Gender Detection & Classification - face recognition dataset

The dataset is created on the basis of Face Mask Detection dataset

Dataset Description:

The dataset comprises a collection of photos of people, organized into folders labeled "women" and "men." Each folder contains a significant number of images to facilitate training and testing of gender detection algorithms or models.

The dataset contains a variety of images capturing female and male individuals from diverse backgrounds, age groups, and ethnicities.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F1c4708f0b856f7889e3c0eea434fe8e2%2FFrame%2045%20(1).png?generation=1698764294000412&alt=media" alt="">

This labeled dataset can be utilized as training data for machine learning models, computer vision applications, and gender detection algorithms.

💴 For Commercial Usage: Full version of the dataset includes 376 000+ photos of people, leave a request on TrainingData to buy the dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, photo_3_extension, photo_4_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, photo_3_extension, photo_4_resolution - photo resolution in the dataset

OTHER BIOMETRIC DATASETS:

• Anti Spoofing Real Dataset
• Antispoofing Replay Dataset
• Selfies, ID Images dataset (5591 sets of 15 files)
• Selfies and video dataset (4 052 sets)
• Dataset of bald people, 5000 images

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to learn about the price and buy the dataset

Content

The dataset is split into train and test folders, each folder includes: - folders women and men - folders with images of people with the corresponding gender, - .csv file - contains information about the images and people in the dataset

File with the extension .csv

• file: link to access the file,
• gender: gender of a person in the photo (woman/man),
• split: classification on train and test

TrainingData provides high-quality data annotation tailored to your needs

keywords: biometric system, biometric system attacks, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, gender detection, supervised learning dataset, gender classification dataset, gender recognition dataset

variables:1. presence of denim pants regardless of colour , Y=yes N=no2. sex, female=F and male=M

Bengali Female VS Male Names Dataset

An NLP dataset that contains 2030 data samples of Bengali names and corresponding gender both for female and male. This is a very small and simple toy dataset that can be used by NLP starters to practice sequence classification problem and other NLP problems like gender recognition from names.

  Background

In Bengali language, name of a person is dependent largely on their gender. Normally, name of a female ends with certain type of… See the full description on the dataset page: https://huggingface.co/datasets/faruk/bengali-names-vs-gender.

As part of the attempt to understand the linguistic origin and cognitive nature of grammatical gender, we designed six psycholinguistic experiments for our language sample from Vanuatu (Merei, Lewo, Vatlongos, North Ambrym) and New Caledonia (Nêlêmwa, Iaai). Each language differs in number of classifiers, and whether nouns can freely occur with different classifiers, or are restricted to just one classifier (similar to grammatical gender).

Free-listing: participants heard a possessive classifier and listed associated nouns. This revealed the different semantic domains of classifiers, the salient nouns associated with each classifier, and showed whether participants listed the same noun with different classifiers.

Card-sorting: Participants free-sorted sixty images, followed by a structured sort according to which classifier they used with each picture. We compared whether similar piles were made across sorting tasks to reveal whether the linguistic classification system provides a structure for general cognition.

Video-vignettes: Participants described 24 video clips which showed different interactions between an actor and their possession, evoking a classifier. This tested both typical and atypical interactions to see if the same or different classifiers were used.

Possessive-labelling: Participants heard 140 nouns in their language and responded by saying the item belonged to them, which meant using a classifier. This measured* inter-speaker variation in the use of classifiers for particular items, reaction times and inter-speaker variation for different possessions.

Storyboards: eight four-picture storyboards were presented to participants. We recorded participant responses, uncovering if the same classifier was used in consecutive parts of the larger story and whether the classifiers were used anaphorically.

Eye-tracking: eight line-drawn pictures were combined in a paired-preference design. An eye tracker recorded fixation times. Participants heard the auditory cue of a classifier before being presented with a pair of images. This provided objective measures of automatic processing to identify patterns in attention.

Context: The very existence of gender is a source of bafflement: why in Russian is 'elbow' masculine, while 'knee' is neuter and 'bone' is feminine? Why do some Dutch speakers distinguish three genders, and others only two? It challenges language learners and excites linguists and psychologists no less. The origin of grammatical gender is a major question in linguistics, and the related issue of how entities are categorised by speakers of different languages is a key question in psychology. We seek to establish empirically whether gender emerged from special classificatory words (classifiers, similar to English 'sheet of paper' vs. 'pack of paper'), while these classifiers in turn developed from nouns. To make our explanation fully convincing, we must also establish how and why languages relinquish a useful, meaningful classificatory system, and adopt a rigid, apparently unmotivated gender system. How do these varying systems impact cognition? Is a gender system more optimal than a classifier system? Do the additional cognitive costs of less optimal systems lead to language change? We shall address these research questions by combining typological enquiry and psycholinguistic experimentation.

Aims: To demonstrate the origin of gender unambiguously, we must track the rise of a completely new gender system, from inception to a fully functioning system. We need to do this in a group of closely related languages, so that we can use differences between the languages as a proxy for development through time. And we need sufficient speakers of each language to enable us to investigate their systems of classification psycholinguistically. Remarkably, we have identified an environment that meets all these requirements - a group of six languages in Vanuatu and New Caledonia that exhibit intriguing signs of this grammatical development in their possessive classifiers.

Our research on how gender emerges will provide insight into the way in which humans categorise entities in the world, and how this categorisation is incorporated into the workings of language. These two aspects of our research provide a rare opportunity to investigate how the mind codifies human experience. We have developed an innovative method for investigating how grammatical categories like gender come to exist. By bringing psycholinguistic experiments to a natural laboratory, we can test hypotheses that would otherwise be out of reach. This approach is timely, given that the key languages are all highly endangered and the chance to conduct this research requires exactly the type of setting identified by our research team.

In order to investigate our hypotheses on the emergence of gender from noun classifier systems and how these differing systems affect cognition, we have tested a suite of experiments. These involve...

The data material was collected in a controlled experiment that investigated the ability of laypeople to visually assess blood loss and to examine factors that may impact accuracy and the classification of injury severity. A total of 125 laypeople watched 78 short videos each of individuals experiencing a hemorrhage. Victim gender, volume of blood lost, and camera perspective were systematically manipulated in the videos.

The data set consists of four variables: volume estimate, volume error, response time, and classification.

Each variable has a separate sheet in the excel document.

The data is from 125 individuals, each listed on a separate row with a unique ID for each individual. Each sheet includes the participant ID (anonymous number), age in years, participant sex (0 = male, 1 = female), perspective of the video clip (0 = top view, 1 = front view), and then one column for each victim gender and loss volume combination (24 total). The column label consists of M or F for male and female victim, followed by underscore and the loss volume (e.g., M_50 for male victim with 50 ml of blood loss, or F_1100 for a female victim with 1100 ml of blood loss).

Volume estimates are the participants' estimate of blood loss in ml. Volume error is the estimate minus the true value, in ml. Response time is the time it took for participants to classify the bleeding as life-threatening or not, in seconds. Classification is a value from 0 to 1 for the proportion of times the participant classified that particular gender-volume combination as depicting a life-threatening blood loss .

The excel sheet provides anonymized data used in Lerback and Hanson, Nature (2017). Identifiers are anonymized across each tab to prevent identification.

This project investigated parent perceptions of COVID19 Schooling from home based on a national survey of parents.

Survey questions are listed below:

•What is your usual employment?

•How many hours a week are you currently employed?

•What is your age?

•What is your gender?

•Country of residence

•State

•Postcode

•How many children are currently under your care?

•How many children are you currently schooling at home?

•What is your child’s age?

•What year of school is your child in?

•What is your child’s gender?

•Does your child have any special learning needs, and if so, what are they?

•What type of school does your child attend?

•In what area is your child’s school located?

•What sort of technology or device does your child most often use for schooling at home (e.g. iPad, Chromebook, ACER laptop, Samsung phone, none)?

•Which would best describe the access that your child has to a device or technology in order to undertake schooling at home?

•Approximately how many weeks in total have you schooled your child from home since the beginning of the COVID-19 pandemic?

•Approximately how many hours a week do you personally support your child to undertake schooling at home?

•Approximately how many hours a week does another adult or adults support your child to undertake schooling at home?

•Please rate your agreement with the following questions:

- Schooling at home has been stressful for me.

- Schooling at home has been difficult for my child.

•What has been most stressful and difficult for you and your child about homeschooling, and why?

•What has worked well/has been beneficial for you or your child during homeschooling, and why?

•How many days each week does your child undertake schooling at home?

•On each schooling at home day, approximately how many hours does your child spend schooling at home?

•Are you generally aware of how your child spends their time completing schooling at home?

•Approximately how many minutes each day (on average) would you estimate your child spends completing each of the following schooling-related activities?

- Paper based activities (e.g. printed worksheets)

- Offline tactile activities (e.g., exercise, science experiments)

- Web-conferencing with a teacher (e.g. via Zoom)

- Online learning games (e.g. Mathletics, Reading Eggs)

- Digital worksheets completed online (e.g. fill-in-the-blank)

- Reading online resources (e.g. links to websites)

- Watching videos (teacher created)

- Watching videos (general public domain)

- Digital creativity tasks (e.g. creating essays, videos, posters)

- Other online tasks (e.g. Google Classroom, Moodle chats)

- Other:

•If you could change anything about your child’s online and offline schooling at home activities, what would it be?

•Does your child learn more, the same or less when schooling from home compared to when learning at school?

•How much more or less do you estimate your child is learning during schooling at home compared to their normal learning when at school?

•Please rate your agreement with the following questions:

- My child is able to learn independently using technology

- I am satisfied with the homeschooling support being offered by my child’s school

•Compared to the first time during the pandemic that you had to do schooling at home, how would you rate schooling at home now?

•Please explain the reasons for your answer to the previous question.

Supporting data for:Tulloch, Ayesha I.T. (2020) Towards more equitable and inclusive conservation and ecology conferences”, Perspective in Nature Ecology and Evolution.4 worksheets:1. Conference Initiatives: Results of review supporting Table 1 and Table S2 in main text of paper. Indicates which of 30 conference events for 10 international conference and ecology conferences implemented different initiatives.To evaluate how ecology and conservation conferences support these principles, the actions and policies of 10 international conferences held by nine academic societies for ecology and conservation were reviewed. Data were collated for the past three events that had been held by each conference targeting an international audience: the biannual International Congress for Conservation Biology (ICCB), International Marine Conservation Congress (IMCC), European Ecological Federation (EEF) Conference and the Society for Ecological Restoration (SER) World Conference on Ecological Restoration, the annual conferences of the Ecological Society of America (ESA), Ecological Society of Australia (ESAus), British Ecological Society (BES) and Association for Tropical Biology and Conservation (ATBC), the conference of the International Association for Ecology (INTECOL), held once every 5 years, and the IUCN World Conservation Congress (WCC) held once every 4 years. Data came from conferences between 2009 and 2020. Data were sourced from conference websites, conference programs and marketing material. Initiatives of interest were those targeted on improving equity and diversity in sex, gender identity and sexual orientation, and associated diversity types and lifestyle choices ̶ marital status, family or carer responsibilities, pregnancy and breastfeeding and physical appearance are categorised according to three broad groups:(a) Minimising discrimination, harassment and implicit bias(b) Minimising barriers to attendance(c) Maximising opportunities for participation & education.2. Conference Affordability: Data on conference registration fees and discounts for students and developing countries.3. Conference Attendance: Data on conference attendee diversity provided by individual conferences and societies on websites and marketing material.4. Conference_equity_forR_200505: Input data (csv file) for GLMM code in R, provided in S3. Code for Statistical Models.

Data format and access

The database consists of full-text patient reviews, reflecting their dissatisfaction with healthcare quality. Materials in Russian have been posted in the «Review list» of the site infodoctor.ru. Publication period: July 2012 to August 2023. The database consists of 18,492 reviews covering 16 Russian cities with population of over one million. Data format: .xlsx.

Data access: 10.5281/zenodo.15257447

Data collection methodology

Based on the fact that negative reviews may be more reliable than positive ones, the authors carried out negative reviews from 16 Russian cities with a population of over one million, for which it was possible to collect representative samples (at least 1000 reviews for each city). We have extracted reviews from the one-star section of this site's guestbook, as they are reliably identified as negative. Duplicates were removed from the database. Personal data in comment texts have been replaced with "##########". The author's gender was determined manually based on his/her name or gender endings in the texts of reviews. Otherwise, we indicated "0" - gender cannot be determined.

For Moscow reviews, classification was carried out using manual markup methods - based on the majority of votes for the review class from 3 annotators (if at least one annotator indicated that it was impossible to determine, the review was classified as #N/A - impossible to clearly determine). For reviews from other cities, classification was made into 3 classes using machine learning methods based on logistic regression. The classification accuracy was 88%.

The medical specialties were distributed into large groups for the convenience of further analysis. The correspondence of medical specialties to large groups is presented in detail in Appendix 1.

Sample structure and description of variables

· CITY – the name of a city with a population of over a million (on a separate sheet – Moscow), the other 15 are Volgograd, Voronezh, Yekaterinburg, Kazan, Krasnodar, Krasnoyarsk, Nizhny Novgorod, Novosibirsk, Omsk, Perm, Rostov-on-Don, Samara, St. Petersburg, Ufa, Chelyabinsk

· TEXT – review text

· GENDER – gender of the review author (2 – female, 1 – male, 0 – cannot be determined)

· CLASS_1 – group of reasons for dissatisfaction with medical care (M – issues of medical content, O – issues of organizational support and economic aspect, C – mixed (combined) class, #N/A – cannot be clearly determined)[1]

· CLASS_2 – group of reasons for dissatisfaction with medical care (0 – issues of medical content, 1 – issues of organizational support and economic aspect, 2 – mixed (combined) class, #N/A – cannot be clearly determined)

· DAY – day of the month the review was posted

· MONTH – month the review was posted

· YEAR – year the review was posted

· DOCTOR_OR_CLINIC – what or who is the review dedicated to – the doctor or the clinic

· SPEC – physician specialty (for observations where the review is dedicated to the physician)

· GROUP_SPEC – a large group of a physician’s specialty

· ID – observation identifier

Database application

The data are suitable for analyzing patient dissatisfaction trends with medical services in Russia over the period from July 2012 to August 2023. This dataset could be particularly useful for healthcare providers, policymakers, and researchers interested in understanding patient experiences and identifying areas for quality improvement in Russian healthcare. Some potential applications include:

• Analyzing geographic patterns of patient complaints across different cities in Russia
• Examining trends in patient dissatisfaction over time
• Identifying common reasons for dissatisfaction with medical care
• Comparing dissatisfaction levels between different medical specialties
• Assessing gender differences in patient complaints

The database provides rich qualitative data through full-text review texts, allowing for in-depth analysis of patient experiences. The structured variables like city, date, doctor/clinic information, etc. enable quantitative analysis as well. This combination of qualitative and quantitative data makes it possible to gain a comprehensive understanding of patient dissatisfaction patterns in Russia's healthcare system over more than a decade.

For researchers specifically interested in healthcare quality issues, this dataset could serve as an important resource for studying patient experiences and outcomes in Russia's medical system. The longitudinal nature of the data (2012-2023) also allows for analysis of changes over time in patient satisfaction.

Overall, this database provides valuable insights into patient perceptions of healthcare quality that could inform policy decisions, quality improvement

BackgroundDiffuse idiopathic skeletal hyperostosis (DISH) is a whole-body disease characterized by ossification or calcification of joints and ligaments, which is present on all continents and in all ethnic groups. However, there is a lack of comprehensive information on the global prevalence and incidence of DISH.ObjectiveTo conduct a systematic review and meta-analysis to investigate the global prevalence of DISH.MethodsThree electronic medical databases (Cochrane Database of Systematic Reviews, MEDLINE, and Embase) were used to conduct a systematic review of population-based and clinical-based studies reporting the prevalence of DISH from the time of commencement to February 2023. “Prevalence or epidemiology” and “diffuse idiopathic skeletal hyperostosis or DISH” were the search terms used. There were no language restrictions. Extract data based on features such as continent, gender, age, and race. Quality was assessed using the Critical Appraisal Tool for Prevalence Data Reporting Studies from the Joanna Briggs Institute, which synthesizes the available evidence using a random effects model.ResultsAmong the 33 studies, the overall estimated prevalence of DISH in the general population (n=36925) was 11.92% (95% CI, 8.68%-15.59%), and the overall prevalence of DISH in clinical patients (n=22969) was about 14.30% (95%CI, 10.10%-19.09%). In 17 population-based studies, the prevalence of DISH was 10.07% (95% CI, 6.76%-13.95%) in Asia, 11.16% (95% CI, 6.19%-17.36%) in Europe, 13.46% in North America (95%CI, 12.20%-14.77%) and 30.07% (95%CI, 25.90%-34.49%) in Oceania. The overall prevalence of DISH by sex was 6.49% (95%CI, 3.65%-10.07%) in women and 17.87% (95%CI, 13.27%-22.98%) in men. The prevalence rate of Asians was 10.07% (95%CI, 6.76%-13.95%), that of white people was 11.90% (95%CI, 7.62%-16.98%), and that of black people was 8.77% (95%CI, 6.39%-11.67%). In 16 clinic-based studies, the prevalence of DISH was 16.32% (95%CI, 10.10%-23.67%) in Asia, 13.20% (95%CI, 9.89%-16.92%) in Europe, and 13.13%(95%CI, 3.79%-26.93%) in North America and 3.93% in Africa. According to gender classification, the overall prevalence of DISH was 10.16% (95%CI, 6.59%-14.38%) in women and 18.73% (95%CI, 12.84%-25.44%) in men. The prevalence rate of Asians was 16.45% (95%CI, 7.45%-28.05%), that of white people was 14.95% (95%CI, 10.28%-20.31%), and that of black people was 5.71% (95%CI, 2.57%-9.98%).ConclusionsThis study identifies the global prevalence of DISH in terms of population distribution, space, and time. The overall prevalence of DISH was approximately 11.92% (95%CI, 8.68%-15.59%) in the general population and 14.30% (95% CI, 10.10%-19.09%) in clinical patients. The prevalence of DISH was higher in males, and those aged 50 and over.

Proportion of Canadian and international student enrolments, by International Standard Classification of Education (ISCED), institution type, Classification of Instructional Programs (CIP) 2021, STEM (science, technology, engineering and mathematics) and BHASE (business, humanities, health, arts, social science and education) groupings, gender and age group.