We support teams to use existing data to uncover new insights and create digital tools for research into anxiety, depression and psychosis in young people.
We support teams to use existing data to uncover new insights and create digital tools for research into anxiety, depression and psychosis in young people.
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. They 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.
Our second data prize was delivered in partnership with The African Population and Health Research Center (APHRC) and open to teams across Africa. The goal was to use existing data to understand and develop new solutions for anxiety, depression and psychosis in Africa.
Ten winning teams have been awarded £200,000 each to develop their projects in 2025 to 2026. The APHRC will deliver training and facilitate access to data sources across the continent. They will also support teams to involve lived experience expertise in their research.
This project will use digitisation and AI to transform hand-written charts for patients with psychosis in Uganda into a standardised, longitudinal database. Led by Dr Emmanul Kiiza Mwesiga at Makerere University, the project’s outcomes could improve understanding of how patients engage with mental health services.
This project will integrate and analyse data from several mental health studies in Malawi into an open access digital tool. Led by Dr Owen Nkoka from the Malawi Epidemiology and Intervention Research Unit, the team will identify risk factors for anxiety and depression in pregnant women as a first use case for the tool.
This project aims to understand the mechanisms underlying depression and identify areas for intervention. Led by Dr Xanthe Hunt from the Africa Health Research Institute, the team will create a new dataset that links data on depression, behaviour and social determinants for adolescents in South Africa.
This project will use healthcare call centre records to train natural language processing and automatic speech recognition models. Led by Dr Joyce Nakatumba-Nabende from Makerere University, the team aims to automatise and improve mental health services in Uganda and Tanzania.
Led by Dr Trust Gangaidzo from the University of the Witwatersrand, this project will develop an open access platform to better use mental health data from longitudinal studies in South Africa. To achieve this, the team will adapt the Harmony data tool developed through Wellcome’s first Data Prize.
More precise and efficient measurement tools are needed to identify adolescents with anxiety and depression in Kenya and South Africa. Led by Dr Bianca Moffet from the University of Witwatersrand, this team will develop Computerised Adaptive Tests, a scalable digital tool for mental health diagnosis.
This project seeks to improve the quality and access to mental health data in Uganda by digitising data from health facilities using the DHIS2 platform. Led by Dr Prosper Behumbiize from the Health Information Systems Program Uganda, the goal is to facilitate the Ministry of Health’s reporting and decision-making.
This project will improve the efficiency of mental health services in South Africa's Western Cape by integrating digital tools. Led by Dr Timothy Mountford at the Western Cape Department of Health and Wellness, the team will use digital tools to address treatment gaps and enhance care for up to five million service users.
This project will analyse data from group support psychotherapy, an intervention used to reduce depressive symptoms for HIV-positive individuals. Led by Dr Etheldreda Nakimulu Mpungu from Makerere University, the goal is to understand what makes the intervention effective, how to improve it and better train treatment providers.
This team has tested several successful interventions for anxiety and depression for young people in Kenya, such as developing a growth mindset and using values affirmations. Led by Dr Tom Osborn from the Shamiri Institute, the team will now analyse data from these trials to identify what makes them effective. They will also build an AI-driven app to personalise treatments for individuals and train treatment providers.
The first Wellcome Data Prize was delivered in partnership with Social Finance and was open to teams in the UK and South Africa. £1.4 million was awarded across three phases, with the top prize of £500,000 shared between the three winning teams. The prize ended in June 2024.
We designed the prize to prioritise inclusivity, creativity and multidisciplinary working. Lived experience was embedded throughout – from design to delivery – and tools were co-created with lived experience experts to ensure they have a lasting impact.
Find out about the eligibility, evaluation criteria and design of the prize.
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
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
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
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.
If you've got any questions or ideas about Wellcome’s Mental Health Data Prize, email dataprize@wellcome.org.