FEATURE
Future technologies
1 February 2017
Robert Roe investigates the use of technologies in HPC that could help shape the design of future supercomputers
February/March 2017
FEATURE
Managing energy efficiency at NASA
1 February 2017
Robert Roe learns that the NASA advanced supercomputing division (NAS) is optimising energy efficiency and water usage to maximise the facility’s potential to deliver computing services to its user community
FEATURE
A cloud is forming
1 February 2017
Robert Roe investigates the growth in cloud technology which is being driven by scientific, engineering and HPC workflows through application specific hardware
FEATURE
Optimising industry
1 February 2017
Robert Roe looks at the latest simulation techniques used in the design of industrial and commercial vehicles
FEATURE
A question of security
1 February 2017
For functionality and security for externalised research, software providers have turned to the cloud, writes Sophia Ktori
Feature
Building a Smart Laboratory 2018 highlights the importance of adopting smart laboratory technology, as well as pointing out the challenges and pitfalls of the process
Feature
Informatics experts share their experiences on the implementing new technologies and manging change in the modern laboratory
Feature
This chapter will consider the different classes of instruments and computerised instrument systems to be found in laboratories and the role they play in computerised experiments and sample processing – and the steady progress towards all-electronic laboratories.
Feature
This chapter considers how the smart laboratory contributes to the requirements of a knowledge eco-system, and the practical consequences of joined-up science. Knowledge management describes the processes that bring people and information together to address the acquisition, processing, storage, use, and re-use of knowledge to develop understanding and to create value
Feature
This chapter takes the theme of knowledge management beyond document handling into the analysis and mining of data. Technology by itself is not enough – laboratory staff need to understand the output from the data analysis tools – and so data analytics must be considered holistically, starting with the design of the experiment