Details of Award
NERC Reference : NE/N000676/1
Provision of Continuing Professional Development in mathematical modelling and statistics for NERC doctoral students
Training Grant Award
- Lead Supervisor:
- Professor M Heath, University of Strathclyde, Mathematics and Statistics
- Grant held at:
- University of Strathclyde, Mathematics and Statistics
- Science Area:
- Atmospheric
- Earth
- Freshwater
- Marine
- Terrestrial
- Overall Classification:
- Atmospheric
- ENRIs:
- Biodiversity
- Environmental Risks and Hazards
- Global Change
- Natural Resource Management
- Pollution and Waste
- Science Topics:
- Earth & environmental
- Complexity Science
- Population Ecology
- Ecosystem Scale Processes
- Ecosystem Scale Processes
- Abstract:
- Our training programme draws on multidisciplinary expertise in the Strathclyde Department of Mathematics and Statistics to deliver 3 courses addressing NERC Headline Skills gaps in mathematical modelling and statistics. The courses build on those delivered for the first time in 2014 and scheduled again for February 2015. Each course uses example data sets and models from widely ranging environmental settings, and in addition we encourage students to bring their own research problems to discuss with co-trainees and tutors, and to be explored as case studies. This feature was so well received by the 2014 students that we extended the duration of courses in the 2015 programme to increase the time available for students to develop their own projects under the guidance of tutors. We are still providing assistance and advice to students of the 2014 cohort, and our contributions have been acknowledged in publications arising from their projects. There has been a steady demand from NERC doctoral students for the 2015 programme, and we aim to build further on this success in a 2016. Course 1: Dynamical systems in environmental science: Build your own models in R. This course will 'lift the lid' on ordinary differential equations (ODEs) used to model processes in the environmental sciences. The emphasis will be on careful model formulation and practical numerical implementations. Drawing from a range of examples from simple predator-prey interactions, through to physical environmental models and realistic ecosystem models, we will show students how to formulate projects from scratch in terms of differential equations, as well as how to code and solve them in the open-source programming environment R. A key part of this course will be an opportunity for all students to bring along their own projects and data and have the course leaders lead them through developing their own research story-line into differential equations, and then into working R code. This 'workshop' aspect of the course was piloted in 2014 and it was very highly valued by the students. A number of the students have remained in contact, and we continue to assist them in their model development. Course 2: Bayesian methods to fit statistical models in environmental science. Using examples from environmental science throughout, Bayesian model fitting methods will be introduced and compared to classical approaches. We will demonstrate how to set up complex structural models where Bayesian methods are necessary. Extensions to spatial and temporal smoothing, for example in disease mapping, will also be covered. The OpenBUGS statistical package will be used in conjunction with R for practical sessions. The focus will be on applications and modelling, particularly in areas cognisant with the students' research, but will also cover sufficient theory to explain the modelling concepts. We assume some previous exposure to elementary statistics. Course 3. Modelling structure and dynamics in complex networks. The study of complex networks has become an important interdisciplinary field of research in 21st century, with large and increasing impacts in biology, society, technology and ecology. The aim is to deliver statistical-mechanics approaches to understand network organisation and function. This involves properties such as expansibility, topological and functional bottlenecks, organisation of clusters, global communicability, "clumpiness" of nodes in a network, and returnability. In this course the basic concepts of network theory are introduced, such as small-worldness and scale-freeness. The study of node centrality for networks and its relevance for biological and ecological networks is also studied. Finally, we will explore models for analysing dynamical processes, such as synchronization, epidemic spreading and replication-mutation, and others from the students' own research fields.
- NERC Reference:
- NE/N000676/1
- Grant Stage:
- Completed
- Scheme:
- Doctoral Training
- Grant Status:
- Closed
- Programme:
- Advanced Training
This training grant award has a total value of £52,452
FDAB - Financial Details (Award breakdown by headings)
| Total - Other Costs |
|---|
| £52,452 |
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