Introduction
The Alan Turing Institute invites ideas for pilot projects from multidisciplinary teams of researchers working in partnership, to apply data science approaches to environmental and sustainability challenges.
The call has three key focus areas:
- The science of climate change
- Reducing carbon emissions
- Climate change impacts and resilience
This call aims to catalyse new collaborations between environmental scientists and data scientists. It is designed to enable groups of researchers with complementary skills and expertise to explore opportunities at the nexus of sustainability and data science research.
Further details can be found in the call guidance.
Application Process
In this first call, up to two 6 month projects are intended to be supported. An award of up to £50,000 will be offered to each selected proposal. We plan to fund further projects in subsequent years.
Applicants should provide a costing which covers 100% of direct costs plus an overhead rate of £32,500 per 1 researcher FTE (pro-rated).
Applications should be completed and submitted by 17:00 GMT, 19 July 2019 to the Turing’s FlexiGrant portal with the following documents:
- A copy of your proposed research case for support (two pages maximum, plus a maximum of one additional page for references)
- This should include background and a description of the proposed research, aims and objectives, tools and methods, relevance and beneficiaries
- A fully completed costing using your Institution’s costing tool
- This should include a budget table (template provided) and narrative justification of resources
Additional FlexiGrant sections should also be completed, details of which can be found on the call guidance linked on this page.
Key Dates
- Call launch: Tuesday 11 June 2019
- Deadline for proposals: Friday 19 July 2019, 17:00
- Results confirmed: From week commencing 5 August 2019
- Projects start: No later than 1 October 2019
- Projects end: No later than 31 March 2020
Further information
For further information please contact [email protected].
This work is supported by The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1
