Reusable FAIR Implementation Profiles as Accelerators of FAIR Convergence

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. Readymade 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.

FAIRification Efforts of Clinical Researchers: The Current State of Affairs

“…A first step toward achieving this is to ensure that there is convergence on FAIR implementations in the healthcare domain. Community-specific FAIR Implementation Profiles [13] can drive this convergence.…”

One Digital Health for more FAIRness

“…Thus, FAIR guiding principles spotlight the need to develop the capacities of computational systems to manage the monitoring, collection, processing, and stewardship of the digital resources to be evaluated, with a minimum of human interventions, due to the “big data” and “cloud computing” revolutions inducing an increase of data volume, velocity, and variability in the health and medicine arena. 11 To this end, an integrative literature review 12 13 was conducted to extract instances of the need—or of the eventual already existing deployment, explicitly reported or not—of FAIR principles with FAIR-enabling digital resources-like 14 (e.g., datasets, metadata, code, protocols, compute resources, data policies, identifier mechanisms, standards) for each of the three layers featured by the ODH framework. The review was processed by seeking for publications, on medical and engineering search engines (e.g., PubMed, IEEE Xplore, Science Direct), totally or partially related to the combinations of, at least, one FAIR principle and, at least, one of the ODH “steering wheel” features, for example, keys, perspectives, or dimensions (or synonyms).…”

Modeling community standards for metadata as templates makes data FAIR

“…The M4M facilitators have also demonstrated that their format can be replicated by other facilitators who are trained in the approach. GO FAIR construes M4M workshops as the vehicle for creating key elements of what it calls FAIR Implementation Profiles (FIPs) 41 -an intentionally generic term for the set of design choices that a given scientific community makes to implement the FAIR Guiding Principles. These design choices largely boil down to decisions about standards.…”