2026-10-14T06:17:48Z https://www.scidb.cn/oai

10.11922/sciencedb.j00104.00067 2020-10-17T19:47:08Z

2020-10-17 A Generic Workflow for the Data FAIRification Process doi:10.11922/sciencedb.j00104.00067 en 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. FAIR data; FAIRification workflow; FAIR data stewardship; Hands-on FAIRification; FAIR dissemination Annika Jacobsen Rajaram Kaliyaperumal Luiz Olavo Bonino da Silva Santos Barend Mons Erik Schultes Marco Roos Mark Thompson PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/10.1162/dint_a_00028 Science Data Bank