Data is the fuel for researchers. Owing to a new awareness for transparent and open science processes, they increasingly aim to make the output of the research lifecycle searchable and available to others. The FAIR Principles guide researchers in their efforts of making data findable, accessible, interoperable and reusable.
As a provider of a FAIR service or tool, you need to consider the FAIRification process and how to facilitate the process of making data FAIR.
- Retrieve Non-FAIR Data
- Describe & Analyze Data
- Rich Metadata
- Persistent Identifier
- Persistent identifiers: working level – ANDS: What, why and when to use PID
- Ontology and Semantic model
- 5 Stars Machine Actionability Model: The 5-star model shows importance and benefits of machine-actionability, open data, and semantic modelling
- Data Storage
- CoreTrustSeal Trustworthy Data Repositories Requirements
- Recommendations on certifying services required to enable FAIR within EOSC: An analysis of activities relevant to certification of the services required to enable FAIR research outputs within EOSC
- Data Licenses
- Deploy FAIRed Data