The data sciences research theme leads innovation in analysing complex data collected from research activities across SEED. This ranges from observational studies across the human lifecourse to lab-based tasks and intervention studies. We also develop digital healthcare solutions to promote good mental health and help people with mental health problems.

Analysing mountains of data

Science needs data. And tracking the social and emotional development of Australians over decades generates a lot of it.

SEED’s oldest data resources have over one million points of data, collected over four decades and crossing three generations.

To make sense of all that information, we need data science.

Data science allows us to develop a clear sense of the developmental story behind problems like anxiety, depression and violence.

How our research uses data science

SEED’s data sciences research theme leads innovation in analysing complex data collected from:

  • long-term cohort studies
  • randomised controlled trials
  • qualitative studies
  • meta-analytic review studies.

We also have expertise in:

  • machine learning
  • digital health study design
  • causal inference methods
  • multi-cohort data harmonisation and analysis
  • data visualisation techniques.

Our team has developed more than 14 apps to promote mental health and treat serious mental disorders, like postnatal depression and eating disorders. We have also developed new tele-mental health platforms that address individual problems like depression and substance use, but also relational problems like intimate partner violence.

We also provide a range of training opportunities to upskill researchers in both quantitative and qualitative approaches to data analysis.

Our purpose

1

To provide academic leadership and support to SEED members around research design and analysis

2

To innovate in use of quantitative and qualitative methods for application in psychologically-based research studies

3

To foster a strong research culture through mentoring, project vetting, and research workshops

Our leaders

Matthew Fuller-Tyszkiewicz Co-leader Data Sciences. Associate Head of School, Research
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Chris Greenwood Co-leader Data Sciences. Research Fellow, Faculty of Health
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Major Projects

Living Knowledge Review System

Digital health studies

Causal modelling

Qualitative Studies

Machine learning