FAIRification of datasets is an ongoing area of research by different consortia. In the context of FAIRplus, we reviewed and selected from existing metrics, which were then applied to pilot datasets before and after our FAIRification process to evaluate its effectiveness. Based on the results in this report and our experiences of the process, we recommend a combination of manual and automated assessments against strong use cases and competency questions. This supports the attainment of the right level of FAIR, which we call “FAIR enough”. Reaching the FAIR enough level ensures we achieve the right balance of providing maximal returns at minimum costs for data owners. The metrics used by FAIRplus have been published on our website and the process for evaluation using these metrics is described in this report.