Making FAIR Easy with FAIR Tools: From Creolization to Convergence

Abstract

Since their publication in 2016 we have seen a rapid adoption of the FAIR principles in many scientific disciplines where the inherent value of research data and, therefore, the importance of good data management and data stewardship, is recognized. This has led to many communities asking “What is FAIR?” and “How FAIR are we currently?”, questions which were addressed respectively by a publication revisiting the principles and the emergence of FAIR metrics. However, early adopters of the FAIR principles have already run into the next question: “How can we become (more) FAIR?” This question is more difficult to answer, as the principles do not prescribe any specific standard or implementation. Moreover, there does not yet exist a mature ecosystem of tools, platforms and standards to support human and machine agents to manage, produce, publish and consume FAIR data in a user-friendly and efficient (i.e., “easy”) way. In this paper we will show, however, that there are already many emerging examples of FAIR tools under development. This paper puts forward the position that we are likely already in a creolization phase where FAIR tools and technologies are merging and combining, before converging in a subsequent phase to solutions that make FAIR feasible in daily practice.

A catalogue of 863 Rett-syndrome-causing MECP2 mutations and lessons learned from data integration

“…We made the prepared genetic variant and phenotype data more Findable, Accessible, Interoperable, and Reusable for humans and computers following the FAIR guiding principles 26 . The data was made machine-readable (in RDF format) using a semantic data model (see below) and a general-purpose FAIRifier tool 27 based on the OpenRefine data cleaning and wrangling tool ( http://openrefine.org/ ) and an RDF plugin ( https://github.com/stkenny/grefine-rdf-extension ). Similarly, machine-readable metadata (information about the data) was generated using the Metadata Editor 27 .…”

FAIRising Pedagogical Documentation for the Research Lifecycle

“…Metadata has a key role in implementing major changes to entrenched tenets of the predominant research culture, notably that of scholarly publications as the sine qua non for research integrity and professional recognition [ 1 , 2 , 10 , 11 ].…”

“…Persistent digital objects are key to this integrated process but do not, in themselves, ensure the success of (re)using resource content. Persistent digital objects must be reliable to sustain the integrity of resource content [ 1 , 2 , 11 ]. By the middle of 2016, FAIR crystallized these concerns and provided a way forward [ 1 ].…”

“…Health and other (inter)disciplinary topics represent especially promising areas in which FAIR principles undergird topic genealogies and how they change over time. Data may be accessed and directed to problems devised by humans, or even by machines [ 11 ]. The tremendous potential for “FAIR-ised” [ 2 , p. 8] curricular resources to enhance the research lifecycle and augment the global FAIR ecosystem lies as yet untapped.…”

FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles

“…In this respect, many recent collaborative works have started to propose ways to implement, adapt and evaluate FAIR principles in several communities (e.g., Herschel et al 2017, Doorn & Timmermann, 2018, Federer et al 2018, Mons et al, 2017, Stall et al, 2018, de Miranda Azevedo & Dumontier, 2019, Erdmann et al, 2019. However, we are also reaching a moment where the FAIR principles now need cross-community convergence and consensus (EC DGRI, 2016;EC DGRI, 2018;Jacobsen et al, 2019;Sustkova et al, 2019;Thompson et al, 2019;Wilkinson et al, 2019). The work on FAIR data standards, repositories and policies is already ongoing as very well illustrated by the FAIRsharing.…”