Efficient data management for putting forward data centric sciences

Abstract

The novel and multidisciplinary data centric and scientific movement promises new and not yet imagined applications that rely on massive amounts of evolving data that need to be cleaned, integrated, and analysed for modelling, prediction, and critical decision making purposes. This paper explores the key challenges and opportunities for data management in this new scientific context, and discusses how data management can best contribute to data centric sciences applications through clever data science strategies.