Money Supply M0 in the United Kingdom increased to 102228 GBP Million in October from 101612 GBP Million in September of 2025. This dataset provides - United Kingdom Money Supply M0 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Family Resources Survey (FRS) has been running continuously since 1992 to meet the information needs of the Department for Work and Pensions (DWP). It is almost wholly funded by DWP.

The FRS collects information from a large, and representative sample of private households in the United Kingdom (prior to 2002, it covered Great Britain only). The interview year runs from April to March.

The focus of the survey is on income, and how much comes from the many possible sources (such as employee earnings, self-employed earnings or profits from businesses, and dividends; individual pensions; state benefits, including Universal Credit and the State Pension; and other sources such as savings and investments). Specific items of expenditure, such as rent or mortgage, Council Tax and water bills, are also covered.

Many other topics are covered and the dataset has a very wide range of personal characteristics, at the adult or child, family and then household levels. These include education, caring, childcare and disability. The dataset also captures material deprivation, household food security and (new for 2021/22) household food bank usage.

The FRS is a national statistic whose results are published on the gov.uk website. It is also possible to create your own tables from FRS data, using DWP’s Stat Xplore tool. Further information can be found on the gov.uk Family Resources Survey webpage.

Secure Access FRS data
In addition to the standard End User Licence (EUL) version, Secure Access datasets, containing unrounded data and additional variables, are also available for FRS from 2005/06 onwards - see SN 9256. Prospective users of the Secure Access version of the FRS will need to fulfil additional requirements beyond those associated with the EUL datasets. Full details of the application requirements are available from http://ukdataservice.ac.uk/media/178323/secure_frs_application_guidance.pdf" style="background-color: rgb(255, 255, 255);">Guidance on applying for the Family Resources Survey: Secure Access.

FRS, HBAI and PI
The FRS underpins the related Households Below Average Income (HBAI) dataset, which focuses on poverty in the UK, and the related Pensioners' Incomes (PI) dataset. The EUL versions of HBAI and PI are held under SNs 5828 and 8503, respectively. The Secure Access versions are held under SN 7196 and 9257 (see above).

505 Economics is on a mission to make academic economics accessible. We've developed the first monthly sub-national GDP data for EU and UK regions from January 2015 onwards.

Our GDP dataset uses luminosity as a proxy for GDP. The brighter a place, the more economic activity that place tends to have.

We produce the data using high-resolution night time satellite imagery and Artificial Intelligence.

This builds on our academic research at the London School of Economics, and we're producing the dataset in collaboration with the European Space Agency BIC UK.

We have published peer-reviewed academic articles on the usage of luminosity as an accurate proxy for GDP.

Key features:

• Granular: Data is provided at the following geographical units:
  • NUTS3 (e.g. London Boroughs),
  • NUTS2 (e.g. London),
  • NUTS1 (e.g. England), and
  • NUTS0 (e.g. United Kingdom) levels.
• Frequent: Data is provided every month from January 2015. This is more frequent than the annualised official datasets.
• Timely: Data is provided with a one month lag (i.e. the data for January 2021 was published at the end of February 2021). This is substantially quicker than the 18 month lag of official datasets.
• Accurate: Our dataset uses Deep Learning to maximise accuracy (RMSE 1.2%).

The dataset can be used by:

• Governments and policy makers - to monitor the performance of local economies, to measure the localised impact of policies, and to get a real-time indication of economic activity.
• Financial services - to get an indication of national-level GDP before official GDP statistics are released
• Engineering companies - to monitor and evaluate the localised impact of infrastructure projects
• Consultancies - to forecast the localised impact of specific projects, to retrospectively monitor and evaluate the localised impact of existing projects
• Economics firms - to create macro forecasts at the national and sub-national level, to assess the impact of policy interventions.
• Academia / Think Tanks - to conduct novel research at the local level. E.g. our dataset can be used to measure the impact of localised COVID-19 lockdowns.

We have created this dataset for all UK sub-national regions, 28 EU Countries and Switzerland.

In Newcastle libraries we are endeavouring to open up as much of our data as possible. We will publish data here on a regular basis. Each file is saved in CSV format and has an accompanying text file detailing what data is contained in each file, who is responsible for it and when it was last updated. If there is any additional data you would like us to release then please contact Luke Burton (luke.burton@newcastle.gov.uk) to discuss. You are under no obligation to do so, but since we know you will make great things with our data we would love for you to tell us about them. Additional information To the extent possible under law, Newcastle Libaries has waived all copyright and related or neighbouring rights to its data published below. This work is published from: United Kingdom. For more information please visit: https://www.newcastle.gov.uk/your-council-and-democracy/open-data-and-access-information/open-data/data-sets/libraries-data-sets

This dataset is about companies in the United Kingdom. It has 245,151 rows. It features 2 columns including revenue type.

Money Supply M2 in the United Kingdom increased to 3178942 GBP Million in October from 3165508 GBP Million in September of 2025. This dataset provides - United Kingdom Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context The UK government amassed traffic data from 2000 and 2018, recording over 1.8 million accidents in the process and making this one of the most comprehensive traffic data sets out there. It's a huge picture of a country undergoing change.

Content There is 1 CSV File in this set. Accidents are the primary ones and have references by Accident_Index to the casualties and vehicles tables. This might be better done as a database.

Description This Dataset contains 33 Features covering over a 1.8million records. this dataset consists of various features like, Accidental / Longitude: Location of Accident

Inspiration Questions to ask about this data are, - What is the number of casualties on each day of the week? - On each day of the week, what is the maximum and minimum speed limit on the roads the accidents happened? - What is the importance of Light and Weather conditions in predicting accident severity? - What does your intuition say and what does the data portray? - To predict the severity of the accidents.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in England: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 22(2.02%) households where the householder is under 25 years old, 374(34.34%) households with a householder aged between 25 and 44 years, 410(37.65%) households with a householder aged between 45 and 64 years, and 283(25.99%) households where the householder is over 65 years old.
• In England, the age group of 45 to 64 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for England median household income by age. You can refer the same here

This dataset is a compilation of processed data on citation and references for research papers including their author, institution and open access info for a selected sample of academics analysed using Microsoft Academic Graph (MAG) data and CORE. The data for this dataset was collected during December 2019 to January 2020.Six countries (Austria, Brazil, Germany, India, Portugal, United Kingdom and United States) were the focus of the six questions which make up this dataset. There is one csv file per country and per question (36 files in total). More details about the creation of this dataset are available on the public ON-MERRIT D3.1 deliverable report.The dataset is a combination of two different data sources, one part is a dataset created on analysing promotion policies across the target countries, while the second part is a set of data points available to understand the publishing behaviour. To facilitate the analysis the dataset is organised in the following seven folders:PRTThe dataset with the file name "PRT_policies.csv" contains the related information as this was extracted from promotion, review and tenure (PRT) policies. Q1: What % of papers coming from a university are Open Access?- Dataset Name format: oa_status_countryname_papers.csv- Dataset Contents: Open Access (OA) status of all papers of all the universities listed in Times Higher Education World University Rankings (THEWUR) for the given country. A paper is marked OA if there is at least an OA link available. OA links are collected using the CORE Discovery API.- Important considerations about this dataset: - Papers with multiple authorship are preserved only once towards each of the distinct institutions their authors may belong to. - The service we used to recognise if a paper is OA, CORE Discovery, does not contain entries for all paperids in MAG. This implies that some of the records in the dataset extracted will not have either a true or false value for the _is_OA_ field. - Only those records marked as true for _is_OA_ field can be said to be OA. Others with false or no value for is_OA field are unknown status (i.e. not necessarily closed access).Q2: How are papers, published by the selected universities, distributed across the three scientific disciplines of our choice?- Dataset Name format: fsid_countryname_papers.csv- Dataset Contents: For the given country, all papers for all the universities listed in THEWUR with the information of fieldofstudy they belong to.- Important considerations about this dataset: * MAG can associate a paper to multiple fieldofstudyid. If a paper belongs to more than one of our fieldofstudyid, separate records were created for the paper with each of those _fieldofstudyid_s.- MAG assigns fieldofstudyid to every paper with a score. We preserve only those records whose score is more than 0.5 for any fieldofstudyid it belongs to.- Papers with multiple authorship are preserved only once towards each of the distinct institutions their authors may belong to. Papers with authorship from multiple universities are counted once towards each of the universities concerned.Q3: What is the gender distribution in authorship of papers published by the universities?- Dataset Name format: author_gender_countryname_papers.csv- Dataset Contents: All papers with their author names for all the universities listed in THEWUR.- Important considerations about this dataset :- When there are multiple collaborators(authors) for the same paper, this dataset makes sure that only the records for collaborators from within selected universities are preserved.- An external script was executed to determine the gender of the authors. The script is available here.Q4: Distribution of staff seniority (= number of years from their first publication until the last publication) in the given university.- Dataset Name format: author_ids_countryname_papers.csv- Dataset Contents: For a given country, all papers for authors with their publication year for all the universities listed in THEWUR.- Important considerations about this work :- When there are multiple collaborators(authors) for the same paper, this dataset makes sure that only the records for collaborators from within selected universities are preserved.- Calculating staff seniority can be achieved in various ways. The most straightforward option is to calculate it as _academic_age = MAX(year) - MIN(year) _for each authorid.Q5: Citation counts (incoming) for OA vs Non-OA papers published by the university.- Dataset Name format: cc_oa_countryname_papers.csv- Dataset Contents: OA status and OA links for all papers of all the universities listed in THEWUR and for each of those papers, count of incoming citations available in MAG.- Important considerations about this dataset :- CORE Discovery was used to establish the OA status of papers.- Papers with multiple authorship are preserved only once towards each of the distinct institutions their authors may belong to.- Only those records marked as true for _is_OA_ field can be said to be OA. Others with false or no value for is_OA field are unknown status (i.e. not necessarily closed access).Q6: Count of OA vs Non-OA references (outgoing) for all papers published by universities.- Dataset Name format: rc_oa_countryname_-papers.csv- Dataset Contents: Counts of all OA and unknown papers referenced by all papers published by all the universities listed in THEWUR.- Important considerations about this dataset :- CORE Discovery was used to establish the OA status of papers being referenced.- Papers with multiple authorship are preserved only once towards each of the distinct institutions their authors may belong to. Papers with authorship from multiple universities are counted once towards each of the universities concerned.Additional files:- _fieldsofstudy_mag_.csv: this file contains a dump of fieldsofstudy table of MAG mapping each of the ids to their actual field of study name.

Overview:

This dataset, titled "🔍 UK Data Science Jobs 2024 - Indeed," provides a detailed snapshot of the Data Science job market in the United Kingdom for the year 2024. It consists of approximately 300 job listings, each ethically sourced through API usage, ensuring a transparent and responsible data collection process. This compilation aims to offer insights into the evolving landscape of Data Science employment opportunities across the UK.

Data Science Applications:

Despite its relatively compact size, the dataset is rich with analytical potential. It is apt for analyses related to job trends, including variations in salary, types of employment, and employer ratings within the Data Science domain. The dataset is invaluable for stakeholders interested in examining the specifics of Data Science roles, including the required qualifications, experience levels, and the distribution of opportunities across different sectors.

Column Descriptors:

The refined dataset includes the following columns, providing structured and accessible data for analysis:

• positionName: Title of the job position.
• salary: Offered salary, presented either as a range or a fixed amount.
• company: Name of the company offering the job.
• rating: Rating of the company, reflecting its reputation and employee satisfaction.
• reviewsCount: Number of reviews for the company, indicating its engagement and visibility on the platform.
• jobTypeConsolidated: A synthesized descriptor of the job type, amalgamating various employment conditions into a single, coherent string (e.g., "Permanent, Full-time" or "Part-time").

Ethical Data Mining:

The dataset is the result of ethical data mining practices, utilizing APIs for data extraction to adhere to privacy standards and platform terms of service. This approach underscores the commitment to ethical standards in data collection and analysis.

Acknowledgments:

Gratitude is extended to the Indeed platform for serving as the primary source of this dataset. The platform's comprehensive job listings database has been crucial in assembling this detailed overview of the Data Science job market in the UK.

Image Credits:

The dataset's thumbnail was generated by Dall-E 3, illustrating the innovative intersection of artificial intelligence and creative design in visualizing data insights.

This dataset serves as a valuable resource for individuals navigating the Data Science job market, researchers conducting academic inquiries, and analysts assessing employment trends within the UK.

This dataset is about news. It has 31,238 rows and is filtered where the keywords includes United Kingdom. It features 10 columns including source, publication date, section, and news link.

Datasets for Analysis

🌍 Countries Dataset

Filename: Countries.csv

Description: This dataset contains information about countries around the world, including their respective codes, continents, and regions. It consists of 251 entries and the following columns:

• Country: The name of the country.
• Code: The ISO code for the country (one entry is missing a code).
• Continent: The continent where the country is located.
• Region: A more specific regional classification within the continent.

This dataset can be useful for geographical analysis, regional studies, and other applications that require country-level data.

✈️ British Airways Reviews Dataset

Filename: ba_reviews.csv

Description: This dataset contains reviews of British Airways flights, capturing various aspects of customer experiences. It consists of 1324 entries and the following columns:

• header: The title or headline of the review.
• author: The name of the reviewer.
• date: The date the review was posted.
• place: The reviewer's location.
• content: The main text of the review.
• aircraft: The type of aircraft flown.
• traveller_type: The type of traveler (e.g., business, leisure).
• seat_type: The class of seat (e.g., Economy Class, Business Class).
• route: The flight route taken.
• date_flown: The date the flight took place.
• recommended: Whether the reviewer recommends the airline.
• trip_verified: Whether the trip is verified.
• rating: The overall rating given by the reviewer (out of 5).
• seat_comfort: The rating for seat comfort (out of 5).
• cabin_staff_service: The rating for cabin staff service (out of 5).
• food_beverages: The rating for food and beverages (out of 5).
• ground_service: The rating for ground service (out of 5).
• value_for_money: The rating for value for money (out of 5).
• entertainment: The rating for in-flight entertainment (out of 5, with -1 indicating no rating).

This dataset is useful for analyzing customer satisfaction, identifying trends in airline service quality, and understanding the factors that contribute to positive or negative flight experiences.

National Grid ESO is the electricity system operator for Great Britain. They have gathered information of the electricity demand in Great Britain from 2009. The is updated twice an hour, which means 48 entries per day. This makes this dataset ideal for time series forecasting.

File information

The dataset consists of three type of files: - Historic_demand_year_20xx.csv: electricity demand in that year - Historic_demand_year_2009_2024.csv: all the yearly datasets merged in one - Historic_demand_year_2009_2024_noNaN.csv: same as above, but NaN values have been removed and the date includes the hour as opposed to only the day

Columns

The columns in the dataset are: * SETTLEMET_DATA: date in format dd/mm/yyyy * SETTLEMENT_PERIOD: half hourly period for the historic outtunr occurred * ND (National Demand). National Demand is the sum of metered generation, but excludes generation required to meet station load, pump storage pumping and interconnector exports. National Demand is calculated as a sum of generation based on National Grid ESO operational generation metering. Measured in MW. * TSD (Transmission System Demand). Transmission System Demand is equal to the ND plus the additional generation required to meet station load, pump storage pumping and interconnector exports. Measured in MW. * ENGLAND_WALES_DEMAND. England and Wales Demand, as ND above but on an England and Wales basis. Measured in MW. * EMBEDDED_WIND_GENERATION. This is an estimate of the GB wind generation from wind farms which do not have Transmission System metering installed. These wind farms are embedded in the distribution network and invisible to National Grid ESO. Their effect is to suppress the electricity demand during periods of high wind. The true output of these generators is not known so an estimate is provided based on National Grid ESO’s best model. Measured in MW. * EMBEDDED_WIND_CAPACITY. This is National Grid ESO’s best view of the installed embedded wind capacity in GB. This is based on publicly available information compiled from a variety of sources and is not the definitive view. It is consistent with the generation estimate provided above. Measured in MW * EMBEDDED_SOLAR_GENERATION. This is an estimate of the GB solar generation from PV panels. These are embedded in the distribution network and invisible to National Grid ESO. Their effect is to suppress the electricity demand during periods of high radiation. The true output of these generators is not known so an estimate is provided based on National Grid ESO’s best model. Measured in MW. * EMBEDDED_SOLAR_CAPACITY. As embedded wind capacity above, but for solar generation. Measured in MW. * NON_BM_STOR (Non-Balancing Mechanism SHort-Term Operating Reserve). For units that are not included in the ND generator definition. This can be in the form of generation or demand reduction. Measured in MW. * PUMP_STORAGE_PUMPING. The demand due to pumping at hydro pump storage units; the -ve signifies pumping load. * IFA_FLOW (IFA Interconnector Flow). The flow on on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * IFA2_FLOW (IFA Interconnector Flow). The flow on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * MOYLE_FLOW (Moyle Interconnector FLow). The flow on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * EAST_WEST_FLOW (East West Innterconnector FLow). The flow on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * NEMO_FLOW (Nemo Interconnector FLow). The flow on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * NSL_FLOW (North Sea Link Interconnector Flow). The flow on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * ELCLINK_FLOW. Blank

United Kingdom UK: Urban Land Area data was reported at 58,698.750 sq km in 2010. This stayed constant from the previous number of 58,698.750 sq km for 2000. United Kingdom UK: Urban Land Area data is updated yearly, averaging 58,698.750 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 58,698.750 sq km in 2010 and a record low of 58,698.750 sq km in 2010. United Kingdom UK: Urban Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Land Use, Protected Areas and National Wealth. Urban land area in square kilometers, based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;

The GBP/USD exchange rate fell to 1.3199 on December 2, 2025, down 0.11% from the previous session. Over the past month, the British Pound has strengthened 0.44%, and is up by 4.14% over the last 12 months. British Pound - values, historical data, forecasts and news - updated on December of 2025.

11th January 2020 Change to vaccination data made available by UK gov - now just cumulative number of vaccines delivered are available for both first and second doses. For the devolved nations the cumulative totals are available for the dates from when given, however for the UK as a whole the total doses given is just on the last date of the index, regardless of when those vaccines were given.

4th January 2020 VACCINATION DATA ADDED - New and Cumulative First Dose Vaccination Data added to UK_National_Total_COVID_Dataset.csv and UK_Devolved_Nations_COVID_Dataset.csv

2nd December 2020:

NEW population, land area and population density data added in file NEW_Official_Population_Data_ONS_mid-2019.csv. This data is scraped from the Office for National Statistics and covers the UK, devolved UK nations, regions and local authorities (boroughs).

20th November 2020:

With European governments struggling with a 'second-wave' of rising cases, hospitalisations and deaths resulting from the SARS-CoV-2 virus (COVID-19), I wanted to make a comparative analysis between the data coming out of major European nations since the start of the pandemic.

I started by creating a Sweden COVID-19 dataset and now I'm looking at my own country, the United Kingdom.

The data comes from https://coronavirus.data.gov.uk/ and I used the Developer's Guide to scrape the data, so it was a fairly simple process. The notebook that scapes the data is public and can be found here. Further information about data collection methodologies and definitions can be found here.

The data includes the overall numbers for the UK as a whole, the numbers for each of the devolved UK nations (Eng, Sco, Wal & NI), English Regions and Upper Tier Local Authorities (UTLA) for all of the UK (what we call Boroughs). I have also included a small table with the populations of the 4 devolved UK nations, used to calculate the death rates per 100,000 population.

As I've said for before - I am not an Epidemiologist, Sociologist or even a Data Scientist. I am actually a Mechanical Engineer! The objective here is to improve my data science skills and maybe provide some useful data to the wider community.

Any questions, comments or suggestions are most welcome! I am open to requests and collaborations! Stay Safe!