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. A full project description can be found here: https://www.oocdtp.ac.uk/meaningful-measurement-qualitative-data-methods-analysis-understanding-real-world-glam-experiences
Closing date: midday (UK time) 9th January 2024.
Proposals for projects should consider how digital humanities techniques can help cultural institutions (museums, libraries, 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.
Potential applicants are encouraged to contact the academic lead supervisor Dr Jaspal Singh (jaspal.singh@open.ac.uk) with questions and for any guidance before submitting their application.