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


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.

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12584 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference1st Workshop on Conceptual Modeling Meets Artificial Intelligence and Data-Driven Decision Making, CMAI 2020, 1st International Workshop on Conceptual Modeling for Life Sciences, CMLS 2020, 2nd Workshop on Conceptual Modeling, Ontologies and (Meta)data Management for Findable, Accessible, Interoperable and Reusable (FAIR) Data, CMOMM4FAIR 2020, 1st Workshop on Conceptual Modeling for NoSQL Data Stores, CoMoNoS 2020 and 3rd International Workshop on Empirical Methods in Conceptual Modeling, EmpER 2020 held at 39th International Conference on Conceptual Modeling, ER 2020


  • Convergence
  • FAIR implementation challenges
  • FAIR implementation choices
  • FAIR implementation community
  • FAIR implementation considerations
  • FAIR implementation profile
  • FAIR principles
  • FAIR-Enabling resource


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