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.

Dataset description

A collection of higher education (HE) and further education (FE) establishments in the UK, dated May 2021.

Further education (Sixth form) includes colleges. Higher education includes universities and post-graduate establishments (e.g. research institutions).

Education establishments are able to be filtered by Local Authority and output areas and include their statistical (ONS) code. These are the most-used statistical area codes for UK statistics.

For more info on the UK education system: Education System in the UK (UK Government document).

Acknowledgements

Search query was performed using https://get-information-schools.service.gov.uk/ Data shared under the Open Government License 3.0 (UK). More info: https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

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.

This dataset compares existing research data policies at UK higher education institutions. It consists of 83 cases. Polices were compared on a range of variables. Variables included policy length in words, whether the policy offers definitions, length of their definition of "data", defines institutional support, requires data management plans, states scope of staff and student coverage, specifies ownership of research outputs, details where external funder rights take precedent, guides on what data and documentation is required to be retained, how long it needs to be retained, reinforces where research ethics prevent open data, finalises where data can be accessed, speaks about open data requirements, includes a statement on funding the costs of Research Data Management, and specifies a review period for the policy. Data also includes the institution's year of foundation and a categorical variable grouping institutions by year of foundation allowing comparison across cohort groups of universities. A further two variables allow for identification of research based universities. Data on total research funding and research council for the year 2014/2015 was added, along with the number of research staff eligible for the 2014 UK Research Excellence Framework (REF). Also included is the institution's Grade Point Average based on its REF score using a Times Higher Education (THES) calculated score. Data collection was based on a list of UK Higher Education Institutions with data policies. This list was provided by the Digital Curation Centre. I also conducted a google search for UK university data policies to discover additional institutions that had adopted Research Data Management requirements. The data does not include 'Roadmaps' to EPSRC compliance.

The student sample for this research was selected from YouthSight’s Student Panel. Based on HESA statistics, the sample comprises national representation of gender, course year, and university type. The data is weighted on these factors. After fieldwork, the sample collected was checked for quality, and any ‘straight-liners’ were removed from the final total. The total student sample size is 2,153 respondents.Fieldwork was carried out between 29th July and 2nd August 2019.The survey instrument was developed by reviewing the limited number of studies and surveys on freedom of expression, consultations with colleagues and informed by our own experience. This resulted in the inclusion of seven comparative statements that are routinely used in surveys on freedom of expression in US universities, and a 15-item Moral Foundations Questionnaire, which enables the data to be interrogated by underlying moral profile. The definition of freedom of expression uses the framing adopted by King’s College London, which was developed through extensive consultation with the Students’ Union.

Published by the Department for Education based on information collected in the January 2015 school census, including information on the number of schools and pupils. It covers all types of school in England including: - local-authority-maintained schools - academies - free schools - studio schools - university technical colleges - independent schools The technical note explains the statistics. Information for London Borough of Barnet can be obtained by carrying out a search query on individual datasets.

These data were generated as part of a two-and-a-half-year ESRC-funded research project examining the digitalisation of higher education (HE) and the educational technology (Edtech) industry in HE. Building on a theoretical lens of assetisation, it focused on forms of value in the sector, and governance challenges of digital data. It followed three groups of actors: UK universities, Edtech companies, and investors in Edtech. The researchers first sought to develop an overview of the Edtech industry in HE by building three databases on Edtech companies, investors in Edtech, and investment deals, using data downloaded from Crunchbase, a proprietary platform. Due to Crunchbase’s Terms of Service, only parts of one database are allowed to be submitted to this repository, i.e. a list of companies with the project’s classification. A report offering descriptive analysis of all three databases was produced and is submitted as well. A qualitative discursive analysis was conducted by analysing seven documents in depth. In the second phase, researchers conducted interviews with participants representing three groups of actors (n=43) and collected documents on their organisations. Moreover, a list of documents collected from Big Tech (Microsoft, Amazon, and Salesforce) were collected to contextualise the role of global digital infrastructure in HE. Due to commercial sensitivity, only lists of documents collected about investors and Big Tech are submitted to the repository. Researchers then conducted focus groups (n=6) with representatives of universities (n=19). The dataset includes transcripts of focus groups and outputs of writing by participants during the focus group. Finally, a public consultation was held via a survey, and 15 participants offered qualitative answers.

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.

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.

The data gives the name, address, postcode, co-ordinates and enrolment data for schools in Northern Ireland. Further information regarding schools can be found on DE's website http://apps.education-ni.gov.uk/appinstitutes/default.aspx and ETI inspection reports on their website https://www.etini.gov.uk/

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 spaces

This is a dataset of schools and educational institutions air quality exposure data broken down by parliamentary constituency based on the current 2013 data from the London Atmospheric Emissions Inventory.This list includes all educational establishments (excluding private nurseries). Note there are some schools which have closed but these are included for consistency with previous data sets. The legal limit for NO2 is an average annual concentration of 40 ug/m3.

Primary Schools' Location in York.

*Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.

The Cambridge Centre for Business Research Survey of Knowledge Exchange Activity with Universities by United Kingdom Companies, 2017-2021 contains the results of an online survey of directors of UK companies in 2020-2021.

The survey was designed to assess the extent and nature of the knowledge exchange interactions of their companies with the university sector. It covers the three-year period to March 2020 prior to the Covid-19 pandemic and questions relating to the subsequent impact of the pandemic on knowledge exchange patterns. The researchers inquired about 33 modes of interaction grouped into four broad categories. These were commercialisation (3 modes), people-based (10 modes), problem-solving (12 modes) and community-based (4 modes).

The survey covers a sample of 3,823 companies in all sectors, regions and countries of the UK and employment sizes ranging from micro-firms less than 10 employees, to the largest public listed corporations. The response rate was 4.4 per cent and a detailed response bias analyses by survey wave and prompt wave showed largely insignificant sample response bias compared to the sampling frame drawn from the FAME database of all UK companies.

The dataset provides a unique source of data on a critical period of challenge for knowledge exchange in the UK. David Sweeney, the then Executive Director of Research England which sponsored the survey commented on an initial report of results in 2022 that "This report which has an exclusive focus on company interactions with universities, is an important addition to our understanding of the collaboration process" (The Changing State of Business-University Interactions in the UK. Centre for Business Research and NCUB. 2022 p2).

The survey dataset contains many variables comparable with a similar previous postal survey of an earlier period by two members of the current research team. The data from this is available from the Data Archive under SN 6464 - Cambridge Centre for Business Research Survey of Knowledge Exchange Activity by United Kingdom Businesses, 2005-2009.

These data were collected as part of a project by Andy Tattersall, Kate O'Neill, Chris Carroll (University of Sheffield) Nick Sheppard (University of Leeds) Thom Blake (University of York).A data request to Altmetric.com was submitted on the 16th April 2019 for entries that included authors from any of the three White Rose universities that are cited at least once in a Wikipedia entry. Data were tabulated and descriptive statistics were produced. The implications of the data are then discussed. We looked at just the White Rose universities of Leeds, Sheffield and York as they have their own shared Open Access repository in addition to their long history of collaboration, research focus; in addition that they are all members of the Russell Group of universities. The Altmetric.com data was tabulated with discipline data extracted from university systems. Wikipedia page entries and embedded citations were collected by Altmetric.com using unique identifiers within the research such as a DOI, PubMed ID or ISBN, this also included the data the research was cited within a Wikipedia entry. They also collected further bibliographic data that included publication title and date. Data collection also included the individual Altmetric.com page corresponding to each Wikipedia citation.We explored the number of Wikipedia citations by discipline for each of the three institutions. We note that the data that Altmetric.com supplies is only as good as the institutional and bibliometric journal that it harvests. Therefore as a consequence we found that certain fields were incomplete and we anticipate that based on a previous study by (Tattersall and Carroll 2018) that a percentage of the data in relation to institutional affiliation and date of publication to be inaccurate. (Tattersall and Carroll 2018) found by looking at citations within policy documents using Altmetric.com a sample of their data that as much as one third of data could be erroneous.Unpaywall data DOIs of all papers that included a Wikipedia citation were subsequently run against the Unpaywall API. Unpaywall is a not for profit service that maintains a database of links to full-text articles harvested from a range of open-access sources. Unpaywall's Simple Query Tool enabled us to submit a large number of DOIs which returned a set of results that we placed into spreadsheet that comprises of information on the open access status including ‘best_oa_url’ and ‘best_oa_licence’. For articles published under the gold model these will typically be the resolvable DOI under a Creative Commons licence whereas for accepted manuscripts from institutional repositories it tended to be the repository URL under a more restrictive licence, often no specific licence. For the purposes of this study, the primary field of interest is designated as ‘is_oa’ which enables us to ascertain the proportion of articles that are available open access (is_oa = TRUE) compared to those that are not (is_oa = FALSE). It is important to note also that any repository record that was under embargo at the time of data collection was returned is_oa = FALSE. Whether the OA version is gold (under a Creative Commons licence) or green (with a more restrictive or no specified licence) is also significant, as Wikipedia citations to gold articles would necessarily be open access with no further intervention, whereas Wikipedia citations to articles made open access from a repository will only be accessible directly from that citation if it includes the appropriate ‘best_oa_url’ which may need to be added manually.Tattersall, Andy, Nick Sheppard, Thom Blake, Kate O’Neill, and Christopher Carroll. 2022. “Exploring Open Access Coverage of Wikipedia-cited Research Across the White Rose Universities”. Insights 35: 3. DOI: http://doi.org/10.1629/uksg.559

The number of failing schools. A failing school is one judged on inspection by Ofsted to be providing inadequate education and lacking the capacity to improve, and therefore placed in special measures.

The first public release of the GRID database. Please note, the csv download only includes IDs, names & locations. See the JSON download for all metadata including types & relationships Please see here for a descriotion of the database format: https://www.grid.ac/format Release notes: Database seeded from research institutes in grant data from over 65 global funders. GeoNames IDs added to all institutes. NUTS codes added to all European institutes. Metadata added for the top 3000 Universities, majority of Germany and Australia and many more. Parent / Child relationships added for 65 super institute members (e.g. Max Planck, Chinese Academy of Sciences, etc.) External identification systems: - HESA institution codes (Higher Education Statistics Agency UK) - UCAS institution codes (Universities and Colleges Admissions Service, UK) - UKPRN institution codes (UK Provider Reference Number, UK) - 4373 Fundref codes

Interested parties can now request extracts of data from the NPD using an improved application process accessed through the following website; GOV.UK The first version of the NPD, including information from the first pupil level School Census matched to attainment information, was produced in 2002. The NPD is one of the richest education datasets in the world holding a wide range of information about pupils and students and has provided invaluable evidence on educational performance to inform independent research, as well as analysis carried out or commissioned by the department. There are a range of data sources in the NPD providing information about children’s education at different phases. The data includes detailed information about pupils’ test and exam results, prior attainment and progression at each key stage for all state schools in England. The department also holds attainment data for pupils and students in non-maintained special schools, sixth form and further education colleges and (where available) independent schools. The NPD also includes information about the characteristics of pupils in the state sector and non-maintained special schools such as their gender, ethnicity, first language, eligibility for free school meals, awarding of bursary funding for 16-19 year olds, information about special educational needs and detailed information about any absences and exclusions. Extracts of the data from NPD can be shared (under strict terms and conditions) with named bodies and third parties who, for the purpose of promoting the education or well-being of children in England, are:- • Conducting research or analysis • Producing statistics; or • Providing information, advice or guidance. The department wants to encourage more third parties to use the data for these purposes and produce secondary analysis of the data. All applications go through a robust approval process and those granted access are subject to strict terms and conditions on the security, handling and use of the data, including compliance with the Data Protection Act. Anyone requesting access to the most sensitive data will also be required to submit a business case. More information on the application process including the User Guide, Application Form, Security Questionnaire and a full list of data items available can be found from the NPD web page at:- https://www.gov.uk/national-pupil-database-apply-for-a-data-extract

This Statistical First Release (SFR) is based upon information collected in the School Census. It includes information on both the number of schools and pupils, and tables showing the number of pupils by age, gender, free school meal eligibility, ethnicity, first language, and gifted and talented status. It also includes a range of class size information. Source agency: Education Designation: National Statistics Language: English Alternative title: (Provisional)

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.