Differences

This shows you the differences between two versions of the page.

--- preface [2026/08/19 12:05] – dkottmeier
+++ preface [2026/08/19 12:32] (current) – dkottmeier
@@ Line 1: / Line 1: @@
 ====== Preface ======
-The publication of **The FAIR Guiding Principles for Scientific Data Management and Stewardship** in 2016 [[start|[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 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 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.
@@ Line 52: / Line 52: @@
 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 addresses this implementation gap through the 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.
+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 ======