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Reusable FAIR Implementation Profiles as Accelerators of FAIR Convergence

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 12584)

Abstract

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.

Keywords

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

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Acknowledgements

This work was carried out in the context of the GO FAIR VODAN Implementation Network (supported also by the GO FAIR Foundation) and ENVRI-FAIR. ENVRI-FAIR has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 824068.

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Correspondence to Barbara Magagna .

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Schultes, E., Magagna, B., Hettne, K.M., Pergl, R., Suchánek, M., Kuhn, T. (2020). Reusable FAIR Implementation Profiles as Accelerators of FAIR Convergence. In: Grossmann, G., Ram, S. (eds) Advances in Conceptual Modeling. ER 2020. Lecture Notes in Computer Science(), vol 12584. Springer, Cham. https://doi.org/10.1007/978-3-030-65847-2_13

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  • DOI: https://doi.org/10.1007/978-3-030-65847-2_13

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