Contents I. Introduction
In recent years, the way we conduct scientific research has undergone a paradigm shift from knowledge-driven to data-driven. This is particularly evident in data-rich industries such as healthcare, transportation, emerging manufacturing and service platforms. Researchers around the world now have the capacity and capability to perform data-intensive experiments that allow them to easily collect, analyses and store large volumes of data acquired through computational simulation, modelling and automated data acquisition. This has been further catalysed by the advancement of state-of-the-art information and communications technologies such as big data, cloud computing and machine learning. Open science is one of the key features of data-intensive research methods [1]. It encompasses unrestricted access to scientific publications, data from publicly funded research, and collaborative research made possible by technological tools and incentives [2]. This is why more and more researchers, journals and funding entities are now discussing on the idea of open science.
