The number of Twitter users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 0.9 million users (+5.1 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 18.55 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

One of the biggest advantages of Twitter is the speed at which information can be passed around. People use Twitter primarily to get news and for entertainment. This is the breakdown of why people use Twitter today.

As of December 2022, X/Twitter's audience accounted for over *** million monthly active users worldwide. This figure was projected to ******** to approximately *** million by 2024, a ******* of around **** percent compared to 2022.

These are the key Twitter user statistics that you need to know.

Computed Tomography (CT) of the Brain - Object Detection dataset

The dataset consists of CT brain scans with cancer, tumor, and aneurysm. Each scan represents a detailed image of a patient's brain taken using CT (Computed Tomography). The data are presented in 2 different formats: .jpg and .dcm.

💴 For Commercial Usage: Full version of the dataset includes much more brain scans of people with different conditions, leave a request on TrainingData to buy the dataset

The dataset of CT brain scans is valuable for research in neurology, radiology, and oncology. It allows the development and evaluation of computer-based algorithms, machine learning models, and deep learning techniques for automated detection, diagnosis, and classification of these conditions.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fd534483d76552e312cf094fbe23d8cc5%2Fezgif.com-optimize.gif?generation=1697211124166914&alt=media" alt="">

Types of brain diseases in the dataset:

• cancer
• tumor
• aneurysm

OTHER MEDICAL DATASETS:

• Computed Tomography (CT) of the Chest
• Computed Tomography (CT) of the Spine - Scoliosis
• Knee X-rays - 4 Stages of Osteoarthritis
• Chest X-ray - 17 Diseases 🏥
• Spine X-ray - Scoliosis and 6 other diseases

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

Content

The folder "files" includes 3 folders:

• corresponding to name of the brain disease and including ct scans of people with this disease (cancer, tumor or aneurysm)
• including brain scans in 2 different formats: .jpg and .dcm.

File with the extension .csv includes the following information for each media file:

• dcm: link to access the .dcm file,
• jpg: link to access the .jpg file,
• type: name of the brain disease on the ct

Medical data might be collected in accordance with your requirements.

TrainingData provides high-quality data annotation tailored to your needs

keywords: aneurysm, cancer detection, cancer segmentation, tumor, computed tomography, head, skull, brain scan, eye sockets, sinuses, medical imaging, radiology dataset, neurology dataset, oncology dataset, image dataset, abnormalities detection, brain anatomy, health, brain formations, imaging procedure, x-rays measurements, machine learning, computer vision, deep learning

Chest X-ray - Object Detection dataset

The dataset consists of a collection of chest X-ray images in .jpg and .dcm formats. The images are organized into folders based on different medical conditions related to the chest. Each folder contains images depicting specific chest abnormalities.

  💴 For Commercial Usage: Full version of the dataset includes much more chest x-rays of people with different conditions, leave a request on TrainingData to buy the dataset… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/chest-x-rays.

Description

Student's Academic Performance Dataset (xAPI-Edu-Data)

Data Set Characteristics: Multivariate

Number of Instances: 480

Area: E-learning, Education, Predictive models, Educational Data Mining

Attribute Characteristics: Integer/Categorical

Number of Attributes: 16

Date: 2016-11-8

Associated Tasks: Classification

Missing Values? No

File formats: xAPI-Edu-Data.csv

Source:

Elaf Abu Amrieh, Thair Hamtini, and Ibrahim Aljarah, The University of Jordan, Amman, Jordan, http://www.Ibrahimaljarah.com www.ju.edu.jo

Dataset Information:

This is an educational data set which is collected from learning management system (LMS) called Kalboard 360. Kalboard 360 is a multi-agent LMS, which has been designed to facilitate learning through the use of leading-edge technology. Such system provides users with a synchronous access to educational resources from any device with Internet connection.

The data is collected using a learner activity tracker tool, which called experience API (xAPI). The xAPI is a component of the training and learning architecture (TLA) that enables to monitor learning progress and learner’s actions like reading an article or watching a training video. The experience API helps the learning activity providers to determine the learner, activity and objects that describe a learning experience. The dataset consists of 480 student records and 16 features. The features are classified into three major categories: (1) Demographic features such as gender and nationality. (2) Academic background features such as educational stage, grade Level and section. (3) Behavioral features such as raised hand on class, opening resources, answering survey by parents, and school satisfaction.

The dataset consists of 305 males and 175 females. The students come from different origins such as 179 students are from Kuwait, 172 students are from Jordan, 28 students from Palestine, 22 students are from Iraq, 17 students from Lebanon, 12 students from Tunis, 11 students from Saudi Arabia, 9 students from Egypt, 7 students from Syria, 6 students from USA, Iran and Libya, 4 students from Morocco and one student from Venezuela.

The dataset is collected through two educational semesters: 245 student records are collected during the first semester and 235 student records are collected during the second semester.

The data set includes also the school attendance feature such as the students are classified into two categories based on their absence days: 191 students exceed 7 absence days and 289 students their absence days under 7.

This dataset includes also a new category of features; this feature is parent parturition in the educational process. Parent participation feature have two sub features: Parent Answering Survey and Parent School Satisfaction. There are 270 of the parents answered survey and 210 are not, 292 of the parents are satisfied from the school and 188 are not.

(See the related papers for more details).

Attributes

1 Gender - student's gender (nominal: 'Male' or 'Female’)

2 Nationality- student's nationality (nominal:’ Kuwait’,’ Lebanon’,’ Egypt’,’ SaudiArabia’,’ USA’,’ Jordan’,’ Venezuela’,’ Iran’,’ Tunis’,’ Morocco’,’ Syria’,’ Palestine’,’ Iraq’,’ Lybia’)

3 Place of birth- student's Place of birth (nominal:’ Kuwait’,’ Lebanon’,’ Egypt’,’ SaudiArabia’,’ USA’,’ Jordan’,’ Venezuela’,’ Iran’,’ Tunis’,’ Morocco’,’ Syria’,’ Palestine’,’ Iraq’,’ Lybia’)

4 Educational Stages- educational level student belongs (nominal: ‘lowerlevel’,’MiddleSchool’,’HighSchool’)

5 Grade Levels- grade student belongs (nominal: ‘G-01’, ‘G-02’, ‘G-03’, ‘G-04’, ‘G-05’, ‘G-06’, ‘G-07’, ‘G-08’, ‘G-09’, ‘G-10’, ‘G-11’, ‘G-12 ‘)

6 Section ID- classroom student belongs (nominal:’A’,’B’,’C’)

7 Topic- course topic (nominal:’ English’,’ Spanish’, ‘French’,’ Arabic’,’ IT’,’ Math’,’ Chemistry’, ‘Biology’, ‘Science’,’ History’,’ Quran’,’ Geology’)

8 Semester- school year semester (nominal:’ First’,’ Second’)

9 Parent responsible for student (nominal:’mom’,’father’)

10 Raised hand- how many times the student raises his/her hand on classroom (numeric:0-100)

11- Visited resources- how many times the student visits a course content(numeric:0-100)

12 Viewing announcements-how many times the student checks the new announcements(numeric:0-100)

13 Discussion groups- how many times the student participate on discussion groups (numeric:0-100)

14 Parent Answering Survey- parent answered the surveys which are provided from school or not (nominal:’Yes’,’No’)

15 Parent School Satisfaction- the Degree of parent satisfaction from school(nominal:’Yes’,’No’)

16 Student Absence Days-the number of absence days for each student (nominal: above-7, under-7)

The students are classified into three numerical intervals based on their total grade/mark:

Low-Level: interval includes values from 0 to 69,

Middle-Level: interval includes values from 70 to 89,

High-Level: interval includes values from 90-100.

Relevant Papers:

-Amrieh, E. A., Hamtini, T., & Aljarah, I. (2016). Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods. International Journal of Database Theory and Application, 9(8), 119-136.

-Amrieh, E. A., Hamtini, T., & Aljarah, I. (2015, November). Preprocessing and analyzing educational data set using X-API for improving student's performance. In Applied Electrical Engineering and Computing Technologies (AEECT), 2015 IEEE Jordan Conference on (pp. 1-5). IEEE.

Citation Request:

Please include these citations if you plan to use this dataset:

• Amrieh, E. A., Hamtini, T., & Aljarah, I. (2016). Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods. International Journal of Database Theory and Application, 9(8), 119-136.

-Amrieh, E. A., Hamtini, T., & Aljarah, I. (2015, November). Preprocessing and analyzing educational data set using X-API for improving student's performance. In Applied Electrical Engineering and Computing Technologies (AEECT), 2015 IEEE Jordan Conference on (pp. 1-5). IEEE.

Sourcing accurate and up-to-date GIS data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent GIS data across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

GapMaps GIS data for Asia and MENA can be utilized in any GIS platform and includes the latest Demographic estimates (updated annually) including:

1. Population (how many people live in your local catchment)
2. Census Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

GapMaps GIS Data also includes Point-Of-Interest (POI) Data updated monthly across a range of categories including Fast Food, Cafe, Health & Fitness and Supermarket/ Grocery

Primary Use Cases for GapMaps GIS Data:

1. Retail Site Selection - identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Integrate GapMaps GIS data with your existing GIS or BI platform to generate powerful visualizations.

This is the breakdown of Twitter users by age group.

Sourcing accurate and up-to-date geodemographic data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent geodemographic datasets across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

Premium geodemographics data for Asia and MENA includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Geodemographic Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

8. Tenant Recruitment

9. Target Marketing

10. Market Potential / Gap Analysis

11. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

12. Customer Profiling

13. Target Marketing

14. Market Share Analysis

The People and Safety Belt Semantic Segmentation Dataset is specifically curated for industrial applications, consisting of CCTV images captured within a factory environment at a resolution of 1920 x 1080 pixels. This dataset focuses on both instance and semantic segmentation, providing annotations for people and the seat belts they are wearing, aimed at enhancing safety compliance monitoring.

The Outdoor Multi-person Panoptic Segmentation Dataset is tailored for the visual entertainment industry, featuring a collection of internet-collected outdoor images with resolutions ranging from 1543 x 2048 to 3072 x 2304 pixels. This dataset focuses on panoptic segmentation, encompassing multiple people and distinguishable objects such as those on individuals, buildings, vehicles, and plants. Each identifiable instance within the images is annotated, providing a comprehensive view of outdoor scenes.

The estimated median household income and estimated median family income are two separate measures: every family is a household, but not every household is a family. According to the U.S. Census Bureau definitions of the terms, a family “includes a householder and one or more people living in the same household who are related to the householder by birth, marriage, or adoption,”[1] while a household “includes all the people who occupy a housing unit,” including households of just one person[2]. When evaluated together, the estimated median household income and estimated median family income provide a thorough picture of household-level economics in Champaign County.

Both estimated median household income and estimated median family income were higher in 2023 than in 2005. The changes in estimated median household income and estimated median family income between 2022 and 2023 were not statistically significant. Estimated median family income is consistently higher than estimated median household income, largely due to the definitions of each term, and the types of household that are measured and are not measured in each category.

Median income data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes datasets on Median Household Income in the Past 12 Months (in 2020 Inflation-Adjusted Dollars) and Median Family Income in the Past 12 Months (in 2020 Inflation-Adjusted Dollars).

[1] U.S. Census Bureau. (Date unknown). Glossary. “Family Household.” (Accessed 19 April 2016).

[2] U.S. Census Bureau. (Date unknown). Glossary. “Household.” (Accessed 19 April 2016).

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (18 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (3 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (7 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (7 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).

In this case study, you are hired by a hospital in Toronto as a deep learning consultant and tasked with automating the detection and classification process of pulmonary diseases.

The team collected extensive X-Ray chest data and asked you to develop a model that could detect and classify diseases in less than a minute.

They provided a dataset consisting of 133 images and divided into 4 classes:

0 - Covid-19 1 - Healty X-ray 2 - Viral Pneumonia X-ray 3 - Bacterial Pneumonia X-ray

However, it's crucial to note that the use of AI in healthcare raises some ethical and social concerns. For instance, there are worries that AI systems may be biased and result in misdiagnoses. Additionally, there are concerns that AI may replace doctors and other healthcare professionals.

The number of Twitter users in the United States was forecast to continuously increase between 2024 and 2028 by in total 4.3 million users (+5.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 85.08 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like Canada and Mexico.

The Multi-person And Appendages Segmentation Dataset is designed for the visual entertainment sector, featuring a collection of internet-collected images with resolutions exceeding 2736 x 3648 pixels. This dataset employs both instance and semantic segmentation techniques to annotate multiple people and their appendages in various scenes. The appendages include shadows, hand-held objects, riding objects, and more, providing a comprehensive view of human interactions with their environment.

The US has historically been the target country for Twitter since its launch in 2006. This is the full breakdown of Twitter users by country.

Context

This dataset is based on the original SpaceNet 7 dataset, with a few modifications.

Content

The original dataset consisted of Planet satellite imagery mosaics, which includes 24 images (one per month) covering ~100 unique geographies. The original dataset will comprised over 40,000 square kilometers of imagery and exhaustive polygon labels of building footprints in the imagery, totaling over 10 million individual annotations.

This dataset builds upon the original dataset, such that each image is segmented into 64 x 64 chips, in order to make it easier to build a model for.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4101651%2F66851650dbfb7017f1c5717af16cea3c%2Fchips.png?generation=1607947381793575&alt=media" alt="">

The images also compare the changes that between each image of each month, such that an image taken in month 1 is compared with the image take in month 2, 3, ... 24. This is done by taking the cartesian product of the differences between each image. For more information on how this is done check out the following notebook.

The differences between the images are captured in the output mask, and the 2 images being compared are stacked. Which means that our input images have dimensions of 64 x 64 x 6, and our output mask has dimensions 64 x 64 x 1. The reason our input images have 6 dimensions is because as mentioned earlier, they are 2 images stacked together. See image below for more details:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4101651%2F9cdcf8481d8d81b6d3fed072cea89586%2Fdifference.png?generation=1607947852597860&alt=media" alt="">

The image above shows the masks for each of the original satellite images and what the difference between the 2 looks like. For more information on how the original data was explored check out this notebook.

Data Structure

The data is structured as follows:
chip_dataset
└── change_detection
└── fname
├── chips
│ └── year1_month1_year2_month2
│ └── global_monthly_year1_month1_year2_month2_chip_x###_y###_fname.tif
└── masks
└── year1_month1_year2_month2
└── global_monthly_year1_month1_year2_month2_chip_x###_y###_fname_blank.tif

The _blank in the mask chips, indicates whether the mask is a blank mask or not.

For more information on how the data was structured and augmented check out the following notebook.

Acknowledgements

All credit goes to the team at SpaceNet for collecting and annotating and formatting the original dataset.

Tuberculosis is a disease that affects many people in developing countries. While treatment is possible, it requires an accurate diagnosis first.

Abstract copyright UK Data Service and data collection copyright owner.The Great Britain Historical Database has been assembled as part of the ongoing Great Britain Historical GIS Project. The project aims to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain at sub-county scales. Further information about the project is available on A Vision of Britain webpages, where users can browse the database's documentation system online. These data were originally collected by the Censuses of Population for England and Wales, and for Scotland. They were computerised by the Great Britain Historical GIS Project and its collaborators. They form part of the Great Britain Historical Database, which contains a wide range of geographically-located statistics, selected to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain, generally at sub-county scales. The census gathered data on levels of educational attainment only from 1951. In 1951 and 1961, attainment was measured simply by the age at which a person's education was completed, rather than by the level of qualifications achieved. These data cover, broadly, the adult population, including many people who had completed their education decades before the relevant census, so the data are indicative of the general level of education of the workforce at the census date, but are a problematic guide to the performance of the education system at that date. The census reports also include cross-tabulations of age of education completion with current age, but not with the level of geographical detail of the transcribed tables. The 1951 data for England and Wales were computerised by Danny Dorling (now of Oxford University), as part of research funded by the Joseph Rowntree Foundation.Latest edition information:For the 2nd edition (June 2021), data for Scotland for 1951 and data for England & Wales and Scotland for 1961 have been added to the study. Normal 0 false false false EN-GB X-NONE X-NONE / Style Definitions / table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0cm; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-fareast-language:EN-US;} Main Topics: Level of education of the adult or occupied population, measured by their age at termination of education. Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research. Purposive selection/case studies