Development and Governance of FAIR Thresholds for a Data Federation

Abstract

The FAIR (findable, accessible, interoperable, and re-usable) principles and practice recommendations provide high level guidance and recommendations that are not research-domain specific in nature. There remains a gap in practice at the data provider and domain scientist level demonstrating how the FAIR principles can be applied beyond a set of generalist guidelines to meet the needs of a specific domain community.We present our insights developing FAIR thresholds in a domain specific context for self-governance by a community (agricultural research). 'Minimum thresholds' for FAIR data are required to align expectations for data delivered from providers' distributed data stores through a community-governed federation (the Agricultural Research Federation, AgReFed). Data providers were supported to make data holdings more FAIR. There was a range of different FAIR starting points, organisational goals, and end user needs, solutions, and capabilities. This informed the distilling of a set of FAIR criteria ranging from 'Minimum thresholds' to 'Stretch targets'. These were operationalised through consensus into a framework for governance and implementation by the agricultural research domain community. 2Wong et al.

Advancing FAIR Agricultural Data: The AgReFed FAIR Assessment Tool

“…During the development of the minimum thresholds for AgReFed, exemplar datasets were initially found to vary in findability, that is, whether they had a permanent identifier, a metadata record and whether that metadata record was indexed in a searchable repository (Wong et al 2022). Fully automated assessment tools are unable to capture any FAIR metrics in scenarios where a dataset doesn't at least have a Uniform Resource Locator (URL) and a machinereadable metadata record.…”

“…AgReFed's common vision is to 'enable Findable, Accessible, Interoperable and Reusable (FAIR) agricultural data to accelerate innovation and increase the profitability and sustainability of Australian agriculture' (Agricultural Research Federation 2023). The AgReFed community developed a domain-specific FAIR assessment that defines thresholds based on minimum standards, acceptable standards and stretch goals (Wong et al 2022). AgReFed recognises that FAIR is a journey that is supported through education and tooling (Mons et al 2020;Devaraju et al 2021) and that FAIR assessments are among the tools and technologies that assist with the provision of FAIR data (Krans et al 2022;Thompson et al 2020).…”

“…AgReFed provides infrastructure, resources, and tools as well as a framework for governance and data stewardship, which are accessible via AgReFed's website. 1 AgReFed, being a data federation, requires data and access controls to be maintained by data providers, ideally within trusted repositories (Wong et al 2022). Data providers are also responsible for the creation and maintenance of adequate metadata and for attaining and sustaining an acceptable level of FAIRness (Box et al 2020).…”

“…FAIR assessments should be conducted on all agricultural research datasets, or at least on exemplars of similar collections. In reflecting on the development of AgReFed's FAIR thresholds, Wong et al (2022) suggest an automated assessment tool as a next step.…”

Attending to the Cultures of Data Science Work

“…Admirably, over the past 20 years, data science organizations and journals have drawn attention to social barriers to data sharing, such as differing incentive structures and levels of training within and across diverse disciplinary communities, scholarly generations, and geographic boundaries (Borgman 2012;Gownaris et al 2022;Laine 2017;van Panhuis et al 2014). Research examining how to support the uptake of best practices, such as FAIR (findable, accessible, interoperable, and reusable) guidelines and data management planning, has shown that adoption not only depends on clear guidance and welldesigned infrastructure but also social advocacy, relationship building, and an amenable financial, legal, and policy landscape (Wong et al 2022). Movements to prioritize the rights and interests of Indigenous peoples in the knowledge economy have highlighted the need for alternative data governance models that privilege self-determination and collective benefits (Carroll et al 2020).…”