Details of Award
NERC Reference : NE/M006077/1
Atmospheric composition data analysis training using openair and R
Training Grant Award
- Lead Supervisor:
- Professor DC Carslaw, King's College London, Analytical & Environmental Sciences
- Grant held at:
- King's College London, Analytical & Environmental Sciences
- Science Area:
- Atmospheric
- Overall Classification:
- Atmospheric
- ENRIs:
- Biodiversity
- Environmental Risks and Hazards
- Global Change
- Natural Resource Management
- Pollution and Waste
- Science Topics:
- Earth & environmental
- Pollution/pollution control
- Boundary Layer Meteorology
- Land - Atmosphere Interactions
- Tropospheric Processes
- Urban & Land Management
- Urban Pollution Management
- Abstract:
- This training proposal aims to develop a course based around the R statistical software and specifically a NERC-funded R package called openair. The openair project was initially funded by NERC from 2008-2011 with additional support from Defra. It has continued to grow and develop since that time. The openair package is used extensively throughout the world to analyse air pollution - or more generally - atmospheric composition data by academia, the public and private sectors. The package is also regularly used by NERC PhD students and PDRAs in their research e.g. leading to theses or journal articles. The applicant is frequently asked about the availability of courses on R/openair by NERC researchers but has yet to develop a course. This proposal addresses that need through the development of a 5-day dedicated course for 20 students. The course will be designed, organised and run by two experienced researchers in the area of atmospheric pollution, the PI (Dr David Carslaw) and Co-I (Dr Anna Font), both at King's College London in partnership with Prof. Alistair Lewis from the National Centre for Atmospheric Research (NCAS). The PI is the lead developer and maintainer of the openair package and has used R extensively in research leading to many new findings and journal publications. Dr Font has extensive experience in using R and openair for a wide range of atmospheric research problems. Prof. Lewis is Director for atmospheric composition, NCAS. The course will comprise three main elements and is focussed on NERC-funded PhD students as well as early career scientists who may work within non-academic settings. Indeed, many openair users work in non-academic settings but carry out extensive air quality data analysis research. An innovative aspect of the course will be the production of a comprehensive course manual based on a 'dynamic reporting' technique that mixes analysis code with text. For students this will provide a lasting resource that allows all analyses to be reproduced exactly as intended (see section 3 below) using freely available cross-platform software. The three main areas that will be considered are: [1] Introduction to R for environmental scientists. The course will require no previous knowledge of R and hence it will be important to introduce R with a focus on environmental science. This part of the course will set out the benefits of using R/openair in scientific research and introduce the underlying philosophies of data analysis. A key component of this part of the course will consider data management. The course will present many case studies of analysing data from different measurement instruments. [2] Use of openair for data analysis. This part of the course will focus specifically on how a dedicated R package (openair) can be used to draw inferences from data analysis. Students will work with a range of data sets from those available (over the Internet) in openair such as the EEA airbase data and NOAA Hysplit back trajectories and more specialist data sets from research instruments. In particular, it will be shown and emphasised that considering data analysis from many perspectives (through different openair functions) can help build up a comprehensive understanding. This part of course will encourage strong interaction - following lines of enquiry from the students as the analyses develop. [3] Reproducible research approaches to data analysis. Will focus on developing strong working practices for effective data analysis encouraging methods of reproducible research. This part of the course will show how these methods can improve data analysis quality and collaboration. By the end of the course students will have a sound understanding of the R/openair software for atmospheric data analysis, be able to work with many data formats, develop robust approaches for drawing inferences from data, as well as providing essential skills for future careers in academia, the public and private sectors.
- NERC Reference:
- NE/M006077/1
- Grant Stage:
- Completed
- Scheme:
- Doctoral Training
- Grant Status:
- Closed
- Programme:
- Advanced Training
This training grant award has a total value of £68,000
FDAB - Financial Details (Award breakdown by headings)
| Total - Other Costs |
|---|
| £68,000 |
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