Reusable FAIR Implementation Profiles as Accelerators of FAIR Convergence

Erik Schultes, Barbara Magagna*, Kristina Maria Hettne, Robert Pergl, Marek Suchánek, Tobias Kuhn

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

10 Citations (Scopus)
127 Downloads (Pure)


Powerful incentives are driving the adoption of FAIR practices among a broad cross-section of stakeholders. This adoption process must factor in numerous considerations regarding the use of both domain-specific and infrastructural resources. These considerations must be made for each of the FAIR Guiding Principles and include supra-domain objectives such as the maximum reuse of existing resources (i.e., minimised reinvention of the wheel) or maximum interoperation with existing FAIR data and services. Despite the complexity of this task, it is likely that the majority of the decisions will be repeated across communities and that communities can expedite their own FAIR adoption process by judiciously reusing the implementation choices already made by others. To leverage these redundancies and accelerate convergence onto widespread reuse of FAIR implementations, we have developed the concept of FAIR Implementation Profile (FIP) that captures the comprehensive set of implementation choices made at the discretion of individual communities of practice. The collection of community-specific FIPs compose an online resource called the FIP Convergence Matrix which can be used to track the evolving landscape of FAIR implementations and inform optimisation around reuse and interoperation. Ready-made and well-tested FIPs created by trusted communities will find widespread reuse among other communities and could vastly accelerate decision making on well-informed implementations of the FAIR Principles within and particularly between domains.

Original languageEnglish
Title of host publicationAdvances in Conceptual Modeling
Subtitle of host publicationER 2020 Workshops CMAI, CMLS, CMOMM4FAIR, CoMoNoS, EmpER, Vienna, Austria, November 3–6, 2020, Proceedings
EditorsGeorg Grossmann, Sudha Ram
Place of PublicationCham
Number of pages10
ISBN (Electronic)978-3-030-65847-2
ISBN (Print)978-3-030-65846-5
Publication statusPublished - 2020
Event39th International Conference on Conceptual Modeling, ER 2020 - Virtual Event
Duration: 3 Nov 20206 Nov 2020
Conference number: 39

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference39th International Conference on Conceptual Modeling, ER 2020
Abbreviated titleER 2020
Internet address


  • Convergence
  • FAIR implementation challenges
  • FAIR implementation choices
  • FAIR implementation community
  • FAIR implementation considerations
  • FAIR implementation profile
  • FAIR principles
  • FAIR-Enabling resource
  • 22/2 OA procedure


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