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  • Digital Humanities and Cultural Heritage
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Spallone, Roberta;
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Publications Open Re...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Publications Open Re...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Panda, Subhajit; Kaur, Navkiran;

    This paper delves into the transformative intersection of emerging technologies and digital libraries, illuminating a path toward an enriched and accessible knowledge landscape. Focusing on Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Augmented Reality (AR), and Virtual Reality (VR), the study explores how these technologies redefine digital library experiences. AI and ML algorithms empower intuitive content curation and recommendation, reshaping the way users interact with digital resources. NLP bridges the gap between human language intricacies and digital systems, enhancing search functionalities and making information retrieval seamless. AR overlays digital information onto the physical world, expanding interactive learning possibilities, while VR immerses users in virtual realms, revolutionizing educational paradigms. The paper critically examines the practical integration of these technologies, ensuring digital libraries not only preserve vast knowledge repositories but also present information in engaging and accessible formats. Through AI-driven metadata generation and content tagging, digital libraries are systematically organized and enriched, amplifying search accuracy. These innovations not only preserve the past but also illuminate a future where knowledge is universally accessible, fostering curiosity, learning, and exploration. The study not only theoretically explores the potential of these technologies but also delves into the perceptions of practical library users, ensuring a user-centric approach in shaping the digital libraries of tomorrow. This research contributes significantly to the evolving landscape of digital libraries, paving the way for inclusive, immersive, and engaging knowledge experiences for diverse users worldwide.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Kévin Roger; David Fiala;

    Programme des journées 2023 du cluster 6 Biblissima+ (3-4 octobre 2023, Bibliothèque musicale La Grange-Fleuret, Paris)

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Blázquez Jiménez, Julia;

    El patrimonio cultural en las últimas décadas ha ido incorporando nuevas herramientas que facilitan su gestión, investigación y difusión. En este sentido, las nuevas tecnologías han supuesto un antes y un después en el mundo cultural permitiendo la “democratización de la cultura” ya que además, gracias a ellas se establece un vínculo entre los bienes culturales y las personas, desarrollando un sentimiento de identidad. Hoy en día, el uso de este tipo de tecnología en el campo patrimonial no resulta novedoso ya que su eficacia está más que probada; sin embargo, la forma en la que esta se emplea en ciertos proyectos sí puede llegar a serlo debido a los resultados que arroja y a la influencia que alcanza. Este es el caso de la ciudad castellanoleonesa de Ávila, cuyo proyecto SHCity (Smart Heritage City) ha destacado por ser pionero en varios aspectos como es el área de aplicación y la herramienta desarrollada. Un estudio en profundidad así como comparado del mismo, muestra el verdadero peso que este proyecto ha tenido en el ámbito cultural internacional debido a la forma de aplicación de tecnologías de información geográfica y comunicación para la gestión y difusión del patrimonio local.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Docta Complutensearrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Docta Complutense
    Other ORP type . 2023
    License: CC BY NC SA
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Docta Complutensearrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Docta Complutense
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  • Authors: Office for National Statistics;

    Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.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 2022The 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. The affected datasets have now been updated. 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 2022APS Well-Being DatasetsFrom 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.APS disability variablesOver time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage. End User Licence and Secure Access APS dataUsers 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. Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS. Multi-stage stratified random sample Face-to-face interview Telephone interview

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  • Authors: Office for National Statistics;

    Abstract copyright UK Data Service and data collection copyright owner.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 filesThe 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 WeightingThe 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. Main Topics:The five-quarter longitudinal datasets include a subset of the most commonly used variables from the Quarterly Labour Force Survey (QLFS), covering the main areas of the survey. See documentation for details Compilation or synthesis of existing material the datasets were created from existing QLFS data. They do not contain all records, but only those of respondents of working age who have responded to the survey in all the periods being linked. The data therefore comprise approximately one third of all QLFS variables. Cases were linked using the QLFS panel design.

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  • Authors: ScotCen Social Research;

    Abstract copyright UK Data Service and data collection copyright owner.The Scottish Health Survey (SHeS) series was established in 1995. Commissioned by the Scottish Government Health Directorates, the series provides regular information on aspects of the public's health and factors related to health which cannot be obtained from other sources. The SHeS series was designed to:estimate the prevalence of particular health conditions in Scotland;estimate the prevalence of certain risk factors associated with these health conditions and to document the pattern of related health behaviours;look at differences between regions and between subgroups of the population in the extent of their having these particular health conditions or risk factors, and to make comparisons with other national statistics for Scotland and England;monitor trends in the population's health over time;make a major contribution to monitoring progress towards health targets.Each survey in the series includes a set of core questions and measurements (height and weight and, if applicable, blood pressure, waist circumference, urine and saliva samples), plus modules of questions on specific health conditions that vary from year to year. Each year the core sample has also been augmented by an additional boosted sample for children. Since 2008 NHS Health Boards have also had the opportunity to boost the number of adult interviews carried out in their area. The Scottish Government Scottish Health Survey webpages contain further information about the series, including latest news and publications. Latest edition informationFor the second edition (April 2023), three 'Intake24' data files have been added to the study, covering foods, nutrients and additional variables. The documentation has been updated and augmented accordingly. Main Topics: The Scottish Health Survey 2021 (SHeS21) is the seventeenth survey in the series. Data collection involved a main computer-assisted telephone interview (CATI), and an online or paper self-completion questionnaire. As interviews were conducted by telephone, no height and weight measurements or biological measures could be taken. Topics covered included household composition, demographics (including ethnicity, religion, educational background and economic activity), general health including caring, mental health and wellbeing, cardiovascular disease, respiratory disease and asthma, physical activity, eating habits, fruit and veg consumption, smoking and drinking, gambling, dental health, discrimination and harassment, social capital and self-reported height and weight measurements. Adults aged 16 and over were also invited to complete an online recall using Intake24.The study also includes combined datasets covering 2017/2018/2019/2021 and 2019/2021. They contain information from the household questionnaires, main individual schedules and self-completions. The combined datasets have been provided to give a larger base for the analysis of variables. The individual year datasets should be used for the analysis of individual years, including comparisons between years. Multi-stage stratified random sample

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  • Authors: Office for National Statistics;

    Abstract copyright UK Data Service and data collection copyright owner.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.New reweighting policyFollowing the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published towards the end of 2020/early 2021. 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. Additional data derived from the QLFS The Archive also holds further QLFS series: End User Licence (EUL) quarterly data; Secure Access datasets; household datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets. Variables DISEA and LNGLST Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018. An article explaining the quality assurance investigations that have been conducted so far is available on the ONS Methodology webpage. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk. Occupation data for 2021 and 2022 data filesThe 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 WeightingThe 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. Main Topics:The two-quarter longitudinal datasets include a subset of the most commonly used variables from the Quarterly Labour Force Survey (QLFS), covering the main areas of the survey. See documentation for details Compilation or synthesis of existing material the datasets were created from existing LFS data. They do not contain all records, but only those of respondents of working age who have responded to the survey in all the periods being linked. The data therefore comprise a subset of variables representing approximately one third of all QLFS variables. Cases were linked using the QLFS panel design.

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  • Authors: Office for National Statistics;

    Abstract copyright UK Data Service and data collection copyright owner.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 filesThe 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 WeightingThe 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. Main Topics:The five-quarter longitudinal datasets include a subset of the most commonly used variables from the Quarterly Labour Force Survey (QLFS), covering the main areas of the survey. See documentation for details Compilation or synthesis of existing material the datasets were created from existing QLFS data. They do not contain all records, but only those of respondents of working age who have responded to the survey in all the periods being linked. The data therefore comprise approximately one third of all QLFS variables. Cases were linked using the QLFS panel design.

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  • Authors: Office for National Statistics;

    Abstract copyright UK Data Service and data collection copyright owner.BackgroundThe 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.Household datasetsUp to 2015, the LFS household datasets were produced twice a year (April-June and October-December) from the corresponding quarter's individual-level data. From January 2015 onwards, they are now produced each quarter alongside the main QLFS. The household datasets include all the usual variables found in the individual-level datasets, with the exception of those relating to income, and are intended to facilitate the analysis of the economic activity patterns of whole households. It is recommended that the existing individual-level LFS datasets continue to be used for any analysis at individual level, and that the LFS household datasets be used for analysis involving household or family-level data. From January 2011, a pseudonymised household identifier variable (HSERIALP) is also included in the main quarterly LFS dataset instead.Change to coding of missing values for household seriesFrom 1996-2013, all missing values in the household datasets were set to one '-10' category instead of the separate '-8' and '-9' categories. For that period, the ONS introduced a new imputation process for the LFS household datasets and it was necessary to code the missing values into one new combined category ('-10'), to avoid over-complication. This was also in line with the Annual Population Survey household series of the time. The change was applied to the back series during 2010 to ensure continuity for analytical purposes. From 2013 onwards, the -8 and -9 categories have been reinstated.LFS DocumentationThe documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each volume alongside the appropriate questionnaire for the year concerned. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance page before commencing analysis.Additional data derived from the QLFSThe Archive also holds further QLFS series: End User Licence (EUL) quarterly datasets; Secure Access datasets (see below); two-quarter and five-quarter longitudinal datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.End User Licence and Secure Access QLFS Household datasetsUsers should note that there are two discrete versions of the QLFS household datasets. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. Secure Access household datasets for the QLFS are available from 2009 onwards, and include additional, detailed variables not included in the standard EUL versions. Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: geography; date of birth, including day; education and training; household and family characteristics; employment; unemployment and job hunting; accidents at work and work-related health problems; nationality, national identity and country of birth; occurrence of learning difficulty or disability; and benefits. For full details of variables included, see data dictionary documentation. The Secure Access version (see SN 7674) has 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.Changes to variables in QLFS Household EUL datasetsIn order to further protect respondent confidentiality, ONS have made some changes to variables available in the EUL datasets. From July-September 2015 onwards, 4-digit industry class is available for main job only, meaning that 3-digit industry group is the most detailed level available for second and last job.Review of imputation methods for LFS Household data - changes to missing valuesA review of the imputation methods used in LFS Household and Family analysis resulted in a change from the January-March 2015 quarter onwards. It was no longer considered appropriate to impute any personal characteristic variables (e.g. religion, ethnicity, country of birth, nationality, national identity, etc.) using the LFS donor imputation method. This method is primarily focused to ensure the 'economic status' of all individuals within a household is known, allowing analysis of the combined economic status of households. This means that from 2015 larger amounts of missing values ('-8'/-9') will be present in the data for these personal characteristic variables than before. Therefore if users need to carry out any time series analysis of households/families which also includes personal characteristic variables covering this time period, then it is advised to filter off 'ioutcome=3' cases from all periods to remove this inconsistent treatment of non-responders. Occupation data for 2021 and 2022 data filesThe 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. Main Topics:The LFS household datasets cover:characteristics of the household: number of people of working age; number of people over working age; number of children aged 0 to 4; number of children aged 5 to 15; number of dependent children (i.e. those in full-time education) aged 16 to 18economic activity in the household: number of people in employment; number of people in full-time employment; number of people in part-time employment; unemployed; economically inactive; students; sick or disabled; economically inactive but would like to work and are not seeking work because they do not believe there is work available ('discouraged workers'); care of dependants Sample drawn from main LFS data. See documentation for details. Compilation/Synthesis

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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Spallone, Roberta;
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Panda, Subhajit; Kaur, Navkiran;

    This paper delves into the transformative intersection of emerging technologies and digital libraries, illuminating a path toward an enriched and accessible knowledge landscape. Focusing on Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Augmented Reality (AR), and Virtual Reality (VR), the study explores how these technologies redefine digital library experiences. AI and ML algorithms empower intuitive content curation and recommendation, reshaping the way users interact with digital resources. NLP bridges the gap between human language intricacies and digital systems, enhancing search functionalities and making information retrieval seamless. AR overlays digital information onto the physical world, expanding interactive learning possibilities, while VR immerses users in virtual realms, revolutionizing educational paradigms. The paper critically examines the practical integration of these technologies, ensuring digital libraries not only preserve vast knowledge repositories but also present information in engaging and accessible formats. Through AI-driven metadata generation and content tagging, digital libraries are systematically organized and enriched, amplifying search accuracy. These innovations not only preserve the past but also illuminate a future where knowledge is universally accessible, fostering curiosity, learning, and exploration. The study not only theoretically explores the potential of these technologies but also delves into the perceptions of practical library users, ensuring a user-centric approach in shaping the digital libraries of tomorrow. This research contributes significantly to the evolving landscape of digital libraries, paving the way for inclusive, immersive, and engaging knowledge experiences for diverse users worldwide.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Kévin Roger; David Fiala;

    Programme des journées 2023 du cluster 6 Biblissima+ (3-4 octobre 2023, Bibliothèque musicale La Grange-Fleuret, Paris)

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    Authors: Blázquez Jiménez, Julia;

    El patrimonio cultural en las últimas décadas ha ido incorporando nuevas herramientas que facilitan su gestión, investigación y difusión. En este sentido, las nuevas tecnologías han supuesto un antes y un después en el mundo cultural permitiendo la “democratización de la cultura” ya que además, gracias a ellas se establece un vínculo entre los bienes culturales y las personas, desarrollando un sentimiento de identidad. Hoy en día, el uso de este tipo de tecnología en el campo patrimonial no resulta novedoso ya que su eficacia está más que probada; sin embargo, la forma en la que esta se emplea en ciertos proyectos sí puede llegar a serlo debido a los resultados que arroja y a la influencia que alcanza. Este es el caso de la ciudad castellanoleonesa de Ávila, cuyo proyecto SHCity (Smart Heritage City) ha destacado por ser pionero en varios aspectos como es el área de aplicación y la herramienta desarrollada. Un estudio en profundidad así como comparado del mismo, muestra el verdadero peso que este proyecto ha tenido en el ámbito cultural internacional debido a la forma de aplicación de tecnologías de información geográfica y comunicación para la gestión y difusión del patrimonio local.

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  • Authors: Office for National Statistics;

    Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.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 2022The 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. The affected datasets have now been updated. 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 2022APS Well-Being DatasetsFrom 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.APS disability variablesOver time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage. End User Licence and Secure Access APS dataUsers 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. Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS. Multi-stage stratified random sample Face-to-face interview Telephone interview

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  • Authors: Office for National Statistics;

    Abstract copyright UK Data Service and data collection copyright owner.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 filesThe 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 WeightingThe 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. Main Topics:The five-quarter longitudinal datasets include a subset of the most commonly used variables from the Quarterly Labour Force Survey (QLFS), covering the main areas of the survey. See documentation for details Compilation or synthesis of existing material the datasets were created from existing QLFS data. They do not contain all records, but only those of respondents of working age who have responded to the survey in all the periods being linked. The data therefore comprise approximately one third of all QLFS variables. Cases were linked using the QLFS panel design.

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  • Authors: ScotCen Social Research;

    Abstract copyright UK Data Service and data collection copyright owner.The Scottish Health Survey (SHeS) series was established in 1995. Commissioned by the Scottish Government Health Directorates, the series provides regular information on aspects of the public's health and factors related to health which cannot be obtained from other sources. The SHeS series was designed to:estimate the prevalence of particular health conditions in Scotland;estimate the prevalence of certain risk factors associated with these health conditions and to document the pattern of related health behaviours;look at differences between regions and between subgroups of the population in the extent of their having these particular health conditions or risk factors, and to make comparisons with other national statistics for Scotland and England;monitor trends in the population's health over time;make a major contribution to monitoring progress towards health targets.Each survey in the series includes a set of core questions and measurements (height and weight and, if applicable, blood pressure, waist circumference, urine and saliva samples), plus modules of questions on specific health conditions that vary from year to year. Each year the core sample has also been augmented by an additional boosted sample for children. Since 2008 NHS Health Boards have also had the opportunity to boost the number of adult interviews carried out in their area. The Scottish Government Scottish Health Survey webpages contain further information about the series, including latest news and publications. Latest edition informationFor the second edition (April 2023), three 'Intake24' data files have been added to the study, covering foods, nutrients and additional variables. The documentation has been updated and augmented accordingly. Main Topics: The Scottish Health Survey 2021 (SHeS21) is the seventeenth survey in the series. Data collection involved a main computer-assisted telephone interview (CATI), and an online or paper self-completion questionnaire. As interviews were conducted by telephone, no height and weight measurements or biological measures could be taken. Topics covered included household composition, demographics (including ethnicity, religion, educational background and economic activity), general health including caring, mental health and wellbeing, cardiovascular disease, respiratory disease and asthma, physical activity, eating habits, fruit and veg consumption, smoking and drinking, gambling, dental health, discrimination and harassment, social capital and self-reported height and weight measurements. Adults aged 16 and over were also invited to complete an online recall using Intake24.The study also includes combined datasets covering 2017/2018/2019/2021 and 2019/2021. They contain information from the household questionnaires, main individual schedules and self-completions. The combined datasets have been provided to give a larger base for the analysis of variables. The individual year datasets should be used for the analysis of individual years, including comparisons between years. Multi-stage stratified random sample

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  • Authors: Office for National Statistics;

    Abstract copyright UK Data Service and data collection copyright owner.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.New reweighting policyFollowing the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published towards the end of 2020/early 2021. 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. Additional data derived from the QLFS The Archive also holds further QLFS series: End User Licence (EUL) quarterly data; Secure Access datasets; household datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets. Variables DISEA and LNGLST Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018. An article explaining the quality assurance investigations that have been conducted so far is available on the ONS Methodology webpage. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk. Occupation data for 2021 and 2022 data filesThe 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 WeightingThe 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. Main Topics:The two-quarter longitudinal datasets include a subset of the most commonly used variables from the Quarterly Labour Force Survey (QLFS), covering the main areas of the survey. See documentation for details Compilation or synthesis of existing material the datasets were created from existing LFS data. They do not contain all records, but only those of respondents of working age who have responded to the survey in all the periods being linked. The data therefore comprise a subset of variables representing approximately one third of all QLFS variables. Cases were linked using the QLFS panel design.

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