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The dataset comprises comments collected from the official Facebook page of the National Institute of Public Health of Kosovo (NIPHK) for a period of 6 months, from March 12 till August 31, 2020. On March 12, the first case of COVID-19 was confirmed in Kosovo. Comments were retrieved using a tool called Comment Exporter. These comments were in Albanian language and reflect the opinions of Kosovo citizens expressed on Facebook about the Covid-19 pandemics. This dataset contains a total of 10,132 comments along with 12 attributes. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

<p>The UK&nbsp;<i>National Surveys of Sexual Attitudes and Lifestyles (</i>Natsal) have been undertaken decennially since 1990 and provide a key data source underpinning sexual and reproductive health (SRH) policy.</p><p> Further information is available from the <a class="external" href="https://www.natsal.ac.uk/" title="Natsal" style="">Natsal</a> website.<br> <br> </p> <p><strong>Natsal-COVID:</strong></p><p>The COVID-19 pandemic disrupted many aspects of sexual lifestyles, triggering an urgent need for population-level data on sexual behaviour, relationships, and service use at a time when gold-standard in-person, household-based surveys with probability sampling were not feasible. The Natsal-COVID study was designed to understand the impact of COVID-19 on the nation's sexual and reproductive health (SRH) and assessed the sample representativeness. The study was funded by the Chief Scientist Office, the Wellcome Trust (with contributions from ESRC and NIHR), the UCL Covid-19 Rapid Response Fund and the Medical Research Council. The Natsal-COVID Wave 1 survey and qualitative follow-up interviews were conducted in 2020. The Wave 2 survey was designed to capture one-year prevalence estimates for key SRH outcomes and measure changes over the first year of the pandemic.</p><p><em>Methods:</em></p><ul> <li>The Natsal-COVID Wave 1 survey was conducted four months after the announcement of Britain's first national lockdown (23 March 2020), between 29 July and 10 August 2020. Wave 1 was an online web-panel survey administered by survey research company, Ipsos MORI. Eligible participants were resident in Britain, aged 18-59 years, and the sample included a boost of those aged 18-29. Questions covered participants' sexual behaviour, relationships, and SRH service use. Quotas and weighting were used to achieve a quasi-representative sample of the British general population. Participants meeting the criteria of interest and agreeing to recontact were selected for qualitative follow-up interviews. Comparisons were made with contemporaneous national probability surveys and Natsal-3 (2010-2012) (see SN 7799) to understand bias.</li> <li>Wave 2 was conducted March-April 2021, approximately one year after the start of Britain’s first national lockdown. Data were collected using an online web-panel survey administered by Ipsos. The sample comprised a longitudinal sample of Wave 1 participants who had agreed to re-contact plus a sample of participants residing in Britain, aged 18-59, including a boost sample comprising people aged 18-29. Questions covered reproductive health, relationships, sexual behaviour and SRH service use. Quotas and weighting were used to achieve a quasi-representative sample of the British population.</li> </ul><p><em>Results:</em></p><ul> <li>Wave 1: 6,654 participants completed the survey and 45 completed follow-up interviews. The weighted Natsal-COVID sample was similar to the general population in terms of gender, age, ethnicity, rurality, and, among sexually-active participants, the number of sexual partners in the past year. However, the sample was more educated, contained more sexually-inexperienced people, and included more people in poorer health.</li> <li>Wave 2: A total of 6,658 individuals completed the survey. In terms of gender, age, ethnicity, and rurality, the weighted Natsal-COVID Wave 2 sample was like the general population. Participants were less likely to be married or to report being in good health than the general population. The longitudinal sample (n=2,098) was broadly similar to participants who only took part in Wave 1 but were older. Among the sexually active, longitudinal participants were less likely to report multiple sexual partners or a new sexual partner in the past year compared to those who only took part in Wave 1.</li> </ul><p><em>Conclusions:</em></p><ul> <li>Wave 1 rapidly collected quasi-representative population data to enable evaluation of the early population-level impact of COVID-19 and lockdown measures on SRH in Britain and inform policy. Although sampling was less representative than the decennial Natsal surveys, Natsal-COVID will complement national surveillance data and Natsal-4 (planned for 2022).</li> <li>Wave 2 collected longitudinal, quasi-representative population data to enable evaluation of the population-level impact of COVID-19 on SRH and to inform policy.</li> </ul><p><strong>Latest edition information</strong></p><p class="x_x_x_MsoNormal"> </p><p>For the second edition (January 2023), data and documentation for Wave 2 were added to the study.</p>

<p class="x_x_x_MsoNoSpacing">The vision of the Smart Energy Research Lab (SERL) is to deliver a unique data resource harnessing the benefits of smart meter data for research. The resource is transforming Great Britain's energy research through the long-term provision of high quality, high-resolution energy data that supports the development of a reliable evidence base for intervention, observational and longitudinal studies across the socio-technical spectrum.</p> <p class="x_x_x_MsoNoSpacing">The goals of&nbsp;the Smart Energy Research Lab are to provide:</p> <ul> <li>A trusted data resource for researchers to utilise large-scale, high-resolution energy data </li><li>An effective mechanism for collecting and linking energy data with other contextual data</li><li>High quality data management to ensure fit-for-purpose data are provisioned to researchers</li></ul> <p class="x_x_x_MsoNoSpacing">Participant recruitment began in August 2019. Approximately 1,700 participants were recruited from central and southern England and from Wales as part of a pilot study that tested different recruitment strategies. The second recruitment wave took place in August-September 2020, and the third wave at the start of 2021. SERL recruited over 13,000 households which are regionally representative across England, Scotland and Wales. Recruitment is also designed to be representative of each Index of Multiple Deprivation (IMD) quintile; an area-based relative measure of deprivation.</p> <p class="x_x_x_MsoNoSpacing">For the latest edition (November 2022), all SERL data up to and including 31 August 2022 were made available. (Users should note that this is the 5th edition of SERL data that has been released, though the citation may refer to the 6th edition.)&nbsp; </p> <p class="x_x_x_MsoNoSpacing">All code provided with the data is now managed on the <a title="SERL GitHub" href="https://github.com/smartEnergyResearchLab">SERL GitHub</a> website.</p> <p class="x_x_x_MsoNoSpacing">Smart meter data:</p> <ul> <li>Daily and half-hourly energy (electricity and gas) consumption data</li><li>Tariff data (available for the first time in the 5th edition)</li><li>Additional smart meter technical data</li></ul> <p class="x_x_x_MsoNoSpacing">Contextual data:</p> <ul> <li>A short SERL survey completed by participant households providing data on household information and building characteristics. Survey data exists for 12,951 participants.</li><li>Energy Performance Certificate (EPC) data</li><li>Weather data</li><li>SERL Covid-19 survey; sent to wave 1 participants in May 2020 to understand their circumstances during the first lockdown.<br> </li></ul> <p></p> <p class="x_x_x_MsoNoSpacing">SERL data will be updated and made available to researchers on a quarterly basis. SERL is an evolving data resource and thus new editions of the data might include:</p> <ul> <li>additional records – more smart meter data, since the previous edition</li><li>additional participants – more participants recruited since the previous release</li><li>additional variables – where new variables become available to SERL. Tariff data is included for the first time in the 5th edition.</li></ul> <p class="x_x_x_MsoNoSpacing">Further information about SERL can be found on&nbsp;<a href="https://serl.ac.uk/" target="_blank">serl.ac.uk</a> and in the associated documentation. The 'Key Documents' section of the SERL website, which links to all publications that use SERL data, can be found at <a href="http://serl.ac.uk/key-documents">serl.ac.uk/key-documents</a>. If you do not see your SERL-data publication listed, please contact the SERL team via info@serl.ac.uk.<br> </p> <p class="x_x_x_MsoNoSpacing"></p> For the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 data users&nbsp;should note<span>&nbsp;</span>that neither the European Commission nor the European Centre for Medium-Range Weather Forecasts will be held responsible for any use that may be made of the<a href="https://apps.ecmwf.int/datasets/licences/copernicus/">&nbsp;Copernicus information</a>&nbsp;or data it contains.<span>&nbsp;</span>The&nbsp;Energy Performance of Buildings Data is also included and users must read and abide<span>&nbsp;</span>by the&nbsp;<a href="https://epc.opendatacommunities.org/docs/copyright">Copyright Information Notice</a>, provided by the Ministry of Housing, Communities and Local Government, that covers the use of Royal Mail information and non-address data provided under the&nbsp;<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">Open Government Licence v3.0</a>.<br>

The survey aimed to gather data on the impact of the COVID19 outbreak on the food support providers active in Greater Manchester. The lockdown created organizational hurdles to many services providing food to the most vulnerable. The survey explored more in depth the obstacles, the needs and the prospects of 55 organizations that were on the frontline in the first months of the crisis.

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 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. Face-to-face interview Telephone interview

The COVID-19 pandemic generated (and keeps generating) a huge corpus of news articles, easily retrievable in Factiva with very targeted queries. This dataset, generated with an ad-hoc parser and NLP pipeline, analyzes the frequency of lemmas and named entities in news articles (in German, French, Italian and English ) regarding Switzerland and COVID-19. The analysis of large bodies of grey literature via text mining and computational linguistics is an increasingly frequent approach to understand the large-scale trends of specific topics. We used Factiva, a news monitoring and search engine developed and owned by Dow Jones, to gather and download all the news articles published between January 2020 and May 2021 on Covid-19 and Switzerland. Due to Factiva's copyright policy, it is not possible to share the original dataset with the exports of the articles' text; however, we can share the results of our work on the corpus. All the information relevant to reproduce the results is provided. Factiva allows a very granular definition of the queries, and moreover has access to full text articles published by the major media outlet of the world. The query has been defined as follows (syntax in bold, explanation in italics): ((coronavirus or Wuhan virus or corvid19 or corvid 19 or covid19 or covid 19 or ncov or novel coronavirus or sars) and (atleast3 coronavirus or atleast3 wuhan or atleast3 corvid* or atleast3 covid* or atleast3 ncov or atleast3 novel or atleast3 corona*)) Keywords for covid19; must appear at least 3 times in the text and ns=(gsars or gout) Subject is “novel coronaviruses” or “outbreaks and epidemics” and “general news” and la=X Language is X (DE, FR, IT, EN) and rst=tmnb Restrict to TMNB (major news and business publications) and wc>300 At least 300 words and date from 20191001 to 20212005 Date interval and re=SWITZ Region is Switzerland It is important to specify some details that characterize the query. The query is not limited to articles published by Swiss media, but to articles regarding Switzerland. The reason is simple: a Swiss user googling for “Schweiz Coronavirus” or for “Coronavirus Ticino” can easily find and read articles published by foreign media outlets (namely, German or Italian) on that topic. If the objective is capturing and describing the information trends to which people are exposed, this approach makes much more sense than limiting the analysis to articles published by Swiss media. Factiva’s field “NS” is a descriptor for the content of the article. “gsars” is defined in Factiva’s documentation as “All news on Severe Acute Respiratory Syndrome”, and “gout” as “The widespread occurrence of an infectious disease affecting many people or animals in a given population at the same time”; however, the way these descriptors are assigned to articles is not specified in the documentation. Finally, the query has been restricted to major news and business publications of at least 300 words. Duplicate check is performed by Factiva. Given the incredibly large amount of articles published on COVID-19, this (absolutely arbitrary) restriction allows retrieving a corpus that is both meaningful and manageable. metadata.xlsx contains information about the articles retrieved (strategy, amount) This work is part of the PubliCo research project. This work is part of the PubliCo research project, supported by the Swiss National Science Foundation (SNF). Project no. 31CA30_195905

This article is a study of mourning among Shi'a Muslims during the COVID-19 pandemic through a call-in talk show called #IAMHUSSEINI. By analyzing the discourses of callers and presenters and locating them within a visual context of the television studio, this article shows how the viewership of #IAMHUSSEINI constitutes a televisual majlis (Arabic: ‘assembly') composed of more than passive asynchronous consumption and resembling what Patrick Eisenlohr refers to as ‘atmospheres'. This article argues that the COVID-19 pandemic drove #IAMHUSSEINI to recalibrate to expectations of a spatially proximate ritual, rather than sustaining a ‘natively digital' aesthetic, repurposing Richard Rogers' approach to digital methods. This change brought about a tacit understanding of the televisual majlis among #IAMHUSSEINI's viewers. This article therefore posits a difference between ‘spatial intercorporeality', in which bodies are mediated by spatial proximity, and ‘functional intercorporeality’, in which they are mediated by the material preconditions of a shared activity.

The project has two main research questions: RQ1 - what is the financial impact of Covid-19 on English professional football clubs so far? RQ2 - what is the wider impact to the local community focusing on four professional football clubs and football community trusts? The data collected for the project is broken down below across the two research questions highlighted above and is split between quantitative data (research question 1) and qualitative data (research question 2). Data collection for RQ1 Quantitative data was extracted from the financial statements of football clubs and the relevant financial data was used to create a bespoke financial database in Microsoft Excel. The data covers all 92 professional football clubs in the EPL and EFL in any given season from 1992/1993 to 2019/2020. At present there are 20 clubs that compete in the EPL and 24 in each of the Championship, League 1, and League 2. Owing to promotion and relegation during the time period analysed, our database covers a total of 112 unique professional football clubs. The financial database contains 28 independent variables in respect of financial and sporting performance that we have defined as Key Performance Indicators (KPIs) for a football club. Data collection for RQ2 Qualitative data was sourced from four professional football clubs that are currently competing in League 1 at the time of writing. Semi-structured interviews were conducted with key individuals at the clubs. A total of 18 interviews were undertaken across the four clubs. Owing to the Covid-19 situation and various lockdowns and restrictions throughout the project, the majority of interviews (apart from one face-to-face visit) were conducted online using Microsoft Teams. Interviews were recorded and transcribed in Teams and then exported to Quirkos (a specialist qualitative analysis programme) for further thematic analysis. Interview schedules were designed based on job role of the interviewee. For example, interviews with CEOs covered all aspects of the business including finance and strategy whereas interviews with Community Managers focused more on the fans of clubs and wider social impact.

The increase in the extent of working-from-home determined by the COVID-19 health crisis has led to a substantial shift of economic activity across geographical areas; which we refer to as a Zoomshock. When a person works from home rather than at the office, their work-related consumption of goods and services provided by the locally consumed service industries will take place where they live, not where they work. Much of the clientèle of restaurants, coffee bars, pubs, hair stylists, health clubs, taxi providers and the like located near workplaces is transferred to establishment located near where people live. These data are our calculations of the Zoomshock at the MSOA level. They reflect estimats of the change in the number of people working in UK neighbourhoods due to home-working.

Each database (*.db) contain three columns.First column: date and time of the tweetSecond column: tweetThird column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live.To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [2] has been appended as the last column. New databases will be added to this dataset every week. Bookmark this page for further updates. [1] https://sentiment.live/ [2] https://textblob.readthedocs.io/en/dev/