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Research Studentship in Knowledge Engineering

Research Studentship in Knowledge Engineering

FAIR synchrotron data, from theory to implementation: understanding and evaluating the technical, social and policy implications

4-year DPhil studentship 

Supervisors: Professor Susanna-Assunta Sansone and Dr Philippe Rocca-Serra, Data Readiness Group (, Department of Engineering Science, University of Oxford; Professor Steve Collins, Diamond Light Source Ltd.


This is a broad description of the scope of the project. The details will be defined with the successful candidate, also according to their skills and ideas.

To meet expectations of governments and funders, new mechanisms are needed to ensure greater transparency and reuse of research data. For scalable, effective and trustworthy data-driven science, we need new technological and social infrastructure, as well as cultural and policy changes. The widely adopted FAIR Principles ( cover four key features of research data, Findability, Accessibility, Interoperability, and Reusability, which are central to open science and public confidence in science. The FAIR Principles have become fundamental to progress in research and are increasingly demanded by funding agencies Worldwide.

This DPhil project is a collaboration between Diamond Light Source and Oxford University, set to understand the implications of adopting the FAIR Principles and the effects of its implementation on synchrotron data, covering a broad range of studies in physics, chemistry, engineering and life sciences. The student will spend time (50/50) in Professor Sansone’s group at Oxford, and in Professor Collins’ team at Diamond, where they will learn about the latest advancement in the FAIR data ecosystem, and synchrotron science, respectively. Professor Tony Hey, Chief Data Scientist at STFC, will serve as Advisor.

The problem is that data rarely follow FAIR principles, and require extensive preparation before the researchers can begin to use the data and answer sophisticated research questions. This DPhil project will examine the current data structure, the types of research question being asked, the evolving landscape of metadata standards, semantic web technologies, and the latest data representation and discovery.

This DPhil project may address the following illustrative questions:

What are the cultural barriers to open science and FAIR data?
What logical frameworks are appropriate for supporting formal vocabularies and metadata models such as NeXus?
What are the most promising technologies for data discovery?
What form of query service would be appropriate to answer the kind of questions that future data consumers will be asking?

We also welcome applications from students who may have alternative ideas about research questions to drive forward our open science programme.

Using specific use cases and scenarios, one or more potential FAIR-enabling frameworks will be demonstrated, along with the delivery of a FAIRness maturity model to guide process improvement.

This DPhil project is designed to deliver novel conceptual and methodological contributions to advance the practices and the infrastructure for research data management necessary to use synchrotron data at scale in a way that is not possible now. Specifically, the DPhil project will define and prototype how to move from the current manually-focused, time-consuming and error-prone operations to a streamlined, unambiguous and AI-ready framework.

This DPhil project will also guide future Diamond data management policy towards achieving goals of better data for better science, where scientific evidence is routinely available in a transparent, trustworthy and persistent manner to drive science forwards.



The studentship is open to Home classified students only. Full details of the EPSRC eligibility requirements:

Award Value

Course fees are covered at the level set for the UK student (c. £8620 p.a. in 2022-23). The stipend is at least £18,062 per annum.

Candidate Requirements

We are searching for somebody with excellent academic potential and an enthusiasm for understanding organizational dynamics, open science/innovation, and data management. A first-class degree in science or computer science is desirable.

An ideal candidate should have experience of data science and digital economy;some familiarity with social science research methods, as well as high degree of independence, excellent communication skills, and fluency in English.

Application Procedure

Informal enquiries are encouraged and should be addressed to Professor Susanna-Assunta Sansone (


Candidates must submit a graduate application form and are expected to meet the graduate admissions criteria.  Details are available on the course page of the University website.

Please quote 23ENGIN_SS in all correspondence and in your graduate application.

Application deadline:  noon on 30 September 2022 

Start date: January 2023