Individuals have the right to access their personal data held by private companies. This operation can be started by different types of data subjects. A 2020 poll conducted among UK managers showed that 31 percent of the requests came from employees or ex-employees. Another 30 percent of Data Subject Access Requests (DSAR) were submitted by customers.

Although a majority of internet users aged between 18 and 75 years in the United Kingdom (UK) are still skeptical when it comes to personal data being collected by companies, a small share (36 percent) would be willing to share this data in return for financial compensation. These types of data mainly included purchase history and location data, while a slightly smaller percentage stated they were willing to sell their browsing history and online media consumption to companies.

Between 2004 and 2024, internet users in the United Kingdom (UK) have seen many significant data breaches. In these incidents, users' passwords were the most frequently leaked type of data, with an overall 234.98 million passwords being leaked in the measured period. Username ranked second, followed by names.

(1) To ascertain how the British public (adults) feel about personal data storage apps and services, and how in control over their personal data they feel, we conducted a UK-wide demographically representative national survey (implemented by survey company, ICM Unlimited, across 15-18 January 2021, 2,065 respondents, online omnibus).

(2) To qualitatively assess UK adults' views on control over personal data, and perceptions of personal data storage apps and services, six two-hour focus groups (35 participants) were conducted in February 2021, split according to age (18-34 year olds in Groups A, D and E, and 65+ in Groups B, C and F).

(3) To ascertain how the British public (adults) feel about being paid for supplying their biometric and emotion personal data in a controlled fashion via personal data storage apps and services, we conducted a UK-wide demographically representative national survey (implemented by survey company, Walnut Unlimited, across 29 Sep – 1 Oct 2021, 2,070 respondents, online omnibus).

Research from academia, industry and regulators finds that most citizens care about their privacy and want greater control over their personal data. However, even the digital cognoscenti struggle to understand how personal data is collected, used and recirculated. Data literacy approaches therefore do not solve the issue of privacy exploitation. The utility of legal approaches is also questionable as European General Data Protection Directive consent processes are problematic. In addition to rights frameworks and regulation, new solutions are needed. As part of a privacy toolkit, privacy-by-design may help to achieve greater data privacy by embedding privacy considerations into systems that process personal data.

Funded by Innovate UK Smart Grants (TS/T019964/1, File reference: 106283) in collaboration with project partner, Cufflink, this project’s central research question is: What empirically generated ethical factors do citizen-level personal data storage services such as Cufflink need to build in their app to empower users to manage their own personal information?

Citizen-level personal data storage services seek to empower users to manage and control their own personal information when linking this to other individuals and organisations. Our project partner, Cufflink is developing a personal data storage app that, uniquely does not require users to prove their identity, and that has a clear, iconography-driven explanation of terms and conditions. However, we do not yet understand the ethical principles by which these apps work, whether their revenue models raise other ethical and privacy externalities, and whether their design adequately reflects citizen concerns with control over their data. If they are a privacy solution that helps structure interaction between citizens and businesses, what features are needed to ensure that everyday citizens, and not just the digital cognoscenti, use it?

To answer these questions, we have collaborated with Cufflink to understand the perceptual, behavioural and ethical contexts in which their product will be used. We have studied the affordances of early iterations of Cufflink’s app and key established competitor apps, and we undertook scoping interviews with relevant governance actors to discuss issues raised by citizen-level personal data storage apps. We conducted 2 demographically representative national surveys: survey 1 establishes UK-level attitudes towards the level of control that people feel they have over personal data, and towards personal data stores; survey 2 establishes UK-level attitudes towards being paid for supplying their biometric and emotion personal data in a controlled fashion via personal data storage apps. We conducted online focus groups of lay users’ comprehension of personal data and privacy provided by personal data stores. We fed our analysis into Cufflink’s product design, thereby improving the product, and we developed an Ethical Impact Assessment toolkit to evaluate all apps that are based on citizen-level personal data storage principles.

ExactOne delivers unparalleled consumer transaction insights to help investors and corporate clients uncover market opportunities, analyze trends, and drive better decisions.

Dataset Highlights - Source: Debit and credit card transactions from 600K+ active users and 2M accounts connected via Open Banking. Scale: Covers 250M+ annual transactions, mapped to 1,800+ merchants and 330+ tickers. Historical Depth: Over 6 years of transaction data. Flexibility: Analyse transactions by merchant/ticker, category/industry, or timeframe (daily, weekly, monthly, or quarterly).

ExactOne data offers visibility into key consumer industries, including: Airlines - Regional / Budget Airlines - Cargo Airlines - Full Service Autos - OEMs Communication Services - Cable & Satellite Communication Services - Integrated Telecommunications Communication Services - Wireless Telecom Consumer - Services Consumer - Health & Fitness Consumer Staples - Household Supplies Energy - Utilities Energy - Integrated Oil & Gas Financial Services - Insurance Grocers - Traditional Hotels - C-corp Industrial - Misc Industrial - Tools And Hardware Internet - E-commerce Internet - B2B Services Internet - Ride Hailing & Delivery Leisure - Online Gambling Media - Digital Subscription Real Estate - Brokerage Restaurants - Quick Service Restaurants - Fast Casual Restaurants - Pubs Restaurants - Specialty Retail - Softlines Retail - Mass Merchants Retail - European Luxury Retail - Specialty Retail - Sports & Athletics Retail - Footwear Retail - Dept Stores Retail - Luxury Retail - Convenience Stores Retail - Hardlines Technology - Enterprise Software Technology - Electronics & Appliances Technology - Computer Hardware Utilities - Water Utilities

Use Cases

For Private Equity & Venture Capital Firms: - Deal Sourcing: Identify high-growth opportunities. - Due Diligence: Leverage transaction data to evaluate investment potential. - Portfolio Monitoring: Track performance post-investment with real-time data.

For Consumer Insights & Strategy Teams: - Market Dynamics: Compare sales trends, average transaction size, and customer loyalty. - Competitive Analysis: Benchmark market share and identify emerging competitors. - E-commerce vs. Brick & Mortar Trends: Assess channel performance and strategic opportunities. - Demographic & Geographic Insights: Uncover growth drivers by demo and geo segments.

For Investor Relations Teams: - Shareholder Insights: Monitor brand performance relative to competitors. - Real-Time Intelligence: Analyse sales and market dynamics for public and private companies. - M&A Opportunities: Evaluate market share and growth potential for strategic investments.

Key Benefits of ExactOne - Understand Market Share: Benchmark against competitors and uncover emerging players. - Analyse Customer Loyalty: Evaluate repeat purchase behavior and retention rates. - Track Growth Trends: Identify key drivers of sales by geography, demographic, and channel. - Granular Insights: Drill into transaction-level data or aggregated summaries for in-depth analysis.

With ExactOne, investors and corporate leaders gain actionable, real-time insights into consumer behaviour and market dynamics, enabling smarter decisions and sustained growth.

Explore Industrial classification in U.K. capital markets : a test of homogeneity through data • Key facts: author, publication date, book publisher, book series, book subjects • Real-time news, visualizations and datasets

The UK Data Center Physical Security Market report segments the industry into By Solution Type (Video Surveillance, Access Control Solutions, Others (Mantraps, Fences, and Monitoring Solutions)), By Service Type (Consulting Services, Professional Services, Other Service Types (System Integration Services)), and End User (IT & Telecommunication, BFSI, Government, Healthcare, Other End Users).

Context

This dataset was created by PromptCloud and DataStock. This dataset has about 30K records in it containing various sections of data fields.

You can download the full dataset here.

Content

This dataset contains the following Data fields: - Uniq_ID, - Crawl_Timestamp, - URL, - Job_Title, - Category, - Company_Name, - City, - State, - Country, - Inferred_City, - Inferred_State, - Inferred_Country, - Post_Date, - Job_Description, - Job_Requirements, - Job_Type, - Salary_Offered, - Job_Board, - Geo, - Fitness_Score 

Acknowledgements

This dataset was created by our in house data crawling and mining teams at PromptCloud and Datastock.

Inspiration

This dataset was made mainly to give companies a sense of the different types of data fields to job data so that they can position themselves better in the current scenario in the job market.

UK: School Enrollment: Primary: % Gross data was reported at 101.909 % in 2015. This records a decrease from the previous number of 107.558 % for 2014. UK: School Enrollment: Primary: % Gross data is updated yearly, averaging 103.899 % from Dec 1971 (Median) to 2015, with 40 observations. The data reached an all-time high of 107.661 % in 1988 and a record low of 99.748 % in 2002. UK: School Enrollment: Primary: % Gross data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s UK – Table UK.World Bank: Education Statistics. Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

The Children’s People and Nature Survey (C-PaNS) provides information on how children and young people experience and think about the natural environment. Each year, the survey samples around 4,000 children and young people aged 8 -15 years across two survey waves, one in term time and one in holiday time.

Waves 5 and 6 of the C-PaNS ran between the 16 and 23 August 2023 (during the school holidays) and 18 and 26 September 2023 (during term-time). Wave 7 and 8 ran between the 14 and 25 August 2024 (during the school holidays) and the 16 September and 2 October 2024 (during the term-time). These data are alongside data from earlier waves already published.

Different versions of the C-PaNS are available from the UK Data Archive under Open Access (SN 9174) conditions, End User Licence (SN 9175), and Secure Access (SN 9176).

The Secure Access version includes the same data as the End User Licence version, but includes more detailed variables including:

• age as a continuous variable
• income (all categories)
• number of people living in household as a continuous variable
• ethnicity
• disability
• home geography variables, including local authority district and urban/rural area
• open answers for thematic analysis in CS_Q14 and CS_Q15

The Open Access version includes the same data as the End User Licence version, but does not include the following variables:

• age band
• number of people living in household (Top coded to ‘6 and over’)
• access to private garden
• income (top coded to £50,000+)
• gender
• places withing walking distance from home

Researchers are advised to review the Open Access and/or the End User Licence versions to determine if these are adequate prior to ordering the Secure Access version.

Accredited official statistics are called National Statistics in the Statistics and Registration Service Act 2007. An explanation can be found on the Office for Statistics Regulation website.

Natural England's statistical practice is regulated by the Office for Statistics Regulation (OSR). OSR sets the standards of trustworthiness, quality and value in the Code of Practice for Statistics that all producers of official statistics should adhere to.

These accredited official statistics were independently reviewed by the Office for Statistics Regulation in January 2023. They comply with the standards of trustworthiness, quality and value in the Code of Practice for Statistics and should be labelled ‘accredited official statistics’.

Users are welcome to contact Natural England directly at people_and_nature@naturalengland.org.uk with any comments about how they meet these standards. Alternatively, users can contact OSR by emailing regulation@statistics.gov.uk or via the OSR website.

Since the latest review by the Office for Statistics Regulation, Natural England have continued to comply with the Code of Practice for Statistics, and have made the following improvements:

1. Published a development plan with timetables for future work, which will be updated annually
2. Ensured that users have opportunities to contribute to development planning through their biannual Research User Group
3. Enabled wider access to the data by publishing raw data sets through the UK Data Service
4. Provided users with guidance on how statistics from their products can be compared with those produced in the devolved nations
5. Published guidance on the differences between PaNS and MENE
6. Improved estimates of the percentage of people visiting nature in the previous 14 days by reducing the amount of respondents answering ‘don’t know’.

These data are available in Excel, SPSS, as well as Open Document Spreadsheet (ODS) formats.

For the second edition (January 2025), data for Wave 7 (during the school holidays in 2024) and Wave 8 (during the term-time in 2024) were added to the study.

Office for National Statistics' national and subnational Census 2021. Schoolchildren and full-time studentsThis dataset provides Census 2021 estimates that classify all usual residents aged 5 years and over in England and Wales. The estimates are as at Census Day, 21 March 2021. Schoolchild or full-time student indicator definition: Indicates whether a person aged 5 years and over was in full-time education on Census Day, 21 March 2021. This includes schoolchildren and adults in full-time education.Schoolchildren and students in full-time education studying away from home are treated as usually resident at their term-time address.Comparability with 2011: Broadly comparable.We have removed the category Schoolchild or full-time student for Census 2021 and replaced it with Student. In the 2011 Census people aged 4 years and over were asked to answer the question, in Census 2021 people aged 5 years and over were asked to answer the question. This data is issued at (BGC) Generalised (20m) boundary type for:Country - England and WalesRegion - EnglandUTLA - England and WalesLTLA - England and WalesWard - England and WalesMSOA - England and WalesLSOA - England and WalesOA - England and WalesIf you require the data at full resolution boundaries, or if you are interested in the range of statistical data that Esri UK make available in ArcGIS Online please enquire at content@esriuk.com.The data services available from this page are derived from the National Data Service. The NDS delivers thousands of open national statistical indicators for the UK as data-as-a-service. Data are sourced from major providers such as the Office for National Statistics, Public Health England and Police UK and made available for your area at standard geographies such as counties, districts and wards and census output areas. This premium service can be consumed as online web services or on-premise for use throughout the ArcGIS system.Read more about the NDS.

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2022 Weighting

The population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.

Latest edition information

For the third edition (September 2023), a new version of the data file with revised SOC variables was deposited. Further information on the SOC revisions can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

• to provide annual data about the nation’s health;
• to estimate the proportion of people in England with specified health conditions;
• to estimate the prevalence of certain risk factors associated with these conditions;
• to examine differences between population subgroups in their likelihood of having specific conditions or risk factors;
• to assess the frequency with which particular combinations of risk factors are found, and which groups these combinations most commonly occur;
• to monitor progress towards selected health targets
• since 1995, to measure the height of children at different ages, replacing the National Study of Health and Growth;
• since 1995, monitor the prevalence of overweight and obesity in children.

The survey includes a number of core questions every year but also focuses on different health issues at each wave. Topics are revisited at appropriate intervals in order to monitor change.

Further information about the series may be found on the NHS Digital Health Survey for England; health, social care and lifestyles webpage, the NatCen Social Research NatCen Health Survey for England webpage and the University College London Health and Social Surveys Research Group UCL Health Survey for England webpage.

Changes to the HSE from 2015:
Users should note that from 2015 survey onwards, only the individual data file is available under standard End User Licence (EUL). The household data file is now only included in the Special Licence (SL) version, released from 2015 onwards. In addition, the SL individual file contains all the variables included in the HSE EUL dataset, plus others, including variables removed from the EUL version after the NHS Digital disclosure review. The SL HSE is subject to more restrictive access conditions than the EUL version (see Access information). Users are advised to obtain the EUL version to see if it meets their needs before considering an application for the SL version.

COVID-19 and the HSE:
Due to the COVID-19 pandemic, the HSE 2020 survey was stopped in March 2020 and never re-started. There was no publication that year. The survey resumed in 2021, albeit with an amended methodology. The full HSE resumed in 2022, with an extended fieldwork period. Due to this, the decision was taken not to progress with the 2023 survey, to maximise the 2022 survey response and enable more robust reporting of data. See the NHS Digital Health Survey for England - Health, social care and lifestyles webpage for more details.

The focus for the HSE 2014 was mental health. The survey also provided updates on core topics including smoking, drinking and general health. Additional topics covered are listed below.

Latest edition information
For the fourth edition (June 2023), the following changes have been made: 1) Variable MEDSNUM2G8 has been corrected and is now derived from MEDNUM2, which is the derived variable that excludes contraceptives and nicotine dependency drugs. 2) Variables SOC2010B and HRPSOC10B have had one category corrected that was incorrectly coded. 3) Variables NATID2 and NATID3 have had their labels corrected to NatID2 "National identity: Welsh" and NatID3 "National identity: Scottish". 4) Variables YNATSC2 and YNATSC3 have had their labels corrected to YNATSC2 "National identity: Welsh (SC)" and YNATSC3 "National identity: Scottish (SC)". 5) Some changes have been made to BMI derived variables: BMIVAL2, BMIVG8, BMIVGDR and WTVAL2 were incorrectly coded for one case, where the value '1' has been changed to '-1'; BMIVG5, BMIVG52, BMIVG53, BMI_GROUP, BMIVG3, BMIVG8 and BMIVGDR have had 3 cases recoded; BMISRG5 has had 4 cases recoded; and BMIVG8 has had one case recoded. 6) The variable label for ANTIBACTAK has been updated. 7) Fruit and Vegetable derived variables PORLGE, PORSML, POROTH, PORPUL, PORSAL, PORVEG, PORVDISH, PORJUICE, PORFRT, PORDRY, PORFROZ, PORFDISH, VEGPOR, FRTPOR, PORFV, PORFTVG, VEGYN, VDISHYN, FRTYN, FDISHYN, DRYYN, FROZYN PULYN, JUICEYN, SALYN, FVYN, PORFV05, VEGTYN, VEGTYN2, FRTTYN and FRTTYN2: these questions were only asked of children but some adult respondents had been included, mistakenly coded to 0 - these cases have now been recoded to '-1'. 8) The documentation has been updated to reflect these changes.

As of July 2023, the majority of internet users in the United Kingdom (UK) were employed full-time. According to a survey conducted in the country between July 2022 and July 2023, 54 percent of internet users said they worked full-time. Furthermore, 13 percent of online users were employed part-time, and a further four percent stated to be self-employed.

The Annual Population Survey (APS) household datasets are produced annually and are available from 2004 (Special Licence) and 2006 (End User Licence). They allow production of family and household labour market statistics at local areas and for small sub-groups of the population across the UK. The household data comprise key variables from the Labour Force Survey (LFS) and the APS 'person' datasets. The APS household datasets include all the variables on the LFS and APS person datasets, except for the income variables. They also include key family and household-level derived variables. These variables allow for an analysis of the combined economic activity status of the family or household. In addition, they also include more detailed geographical, industry, occupation, health and age variables.

For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.

Occupation data for 2021 and 2022
The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022

End User Licence and Secure Access APS data
Users should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to:

• age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child
• family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family
• nationality and country of origin
• geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district
• health: including main health problem, and current and past health problems
• education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships
• industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from
• occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from
• system variables: including week number when interview took place and number of households at address

The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

Background:
A household food consumption and expenditure survey has been conducted each year in Great Britain (excluding Northern Ireland) since 1940. At that time the National Food Survey (NFS) covered a sample drawn solely from urban working-class households, but this was extended to a fully demographically representative sample in 1950. From 1957 onwards the Family Expenditure Survey (FES) provided information on all household expenditure patterns including food expenditure, with the NFS providing more detailed information on food consumption and expenditure. The NFS was extended to cover Northern Ireland from 1996 onwards. In April 2001 these surveys were combined to form the Expenditure and Food Survey (EFS), which completely replaced both series. From January 2008, the EFS became known as the Living Costs and Food (LCF) module of the Integrated Household Survey (IHS). As a consequence of this change, the questionnaire was altered to accommodate the insertion of a core set of questions, common to all of the separate modules which together comprised the IHS. Some of these core questions are simply questions which were previously asked in the same or a similar format on all of the IHS component surveys. For further information on the LCF questionnaire, see Volume A of the LCF 2008 User Guide, held with SN 6385. Further information about the LCF, including links to published reports based on the survey, may be found by searching for 'Living Costs and Food Survey' on the ONS website. Further information on the NFS and Living Costs and Food Module of the IHS can be found by searching for 'Family Food' on the GOV.UK website.

History:
The LCF (then EFS) was the result of more than two years' development work to bring together the FES and NFS; both survey series were well-established and important sources of information for government and the wider community, and had charted changes and patterns in spending and food consumption since the 1950s. Whilst the NFS and FES series are now finished, users should note that previous data from both series are still available from the UK Data Archive, under GNs 33071 (NFS) and 33057 (FES).

Purpose of the LCF
The Office for National Statistics (ONS) has overall project management and financial responsibility for the LCF, while the Department for Environment, Food and Rural Affairs (DEFRA) sponsors the food data element. As with the FES and NFS, the LCF continues to be primarily used to provide information for the Retail Prices Index, National Accounts estimates of household expenditure, analysis of the effect of taxes and benefits, and trends in nutrition. The results are multi-purpose, however, providing an invaluable supply of economic and social data. The merger of the two surveys also brings benefits for users, as a single survey on food expenditure removes the difficulties of reconciling data from two sources.

Design and methodology The design of the LCF is based on the old FES, although the use of new processing software by the data creators has resulted in a dataset which differs from the previous structure. The most significant change in terms of reporting expenditure, however, is the introduction of the European Standard Classification of Individual Consumption by Purpose (COICOP), in place of the codes previously used. An additional level of hierarchy has been developed to improve the mapping to the previous codes. The LCF was conducted on a financial year basis from 2001, then moved to a calendar year basis from January 2006 (to complement the IHS) until 2015-16, when the financial year survey was reinstated at the request of users. Therefore, whilst SN 5688 covers April 2005 - March 2006, SN 5986 covers January-December 2006. Subsequent years cover January-December until 2014. SN 8210 returns to the financial year survey and currently covers April 2015 - March 2016.

Northern Ireland sample
Users should note that, due to funding constraints, from January 2010 the Northern Ireland (NI) sample used for the LCF was reduced to a sample proportionate to the NI population relative to the UK.

Family Food database:
'Family Food' is an annual publication which provides detailed statistical information on purchased quantities, expenditure and nutrient intakes derived from both household and eating out food and drink. Data is collected for a sample of households in the United Kingdom using self-reported diaries of all purchases, including food eaten out, over a two week period. Where possible quantities are recorded in the diaries but otherwise estimated. Energy and nutrient intakes are calculated using standard nutrient composition data for each of some 500 types of food. Current estimates are based on data collected in the Family Food Module of the LCFS. Further information about the LCF food databases can be found on the GOV.UK Family Food Statistics web pages.

Secure Access version
A Secure Access version of the LCF from 2006 onwards is available from the UK Data Archive under SN 7047, subject to stringent access conditions. The Secure Access version includes variables that are not included in the standard End User Licence (EUL) version, including geographical variables with detail below Government Office Region, to postcode level; urban/rural area indicators; other sensitive variables; raw diary information files (derived variables are available in the EUL) and the family expenditure codes files. Users are strongly advised to check whether the EUL version is sufficient for their needs before considering an application for the Secure Access version.

Occupation data for 2021 and 2022 data files
The ONS have identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. For further information on this issue, please see: https://www.ons.gov.uk/news/statementsandletters/occupationaldatainonssurveys.

Users should note from 2006, the EFS has moved to a calendar year rather than financial year basis, in anticipation of the new ONS CPS survey (see above). Further details on the impact of this change are available in the documentation. In addition, the documentation for 2006 is in MS Excel format apart from Volume A.

For the second edition (February 2009), revised mortgage variables (quarters 1-4) and new weighting variables were deposited, and merged with the dvhh file. The new weighting variables are included alongside the previous ones (weighta and weightq), and are NewWeightA 'Annual weight using the new 2001-based weighting method' and NewWeightQ 'Quarterly weight using the new 2001-based weighting method'. The following mortgage and housing expenditure variables have been replaced: B150, B200, P281p, P515p, P515TP, P516TP, P516p, P531, P531T, P532, P535TP, P535p, P536p, P536tp, P550p, P550tp, P551p, P551tp, P620p, P630p, P630cp, P620tp, P630tp, FSK, FSK1, FSK11, FSALL, FS145, FS1452.

For the third edition (June/July 2009), the DEFRA food database was added to the dataset, and the Incanon variable available in the dvhh file was replaced with a corrected version. Users are advised to download a new copy of the dvhh file.