Details of Award
NERC Reference : NE/R012415/1
Big Data in environmental biology: Applying advanced statistics to explore and analyse large data sets in ecology and environmental science
Training Grant Award
- Lead Supervisor:
- Dr T Hesselberg, University of Oxford, Continuing Education
- Grant held at:
- University of Oxford, Continuing Education
- Science Area:
- Atmospheric
- Earth
- Freshwater
- Marine
- Terrestrial
- Overall Classification:
- Terrestrial
- ENRIs:
- Biodiversity
- Environmental Risks and Hazards
- Global Change
- Natural Resource Management
- Pollution and Waste
- Science Topics:
- Ecosystems
- Environmental modelling
- Earth & environmental
- Environmental Geography
- Geography and citizen science
- Multivariate
- Statistics & Appl. Probability
- Statistical Ecology
- Abstract:
- The proposed 4-day residential postgraduate-level training course in exploring, visualising and analysing very large datasets (so called Big Data) aims to equip participants with the confidence and necessary skills to employ advanced statistical techniques to their own data, and to apply them in a professional environment. The course provides an overview of the most widely used statistical modelling tools in ecological research for dealing with large data-sets including data mining, generalised linear models, generalised linear mixed models, principal component analysis and ordination statistics. Participants will be working on real data-sets centred around four case-studies. Case study 1 focuses on managing and locating relevant subset of data in a big data system relating to global environmental data. Case study 2 looks at how advanced linear models can shed light on the complex drivers behind deforestation during the development of the Seima Protection Forest REDD+ project in Cambodia. Case study 3 introduces the increasingly popular method of obtaining large ecological data-sets through citizen science and demonstrates how generalised linear mixed models were used to determine the environmental and habitat variables influencing the timing of ant mating flights in the UK. Finally, case study 4 follows on from case study 1, by showing how the same big data resource can be accessed by R scripts to visualise and analyse the available data. The core statistical techniques outlined above will be related to the needs of the environmental industry sector and the course will be framed by two guest lectures by experienced business consultants; one on the use of big data in industry on day 1 and another one on business perspectives of big data with a focus on business questions around innovation and commercialisation of big data project on day 4. Uniqueness The proposed bespoke course introduces postgraduate students in ecological and environmental science to R packages for managing, visualising and analysing very large data-sets. The core team of three tutors will provide a supportive, but rigorous forum in which students can improve their analytical skills as well as address questions relevant to their own research. The course is furthermore unique in involving two business consultants that will provide advice on the teaching material to ensure its relevance and connection to the environmental industry sector as well as delivering two guest lectures on the current use and potential of big data in business. Finally, participants will have the option to introduce their own research and data-sets with opportunities to discuss their analysis with the course tutors. Impact The course aims to fill a widening gap in skill provision in advanced ecological data analysis by focussing on the most widely used advanced techniques for exploring and analysing very large data-sets. The ability to analyse and interpret data underpins all ecological research and decision making, and is highly valued by academics, NGOs, consultants and industrial employers. The design of the proposed course is based on the participant feedback and experiences gained through delivering two successful NERC funded face-to-face training courses in multivariate statistics and one in data visualisation during the past four years. Throughout the course, students will be signposted to a wide range of resources including books, websites and R user groups, which will allow them to continue improving their understanding of, and skills in, working with Big Data. The impact of this training project will extend beyond the period of the grant as the participating students will be able to use the skills learned throughout their studentship. The training course furthermore equips students with relevant skills for continuing their careers in both academic research or in data-driven roles in industry and policy related sectors.
- NERC Reference:
- NE/R012415/1
- Grant Stage:
- Completed
- Scheme:
- Doctoral Training
- Grant Status:
- Closed
- Programme:
- NPIF Training
This training grant award has a total value of £26,805
FDAB - Financial Details (Award breakdown by headings)
| Total - Other Costs |
|---|
| £26,805 |
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