Meaningful measurement: Qualitative data methods & analysis for understanding real-world GLAM experiences

Applications are invited for an Open-Oxford-Cambridge AHRC DTP-funded Collaborative Doctoral Award at The Open University (OU) in partnership with the Gardens, Libraries, and Museums (GLAM) Division at the University of Oxford. This fully-funded studentship is available from October 2024 on a full or part-time basis. Further details about the value of an Open-Oxford-Cambridge AHRC DTP award are available on our Studentships page.

Closing date: midday (UK time) 9th January 2024.

Project overview

GLAM institutions have a wealth of data, from audience evaluation, digital engagement and visitor figures, online reviews such as Google or TripAdvisor, as well as collections data and business data (such as shop sales), and some organisations are using these data to make evidence-based decisions. Many institutions pay external agencies (such as Morris Hargreaves McIntyre or Audience Agency) to conduct large scale annual surveys, and also undertake smaller scale internal evaluation. However, these data often sit in silos, either because the data are ‘owned’ by different departments, or because staff do not have the technical skills to connect the data. A further issue is lack of skills in being able to analyse the data. There are some large institutions who employ data scientists or software engineers, but this is the exception rather than the norm. Most organisations are able to draw some insight from their quantitative data, but the masses of qualitative data from reviews, interviews, audience feedback, and social media remain largely untapped by the institutions themselves. The lack of expertise in some of these methods also means that GLAM organisations are not undertaking some forms of data capture because they lack the skills to analyse and interpret it, such as non-verbal data collection (e.g. drawing or creative outputs) and observational or ethnographic fieldwork.

This matters because GLAM institutions want to understand their audiences better, remain relevant and responsive to those audiences’ requirements from engagement with cultural heritage, and run their businesses more effectively. In addition there is a push from funders to have robust reporting in place for funded projects. Given the funding situation within the sector, there is unlikely to be a growth in data scientist or digital humanist roles within museums, so this project examines what interventions can be used to add value to GLAM institutions at low costs, and harness the power of their data to improve their visitor experience and business models.

Proposals for projects should consider how digital humanities techniques can help cultural institutions (museums, galleries, and other cultural and natural heritage sites) better understand their qualitative data. Specifically project proposals should:

  • Diagnose the rigour and standards of qualitative research as typically deployed as part of the variety of methods typically used to research real-world experiences in museums;
  • Identify how digital humanities tools and techniques, including computational linguistics and AI tools, can increase the quality and value of language-rich data collected by museums (e.g. online review repositories, social media or in-house user-generated information gathered through interviews or feedback);
  • Identify interdisciplinary research questions that digital humanities analysis can help GLAM institutions answer;
  • Demonstrate how these tools can be implemented by GLAM institutions with low level of data science or digital humanities specialist skills;

The project should identify and engage with relevant case studies across the sector but make specific use of, and reference to, existing datasets and prototyping opportunities at the Oxford University Museums and Bodleian Libraries to test techniques and serve as a ‘living laboratory’.

The successful applicant could explore some (not all) of the following methods to assist GLAM institutions understand their data better.

  • Social post keyword tagging (digital content analysis);
  • Ways to accelerate the process of turning verbal or written material into machine-readable, searchable and retrievable data. These might include handwriting analysis, Optical Character Recognition (OCR) or speech-to-text;
  • Natural Language Processing techniques (e.g. topic modelling) to understand large volumes of textual data extracted from review platforms or transcribed interviews.
  • Qualitative analysis of textual and linguistic meaning;
  • Machine Learning and distant reading techniques typically employed in literary scholarship to identify recurring patterns or developments over time. 

Training and mentoring in digital humanities methods, qualitative data measurement, digital humanities, ethnographic fieldwork, linguistic meaning analysis, and public engagement will be provided to support this work. In this way, the applicant will be equipped with additional skills and experience from the academic and heritage sector that will considerably enhance their employability prospects upon graduation.


The successful applicant would be by a team of two academic supervisors and two supervisors from the partner institution. Lead academic supervisor will be Dr Jaspal Singh, Lecturer in Applied Linguistics and English Language at the Faculty of Wellbeing, Education and Language Studies (WELS) at the Open University. Jaspal has expertise in qualitative data methods, ethnography, interviewing and linguistic meaning analysis.

The academic co-supervisor will be Prof David De Roure, Professor of e-Research, with interdisciplinary expertise in digital humanities and digital social research.

The two supervisors from the partner institution will be Helen Adams, Head of Audience and Engagement Support, Gardens, Libraries and Museums, University of Oxford with expertise in audience-led cultural programmes; and Dr Megan Gooch, Head of the Centre for Digital Scholarship, University of Oxford, Bodleian Libraries who has expertise in digital humanities and cultural audience evaluation.

In addition to working with this supervisory team of experts, the applicant will benefit from the extensive programme of research training events provided by the Open University and by the Open-Oxford-Cambridge Doctoral Training Partnership.

How to apply

While the successful candidate will complete a PhD in languages and applied linguistics, their academic background may be in any related discipline, including but not limited to linguistics, heritage and museum studies, classical studies, history, cultural informatics, archaeology, anthropology or sociology. It is important that the candidate is confident in qualitative research methods and is eager to expand their knowledge of and keep up-to-date with the rapid developments in digital humanities. We invite applications from candidates from all social backgrounds, all genders, all abilities, all ages and all ethnicities. Applicants will normally hold a Master's Degree in linguistics or a related discipline as outlined above, and/or a first class degree with a substantial original-source dissertation. However, applicants without those qualifications can be considered provided they can demonstrate the capacity to conduct high quality research and competently write according to highest academic standards.

Potential applicants are encouraged to contact Dr Jaspal Singh ( with questions and for any guidance before submitting their application.

To apply for an Open-Oxford-Cambridge AHRC DTP studentship, please complete OOC DTP Application Form, OU application form and supporting documents listed on the application from, Research proposal and a covering letter indicating your suitability to the project  and  send to by 9th January 2024 (midday, UK time)