Data ScienceData 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)
|Christopher Greenwood (PhD student)|
Research Focus Areas
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).
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