Details of Award
NERC Reference : NE/N000161/1
Environmental Statistics and Data analytics
Training Grant Award
- Lead Supervisor:
- Professor M Scott, University of Glasgow, School of Mathematics & Statistics
- Grant held at:
- University of Glasgow, School of Mathematics & 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:
- Climate & Climate Change
- Regional & Extreme Weather
- Statistics & Appl. Probability
- Earth Resources
- Pollution
- Abstract:
- Building on our previous highly successful training initiatives, our new proposal contains 3 main components: (1) a residential 4-day training course giving core statistical and data analytics training; (2) a second 4-day training course on advanced statistical topics and computational aspects related to sensor technology and (3) supplementing each training course, a 2 day challenge workshop/research seminar, the first such workshop will be on data analytics and visualisation around earth observation and environmental sensors, the second on environmental risk and resilience The proposed training covers Statistics, risk, and uncertainty and will provide specialised training and practical experience in Statistical modelling for the environmental sciences and scientific computing. This will include managing and manipulating large data sets, data visualisation, quantifying risk and uncertainty, understanding the power and limitations of statistical methodologies and data and information management. An important feature of each training course and workshop will be guest lecturers from other research institutes, business and government agencies. The first training course will provide participants with a fundamental grounding in both the theory and computation of modern statistical methodology. This will then be enhanced by the linked workshop which will focus on diverse data streams from earth observation and environmental sensors and cover appropriate strategies, methodologies and platforms to manage, manipulate and explore large and complex data structures, and appropriate methods for visualising such data. The second training week will extend participants' core statistical knowledge by providing expertise in using advanced statistical modelling tools, and will be enhanced by a linked 2-day workshop on the need for and use of statistical evidence in support of policy, regulation, environmental risk and resilience. We will accept a maximum of 25 students for each course (over the past 10 years, similar courses we have run have been oversubscribed by 100%, and fully booked within days of the announcement). Similarly we will restrict the linked 2-day workshops to 25, with first choice given to those attending the training courses. All the material will be available online and students who register for the courses will be provided with some revision material to reinforce their basic statistical and programming skills. Throughout the courses, students will be immersed in modern statistical computation using R, which will provide transferable skills in scientific computing. The overarching goal in the training courses is to offer practical training, so that as well as covering specific skills, the students will also be trained in the actual implementation and interpretation of the analysis. One key component of all the sessions is to give students experience and confidence in understanding the power and limitations of these methodologies. This proposal addresses directly the need that Environmental Scientists should understand the power and limitations of leading edge statistical methodologies in advancing the environmental sciences. The training courses provide a wide ranging introduction and application of statistical methodologies as they can be applied in a wide variety of environmental contexts. The underpinning concept is that of building explanatory, descriptive and predictive models, with an appropriate quantification of uncertainties.
- NERC Reference:
- NE/N000161/1
- Grant Stage:
- Completed
- Scheme:
- Doctoral Training
- Grant Status:
- Closed
- Programme:
- Advanced Training
This training grant award has a total value of £70,000
FDAB - Financial Details (Award breakdown by headings)
| Total - Other Costs |
|---|
| £70,000 |
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