Sharing reproducible analyses in Big Healthcare Data Infrastructures at The University of Manchester

Funding:

This funding is available for home students, at the current UKRI rate (tax free stipend of 18,622 GBP and tuition fees). 

Qualifications:

Applicants should hold a 2:1 undergraduate degree or better, and a masters degree in Computer Science or a subject relevant to one of data science, information modelling, Web technologies, Linked Data, knowledge graphs, or health care data processing; or equivalent international qualifications.  

About the Project

An exciting new research collaboration between Health Data Research UK and the University of Manchester is funding a UK PhD studentship within the Department of Computer Science.  

The University of Manchester, the University of Nottingham and the University of Dundee have established a collaborative network to explore innovative ways to use health data for research and healthcare improvement. These initiatives involve developing new analytical techniques, data linkage methods, and tools for data sharing while ensuring patient privacy and data security. This funded PhD is an opportunity for training and collaboration with a wide network of researchers within those institutions. 

In particular, at the University of Manchester, we want to better understand the challenges of computational reproducibility and FAIR data sharing within HDR UK federated data infrastructures, especially focussing on technology potential, limitations, integrity measures and handling of sensitive data.  

We are looking to recruit a PhD student with an interest in developing and applying emerging approaches for federated computational analytics (e.g. scientific workflow systems) and metadata management across distributed systems.  

You will be collaborating with and be supported by our team that concurrently is further developing HDR UK’s infrastructure for federated analytics, and will be in a prime position to propose technological advances to be tested on real use cases, working with UK’s leading experts of healthcare data management and trusted research environments within the HDR UK network and the newly established BioFAIR infrastructure for UK life science researchers. 

Project objectives: 

1. The project will aim to meet the following objectives. These are broadly outlined below, and will be finalised and made more specific during the PhD. 
2. Systematic review exploring existing literature on workflow management systems and methods to capture federated provenance and structured metadata
3. Systematic review limitations, opportunities, ethical and privacy concerns that restricts FAIR sharing of sensitive healthcare data, identifying potential countermeasures
4. Prototyping of a system that can filter federated workflow provenance to make it publishable as open datasets
5. Development of framework for open sharing of provenance for sensitive data analysis
6. Development of metadata models for mapping sensitive data identifiers  

Further Information:

You will work with Carole Goble (Professor of Computer Science) and Stian Soiland-Reyes (Research Fellow) in the eScience Lab at The University of Manchester; they both have a long track record of international research and development of scientific workflow systems, provenance standards and FAIR data sharing practices across life sciences. 

You may be expected to participate in some HDR UK activities along with the other PhD studentship recipients.