Research Technology
Our LASER platform for sensitive data analysis has continued to operate smoothly and currently supports 147 LIDA researchers working on 53 different projects. The occasional issues that have occurred have been resolved efficiently by the teamwork of the University’s IT teams and LIDA’s data analytics team (the DAT). That is impressive given the variety of components, from different industry-standard suppliers, that make up the LASER system, and the ever-changing landscape of threats and updates that apply to any such system.
During the year, two new components of LASER have been completed. One, the Dashboard, is a suite of Power BI reports developed by the DAT to help researchers easily monitor and manage their activity, usage and costs within LASER. The Dashboard is transformational when compared with having to do that monitoring via the native Azure Portal, which required researchers to have an understanding of Azure infrastructure and was hard to interpret.
The other is a capability for cloud-driven interactive data visualization on super-high resolution displays such as our 48 megapixel Powerwalls. Achieving that involved detailed investigations and parameter optimisation across components in several layers of the LASER architecture. In doing so, we have identified deficiencies in the way that some established software handles user input events (e.g., mouse operations) and a design solution that would adopt the dynamic level of detail approach that CADCAM software utilised in the 1980s to enable users to manipulate complex 3D models before today’s powerful graphics cards became available.
Looking forward, LIDA staff are playing key roles in the procurement of new infrastructure for high-performance computing (HPC), AI, data storage and general computing capability, funded by the University’s £28 million strategic investment in Research IT. Other initiatives concern increasing LIDA’s capacities in research software engineering and data science methods, and broadening the DAT’s contributions. The former will benefit both research and impactful outputs, as well as opening up new opportunities for staff and students seeking experience in data science. The latter aims to transform career trajectories, engagement with stakeholders and professional standing.