This dataset contains results from an interview study deployed between April and June 2020 to understand the changing situation in research software engineering work environments as a result of the COVID-19 pandemic. The study took place over an eight-week period, during which 17 self-identified research software engineers (RSEs) recorded their thoughts about the impact of the pandemic on their work and lifestyles. Each weekly entry included a series of questions based on the agile software engineering retrospective, a technique used within agile teams to look back on previous work. The first week followed a basic retrospective format, asking participants to assess what went well and didn’t go well, and to identify areas that could be improved going forward. To encourage ongoing participation, questions in subsequent weeks were adapted from creative retrospective plans designed by agile practitioners. An invitation to take part was issued via various international RSE social media channels in two batches, resulting in 11 participants starting in the week commencing on the 6th of April, and six starting in the week of the 20th of April. In total, 17 participants responded to the invitation; 15 agreed to participate after the first week. Participants were sent an email each week inviting them to complete a diary entry for a total of eight weeks; data were collected through a survey deployed via JISC’s Online Surveys.The consent form and a pdf of the first week of questions are included in the materials to provide an example of how the survey was administered. The entry week and questions are reported in full in columns A and B in the spreadsheet accordingly. To avoid identification of individuals, demographic information and some contextual information has been redacted. Redactions are indicated by *** in the response.The study was conducted as part of the STRIDE project: https://stride.org.uk.

Analysis of populations in the UK by likelihood of having received a third vaccination against COVID-19 using the Coronavirus (COVID-19) Infection Survey. This survey is being delivered in partnership with University of Oxford, University of Manchester, UK Health Security Agency and Wellcome Trust.

Roadside advertising impacts have been affected by the coronavirus outbreak in the United Kingdom (UK). However, in Edinburgh, Greater London, and Greater Manchester on the Thursday and Saturday before Easter, impacts increased compared to the week before. There was a decrease in roadside ad impacts on Good Friday and Easter Sunday. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Anonymous data from study.

Abstract copyright UK Data Service and data collection copyright owner.

The Evidence for Equality National Survey (EVENS) is a national survey that documents the experiences and attitudes of ethnic and religious minorities in Britain. EVENS was developed by the Centre on the Dynamics of Ethnicity (CoDE) in response to the disproportionate impacts of COVID-19 and is the largest and most comprehensive survey of the lives of ethnic and religious minorities in Britain for more than 25 years. EVENS used pioneering, robust survey methods to collect data in 2021 from 14,200 participants of whom 9,700 identify as from an ethnic or religious minority. The EVENS main dataset, which is available from the UK Data Service under SN 9116, covers a large number of topics including racism and discrimination, education, employment, housing and community, health, ethnic and religious identity, and social and political participation.

The EVENS Teaching Dataset provides a selection of variables in an accessible form to support the use of EVENS in teaching across a range of subjects and levels of study. The dataset includes demographic data and variables to support the analysis of:

• racism and belonging
• health and well-being during COVID-19
• political attitudes and trust.

Racism, belonging, impact of COVID-19, health, well-being, financial position, political attitudes and trust.

Abstract copyright UK Data Service and data collection copyright owner.

As the UK went into the first lockdown of the COVID-19 pandemic, the team behind the biggest social survey in the UK, Understanding Society (UKHLS), developed a way to capture these experiences. From April 2020, participants from this Study were asked to take part in the Understanding Society COVID-19 survey, henceforth referred to as the COVID-19 survey or the COVID-19 study.

The COVID-19 survey regularly asked people about their situation and experiences. The resulting data gives a unique insight into the impact of the pandemic on individuals, families, and communities. The COVID-19 Teaching Dataset contains data from the main COVID-19 survey in a simplified form. It covers topics such as

• Socio-demographics
• Whether working at home and home-schooling
• COVID symptoms
• Health and well-being
• Social contact and neighbourhood cohesion
• Volunteering

The resource contains two data files:

• Cross-sectional: contains data collected in Wave 4 in July 2020 (with some additional variables from other waves);
• Longitudinal: Contains mainly data from Waves 1, 4 and 9 with key variables measured at three time points.

Key features of the dataset

• Missing values: in the web survey, participants clicking "Next" but not answering a question were given further options such as "Don't know" and "Prefer not to say". Missing observations like these are recorded using negative values such as -1 for "Don't know". In many instances, users of the data will need to set these values as missing. The User Guide includes Stata and SPSS code for setting negative missing values to system missing.

• The Longitudinal file is a balanced panel and is in wide format. A balanced panel means it only includes participants that took part in every wave. In wide format, each participant has one row of information, and each measurement of the same variable is a different variable.

• Weights: both the cross-sectional and longitudinal files include survey weights that adjust the sample to represent the UK adult population. The cross-sectional weight (betaindin_xw) adjusts for unequal selection probabilities in the sample design and for non-response. The longitudinal weight (ci_betaindin_lw) adjusts for the sample design and also for the fact that not all those invited to participate in the survey, do participate in all waves.

• Both the cross-sectional and longitudinal datasets include the survey design variables (psu and strata).

A full list of variables in both files can be found in the User Guide appendix.

Who is in the sample?

All adults (16 years old and over as of April 2020), in households who had participated in at least one of the last two waves of the main study Understanding Society, were invited to participate in this survey. From the September 2020 (Wave 5) survey onwards, only sample members who had completed at least one partial interview in any of the first four web surveys were invited to participate. From the November 2020 (Wave 6) survey onwards, those who had only completed the initial survey in April 2020 and none since, were no longer invited to participate

The User guide accompanying the data adds to the information here and includes a full variable list with details of measurement levels and links to the relevant questionnaire.

• Socio-demographics;
• Whether working at home and home-schooling;
• COVID symptoms;
• Health and well-being;
• Social contact and neighbourhood cohesion;
• Volunteering.

These indicators are designed to accompany the SHMI publication. The SHMI methodology does not make any adjustment for deprivation. This is because adjusting for deprivation might create the impression that a higher death rate for those who are more deprived is acceptable. Patient records are assigned to 1 of 5 deprivation groups (called quintiles) using the Index of Multiple Deprivation (IMD). The deprivation quintile cannot be calculated for some records e.g. because the patient's postcode is unknown or they are not resident in England. Contextual indicators on the percentage of provider spells and deaths reported in the SHMI belonging to each deprivation quintile are produced to support the interpretation of the SHMI. Notes: 1. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. Activity that is being coded as COVID-19, and therefore excluded, is monitored in the contextual indicator 'Percentage of provider spells with COVID-19 coding' which is part of this publication. 2. Please note that there was a fall in the overall number of spells from March 2020 for England due to COVID-19 impacting on activity and the number has not returned to pre-pandemic levels. Further information at Trust level is available in the contextual indicator ‘Provider spells compared to the pre-pandemic period’ which is part of this publication. 3. There is a shortfall in the number of records for Frimley Health NHS Foundation Trust (trust code RDU), Manchester University NHS Foundation Trust (trust code R0A), Royal Surrey County Hospital NHS Foundation Trust (trust code RA2), and Wrightington, Wigan and Leigh NHS Foundation Trust (trust code RRF). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 4. There is a high percentage of invalid diagnosis codes for Hampshire Hospitals NHS Foundation Trust (trust code RN5). Values for this trust should therefore be interpreted with caution. 5. A number of trusts are currently engaging in a pilot to submit Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS), rather than the Admitted Patient Care (APC) dataset. As the SHMI is calculated using APC data, this does have the potential to impact on the SHMI value for these trusts. Trusts with SDEC activity removed from the APC data have generally seen an increase in the SHMI value. This is because the observed number of deaths remains approximately the same as the mortality rate for this cohort is very low; secondly, the expected number of deaths decreases because a large number of spells are removed, all of which would have had a small, non-zero risk of mortality contributing to the expected number of deaths. We are working to better understand the planned changes to the recording of SDEC activity and the potential impact on the SHMI. The trusts affected in this publication are: Barts Health NHS Trust (trust code R1H), Cambridge University Hospitals NHS Foundation Trust (trust code RGT), Croydon Health Services NHS Trust (trust code RJ6), Epsom and St Helier University Hospitals NHS Trust (trust code RVR), Frimley Health NHS Foundation Trust (trust code RDU), Imperial College Healthcare NHS Trust (trust code RYJ), Manchester University NHS Foundation Trust (trust code R0A), Norfolk and Norwich University Hospitals NHS Foundation Trust (trust code RM1), and University Hospitals of Derby and Burton NHS Foundation Trust (trust code RTG). 6. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page.

The number of overseas visits to the United Kingdom rose significantly in 2022 over the previous year, after falling dramatically due to the impact of the coronavirus (COVID-19) pandemic. Despite the sharp annual increase, inbound visits remained below pre-pandemic levels. In England, the most visited country by inbound travelers in the UK, international visits totaled around 27.8 million in 2022, remaining 23 percent lower than in 2019. Meanwhile, London, Edinburgh, and Manchester topped the ranking of the cities in the UK with the highest number of inbound visits in 2022.