographic Data.

Key Benefits of Demographic Data: - High Accuracy: Our Demographic Data is verified with 96% valid postal codes, 98% valid emails, and 70% valid phone numbers. - Comprehensive Dataset: Access a large dataset of approximately 1.3M records, including detailed demographic information and identity data. - Fresh Data: The Demographic Data has been resting for about 3 months, ensuring it is not overused. - Versatile Use: Suitable for various applications such as marketing, research, advertising and product development. - Compliance Ready: Our Demographic Data is ready to use under QuoteWay Canada Inc., ensuring compliance and ease of use. - Life Insurance Focused: The data was initially collected for life insurance purposes, providing a unique consumer segment. - Reliable Source: QuoteWay's commitment to quality ensures that our Demographic Data is reliable and effective, using our Advertising Data.

By leveraging QuoteWay Canada's Demographic and Identity Data, businesses can achieve greater accuracy and success in their consumer outreach efforts. The comprehensive and verified nature of our Demographic Data makes it an invaluable resource for any organization looking to enhance its audience targeting strategies.

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals may increase or decrease.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups.

B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health.

Data on the population characteristics of COVID-19 deaths are from: *Case reports *Medical records *Electronic lab reports *Death certificates

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

To protect resident privacy, we summarize COVID-19 data by only one population characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more.

Data notes on select population characteristic types are listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases.

Gender * The City collects information on gender identity using these guidelines.

C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week.

Dataset will not update on the business day following any federal holiday.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a dataset based on the San Francisco Population and Demographic Census dataset.These population estimates are from the 2018-2022 5-year American Community Survey (ACS).

This dataset includes several characteristic types. 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 number of cumulative deaths.

Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.

To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset.

E. CHANGE LOG

Percentage of persons aged 15 years and over by perceived health, by gender and other selected sociodemographic characteristics: age group; immigrant status; visible minority group; Indigenous identity; persons with a disability, difficulty or long-term condition; LGBTQ2+ people; highest certificate, diploma or degree; main activity; and urban and rural areas.

The Characteristics and Financial Circumstances of TANF Recipients tables provide demographic data on the age, gender, and race/ethnicity of adults and children in TANF and Separate State Program (SSP)-Maintenance-of-Effort (MOE) active families and closed cases, as well as data on the financial circumstances of TANF cash assistance recipients. Units of Response: TANF Recipients, States Type of Data: Administrative Tribal Data: No COVID-19 Data: No Periodicity: Annual SORN: Not Applicable Data Use Agreement: https://www.ndacan.acf.hhs.gov/datasets/order_forms/termsofuseagreement.pdf Data Use Agreement Location: Unavailable Equity Indicators: Disability;Ethnicity;Gender Identity;Household Size;Race Granularity: State

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study examined life histories and experiences of individuals involved in the sex trade in New York City. Also interviewed were twenty-eight criminal justice policymakers, practitioners, and community representatives affiliated with New York City's Human Trafficking Intervention Courts (HTICs). The collection contains 1 SPSS data file (Final-Quantitative-Data-resubmission.sav (n=304; 218 variables)). Demographic variables include gender, age, race, ethnicity, education level, citizenship status, current housing, family size, sexual orientation, and respondent's place of birth.

Socioeconomic characteristics of the population aged 15 and older that is Two-Spirit, lesbian, gay, bisexual, transgender, and queer or who use other terms related to gender or sexual diversity (2SLGBTQ+), 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.

25% sample data.

Abstract copyright UK Data Service and data collection copyright owner. This research focused on crime and its relation to risk of victimisation and the suggestion that high-risk groups, in particular, young men, report lower fear than low-risk groups, in particular, older women. The notion of anxiety as a mediating influence in the relationship between risk of victimisation and fear of crime was examined. Through a set of face-to-face interviews, the research found that the effect of risk figured prominently in interviewee's accounts of their fear of crime and their previous victimisation. They not only positioned themselves as more or less at risk but more actively were recurrently engaged in more or less ‘risky’ practices. The research suggests that the relations between risk and fear of crime cannot be understood without theorising the multiple meanings attaching to a person’s identity which become invested with anxiety. The data consists of interview transcripts with men and women living on estates where the incidence of crime was either high or low. The interviews aimed to understand the differences in fear of crime among different social groups, integrating demographic characteristics, analyses of gender, ethnicity and age. Main Topics: Anxiety; childhood; community life; crime; crime victims; fear of crime; gender; psychoanalysis; risk; violence. Purposive selection/case studies Face-to-face interview

In compliance with the 2015 Racial Identity Profiling Act, the Long Beach Police Department was one of seven law enforcement agencies required to begin collecting stop data on January 1, 2019, for individuals stopped by police and consensual encounters that resulted in a search. The Department will collect data for each calendar year and will submit the data to the California Department of Justice on an annual basis.

Data elements collected include demographic information of the stopped individuals that is perceived by the officer. This demographic information consists of race/ethnicity, gender, LGBT identity, age, English fluency, and perceived or known disability. The date, time, location, reason for stop, actions taken, contraband/evidence discovered, property seized, and result of stop are also included in the data collected.

Percentage of persons aged 15 years and over by level of satisfaction with their personal relationships, by gender and other selected sociodemographic characteristics: age group; immigrant status; visible minority group; Indigenous identity; persons with a disability, difficulty or long-term condition; LGBTQ2+ people; highest certificate, diploma or degree; main activity; and urban and rural areas.

A number of characteristics of individuals are protected under the 2010 Equality Act, in order to limit the discrimination and disadvantage of groups with one or several shared characteristics. This table brings together a range of sources to present estimates of London's population by gender, age, ethnicity, religion, disability status, country of birth and sexual identity. It also shows population breakdowns for subgroups in each of these categories by broad age group and ethnicity.

The socio-economic position of individuals is not a protected characteristic, but is nonetheless an important factor affecting outcomes. The table therefore also includes social class at the household level.

Abstract copyright UK Data Service and data collection copyright owner.

The Integrated Household Survey (IHS), which ran from 2009-2014, was a composite survey combining questions asked on a number of social surveys conducted by the Office for National Statistics (ONS) to produce a dataset of 'core' variables. The ONS stopped producing IHS datasets from 2015 onwards; variables covering health, smoking prevalence, forces veterans, sexual identity and well-being will be incorporated into the Annual Population Survey - see the Which surveys (or modules) are included in the IHS? and What is the IHS? FAQ pages for further details.

Background and history of the IHS
The aim of the IHS was to produce high-level estimates for particular themes to a higher precision and lower geographic level than current ONS social surveys. The 'core' set contained around 100 questions, but a respondent was only asked a proportion of those depending on routing from answers to questions. The core questions were asked, where possible, at the beginning of the component surveys.
In January 2008, a set of core questions was introduced within three ONS surveys in the General Lifestyle Survey, Living Costs and Food Survey, and the Opinions and Lifestyle Survey. In April 2008 the IHS core questions were also introduced on the English Housing Survey, bringing the family of modules on the IHS up to four. The IHS dataset for 2008-2009 was used as a pilot for the concept, developing the systems and designing the weighting methodology. The IHS data for that period have not been published as they do not provide better quality information than that within existing surveys. Hence, the earliest IHS data currently available cover 2009-2010. In April 2009 the IHS core questions were introduced on the Labour Force Survey (LFS) and Annual Population Survey (APS) questionnaires and from June 2009 the Life Opportunities Survey (LOS, which also ran from 2009-2014) was included in the IHS family of modules. With the inclusion of these new surveys the IHS became complete, with an achieved annual sample size of approximately 450,000 individuals from interviews undertaken in Great Britain and Northern Ireland. Therefore, the first IHS dataset released covers the period April 2009-March 2010, starting the IHS data series from the point that all surveys were included. The large sample size and UK-wide coverage meant that various geographical breakdowns were possible in the IHS, and it is possible to use a geographical hierarchy to drill down to lower level detail within an area. The IHS also contained data collected from the following surveys: General Lifestyle Survey; Living Costs and Food Survey; Opinions and Lifestyle Survey; English Housing Survey; Labour Force Survey; Annual Population Survey; and Life Opportunities Survey. All questions had been removed from the component surveys by 2014 and the IHS closed that year. Further information is available from the ONS Integrated Household Survey (Experimental statistics): January to December 2014 webpage.

Available IHS data: End User Licence and Secure Access
Users should note that there are two versions of the IHS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version (SN 8075). The Secure Access version contains more detailed variables relating to age, age of youngest dependent child, country of birth, family unit type, household and household reference person, industry class, sub-class and division, month left last job, cohabitation, country of residence history, multiple households at address, nationality, New Deal training types, National Statistics Socio-Economic Classification (NS-SEC) long version, qualifications, household relationships, minor Standard Occupational Classification (SOC) groups, sexual identity, training and working age. The more detailed geographic variables present include county, unitary/local authority, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions and Training and Enterprise Councils (TECs). Users should note that the user guide also mentions variables that are not included in either the EUL or Secure Access datasets held at the Archive.

The EUL version contains less detailed variables. For example, the lowest geography available is Government Office Region, only major (3-digit) SOC groups are included for main, second and last job, and only industry sector for main, second and last job. Users are advised to first obtain the standard EUL version of the data before making an application for the Secure Access version to see if they are sufficient for their research requirements.


The SL version of the IHS October 2010 - September 2011 is available under SN 7002.


Main Topics:
The IHS core questions cover several themes. These include:

• economic activity
• education
• health and...

The aggregated data in this dataset explores representation and exclusion in educational environments, analyzing diverse demographic variations. It investigates the impact of Universal Design for Learning (UDL) practices on academic experiences. Using mixed methods, it combines quantitative data and qualitative insights from open-ended survey responses. Encompassing various demographic factors, including gender identity, sexuality, disability, race, and culture, it provides a holistic view. The dataset captures positive and negative impacts, offering a balanced assessment of UDL effectiveness and exclusion prevalence in education.

Percentage of persons aged 15 years and over by strength of sense of belonging to their local community, by gender and other selected sociodemographic characteristics: age group; immigrant status; visible minority group; Indigenous identity; persons with a disability, difficulty or long-term condition; LGBTQ2+ people; highest certificate, diploma or degree; main activity; and urban and rural areas.

The 2015 U.S. Transgender Survey (USTS) was conducted by the National Center for Transgender Equality (NCTE) to examine the experiences of transgender adults in the United States. The USTS questionnaire was administered online and data were collected over a 34-day period in the summer of 2015, between August 19 and September 21. The final sample included respondents from all fifty states, the District of Columbia, American Samoa, Guam, Puerto Rico, and U.S. military bases overseas. The USTS Public Use Dataset (PUDS) features survey results from 27,715 respondents and details the experiences of transgender people across a wide range of areas, such as education, employment, family life, health, housing, and interactions with police and prisons. The survey instrument had thirty-two sections that covered a broad array of topics, including questions related to the following topics (in alphabetical order): accessing restrooms; airport security; civic participation; counseling; family and peer support; health and health insurance; HIV; housing and homelessness; identity documents; immigration; intimate partner violence; military service; police and incarceration; policy priorities; public accommodations; sex work; sexual assault; substance use; suicidal thoughts and behaviors; unequal treatment, harassment, and physical attack; and voting. Demographic information includes age, racial and ethnic identity, sex assigned at birth, gender and preferred pronouns, sexual orientation, language(s) spoken at home, education, employment, income, religion/spirituality, and marital status.