Data scientists spend much time with data cleaning tasks, and this is especially important when dealing with data gathered from sensors, as finding failures is not unusual (there is an abundance of research on anomaly detection in sensor data).

In the field of plant disease protection, many approaches exist, but all acknowledge the necessity of fast and accurate identification of the source in order to make the most efficient applications.

In general, one of the complexities of large simulations is related to the usage of the heterogeneous computational resources that are needed to execute them. The definition of workflows, usually linked to concrete orchestrations solutions, has reduced most of that complexity.

Application of Pocket mHealth as a tool for occupational health, presented in the Workshop on Personalized Health & Intelligent Workplaces Transforming Ergonomics ‘18 - Acknowledgement to EIT Health.

Accurate digital twinning of the global challenges (GC) leads to computationally expensive coupled simulations.