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--- preface [2026/08/04 16:35] – [Preface] esoeding
+++ preface [2026/08/19 12:32] (current) – dkottmeier
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 ====== Preface ======
-The publication of **The FAIR Guiding Principles for Scientific Data Management and Stewardship** [[start|[1]]] addressed a persistent challenge within the research data management community. Although the importance of responsible research data management was widely recognized among data professionals, its objectives and benefits often remained difficult to communicate to researchers, decision-makers, and other stakeholders outside dedicated RDM teams. In particular, data managers frequently encountered uncertainty regarding the value of data documentation, the manner in which such documentation should be structured, and the benefits that systematic data stewardship could provide.
+The publication of **The FAIR Guiding Principles for Scientific Data Management and Stewardship** in 2016 [1] addressed an ongoing challenge within the research data management (RDM) community: although the importance of responsible research data management was widely recognized among data professionals, its objectives and benefits often remained difficult to communicate to researchers, decision-makers, and other stakeholders outside dedicated RDM teams. In particular, data managers frequently encountered uncertainty regarding the value of data documentation, the manner in which such documentation should be structured, and the benefits that systematic data stewardship could provide.
-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 Guiding Principles offered a concise and broadly applicable framework through which the objectives of RDM 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 subsequently readily adopted by the RDM community and increasingly shaped 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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 . The principles do not explain exactly how FAIRness should be achieved.
-Because they deliberately “precede implementation choices,” considerable interpretation is required. Communities and repositories must decide which identifiers, metadata schemas, ontologies, protocols, licenses, and provenance models to use. Consequently, two implementations may both claim to follow FAIR while remaining technically incompatible.
+Because they deliberately “precede implementation choices,” considerable interpretation is required. Communities and repositories must decide which persistent identifiers, metadata schemas, semantic artefacts, protocols, licenses, and provenance practices to use. Consequently, two implementations may both claim to follow FAIR while remaining technically incompatible.
 . FAIR depends on community agreement outside the principles themselves.
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 The original principles do not constitute a comprehensive framework for evaluating scientific quality, correctness, ethical acceptability, long-term preservation, social justice, governance, or trustworthiness. These matters may affect reuse, but they are not specified as independent FAIR requirements. This is a boundary of the framework rather than an explicit criticism made by the authors.
-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.
+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 artefacts, protocols, licenses, and provenance practices.
-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.
+This wiki addresses this implementation gap through the [[wiki:1_road-to-fair-strategy:|Road-to-FAIR Strategy]], which provides a framework for the coordinated application of FAIR practices across the Helmholtz Association. Rather than simply assessing individual elements of the FAIR framework, it provides guidance on the selection, prioritization, and consistent implementation of relevant standards, technologies, and community practices. Its aim is to support research data products in achieving the highest practicable level of FAIRness, by harmonizing critical parts of the research data documentation across infrastructures, institutions and organizations.
 ====== References ======