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 The FAIR Guiding Principles offered a concise and broadly applicable framework through which these objectives could be articulated. By defining four high-level properties—Findability, Accessibility, Interoperability, and Reusability—the principles provided a common vocabulary for describing the intended outcomes of research data management. These overarching objectives were complemented by 15 numbered elements of the FAIR framework—F1–F4, A1–A2, I1–I3, and R1–R1.3—which describe characteristics and practices intended to support their realization.
-The FAIR framework was readily adopted by the RDM community and increasingly informed the development of data infrastructures, services, and research data ecosystems. In many cases, these systems were designed or adapted to satisfy the more detailed FAIR sub-principles. However, the principles were deliberately formulated at a high level and therefore present several limitations for their practical implementation:
+The FAIR framework was readily adopted by the RDM community and increasingly informed the development of data infrastructures, services, and research data ecosystems. In many cases, these systems were designed or adapted to satisfy the more detailed elements of the FAIR framework. However, the principles were deliberately formulated at a high level and therefore present several limitations for their practical implementation:
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 FAIR thus defines the properties that a well-managed digital research object should exhibit, but it does not provide the common standards or coordinated implementation required to produce those properties. Its practical success depends on research communities agreeing upon and consistently applying persistent identifiers, metadata schemas, semantic vocabularies, protocols, provenance practices, and licenses.
-This wiki seeks to address this implementation gap by supporting the coordinated application of FAIR practices across the Helmholtz Association. It provides guidance on the selection and consistent use of relevant standards, technologies, and community practices, with the aim of enabling research data products to attain the highest practicable level of FAIRness.
+This wiki seeks to address this implementation gap by supporting the coordinated application of FAIR practices across the Helmholtz Association. It goes beyond the simple checkmarking of elements in the FAIR framework. It provides guidance on the selection, prioritization and consistent use of relevant standards, technologies, and community practices, with the aim of enabling research data products to attain the highest practicable level of FAIRness.
+====== References ======
+[1] Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3:160018 doi: 10.1038/sdata.2016.18 (2016)