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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

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.

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.

Several studies in different countries have reported that part of the population does not fully comply with the measures recommended to prevent COVID-19, and therefore poses a risk to public health. For this reason, several measures have been developed to assess the level of compliance, although many of them have methodological limitations or do not include a comprehensive set of items. The main goal of the current study was to develop a new instrument with suitable psychometric properties, which includes a more complete set of items and controls the impact of acquiescence bias. The participants were 1410 individuals (59.2% women) from Spain, who answered the new questionnaire and several items on sociodemographic and attitudinal issues. Exploratory and confirmatory factor analyses were carried out, and the results suggested that only one content factor was underlying the data. This solution was replicated in a different subsample, which shows the stability of the solution. Furthermore, the relationships between the scores of the new questionnaire and the sociodemographic and attitudinal variables are similar to those obtained in previous studies, which can be regarded as evidence of the validity of the new questionnaire.

There is an extensive literature on the importance of the use of scientific evidence on teaching methods in higher education institutions. However, there is a gap in how evidence is used for decision making that affects students and staff in universities. This article is a contribution to the existing gap, making the analysis of a specific case on how Catalan universities have managed the decision making regarding their staff in the face of the pandemic situation with the COVID-19. In this article, through the interview with managers and faculty from different Catalan universities, it will be shown to what extent these university institutions have opted for decision making in the management of the situation based on scientific evidence or simply compliance with government regulations.

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.

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)

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, then between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also available). Further information on the background to the QLFS may be found in the documentation.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.New reweighting policyFollowing the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published towards the end of 2020/early 2021.Additional data derived from the QLFSThe Archive also holds further QLFS series: Secure Access datasets (see below); household datasets; two-quarter and five-quarter longitudinal datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.End User Licence and Secure Access QLFS dataUsers should note that there are two discrete versions of the QLFS. 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.Changes to Country of Birth and Nationality variables, 2017:Following a disclosure review in 2016 by the ONS Data Access Team, changes have been made to the LFS Country of Birth and Nationality variables from the July-September 2017 quarter. Four new variables have been created and four variables removed. The new groupings are consistent with those published by the Migration Statistics Unit and so should facilitate users to carry out required analysis of Country of Birth and Nationality. The variables added are: CRYOX7_EUL_Main, CRYOX7_EUL_Sub, NATOX7_EUL_Main and NATOX7_EUL_Sub. The variables removed are: CRYO7, CRYOX7, NATO7 and NATOX7.Variables DISEA and LNGLSTDataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018. An ONS Methodology section article on Analysis of the discontinuity in the Labour Force Survey disability data: April to June 2017 to July to September 2017 has also been published. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk ONS methodology reports on the Labour Force Survey, published 2019: A report on progress to assess potential bias in the LFS through a comparison against alternative data sources including proxy labour measures from administrative data: Exploring the use of external data to assess for observed bias in Labour Force Survey estimates: interim findings An update on Progress against the Labour Force Survey National Statistics Quality Review recommendationsLFS response to COVID-19Since April 2020, additional non-calendar quarter LFS microdata have been delivered to Government Departments and the wider research community through the ONS Secure Research Service and UK Data Service. The first additional microdata to be released covered the period February to April 2020, to coincide with Labour Market Statistical Bulletin publication on 16 June. Further guidance was also provided with the release of the February to April 2020 microdata. Please consult the documentation for full details. Users should note that within the additional COVID-19 quarters, the pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables are only produced once a quarter by ONS, and so are not available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. It is intended that the Casenop and Hserialp variables in the COVID-19 datasets will be updated at the release of the next standard calendar quarter, when the values for the missing cases will become available. Users should also note that the Income Weight variable, PIWT, is not available in the non-standard quarters, although the Person Weight (PWT) is included.Weighting methodology information, May 2021 Following advice from ONS Labour Market Division regarding concerns over the estimates for Ethnicity, COB, Nationality and Disability from the LFS and APS, users are advised that levels and changes in levels should be used with caution. Rates published from the LFS and APS remain robust. This will particularly affect estimates for country of birth, nationality, ethnicity and disability, so any analysis using levels for these topics should be suppressed.LFS and APS responses are weighted to official 2018-based population projections on demographic trends that pre-date the coronavirus pandemic. In the Labour Market Division's Coronavirus and the impact on payroll employment article, analysis of the population totals currently used in the LFS weighting process is explained, and the intention to continue to make adjustments when appropriate.The document Labour Force Survey weighting methodology details the reweighting methodology and includes release dates for reweighted estimates.Occupation data for 2021 and 2022 data files The ONS have identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. For further information on this issue, please see: https://www.ons.gov.uk/news/statementsandletters/occupationaldatainonssurveys.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. Latest edition informationFor the second edition (June 2022), 2022 weighting variables PIWT22 and PWT22 were added to the study, and 2020 weights 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. Face-to-face interview Telephone interview

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.

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. A 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. This 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.