In this joint blog post, Allyson Lister and Susanna Sansone with guest authors Barbara Magagna, Tobias Kuhn, and Erik Schultes describe the collaboration between the FIP wizard and FAIRsharing
The challenge we address
At the core of putting FAIR into practice are the choices that have to be made when selecting the appropriate standards (i.e., terminologies, models, formats, minimal information requirements, and identifier schema) and databases (repositories and knowledge bases). These resources are essential to describe, report and identify, as well as share research objects (e.g., datasets, code, workflows). Often each project, group or organisation (i.e., community) has its own norms. To map this landscape, the concept of the FAIR Implementation Profile (FIP) was created in 2019 together with ENVRI-FAIR and developed in a series of projects guided by the GO FAIR Foundation, to represent a collection of declarations a community makes about the usage of FAIR Enabling Resources (FERs), including standards, services and policies.
FAIRsharing and FIPs
Across all disciplines there are thousands of standards, databases and policies, all of which are core FERs; this is where FAIRsharing comes to the rescue. An informative and educational service on standards, inter-related to databases and data policies, across all disciplines, FAIRsharing guides consumers and producers to discover, select and use these resources with confidence, producers to make their resource more discoverable, more widely adopted and cited. FAIRsharing records describe these resources and are curated and tagged according to the maturity (i.e., ready, in development, deprecated, uncertain) to reflect their very dynamic evolution. Communities can also create FAIRsharing Collections of standards, databases and policies to list the resources they use within their FIP or signpost or recommend to others. FAIRsharing content is also machine-actionable, allowing third party tools to connect and provide services that can answer questions on the characteristics of standards, databases and their relationships, such as: “Which repositories have controlled data access? Which identification schema does this repository use? Which standard is suitable for describing software? Is it ready or in development?”. Via the FAIRsharing API, curated and trustworthy content is provided to enable various data management tasks, for example when assessing or assisting with FAIRness or when creating data management plans, as illustrated with the Data Stewardship Wizard (DSW).
FIP Wizard and nanopublications
Developed as an instance of the DSW software, the FIP Wizard is specialised for the creation of FIPs. The FIP Wizard provides project templates organised as questionnaires for creating the FIP itself, but also for creating descriptions of the FERs that are used to compose the FIP, such as identifier services, metadata schemata, FAIR vocabularies, data usage licences etc. Both FIPs and FERs can be published from the Wizard as machine-readable nanopublications based on the FIP ontology and the FER typology. The integration of the FIP Wizard with FAIRsharing makes use of its rich content. Where FER nanopublications describe resources that are already registered within FAIRsharing, the curated information about the relevant resources is retrieved via the FAIRsharing API and the FAIRsharing DOIs and descriptions are automatically added to the assertion part of the nanopublication. Furthermore, the citations of the FAIRsharing record are added for richer provenance (as in the figure). Where FER nanopublications describe standards, services and policies that are not already registered in FAIRsharing, users are prompted to create a corresponding FAIRsharing record. This collaborative process provides valuable input to FAIRsharing, allowing FIP authors to contribute to FAIRsharing content and coverage.
Next steps
As with many resources that strive to interoperate, the FAIRsharing team and GO FAIR Foundation are doing this work mostly without dedicated funding; we believe that piloting is important! We know that the curation workflow – needed to accurately import key descriptions from the FAIRsharing records to the FER nanopublications – is semi-automated and still requires manual effort. Going forward, we will focus on mapping the common attributes and streamline the process, and perhaps explore interim solutions such as an exporter service from FAIRsharing into the FER nanopublications, with provenance and attribution. Watch this space!