A Generic Workflow for the Data FAIRification Process
收藏科学数据银行2020-10-17 更新2026-04-23 收录
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资源简介：
The FAIR guiding principles aim to enhance the Findability, Accessibility, Interoperability and Reusability of digital resources such as data, for both humans and machines. The process of making data FAIR (“FAIRification”) can be described in multiple steps. Figure 1 shows a generic step-by-step workflow for the process of making data FAIR (“FAIRification”). The workflow is divided into three “phases”: Pre-FAIRification, FAIRification, and Post-FAIRification (dark grey boxes) that are further specified by “steps” indicating typical aspects of practical FAIRification (light grey boxes): 1) identify FAIRification objective, 2) analyze data, 3) analyze metadata, 4a) define semantic data model, 4b) define semantic metadata model, 5a) make data linkable, 5b) make metadata linkable, 6) host FAIR data, and 7) assess FAIR data. The order is not strict and can be iterative.
提供机构：
Leiden University Medical Center; Rajaram Kaliyaperumal; GO FAIR International Support & Coordination Office; Luiz Olavo Bonino Da Silva Santos
创建时间：
2020-10-17
