Abstract:

[Objective] Research in data-intensive sciences is highly dependent on data. This article aims to promote the dissemination of the concept of the FAIR principles by introducing typical cases of their integration into data-intensive science practices, while also providing a reference for the construction of scientific data centers in China. [Method] Taking the practice of the FAIR principles in the EU Open Data Portal, the High Energy Physics and Geoscience Community as examples, we explore the constructive role of the FAIR principles in data management and summarize the experience of building data-intensive communities. [Result] The case studies show that the FAIR Principles are of good compatibility and have been rapidly adopted in the field of AI. And also, the FAIR Principles need to be integrated into current data infrastructures and normative standards. [Conclusions] The FAIR principles have been gradually adopted by international scientific research institutions and demonstrated their effectiveness in data-intensive scientific research fields. In contrast, the promotion of the FAIR principles in China faces numerous challenges, including regulation, data infrastructure, cross-disciplinary FAIR implementation, and cultural awareness.

Key words: data intensive science, FAIR principles, application practice, data management