Details of Award
NERC Reference : NE/R012326/1
Advanced Scripting and Computing Techniques: Become More Efficient and Productive
Training Grant Award
- Lead Supervisor:
- Professor DM Schultz, The University of Manchester, Earth Atmospheric and Env Sciences
- Grant held at:
- The University of Manchester, Earth Atmospheric and Env Sciences
- 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:
- Environmental modelling
- Earth & environmental
- Climate modelling
- Land - Atmosphere Interactions
- Circulation modelling
- Large scale atmos modelling
- Weather modelling
- Large Scale Dynamics/Transport
- Abstract:
- Low productivity is the key weakness identified by the Prime Minister in her Foreword to the Industrial Strategy Green Paper. One way to increase productivity is through gains in supercomputing and advanced modelling. These are skills that many NERC students already excel in because of their use and expertise with weather, climate, and other types of environmental models. Thus, NERC students are ideally suited to help solve the UK's productivity gap. This proposal helps develop NERC-funded students to reduce this productivity gap by providing training to increase their productivity through three skills: efficient scripting, cloud computing, and big-data approaches. These skills have been identified by our Project Partners Risk Management Solutions and Bolt Forecasting Limited as the kinds of skills that would elevate a recent graduate's CV for an entry-level research position to the top of the pile. Thus, these skills are recognized as meeting the national training need. To give these students an employability advantage, we propose a training course that addresses these three skills. The course has two components: a five-day interactive workshop at the University of Manchester and an online set of videos on common solutions to computing dilemmas. These online resources would be publicly available, linked from the Manchester-Liverpool Understanding the Earth, Atmosphere and Oceans DTP web site, and hosted on YouTube, ensuring widespread visibility. The training and learning outcomes of the in-person training and YouTube resources are: 1. Demonstrate improved abilities in scripting applicable for automating numerical model operation and parallelizing data analysis, increasing computational performance and efficiency. 2. Implement cloud-computing on an example relevant to the student. 3. Demonstrate the utility of big-data techniques to a student research problem. More specifically, the training content would include the following. The students would come to the workshop in Manchester with their own problem to work on. For students without project ideas yet, example datasets would be provided. Concepts would be presented during lectures in the mornings, with a guided interactive session in the afternoon where they would work on their own problem under the supervision of the instructor and teaching assistants. The first four days would focus on automation of numerical modelling operations, the concept of job arrays and massive data analysis, big-data approaches to datasets, cloud computing, and applications of these techniques to industrial markets. Day 5 would be student presentations where the students would show how they have implemented the tools discussed during the week to their research. The lectures and student presentations during the workshop will be recorded. Videos of some of the most common questions and how-to segments would also be created. These videos will be produced, made available on YouTube and linked from the Manchester-Liverpool DTP web site in a specially designed training-resource area.
- NERC Reference:
- NE/R012326/1
- Grant Stage:
- Completed
- Scheme:
- Doctoral Training
- Grant Status:
- Closed
- Programme:
- NPIF Training
This training grant award has a total value of £69,056
FDAB - Financial Details (Award breakdown by headings)
| Total - Other Costs |
|---|
| £69,056 |
If you need further help, please read the user guide.