Wellcome Data Prizes
We support teams to use existing data to uncover new insights and build digital tools.
Participants in Wellcome's Data Prize are supported to co-create digital solutions such as new software packages, algorithms or scripts.
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 Data Prize in Mental Health
The Wellcome Data Prize in Mental Health is supporting collaborative approaches to research into anxiety and depression in young people. Teams are exploring existing data to find new insights and build digital tools that enable future research.
We have designed the prize to prioritise inclusivity, creativity and multidisciplinary working. Find out more detail about the criteria and design of the data prize.
The prize has three phases, each six months long and with different levels of funding and support on offer. £1.4 million will be awarded across the three phases, with the top £500,000 prize to be shared between three winning teams.
We have partnered with Social Finance to deliver the prize, and their team is working with Wellcome’s Data for Science and Health and Mental Health teams.
Who and what we’re funding
Phase one: discovery
The first phase of the Data Prize in Mental Health ran from Autumn 2022 to Spring 2023.
For this initial stage, we funded eleven teams who used existing datasets to explore a wide range of research topics related to youth mental health:
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.
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.
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 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 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.
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.
Five of the eleven teams in phase one were selected to progress to phase two.
Phase two: prototyping
We are now in the second phase of the Data Prize in Mental Health. Five teams will create a prototype digital tool, and refine and disseminate their research from the first phase:
Students who feel connected to their school are less likely to experience anxiety and depression, but there are gaps in our understanding of which aspects of the school environment are important. This project will investigate the impact of school environments and policies on how connected students report being to their school. Led by Dr Jeremy Segrott at Cardiff University, the team will develop a school-level digital dashboard which will share insights with schools and young people and provide a tool which other researchers can utilise.
The team: Jeremy Segrott, Hayley Reed, Frances Rice, Simon Murphy, Rhys Bevan-Jones, Yulia Shenderovich, Olga Eyre, Nicholas Page, Maria Boffey, Edna Ogada.
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
This team will support better integration of mental health research through a natural language processing harmonisation tool, allowing researchers to compare data from existing studies to investigate the active ingredients of mental health. Led by Dr Eoin McElroy at Ulster University, the team will develop and demonstrate Harmony to answer research questions around human connection and its influence on the development of depression and anxiety in young people.
The team: Eoin McElroy, Bettina Moltrecht, Thomas Wood, Mauricio Scopel Hoffmann, George B. Ploubidis
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
This team will bring together expertise in young people’s mental health, cognitive psychology, medical and health humanities, and applied statistics. Led by Dr Aja Murray at the University of Edinburgh, the team will develop a digital tool to facilitate counterfactual analysis of mental health data. The tool can help accelerate progress in identifying the most promising active ingredients in mental health that can form the basis of preventive interventions and treatments.
The team: Aja Murray, Marie Allitt, Ingrid Obsuth, Josiah King, Dan Mirman, Patrick Errington & Helen Wright
In the next and final phase of the Mental Health Data Prize – the sustainability phase – the three winning teams will implement and scale up their prototype digital tool.
We’ll update this page in Autumn 2023 to show which of the teams have progressed to the next stage.