2022/01/19 by Natalia Queiroz de Oliveira, Vânia Borges, de Oliveira, Natalia Queiroz +7
Business, Management and Accounting · Computer Science · Health Professions · #Artificial Intelligence in Healthcare #Big Data and Business Intelligence #Databases (cs.DB) #FOS: Biological sciences #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Quantitative Methods (q-bio.QM) #Research Data Management Practices
paper · pdf · doi:10.48550/arxiv.2201.07866
openalex publication_date 2022/01/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Since their proposal in 2016, the FAIR principles have been largely discussed by different communities and initiatives involved in the development of infrastructures to enhance support for data findability, accessibility, interoperability, and reuse. One of the challenges in implementing these principles lies in defining a well-delimited process with organized and detailed actions. This paper presents a workflow of actions that is being adopted in the VODAN BR pilot for generating FAIR (meta)data for COVID-19 research. It provides the understanding of each step of the process, establishing their contribution. In this work, we also evaluate potential tools to (semi)automatize (meta)data treatment whenever possible. Although defined for a particular use case, it is expected that this workflow can be applied for other epidemical research and in other domains, benefiting the entire scientific community.