Wellcome Data Prizes
A series of prizes that will use data to transform how science solves urgent health challenges.
An opportunity for multidisciplinary teams to receive funding and support to use existing data to answer important research questions.
Participants will 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.
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.
The overarching questions for the prize are:
What are the ‘active ingredients' that make a difference in preventing, treating, and managing anxiety and depression in young people? What works, for whom, in what contexts, and why?
Who we’re working with
Teams in the UK and South Africa are exploring existing data to find new insights and build digital tools that enable future research.
We have partnered with Social Finance to deliver the prize, working with Wellcome’s data for science and health and mental health teams.
What we’re funding
The first stage of the prize – the discovery phase - has started with 11 teams exploring a wide range of research topics.
Developing a counterfactual analysis digital tool to illuminate active ingredients in mental health
This team will bring together expertise in young people’s mental health, cognitive psychology, medical and health humanities, and creative theory. Led by Dr Aja Murray at the University of Edinburgh, the team will assess the impact of reading for pleasure in young people’s mental health. The team will then create a digital tool for assessing the effect of an intervention.
Mental health trajectories following pharmacological and psychological treatment
This project will examine how depression develops following pharmacological and psychological interventions. The goal is to identify how different interventions could be combined to improve people’s mental health over time. Led by Dr Heather Whalley at the University of Edinburgh, the team will co-produce an open-source digital tool for researchers without statistical backgrounds. It will allow researchers to incorporate trajectory modelling into their research to understand changes over time when exploring other active ingredients such as emotion regulation or sleep improvements.
Anxiety and depression in young people: who do they affect, who seeks treatment, and who responds to treatment?
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.
Mental Health Researchers in Discovering Active Ingredients in longitudinal Datasets using Artificial Intelligence (MHR-DAIDAI)
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.
Development and validation of a digital tool for identifying young people at risk for depression in South Africa
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.
Harmony - A global platform for contextual harmonisation, translation and cooperation in mental health research
This team will support better integration of mental health research through a natural language processing harmonisation tool (Harmony), 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.
Prevention of persistent high levels of depression across adolescence and young adulthood: the role of active ingredients
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.
Understanding the role of secondary school environments and policies as drivers of school connectedness to prevent anxiety and depression
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 digital tool to share insights with researchers, policy makers, schools and young people.
The impact of stop and search on young Black people’s mental health
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.
Unravelling patterns in social connection for the prevention of depression and anxiety in adolescents and young adults
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.
Developing a rapid AI-based policy probing and observational research tool
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.
Our design principles
We have designed the prize to prioritise inclusivity, creativity and multidisciplinary working.
With the prize, we want to:
1. Gain fresh insights from existing data
We will use data related to mental health to understand which ‘active ingredients’ prevent, treat, or help manage ongoing anxiety and depression in young people (aged 0 to 30). We will prioritise datasets that could be used in new ways to offer insights into the social and environmental context of mental health.
This includes data sources created both inside and outside of academia, like data on medication use, access to green space, financial situation, cognitive processes, genetics and personal relationships. For this data prize, Wellcome also helped fund the creation of a new dataset focused on adolescent mental health in South Africa.
2. Build a multidisciplinary mental health data community
Solving urgent global health problems requires a range of different perspectives. Our data prizes have been designed to make sure the participation of different groups is at the centre of the projects we fund. In particular, we will involve people with lived experience because we value their important (and often missing) insight on mental health research priorities.
3. Create trustworthy digital tools with tangible impact
We want to show how to build trustworthy digital tools that really make a difference to future research into youth anxiety and depression.
4. Raise awareness around the health challenge and inspire solutions
We will talk about our work openly and share what we’re learning during this process. We want to inspire policy and decision makers to think about new approaches to developing digital tools to address global health challenges.
In the next stage of the prize – the prototyping phase - five successful teams will create a prototype of a digital tool and refine their research from the discovery phase.
We’ll update this page in spring 2023 to show which of the teams have progressed to the next stage.