Scientific data are generated at increasing speed and abundance due to the miniaturization and parallelization of experiments, the deployment of (remote) sensors and the digitization of experimental practices. In the internet age, data can be shared as rapidly as they are generated, facilitating contemporary collaborative science and knowledge sharing.
The development and application of methods to enable scientists to access and analyze voluminous and rapidly generated data, from radio telescopes to social media, are becoming universally important.
Example: Sensor networks
One specific area of optimized data handling in which we develop expert knowledge is the management and analysis of sensor network systems. Around us is an increasingly complex set of ever more sophisticated, in many cases distributed, sensor networks monitoring any number of dynamic processes.
Sensor networks are used to monitor climate conditions, traffic flow, human physiology and health, emergency detection (fires etc), structural health of buildings, vehicle telemetry and machine process management.
At the same time, novel scientific instruments also generate increasingly large sensor data streams. The science to manage and analyse sensor networks is dependent on disciplines such as wireless communications, protocols, signal processing, embedded systems, streameddata analysis, distributed algorithms, and data management.
This expertise area includes