Alignment with Community Efforts

How FAIRsharing helps resources with FAIRness

The FAIR Principles, as described on the GO-FAIR website:

provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data. https://www.go-fair.org/fair-principles/

Although traditionally applied to research data objects and other digital assets, within FAIRsharing our descriptions of standards, databases and data policies are our digital assets. Therefore, you will find that within the FAIRsharing documentation are notes that help you learn why adding the metadata we request help us describe your resources FAIRly, and more importantly, why these fields are so important in enabling the FAIRness of your resources. Here's an example from one of our documentation pages:

Ensuring that your homepage is up to date with us helps the FAIRsharing user community to find your resource and the data/digital objects that might make use of it in alignment with F2: Data are described with rich metadata and F3 Metadata clearly and explicitly include the identifier of the data they describe.

Example from our homepage documentation.

So, how does providing a homepage, a resource name or, indeed, any of the other fields that we for your FAIRsharing record help with making a digital research object more FAIR? FAIRsharing helps enable FAIR data by structuring and standardising the descriptions of the standards, databases and policies used by the research community, and therefore allowing our community of users to determine which resources have the qualities they need to make their own data FAIR.

Further, our metadata aligns with the FAIR Principles, and is used in a number of FAIR evaluation and assessment tools. Our metadata provides human- and machine-accessible information regarding the findability, accessibility, interoperability and re-usability of the resources we describe. FAIRsharing metadata aligns with the FAIR principles to help you make an informed choice about the resources you use to describe and store your research data.

The majority of FAIRsharing metadata improves the findability of a resource; that is, providing human- and machine-accessible metadata to help users find the resources they need. Ultimately, this will enable the FAIRness of the data they need to share. Many fields are also relevant to the re-usability of a resource as well, which deals with the ability of a user to see if the resource (and ultimately the data it is associated with) is useful for them. While the F is generally about discovery of a resource, the R tends to be more about the utility of the digital object within a particular context, as well as its legal interoperability (e.g. licencing). In an wider context, FAIRsharing helps the resources it describes to themselves align with F4: (Meta)data are registered or indexed in a searchable resource by providing a registry to describe themselves in.

How FAIRsharing aligns with FAIR

F1. (Meta)data are assigned a globally unique and persistent identifier: FAIRsharing assigns DOIs to all records within its three registries (standards, databases and data policies) according to this DOI minting schedule. Users may identify themselves with their ORCID, and we integrate with the ORCID registry as a trusted organisation. Organisations may be linked to their ROR id, and new organisations can be added directly from ROR. Read our news items about adding ROR organisations and mapping to ROR.

F2. Data are described with rich metadata (defined by R1 below): This principle focuses on metadata used to help "help people to locate your data, and increase re-use and citations". FAIRsharing provides over 40 different metadata fields for resource developers to use to describe their resources.

F3. Metadata clearly and explicitly include the identifier of the data they describe: This item is not applicable for FAIRsharing, as we do not have separate data and metadata objects.

F4. (Meta)data are registered or indexed in a searchable resource: We have marked up our site with Bioschemas and schema.org to improve the discovery of our records within search engines.

A1. (Meta)data are retrievable by their identifier using a standardised communications protocol, A1.1 The protocol is open, free, and universally implementable, A1.2 The protocol allows for an authentication and authorisation procedure, where necessary, A2. Metadata are accessible, even when the data are no longer available: All of these are implemented through the use of our standard web interface for human access

I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation: Our REST API provides responses using JSON.

I2. (Meta)data use vocabularies that follow FAIR principles: The controlled vocabularies we use to describe our records are publicly available and resolvable and make use of globally unique and persistent identifiers. Subjects: GitHub repository, FAIRsharing subject browser, OLS browser. Domains: GitHub repository Taxonomy: based on NCBI Taxonomy, work is ongoing to align fully with their hierarchy.

I3. (Meta)data include qualified references to other (meta)data: Cross-references to a variety of repositories that also describe the resource referenced in a given FAIRsharing record. Our links to ROR and ORCID might also be relevant to this item as well as to F1.

R1. (Meta)data are richly described with a plurality of accurate and relevant attributes: As stated in the principles, "Principle R1 is related to F2, but R1 focuses on the ability of a user (machine or human) to decide if the data is actually USEFUL in a particular context. To make this decision, the data publisher should provide not just metadata that allows discovery, but also metadata that richly describes the context under which the data was generated." In this respect, our >40 metadata descriptors also are used to fulfil this requirement.

R1.1. (Meta)data are released with a clear and accessible data usage license. FAIRsharing is released under a CC BY-SA 4.0 licence.

R1.2. (Meta)data are associated with detailed provenance. Detailed history of all changes in each FAIRsharing record is available publicly, including information about which user made each edit.

R1.3. (Meta)data meet domain-relevant community standards: we work with many communities such as the RDA to ensure that the metadata we collect aligns with community efforts for minimal metadata criteria for policies, standards and databases.

Database Attributes

A number of efforts are ongoing to provide a minimal set of database attributes for describing such resources, to aid database discovery and comparison. FAIRsharing strives to integrate with any such community effort, and details of any alignments between them and FAIRsharing database metadata are integrated into the descriptions of those fields throughout this documentation. In short, FAIRsharing metadata is aligned with both RDA and NIH repository attribution efforts. To keep it all in a single documented location, please visit our Database Conditions 'Alignment with Existing Efforts' subsection for full details of alignment with the output of this group.

Policies and Data Management Plans

The alignment file for the four resources (FAIRsharing, FAIR-enabling Data Policy Checklist, RDA Data policy IG output, and Concordat on Open Research Data) is available at:

Lister, A., & Davidson, J. (2024). Alignment of FAIRsharing Policy Attributes with Community Efforts (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10658162

There are two recent efforts that have drawn upon the research community to produce a set of common metadata attributes for data policies with the goal of standardising what metadata is most relevant to the research community when evaluating and comparing data policies.

• FAIRsFAIR: FAIR-enabling Data Policy Checklist

• RDA's Data policy standardisation and implementation Interest Group (IG): Developing a Research Data Policy Framework for All Journals and Publishers

Additionally, the a third community effort in the UK has created a concordat that ensures that the research data gathered and generated by members of the UK research community is made openly available for use by others wherever possible.

• Concordat on Open Research Data

These three efforts are related, although they do not completely overlap in scope. In a collaboration with the UKRN and the DCC (2024 news item, 2022 news item), we have ensured our policy metadata fields allow you to create machine-actionable data policy descriptions within FAIRsharing that align with these three outputs. More details of this are available within our Policy Content and Scope documentation.

Tools utilising FAIRsharing

A number of tools providing a wide range of services link to FAIRsharing's API, and a selection of these are listed in our Adopters page. If you would like your tool listed with us, please follow the instructions on that page.

Standards

Identifier schema regular expressions

When manually curating our identifier schema records, we use both our curation expertise and the information provided by identifiers.org (https://identifiers.org/) and bioregistry (https://bioregistry.io/).

Terminology Artifacts

Corcho et al have published paper describing 'A maturity model for catalogues of semantic artefacts'[1], written by a group within the EOSC Task Force on Semantic Interoperability and aimed at identifying those dimensions and features that might be used to assess the maturity of catalogues of semantic artefacts. This paper:

presents a maturity model for assessing catalogues of semantic artefacts, one of the keystones that permit semantic interoperability of systems. We defined the dimensions and related features to include in the maturity model by analysing the current literature and existing catalogues of semantic artefacts provided by experts. In addition, we assessed 26 different catalogues to demonstrate the effectiveness of the maturity model, which includes 12 different dimensions (Metadata, Openness, Quality, Availability, Statistics, PID, Governance, Community, Sustainability, Technology, Transparency, and Assessment) and 43 related features (or sub-criteria) associated with these dimensions. [1]

FAIRsharing is one of the 26 resources assessed, and has scored very highly according to the set of attributes of such catalogues that the authors deem important for maturity. Incorporating those modifications to the assessment as described below, and ignoring those features/attributes that are lower/equivalent in stringency (5/43) or out of scope for a registry like FAIRsharing (4/43), we implement 31/34 maturity features as defined by the EOSC TF on Semantic Interoperability. Details of our alignment with this EOSC TF maturity model are below available in a google spreadsheet.

A partial screenshot of the update on the assessment of FAIRsharing in the context of semantic artifacts by Corcho et al[1]. Full details at this spreadsheet.

FAIRsharing provides not only manually-curated records for standards, databases and policies, but also a rich graph of the connectivity among these resources, placing each record within the broader context of the research data landscape. When you visit FAIRsharing to register, compare or help discover resources such as terminologies, you are able to discover and describe both their features and their interconnectedness.

A graph of the standards, databases and policies that are related to the Gene Ontology record at https://doi.org/10.25504/FAIRsharing.6xq0ee.

Find out more at our blog post on this alignment work.

[1] Corcho, O., Ekaputra, F.J., Heibi, I. et al. A maturity model for catalogues of semantic artefacts. Sci Data 11, 479 (2024). https://doi.org/10.1038/s41597-024-03185-4