Selected socioeconomic characteristics of the transgender or non-binary population aged 15 and older, by age group. 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.

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.

The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. The Sexual and Gender measures in this release include the proportion of a state's population identifying as LGBTQ+ in the U.S. Census Bureau's Household Pulse Survey, Phases 3.2 (07/21/2021-10/11/2021), 3.3 (12/01/2021-02/07/2022), 3.4 (03/02/2022-05/09/2022), and 3.5 (06/01/2022-08/08/2022). To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable.

The European Union Fundamental Rights Agency (FRA) ran the largest survey of its kind in 2020. The aggregated data (i.e. answers are shown by group without any individual information) was made public and available on their site.

Content Below are the column labels and what they mean: CountryCode - country. target_group - Lesbian women, Gay men, Bisexual women, Bisexual men, Trans people, Intersex people or "All" subset - additional filter. In the trans subgroup file, this includes trans men, trans women, non-binary people etc. question_code - unique label for each question in the survey. question_label - text of the question. answer - could be unique or allow multiple selections. percentage - percentage of people who selected each answer to a question.

Notes ‡ - Data was flagged due to the small number of respondents (20-49) [1] - Data was deemed unusable due to the small number of respondents (<20)

Extra Viz I made with the Discrimination and Daily Life datasets https://public.tableau.com/app/profile/m.mariano/viz/DiscriminationoftranspeopleinEurope/Dashboard1?publish=yes

Coming soon Dataset grouped by age, education, employment status etc.

Feel free to reach out if you'd like to help or have some feedback!

Description This dataset contains demographic information from the Pakistan Population Census conducted in 2017. It provides detailed population data at various administrative levels within Pakistan, including provinces, divisions, districts, and sub-divisions. The dataset also includes information on urban and rural populations, gender distribution, transgender individuals, sex ratios, population figures from the 1998 census, and annual growth rates.

Features Province: The administrative provinces or regions of Pakistan where the census data was collected.

Division: The divisions within each province. Divisions are the second level of administrative divisions in Pakistan.

District: Districts within each division, representing larger administrative units.

Sub-Division: Sub-divisions or tehsils within each district, providing more localized data.

Area: The land area (in square kilometers) of each sub-division.

Urban Population 2017: The population of urban areas within each sub-division for the year 2017.

Rural Population 2017: The population of rural areas within each sub-division for the year 2017.

Male Population 2017: The male population within each sub-division for the year 2017.

Female Population 2017: The female population within each sub-division for the year 2017.

Transgender Population 2017: The population of transgender individuals within each sub-division for the year 2017.

Sex Ratio 2017: The sex ratio, calculated as the number of females per 1000 males, within each sub-division for the year 2017.

Population in 1998: The total population of each sub-division as recorded in the 1998 census.

Annual Growth Rate: The annual growth rate of the population in each sub-division, calculated as the percentage increase from 1998 to 2017.

Data Source The data in this dataset was collected from official Pakistan Population Census reports and may include data from various government sources. It is essential to provide proper attribution and reference the original sources when using this dataset for analysis or research.

Data Usage Researchers and analysts can use this dataset to explore demographic trends, population growth, urbanization rates, gender distribution, and more within Pakistan at different administrative levels. Ensure compliance with ethical and legal guidelines when using this data for research or public sharing.

Please note that this description is a template, and you should adapt it based on the actual data sources and specific details of your dataset when creating it for Kaggle or any other platform.

Shark Tank India Data set.

Shark Tank India - Season 1 to season 4 information, with 80 fields/columns and 630+ records.

All seasons/episodes of 🦈 SHARKTANK INDIA 🇮🇳 were broadcasted on SonyLiv OTT/Sony TV.

Here is the data dictionary for (Indian) Shark Tank season's dataset.

• Season Number - Season number
• Startup Name - Company name or product name
• Episode Number - Episode number within the season
• Pitch Number - Overall pitch number
• Season Start - Season first aired date
• Season End - Season last aired date
• Original Air Date - Episode original/first aired date, on OTT/TV
• Episode Title - Episode title in SonyLiv
• Anchor - Name of the episode presenter/host
• Industry - Industry name or type
• Business Description - Business Description
• Company Website - Company Website URL
• Started in - Year in which startup was started/incorporated
• Number of Presenters - Number of presenters
• Male Presenters - Number of male presenters
• Female Presenters - Number of female presenters
• Transgender Presenters - Number of transgender/LGBTQ presenters
• Couple Presenters - Are presenters wife/husband ? 1-yes, 0-no
• Pitchers Average Age - All pitchers average age, <30 young, 30-50 middle, >50 old
• Pitchers City - Presenter's town/city or place where company head office exists
• Pitchers State - Indian state pitcher hails from or state where company head office exists
• Yearly Revenue - Yearly revenue, in lakhs INR, -1 means negative revenue, 0 means pre-revenue
• Monthly Sales - Total monthly sales, in lakhs
• Gross Margin - Gross margin/profit of company, in percentages
• Net Margin - Net margin/profit of company, in percentages
• EBITDA - Earnings Before Interest, Taxes, Depreciation, and Amortization
• Cash Burn - In loss in current year; burning/paying money from their pocket (yes/no)
• SKUs - Stock Keeping Units or number of varieties, at the time of pitch
• Has Patents - Pitcher has Patents/Intellectual property (filed/granted), at the time of pitch
• Bootstrapped - Startup is bootstrapped or not (yes/no)
• Part of Match off - Competition between two similar brands, pitched at same time
• Original Ask Amount - Original Ask Amount, in lakhs INR
• Original Offered Equity - Original Offered Equity, in percentages
• Valuation Requested - Valuation Requested, in lakhs INR
• Received Offer - Received offer or not, 1-received, 0-not received
• Accepted Offer - Accepted offer or not, 1-accepted, 0-rejected
• Total Deal Amount - Total Deal Amount, in lakhs INR
• Total Deal Equity - Total Deal Equity, in percentages
• Total Deal Debt - Total Deal debt/loan amount, in lakhs INR
• Debt Interest - Debt interest rate, in percentages
• Deal Valuation - Deal Valuation, in lakhs INR
• Number of sharks in deal - Number of sharks involved in deal
• Deal has conditions - Deal has conditions or not? (yes or no)
• Royalty Percentage - Royalty percentage, if it's royalty deal
• Royalty Recouped Amount - Royalty recouped amount, if it's royalty deal, in lakhs
• Advisory Shares Equity - Deal with Advisory shares or equity, in percentages
• Namita Investment Amount - Namita Investment Amount, in lakhs INR
• Namita Investment Equity - Namita Investment Equity, in percentages
• Namita Debt Amount - Namita Debt Amount, in lakhs INR
• Vineeta Investment Amount - Vineeta Investment Amount, in lakhs INR
• Vineeta Investment Equity - Vineeta Investment Equity, in percentages
• Vineeta Debt Amount - Vineeta Debt Amount, in lakhs INR
• Anupam Investment Amount - Anupam Investment Amount, in lakhs INR
• Anupam Investment Equity - Anupam Investment Equity, in percentages
• Anupam Debt Amount - Anupam Debt Amount, in lakhs INR
• Aman Investment Amount - Aman Investment Amount, in lakhs INR
• Aman Investment Equity - Aman Investment Equity, in percentages
• Aman Debt Amount - Aman Debt Amount, in lakhs INR
• Peyush Investment Amount - Peyush Investment Amount, in lakhs INR
• Peyush Investment Equity - Peyush Investment Equity, in percentages
• Peyush Debt Amount - Peyush Debt Amount, in lakhs INR
• Ritesh Investment Amount - Ritesh Investment Amount, in lakhs INR
• Ritesh Investment Equity - Ritesh Investment Equity, in percentages
• Ritesh Debt Amount - Ritesh Debt Amount, in lakhs INR
• Amit Investment Amount - Amit Investment Amount, in lakhs INR
• Amit Investment Equity - Amit Investment Equity, in percentages
• Amit Debt Amount - Amit Debt Amount, in lakhs INR
• Guest Investment Amount - Guest Investment Amount, in lakhs INR
• Guest Investment Equity - Guest Investment Equity, in percentages
• Guest Debt Amount - Guest Debt Amount, in lakhs INR
• Invested Guest Name - Name of the guest(s) who invested in deal
• All Guest Names - Name of all guests, who are present in episode
• Namita Present - Whether Namita present in episode or not
• Vineeta Present - Whether Vineeta present in episode or not
• Anupam ...

Source: 48-Hour Opioid Overdose Reporting System, Rhode Island Department of Health (RIDOH)Note: Percentages are displayed as decimals. Data for individuals identifying as transgender represented <1%.These data were not included due to RIDOH’s Small Numbers Reporting Policy. Due to RIDOH's Small Numbers Reporting Policy, values less than 5 (and their corresponding percentages) are censored. Data from 2016 does not include January.

Sexual minority men (SMM) and transgender women in South Africa engage in HIV care at lower rates than other persons living with HIV and may experience population-specific barriers to HIV treatment and viral suppression (VS). As part of a pilot trial of an SMM-tailored peer navigation (PN) intervention in Ehlanzeni district, South Africa, we assessed factors associated with ART use and VS among SMM at trial enrolment. A total of 103 HIV-positive SMM and transgender women enrolled in the pilot trial. Data on clinical visits and ART adherence were self-reported. VS status was verified through laboratory analysis (

Quality assessment of mixed method studies with the MMAT.

Number of Times a Reason was Ticked (and Percentage of Participants that Selected the Reason) in the Dutch (NL) and United Kingdom (UK) Survey in Response to the Question:: “Have You Ever Been Discriminated Against for any of the Following Reasons:”.