Presents statistics on student support paid to students in the form of loans and grants or to their University/College in the form of tution fees. The students are English domiciles studying anywhere in the UK or EU students studying in England. Source agency: Student Loans Company Designation: National Statistics Language: English Alternative title: Student Support for Higher Education in England Data and Resources 2012/13 (Final) and 2013/14 (Provisional)HTML 2012/13 (Final) and 2013/14 (Provisional)

This dataset shows the location of Higher Education (HE) and Further Education (FE) institutes in the Great Britain. This should cover Universities and Colleges. Many institutes have more than one campus and where possible this is refelcted in the data so a University may have more than one entry. Postcodes have also been included for instities where possible. This data was collected from various sources connected with HEFE in the UK including JISC and EDINA. This represents the fullest list that the author could compile from various sources. If you spot a missing institution, please contact the author and they will add it to the dataset. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-02-01 and migrated to Edinburgh DataShare on 2017-02-21.

Presents statistics on student support paid to students in the form of loans and grants or to their University/College in the form of tution fees. The students are Northern Ireland domiciles studying anywhere in the UK, ROI or EU students studying in Northern Ireland. Source agency: Student Loans Company Designation: Official Statistics not designated as National Statistics Language: English Alternative title: Student Support for Higher Education in Northern Ireland

The information refers to NI domiciled students enrolled at higher education institutions in the UK. The dataset is collected annually and is based on enrolments in higher education institutions in the UK on 1st December each year. The dataset is collected by the Higher Education Statistics Agency from higher education institutions throughout the UK and provided to the Department for Employment and Learning, Northern Ireland, for analysis. For 2013/14, NI Domiciled enrolments and qualifications at Open University are available. In previous years, these figures were included in NI students studying in England, as the administrative centre of the Open University is located in England. The specification of the HESA Standard Registration Population has changed for 2007/08 enrolments onwards. Writing up and sabbatical students are now excluded from this population where they were previously included in published enrolment data and therefore 2007/08 data onwards cannot be directly compared to previous years.

The information refers to NI domiciled students gaining higher education qualifications from UK higher education institutions. The dataset is collected annually and is based on students obtaining a qualification at UK higher education institutions. The dataset is collected by the Higher Education Statistics Agency from higher education institutions throughout the UK and provided to the Department for Employment and Learning, Northern Ireland, for analysis. For 2013/14, NI Domiciled enrolments and qualifications at Open University are available. In previous years, these figures were included in NI students studying in England, as the administrative centre of the Open University is located in England.

Presents statistics on student support paid to students in the form of loans and grants or to their University/College in the form of tution fees. The students are Welsh domiciles studying anywhere in the UK or EU students studying in Wales. Source agency: Student Loans Company Designation: Official Statistics not designated as National Statistics Language: English Alternative title: Student Support for Higher Education in Wales Data and Resources Academic Year 2012/13 (Final) and 2013/14 (Provisional)HTML Academic Year 2012/13 (Final) and 2013/14 (Provisional)

Data product is provided by ASL Marketing. It contains current college students who are attending colleges and universities nationwide. Connect with this market by: Class Year Field of Study Home/School address College Attending Ethnicity School Type Region Sports Conference Gender eSports Email

This dataset provides Census 2021 estimates that classify schoolchildren and full-time students aged 5 years and over in England and Wales by student accommodation and by age. The estimates are as at Census Day, 21 March 2021.

Estimates for single year of age between ages 90 and 100+ are less reliable than other ages. Estimation and adjustment at these ages was based on the age range 90+ rather than five-year age bands. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Coverage

Census 2021 statistics are published for the whole of England and Wales. Data are also available in these geographic types:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

Student accommodation type

Combines the living situation of students and school children in full-time education, whether they are living:

• alone
• in a student household
• with parents
• in a communal establishment, split by university and other communal establishment type
• in other household types

It also includes whether these households contain one or multiple families.

This variable is comparable with the student accommodation variable but splits the communal establishment type into “university” and “other” categories.

Age

A person’s age on Census Day, 21 March 2021 in England and Wales. Infants aged under 1 year are classified as 0 years of age.

This dataset presents a cluster analysis of UK universities based on four synthetic environments: social, cultural, physical and economic. These were developed based on variables that represented an educational ecosystem of well-being. The cluster analysis was initially linked to the LSYPE-Secure dataset using the UKPRNs (i.e. higher education institutional number) and hence the cluster analysis used data from around 2009-2012 to represent Wave 6 and Wave 7 of the LSYPE-Secure dataset. The cluster analysis was based on using a variety of variables available from HESA and the Office for Students (OfS) to represent these environments, for example: Social: had demographics of students and staff including ethnicity and sex Cultural: had data on research and teaching scores Economic: had data on student: staff ratio and expenditure Physical: had data related to the built and natural environment including residential sites, blue and green spacesEarlier last year (April 2018), the UK Office for Students (OfS) noted that students from underrepresented groups such as black and minority ethnic (BME) students and those from disadvantaged backgrounds were less likely to succeed at university. Coupled with this, research has shown that students from these groups are also more likely to have poorer mental health and wellbeing. However, there is substantial social and political pressure on universities to act to improve student mental health. For example, the Telegraph ran the headline "Do British universities have a suicide problem?" Thus, in June 2018, the Hon. Sam Gyimah, the then UK universities minister, informed university vice-chancellors that student mental health and wellbeing has to be one of their top priorities. Universities are investing substantive sums in activities to tackle student mental health but doing so with no evidence base to guide strategic policy and practice. These activities may potentially be ineffective, financially wasteful, and possibly, counter-productive. Therefore, we need a better evidence base which this project intends to fulfil. Currently, there is a lack of evidence and understanding about which groups of young people going to universities may have poorer life outcomes (such as education, employment, and mental health and well-being) as a result of their mental health and wellbeing during their adolescent years. These life outcomes and their mental health and wellbeing, however, are important for understanding the context of the complex social identities of the young people, such as the intersections between their gender, ethnicity, sexuality, religion and socio-economic status. Otherwise, these young people may feel misunderstood or judged. Most of the large body of quantitative research on life outcomes tend to focus on one social characteristic/identity of the student, such as the young person's gender or ethnicity or socio-economic status, but not the combination of all of these, i.e. the intersectionalities. Primarily, the reason for this has been the lack of sufficient data. This research draws on data from the Longitudinal Study of Young People in England (LSYPE), which tracked over 15,000 adolescents' education and health over 7 years between 2004-2010 (from when they were 13-19 years old), and the Next Steps Survey, which collected data from the same individuals in 2015 when they were 25 years and in the job market. This dataset also had an ethnic boost, which thus allows for the exploratory analysis of intersectionalities. Currently, there are a number of interventions being implemented to improve the university environment. However, there is a lack of evidence on how the university environment (such as their its size, amount of academic support available, availability of sports activities, students' sense of belonging, etc.) can affect the young person'students' mental health and wellbeing life outcomes. This evidence can be determined through by using the LSYPE data supplemented and by university environment data supplemented from the National Student Survey (NSS) and the Higher Education Statistics Agency (HESA). Thus this research uses an intersectional approach to investigate the extent to which the life outcomes of young persons who go to university are affected by their social inequality groupings and mental health and well-being during adolescence. Additionally, this research also aims to determine the characteristics of university environments that can improve the life outcomes of these young people depending on their social and mental health/wellbeing background. We use secondary data analysis of mainly HESA and OfS variables and created derived variables.

Abstract copyright UK Data Service and data collection copyright owner.The purpose of this study was to investigate the factors which influence young people in their demand for higher education in its various forms - at universities, colleges of education (teacher training colleges), polytechnics and colleges of further education. Six of these eight surveys are the main study which was carried out on (a) the schools and the fifth-formers and the sixth-formers in them, and (b) the colleges of further education and their home students studying A' level subjects full-time. The material from the young people includes that given by them at two stages, first from the main survey which took place before they sat GCE examinations and before the results of higher education applications were available and secondly, from the follow-up survey after the results of the GCE examinations were known and the young people already embarked on courses the following session. For the fifth and sixth-form surveys (67001, 67002 and 68005) there is also incorporated the form teachers' broad assessment of ability (three categories) examination prospects and higher education and career aspirations. For the schools the main survey was carried out in the Spring term 1967 with the follow-up in the autumn. The equivalent dates in the colleges of further education were May 1967 and January 1968. (The remaining two surveys are subsidiary to the project; 66023 is the pilot stage of the main survey part of 68004, i.e. home students studyingA' levels full-time in the further education colleges, whilst 67005 (fifth-formers in the fast stream in schools) comprises a sub-set of material from the main fifth-form survey for an enlarged sample of those pupils in schools with fast streams). The six surveys in the main study are interlinked with information from the school or college complementing that from the pupil or student. In addition there is standardisation - as far as was practicable - between sections of the questionnaire used for the fifth-formers, lower and upper sixth-formers and students in further education (e.g. general background). The contents of the questionnaire for the upper sixth-formers and further education students corresponded particularly closely. Copies of all reports on the surveys are in the Library of the Royal Statistical Society. Mainly they deal with specific aspects of the data e.g. 'Subject commitments and the demand for higher education', G. A. Barnard and M. D. McCreath (1970) Journal of the Royal Statistical Society Series A (General) 133 (3) 358 - 408, 'Report of the surveys of full-time 'A' level students (home) in colleges of further education', by M. D. McCreath (1970). All the material which is available is listed in the most recent report written in 1972, Factors influencing choice of higher education: surveys carried out by Margaret D McCreath under the direction of Professor G A Barnard, Department of Mathematics, University of Essex. This 1972 report includes data from both the school and further education surveys. The extensive tables are based on the following variables: social class, expectations about leaving school and reasons for doing so, source of the most useful discussion on what to do after school, family experience of higher education, O' andA' level attempts and passes, knowledge of higher education entry requirements and with whom these were discussed, as well as intended and actual destinations in higher education. The technical note on the sample design by Judith Doherty was published in 1970 as Appendix 1 of Volume 1 of the Schools Council Sixth-Form Survey, Sixth-Form Pupils and Teachers. Details of the response rates are given in the 1972 report mentioned above.

This dataset is a compilation of processed data on citation and references for research papers including their author, institution and open access info for a selected sample of academics analysed using Microsoft Academic Graph (MAG) data and CORE. The data for this dataset was collected during December 2019 to January 2020.Six countries (Austria, Brazil, Germany, India, Portugal, United Kingdom and United States) were the focus of the six questions which make up this dataset. There is one csv file per country and per question (36 files in total). More details about the creation of this dataset are available on the public ON-MERRIT D3.1 deliverable report.The dataset is a combination of two different data sources, one part is a dataset created on analysing promotion policies across the target countries, while the second part is a set of data points available to understand the publishing behaviour. To facilitate the analysis the dataset is organised in the following seven folders:PRTThe dataset with the file name "PRT_policies.csv" contains the related information as this was extracted from promotion, review and tenure (PRT) policies. Q1: What % of papers coming from a university are Open Access?- Dataset Name format: oa_status_countryname_papers.csv- Dataset Contents: Open Access (OA) status of all papers of all the universities listed in Times Higher Education World University Rankings (THEWUR) for the given country. A paper is marked OA if there is at least an OA link available. OA links are collected using the CORE Discovery API.- Important considerations about this dataset: - Papers with multiple authorship are preserved only once towards each of the distinct institutions their authors may belong to. - The service we used to recognise if a paper is OA, CORE Discovery, does not contain entries for all paperids in MAG. This implies that some of the records in the dataset extracted will not have either a true or false value for the _is_OA_ field. - Only those records marked as true for _is_OA_ field can be said to be OA. Others with false or no value for is_OA field are unknown status (i.e. not necessarily closed access).Q2: How are papers, published by the selected universities, distributed across the three scientific disciplines of our choice?- Dataset Name format: fsid_countryname_papers.csv- Dataset Contents: For the given country, all papers for all the universities listed in THEWUR with the information of fieldofstudy they belong to.- Important considerations about this dataset: * MAG can associate a paper to multiple fieldofstudyid. If a paper belongs to more than one of our fieldofstudyid, separate records were created for the paper with each of those _fieldofstudyid_s.- MAG assigns fieldofstudyid to every paper with a score. We preserve only those records whose score is more than 0.5 for any fieldofstudyid it belongs to.- Papers with multiple authorship are preserved only once towards each of the distinct institutions their authors may belong to. Papers with authorship from multiple universities are counted once towards each of the universities concerned.Q3: What is the gender distribution in authorship of papers published by the universities?- Dataset Name format: author_gender_countryname_papers.csv- Dataset Contents: All papers with their author names for all the universities listed in THEWUR.- Important considerations about this dataset :- When there are multiple collaborators(authors) for the same paper, this dataset makes sure that only the records for collaborators from within selected universities are preserved.- An external script was executed to determine the gender of the authors. The script is available here.Q4: Distribution of staff seniority (= number of years from their first publication until the last publication) in the given university.- Dataset Name format: author_ids_countryname_papers.csv- Dataset Contents: For a given country, all papers for authors with their publication year for all the universities listed in THEWUR.- Important considerations about this work :- When there are multiple collaborators(authors) for the same paper, this dataset makes sure that only the records for collaborators from within selected universities are preserved.- Calculating staff seniority can be achieved in various ways. The most straightforward option is to calculate it as _academic_age = MAX(year) - MIN(year) _for each authorid.Q5: Citation counts (incoming) for OA vs Non-OA papers published by the university.- Dataset Name format: cc_oa_countryname_papers.csv- Dataset Contents: OA status and OA links for all papers of all the universities listed in THEWUR and for each of those papers, count of incoming citations available in MAG.- Important considerations about this dataset :- CORE Discovery was used to establish the OA status of papers.- Papers with multiple authorship are preserved only once towards each of the distinct institutions their authors may belong to.- Only those records marked as true for _is_OA_ field can be said to be OA. Others with false or no value for is_OA field are unknown status (i.e. not necessarily closed access).Q6: Count of OA vs Non-OA references (outgoing) for all papers published by universities.- Dataset Name format: rc_oa_countryname_-papers.csv- Dataset Contents: Counts of all OA and unknown papers referenced by all papers published by all the universities listed in THEWUR.- Important considerations about this dataset :- CORE Discovery was used to establish the OA status of papers being referenced.- Papers with multiple authorship are preserved only once towards each of the distinct institutions their authors may belong to. Papers with authorship from multiple universities are counted once towards each of the universities concerned.Additional files:- _fieldsofstudy_mag_.csv: this file contains a dump of fieldsofstudy table of MAG mapping each of the ids to their actual field of study name.

Abstract copyright UK Data Service and data collection copyright owner. The purpose of this study was the construction and analysis of a database of the records relating to students who attended the University of Aberdeen from 1860 to 1920, in order to create a comprehensive textual dossier of information about individual students which could be accessed easily by the University Archivist in answering frequent enquiries about past students; to facilitate the researches of scholars preparing publications on different aspects of University life for the institution's quincentenary in 1995; to demonstrate trends in the geographical and social mobility of the student population, as well as the impact on academic life of major changes in the curriculum. Main Topics: Three broad categories of data have been sought in constructing a database of students at the University of Aberdeen from 1860-1920. Out of a possible total of 52 fields, approximately 16 are devoted to biographical and background information, including dates of birth and death, place of origin, schools attended and father's occupation. A further 21 fields are concerned with the student's university education and experiences, including actual and total periods of study, classes attended, examinations passed (where applicable), degree(s) obtained, both at Aberdeen and at other universities, bursaries, prizes and medals awarded, the location of lodgings (where applicable), and membership of university societies. The third major area of investigation was the student's post-university life, incorporating information on locations, careers, and civil, military and academic honours and awards. Several further fields have been allocated to identifying sources of information. The database has been designed to allow not only minute documentation of the backgrounds and careers of individual students, but also global analysis of changing patterns of geographical and social mobility among the student population as a whole. Data on students' (and parents') occupations have been classified in conformity with the Registrar General's classification. No sampling (total universe)

The Open University (OU) dataset is an open database containing student demographic and click-stream interaction with the virtual learning platform. The available data are structured in different CSV files. You can find more information about the original dataset at the following link: https://analyse.kmi.open.ac.uk/open_dataset.

We extracted a subset of the original dataset that focuses on student information. 25,819 records were collected referring to a specific student, course and semester. Each record is described by the following 20 attributes: code_module, code_presentation, gender, highest_education, imd_band, age_band, num_of_prev_attempts, studies_credits, disability, resource, homepage, forum, glossary, outcontent, subpage, url, outcollaborate, quiz, AvgScore, count.

Two target classes were considered, namely Fail and Pass, combining the original four classes (Fail and Withdrawn and Pass and Distinction, respectively). The final_result attribute contains the target values.

All features have been converted to numbers for automatic processing.

Below is the mapping used to convert categorical values to numeric:

code_module: 'AAA'=0, 'BBB'=1, 'CCC'=2, 'DDD'=3, 'EEE'=4, 'FFF'=5, 'GGG'=6

code_presentation: '2013B'=0, '2013J'=1, '2014B'=2, '2014J'=3

gender: 'F'=0, 'M'=1

highest_education: 'No_Formal_quals'=0, 'Post_Graduate_Qualification'=1, 'HE_Qualification'=2, 'Lower_Than_A_Level'=3, 'A_level_or_Equivalent'=4

IMBD_band: 'unknown'=0, 'between_0_and_10_percent'=1, 'between_10_and_20_percent'=2, 'between_20_and_30_percent'=3, 'between_30_and_40_percent'=4, 'between_40_and_50_percent'=5, 'between_50_and_60_percent'=6, 'between_60_and_70_percent'=7, 'between_70_and_80_percent'=8, 'between_80_and_90_percent'=9, 'between_90_and_100_percent'=10

age_band: 'between_0_and_35'=0, 'between_35_and_55'=1, 'higher_than_55'=2

disability: 'N'=0, 'Y'=1

student's outcome: 'Fail'=0, 'Pass'=1

For more detailed information, please refer to:

Casalino G., Castellano G., Vessio G. (2021) Exploiting Time in Adaptive Learning from Educational Data. In: Agrati L.S. et al. (eds) Bridges and Mediation in Higher Distance Education. HELMeTO 2020. Communications in Computer and Information Science, vol 1344. Springer, Cham. https://doi.org/10.1007/978-3-030-67435-9_1

This dataset provides Census 2022 estimates for distance travelled to place of study of people aged 4 and over studying by age (in 4 categories) in Scotland.

Age

A person's age on Census Day, 20 March 2022. Infants aged under 1 year are classified as 0 years of age.

Distance travelled to work or place of study

The distance between a person’s home address and their main place of work or study (Grouped).

Address of place of work or study is used (along with home address) to explore the relationship between where people live and where they work or study. Used in conjunction with information from the method of travel question, the data helps to identify commuter patterns and routes and provide a reliable indicator for the demands placed on public and private transport.

It is used to inform the balance of housing and jobs in particular areas and assess the need for services such as new schools. Information on where people live and work is used by government departments to define “Travel to Work Areas” - these are approximations of self-contained labour markets and are the smallest areas for which unemployment rates are published. Collecting information on both work and study address enables a more accurate count of daytime populations to be obtained, which is particularly useful for areas accommodating universities and businesses. It also allows the differences in travel patterns between these groups to be compared.

Details of classification can be found here

The quality assurance report can be found here

The project uses a unique dataset collected from UK higher education institutions comprised of individual-level data on undergraduate students from the UK and EU (i.e. those potentially eligible for bursaries), including the bursary they are awarded each year, academic outcomes, prior attainment and other demographic information. Collection consists of data from 10 English universities on bursary awards, student characteristics, and student outcomes over the period 2006-2011. The aim is to identify the impact of bursaries on the academic outcomes of students by exploiting variation in bursary rules across institutions. This will be achieved by comparing students with similar characteristics but receiving different levels of bursary due to the institution they are attending. To account for underlying differences across universities we will exploit changes in bursary eligibility rules within a university over time. The findings should be useful for universities and policy makers when considering the role of bursaries in improving student outcomes. Higher education bursaries and performance: annual test scores, drop out and degree outcomes Despite some £300m per year being spent on higher education bursaries in the UK, there remains no empirical research that examines the effectiveness of this element of financial aid as a means to improve student outcomes whilst at university. The aim of this project is to investigate the impact of bursaries on students’ academic outcomes – including annual test results, completion rates and degree classification. All universities in England where contacted, requesting individual level data on undergraduates, on the following: Student-level data for all UK/EU full-time undergraduate students (i.e. only those eligible for bursaries), with, for each undergraduate: • Their year of entry (from 2006 onwards - or any previous years, if available) • Their A level grades (or other qualifications such as BTEC, HND etc) on entry (with subject of study, if possible) • The subject of degree student studying • Amount of bursary awarded each year, including zeros (i.e. a report for every student, whether they got a bursary or not) • Their annual examination/module scores (by subject if possible) • Their final degree classification • Whether dropped out, and year of drop out • Basic demographics such as age at point of entry, gender, ethnicity, SES, parental income and any other demographic information available • Outline of means-testing rules for bursary awards (as detailed as possible - i.e. parents income>£40k = no bursary; parents income>£10k & <£15k=£2000 bursary etc) each year. 22 universities provided data. 17 are useable.

Background:
The Millennium Cohort Study (MCS) is a large-scale, multi-purpose longitudinal dataset providing information about babies born at the beginning of the 21st century, their progress through life, and the families who are bringing them up, for the four countries of the United Kingdom. The original objectives of the first MCS survey, as laid down in the proposal to the Economic and Social Research Council (ESRC) in March 2000, were:

• to chart the initial conditions of social, economic and health advantages and disadvantages facing children born at the start of the 21st century, capturing information that the research community of the future will require
• to provide a basis for comparing patterns of development with the preceding cohorts (the National Child Development Study, held at the UK Data Archive under GN 33004, and the 1970 Birth Cohort Study, held under GN 33229)
• to collect information on previously neglected topics, such as fathers' involvement in children's care and development
• to focus on parents as the most immediate elements of the children's 'background', charting their experience as mothers and fathers of newborn babies in the year 2000, recording how they (and any other children in the family) adapted to the newcomer, and what their aspirations for her/his future may be
• to emphasise intergenerational links including those back to the parents' own childhood
• to investigate the wider social ecology of the family, including social networks, civic engagement and community facilities and services, splicing in geo-coded data when available

Additional objectives subsequently included for MCS were:

• to provide control cases for the national evaluation of Sure Start (a government programme intended to alleviate child poverty and social exclusion)
• to provide samples of adequate size to analyse and compare the smaller countries of the United Kingdom, and include disadvantaged areas of England

Further information about the MCS can be found on the Centre for Longitudinal Studies web pages.

The content of MCS studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The first sweep (MCS1) interviewed both mothers and (where resident) fathers (or father-figures) of infants included in the sample when the babies were nine months old, and the second sweep (MCS2) was carried out with the same respondents when the children were three years of age. The third sweep (MCS3) was conducted in 2006, when the children were aged five years old, the fourth sweep (MCS4) in 2008, when they were seven years old, the fifth sweep (MCS5) in 2012-2013, when they were eleven years old, the sixth sweep (MCS6) in 2015, when they were fourteen years old, and the seventh sweep (MCS7) in 2018, when they were seventeen years old.

End User Licence versions of MCS studies:
The End User Licence (EUL) versions of MCS1, MCS2, MCS3, MCS4, MCS5, MCS6 and MCS7 are held under UK Data Archive SNs 4683, 5350, 5795, 6411, 7464, 8156 and 8682 respectively. The longitudinal family file is held under SN 8172.

Sub-sample studies:
Some studies based on sub-samples of MCS have also been conducted, including a study of MCS respondent mothers who had received assisted fertility treatment, conducted in 2003 (see EUL SN 5559). Also, birth registration and maternity hospital episodes for the MCS respondents are held as a separate dataset (see EUL SN 5614).


How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from MCS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Secure Access datasets:
Secure Access versions of the MCS have more restrictive access conditions than versions available under the standard End User Licence or Special Licence (see 'Access data' tab above).

Secure Access versions of the MCS include:

• detailed sensitive variables not available under EUL. These have been grouped thematically and are held under SN 8753 (socio-economic, accommodation and occupational data), SN 8754 (self-reported health, behaviour and fertility), SN 8755 (demographics, language and religion) and SN 8756 (exact participation dates). These files replace previously available studies held under SNs 8456 and 8622-8627
  
• detailed geographical identifier files which are grouped by sweep held under SN 7758 (MCS1), SN 7759 (MCS2), SN 7760 (MCS3), SN 7761 (MCS4), SN 7762 (MCS5 2001 Census Boundaries), SN 7763 (MCS5 2011 Census Boundaries), SN 8231 (MCS6 2001 Census Boundaries), SN 8232 (MCS6 2011 Census Boundaries), SN 8757 (MCS7), SN 8758 (MCS7 2001 Census Boundaries) and SN 8759 (MCS7 2011 Census Boundaries). These files replace previously available files grouped by geography SN 7049 (Ward level), SN 7050 (Lower Super Output Area level), and SN 7051 (Output Area level)
• linked education administrative datasets for Key Stages 1, 2, 4 and 5 held under SN 8481 (England). This replaces previously available datasets for Key Stage 1 (SN 6862) and Key Stage 2 (SN 7712)
  
• linked education administrative datasets for Key Stage 1 held under SN 7414 (Scotland)
• linked education administrative dataset for Key Stages 1, 2, 3 and 4 under SN 9085 (Wales)
  
• linked NHS Patient Episode Database for Wales (PEDW) for MCS1 – MCS5 held under SN 8302
• linked Scottish Medical Records data held under SNs 8709, 8710, 8711, 8712, 8713 and 8714;
• Banded Distances to English Grammar Schools for MCS5 held under SN 8394
• linked Health Administrative Datasets (Hospital Episode Statistics) for England for years 2000-2019 held under SN 9030
• linked Hospital of Birth data held under SN 5724.

The linked education administrative datasets held under SNs 8481,7414 and 9085 may be ordered alongside the MCS detailed geographical identifier files only if sufficient justification is provided in the application.

Researchers applying for access to the Secure Access MCS datasets should indicate on their ESRC Accredited Researcher application form the EUL dataset(s) that they also wish to access (selected from the MCS Series Access web page).

The seventh sweep of the Millennium Cohort Study (MCS7) was carried out when the cohort members were 17 years old. As 17 is a key transitional age, the sweep purposefully focused on engaging with the cohort members themselves (in addition to their parents). MCS7 marks an important transitional time in the cohort members' lives, where educational and occupational paths can diverge significantly. It is also an important age in data collection terms since it may be the last sweep at which parents are interviewed and it is an age when direct engagement with the cohort members themselves rather than their families is crucial to the long term viability of the study. To reflect this, face-to-face interviews with the cohort members have been conducted for the first time. Cohort members were also asked to do a range of other activities including filling in a self-completion questionnaire on the interviewer's tablet, completing a cognitive assessment (number activity) and having their height, weight and body fat measurements taken. In addition, they were asked to complete a short online questionnaire after the visit.

Parents were still interviewed at MCS7. Resident parents were asked to complete a household interview and a short online questionnaire, and one parent was asked to complete a Strengths and Difficulties Questionnaire (SDQ) about the cohort member. Cohort members who were either unable or unwilling to complete the main survey were asked to complete a short follow up questionnaire online after the fieldwork finished. This contained some key questions and was designed to boost response and maintain engagement.

For the second edition (March 2021), two new data files have been added (mcs7_cm_qualifications and mcs7_parent_derived), and five existing data files have been updated (mcs7_cm_derived, mcs7_cm_interview, mcs7_hhgrid, mcs7_parent_cm_interview, cs7_parent_interview). In addition the User Guide, the Derived Variables User Guide and the Longitudinal Data Dictionary have all been updated.

This dataset was created and deposited onto the University of Sheffield Online Research Data repository (ORDA) on 14-Dec-2023 by Dr. Matthew S. Hanchard, Research Associate at the University of Sheffield iHuman Institute. The dataset forms part of the outputs from a project titled ‘Fostering cultures of open qualitative research’ which ran from January 2023 to June 2023, and was funded with £13,913.85 of Research England monies held internally by the University of Sheffield as part of their ‘Enhancing Research Cultures’ scheme 2022-2023. The dataset aligns with ethical approval granted by the University of Sheffield School of Sociological Studies Research Ethics Committee (ref: 051118) on 23-Jan-2023. This includes due concern for participant anonymity and data management. ORDA has full permission to store this dataset and to make it open access for public re-use on the basis that no commercial gain will be made from reuse. It has been deposited under a CC-BY-NC license. Overall, this dataset comprises: 1 x Workshop transcript - in .docx file format which can be opened with Microsoft Word, Google Doc, or an open-source equivalent. The workshop took place on 18-Jul-2023 at the Wave Building, University of Sheffield. All five attendees have read and approved a portion of transcripts containing their own discussion. All workshop attendees have had an opportunity to retract details should they wish to do so. All workshop attendees have chosen whether to be pseudonymised or named directly. The pseudonym or real name can be used to identify individual participant responses in the qualitative coding held within accompanying dataset from the same project - Survey Responses: Hanchard M and San Roman Pineda I (2023) Fostering cultures of open qualitative research: Dataset 1 – Survey Responses. The University of Sheffield. DOI: 10.15131/shef.data.23567250.v1. Interviews: Hanchard M and San Roman Pineda I (2023) Fostering cultures of open qualitative research: Dataset 2 – Interview Transcripts. The University of Sheffield. DOI: 10.15131/shef.data.23567223.v2. As a limitation, the audio recording of the workshop session that this transcript is based upon is missing a section (due to a recording error) and may contain errors/inaccuracies (due to poor audio conditions within the workshop room). Every effort has been taken to correct these, including participants themselves reviewing their discussion/quotes, but the transcript may still contain minor inaccuracies, typos, and/or other errors in the text - as is noted on the transcript itself. The project was undertaken by two staff: Co-investigator: Dr. Itzel San Roman Pineda (Postdoctoral Research Assistant) ORCiD ID: 0000-0002-3785-8057 i.sanromanpineda@sheffield.ac.uk Labelled as ‘Researcher 1’ throughout all project datasets. Principal Investigator (corresponding dataset author): Dr. Matthew Hanchard (Research Associate) ORCiD ID: 0000-0003-2460-8638 m.s.hanchard@sheffield.ac.uk iHuman Institute, Social Research Institutes, Faculty of Social Science Labelled as ‘Researcher 2’ throughout all project datasets.

At the end of 2019 the Greater London Authority (GLA) commissioned the Social Market Foundation (SMF) to conduct research focusing on how the outcomes of graduates who have studied in London and those from London vary, by a range of different characteristics. This research uses a range of methods to gain insight into the outcomes of graduates who were domiciled in London prior to university and those who studied at a London institution. In particular, the SMF undertook a literature review of academic, government and policy papers on degree outcomes and the factors that interact with these; conducted descriptive analysis of data provided by the Higher Education Statistics Agency (HESA); and ran a series of logit regression models to look further into how different characteristics influence graduate outcomes when controlling for other variables. The data includes young first degree students studying at a Higher Education Institution within London and students domiciled in London prior to university who study outside of the capital. The data includes four cohorts from the academic years 2010/11 to 2013/14.

We recruited 835 faculty members from 40 universities in the United Kingdom (UK) via our networks within UK STEM departments. Participants were drawn from various STEM departments, including biological science (18%), computer science (7%), engineering (28%) mathematical science (16%), and physics (13%). Respondents completed an online survey in which details about their employment were collected at the beginning and additional demographic information was collected at the end. The middle section of the survey contained measures of: identity and career perceptions; staying in academia; collaborative working style, received opportunities; workplace diversity and inclusion and affective workplace climate; experience of harassment; and assessment of a workshop intervention.

The Quarterly Labour Force Survey July - September 2018: Teaching Dataset is based on the Quarterly Labour Force Survey, July - September 2018 (QLFS JS18; available from the UK Data Archive under SN 8407) and constitutes real data which are used by the government and are behind many headlines. The teaching dataset contains fewer variables and has been subjected to certain simplifications and additions for the purpose of learning and teaching.

The main differences are:

• the number of variables has been reduced;
• extra variables (based on variables from the original QLFS JS18 dataset) have been added, including a weight for which mean=1;
• a reduced codebook is provided.

Further information is available in the study documentation which includes a dataset user guide.