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100 Research products

  • COVID-19
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  • 2013-2022
  • Other ORP type
  • COVID-19
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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: Kelder, J.M.;

    From an Ancient Egyptian plague to the Black Death and Spanish flu, epidemics have often spurred societal transformations. Understanding why can help us create a better world after covid-19

    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 NARCISarrow_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
    NARCIS
    Other ORP type . 2022
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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 NARCISarrow_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 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: Dronseikaitė, Roberta;

    Every teacher faces the challenge of getting students interested. This problem was exacerbated especially after the covid-19 pandemic, during which students learned remotely and became no longer accustomed to long-term focus, gaining many knowledge gaps. After returning to school after almost two years of learning from home, the problem became especially apparent even among students who were really interested - they simply could not concentrate. Working with students revealed another problem - basic learning tools have provided only one perspective on history, while it has more than one. The solution to these problems was to show Soviet-era newspapers in class that contradict the information in the textbook. These sources were taken from the electronic cultural heritage source database www.epaveldas.lt. The main source was the pro-Russian newspaper „Tiesa“ and the newspaper „Dirva“ was used on the last lesson. The articles were chosen to generate the most emotion for the students or to provide a basis for discussion. This solution paid off, as almost twice as many students began to take part in the lessons, and the discussions, which were sometimes stimulated by the children themselves, intensified. One student even brought his own collection of Soviet medals and newspaper articles. This method of lessons was acceptable for the children, because they were interested in finding out what the articles were like at the time, they liked to discuss possible different approaches to history, they remembered the material in the lesson better. Positive feedback was received not only from the students but also from their parents. The success of the textbook section was crowned by the students' reporting work, in which they done more than were asked to, and were able to compare the information provided in the textbook with the new information. Kiekvienas mokytojas susiduria su problema, kaip sudominti mokinius. Ši problema ypač paaštrėjo po covid-19 pandemijos, kurios metu mokiniai mokėsi nuotoliniu būdu ir atprato ilgai koncentruoti dėmesį, įgijo daug spragų. Grįžus į mokyklas po beveik dviejų metų mokymosi iš namų ši problema tapo itin akivaizdi netgi tarp mokinių, kuriems iš tiesų buvo įdomu – jie paprasčiausiai negebėjo koncentruoti dėmesio. Dirbant su mokiniais išaiškėjo dar viena problema – pagrindinės mokymosi priemonės pateikė tik vieną požiūrį į istoriją, kuri tuo tarpu turi daugiau negu vieną perspektyvą. Šių problemų sprendimo būdu buvo pasirinkta pamokose demonstruoti sovietmečiu leistus laikraščius, kurie prieštarauja vadovėlyje pateiktai informacijai. Šie šaltiniai buvo paimti iš elektroninės kultūros paveldo šaltinių bazės www.epaveldas.lt. Pagrindiniu šaltiniu buvo prorusiškas laikraštis „Tiesa“, o paskutinę pamoką buvo panaudotas laikraštis „Dirva“. Straipsniai buvo parenkami tokie, kurie mokiniams sukeltų daugiausiai emocijų arba teiktų pagrindą diskusijoms. Šis sprendimo būdas pasiteisino, nes į pamokas pradėjo įsitraukti kone dvigubai daugiau mokinių, daug intensyviau vyko diskusijos, kurias kartais patys vaikai ir paskatindavo. Vienas mokinys netgi atsinešė savo turimą sovietinių medalių ir straipsnių kolekciją. Vaikams toks pamokų būdas buvo priimtinas, nes jiems buvo įdomu sužinoti, kokie buvo tais laikais spausdinami straipsniai, patiko diskutuoti apie galimus skirtingus požiūrius į istoriją, jie geriau įsiminė pamokoje pateikiamą medžiagą. Teigiamų atsiliepimų buvo sulaukta ne tik iš mokinių, bet ir iš jų tėvų. Vadovėlio skyriaus sėkmę vainikavo mokinių atsiskaitymo darbai, kuriuose jie padarė daugiau, negu buvo prašoma, gebėjo palyginti vadovėlyje pateiktą ir naujai sužinotą informaciją.

    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/ Vytautas Magnus Univ...arrow_drop_down
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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/ Vytautas Magnus Univ...arrow_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 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

    Covid-19 je na svetovni ravni okrepil državni nadzor, vse pogosteje moramo posegati po osebnih dokumentih, pri čemer je osrednji subjekt identifikacije postal človeški obraz – na katerem temeljijo tudi najnovejše tehnologije nadzora.

    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 Repository of Univer...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 Repository of Univer...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
    Authors: Oliveira, Daniel Filipe Nunes;

    Os recursos humanos são um dos maiores ativos de qualquer empresa visto que estes providenciam a possibilidade para realizar produtos ou serviços. A revolução tecnológica, a pandemia do COVID-19 e a competitividade do mercado laboral contribuem para um clima de incerteza e permanente renovação de staff dentro das empresas. Isto significa permanências curtas dos funcionários, mas, mais importante, leva a que os repositórios de competências de uma empresa possam ficar, por vezes, empobrecidos e, deste modo, pode pôr em causa a execução dos produtos e serviços pelos quais uma empresa é reconhecida. Recentemente, têm surgido plataformas online com o objetivo de atrair, designar funções, treinar, mas sobretudo reter os talentos, tudo isto só é alcançado revendo e melhorando permanentemente as competências de cada recurso. Estas plataformas utilizam quase sempre mecanismos de inteligência artificial. Neste trabalho, apresenta-se uma revisão literária das técnicas de inteligência artificial que podem estar presentes nestas plataformas. Esta revisão literária apresenta 4 questões de investigação que, juntas, respondem a uma questão de investigação mais ampla: “Como implementar uma destas plataformas, sendo inovador?”. A primeira questão é relativa aos processos automatizados de leitura e extração de informação de currículos. A segunda questão é relativa à inferência de competências, através de outras competências ou informações previamente extraídas de currículos, e é neste ponto que se pretende inovar quando em comparação com as soluções existentes. A terceira questão é relativa à existência de sistemas multiagente que associam recursos a tarefas de uma forma otimizada. Por último, a quarta questão de investigação é relativa à aplicabilidade de algoritmos genéticos também na associação de funcionários a tarefas numa empresa. Os resultados da revisão literária mostram que as 4 questões foram respondidas com sucesso. A revisão literária seguiu a metodologia PRISMA, tendo sido realizada a pesquisa em 2 fontes distintas. Foram selecionados 44 artigos, de entre os 27361 encontrados, que foram filtrados através de controlo de qualidade ou leituras rápidas de títulos e resumos. Por último, tendo em consideração a revisão literária realizada, foi implementado e testado um sistema que se assemelha a um mercado de talentos, com inferência de competências. O emparelhamento entre recursos e tarefas foi feito através de um sistema multiagente e de um algoritmo genético. Foi, ainda, realizada uma análise de usabilidade. Desta forma, podemos concluir que o resultado final foi atingido, pois quer a revisão literária quer o sistema implementado foram atividades realizadas com sucesso. Human resources are one of the biggest assets in companies since they possess the skills and expertise to deliver products and services. The COVID-19 pandemic and the technological revolution both increased employee turnover to a level where companies can hardly keep up with the pace, leading to worst talents management. Recently, online platforms, known as talent markets, have become more and more popular and they have the main objective to attract, designate tasks, train and, above all, retain existing employees. Most of these online platforms use artificial intelligence. This work presents a systematic review in artificial intelligence techniques that allow automatization of the processes of talent management. Four research questions were elaborated that, altogether, answer a broader research question which is: “How to implement an innovative talent market?”. The first question is relative to the automatized processes of information extraction out of resumes. The second question is related to the skill inference process, it is here that innovation is expected when comparing with existing solutions. In the third and fourth research questions, literature over multiagent systems and genetic algorithms dedicated to the optimization of task execution is provided. In the review, 44 papers were selected out of an initial set of 27361. In addition to the systematic review, a system is also proposed that resembles already existing solutions. Innovation is achieved by exploring skill inference, in addition to using already existing techniques in the area of information recognition. A multiagent system and genetic algorithms were also developed for an optimized task and employee pairing. This document also presents various tests to the system including a usability analysis. All in all, the outcome was rewarding, the systematic literature review was a success and so was the proposed solution.

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  • Authors: Department for Work and Pensions; NatCen Social Research;

    Abstract copyright UK Data Service and data collection copyright owner.The Family Resources Survey (FRS) has been running continuously since 1992 to meet the information needs of the Department for Work and Pensions (DWP). It is almost wholly funded by DWP. The FRS collects information from a large, and representative sample of private households in the United Kingdom (prior to 2002, it covered Great Britain only). The interview year runs from April to March.The focus of the survey is on income, and how much comes from the many possible sources (such as employee earnings, self-employed earnings or profits from businesses, and dividends; individual pensions; state benefits, including Universal Credit and the State Pension; and other sources such as savings and investments). Specific items of expenditure, such as rent or mortgage, Council Tax and water bills, are also covered.Many other topics are covered and the dataset has a very wide range of personal characteristics, at the adult or child, family and then household levels. These include education, caring, childcare and disability. The dataset also captures material deprivation, household food security and (new for 2021/22) household food bank usage. The FRS is a national statistic whose results are published on the gov.uk website. It is also possible to create your own tables from FRS data, using DWP’s Stat Xplore tool. Further information can be found on the gov.uk Family Resources Survey webpage. Safe Room Access FRS data In addition to the standard End User Licence (EUL) version, Safe Room access datasets, containing unrounded data and additional variables, are also available for FRS from 2005/06 onwards - see SN 7196, where the extra contents are listed. The Safe Room version also includes secure access versions of the Households Below Average Income (HBAI) and Pensioners' Incomes (PI) datasets. The Safe Room access data are currently only available to UK HE/FE applicants and for access at the UK Data Archive's Safe Room at the University of Essex, Colchester. Prospective users of the Safe Room access version of the FRS/HBAI/PI will need to fulfil additional requirements beyond those associated with the EUL datasets. Full details of the application requirements are available from Guidance on applying for the Family Resources Survey: Secure Access.FRS, HBAI and PIThe FRS underpins the related Households Below Average Income (HBAI) dataset, which focuses on poverty in the UK, and the related Pensioners' Incomes (PI) dataset. The EUL versions of HBAI and PI are held under SNs 5828 and 8503 respectively. The secure access versions are held within the Safe Room FRS study under SN 7196 (see above). FRS 2020-21 and the coronavirus (COVID-19) pandemicThe coronavirus (COVID-19) pandemic affected the FRS 2020-21 in the following ways:Fieldwork operations for the FRS were rapidly changed in response to the coronavirus (COVID-19) pandemic and the introduction of national lockdown restrictions. The established face-to-face interviewing approach employed on the FRS was suspended and replaced with telephone interviewing for the whole of the 2020-21 survey year. This change impacted both the size and composition of the achieved sample. This shift in mode of interview has been accompanied by a substantial reduction in the number of interviews achieved: just over 10,000 interviews were achieved this year, compared with 19,000 to 20,000 in a typical FRS year. It is also recognised that older, more affluent participants were over-sampled. The achieved sample was particularly small for April, and was more unbalanced across the year, with a total of 4,000 households representing the first 6 months of the survey year. While we made every effort to address additional biases identified (e.g. by altering our weighting regime), some residual bias remains. Please see the FRS 2020-21 Background Information and Methodology document for more information.The FRS team have published a technical report for the 2020-21 survey, which provides a full assessment of the impact of the pandemic on the statistics. In line with the Statistics Code of Practice, this is designed to assist users with interpreting the data and to aid transparency over decisions and data quality issues.Latest version informationIn June 2023, a new variable, CTAMTBND (Annual council tax payment bands), was added to the HOUSEHOL file. The documentation has been updated accordingly. Main Topics: Household characteristics (family composition, tenure); COVID-19, housing costs including rent or details of mortgage; household bills including Council Tax, buildings and contents insurance, water and sewerage rates; receipt of state support from all state benefits, including Universal Credit and Tax Credits; educational level and grants and loans; children in education; care, both those receiving care and those caring for others; childcare; occupation, employment, self-employment and earnings/wage details; income tax payments and refunds; National Insurance contributions; earnings from odd jobs; health, restrictions on work, children's health, and disability or limiting long-standing illness; personal and occupational pension schemes; income from pensions and trusts, royalties and allowances, and other sources; children's earnings; interest and dividends from investments including National Savings products, stocks and shares; and total household assets. Multi-stage stratified random sample Telephone interview: Computer-assisted (CATI)

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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 Annual Population Survey, also held at the UK Data Archive, is derived from the LFS.The LFS was first conducted biennially from 1973-1983, then annually between 1984 and 1991, comprising a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter. From 1992 it moved to a quarterly cycle with a sample size approximately equivalent to that of the previous annual data. Northern Ireland was also included in the survey from December 1994. Further information on the background to the QLFS may be found in the documentation.The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.LFS DocumentationThe 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 (the latest questionnaire available covers July-September 2022). Volumes are updated periodically, 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.LFS response to COVID-19From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.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.End User Licence and Secure Access QLFS dataTwo versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).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 finer detail 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, and other categories; 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 5-digit industry subclass and 4-digit 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 other additional detailed variables may also be included. The Secure Access datasets (SNs 6727 and 7674) 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. Latest edition informationFor the third edition (July 2023), SOC variables NSECM20, NSECMJ20, SC20LMJ, SC20LMN, SC20MMJ, SC20MMN, SC20SMJ, SC20SMN, SOC20M, SC2010M and the person income weight PIWT22 were replaced with revised versions. 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 QLFS questionnaire comprises a 'core' of questions which are included in every survey, together with some 'non-core' questions which vary from quarter to quarter.The questionnaire can be split into two main parts. The first part contains questions on the respondent's household, family structure, basic housing information and demographic details of household members. The second part contains questions covering economic activity, education and health, and also may include a few questions asked on behalf of other government departments (for example the Department for Work and Pensions and the Home Office). Until 1997, the questions on health covered mainly problems which affected the respondent's work. From that quarter onwards, the questions cover all health problems. Detailed questions on income have also been included in each quarter since 1993. The basic questionnaire is revised each year, and a new version published, along with a transitional version that details changes from the previous year's questionnaire. Four sampling frames are used. See documentation for details. Face-to-face interview Telephone interview The first interview is conducted face-to-face, and subsequent interviews by telephone where possible.

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  • Authors: Department for Digital, Culture, Media and Sport;

    Abstract copyright UK Data Service and data collection copyright owner.The Participation Survey is a continuous push-to-web survey of adults aged 16 and over in England. It serves as a successor to the Taking Part survey, which ran for 16 years as a continuous face to face survey. Paper surveys are available for those not digitally engaged. Fieldwork started in October 2021 and it is envisaged that the survey will be a key evidence source for Department for Digital, Culture, Media and Sport (DCMS) and its sectors by providing statistically representative national estimates of adult engagement with the DCMS sectors. The survey’s main objectives are to: Provide a central, reliable evidence source that can be used to analyse cultural, digital, and sporting engagement, providing a clear picture of why people do or do not engage. Provide data at a county level to meet user needs, including providing evidence for the levelling up agenda. Underpin further research on driving engagement and the value and benefits of engagement.Further information on the survey can be found on the gov.uk Participation Survey webpage. Three versions of the Participation Survey 2021-2022 are available:An open access version (SN 9013). This version is freely available to download and does not require UK Data Service registration. This safeguarded dataset (SN 9012), which includes some additional detail. It is only available to registered UKDS users who have agreed to abide by the conditions of the End User Licence. A Secure Access version (SN 9014), which contains further detailed information. Access to this version is very restricted and requires UKDS registration, completion of an extensive application form, approval from the depositor, and successful completion of a Safe Researcher Training course before access can be granted. Users are advised to first download the safeguarded version (SN 9012) to check whether it includes sufficient detail for their research, before considering making an application for the Secure Access version.Details of all variables available for the version concerned can be found in the UKDS Data Dictionary - see the Documentation section. Main Topics:The Participation Survey collects data on engagement in: the arts libraries heritage museums and galleries tourism major cultural events major sporting events sport gambling digital sectors The survey includes information on frequency of participation, reasons for participating, barriers to participation and attitudes to the sectors. Information is also gathered on demographics (e.g. age, education), and related areas including wellbeing, loneliness, and use of digital technology.

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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 Annual Population Survey, also held at the UK Data Archive, is derived from the LFS. The LFS was first conducted biennially from 1973-1983, then annually between 1984 and 1991, comprising a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter. From 1992 it moved to a quarterly cycle with a sample size approximately equivalent to that of the previous annual data. Northern Ireland was also included in the survey from December 1994. Further information on the background to the QLFS may be found in the documentation.The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.LFS DocumentationThe 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.LFS response to COVID-19From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.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.End User Licence and Secure Access QLFS dataTwo versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).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 finer detail 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, and other categories; 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 5-digit industry subclass and 4-digit 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 other additional detailed variables may also be included. The Secure Access datasets (SNs 6727 and 7674) 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. Latest edition information:For the third edition (June 2022), 2022 weighting variable PWT22 was added to the study, and the 2020 weight removed. Main Topics:The QLFS questionnaire comprises a 'core' of questions which are included in every survey, together with some 'non-core' questions which vary from quarter to quarter.The questionnaire can be split into two main parts. The first part contains questions on the respondent's household, family structure, basic housing information and demographic details of household members. The second part contains questions covering economic activity, education and health, and also may include a few questions asked on behalf of other government departments (for example the Department for Work and Pensions and the Home Office). Until 1997, the questions on health covered mainly problems which affected the respondent's work. From that quarter onwards, the questions cover all health problems. Detailed questions on income have also been included in each quarter since 1993. The basic questionnaire is revised each year, and a new version published, along with a transitional version that details changes from the previous year's questionnaire. Four sampling frames are used. See documentation for details. Face-to-face interview Telephone interview The first interview is conducted face-to-face, and subsequent interviews by telephone where possible.

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  • Authors: University of Essex, Institute for Social and Economic Research;

    Abstract copyright UK Data Service and data collection copyright owner.Understanding Society (the UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Kantar Public and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991. The Understanding Society: Calendar Year Dataset, 2020, is designed to enable cross-sectional analysis of individuals and households relating specifically to their annual interviews conducted in the year 2020, and, therefore, combine data collected in three waves (Waves 10, 11 and 12). It has been produced from the same data collected in the main Understanding Society study and released in the longitudinal datasets SN 6614 (End User Licence) and SN 6931 (Special Licence). Such cross-sectional analysis can, however, only involve variables that are collected in every wave in order to have data for the full sample panel. The 2020 dataset is the first of a series of planned Calendar Year Datasets to facilitate cross-sectional analysis of specific years. Full details of the Calendar Year Dataset sample structure (including why some individual interviews from 2021 are included), data structure and additional supporting information can be found in the document '8987_calendar_year_dataset_2020_user_guide'. As multi-topic studies, the purpose of Understanding Society is to understand short- and long-term effects of social and economic change in the UK at the household and individual levels. The study has a strong emphasis on domains of family and social ties, employment, education, financial resources, and health. Understanding Society is an annual survey of each adult member of a nationally representative sample. The same individuals are re-interviewed in each wave approximately 12 months apart. When individuals move they are followed within the UK and anyone joining their households are also interviewed as long as they are living with them. The fieldwork period for a single wave is 24 months. Data collection uses computer-assisted personal interviewing (CAPI) and web interviews (from wave 7), and includes a telephone mop up. From March 2020 (the end of wave 10 and 2nd year of wave 11), due to the coronavirus pandemic, face-to-face interviews were suspended and the survey has been conducted by web and telephone only, but otherwise has continued as before. One person completes the household questionnaire. Each person aged 16 or older participates in the individual adult interview and self-completed questionnaire. Youths aged 10 to 15 are asked to respond to a paper self-completion questionnaire. In 2020 an additional frequent web survey was separately issued to sample members to capture data on the rapid changes in people’s lives due to the COVID-19 pandemic (see SN 8644). The COVID-19 Survey data are not included in this dataset. Further information may be found on the Understanding Society main stage webpage and links to publications based on the study can be found on the Understanding Society Latest Research webpage. Co-funders In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency. End User Licence and Special Licence versions: There are two versions of the Calendar Year 2020 data. One is available under the standard End User Licence (EUL) agreement, and the other is a Special Licence (SL) version. The SL version contains month and year of birth variables instead of just age, more detailed country and occupation coding for a number of variables and various income variables have not been top-coded (see xxxx_eul_vs_sl_variable_differences for more details). Users are advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL data have more restrictive access conditions; prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. The main longitudinal versions of the Understanding Society study may be found under SNs 6614 (EUL) and 6931 (SL). Low- and Medium-level geographical identifiers produced for the mainstage longitudinal dataset can be used with this Calendar Year 2020 dataset, subject to SL access conditions. See the User Guide for further details. Suitable data analysis software These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Stata SE or MP software is needed to analyse the larger files, which contain about 1,900 variables. Main Topics: The survey instrument is constructed with modules. For a fuller listing of modules and questionnaire content see the User Manual or the online documentation system. The household grid or enumeration grid has a listing of all household members with information about gender, date of birth, marital and employment status, and relationship to the household respondent. The household questionnaire has questions about housing, mortgage or rent payments, material deprivation, and consumer durables and cars. The individual adult interview is asked of every person in the household aged 16 or over. It has questions about demographics, baseline information, family background, ethnicity and language use; migration, partnership and fertility histories; health, disability and caring; current employment and earnings; employment status; parenting and childcare arrangements; family networks; benefit payments; political party identification; household finances; environmental behaviours; consents to administrative data linkage. A proxy module is a much-shortened version of the individual questionnaire that collects demographic, health and employment information, as well as a summary income measure. It is completed by one person on behalf of another. Those who completed an individual adult interview also complete a self-completion questionnaire. It includes subjective questions, particularly those which are potentially sensitive or require more privacy. For example, feelings of depression (GHQ-12) and well-being, sleep behaviour, environmental attitudes and beliefs, neighbourhood participation and belonging, life satisfaction, activities with partner and relationship quality. A youth self-completed questionnaire is completed by 10-15 year olds. It includes questions on computer and technology use, family support, sibling relationships, feelings about areas of life, Strengths and Difficulties Questionnaire, health behaviours, smoking and drinking, and aspirations. Multi-stage stratified random sample Web-based interview Telephone interview Self-administered questionnaire Face-to-face interview

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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 Annual Population Survey, also held at the UK Data Archive, is derived from the LFS.The LFS was first conducted biennially from 1973-1983, then annually between 1984 and 1991, comprising a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter. From 1992 it moved to a quarterly cycle with a sample size approximately equivalent to that of the previous annual data. Northern Ireland was also included in the survey from December 1994. Further information on the background to the QLFS may be found in the documentation.The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.LFS DocumentationThe 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 (the latest questionnaire available covers July-September 2022). Volumes are updated periodically, 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.LFS response to COVID-19From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.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.End User Licence and Secure Access QLFS dataTwo versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).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 finer detail 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, and other categories; 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 5-digit industry subclass and 4-digit 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 other additional detailed variables may also be included. The Secure Access datasets (SNs 6727 and 7674) 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. Latest edition informationFor the third edition (July 2023), SOC variables NSECM20, NSECMJ20, SC20LMJ, SC20LMN, SC20MMJ, SC20MMN, SC20SMJ, SC20SMN, SOC20M, SC2010M, SMSOC201, SMSOC203 and the person income weight PIWT22 were replaced with revised versions. 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 QLFS questionnaire comprises a 'core' of questions which are included in every survey, together with some 'non-core' questions which vary from quarter to quarter.The questionnaire can be split into two main parts. The first part contains questions on the respondent's household, family structure, basic housing information and demographic details of household members. The second part contains questions covering economic activity, education and health, and also may include a few questions asked on behalf of other government departments (for example the Department for Work and Pensions and the Home Office). Until 1997, the questions on health covered mainly problems which affected the respondent's work. From that quarter onwards, the questions cover all health problems. Detailed questions on income have also been included in each quarter since 1993. The basic questionnaire is revised each year, and a new version published, along with a transitional version that details changes from the previous year's questionnaire. Four sampling frames are used. See documentation for details. Face-to-face interview Telephone interview The first interview is conducted face-to-face, and subsequent interviews by telephone where possible.

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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: Kelder, J.M.;

    From an Ancient Egyptian plague to the Black Death and Spanish flu, epidemics have often spurred societal transformations. Understanding why can help us create a better world after covid-19

    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 NARCISarrow_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 NARCISarrow_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 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: Dronseikaitė, Roberta;

    Every teacher faces the challenge of getting students interested. This problem was exacerbated especially after the covid-19 pandemic, during which students learned remotely and became no longer accustomed to long-term focus, gaining many knowledge gaps. After returning to school after almost two years of learning from home, the problem became especially apparent even among students who were really interested - they simply could not concentrate. Working with students revealed another problem - basic learning tools have provided only one perspective on history, while it has more than one. The solution to these problems was to show Soviet-era newspapers in class that contradict the information in the textbook. These sources were taken from the electronic cultural heritage source database www.epaveldas.lt. The main source was the pro-Russian newspaper „Tiesa“ and the newspaper „Dirva“ was used on the last lesson. The articles were chosen to generate the most emotion for the students or to provide a basis for discussion. This solution paid off, as almost twice as many students began to take part in the lessons, and the discussions, which were sometimes stimulated by the children themselves, intensified. One student even brought his own collection of Soviet medals and newspaper articles. This method of lessons was acceptable for the children, because they were interested in finding out what the articles were like at the time, they liked to discuss possible different approaches to history, they remembered the material in the lesson better. Positive feedback was received not only from the students but also from their parents. The success of the textbook section was crowned by the students' reporting work, in which they done more than were asked to, and were able to compare the information provided in the textbook with the new information. Kiekvienas mokytojas susiduria su problema, kaip sudominti mokinius. Ši problema ypač paaštrėjo po covid-19 pandemijos, kurios metu mokiniai mokėsi nuotoliniu būdu ir atprato ilgai koncentruoti dėmesį, įgijo daug spragų. Grįžus į mokyklas po beveik dviejų metų mokymosi iš namų ši problema tapo itin akivaizdi netgi tarp mokinių, kuriems iš tiesų buvo įdomu – jie paprasčiausiai negebėjo koncentruoti dėmesio. Dirbant su mokiniais išaiškėjo dar viena problema – pagrindinės mokymosi priemonės pateikė tik vieną požiūrį į istoriją, kuri tuo tarpu turi daugiau negu vieną perspektyvą. Šių problemų sprendimo būdu buvo pasirinkta pamokose demonstruoti sovietmečiu leistus laikraščius, kurie prieštarauja vadovėlyje pateiktai informacijai. Šie šaltiniai buvo paimti iš elektroninės kultūros paveldo šaltinių bazės www.epaveldas.lt. Pagrindiniu šaltiniu buvo prorusiškas laikraštis „Tiesa“, o paskutinę pamoką buvo panaudotas laikraštis „Dirva“. Straipsniai buvo parenkami tokie, kurie mokiniams sukeltų daugiausiai emocijų arba teiktų pagrindą diskusijoms. Šis sprendimo būdas pasiteisino, nes į pamokas pradėjo įsitraukti kone dvigubai daugiau mokinių, daug intensyviau vyko diskusijos, kurias kartais patys vaikai ir paskatindavo. Vienas mokinys netgi atsinešė savo turimą sovietinių medalių ir straipsnių kolekciją. Vaikams toks pamokų būdas buvo priimtinas, nes jiems buvo įdomu sužinoti, kokie buvo tais laikais spausdinami straipsniai, patiko diskutuoti apie galimus skirtingus požiūrius į istoriją, jie geriau įsiminė pamokoje pateikiamą medžiagą. Teigiamų atsiliepimų buvo sulaukta ne tik iš mokinių, bet ir iš jų tėvų. Vadovėlio skyriaus sėkmę vainikavo mokinių atsiskaitymo darbai, kuriuose jie padarė daugiau, negu buvo prašoma, gebėjo palyginti vadovėlyje pateiktą ir naujai sužinotą informaciją.

    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/ Vytautas Magnus Univ...arrow_drop_down
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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

    Covid-19 je na svetovni ravni okrepil državni nadzor, vse pogosteje moramo posegati po osebnih dokumentih, pri čemer je osrednji subjekt identifikacije postal človeški obraz – na katerem temeljijo tudi najnovejše tehnologije nadzora.

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    Authors: Oliveira, Daniel Filipe Nunes;

    Os recursos humanos são um dos maiores ativos de qualquer empresa visto que estes providenciam a possibilidade para realizar produtos ou serviços. A revolução tecnológica, a pandemia do COVID-19 e a competitividade do mercado laboral contribuem para um clima de incerteza e permanente renovação de staff dentro das empresas. Isto significa permanências curtas dos funcionários, mas, mais importante, leva a que os repositórios de competências de uma empresa possam ficar, por vezes, empobrecidos e, deste modo, pode pôr em causa a execução dos produtos e serviços pelos quais uma empresa é reconhecida. Recentemente, têm surgido plataformas online com o objetivo de atrair, designar funções, treinar, mas sobretudo reter os talentos, tudo isto só é alcançado revendo e melhorando permanentemente as competências de cada recurso. Estas plataformas utilizam quase sempre mecanismos de inteligência artificial. Neste trabalho, apresenta-se uma revisão literária das técnicas de inteligência artificial que podem estar presentes nestas plataformas. Esta revisão literária apresenta 4 questões de investigação que, juntas, respondem a uma questão de investigação mais ampla: “Como implementar uma destas plataformas, sendo inovador?”. A primeira questão é relativa aos processos automatizados de leitura e extração de informação de currículos. A segunda questão é relativa à inferência de competências, através de outras competências ou informações previamente extraídas de currículos, e é neste ponto que se pretende inovar quando em comparação com as soluções existentes. A terceira questão é relativa à existência de sistemas multiagente que associam recursos a tarefas de uma forma otimizada. Por último, a quarta questão de investigação é relativa à aplicabilidade de algoritmos genéticos também na associação de funcionários a tarefas numa empresa. Os resultados da revisão literária mostram que as 4 questões foram respondidas com sucesso. A revisão literária seguiu a metodologia PRISMA, tendo sido realizada a pesquisa em 2 fontes distintas. Foram selecionados 44 artigos, de entre os 27361 encontrados, que foram filtrados através de controlo de qualidade ou leituras rápidas de títulos e resumos. Por último, tendo em consideração a revisão literária realizada, foi implementado e testado um sistema que se assemelha a um mercado de talentos, com inferência de competências. O emparelhamento entre recursos e tarefas foi feito através de um sistema multiagente e de um algoritmo genético. Foi, ainda, realizada uma análise de usabilidade. Desta forma, podemos concluir que o resultado final foi atingido, pois quer a revisão literária quer o sistema implementado foram atividades realizadas com sucesso. Human resources are one of the biggest assets in companies since they possess the skills and expertise to deliver products and services. The COVID-19 pandemic and the technological revolution both increased employee turnover to a level where companies can hardly keep up with the pace, leading to worst talents management. Recently, online platforms, known as talent markets, have become more and more popular and they have the main objective to attract, designate tasks, train and, above all, retain existing employees. Most of these online platforms use artificial intelligence. This work presents a systematic review in artificial intelligence techniques that allow automatization of the processes of talent management. Four research questions were elaborated that, altogether, answer a broader research question which is: “How to implement an innovative talent market?”. The first question is relative to the automatized processes of information extraction out of resumes. The second question is related to the skill inference process, it is here that innovation is expected when comparing with existing solutions. In the third and fourth research questions, literature over multiagent systems and genetic algorithms dedicated to the optimization of task execution is provided. In the review, 44 papers were selected out of an initial set of 27361. In addition to the systematic review, a system is also proposed that resembles already existing solutions. Innovation is achieved by exploring skill inference, in addition to using already existing techniques in the area of information recognition. A multiagent system and genetic algorithms were also developed for an optimized task and employee pairing. This document also presents various tests to the system including a usability analysis. All in all, the outcome was rewarding, the systematic literature review was a success and so was the proposed solution.

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  • Authors: Department for Work and Pensions; NatCen Social Research;

    Abstract copyright UK Data Service and data collection copyright owner.The Family Resources Survey (FRS) has been running continuously since 1992 to meet the information needs of the Department for Work and Pensions (DWP). It is almost wholly funded by DWP. The FRS collects information from a large, and representative sample of private households in the United Kingdom (prior to 2002, it covered Great Britain only). The interview year runs from April to March.The focus of the survey is on income, and how much comes from the many possible sources (such as employee earnings, self-employed earnings or profits from businesses, and dividends; individual pensions; state benefits, including Universal Credit and the State Pension; and other sources such as savings and investments). Specific items of expenditure, such as rent or mortgage, Council Tax and water bills, are also covered.Many other topics are covered and the dataset has a very wide range of personal characteristics, at the adult or child, family and then household levels. These include education, caring, childcare and disability. The dataset also captures material deprivation, household food security and (new for 2021/22) household food bank usage. The FRS is a national statistic whose results are published on the gov.uk website. It is also possible to create your own tables from FRS data, using DWP’s Stat Xplore tool. Further information can be found on the gov.uk Family Resources Survey webpage. Safe Room Access FRS data In addition to the standard End User Licence (EUL) version, Safe Room access datasets, containing unrounded data and additional variables, are also available for FRS from 2005/06 onwards - see SN 7196, where the extra contents are listed. The Safe Room version also includes secure access versions of the Households Below Average Income (HBAI) and Pensioners' Incomes (PI) datasets. The Safe Room access data are currently only available to UK HE/FE applicants and for access at the UK Data Archive's Safe Room at the University of Essex, Colchester. Prospective users of the Safe Room access version of the FRS/HBAI/PI will need to fulfil additional requirements beyond those associated with the EUL datasets. Full details of the application requirements are available from Guidance on applying for the Family Resources Survey: Secure Access.FRS, HBAI and PIThe FRS underpins the related Households Below Average Income (HBAI) dataset, which focuses on poverty in the UK, and the related Pensioners' Incomes (PI) dataset. The EUL versions of HBAI and PI are held under SNs 5828 and 8503 respectively. The secure access versions are held within the Safe Room FRS study under SN 7196 (see above). FRS 2020-21 and the coronavirus (COVID-19) pandemicThe coronavirus (COVID-19) pandemic affected the FRS 2020-21 in the following ways:Fieldwork operations for the FRS were rapidly changed in response to the coronavirus (COVID-19) pandemic and the introduction of national lockdown restrictions. The established face-to-face interviewing approach employed on the FRS was suspended and replaced with telephone interviewing for the whole of the 2020-21 survey year. This change impacted both the size and composition of the achieved sample. This shift in mode of interview has been accompanied by a substantial reduction in the number of interviews achieved: just over 10,000 interviews were achieved this year, compared with 19,000 to 20,000 in a typical FRS year. It is also recognised that older, more affluent participants were over-sampled. The achieved sample was particularly small for April, and was more unbalanced across the year, with a total of 4,000 households representing the first 6 months of the survey year. While we made every effort to address additional biases identified (e.g. by altering our weighting regime), some residual bias remains. Please see the FRS 2020-21 Background Information and Methodology document for more information.The FRS team have published a technical report for the 2020-21 survey, which provides a full assessment of the impact of the pandemic on the statistics. In line with the Statistics Code of Practice, this is designed to assist users with interpreting the data and to aid transparency over decisions and data quality issues.Latest version informationIn June 2023, a new variable, CTAMTBND (Annual council tax payment bands), was added to the HOUSEHOL file. The documentation has been updated accordingly. Main Topics: Household characteristics (family composition, tenure); COVID-19, housing costs including rent or details of mortgage; household bills including Council Tax, buildings and contents insurance, water and sewerage rates; receipt of state support from all state benefits, including Universal Credit and Tax Credits; educational level and grants and loans; children in education; care, both those receiving care and those caring for others; childcare; occupation, employment, self-employment and earnings/wage details; income tax payments and refunds; National Insurance contributions; earnings from odd jobs; health, restrictions on work, children's health, and disability or limiting long-standing illness; personal and occupational pension schemes; income from pensions and trusts, royalties and allowances, and other sources; children's earnings; interest and dividends from investments including National Savings products, stocks and shares; and total household assets. Multi-stage stratified random sample Telephone interview: Computer-assisted (CATI)

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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 Annual Population Survey, also held at the UK Data Archive, is derived from the LFS.The LFS was first conducted biennially from 1973-1983, then annually between 1984 and 1991, comprising a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter. From 1992 it moved to a quarterly cycle with a sample size approximately equivalent to that of the previous annual data. Northern Ireland was also included in the survey from December 1994. Further information on the background to the QLFS may be found in the documentation.The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.LFS DocumentationThe 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 (the latest questionnaire available covers July-September 2022). Volumes are updated periodically, 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.LFS response to COVID-19From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.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.End User Licence and Secure Access QLFS dataTwo versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).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 finer detail 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, and other categories; 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 5-digit industry subclass and 4-digit 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 other additional detailed variables may also be included. The Secure Access datasets (SNs 6727 and 7674) 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. Latest edition informationFor the third edition (July 2023), SOC variables NSECM20, NSECMJ20, SC20LMJ, SC20LMN, SC20MMJ, SC20MMN, SC20SMJ, SC20SMN, SOC20M, SC2010M and the person income weight PIWT22 were replaced with revised versions. 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 QLFS questionnaire comprises a 'core' of questions which are included in every survey, together with some 'non-core' questions which vary from quarter to quarter.The questionnaire can be split into two main parts. The first part contains questions on the respondent's household, family structure, basic housing information and demographic details of household members. The second part contains questions covering economic activity, education and health, and also may include a few questions asked on behalf of other government departments (for example the Department for Work and Pensions and the Home Office). Until 1997, the questions on health covered mainly problems which affected the respondent's work. From that quarter onwards, the questions cover all health problems. Detailed questions on income have also been included in each quarter since 1993. The basic questionnaire is revised each year, and a new version published, along with a transitional version that details changes from the previous year's questionnaire. Four sampling frames are used. See documentation for details. Face-to-face interview Telephone interview The first interview is conducted face-to-face, and subsequent interviews by telephone where possible.

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  • Authors: Department for Digital, Culture, Media and Sport;

    Abstract copyright UK Data Service and data collection copyright owner.The Participation Survey is a continuous push-to-web survey of adults aged 16 and over in England. It serves as a successor to the Taking Part survey, which ran for 16 years as a continuous face to face survey. Paper surveys are available for those not digitally engaged. Fieldwork started in October 2021 and it is envisaged that the survey will be a key evidence source for Department for Digital, Culture, Media and Sport (DCMS) and its sectors by providing statistically representative national estimates of adult engagement with the DCMS sectors. The survey’s main objectives are to: Provide a central, reliable evidence source that can be used to analyse cultural, digital, and sporting engagement, providing a clear picture of why people do or do not engage. Provide data at a county level to meet user needs, including providing evidence for the levelling up agenda. Underpin further research on driving engagement and the value and benefits of engagement.Further information on the survey can be found on the gov.uk Participation Survey webpage. Three versions of the Participation Survey 2021-2022 are available:An open access version (SN 9013). This version is freely available to download and does not require UK Data Service registration. This safeguarded dataset (SN 9012), which includes some additional detail. It is only available to registered UKDS users who have agreed to abide by the conditions of the End User Licence. A Secure Access version (SN 9014), which contains further detailed information. Access to this version is very restricted and requires UKDS registration, completion of an extensive application form, approval from the depositor, and successful completion of a Safe Researcher Training course before access can be granted. Users are advised to first download the safeguarded version (SN 9012) to check whether it includes sufficient detail for their research, before considering making an application for the Secure Access version.Details of all variables available for the version concerned can be found in the UKDS Data Dictionary - see the Documentation section. Main Topics:The Participation Survey collects data on engagement in: the arts libraries heritage museums and galleries tourism major cultural events major sporting events sport gambling digital sectors The survey includes information on frequency of participation, reasons for participating, barriers to participation and attitudes to the sectors. Information is also gathered on demographics (e.g. age, education), and related areas including wellbeing, loneliness, and use of digital technology.

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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 Annual Population Survey, also held at the UK Data Archive, is derived from the LFS. The LFS was first conducted biennially from 1973-1983, then annually between 1984 and 1991, comprising a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter. From 1992 it moved to a quarterly cycle with a sample size approximately equivalent to that of the previous annual data. Northern Ireland was also included in the survey from December 1994. Further information on the background to the QLFS may be found in the documentation.The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.LFS DocumentationThe 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.LFS response to COVID-19From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.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.End User Licence and Secure Access QLFS dataTwo versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).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 finer detail 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, and other categories; 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 5-digit industry subclass and 4-digit 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 other additional detailed variables may also be included. The Secure Access datasets (SNs 6727 and 7674) 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. Latest edition information:For the third edition (June 2022), 2022 weighting variable PWT22 was added to the study, and the 2020 weight removed. Main Topics:The QLFS questionnaire comprises a 'core' of questions which are included in every survey, together with some 'non-core' questions which vary from quarter to quarter.The questionnaire can be split into two main parts. The first part contains questions on the respondent's household, family structure, basic housing information and demographic details of household members. The second part contains questions covering economic activity, education and health, and also may include a few questions asked on behalf of other government departments (for example the Department for Work and Pensions and the Home Office). Until 1997, the questions on health covered mainly problems which affected the respondent's work. From that quarter onwards, the questions cover all health problems. Detailed questions on income have also been included in each quarter since 1993. The basic questionnaire is revised each year, and a new version published, along with a transitional version that details changes from the previous year's questionnaire. Four sampling frames are used. See documentation for details. Face-to-face interview Telephone interview The first interview is conducted face-to-face, and subsequent interviews by telephone where possible.

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  • Authors: University of Essex, Institute for Social and Economic Research;

    Abstract copyright UK Data Service and data collection copyright owner.Understanding Society (the UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Kantar Public and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991. The Understanding Society: Calendar Year Dataset, 2020, is designed to enable cross-sectional analysis of individuals and households relating specifically to their annual interviews conducted in the year 2020, and, therefore, combine data collected in three waves (Waves 10, 11 and 12). It has been produced from the same data collected in the main Understanding Society study and released in the longitudinal datasets SN 6614 (End User Licence) and SN 6931 (Special Licence). Such cross-sectional analysis can, however, only involve variables that are collected in every wave in order to have data for the full sample panel. The 2020 dataset is the first of a series of planned Calendar Year Datasets to facilitate cross-sectional analysis of specific years. Full details of the Calendar Year Dataset sample structure (including why some individual interviews from 2021 are included), data structure and additional supporting information can be found in the document '8987_calendar_year_dataset_2020_user_guide'. As multi-topic studies, the purpose of Understanding Society is to understand short- and long-term effects of social and economic change in the UK at the household and individual levels. The study has a strong emphasis on domains of family and social ties, employment, education, financial resources, and health. Understanding Society is an annual survey of each adult member of a nationally representative sample. The same individuals are re-interviewed in each wave approximately 12 months apart. When individuals move they are followed within the UK and anyone joining their households are also interviewed as long as they are living with them. The fieldwork period for a single wave is 24 months. Data collection uses computer-assisted personal interviewing (CAPI) and web interviews (from wave 7), and includes a telephone mop up. From March 2020 (the end of wave 10 and 2nd year of wave 11), due to the coronavirus pandemic, face-to-face interviews were suspended and the survey has been conducted by web and telephone only, but otherwise has continued as before. One person completes the household questionnaire. Each person aged 16 or older participates in the individual adult interview and self-completed questionnaire. Youths aged 10 to 15 are asked to respond to a paper self-completion questionnaire. In 2020 an additional frequent web survey was separately issued to sample members to capture data on the rapid changes in people’s lives due to the COVID-19 pandemic (see SN 8644). The COVID-19 Survey data are not included in this dataset. Further information may be found on the Understanding Society main stage webpage and links to publications based on the study can be found on the Understanding Society Latest Research webpage. Co-funders In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency. End User Licence and Special Licence versions: There are two versions of the Calendar Year 2020 data. One is available under the standard End User Licence (EUL) agreement, and the other is a Special Licence (SL) version. The SL version contains month and year of birth variables instead of just age, more detailed country and occupation coding for a number of variables and various income variables have not been top-coded (see xxxx_eul_vs_sl_variable_differences for more details). Users are advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL data have more restrictive access conditions; prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. The main longitudinal versions of the Understanding Society study may be found under SNs 6614 (EUL) and 6931 (SL). Low- and Medium-level geographical identifiers produced for the mainstage longitudinal dataset can be used with this Calendar Year 2020 dataset, subject to SL access conditions. See the User Guide for further details. Suitable data analysis software These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Stata SE or MP software is needed to analyse the larger files, which contain about 1,900 variables. Main Topics: The survey instrument is constructed with modules. For a fuller listing of modules and questionnaire content see the User Manual or the online documentation system. The household grid or enumeration grid has a listing of all household members with information about gender, date of birth, marital and employment status, and relationship to the household respondent. The household questionnaire has questions about housing, mortgage or rent payments, material deprivation, and consumer durables and cars. The individual adult interview is asked of every person in the household aged 16 or over. It has questions about demographics, baseline information, family background, ethnicity and language use; migration, partnership and fertility histories; health, disability and caring; current employment and earnings; employment status; parenting and childcare arrangements; family networks; benefit payments; political party identification; household finances; environmental behaviours; consents to administrative data linkage. A proxy module is a much-shortened version of the individual questionnaire that collects demographic, health and employment information, as well as a summary income measure. It is completed by one person on behalf of another. Those who completed an individual adult interview also complete a self-completion questionnaire. It includes subjective questions, particularly those which are potentially sensitive or require more privacy. For example, feelings of depression (GHQ-12) and well-being, sleep behaviour, environmental attitudes and beliefs, neighbourhood participation and belonging, life satisfaction, activities with partner and relationship quality. A youth self-completed questionnaire is completed by 10-15 year olds. It includes questions on computer and technology use, family support, sibling relationships, feelings about areas of life, Strengths and Difficulties Questionnaire, health behaviours, smoking and drinking, and aspirations. Multi-stage stratified random sample Web-based interview Telephone interview Self-administered questionnaire Face-to-face interview

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    CESSDA
    Other ORP type . 2022
    Data sources: B2FIND