Alignment with Community Efforts
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The , 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.
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 and .
Example from our 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 by providing a registry to describe themselves in.
: FAIRsharing assigns DOIs to all records within its three registries (standards, databases and data policies) according to this . Users may identify themselves with their ORCID, and we integrate with the registry as a . Organisations may be linked to their ROR id, and new organisations can be added directly from ROR. Read our news items about and .
: 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.
: This item is not applicable for FAIRsharing, as we do not have separate data and metadata objects.
: We have marked up our site with and schema.org to improve the discovery of our records within search engines.
, , , : All of these are implemented through the use of our standard web interface for human access
Transparency: mission statement, scope, terms of use, and minimum digital preservation timeframe. FAIRsharing's mission statement is available from its front page, and is the first thing a user sees when visiting the site.
Guides consumers to discover, select and use these resources with confidence. Helps producers to make their resources more visible, more widely adopted and cited. Provides humans and tools with access to trustworthy content to enable data management tasks.
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:
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.
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.
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 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.
: Our REST API provides responses using JSON.
: The controlled vocabularies we use to describe our records are publicly available and resolvable and make use of globally unique and persistent identifiers. Subjects: , ,. Domains: Taxonomy: based on NCBI Taxonomy, work is ongoing to align fully with their hierarchy.
: 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.
: 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.
. FAIRsharing is released under a licence.
. Detailed history of all changes in each FAIRsharing record is available publicly, including information about which user made each edit.
: we work with many communities such as the to ensure that the metadata we collect aligns with community efforts for minimal metadata criteria for policies, standards and databases.
This section provides a summary of how FAIRsharing itself, as a registry of standards, databases and policies, attributes align with the , developed in collaboration with the . Work on this alignment is ongoing, with iterative feedback from the .
For more information on TRUST: Lin, D., Crabtree, J., Dillo, I. et al. The TRUST Principles for digital repositories. Sci Data 7, 144 (2020).
Responsibility: community-defined metadata and curation standards, including persistence and other stewardship provisions; data services including download and machine interfaces; and IP management and sensitive data security. Our application ontologies, used to build our tagging systems and hierarchical searches, are drawn from 50+ community ontologies. We implement schema.org and provide JSON and JSON-LD metadata, and our records are given DOIs. Our was initially modelled on the best practices for community development as outlined at . We have a variety of available. There is no sensitive data stored at FAIRsharing, so those considerations are not applicable.
Usability: Implementing and publishing relevant data metrics, providing community catalogues, and monitoring community expectations. We have a variety of relating to the registry, and our stakeholders regularly use our content for landscape analyses, e.g. of the attributes of databases or policies across a region or resource type. We are very active within the research data community to ensure that our content always reflects the need of the community. Our are also key to ensuring that our community of stakeholders have access to FAIRsharing and can easily influence and comment on updates to the registry.
Sustainability: risk mitigation, continuity, funding, governance and long-term preservation. FAIRsharing involves the engagement of a Stakeholder Advisory Board from a variety of roles, geographical locations, and subject areas. Our plans provide a robust plan for longevity and availability of our content. Our funding is ongoing from a variety of sources including OSTrails and TIER2.
Technology: standards and tools for data management and curation, and mechanisms for responding to cyber or physical security threats. Our software and technical infrastructure is kept up to date by our technical team and through the support of University of Oxford. We utilise open source tools such as for team discussions and development.
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 'Alignment with Existing Efforts' subsection for full details of alignment with the output of this group.
Lister, A., & Davidson, J. (2024). Alignment of FAIRsharing Policy Attributes with Community Efforts (1.0.0). Zenodo.
FAIRsFAIR:
RDA's Data policy standardisation and implementation Interest Group (IG):
These three efforts are related, although they do not completely overlap in scope. In a collaboration with the UKRN and the DCC (, ), 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 documentation.
A number of tools providing a wide range of services link to FAIRsharing's API, and a selection of these are listed in our page. If you would like your tool listed with us, please follow the instructions on that page.
When manually curating our identifier schema records, we use both our curation expertise and the information provided by identifiers.org () and bioregistry ().
Corcho et al have published paper describing ''[1], written by a group within the and aimed at identifying those dimensions and features that might be used to assess the maturity of catalogues of semantic artefacts. This paper:
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 .
Find out more at our 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).
