Wednesday, 28th September 2022

10:00 - 16:00

We are collaborating with HDR UK's new hub - DATAMIND - who are working to enable researchers and others to find and use the UK's rich datasets and health records for mental health research.

This Data Science event aims to catalyse cross-sectoral and interdisciplinary collaboration for mental health research.

The meeting will be hosted by Lea Milligan, CEO of MQ Mental Health Research and Professor Andrew McIntosh from the University of Edinburgh.

We are very pleased to announce that our keynote speaker is Dr Greg Farber , Director, Office of Technology Development and Coordination at the National Institute of Mental Health.

Also featured:

• Professor Andrew Morris, Inaugural Director of Health Data Research UK
• Dr Catherine Sebastian, Head of Evidence at Wellcome Trust
• Dr David Baghurst , Director Research Programmes at NIHR Central Commissioning Facility
• Professor Thalia Eley, Professor of Developmental Behavioural Genetics at King’s College London and Deputy Chair of MQ Fellows Panel
• Author John Niven
• Dr Max Taquet, NIHR Academic Clinical Fellow, University of Oxford
• Professor Louise Arseneault, Professor of Developmental Psychology at KCL
• Dr Joanna Latimer, Head of Neurosciences and Mental health Board at MRC
• Professor Mina Fazel, Professor of Adolescent Psychiatry, University of Oxford

The full agenda will be announced very soon.

What do we mean by Data Science?

Data Science can refer to using existing administrative or healthcare datasets, or linking of datasets, for research. It can also involve novel analytic approaches to complex datasets, such as those combining expertise from statistics and/or computing science.

Would you like to present your research?

Please submit your abstract by registering for the event before 14:00 (BST) 13th of July 2022, completing this form. Abstracts will be assessed based on:

• Contribution to data science in mental health. We are specifically interested in research using administrative and or healthcare datasets, data linkage, and in novel approaches to analysis of complex datasets, data discovery, open science, and co-production.

• Interdisciplinarity

• Preference will be given to research integrating Patients and Public Involvement.