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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 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

Screenshots from the Telegram channels that I analyzed for my MA Thesis 'Vaccine Damages' and 'Lethal Injections': Strategic Communication in German Language Covid-19 Conspiracy Telegram Channel Screenshots were taken on February 10, 2022.

<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>

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/

Version 1.0 of the EXCEPTIUS project dataset (v.1.0) tracked all exceptional measures in the field of democratic governance/ human rights and international cooperation issued by national authorities in European Union (EU) countries (including UK and Switzerland, excluding Iceland) in order to tackle the COVID-19 pandemic from January to June 2020. The dataset also provides data on the implementation modalities of such measures. Version 2.0 now includes data on measures issued for subnational authorities (Austria, Belgium, Finland, France, Germany, Italy, Spain, Switzerland, UK) and covers the second (July- December 2020) and third (January-April 2021) waves. It also includes a new type event (10, emergency decision-making) allowing to distinguish between countries who implemented a state of emergency (type event 1) and countries which implemented emergency measures without activating state of emergency provisions.

<p>The <span style="font-style: italic;">Active Lives Survey</span> (ALS) commenced in November 2015. It replaces the Active People Survey, which ran from 2005 to 2015. The survey provides the largest sample size ever established for a sport and recreation survey and allows levels of detailed analysis previously unavailable. It identifies how participation varies from place to place, across different sports, and between different groups in the population. The survey also measures levels of activity (active, fairly active and inactive), the proportion of the adult population that volunteer in sports on a weekly basis, club membership, sports spectating and wellbeing measures such as happiness and anxiety, etc. The questionnaire was designed to enable analysis of the findings by a broad range of demographic information, such as gender, social class, ethnicity, household structure, age, and disability.<br><br>More general information about the study can be found on the Sport England <a href="https://www.sportengland.org/research/active-lives-survey/">Active Lives Survey</a> webpage and the <a href="https://activelives.sportengland.org/">Active Lives Online</a> website, including reports and data tables.<br> </p> <p><span style="font-style: italic;">Active Lives Adults Survey, 2020-2021</span><br></p> <p>The Coronavirus (COVID-19) pandemic developed rapidly during 2020 and 2021. Fieldwork for the Active Lives survey continued throughout the pandemic. This data, therefore, reflects the impact of coronavirus (COVID-19) on activity levels and the government’s policies to contain its spread. The survey instrument was largely unchanged. More general information about the study can be found on the Sport England&nbsp;<a href="https://www.sportengland.org/research/about-our-research/active-people-survey/" style="background-color: rgb(255, 255, 255);">Active People Survey</a>&nbsp;and&nbsp;<a href="https://www.sportengland.org/research/active-lives-survey/" style="background-color: rgb(255, 255, 255);">Active Lives Survey</a>&nbsp;webpages and&nbsp;<a href="https://activelives.sportengland.org/" style="background-color: rgb(255, 255, 255);">Active Lives Online</a>&nbsp;website.</p>

<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>

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.

TwinLife ist eine auf zwölf Jahre angelegte repräsentative verhaltensgenetische Studie zur Entwicklung von sozialen Ungleichheiten. Für eine detaillierte Studien-Dokumention besuchen Sie bitte https://www.twin-life.de/documentation/. Das Langfristvorhaben begann im Jahr 2014 und befragt in einem jährlichen Turnus über 4000 in Deutschland lebende Zwillingspaare und deren Familien zu unterschiedlichen Lebensbereichen. Durch den Vergleich von ein- und zweieiigen, gleichgeschlechtlichen Zwillingspaaren können neben sozialen Mechanismen auch genetische Differenzen zwischen Individuen, sowie die Kovariation und Interaktion sozialer und genetischer Einflussgrößen analysiert werden. Um die individuelle Entwicklung unterschiedlicher Einflussfaktoren zu dokumentieren werden die Familien über mehrere Jahre hinweg umfassend untersucht. Inhaltlich wird dabei auf sechs für soziale Ungleichheiten bedeutsame Lebensbereiche fokussiert: 1. Bildung und Kompetenzerwerb, 2. Karriere und Erfolg auf dem Arbeitsmarkt, 3. Integration und Teilhabe am sozialen, kulturellen und politischen Leben, 4. Lebensqualität und wahrgenommene Handlungsmöglichkeiten, 5. physische und psychologische Gesundheit sowie 6. Verhaltensprobleme und abweichendes Verhalten. In 2020 und 2021 fanden zwei zusätzliche Befragungen zu den Einflüssen und Folgen der COVID-19-Pandemie statt. Die erste Zusatzerhebung hatte zum Ziel, retrospektiv das Verhalten, die Einstellungen, Belastungen, Gesundheit und sozioökonomische Veränderungen im Leben der Befragten während der ersten Welle der COVID-19-Pandemie von März 2020 bis hin zu den ersten Lockerungen der Lockdown-Maßnahmen zu erfassen. Die zweite Zusatzbefragung zielte darauf ab, aktuelle Verhaltensweisen, Einstellungen, Belastungen, gesundheitliche und sozioökonomische Veränderungen während der COVID-19-Pandemie zu erfassen. TwinLife is a 12-year representative behavior genetic study investigating the emergence and development of social inequalities over the life course. For a detailed documentation of the study please visit https://www.twin-life.de/documentation/. The long-term project began in 2014 and surveys more than 4,000 pairs of twins and their families in different stages of life on a yearly basis. All of the subjects reside in Germany. Not only social, but also genetic mechanisms as well as covariations and interactions between these two factors can be examined with the help of identical and fraternal same-sex twins. In order to document the individual development of different aspects it is important to examine the families extensively over the course of several years. Six important contextual domains are focused on: 1. Education and academic performance / skill development, 2. career and labor market attainment, 3. integration and participation in social, cultural and political life, 4. quality of life and perceived capabilities, 5. physical and psychological health and 6. behavioral problems and deviant behavior. In 2020 and 2021, two a supplementary surveys on the influences and consequences of the COVID-19 pandemic took place. The first supplemental survey aimed to retrospectively capture the behavior, attitudes, stresses, health, and socioeconomic changes during the first wave of the COVID-19 pandemic from March 2020 through the first relaxations of the lockdown measures. The second supplemental survey aimed to assess current behaviors, attitudes, stresses, health, and socioeconomic changes during the COVID-19 pandemic. Ein- und zweieiige gleichgeschlechtliche Zwillingspaare der Geburtsjahrgänge 1.) 1990 bis 1993, 2.) 1997/1998, 3.) 2003/2004 und 4.) 2009/2010 (4 Geburtsjahrgangskohorten in 2 Teilstichproben) + mindestens ein biologischer Elternteil (+ ggf. der andere biologische Elternteil, Stiefeltern(-teile), ein Geschwister und die Partner der Zwillinge) Twins and their families (Extended Twin Family Design, ETFD): Monozygotic and dizygotic same-sex twin pairs born in the following years: 1.) 1990 - 1993, 2.) 1997/1998, 3.) 2003/2004 and 4.) 2009/2010 (4 birth cohorts in two subsamples) + at least one biological parent (+ if possible the other biological parent, step-parent(-s), one sibling and the twins´ partners) Wahrscheinlichkeitsauswahl: Mehrstufige Zufallsauswahl; Auswahlverfahren Kommentar: Wahrscheinlichkeitsauswahl: Mehrstufige Zufallsauswahl Probability: Multistage; Sampling Procedure Comment: Probability Sample: Multistage Sample Erhebungseinheit: Erwachsene; Jugendliche; Vorschulkinder; Eltern; sonstiges Die Datenlieferung v6-0-0 besteht aus 19 Datendateien im SPSS- und Stata-Format: • Master (ZA6701_master_v$): Der Masterdatensatz enthält Informationen über die Bruttostichprobe, wie z.B. konsistenzgeprüfte, zeitinvariante Variablen (Geschlecht, Geburtsjahr, Beziehung zu den Zwillingen, Zygotie, Migrationshintergrund) und wellenspezifische Variablen (Personentyp, Befragungsstatus) zu allen in TwinLife erfassten Personen in jeder Datenerhebung. • Befragungsdaten im Personenformat mit Filterfehlerbereinigung (ZA6701_person_wid$_v$ bzw. ZA6701_person_cov$_v$): Für jede Datenerhebung gibt es einen Datensatz. Die Kennung der Datenerhebung ist die Variable wid. Jede Befragungsperson (pid) erhält eine Datenzeile. • Befragungsdaten der COVID-Zusatzerhebungen (ZA6701_person_con$_v$): Für jede COVID-Zusatzerhebung gibt es einen Datensatz. Die Kennung der Datenerhebung ist die variable cov. • Befragungsdaten im Personenformat ohne Filterfehlerbereinigung (ZA6701_person_unadj_wid$_v$): Variablen, die zumindest teilweise im PAPI-Modus (selbstadministriert) erhoben wurden, werden hier noch einmal ohne Filterfehlerbereinigung abgelegt. Die Entscheidung, wie mit den Angaben der Befragten umzugehen ist, obliegt den Nutzer*innen. • Befragungsdaten im Familienformat (ZA6701_family_wide_wid$_v$): Es gibt einen Datensatz für jede Datenerhebung, außer der Corona-Zusatzbefragungen. Jede Familie hat eine Datenzeile mit Informationen zu jeder teilnehmenden Person in der Familie, die in separaten Variablen/Spalten gespeichert sind. Die Datensätze im Personen- und Familienformat enthalten dieselben Daten mit unterschiedlichen Strukturen. • Umfragegewichte (ZA6701_weights_v$): Datendatei mit den Gewichten der Erhebung (Designgewicht, Nonresponsegewicht und Panelgewichte). • Zudem sind jeweils ein Datensatz mit den Informationen aus der Befragung zur Feststellung der Zygotie der Zwillingspaare (ZA6701_zygosity_v$) und ein Datensatz, der für jede Variable auf Personenebene den Befragungsmodus der 1. F2F-Befragung dokumentiert (ZA6701_mode_wid1_v$), Teil des Datenrelease. Alle Daten sind mit englischen und deutschen Variablenbeschreibungen versehen. In Stata sind diese Sprachen in einem Datensatz enthalten, während es sich in SPSS um separate Datendateien handelt. Die Daten sind auf Inkonsistenzen geprüft und filterbereinigt. Variablen und Instrumente werden unter http://www.paneldata.org dokumentiert. Detaillierte Informationen zur Studie und ihren Besonderheiten finden Sie unter https://www.twin-life.de/documentation/. Bei Fragen zu den Dateninhalten wenden Sie sich bitte an data(at)twin-life.de. Die Bestellgebühren für diese Studie werden von TwinLife selbst übernommen, so dass für die Nutzung keine weiteren Gebühren anfallen. Survey unit: adults; teenagers; preschool children; parents; others The data delivery v6-0-0 consists of 19 data files in SPSS and Stata format: • Master data (ZA6701_master_v$): Includes information on the gross sample, such as consistency checked variables that are stable over time (sex, year of birth, relation to the twins, zygosity, migration background) and wave-specific variables (person type, response status, family composition) about all individuals included in TwinLife in each wave. • Survey data in person format with filter error adjustment (ZA6701_person_wid$_v$): There is one data set for each data collection. The data collection identifier is the variable wid. Each surveyed person has one data row (pid). • Data of covid supplemental surveys (ZA6701_person_cov$_v$): There is one data set for each covid supplemental survey. Each surveyed person has one data row. The data collection identifier is the variable cov. • Survey data in family format (ZA6701_family_wide_wid$_v$): There is one data set for each data collection except the COVID-19. Each family has one data row with information of each participating person in the family being stored in separate variables/columns). Person format and family format data sets contain the same data using different structures. • Survey weights (ZA6701_weights_v$): A data file containing the survey weights (design, nonresponse, and panel weights). • Twin zygosity assessment (ZA6701_zygosity_v$): A data file with information of the twin zygosity assessment in F2F 1. • Survey mode (ZA6701_mode_wid1_v$): A datafile with Contains information on the survey mode for each variable in F2F 1. • Unadjusted data of all variables collected in the PAPI survey mode (ZA6701_person_unadj_wid$_v$): One data file for each data collection with data unadjusted for filter errors for all constructs/variables that were at least partly surveyed in the PAPI mode (as of data release v4-1-0 in autumn 2020). All data is provided with English and German variable descriptions. In Stata, these languages are included in one data set while in SPSS, these are separate data files. The data are checked for inconsistencies and adjusted for filter errors. Variables and instruments are documented at http://www.paneldata.org. Detailed information on the study and special features can be found at https://www.twin-life.de/documentation/. For questions regarding the content of the data, please contact data(at)twin-life.de. Charges for downloading this data will be paid by the TwinLife project, so the use of the data is free of charge! Face-to-face interview: CAPI/CAMI Self-administered questionnaire: Computer-assisted (CASI) Self-administered questionnaire: Paper Educational measurements and tests Recording Self-administered questionnaire: Web-based Telephone interview: CATI F2F 1: Persönliche Haushaltsinterviews auf Basis dreier verschiedener Interview-Modi (Persönliches Interview: CAPI (Computerunterstützte persönliche Befragung), Selbstausfüller: CASI (Computerunterstützte Selbstbefragung), Selbstausfüller: Papier) plus Messungen und Tests der kognitiven Fähigkeiten sowie Scans/Fotos von Zeugnissen; Fragebögen für außerhäusig lebende Teilnehmer in zwei Modi: Selbstausfüller: CAWI (Computerunterstütztes Web-Interview) und Selbstausfüller: Papier. CATI 1: Telefonische Haushalts- und Personenbefragung. F2F 2: Persönliche Haushaltsinterviews auf Basis dreier verschiedener Interview-Modi (Persönliches Interview: CAPI (Computerunterstützte persönliche Befragung), Selbstausfüller: CASI (Computerunterstützte Selbstbefragung), Selbstausfüller: Papier) plus Gummibärchentest sowie Scans/Fotos von Zeugnissen CATI 2: Telefonische Haushalts- und Personenbefragung. Corona-Zusatzbefragung CoV 1: CAWI (Computerunterstütztes Web-Interview) F2F 3: Persönliche Haushaltsinterviews auf Basis verschiedener Interview-Modi (Persönliches Interview: CAPI (Computerunterstützte persönliche Befragung), Selbstausfüller: CASI (Computerunterstützte Selbstbefragung) und CAWI (Computerunterstütztes Web-Interview), Selbstausfüller: Papier plus Aufzeichnungen der APGAR-scores, Scans/Fotos von Zeugnissen und Speichelproben). CoV 2: CAPIbyPhone (Computerunterstützte persönliche Befragung), CATI (Computerunterstütztes Telefon-Interview) oder CAWI (Computerunterstütztes Web-Interview) F2F 1: Household interviews with the family via three different interview modes (Face-to-face interview: CAPI (Computer Assisted Personal Interview), Self-administered questionnaire: CASI (Computer Assisted Self-Interview), and Self-administered questionnaire: Paper plus cognitive tests, scans/photos of certificates; interviews with family members living outside the interviewed households by two modes (Self-administered questionnaire: CAWI (Computer Assisted Web Interview), Self-administered questionnaire: Paper. CATI 1: individual and household interviews by phone. F2F 2: Household interviews with the family via three different interview modes (Face-to-face interview: CAPI (Computer Assisted Personal Interview), Self-administered questionnaire: CASI (Computer Assisted Self-Interview), and Self-administered questionnaire: Paper plus gummy bear tests, scans/photos of certificates. CATI 2: individual and household interviews by phone. CoV 1: CAWI (Computer Assisted Web Interview) F2F 3: Household interviews with the family via different interview modes (Face-to-face interview: CAPI (Computer Assisted Personal Interview), Self-administered questionnaire: CASI (Computer Assisted Self-Interview), CAWI (Computer Assisted Web Interview), Self-administered questionnaire: Paper plus recording of the APGAR score, scans/photos of school reports, saliva sample). CoV 2: CAPIbyPhone (Computer Assisted Personal Interview), CATI (Computer Assisted Telephone Interview) or CAWI (Computer Assisted Web Interview)