Estimates for UK immigration, emigration and net migration, year ending June 2012 to year ending December 2024. These are official statistics in development. To access the most up-to-date data for each time period, please use the most recently published dataset.

National and subnational mid-year population estimates for the UK and its constituent countries by administrative area, age and sex (including components of population change, median age and population density).

Gender-Based Average School Workforce in England

Exploring Differences within Geographic Levels, Grades, Types, and Ages

By Charlie Hutcheson [source]

About this dataset

This dataset contains information about the average school workforce in England for the 2019 reporting year. It includes gender, school type, grade, age category, geographic level and average mean of the school workforce in England. With this data we can gain insight into how different demographics are represented educationally across different locations. By analyzing this data we can start to better understand where educational disparities exist and how to bridge those gaps in order to make education more equitable and accessible for all students across England

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How to use the dataset

Let's take a look at what is included in this dataset. Each row represents an observation reporting year and includes columns for aspects of each observation such as location, geographic level, grade range/school type, age category, gender groupings and average mean (for full-time equivalents or FTEs). There is also a location code column that can be used as an identifier should users wish to separate observations geographically.

Some possible uses for this dataset include studying trends related to gender distribution within specific school populations or across different geographic regions. It can also be used to investigate patterns related to age categories or types of schools within a given location or region. You may even wish to compare overall averages among certain groups such as teachers versus support staff or high schools versus elementary schools across regions or groups within England.

In any case, with all variables considered one can generate some interesting insights about the demographics makeup of staff within an educational system and further research particular questions about its nature The possibilities are practically endless! Thanks for checking out our dataset; we hope that you find something useful during your exploration!

Research Ideas

• Investigating whether gender is a factor in the type of school workforce found in different locations
• Examining the average mean across varying school types, grades, and age categories throughout England
• Visualizing the geographic level of specific school workforces based on their location

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: data-school-workforce-in-england.csv | Column name | Description | |:---------------------|:---------------------------------------------------------------------------------| | location | The geographic location of the school. (String) | | location_code | The code for the geographic location of the school. (String) | | geographic_level | The geographic level of the school (e.g. county, region, etc.). (String) | | time_period | The time period for which the data was collected. (String) | | gender | The gender of the school workforce. (String) | | school_type | The type of school (e.g. maintained nursery, maintained primary, etc.). (String) | | grade | The grade level of the school. (String) | | age_category | The age category of the school workforce. (String) | | average_mean | The average mean salary of the school workforce. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Charlie Hutcheson.

The English TTS Monologue Speech Dataset is a professionally curated resource built to train realistic, expressive, and production-grade text-to-speech (TTS) systems. It contains studio-recorded long-form speech by trained native English voice artists, each contributing 1 to 2 hours of clean, uninterrupted monologue audio.

Unlike typical prompt-based datasets with short, isolated phrases, this collection features long-form, topic-driven monologues that mirror natural human narration. It includes content types that are directly useful for real-world applications, like audiobook-style storytelling, educational lectures, health advisories, product explainers, digital how-tos, formal announcements, and more.

All recordings are captured in professional studios using high-end equipment and under the guidance of experienced voice directors.

Recording & Audio Quality

Only clean, production-grade audio makes it into the final dataset.

Voice Artist Selection

All voice artists are native English speakers with professional training or prior experience in narration. We ensure a diverse pool in terms of age, gender, and region to bring a balanced and rich vocal dataset.

Script Quality & Coverage

Scripts are not generic or repetitive. Scripts are professionally authored by domain experts to reflect real-world use cases. They avoid redundancy and include modern vocabulary, emotional range, and phonetically rich sentence structures.

Transcripts & Alignment

While the script is used during the recording, we also provide post-recording updates to ensure the transcript reflects the final spoken audio. Minor edits are made to adjust for skipped or rephrased words.

A full list of PSC's provided to Companies House. A daily snapshot. Since 30 June 2016, UK companies, Societates Europaeae (SEs) and limited liability partnerships (LLPs) are required to identify and record the people who own or control their company. Companies, SEs and LLPs will need to keep a PSC register, in addition to existing registers such as the register of directors and register of members (shareholders), and must file the PSC information with the central public register at Companies House. Each data file is provided in JSON format and can be downloaded as a single file or as multiple files for ease of downloading. All files are overwritten daily with the latest information. This snapshot is provided free of charge and will not be supported. The latest snapshot will be updated every morning before 10am GMT. The contents of the snapshot have been compiled up to the end of the previous day.

This data has been taken from LG Inform at http://lginform.local.gov.uk/ data reference ID 31. It shows the percentage of vulnerable people achieving independent living in Plymouth from financial year 2006/2007 to 2010/2011 Percentage of vulnerable people achieving independent living. This is the number of service users (i.e. people who are receiving a Supporting People Service) who have moved on from supported accommodation in a planned way, as a percentage of total service users who have left the service. This was previously reported as NI 141. Source name: Communities and Local Government Collection name: Supporting People Local System (SPLS) Polarity: High is good Polarity is how sentiment is measured "Sentiment is usually considered to have "poles" positive and negative these are often translated into "good" and "bad" sentiment analysis is considered useful to tell us what is good and bad in our information stream

Transport For Londons's key London Underground performance measures (since May-11). The key measures of underground performance contained in the Excel spreadsheet are: Total operated kilometres, Total number of lost customer hours (LCH) (all causes), Average excess journey time, and Percentage of scheduled operated. More indicators are available from the TFL monthly performance data Almanac. Also available from TFL are a list of tube stations containing passenger entry and exit statistics. London Underground conducts surveys each year. We count the number of people using our service, where they travel to and from and how long their journeys last. TFL also monitor performance and assets, measuring how far trains have travelled and how many trains, lifts and escalators are in service. TFL are committed to providing passengers with details of how well the network is working and regularly publish a performance update. This is updated every four weeks - defined as a period - and gives figures for a range of service and network-related matters. The following information is provided on the TfL website: 1) A PDF report of the latest periods performance 2) Spreadsheet reports (almanac) of historic performance data dating back to 2002 There are also links to other reports of interest (station entry/exit figures, Tube upgrade plan and Quarterly Investment Programme reports). This data package is similar to the information previously made available on the Datastore as part of a London Assembly Scrutiny investigation in 2011.

Families by size and children in families by age. A National Statistics Publication. Child Benefit is paid to those responsible for children (aged under 16) or qualifying young people. The latter includes: a) those in full-time non-advanced education or (from April 2006) on certain approved vocational training courses and who are under 19, or are aged 19 and have been on the same course since their 19th birthdays. (Note: those reaching 19 up to 9 April 2006 ceased to qualify on their 19th birthdays); b) those entered for future external examinations, or are in the period between leaving education (or exams finishing) and the week containing the first Monday in September (or similar dates after Easter and in early January, if earlier), and are not in work (there are slight variations for Scotland); c) those aged under 18 who have moved directly from full-time education to being registered for work or training with the Careers service or with Connexions. As of January 2013, claimants may be liable to a tax charge called the 'High Income Child Benefit charge'. Being liable for this charge does not affect a claimant's eligibility but any Child Benefit recipient is liable to repay some or all of their Child Benefit back if they or their partner has an individual income of more than £50,000 per year. For every additional £100 over the £50,000 threshold that an individual earns, the tax charge due increases by 1%. This means that any recipient whose income (or partner’s income) is over £60,000 will be liable to repay their entire Child Benefit entitlement. Alternatively, claimants affected by the High Income Child Benefit charge have the option to opt-out of receiving Child Benefit, thereby ceasing their payments. Child benefit data for Lower Super Output Areas (LSOA) can be accessed on the HMRC website. Related to: https://www.gov.uk/government/collections/child-benefit-geographical-statistics

This is a repository for a UKRI Economic and Social Research Council (ESRC) funded project to understand the software used to analyse social sciences data. Any software produced has been made available under a BSD 2-Clause license and any data and other non-software derivative is made available under a CC-BY 4.0 International License. Note that the software that analysed the survey is provided for illustrative purposes - it will not work on the decoupled anonymised data set. Exceptions to this are: Data from the UKRI ESRC is mostly made available under a CC BY-NC-SA 4.0 Licence. Data from Gateway to Research is made available under an Open Government Licence (Version 3.0). Contents Survey data & analysis: esrc_data-survey-analysis-data.zip Other data: esrc_data-other-data.zip Transcripts: esrc_data-transcripts.zip Data Management Plan: esrc_data-dmp.zip Survey data & analysis The survey ran from 3rd February 2022 to 6th March 2023 during which 168 responses were received. Of these responses, three were removed because they were supplied by people from outside the UK without a clear indication of involvement with the UK or associated infrastructure. A fourth response was removed as both came from the same person which leaves us with 164 responses in the data. The survey responses, Question (Q) Q1-Q16, have been decoupled from the demographic data, Q17-Q23. Questions Q24-Q28 are for follow-up and have been removed from the data. The institutions (Q17) and funding sources (Q18) have been provided in a separate file as this could be used to identify respondents. Q17, Q18 and Q19-Q23 have all been independently shuffled. The data has been made available as Comma Separated Values (CSV) with the question number as the header of each column and the encoded responses in the column below. To see what the question and the responses correspond to you will have to consult the survey-results-key.csv which decodes the question and responses accordingly. A pdf copy of the survey questions is available on GitHub. The survey data has been decoupled into: survey-results-key.csv - maps a question number and the responses to the actual question values. q1-16-survey-results.csv- the non-demographic component of the survey responses (Q1-Q16). q19-23-demographics.csv - the demographic part of the survey (Q19-Q21, Q23). q17-institutions.csv - the institution/location of the respondent (Q17). q18-funding.csv - funding sources within the last 5 years (Q18). Please note the code that has been used to do the analysis will not run with the decoupled survey data. Other data files included CleanedLocations.csv - normalised version of the institutions that the survey respondents volunteered. DTPs.csv - information on the UKRI Doctoral Training Partnerships (DTPs) scaped from the UKRI DTP contacts web page in October 2021. projectsearch-1646403729132.csv.gz - data snapshot from the UKRI Gateway to Research released on the 24th February 2022 made available under an Open Government Licence. locations.csv - latitude and longitude for the institutions in the cleaned locations. subjects.csv - research classifications for the ESRC projects for the 24th February data snapshot. topics.csv - topic classification for the ESRC projects for the 24th February data snapshot. Interview transcripts The interview transcripts have been anonymised and converted to markdown so that it's easier to process in general. List of interview transcripts: 1269794877.md 1578450175.md 1792505583.md 2964377624.md 3270614512.md 40983347262.md 4288358080.md 4561769548.md 4938919540.md 5037840428.md 5766299900.md 5996360861.md 6422621713.md 6776362537.md 7183719943.md 7227322280.md 7336263536.md 75909371872.md 7869268779.md 8031500357.md 9253010492.md Data Management Plan The study's Data Management Plan is provided in PDF format and shows the different data sets used throughout the duration of the study and where they have been deposited, as well as how long the SSI will keep these records.

Young people aged 18, 19 and 20 who ceased to be looked after in a foster placement on their 18th birthday, were eligible for care leavers support and who were living with their former foster carer. Data formerly in table F2 and F4.

Background:

A PIONEER synthetic dataset of 20,000 ethnically diverse hypertrophic cardiomyopathy patients created using CT-GAN generative AI. Data includes clinical & biological phenotyping, co-morbidities, investigations (ECG, ECHO), procedures & outcomes.

Well-created synthetic data establishes a governance risk-free environment for algorithm development & experimentation. This includes evaluating new treatment models, care management systems, clinical decision support, and more. Synthetic data is of particular use in rare diseases, where real data may be in short supply, or to replicate disease in less common patient demographics (e.g. ethnicities).

Familial hypertrophic cardiomyopathy (HCM) is a rare genetic condition characterised by thickening (hypertrophy) of the cardiac muscle, usually of the interventricular septum. Arrhythmias can be life threatening and HCM is associated with an increased risk of sudden death. Some affected individuals develop potentially fatal heart failure, which may require heart transplantation. Approximately 130,000 people have HCM in the UK, but there is a significant burden of undiagnosed disease and diagnostic delay.

Geography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can provide real world data to meet bespoke requirements.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

English Monarchs and Marriages

This data contains English Monarchs and Marriages!

The dataset focuses on the names, ages, and marriages of various 'kings' and 'consorts'. and ranges all the way back to 850 where the details are a bit fuzzy, spanning all the way to current day. Names contain special characters; ages & years can be a bit of a regex puzzle as well. Additionally, the age of kings and consorts may show quite a bit of an age gap.

Data Dictionary

english_monarchs_marriages.csv

The data was scraped from Ian Visits by f. hull, who also curated the data.

Unemployment Rate in the United Kingdom increased to 5 percent in September from 4.80 percent in August of 2025. This dataset provides the latest reported value for - United Kingdom Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Source:

Dr. Daqing Chen, Course Director: MSc Data Science. chend '@' lsbu.ac.uk, School of Engineering, London South Bank University, London SE1 0AA, UK.

Data Set Information:

This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers.

Attribute Information:

InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation. StockCode: Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product. Description: Product (item) name. Nominal. Quantity: The quantities of each product (item) per transaction. Numeric. InvoiceDate: Invice date and time. Numeric. The day and time when a transaction was generated. UnitPrice: Unit price. Numeric. Product price per unit in sterling (£). CustomerID: Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer. Country: Country name. Nominal. The name of the country where a customer resides.

Relevant Papers:

Chen, D. Sain, S.L., and Guo, K. (2012), Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208. doi: [Web Link]. Chen, D., Guo, K. and Ubakanma, G. (2015), Predicting customer profitability over time based on RFM time series, International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 1, pp.1-18. doi: [Web Link]. Chen, D., Guo, K., and Li, Bo (2019), Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, 28-31 Oct, 2019. Laha Ale, Ning Zhang, Huici Wu, Dajiang Chen, and Tao Han, Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network, IEEE Internet of Things Journal, Vol. 6, Issue 3, pp. 5520-5530, 2019. Rina Singh, Jeffrey A. Graves, Douglas A. Talbert, William Eberle, Prefix and Suffix Sequential Pattern Mining, Industrial Conference on Data Mining 2018: Advances in Data Mining. Applications and Theoretical Aspects, pp. 309-324. 2018.

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The number of Instagram users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 2.1 million users (+7.02 percent). After the ninth consecutive increasing year, the Instagram user base is estimated to reach 32 million users and therefore a new peak in 2028. Notably, the number of Instagram users of was continuously increasing over the past years.User figures, shown here with regards to the platform instagram, 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 and count multiple accounts by persons only once.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).