From supporting leading research projects to presenting at respected conferences and contributing to widely used Open Source Software, we invest in promoting our work and that of our collaborators. 

You can find out more about the type of work we support by reading the case studies in the tabs below. 

Case studies

MAGDA

Background 

At the conclusion of a successful project, researchers are often the custodians of large datasets of great potential value to the wider research community. Publishing such data was the challenge faced by the Space and Atmospheric Physics group at the end of the long-running and highly productive Cassini mission. After collecting data from two instruments on the probe over 19 years of operation the group held 2TB of data that, due to funder requirements, needed to be made readily accessible to the global community of space researchers. 

Our contribution  

An internal webserver and shared filesystem provided temporary means of accessing the data but were reliant on legacy software and obsolete hardware. The Research Computing Service's RSE team were engaged by the Head of Department and the Vice-Provost (Research and Enterprise) to develop a replacement system suitable for public release of the data. A key requirement was that the solution would be effective for time scales of 10+ years with only minimal maintenance. The team addressed this challenge by embracing open source technologies with well-established user communities. A hardware independent solution was developed through the use of containerisation so that the webserver can be hosted flexibly on virtual infrastructure and upgraded seamlessly. 

Outcomes 

Users of the new MAGDA system are able to browse and filter the available data by date range and other properties. After identifying relevant data it can be visualised to search for interesting features and downloaded in a variety of formats. Completing this project required close collaboration to design a suitably intuitive and reliable user interface, and to produce informative visualisations of the data including comparison against numerical models. 

Testimonials 

Dr Richard Bantges, Scientific Project Manager, MAGDA (to be approved): 

The scientific elements of this project precluded using a contract web developer but the expertise of the RSE team was a perfect fit. They quickly understood the problem at hand, rapidly prototyping web pages and visualisations for us to provide feedback on. Without the help of RSE team we would have struggled to comply with our funder's stipulations to publish the Cassini mission data. 

Keywords

Python, Application development, Data visualisation, Astrophysics, Data preservation 

MUSE

Background 

Dr Adam Hawkes and Dr Sara Giarola of the Sustainable Gas Institute at Imperial College London are building MUSE: a novel technology-rich, whole-systems model of the energy sector. The model is capable of differentiating between regions, between technologies (from the gas boilers consuming energy to nuclear plants producing energy) and between investors with different behaviours and preferences. The RSE team in Imperial’s Research Computing Service became involved when the complexity of the model's implementation grew large enough to hamper further development of MUSE. 

The model tackles a large track of the world economy in a detailed fashion. It combines vastly different concepts, e.g. the preference of green-minded investors and development minded investors versus the characteristics of a nuclear plant and that of a kettle. It combines data across multiple dimensions, e.g. a regional axis with a year axis, or a seasonal axis with a commodity axis. It aims to be modular, so that users drive their own research by tailoring, modifying, or even overriding any part of the model. Managing this modelling complexity so that it does not overwhelm users and developers requires modern and professional approaches to software design and development. 

Our contribution 

Initial assistance from the RSE team involved the introduction of tools and methodologies that make complex projects sustainable. The code was outfitted with regression tests and a unit testing framework, enabling automated cross-platform quality assurance. The MUSE development team was encouraged to make full use of the GitHub platform to host, communicate and plan development. In later stages implementation complexity was managed by using self-describing structures fully reflecting the heterogeneity and complexity of the data, and by consolidating much of MUSE into smaller, self-contained, modular blocks of legible code – supported by the new testing framework. 

Outcomes 

The code behind MUSE will be open-sourced, including a simplified set of the data that makes it a complete economic model for energy usage across the world. It will be packaged as a native application for Windows and macOS, so that users with no programming experience can use it directly. For more experienced users, HPC users, and developers it is available as a standard Python library that can be installed using the pip package manager. User and developer guides are also tested and compiled automatically, providing a comprehensive documentation suite that further encourages re-use and ultimately citation of the software. 

Ultimately this collaboration between the MUSE and RSE teams resulted in a reliable, performant modelling framework capable of being maintained by a research team with enhanced software development capacity. Its accessibility now lends itself to a much stronger community of users and developers, and materially increased impact for the underlying research. 

Testimonials 

Dr Adam Hawkes, Reader in Energy Systems and MUSE PI: 

The RCS has helped manage the complexity inherent in MUSE while greatly increasing its modularity and simplifying the process of adding new features. The RSE team took a proof-of-concept model and helped us shape it into a reliable, legible code we are confident to release to the community and use as a foundation for further development. Their support has been invaluable to our research. 

Keywords

Python, Reproducibility, Energy Systems, Modelling

POWBAL

Background 

POWBAL is an innovative energy demand management study co-ordinated by the Imperial College Business School and the Grantham Institute of Climate Change. Participants are provided with domestic smart plugs and usage incentives on the understanding that the devices can be remotely controlled. Analysis and understanding of consumer attitudes to power consumption are intended to inform the development of relevant policy, including effective incentives for energy conservation. 

Our contribution 

The first iteration of the POWBAL technology platform was developed by a third-party software company but was not considered a suitable long-term solution for technical and functional reasons. The College’s Research Computing Service was commissioned by Dr Ralf Martin (leader of the project at Imperial) to deliver a sustainable re-implementation according to software engineering best-practice and well-integrated with the College's technology infrastructure. The schedule was ambitious, based on enrolling study participants at the busiest time of the academic year. 

Outcomes 

The RSE team worked in collaboration with Dr Martin and the College’s ICT department to deliver the project on schedule and within budget following an iterative development strategy that involved the project PI at every stage. The new system prioritised security, maintainability, and durability in addition to providing an intuitive user interface for study participants. At the time of writing it has successfully logged over 500,000 energy readings for analysis by the POWBAL team - transforming a project proposal into a working solution within a matter of weeks. 

Testimonials 

Dr Ralf Martin, Associate Professor of Economics, Imperial College Business School and POWBAL PI: 

The Research Computing Service provided us with a welcome alternative to third-party software development: offering face-to-face support, long-term continuity and a thorough understanding of our requirements. The solution they delivered exceeded our expectations and has proved a solid foundation for our research study. We highly value the RSE team's contribution to the POWBAL project. 

Keywords

Python, Application development, IOT, Data aggregation, Energy systems

Smart Forming

Background 

One of the Research Computing Service's earliest RSE projects was a proof-of-concept built for the Metal Forming Technologies (MFT) group in Mechanical Engineering. The RSE team worked with Omer El-Fakir and Dr Li-Liang Wang to develop a bespoke software platform demonstrating broad applicability, teaching utility and the potential for long-term commercialisation. This resulting solution was not only of immediate use in postgraduate tuition but also enabled the submission of ambitious follow-up funding proposals. 

Omer has a strong track record in technology and innovation (including as founder of BLOCKS) and contacted the RCS after seeing the initial announcement of the RSE service. He proposed a system enabling postgraduate students to collaborate on the development of mathematical models relevant research carried out by Dr Wang's group. The deadline (determined to the MSc teaching schedule) was ambitious but the work suited the RSE team's specialist expertise in software engineering and High-Performance Computing, and their interest in developing tools to support both research as well as teaching and learning. 

Our contribution  

In order to fulfil the MFT group's requirements alongside initial demand for the RSE service, the team worked in collaboration with a postgraduate student in Engineering. His coding knowledge and desire to take on practical projects as preparation for a career in software engineering was combined with our experience in transferring knowledge and efficiently developing solutions alongside researchers. The result was a reliable and intuitive system that was well-integrated with the College's existing infrastructure and that made high-performance computing readily accessible: enabling the upload, sharing and remote execution of MATLAB models. This enabled the relevant students to efficiently run their code whilst focusing on tuning the models, and our mentorship provided the contributing student valuable practical experience in a supervised environment. 

Outcomes  

The RCS ultimately delivered the MFT group a fully functional proof-of-concept to enable further funding applications and raise awareness of their work. The RSE team worked very closely with Omer and Dr Wang, iterating rapidly to design, develop and deploy a bespoke solution while allowing them to retain ownership of the project and the outputs. This provided early evidence of how investment from the College and ICT in building a Research Software Engineering team within the RCS offered material benefits to research leaders and students by providing specialist software engineering expertise. 

Keywords

Python, MATLAB, Proof-of-concept, Learning and Teaching, High-Performance Computing, Research funding