Data Science

Data Sciences leads innovation in approaches to analysing complex data collected from long term cohort studies, randomised controlled trials (clinical and community), and meta-analytic review studies. Data Sciences provides a range of training opportunities each year that are designed to up skill researchers in both quantitative and qualitative approaches to data analysis.


A/Prof Matthew Fuller-Tyszkiewicz (Group leader)

Research Team

Jeromy Anglim
Gill Clarke
Kerri Coomber
Ian Fuelscher
Matthew Fuller-Tyszkiewicz
Alexa Hayley
Christian Hyde
Shannon Hyder
Anna Klas
Tess Knight
Emily Kothe
Jake Linardon
Mathew Ling
Mark Stokes
George Youssef
Christopher Greenwood (PhD student)

Research Focus Areas

Cohort Studies

Led by Dr George Youssef, this stream focuses on:

  • causal inference
  • approaches to longitudinal data analysis (LGM, MLM, GEE, etc.)
  • open science principles
  • machine learning
  • network analysis.

Clinical trials and experiments

Led by Dr Christian Hyde, this stream focuses on:

  • small sample approaches
  • analysis of neurophysiological data (e,g., diffusion MRI approaches, functional MRI and multivariate pattern analysis)
  • power analysis.

Systematic reviews and meta-analysis

Led by Dr Emily Kothe, this stream focuses on:

  • identifying and evaluating relevant literature
  • reproducible and replicable analysis and reporting
  • open source software for meta-analysis (including: R, MAJOR, MAVIS)
  • range of meta-analytic techniques (including: RMA, RVA, PET-PEESE, MLM).

Qualitative methods

Led by A/Prof Tess Knight, this stream focuses on:

  • data collection and analysis for in-depth understanding of a phenomenon
  • methodological approaches for specific research questions
  • trustworthiness and ethical considerations
  • qualitative synthesis