FAIR Principles and Big Data: A Software Reference Architecture for Open Science

Abstract

Open Science pursues the assurance of free availability and usability of every digital outcome originated from scientific research, such as scientific publications, data, and methodologies. It motivated the emergence of the FAIR Principles, which introduce a set of requirements that contemporary data sharing repositories must adopt to provide findability, accessibility, interoperability, and reusability. However, implementing a FAIR-compliant repository has become a core problem due to two main factors. First, there is a significant complexity related to fulfilling the requirements since they demand the management of research data and metadata. Second, the repository must be designed to support the inherent big data complexity of volume, variety, and velocity. In this paper, we propose a novel FAIR-compliant software reference architecture to store, process, and query massive volumes of scientific data and metadata. We also introduce a generic metadata warehouse model to handle the repository metadata and support analytical query processing, providing different perspectives of data insights. We show the applicability of the architecture through a case study in the context of a real-world dataset of COVID-19 Brazilian patients, detailing different types of queries and highlighting their importance to big data analytics.