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/

Presents an overview of all aspects of higher education in the UK. It draws together data on students, staff and institutional finance, applicants via the Universities and Colleges Admission Service (UCAS), graduates and graduate destinations, student support, and international comparisons. Source agency: Business, Innovation and Skills Designation: National Statistics Language: English Alternative title: Higher Education Statistics for the United Kingdom

The USR consists of records of undergraduate students on courses of one academic year or more; postgraduate students on courses of one academic year or more; academic and related staff holding regular salaried appointments, and finance data for all UK universities.
The Finance dataset contains details of income and expenditure for all of the UK universities. These data are contained in a series of files for each year. For detailed information on structure and content of these files users should refer to the documentation that accompanies this dataset. Also included in the Finance dataset is the Student Load data. Student Load is, in the USR context, a reallocation of student-head count numbers, by apportioning them as a percentage to the departmental cost centres where they are taught, thus enabling student load, staff and financial data to be brought together.

This dataset is about books. It has 4 rows and is filtered where the book subjects is Universities and colleges-England-Finance. It features 9 columns including author, publication date, language, and book publisher.

Abstract copyright UK Data Service and data collection copyright owner. This study is comprised by the data collected for a wider project exploring the historical relationship between higher education and the UK economy. The project sought to provide a long-term explanation of the relationships between funding, widening access and socio-economic aspects of higher education. Three main areas were considered: -The provision of an in-depth historical account and analysis of the numbers and extent of students and staff for the purposes of evaluating the main characteristics of UK higher education development back the 1920s. -The provision of an in-depth historical account and evaluation of levels and structures of income and expenditure in higher education -The interpretation of these data with reference to major socio-economic indicators. Main Topics: This study is a collation and analysis of statistics on UK higher education which refers to pre-1992 universities and includes all institutions delivering degrees afterwards. The dataset, which gathers historical series on funding and development of universities from the early 1920s, is the result of research into primary and secondary governmental and institutional sources. Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research. No sampling (total universe) Compilation or synthesis of existing material

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.

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.

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.

Dataset of the E, I and XE indices for the univerities that educated cabinet ministers in the UK between 1922 and 2021

Information on all schools in England including local authority maintained schools, academies, free schools, studio schools, university technical colleges and independent schools. The information includes address, school type and phone number. This information comes from EduBase, DfE’s register of schools, and will be updated every month. http://www.education.gov.uk/edubase/home.xhtml

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 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.

This dataset is about books. It has 27 rows and is filtered where the book publisher is Universities UK. It features 7 columns including author, publication date, language, and book publisher.

Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard Safeguarded Licence (see 'Access' section).

Secure Access versions of the Next Steps include:

• sensitive variables from the questionnaire data for Sweeps 1-9. These are available under Secure Access SN 8656.
  
• National Pupil Database (NPD) linked data at Key Stages 2, 3, 4 and 5, England. These are available under SN 7104.
• Linked Individualised Learner Records learner and learning aims datasets for academic years 2005 to 2014, England. These are available under SN 8577.
  
• detailed geographic indicators for Sweep 1 and Sweep 8 (2001 Census Boundaries) are available under SN 8189, geographic indicators for Sweep 8 and 9 (2011 Census Boundaries) are available under SN 8190, and geographic indicators for Sweep 9 (2021 Census Boundaries) are available under SN 9337. The Sweep 1 geography file was previously held under SN 7104.
• Linked Health Administrative Datasets (Hospital Episode Statistics) for financial years 1997-2022 held under SN 8681.
• Linked Student Loans Company Records for years 2007-2021 held under SN 8848.

When researchers are approved/accredited to access a Secure Access version of Next Steps, the Safeguarded (EUL) version of the study - Next Steps: Sweeps 1-9, 2004-2023 (SN 5545) - will be automatically provided alongside.

The Student Loans Company (SLC) is a non-profit making government-owned organisation that administers loans and grants to students in colleges and universities in the UK. The Next Steps: Linked Administrative Datasets (Student Loans Company Records), 2007 - 2021: Secure Access includes data on higher education loans for those Next Steps participant who provided consent to SLC linkage in the age 25 sweep. The matched SLC data contains information about participant's applications for student finance, payment transactions posted to participant's accounts, repayment details and overseas assessment details.

DOI Abstract copyright UK Data Service and data collection copyright owner.The USR consists of records of undergraduate students on courses of one academic year or more; postgraduate students on courses of one academic year or more; academic and related staff holding regular salaried appointments, and finance data for all UK universities. The Finance dataset contains details of income and expenditure for all of the UK universities. These data are contained in a series of files for each year. For detailed information on structure and content of these files users should refer to the documentation that accompanies this dataset. Also included in the Finance dataset is the Student Load data. Student Load is, in the USR context, a reallocation of student-head count numbers, by apportioning them as a percentage to the departmental cost centres where they are taught, thus enabling student load, staff and financial data to be brought together. Main Topics: Finance: income and expenditure; university; cost centre. Student load: undergraduate, postgraduate (taught course or research); cost centre. No information recorded Annual returns from each university.

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

The datasets provided by UK based online learning university "Open University". More about the dataset: https://analyse.kmi.open.ac.uk/open_dataset

Abstract copyright UK Data Service and data collection copyright owner. The Universities and Colleges Admissions Service (UCAS) Data, 2007-2021: Secure Access includes information about applicants and applications to UK Higher Education with UCAS registered Higher Education Providers. The data runs from the 2007 application cycle, up to and including the 2019 cycle. All applicants to UK Higher Education are included which covers those domiciled within the UK and those outside. Latest edition informationFor the third edition (April 2024), all FACT 349, b36, 8d1, d5f and 7d7 datasets for 2007-2021 (i.e. all datasets apart from the UCAS variable codes) have been replaced with new versions, edited to reduce the risk of disclosure. Main Topics: