Describes the dynamics of the UK population. It includes information on changes in the age structure, population growth and the role of fertility and migration in driving population change. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: FoPM

Context

This dataset was created by our in-house Web Scraping and Data Mining teams at PromptCloud and DataStock. You can download the full dataset here. This sample contains 30K records. You can download the full dataset here

Content

Total Records Count : 330216  Domain Name : monster.uk  Date Range : 01st Dec 2020 - 31st Mar 2021   File Extension : ldjson

Available Fields : uniq_id, crawl_timestamp, url, job_title, category, company_name, city, state, country, post_date, job_description, inferred_salary_from, inferred_salary_to, inferred_salary_time_unit, job_board, geo, valid_through, html_job_description, test1_cities, test1_states, test1_countries, site_name, domain, postdate_yyyymmdd, predicted_language, inferred_iso3_lang_code, test1_inferred_city, test1_inferred_state, test1_inferred_country, inferred_city, inferred_state, inferred_country, inferred_salary_currency, has_expired, last_expiry_check_date, latest_expiry_check_date, duplicate_status, dataset, is_remote, postdate_in_indexname_format, segment_name, fitness_score    

Acknowledgements

We wouldn't be here without the help of our in house web scraping and data mining teams at PromptCloud, DataStock and live job data from JobsPikr.

Inspiration

This dataset was created keeping in mind our data scientists and researchers across the world.

UK residents by individual countries of birth and citizenship, broken down by UK country, local authority, unitary authority, metropolitan and London boroughs, and counties. Estimates from the Annual Population Survey.

Mid-year population estimates relate to the usually resident population. They account for long-term international migrants (people who change their country of usual residence for a period of 12 months or more) but do not account for short-term migrants (people who come to or leave the country for a period of less than 12 months). This approach is consistent with the standard UN definition for population estimates which is based upon the concept of usual residence and includes people who reside, or intend to reside, in the country for at least twelve months, whatever their nationality.

The provided dataset contains financial and operational metrics spanning from January to September 2020 for a company operating in the UK. It reflects key aspects like revenue, expenses, profit, customer count, transactions, stock price, market sentiment, loan approval rate, employee count, and marketing spend.

London, as a part of the UK, likely shares these trends but could have its specific nuances due to being a distinct economic hub within the country. In this period:

1. Financial Performance: The company's revenue fluctuates throughout the months, peaking at £65,090 in June and dipping to £35,184 in July. Despite varying expenses, profits generally stay positive, showcasing resilience in managing costs against revenue. London, being a financial center, might witness higher revenue or fluctuations due to specific industries concentrated there.

2. Customer Engagement: Customer metrics show variation. Customer count ranges from 131 to 426, with transactions varying from 57 to 188. This indicates fluctuations in customer activity, potentially influenced by market trends, seasonal patterns, or even regional events.

3. Stock Performance: Stock prices show fluctuation, hitting a high of 138.53 and a low of 78.79. Market sentiment, indicating public confidence, also fluctuates, potentially influencing stock prices. London's stock market might reflect similar volatility but could be influenced by the performance of prominent companies headquartered there.

4. Business Operations: Loan approval rates stay relatively stable between 70% to 97%, indicating a consistent approach to risk management. Employee count remains somewhat constant, which could signify stable operations without significant expansion or downsizing.

5. Marketing and Growth: The company's marketing spend varies, suggesting a willingness to adapt strategies based on performance or seasonal demands. London might have higher marketing expenditures due to the competitive market and the need to stand out amidst numerous businesses.

6. Economic Impact: Economic factors affecting the UK market—Brexit discussions, global economic shifts, or even local policies—might influence these metrics. London, as a financial center, could be more sensitive to global economic changes, impacting revenue, market sentiment, and stock prices more profoundly.

7. Covid-19 Influence: Given the timeframe (2020), the dataset might reflect the initial impact of the COVID-19 pandemic. The varying metrics could illustrate the company's adaptation strategies in response to changing consumer behaviors and economic uncertainties.

In London specifically, these trends might amplify due to its prominence in finance, trade, and services. The city's diverse industries and international connections might lead to more pronounced fluctuations in financial indicators like stock prices and market sentiment. Moreover, its position as a global economic hub might expose businesses to unique challenges and opportunities, potentially reflected in the provided dataset.

Understanding London's specific dynamics within the UK would require deeper analysis, considering sector-specific influences, competitive landscape, and regional economic factors. Nevertheless, this dataset offers insights into the company's adaptability and performance within the broader context of the UK's economic landscape.

About the Dataset:

Amazon is one of the biggest online retailers in the UK. With this dataset, you can get an in-depth idea of what products sell best, which SEO titles generate the most sales, the best price range for a product in a given category, and much more.

It took a lot of time and energy to prepare this original dataset, so don't forget to hit the upvote button! 😊💝

Similar Datasets:

Amazon Canada Products

Amazon India Products

Amazon USA Products

Interesting Task Ideas:

1. Uncover trending product categories and their sales performance.
2. Analyze customer ratings to find top-rated products.
3. Train a product title generator that can generate sales-worthy product titles based on products with the most sales.
4. Gain insight into the best price for any given product based on sales data and competition.
5. Identify which niches are the easiest to make sales in.
6. Gain insights into the general spending habits of online shoppers.
7. Use as a seed dataset for practicing database management and performance optimization.
8. Train (or learn to train) an AI-based search model to recommend Amazon products.

Checkout my other datasets

USA Unemployment Rates by Demographics & Race

USA Hispanic-White Wage Gap Dataset

Median and Avg Hourly Wages in the USA

Health Insurance Coverage in the USA

Black-White Wage Gap in the USA Dataset

Photo by Canonicalized

Dataset

This dataset was created by peter mushemi

Released under MIT

Contents

The statistics of the Brexit referendum, as voted by British citizens, showing the number of people voting to either leave or remain in each of the four zones selected. **Motivation ** To exhibit our expertise to create a comprehensive solution for web-based interactive charts by using cutting-edge technologies.

Contains all the current domains and measures of national well-being for young people. As well as providing the latest data for each measure, where available a time series of data are also presented along with useful links to data sources and other websites which may be of interest.

Context

This dataset is for showing how to visualize OD datasets

Content

This dataset contains all the cities where the british queen has visited in her lifetime.

Acknowledgements

The dataset is obtained from the internet.

Past Research

No

Inspiration

Showing OD dataset is very fun.

The dataset comes from The UK Office for National Statistics. It was explored in the July 2023 article "Why do children and young people in smaller towns do better academically than those in larger towns?".

This dataset is about book subjects. It has 3 rows and is filtered where the books is Educating the Germans : people and policy in the British zone of Germany, 1945-1949. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Datasets relating to Disabled People’s Perceptions of Public Transport Service in the UK24th February 2023 - 24th February 2024This exploratory study is to better understand the United Kingdom (UK) transport service for those with disabilities through Transformative Service Research (TSR). This TSR considers Field et al. (2021) Service Research Priority 7 (SRP7 Services for disadvantaged consumers and communities) in the context of Disabled people’s perceptions and lived experiences of public transport service in the UK. Greater insight into Disabled consumer transport experiences at a local/regional level and service ecosystem mindset contributes to much needed innovation on a more diverse, inclusive transport service ecosystem revolution.Datasets 2-7, attached here, are anonymised responses to an online survey. Dataset 1 contained personal data of participants and is not provided here. The aims of the survey were:To explore insights of Disabled people's experiences of public transport in the UK.To consider positive sentiments, barriers and facilitators linked to public transport.To identify suggestions for short and long-term solutions for overcoming some of the barriers.The study results have been submitted for publication and a link will be provided here when available.

What does the data show?

The data shows projections of population age structure (thousands of people per age class) from the UK Climate Resilience Programme UK-SSPs project. The data is available for each Office for National Statistics Local Authority District (ONS LAD) shape simplified to a 10m resolution.

The age structure is split into 19 age classes e.g. 10-14 and is available for the end of each decade. For more information see the table below.

This dataset contains only SSP2, the 'Middle of the Road' scenario.

Indicator

Demography

Metric

Age Structure

Unit

Thousands per age class

Spatial Resolution

LAD

Temporal Resolution

Decadal

Sectoral Categories

19 age classes

Baseline Data Source

ONS 2019

Projection Trend Source

IIASA

What are the naming conventions and how do I explore the data?

This data contains a field for the year at the end of each decade. A separate field for 'Age Class' allow the data to be filtered e.g. by age class '10-14'.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to 2020 values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links:

Further information on the UK SSPs can be found on the UK SSP project site and in this storymap. Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.

Introduction

This UK English Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of English speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native UK English speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

Introduction

Welcome to the UK English General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of English speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world UK English communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade English speech models that understand and respond to authentic British accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of UK English. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple English speech and language AI applications:

There is an urgent need to understand the factors that mediate and mitigate the impact of the Covid-19 pandemic on behaviour and wellbeing. However, the onset of the outbreak was unexpected and the rate of acceleration so rapid as to preclude the planning of studies that can address these critical issues. Coincidentally, in January 2020, just prior to the outbreak in the UK, my team launched a study that collected detailed (~50 minute) cognitive and questionnaire assessments from >200,000 members of the UK public as part of a collaboration with the BBC. This placed us in a unique position to examine how aspects of mental health subsequently changed as the pandemic arrived in the UK. Therefore, we collected data from a further ~120,000 people in May, including additional detailed measures of self-perceived pandemic impact and free text descriptions of the main positives, negatives and pragmatic measures that people found helped them maintain their wellbeing.

In this data archive, we include the survey data from January and May 2020 examining impact of Covid-19 on mood, wellbeing and behaviour in the UK population. This data is reported in a preprint article, where we apply a novel fusion of psychometric, multivariate and machine learning analyses to this unique dataset, in order to address some of the most pressing questions regarding wellbeing during the pandemic in a data-driven manner. The preprint is available on this URL. https://www.medrxiv.org/content/10.1101/2020.06.18.20134635v1

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for all MSOAs and compare this with Leicester overall statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsProficiency in EnglishThis dataset provides Census 2021 estimates that classify usual residents in England and Wales by their proficiency in English. The estimates are as at Census Day, 21 March 2021.Definition: How well people whose main language is not English (English or Welsh in Wales) speak English.This dataset provides details for the MSOAs of Leicester city.

Dataset description

Daily official UK Covid data. The data is available per country (England, Scotland, Wales and Northern Ireland) and for different regions in England. The different regions are split into two different files as part of the data is directly gathered by the NHS (National Health Service). The files that contain the word 'nhsregion' in their name, include data related to hospitals only, such as number of admissions or number of people in respirators. The files containing the word 'region' in their name, include the rest of the data, such as number of cases, number of vaccinated people or number of tests performed per day. The next paragraphs describe the columns for the different file types.

Region files

Files related to regions (word 'region' included in the file name) have the following columns: - "date": date in YYYY-MM-DD format - "area type": type of area covered in the file (region or nation) - "area name": name of area covered in the file (region or nation name) - "daily cases": new cases on a given date - "cum cases": cumulative cases - "new deaths 28days": new deaths within 28 days of a positive test - "cum deaths 28days": cumulative deaths within 28 days of a positive test - "new deaths_60days": new deaths within 60 days of a positive test - "cum deaths 60days": cumulative deaths within 60 days of a positive test - "new_first_episode": new first episodes by date - "cum_first_episode": cumulative first episodes by date - "new_reinfections": new reinfections by specimen data - "cum_reinfections": cumualtive reinfections by specimen data - "new_virus_test": new virus tests by date - "cum_virus_test": cumulative virus tests by date - "new_pcr_test": new PCR tests by date - "cum_pcr_test": cumulative PCR tests by date - "new_lfd_test": new LFD tests by date - "cum_lfd_test": cumulative LFD tests by date - "test_roll_pos_pct": percentage of unique case positivity by date rolling sum - "test_roll_people": unique people tested by date rolling sum - "new first dose": new people vaccinated with a first dose - "cum first dose": cumulative people vaccinated with a first dose - "new second dose": new people vaccinated with a first dose - "cum second dose": cumulative people vaccinated with a first dose - "new third dose": new people vaccinated with a booster or third dose - "cum third dose": cumulative people vaccinated with a booster or third dose

Country files

Files related to countries (England, Northern Ireland, Scotland and Wales) have the above columns and also: - "new admissions": new admissions, - "cum admissions": cumulative admissions, - "hospital cases": patients in hospitals, - "ventilator beds": COVID occupied mechanical ventilator beds - "trans_rate_min": minimum transmission rate (R) - "trans_rate_max": maximum transmission rate (R) - "trans_growth_min": transmission rate growth min - "trans_growth_max": transmission rate growth max

NHS Region files

Files related to nhsregion (word 'nhsregion' included in the file name) have the following columns: - "new admissions": new admissions, - "cum admissions": cumulative admissions, - "hospital cases": patients in hospitals, - "ventilator beds": COVID occupied mechanical ventilator beds - "trans_rate_min": minimum transmission rate (R) - "trans_rate_max": maximum transmission rate (R) - "trans_growth_min": transmission rate growth min - "trans_growth_max": transmission rate growth max

It's worth noting that the dataset hasn't been cleaned and it needs cleaning. Also, different files have different null columns. This isn't an error in the dataset but the way different countries and regions report the data.