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.

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.

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.

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.

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.

The Health Survey for England (HSE), 2002: Teaching Dataset has been prepared solely for the purpose of teaching and student use. The dataset will help class tutors to incorporate empirical data into their courses and thus to develop students’ skills in quantitative methods of analysis.

All the variables and value labels are those used in the original HSE files, with one exception (New-wt) which is a new weighting variable.

Users may be interested in the Guide to using SPSS for Windows available from Online statistical guides and which explores this dataset.

The original HSE 2002 dataset is held at the UK Data Archive under SN 4912.

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.

Understanding Society, (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

The Understanding Society: Calendar Year Dataset, 2022, is designed for analysts to conduct cross-sectional analysis for the 2022 calendar year. The Calendar Year datasets combine data collected in a specific year from across multiple waves and these are released as separate calendar year studies, with appropriate analysis weights, starting with the 2020 Calendar Year dataset. Each subsequent year, an additional yearly study is released.

The Calendar Year data is designed to enable timely cross-sectional analysis of individuals and households in a calendar year. Such analysis can, however, only involve variables that are collected in every wave (excluding rotating content, which is only collected in some of the waves). Due to overlapping fieldwork, the data files combine data collected in the three waves that make up a calendar year. Analysis cannot be restricted to data collected in one wave during a calendar year, as this subset will not be representative of the population. Further details and guidance on this study can be found in the document 9333_main_survey_calendar_year_user_guide_2022.

These calendar year datasets should be used for cross-sectional analysis only. For those interested in longitudinal analyses using Understanding Society please access the main survey datasets: End User Licence version or Special Licence version.

Understanding Society: the UK Household Longitudinal Study, started in 2009 with a general population sample (GPS) of UK residents living in private households of around 26,000 households and an ethnic minority boost sample (EMBS) of 4,000 households. All members of these responding households and their descendants became part of the core sample who were eligible to be interviewed every year. Anyone who joined these households after this initial wave was also interviewed as long as they lived with these core sample members to provide the household context. At each annual interview, some basic demographic information was collected about every household member, information about the household is collected from one household member, all 16+-year-old household members are eligible for adult interviews, 10-15-year-old household members are eligible for youth interviews, and some information is collected about 0-9 year-olds from their parents or guardians. Since 1991 until 2008/9 a similar survey, the British Household Panel Survey (BHPS), was fielded. The surviving members of this survey sample were incorporated into Understanding Society in 2010. In 2015, an immigrant and ethnic minority boost sample (IEMBS) of around 2,500 households was added. In 2022, a GPS boost sample (GPS2) of around 5,700 households was added. To know more about the sample design, following rules, interview modes, incentives, consent, and questionnaire content, please see the study overview and user guide.

Co-funders

In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency.

End User Licence and Special Licence versions:

There are two versions of the Calendar Year 2022 data. One is available under the standard End User Licence (EUL) agreement (SN 9333), and the other is a Special Licence (SL) version (SN 9334). The SL version contains month and year of birth variables instead of just age, more detailed country and occupation coding for a number of variables and various income variables have not been top-coded (see document 9333_eul_vs_sl_variable_differences for more details). Users are advised first to obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL data have more restrictive access conditions; prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. The main longitudinal versions of the Understanding Society study may be found under SNs 6614 (EUL) and 6931 (SL).

Low- and Medium-level geographical identifiers produced for the mainstage longitudinal dataset can be used with this Calendar Year 2022 dataset, subject to SL access conditions. See the User Guide for further details.

Suitable data analysis software

These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Stata SE or MP software is needed to analyse the larger files, which contain about 1,800 variables.

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.

For the academic year of 2024/2025, the University of Oxford was ranked as the best university in the world, with an overall score of 98.5 according the Times Higher Education. The Massachusetts Institute of Technology and Harvard University followed behind. A high number of the leading universities in the world are located in the United States, with the ETH Zürich in Switzerland the highest ranked neither in the United Kingdom nor the U.S.

This dataset is about book series. It has 1 row and is filtered where the books is How much is that star in the window? : professorial salaries and research performance in UK universities. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Interest in the relationship between the activities of universities and action on climate change is growing, but until recently there has been little focus on the role of researchers, particularly with regards to how research practices and culture can enable or inhibit change. This study addresses this gap, exploring researchers’ perceptions of universities’ measures to tackle their own emissions, their own engagement on issues surrounding the climate crisis, and challenges and opportunities for researchers to contribute to them. We present findings from a large, mixed methods survey of 1,853 researchers from 127 UK universities across disciplines and career stages, including comparing responses across these professional differences, and analysis based on over 5,000 open text responses provided by the survey participants. The results show that while most have some knowledge of the actions being taken and feel that climate emergency declarations are making a (small) difference, many think not enough is being done. They feel that responsibility for university climate action sits across government, universities and research councils, but almost all researchers are also personally worried about climate change and want to do more themselves to address it. For the most part, they also strongly support climate advocacy by those engaged in research. Yet high workload, uncertainty about what actions to take, perceived lack of agency or power, inflexible university processes and pressure to travel are just some of the many barriers researchers face in taking action. The study highlights how these barriers can be overcome, and the steps universities and researchers can take to better incorporate climate action into their research culture and practices.The Centre for Climate Change Transformations (C3T) will be a global hub for understanding the profound changes required to address climate change. At its core, is a fundamental question of enormous social significance: how can we as a society live differently - and better - in ways that meet the urgent need for rapid and far-reaching emission reductions? While there is now strong international momentum on action to tackle climate change, it is clear that critical targets (such as keeping global temperature rise to well within 2 degrees Celsius relative to pre-industrial levels) will be missed without fundamental transformations across all parts of society. C3T's aim is to advance society's understanding of how to transform lifestyles, organisations and social structures in order to achieve a low-carbon future, which is genuinely sustainable over the long-term. Our Centre will focus on people as agents of transformation in four challenging areas of everyday life that impact directly on climate change but have proven stubbornly resistant to change: consumption of goods and physical products, food and diet, travel, and heating/cooling. We will work across multiple scales (individual, community, organisational, national and global) to identify and experiment with various routes to achieving lasting change in these challenging areas. In particular, we will test how far focussing on 'co-benefits' will accelerate the pace of change. Co-benefits are outcomes of value to individuals and society, over and above the benefits from reducing greenhouse gas emissions. These may include improved health and wellbeing, reduced waste, better air quality, greater social equality, security, and affordability, as well as increased ability to adapt and respond to future climate change. For example, low-carbon travel choices (such as cycling and car sharing) may bring health, social and financial benefits that are important for motivating behaviour and policy change. Likewise, aligning environmental and social with economic objectives is vital for behaviour and organisational change within businesses. Our Research Themes recognise that transformative change requires: inspiring yet workable visions of the future (Theme 1); learning lessons from past and current societal shifts (Theme 2); experimenting with different models of social change (Theme 3); together with deep and sustained engagement with communities, business and governments, and a research culture that reflects our aims and promotes action (Theme 4). Our Centre integrates academic knowledge from disciplines across the social and physical sciences with practical insights to generate widespread impact. Our team includes world-leading researchers with expertise in climate change behaviour, choices and governance. We will use a range of theories and research methods to fill key gaps in our understanding of transformation at different spatial and social scales, and show how to target interventions to impactful actions, groups and moments in time. We will partner with practitioners (e.g., Climate Outreach, Greener-UK, China Centre for Climate Change Communication), policy-makers (e.g., Welsh Government) and companies (e.g., Anglian Water) to develop and test new ways of engaging with the public, governments and businesses in the UK and internationally. We will enhance citizens', organisations' and societal leaders' capacity to tackle climate change through various mechanisms, including secondments, citizens' panels, small-scale project funding, seminars, training, workshops, papers, blog posts and an interactive website. We will also experiment with transformations within academia itself, by trialling sustainable working practices (e.g., online workshops), being 'reflexive' (studying our own behaviour and its impacts on others), and making our outputs and data publically available. An anonymous survey of researchers (N = 1,853) was self-administered online using Qualtrics survey software. The population for the survey was researchers at UK universities. The aim of this broad approach was to reach participants from across different disciplines, career and level of professional involvement with climate change. Universities UK provided the clearest list of UK universities to work from and use as the sampling frame – 140 in total. While participants from other UK universities were eligible to complete the survey, only universities from the Universities UK list were directly contacted. Participants were recruited via email through Heads of Departments (or equivalent). However, the email asked Heads of Departments to forward the survey to researchers in their department rather than asking for permission to contact the researchers directly.

RESPOND project produced a high level of empirical material in 11 countries (Sweden, the UK, Germany, Italy, Poland, Austria, Greece, Bulgaria, Turkey, Iraq, and Lebanon) where the research is conducted between the period 2017-2020. The country teams gathered macro (policies), meso (implementation/stakeholders) and micro (individuals/asylum seekers and refuges) level data related to the thematic fields formulated in four work packages: borders, protection regimes, reception, and integration. An important contribution of this research has been its micro/individual focus which enabled the research teams to capture and understand the migration experiences of asylum seekers and refugees and their responses to the policies and obstacles that they have encountered.

Country teams conducted in total 539 interviews with refugees and asylum seekers, and more than 210 interviews with stakeholders (state and non-state actors) working in the field of migration. Additionally, the project has conducted a survey study in Sweden and Turkey (n=700 in each country), covering similar topics.

This dataset is only about the micro part of the Respond research, and reflects data derived out of 539 interviews conducted with asylum seekers and refugees in 11 countries and here presented in a quantitative form. The whole dataset is structured along the work package topics: Border, Protection, Reception and Integration.

This dataset is prepared as part of Work Package D4.4 (Dataset on Reception) the Horizon 2020 RESPOND project as a joint effort of the below listed project partners.

• • Uppsala University (dataset entries from Sweden)
• • Göttingen University (dataset entries from Germany)
• • Glasgow Caledonian University (dataset entries from the UK and Hungary)
• • Istanbul Bilgi University (dataset entries from Turkey)
• • University of Cambridge (dataset entries from the UK, Sweden and Germany)
• • Swedish Research Institute Istanbul (dataset entries from Turkey)
• • University of Florence (dataset entries from Italy)
• • Özyegin University (dataset entries from Turkey)
• • University of Aegean (dataset entries from Greece)
• • University of Warsaw (dataset entries from Poland)
• • Hammurabi Human Rights Organization (dataset entries from Iraq)
• • Lebanon Support (dataset entries from Lebanon)
• • Austrian Academy of Sciences (dataset entries from Austria)

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 data relates to careers advice and guidance to year 11 and 12 pupils in Northern Ireland and their university aspirations. Data is presented for year 11 and 12 pupils on their confidence making career decisions, the support they require to achieve their career goals, their awareness of the all-age Careers Service, their knowledge of how to contact a Careers Adviser outside school and also their university aspirations. The data is derived from the Young Persons’ Behaviour and Attitudes Survey, carried out between September 2019 and February 2020.

This project uses interview data to investigate the implications, implementation and consequences of Brexit for UK universities, including the effects in relation to migration, international education and financial sustainability. The generic research questions are: 1) What are the perceived implications of Brexit for UK universities as leaders and others see it? 2) What are the principal responses of universities and what are their capabilities to monitor, judge, strategies, respond, initiate and make internal changes, in relation to Brexit? 3) How do these factors vary by UK nation; university mission, status, resources; and discipline? The dataset includes 124 semi-structured transcripts of semi-structured interviews conducted between November 2017 to September 2018. Participants were from 12 universities in the UK. This project is part of the ESRC’s 'The UK in a Changing Europe' initiative which supports research into the relationship between the UK and the European Union (EU).UK universities are extensively engaged in Europe, in collaborative research and infrastructure and through EU citizen staff and students. The UK’s departure from the EU has many potential consequences for UK universities and their staffing, research, international education and financial sustainability. Brexit is an unprecedented development with implications in almost every domain of UK higher education (HE) and a range of possible forms and consequences for individual UK HEIs, with marked potential for differential effects (e.g. in research capability, international students, staffing, mission, income) across the variation of HEI types. Though Brexit has many possible forms, in any form it is likely to disrupt existing projects, networks and activities, and could imply sharp reductions in staff, students and/or income, in some or all HEIs. It also calls for new and innovative lines of institutional and discipline-based development on and off shore.In an uncertain and fast changing setting characterised by multiple possibilities and sudden shocks, HEIs will be required to monitor, respond, adjust, strategize, reorient and initiate with unprecedented speed and effectiveness; to build new relations and activity portfolios in Europe and beyond; and to grapple with new challenges to human resource management, risk management, financial sustainability, mission, governance and local implementation systems. This research investigates the policy implications, implementation and consequences of Brexit for UK HE, in two priority areas identified by the Economic and Social Research Council: implications of Brexit for migration, and impacts in the economy and future trade arrangements. UK higher education institutions (HEIs) are extensively engaged in Europe and in this sector EU relations have been unambiguously positive and productive. While there is a range of possible Brexit scenarios, UK HE is closely affected by the Brexit-related policy settings for staff mobility, retention and recruitment ('migration'); for international student policy and regulation, with consequences for tuition revenues and balance sheets ('trade'); and by the effects of Brexit in research relations between UK and European HEIs. Research papers co-authored with colleagues in Europe outweigh total papers co-authored with US and other English-speaking countries, more than 20 per cent of UK R&D funding is from international sources with much from collaborative European research schemes. The role of UK universities in Europe is central to their outstanding global research performance: UK accounts for 3.2 per cent of global R&D spending, 9.5 per cent of scientific papers downloaded, 11.6 per cent of citations, and 15.9 per cent of the most highly-cited papers. EU frameworks enable many UK researchers to lead, while sharing the best ideas and people from other EU member countries. The research capacity and reputation of UK HEIs also underpins the nation's role as the world's second largest exporter of international education after the US. The government has stated that it hopes to raise education exports by almost 50 per cent to 30 billion pa in 2020. The main data collection consists of qualitative case studies in 12 UK HEIs, with participating institutions selected from all four nations and illustrating the diversity of the sector. There are 127 semi-structured interviews, with senior academic leaders of HEIs, chief financial officers, heads of human resources, executive deans in three disciplines (health, science, social science), research professors from these disciplines, and student representatives. The project also conducted policy-oriented seminars which will have both data gathering and dissemination/public discussion purposes. The practical outcomes of the research are (a) through research, public events and briefings, to draw to the attention of policy makers and public the implications of different Brexit scenarios in higher education, (b) within HE, to investigate and make recommendations on the capacity of UK HEIs to respond effectively to the challenges triggered by Brexit under the different possible Brexit scenarios, in the context also of other policy developments (Office for Students, TEF). Interviews were conducted between November 2017 to September 2018. Participants were from 12 universities in the UK. We have sampled universities based on the following criteria to include a variety of case study universities: (1) Nations: We aimed to include universities from the four nations of the UK and had eight case study universities in England, two in Scotland, one in Wales, and one in Northern Ireland. (2) Type of universities: We sampled universities to include those from different groupings and had four Russell Group universities, five other pre-1992 universities, and three post-1992 universities. Within each case study university, we aimed to interview participants with different level of responsibilities, including 44 senior executives (e.g. vice-chancellor), 23 senior administrators (e.g. director of finance), 10 members of governing body, 28 academic leaders (e.g. department head), 8 students, and 14 academics in Health Sciences, Sciences, Social Sciences.

The Changing Arctic Ocean (CAO) oceanographic dataset comprises data collected in the Arctic Ocean, including the Barents Sea and Fram Strait, as part of the Changing Arctic Ocean programme. The data were collected over multiple research cruises starting in June 2017. The majority of these cruises were conducted during the Arctic summer on board the RRS James Clark Ross, with further winter cruises completed in collaboration with the Nansen Legacy project on board the RV Helmer Hanssen. Shipboard data collection included the deployment of conductivity-temperature-depth (CTD) packages, ocean seagliders, mulitcorers, grabs, nets, trawls, and a shelf underwater camera system. The CAO programme aims to understand the changes in Arctic marine ecosystem in a quantifiable way, enabling computer models to help predict the consequences of these changes on, for example; surface ocean productivity; species distributions; food webs; and ecosystems, and the services they provide (ecosystem services). It was initially a Natural Environment Research Council (NERC) funded programme comprising four projects: Arctic PRIZE (Arctic productivity in the seasonal ice zone), led by Finlo Cottier (Scottish Association for Marine Science - SAMS); ARISE (Can we detect changes in Arctic ecosystems?), led by Claire Mahaffey (University of Liverpool); ChAOS (The Changing Arctic Ocean Seafloor), led by Christian Maerz (University of Leeds) and DIAPOD (Mechanistic understanding of the role of diatoms in the success of the Arctic Calanus complex and implications for a warmer Arctic), led by David Pond (University of Stirling). Additional projects were added to the programme in July 2018 through funding provided by NERC and the German Federal Ministry of Education and Research (BMBF). The majority of data are held by the British Oceanographic Data Centre (BODC) but a proportion of the data, primarily biological, are stored at the British Antarctic Survey Polar Data Centre (polardatacentre@bas.ac.uk) and any BMBF funded data are held by Pangaea (https://www.pangaea.de/).

I recently submitted my dissertation for my MSc in Business Analytics titled: Understanding & Predicting Student Rental Prices in a U.K. city: Machine Learning & Traditional Methods.

I chose this dissertation research area due to the lacking literature investigating U.K. rental dynamics (particularly in Northern Ireland) and due to the real and very current issue of rising rent felt in Belfast by students.

Based on a selection of 36 property variables such as geographic location, bedroom number & property size - I built multiple machine learning models to predict the price of rent and to understand the most important variables in selected models.

No existing dataset was available that combined all the required information for Belfast and therefore I chose to complete the task of data mining and cleaning the information, pulling it all into one dataset. I sourced the info from Property Pal and Property News. Please check the dataset as there may be minor repetition or some columns which should not be used.

Finally, I leveraged these findings into an interactive dashboard (you can view a link below) which enables students to view all available properties and determine which one has the required features alongside appropriate pricing.