(Turing) Decentralised Reproducibility - the development of an ontology and standards of methodologies for health data science reproducibility

The crisis of reproducibility and replicability in science is well known1. The challenges of reproducibility in health data science are particularly complex and it remains uncommon2. Even when the same intervention is being measured there can be different environment issues, such as software implementation, local processes for data collection and user experience factors, that can lead to significant differences in outcomes. Understanding the requirements and the development of reliable software tools and services are a critical step in enabling reproducibility to be appropriately planned into the design of health informatics studies and then evaluated3. Distributed Ledger Technologies (DLT) could provide a mechanism for Decentralised Reproducibility. Smart contracts can enable management of permissions of use of data, as well as provide a platform for management of computational containers, the running of experiments and the capture of output independently of the original researchers. This would provide an immutable audit trail of the experiments and could enable the use of tokens in exchange for demonstration of reproducibility as a method of demonstration of value of the research. Key areas to investigate include: the development of an ontology and standards of methodologies for health data science reproducibility; a platform for researchers to use DLT for management of research objects for planning and test future reproducibility and the re-use of data with managed permission and attribution; creation of components that could provide automated validation of research publicly shared using DLT; provide a catalogue and evidence base for different types of analyses of health data; and support selection of appropriate replication methodologies for validation in the initial design of research.

www.herc.ac.uk
www.research.manchester.ac.uk/portal/john.ainsworth.html

The Alan Turing Institute – About the studentship
The Alan Turing Institute and The University of Manchester offer a number of places each year to motivated graduate students to complete a fully funded PhD. The Turing doctoral studentship scheme combines the strengths and expertise of world-class universities with the Turing’s unique position as the UK’s national institute for data science and artificial intelligence, to offer an exceptional PhD programme.

Turing students will have access to a wide range of exceptional benefits:
• Spend time in both a university research environment and at The Alan Turing Institute.
• Access to a range of training, events, seminars, reading groups and workshops delivered by leaders in research, government and industry.
• Opportunities to collaborate on real-world projects for societal impact with current and emerging industry partners.
• Expert support and guidance through all stages of the studentship, delivered by supervisors who are Fellows of the Turing or substantively engaged with the Turing.
• Networking opportunities and brilliant minds researching a range of subjects with opportunities to collaborate and join or start interest groups.
• Opportunities to supercharge your research with access to cutting edge resources.

Find out more at turing.ac.uk/PhD https://www.turing.ac.uk/phd-at-turing

Entry Requirements
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.