April 27, 2017, by Erin Snyder
Research Software Engineering for Data Driven Discovery
The Data Driven Discovery research priority area includes researchers from across all five faculties of the university, all working with novel ways of interacting with data. When we were looking to test a research software engineering service, to see what value it might bring to researchers and how it might work, the RPA members were natural partners.
We asked for expressions of interest for existing projects that would benefit from software engineering time, and received two. The first project is based in the Business School, and is working with mathematical models of decision-making. The second is based in MHS, and involves genetic sequencing. Happily, we had the capacity available to pick up both projects and help move them forward.
Both of these (very different!) projects will allow us to explore what a research software engineering service can bring to researchers. Can we improve the quality of the research, or speed up the way that researchers are working, with a short engagement? What’s the value to our researchers of having this kind of expertise available?
In addition, we can explore questions that will be of relevance as we design services in the future: what does it take to scale people up to working with higher levels of computational power, both technologically, and in terms of help and support? What roadblocks are in the way, and how can we get around them? What lessons will we need to keep in mind in service design? Perhaps most relevant: what do we not yet even know we should be considering?
This sort of quick trial will help us experiment and learn a large amount through a short engagement, all while helping our researchers with their projects.