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Details of Grant 

EPSRC Reference: EP/R01860X/1
Title: Data Science of the Natural Environment
Principal Investigator: Leslie, Professor D
Other Investigators:
Dondelinger, Dr F Nemeth, Professor CJ Taylor, Dr BM
Reis, Professor S Young, Dr PJ Blair, Professor G
Atkinson, Professor PM Leeson, Dr AA Sedda, Dr L
Henrys, Dr P Watkins, Mr JW Harrison, Professor PA
Diggle, Professor PJ Tawn, Professor J
Researcher Co-Investigators:
Project Partners:
Asthma UK Centre for Applied Research BT Centre for Env Fisheries Aqua Sci CEFAS
Centre for Polar Obs & Modelling (CPOM) Dept for Env Food & Rural Affairs DEFRA EDF
Environment Agency (Grouped) Government of Scotland JBA Trust
JNCC (Joint Nature Conserv Committee) Met Office Microsoft
National Centre for Atmospheric Research Natural England Natural Resources Wales
NERC Grouped NOC (Up to 31.10.2019) Research Centre Juelich GmbH (Helmholtz)
Scottish Environmental Protection Agency Small World Consulting Ltd University of Oxford
Department: Mathematics and Statistics
Organisation: Lancaster University
Scheme: Standard Research
Starts: 16 April 2018 Ends: 15 April 2024 Value (£): 2,656,400
EPSRC Research Topic Classifications:
Artificial Intelligence Information & Knowledge Mgmt
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
09 Oct 2017 New Approaches to Data Science Interviews 9 and 10 October 2017 Announced
Summary on Grant Application Form
We will develop a data science of the natural environment, deploying modern machine learning and statistical techniques to enable better-informed decision-making as our climate changes. While an explosion in data science research has fuelled enormous advances in areas as diverse as eCommerce and marketing, smart cities, logistics and transport, health and wellbeing, these tools have yet to be fully deployed in one of the most pressing problems facing humanity, that of mitigating and adapting to climate change. This project brings together world-leading statisticians, computer scientists and environmental scientists alongside an extensive array of key public and private stakeholder organisations to effect a step change in data culture in the environmental sciences.

The project will develop a new approach to data science of the natural environment driven by three representative grand challenges of environmental science: predicting ice sheet melt, modelling and mitigating poor air quality, and managing land use for maximal societal benefit. In each motivational challenge, there is already an extensive scientific expertise, with intricate models of processes at multiple scales. However this sophisticated modelling of system components is usually let down by naive integration of these components together, and inadequate calibration to observed data. The consequence is poor predictions with a high level of uncertainty and hence poorly-informed policy making. As new forms of environmental data become available, and the pressures on our natural environment from climate change increase, this gap is becoming a pressing concern, and we bring an impressive team to bear on the problem.

A key theme of the project is integration, developing a suite of novel data science tools which work together in a modular fashion, and with existing scientifically-informed process models. By building a team that spans the inter-disciplinary divisions between data and environmental scientists we can ensure the necessary interoperability of methods that is currently lacking. Working with the full range of stakeholder environmental organisations will enable continual co-design of the programme and training of end-user scientists to ensure a reduction of the skills gap in this area. The resultant culture shift in the data literacy of the environmental sciences will enable better decision-making as climate change places ever greater strains on our society.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Further Information:  
Organisation Website: http://www.lancs.ac.uk