Wellcome Mental Health Data Prizes
We support teams to use existing data to uncover new insights and create digital tools for research into anxiety and depression in young people.
What are data prizes?
Data prizes are an open competition or challenge where participants use data to solve a societal problem.
They are increasingly used to find solutions to complex issues and can be especially powerful for areas blocked by siloed research fields, where funding is limited, or when there is a lack of motivation because progress has slowed.
Wellcome Mental Health Data Prize: Africa, 2024
Our newest data prize will fund teams across the African continent to analyse data and build digital tools to answer the question: “What can help us tackle anxiety, depression and psychosis in Africa?”
We are working with The African Population and Health Research Center (APHRC) to run this prize. They will deliver training, facilitate access to data sources from across the continent and support teams through the prize.
Wellcome Mental Health Data Prize: UK and South Africa, 2022-2023
The first Wellcome Data Prize was delivered in partnership with Social Finance. £1.4 million was awarded across three phases, with the top prize of £500,000 shared between the three winning teams.
We designed the prize to prioritise inclusivity, creativity and multidisciplinary working. It was open to teams in the UK and South Africa, and we are using what we learned to inform the structure of the new Data Prize in Africa. Find out about the eligibility, evaluation criteria and design of the prize.
"I have been a member of many funding panels and the Wellcome Mental Health Data Prize stood out in terms of the diversity of applicants. The prize really reached beyond the usual suspects with applicants from various geographies, career stages and institutions from both inside and outside the academic landscape, which will bring new perspectives to the research area."
Find out about the winning projects:
Harmony – Ulster University
A free-to-use AI tool for researchers to make better use of existing mental health questionnaire data, by bringing together different studies.
The team: Eoin McElroy, Bettina Moltrecht, Thomas Wood, Mauricio Scopel Hoffmann, George B. Ploubidis
DigiCAT – Edinburgh University
A digital tool that analyses cause and effect in observational mental health data. This can accelerate progress in identifying potential intervention targets.
The team: Aja Murray, Marie Allitt, Ingrid Obsuth, Josiah King, Dan Mirman, Patrick Errington and Helen Wright
School Health Research Network (SHRN) – Cardiff University
A digital dashboard that empowers schools to use bespoke data to create environments that promote good mental and physical health.
The team: Jeremy Segrott, Hayley Reed, Frances Rice, Simon Murphy, Rhys Bevan-Jones, Yulia Shenderovich, Olga Eyre, Nicholas Page, Maria Boffey and Edna Ogada
Projects funded during previous phases:
Anxiety and depression are common conditions – yet we do not understand what affects vulnerability, who seeks support, or why people respond to treatments in different ways. Led by Dr Alexandra Pike at the University of York, the team will use machine learning techniques to understand the factors that affect these aspects of mental health over time. The team will produce an online application so researchers, educators, clinicians and policy makers can use the results, and young people and their families can access insights as well.
Led by Prof Paul Tiffin at the University of York, this team will co-produce an AI-based tool to rapidly evaluate the impact of healthcare and policy interventions, and how different groups respond. They will initially focus on the active ingredient of physical activity and its impact on youth depression. Alongside the tool, the team will develop a framework for the transparent and replicable reporting of their methods.
Led by Dr Darshini Govindasamy at the South African Medical Research Council, this team will develop and validate a digital tool to predict symptoms of depression and anxiety among young people in South Africa. The discovery phase will use multi-level modelling and machine learning techniques to investigate the socio-economic determinants of anxiety and depression. The team’s approach will be informed by lived experience youth experts.
The team will build and test a tool that uses natural language processing to help mental health researchers decide what variables to use in longitudinal studies. Led by Dr Anesa Hosein at the University of Surrey, the team will explore how physical activity affects young people’s mental health during key periods of their school and university life and the impact of sociological and demographic factors.
Mental health trajectories are crucial for examining why, when and how mental health traits change over time, offering insight into key periods of change. However, these models can be difficult to implement and interpret. Led by Dr Heather Whalley at the University of Edinburgh, the team will co-produce an open-source digital tool for researchers without statistical background. This will allow researchers to facilitate analyses of mental health traits, including features associated with worse/improved health and potential interventions.
The team: Heather Whalley, Alex Kwong, Andrew McIntosh, Liana Romaniuk, Iona Beange, Amelia Edmondson-Stait, Thalia Eley, Ellen Thompson, Rebecca Pearson, Kate Tilling, Richard Parker, Ahmed Elhakeem
The team will build a digital tool using machine learning models that will reliably predict combinations of active ingredients that are associated with a lower risk of depression. Led by Dr Isabel Morales-Munoz at the University of Birmingham, the goal is for the tool to be tested against routinely collected data. It will also form the basis of a tool for clinicians to guide early interventions for young people with depression.
Led by Dr Jolyon Miles-Wilson at Black Thrive Global, this project will investigate how the disproportional use of stop and search powers by police impacts young Black people's mental health. The team will combine data from the UK Police’s stop and search database with mental health measurements to study differences across locations. The goal is to produce insights for multiple stakeholders, including young Black people, policy makers, researchers, and the police. The team hopes to stimulate public discourse on this topic, facilitate further investigation and drive positive social change.
The team: Jolyon Joseph Miles-Wilson, Samantha Davis, Craig Morgan, Celestin Okoroji, Gareth Rees, Graeme Porteus, Lucas Cumsille Montesinos, Katrina Ffrench
Led by Dr Josefien Breedvelt at the National Centre for Social Research, this project aims to explore the relationship between social connection and the development of depression and anxiety in young people by identifying key transition points, for example, significant life events that lead to changes in relationships. The team will co-produce a tool for researchers, policy makers and lived experience experts to explore the dynamics of social connections and their effects on the development of mental health problems over time.
Contact us
If you've got any questions or ideas about Wellcome’s Mental Health Data Prize, email dataprize@wellcome.org.
