The NIHR Team Science Camp is an opportunity for you to work as part of an interdisciplinary team. It is open to researchers from all career stages but you must be actively conducting research in the relevant theme area to be eligible to apply.
The theme for the 2026 Team Science Camp is ‘data-science driven health and social care research. At the camp, you will be coached in the principles of team science and supported to develop an interdisciplinary team with other attendees with the aim of applying for a Team Science Award for further funding and support. You are eligible to apply for the Team Science Award even if you do not attend the camp.
The camp will take place over 3 days. It is fully funded and facilitated, but costs associated with travel and visas are yours and your employers responsibility.
Location
The location of the camp will be De Vere Cranage Estate, Byley Ln, Cranage, Crewe, CW4 8EW
Time
Registration on-site will open on Day 1 from 11am with refreshments and networking before the main agenda commences at 1pm.
The camp will end at 1:30pm on Day 3.
Theme
This year’s camp will focus on data-science driven health and social care research.
Background
The emerging field of data-enabled health and care research represents a critical intersection of data science and clinical and applied research that will accelerate health and social care innovation, thereby driving the Government’s Health and Growth Missions (.PDF). The goal is to encourage and facilitate joint projects from data scientists and health and care professionals that will deliver clear benefits to the NHS and wider health and social care systems.
Definition
Data-enabled health and social care is an interdisciplinary approach that leverages advanced computational and statistical methods to extract insights from complex health and social care datasets. By integrating multidisciplinary expertise from data science, clinical practice, and health and social care, data enabled health and care research will improve patient care, support the move towards prevention and drive better outcomes.
Rationale
Following the publications of the Goldacre Review (2022) and the Sudlow Review (November 2024), it is clear that there is high demand for skilled interdisciplinary data-enabled health and social care research. Whilst the UK researchers have a huge opportunity to power cutting edge health and social care research with access to NHS data, there are many challenges to accessing, curating and linking relevant data. This challenge requires multidisciplinary teams that can understand and harness data whilst incorporating clinical expertise of impact and ambition. The Team Science approach would provide huge value to this area as it will encourage research teams to design ambitious research applications. Adopting a Team Science approach would help to fully realise the benefits of data in health and care research, and to deliver the Government’s Health and Growth Missions.
You can use the Team Science award to develop skills in data science as applied in clinical, biomedical or population health settings.
Health Data Science (HDS) is a recently established inter-disciplinary field. Its importance to all aspects of health research will continue to grow. The 3 pillars of HDS are:
- statistics
- computation
- domain knowledge
The first 2 of the HDS pillars are underpinning methodologies. The 3rd is context-specific, ranging from molecular to whole-population studies. It is rare to find this combination of expertise in a single individual. Hence, HDS is best approached as a team science endeavour in which each member of the team is an expert in 1 of the 3 pillars but conversant in all 3. We are interested in applications from individuals from all types of professional and research backgrounds who are seeking to develop skills in HDS.
Relevant areas of interest, these examples are illustrative only and are not exhaustive:
Machine learning
A key characteristic of data science is that many of its methodological underpinnings bridge the traditionally separate disciplines of statistics and computer science.
Statistics
Examples of relevant areas of interest can include:
- study design principles: formulating the research question, validity, efficiency, controlling for extraneous variation
- choosing a study design: observational, interventional or randomised trial
- probability: quantifying uncertainty in data, and in conclusions drawn from data
- inference: turning data into evidence – testing, estimation or prediction?
- critical appraisal of research evidence
- machine learning
Computing
Examples of relevant areas of interest can include:
- data collection, processing and management
- programming
- machine learning
- reproducibility of data-driven research
- user-interfaces
Domain knowledge
Examples of relevant areas of interest can include:
- biology
- epidemiology
- public health
- health services
Generic
Examples of relevant areas of interest can include:
- multidisciplinary team science: leadership, networking, collaboration
- communication: within and beyond the research team
- governance, including regulatory requirements and research ethics
- patient and public involvement (PPI)
Visit funding web page
(https://www.nihr.ac.uk/funding/team-science-camp-cohort-4/2026440)
