According to a survey, ** percent of online shoppers in the United States and Canada leave product reviews online at least sometimes in 2024. Almost ** percent of online shoppers never left a review, while * percent did it always after buying a product. During 2024, ** percent of online shoppers in the United States and Canada were very likely to use AI-powered size and fit recommendations while shopping.

This statistic displays the frequency of writing online reviews or app reviews among internet users in the United Kingdom in 2013, broken down by subject matter. In 2013, 27 percent of respondents reported writing reviews for products available to buy online sometimes or rarely.

This survey collected information about respondents’ participation in leisure, cultural and sporting activities, and acquisition of digital skills during the COVID-19 coronavirus pandemic in England. There are three waves of data, which were collected during May, June and July 2020, and all three used the same sampling methods and data collection instruments.

The main Taking Part adult and child studies for 2019-2020 are available at UK Data Archive SNs 8745 (EUL version) and 8746 (Special Licence version).

Latest edition information

For the second edition (April 2020), updated versions of the data files for all three waves of the survey were deposited, with additional variables included to help identify how many respondents completed more than one wave of the survey.

According to a survey of internet users in the United States conducted in March 2024, ** percent of respondents aged between 30 and 44 reported having already used artificial intelligence (AI) tool to write e-mails. Approximately ** percent of respondents aged between 45 and 64 years had not used AI for writing their e-mails, and would not consider the possibility. In comparison, ** respondents aged between 18 and 29 years would consider employing AI to compose their electronic communications.

This dataset provides a rich set of linked open data representing women's literary history from the beginnings to the present, concentrated on writing in English in the British Isles but with tentacles out to other languages, literary traditions, and parts of the world. It emerges from the ongoing experiments in literary history conducted by The Orlando Project, whose textbase is published by Cambridge University Press (2006-present) as Orlando: Women's Writing in the British Isles from the Beginnings to the Present, edited by Susan Brown, Patricia Clements, and Isobel Grundy and created, updated, and augmented by a large interdisciplinary team (see the Orlando Project website). The dataset was created as part of the Canadian Writing Research Collaboratory's work in Linked Open Data as a means of enabling digital scholarship and collaboration in the humanities. The Orlando Textbase is a semi-structured collection of biocritical entries providing detailed information on the lives and writing of more than 1400 writers with accompany literary, social, and political materials to provide context to its representation of literary history. It does not contain digitized versions of primary texts. The aim of extracting linked data from Orlando's textbase is to make the data accessible in new ways to discovery, querying, analysis, and visualization; to promote interlinking between Orlando and other related materials on the web; and to experiment with the potential of Linked Open Data technologies to support knowledge production and dissemination in the humanities. This is a first release of the dataset, which will be augmented and refined over time. This release is focused on the internal linking of biographical information using the CWRC ontology, with selective linking out to other ontology terms and other linked data entities. All of Orlando's bibliographical data, linked to Orlando authors, is also included in this release. This dataset comprised approximately 5 million triples. The CWRC Ontology Specification 0.99.80 should be used in conjunction with the RDF data. For fuller information see the "Release Notes" archived with this dataset.

The Online Writing Assistant market has emerged as a vital segment within the broader technology landscape, driven by the increasing demand for effective communication across various industries. As businesses and individuals alike seek to enhance their writing quality, improve productivity, and create engaging conte

Snapshot img

Writing And Marking Instruments Market Size 2025-2029

The writing and marking instruments market size is forecast to increase by USD 13.20 billion at a CAGR of 7.7% between 2024 and 2029.

The market is experiencing significant shifts driven by the increasing adoption of e-commerce platforms and the rising trend of mergers and acquisitions among key players. The e-commerce sector's growth is fueling the demand for writing and marking instruments, as consumers increasingly prefer the convenience of purchasing these products online. Additionally, the digitization trend, while presenting opportunities for innovation in digital pens and digital markers, also poses a challenge to traditional writing instrument manufacturers. The market's competitive landscape is dynamic, with companies continually seeking strategic collaborations and acquisitions to expand their product offerings and strengthen their market position. Companies looking to capitalize on these opportunities must stay abreast of the latest trends and consumer preferences while navigating the challenges presented by digitization and e-commerce competition. By focusing on product innovation, strategic partnerships, and effective marketing strategies, companies can differentiate themselves and thrive in this evolving market.

What will be the Size of the Writing And Marking Instruments Market during the forecast period?

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The market encompasses a diverse range of products, including pencils, markers, highlighters, fountain pens, ballpoint pens, gel pens, mechanical pencils, chalk, charcoal, crayons, pastels, erasers, ink cartridges, ink bottles, printing and writing paper, paper notebooks, sketchbooks, diaries, writing instrument stands, and writing instrument pouches. This market exhibits steady growth, driven by the increasing demand for high-quality, innovative writing tools that cater to various applications and user preferences. Factors such as the rise of remote work and e-learning, the proliferation of digital note-taking apps, and the growing popularity of art and creativity hobbies contribute to the market's expansion.
Additionally, advancements in technology lead to the development of eco-friendly, ergonomic, and multi-functional writing instruments that cater to the evolving needs of consumers. Overall, the market is a dynamic and vibrant industry that continues to evolve in response to changing consumer demands and technological innovations.

How is this Writing And Marking Instruments Industry segmented?

The writing and marking instruments industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Distribution Channel

  Offline
  Online


Application

  Students
  Working professionals
  Institutions


Material

  Plastic
  Wood
  Metal
  Recycled materials


Geography

  APAC

    China
    India
    Japan
    South Korea


  Europe

    France
    Germany
    Italy
    UK


  North America

    US
    Canada


  South America



  Middle East and Africa

By Distribution Channel Insights

The offline segment is estimated to witness significant growth during the forecast period. Writing and marking instruments continue to be essential tools for various industries and individuals. Offline stores, including stationery retail outlets, supermarkets and hypermarkets, convenience stores, and discount stores, remain the primary purchasing channel for consumers, particularly in emerging markets. Globalization has led to increased investments in traditional commerce, resulting in a higher number of hypermarkets and supermarkets worldwide. Manufacturers offer innovative writing and marking instruments through these channels at competitive prices. While e-commerce channels and internet infrastructure have high penetration globally, offline stores remain preferred for purchasing these items, especially in countries like India. Companies focus on organized retailing and visual merchandising in these markets, utilizing creative product banners, posters, and standees to attract consumers.

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The offline segment was valued at USD 18.83 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 58% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market size of various regions, Request Free Sample

The market in Asia Pacific (APAC) is experiencing notable expansion, driven by innovative product designs and features. Increasing disposable income levels in

Context

Dataset is based upon IMDb movies extensive dataset and is intended for prediction of movie rating based on personal preference. I have scraped my ratings with this script but felt that as a dataset it is too small for making good predictions and I'm missing a lot of information in the IMDb movies dataset by simply doing an inner join.

Content

The dataset contains ratings for average, worst, and best movies produced by each production company, each writer, director, and actor/actress. Worst and best movies do not contain outliers (movies that are rated more than 2 standard deviations apart from the mean rating for this production company (or writer, etc.). Each rating is calculated separately for each age and gender user bin.

Notebooks that generate dataset: https://www.kaggle.com/hurrdurrrderp/features-based-upon-imdb-movies-dataset https://www.kaggle.com/hurrdurrrderp/enrich-imdb-movies-datasets-with-vote-statistics

This statistic shows the results of a survey on the internet usage frequency for writing e-mails in Germany from 2013 to 2016. In 2016, there were about ** million internet users among the German-speaking population aged 14 years and older, who frequently used the internet for writing e-mails.

According to Cognitive Market Research, The Global Content Writing Services market is expected to grow at a compound annual growth rate (CAGR) of 5.50% from 2023 to 2030. Demand for SEO and Content Marketing Drive the Market Expansion

Businesses recognized the importance of SEO-optimized content for improved search engine visibility and content marketing strategies. SEO (Search Engine Optimisation) is the process of improving the visibility of content and websites in search engine results pages (SERPs). Businesses understand that when their content ranks higher in search results, they are more likely to attract organic (non-paid) traffic from users actively searching for relevant information or solutions. Organic traffic is valuable because it often represents genuinely interested users in a business's products or services. SEO-optimized content allows businesses to align their content with the specific keywords and phrases their target audience uses to search for information.

For instance, in August 2023, according to the Economic Times Business Verticals, SEO is becoming increasingly popular among Indian businesses, whether they are e-commerce brands like Flipkart, Nykaa, BigBasket, BFSI brands like Grow and ICICI bank, or travel brands like TripAdvisor, Makemytrip, and others. With sponsored channels like Google, Facebook, and others growing costlier by the day, marketers continuously seek strategies to expand efficiently while maintaining a consistent Return on Investment (ROI).

(Source:brandequity.economictimes.indiatimes.com/news/digital/seo-trends-every-marketer-should-know-in-2023/98550669)

Expansion of E-Commerce will Create Profitable Opportunities

E-commerce growth also emphasizes the importance of visual content. High-quality images, videos, and interactive media enhance the customer's understanding of the product. Content writers often collaborate with designers and photographers to create visual and written content that tells a cohesive and appealing story about the product. Detailed product descriptions and reviews improve user experience by reducing uncertainty. Customers who feel well-informed about a product are more likely to trust the e-commerce platform and complete a purchase. Quality content can help alleviate doubts and reduce the likelihood of returns due to mismatched expectations.

For instance, in July 2021, According to Indian Retailer, From high-end luxury apparel and jewelry to ordinary groceries, e-commerce has become the future of all forms of shopping. The ease of being anywhere and having your selected items or services delivered to your door with the touch of a finger. India is a rapidly growing/developing country with a large population and unexplored markets. Most of the population is of working-class/aspirational age, providing greater opportunities for the rest of the globe to profit and enter the market. With the changing times exacerbated by the pandemic, e-commerce has grown even more popular and accessible to all demographics.

(Source:www.indianretailer.com/article/whats-hot/trends/E-commerce-Tools-to-Lead-the-way-for-a-more-Inclusive-Global-Economy-A-Brighter-Future-for-Exporters.a6323)

The factors are restricting the Content Writing Services market's growth

Economic Challenges Caused by the Pandemic Restrict Market Growth

Economic challenges caused by the pandemic led some businesses to cut down on marketing budgets, impacting demand for content writing services. During economic uncertainty, businesses might focus on short-term goals like cash flow management and cost reduction. Marketing efforts perceived as long-term investments, such as content creation, maybe deprioritized in favor of more immediate needs.

Trend Factor for the Content Writing Servicesl Market

As companies in various sectors realize the importance of high-quality, SEO-optimized content for enhancing online visibility, engagement, and conversions, the market for content writing services is growing quickly. The increasing demand for digital marketing, brand storytelling, and tailored user experiences across e-commerce sites, social media, blogs, and websites drives demand. To access a variety of skill sets and scale effectively, businesses are progressively outsourcing content production to specialized agencies and freelance authors. AI-assisted writing tools, multilingual content strategies, and data-driven content planni...

The Scientific Author's Writing Style Corpus 2017 is composed by 66 experiments in which three evaluators ranked four short text snippets ('targets') with regard to their similarity in writing style to one other snippet ('source'). The snippets were selected from the introduction of scientific articles written by single authors. Additionally, the snippets were manually checked for not having any clear hint on authorship for the evaluators.

Technical and professional communication (TPC) scholars have called for increased attention to creative thinking in the field’s writing practices. This article examines posts about creativity on two social networking websites and generates challenges, skills, and practices relevant to posters’ creative work.

This is the dataset for the shared task on Multi-Author Writing Style Analysis PAN@CLEF2023. Please consult the task's page for further details on the format, the dataset's creation, and links to baselines and utility code.

Task: We ask participants to solve the following intrinsic style change detection task: for a given text, find all positions of writing style change on the paragraph-level (i.e., for each pair of consecutive paragraphs, assess whether there was a style change). The simultaneous change of authorship and topic will be carefully controlled and we will provide participants with datasets of three difficulty levels:

1. Easy: The paragraphs of a document cover a variety of topics, allowing approaches to make use of topic information to detect authorship changes.
2. Medium: The topical variety in a document is small (though still present) forcing the approaches to focus more on style to effectively solve the detection task.
3. Hard: All paragraphs in a document are on the same topic.

All documents are provided in English and may contain an arbitrary number of style changes. However, style changes may only occur between paragraphs (i.e., a single paragraph is always authored by a single author and contains no style changes).

Data: To develop and then test your algorithms, three datasets including ground truth information are provided (dataset1 for the easy task, dataset2 for the medium task, and dataset3 for the hard task).

Each dataset is split into three parts:

1. training set: Contains 70% of the whole dataset and includes ground truth data. Use this set to develop and train your models.
2. validation set: Contains 15% of the whole dataset and includes ground truth data. Use this set to evaluate and optimize your models.
3. test set: Contains 15% of the whole dataset, no ground truth data is given. This set is used for evaluation.

You are free to use additional external data for training your models. However, we ask you to make the additional data utilized freely available under a suitable license.

Versioning:

• 1.0: initial upload

The need for much better preparation of faculty on reading arises from evidence in three areas: students’ problems with critical reading and thinking, lack of extant faculty preparation in reading pedagogy, and an absence of focused faculty development to improve student reading. Many recent studies show clearly that students do not read as well as they might, online and off. Both quantitative studies like the ACT’s data on over a million students in the U.S. and Canada and qualitative studies like the Citation Project show that half or more of current college students lack the skills to analyze, synthesize, evaluate and use material they have read for their own purposes, in school and beyond. Critical and analytical skills are particularly lacking as shown in untimed tests by Stanford University researchers of students’ ability to evaluate online material. To address students’ needs, clear goals for faculty development can help. Pre-service faculty should be trained in the psycholinguistics of reading as well as in teaching techniques. In-service faculty should have access to professional development to understand students’ reading needs and address them more effectively. Collaborations across campus with library faculty can also provide useful approaches to building students’ online critical reading skills.

Creativity is core to being human. Generative AI—made readily available by powerful large language models (LLMs)—holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on generative AI ideas. We study the causal impact of generative AI ideas on the production of short stories in an online experiment where some writers obtained story ideas from an LLM. We find that access to generative AI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers. However, generative AI-enabled stories are more similar to each other than stories by humans alone. These results point to an increase in individual creativity at the risk of losing collective novelty. This dynamic resembles a social dilemma: with generative AI, writers are individually better off, but collectively a narrower scope of novel content is produced. Our results have implications for researchers, policy-makers, and practitioners interested in bolstering creativity. Methods This dataset is based on a pre-registered, two-phase experimental online study. In the first phase of our study, we recruited a group of N=293 participants (“writers”) who are asked to write a short, eight sentence story. Participants are randomly assigned to one of three conditions: Human only, Human with 1 GenAI idea, and Human with 5 GenAI ideas. In our Human only baseline condition, writers are assigned the task with no mention of or access to GenAI. In the two GenAI conditions, we provide writers with the option to call upon a GenAI technology (OpenAI’s GPT-4 model) to provide a three-sentence starting idea to inspire their own story writing. In one of the two GenAI conditions (Human with 5 GenAI ideas), writers can choose to receive up to five GenAI ideas, each providing a possibly different inspiration for their story. After completing their story, writers are asked to self-evaluate their story on novelty, usefulness, and several emotional characteristics. In the second phase, the stories composed by the writers are then evaluated by a separate group of N=600 participants (“evaluators”). Evaluators read six randomly selected stories without being informed about writers being randomly assigned to access GenAI in some conditions (or not). All stories are evaluated by multiple evaluators on novelty, usefulness, and several emotional characteristics. After disclosing to evaluators whether GenAI was used during the creative process, we ask evaluators to rate the extent to which ownership and hypothetical profits should be split between the writer and the AI. Finally, we elicit evaluators’ general views on the extent to which they believe that the use of AI in producing creative output is ethical, how story ownership and hypothetical profits should be shared between AI creators and human creators, and how AI should be credited in the involvement of the creative output. The data was collected on the online study platform Prolific. The data was then cleaned, processed and analyzed with Stata. For the Writer Study, of the 500 participants who began the study, 169 exited the study prior to giving consent, 22 were dropped for not giving consent, and 13 dropped out prior to completing the study. Three participants in the Human only condition admitted to using GenAI during their story writing exercise and—as per our pre-registration—they were therefore dropped from the analysis, resulting in a total number of writers and stories of 293. For the Evaluator Study, each evaluator was shown 6 stories (2 stories from each topic). The evaluations associated with the writers who did not complete the writer study and those in the Human only condition who acknowledged using AI to complete the story were dropped. Thus, there are a total of 3,519 evaluations of 293 stories made by 600 evaluators. Four evaluations remained for five evaluators, five evaluations remained for 71, and all six remained for 524 evaluators.

The Ink and Identity dataset presents a comprehensive collection of 374 handwritten characters consisting of all lowercase and 12 uppercase alphabets, including a few digits, gathered from participants spanning diverse age groups. Utilized A4-size unruled paper for writing. As of now, the datasets available are on ruled paper containing only a single line or single word, which limits analyzing data in graphology, and all the datasets do not contain all alphabets, like numbers and characters. These limitations are covered in this dataset.

The dataset was collected using two methods: an in-person (300 images) and a web form (150 images). Total dataset: 450 images. The dataset comprises two folders, in-person and web form, and the image was captured in a 1:1 ratio on an iPhone 14 Plus 12MP. Web form images were instructed to be captured in a 1:1 ratio, and each one has been pre-processed with a unique ID like “001” and stored in .jpg format.

Additionally, researchers can employ this dataset as a reference standard for age-based handwriting analysis, personality trait prediction, AI/ML model training, cognitive and neurological research, and health and mental well-being.

It is presumed that there is a dearth of research on how best to acquire academic writing online at this critical juncture. To fill this gap, 126 Saudi students were surveyed online, and 20 of them were chosen to complete a semi-structured interview. Five main obstacles were identified: lack of active student-teacher communication, technological difficulties, time allotted to perform tasks, insufficient academic assistance, and issues related to feedback from the teacher. Four main mechanisms for coping with these obstacles have also been identified: flexibility regarding limiting the time allotted to complete tasks online; choosing a student in each group to present problems regarding online academic writing; making initiatives to develop students' awareness of dealing with educational tools in academic writing; and activating feedback from teachers and peers. Two benefits of online writing education were found: extensive use of educational materials and online teaching platforms, and available opportunities to practice academic writing online.

The number of the internet authors and writers in China had expanded by almost **** times from 2015 to 2024. In 2024, there were around ** million online publication authors in China, up from around ** million authors in 2023.

Title: A review of educational dialogue strategies to improve academic writing skills. Methods Search strategy: In April 2017, we searched the following online databases: Web of Science, EMBASE, ERIC, CINAHL, PsycINFO and Google Scholar. At first, we searched on ‘feed up’, ‘feed back’ and ‘feed forward’ but this strategy did not produce enough suitable articles so we added the term ‘feedback’. To minimize the chance of missing relevant articles, the scope was broad and included the following string of keywords and Boolean operators: ‘dialogue OR discussion OR conversation’ AND ‘feedback’ AND ‘writing’. Inclusion and exclusion criteria: The electronic literature search was limited to English full-text studies published since 1990. Only articles that met the following inclusion criteria were selected: peer-reviewed, empirical studies with a particular focus on academic writing, published in the field of academic education, including all disciplines that discussed interventions employing face-to-face feedback dialogue. We excluded literature reviews and case studies, studies that did not focus on academic writing or studies that only addressed the online, digital or ICT aspects of the main topics. Data extraction: The first author performed the search, yielding 1508 records. After removal of duplicates, the titles and abstracts of the remaining records (N=1182) were screened on the inclusion and exclusion criteria. The resulting records (N=304) contained the topics ‘dialogue’, ‘feedback’ and ‘writing’. Further eligibility was subsequently assessed by reading the full articles on this list. After this phase, 102 articles remained for consideration. Of those, only articles that discussed a feedback intervention involving ‘face-to-face dialogue’ before submission of an academic writing assignment were included. As a result, the final review was based on 19 studies (Figure 1). Data analysis: We scrutinized each intervention for the presence of feed-up, feed-back and feed-forward information (Black and Wiliam, 2009; Hattie and Timperley, 2007; Jonsson, 2012; Nicol and Macfarlane-Dick, 2006; Price et al., 2010; Rae and Cochrane, 2008). For the purpose of this review, we considered educational strategies such as assessment criteria, exemplars, worked examples and training (e.g. instructions or workshops) as expressions of feed-up information; written lecturer feedback and written peer review/assessments as feed-back information; and instructions to revise draft products as feed-forward information. In the next step, we checked which and how many participants were involved in the dialogue (student-student, lecturer-student or a combination of both). Since the studies did not describe the content of the face-to-face dialogues, we did not categorize them in terms of feed up, feed back and/or feed forward. Third, we operationalized intervention outcomes in terms of students’ perceptions of the intervention, their marks and by text/dialogue analysis. Finally, in assessing the effectiveness of each intervention, we took into account the methodological characteristics of each study, including their study design, data sources and data collection methods (Creswell, 2014).

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.

DEFRA Family Food database:
This is available as a separate Access download zip file for those users who require it.

Latest edition information:
For the fifth edition (November 2023), the DEFRA Family Food Database has been updated; one case has been removed.