Search

The EJP RD stewards' team was established in July 2020 to support European Reference Networks (ERNs) in creating Findable, Accessible, Interoperable and Reusable (FAIR) patient registries. To track the evolving registry FAIR implementations, the steward's team collected the ERNs' reported updates in an implementation status inventory. We here analyze the progress registered to understand the impact of the team's approach on supporting ERNs. The team conducted two rounds of online interviews in 2020 and 2021 to collect the ERNs implementation status. Prior to the interview, they sent ERNs an inventory structured into five phases: a) data modelling; b) data collection; c) registering the registry; d) data accessibility, e) Data querying; and instructed ERNs to fill it using the terminology "Implemented", "Implementing assisted by expert", "Plans to Implement", "Needs Expert help" and "Not applicable". From a total of 24 ERNs, 20 completed the inventory. The results indicate an increase in the number of FAIR tools implemented from 2020 (32.6%) to 2021 (56.0%). Additionally, the percentage of tools that shifted from "Plans to implement" or "Needs expert help" to "Implemented" in 2021 were, respectively, 35.8% and 18.8%. In both years, the most significant increases in implemented tools are related to the modelling phase - 17.3% in 2020 and 29.2% in 2021 - and "making data accessible for querying" - 5.2% in 2020 to 13.2% in 2021. Out of all five stages, the tools more frequently marked as "Plans to Implement" in 2020 concerned the modelling phase (9.8%), which in 2021 decreased to 4.6%. Changes in staff and different people completing the inventory are limitations of this analysis, as they could result in different interpretations of categorizing the tools and, occasionally, the loss of reported evolution. Regardless, the joint approach between ERNs and Stewards appears to have resulted in more implemented tools, mainly in the modelling stage. It also allows prioritizing future efforts on accessibility and discoverability for querying, and data reuse. We expect the number of implemented tools to plateau and the tools planned for implementation or needing expert help to decrease as a result of the ERNs learning more about the options for FAIR data.&nbsp

The process of making data Findable, Accessible, Interoperable and Reusable (FAIR - FAIRification) varies across projects, depending on their objectives and domain needs. However, such variation can complicate identifying the best workflow for an efficient large-scale national implementation. It is therefore necessary to align the different FAIRification processes and workflows. With this aim in mind, we mapped a set of FAIRification workflows to understand the divergence among them and identify critical common FAIRification and domain-specific steps. This work was executed in the context of the DutchHealth-RIand theEuropean Joint Programme on Rare Diseases (EJP RD), which both rely on the FAIR principles to enable sustainable data reuse in the healthcare domain. First, we gathered a group of FAIR experts amongst the collaborators in the two initiatives. Next, we identified a set of relevant workflows used within the rare disease domain and the Dutch health research infrastructure context. A total of seven workflows were selected, including the EJP RDFAIRopoly, given its compilation of theDe NovoandGenericworkflows. Through several hands-on meetings, we discussed the interpretation and best mapping of the workflows’ steps. Finally, we considered any steps that were mapped in more than four workflows (>50%) as critical. Preliminary results show three critical steps: a) identification of FAIRification objectives and expertise, b) (meta)data assessment, definition and semantic modelling, and c) defining data licensing (or consent), access and use conditions. The first can impact the success of the FAIRification of projects, whereas (meta)data assessment and modelling are intrinsically related to the core of the FAIR principles: semantic interoperability and machine actionability of (meta)data. Finally, licensing (consenting), accessing and reusing conditions are equally crucial for healthcare data reuse as they protect sensitive information. Future work includes identifying and analysing domain-specific steps and detailing each critical step into recommended resources, checklists and needed expertise. Any training needs regarding the FAIR expertise capacity can then be identified from such descriptions. We expect that embedding critical steps in the national research infrastructure can improve FAIR data availability.

Objectives: Existing individual-level human data cover large populations on many dimensions such as lifestyle, demography, laboratory measures, clinical parameters, etc. Recent years have seen large investments in data catalogues to FAIRify data descriptions to capitalise on this great promise, i.e. make catalogue contents more Findable, Accessible, Interoperable and Reusable. However, their valuable diversity also created heterogeneity, which poses challenges to optimally exploit their richness.Methods: In this opinion review, we analyse catalogues for human subject research ranging from cohort studies to surveillance, administrative and healthcare records.Results: We observe that while these catalogues are heterogeneous, have various scopes, and use different terminologies, still the underlying concepts seem potentially harmonizable. We propose a unified framework to enable catalogue data sharing, with catalogues of multi-center cohorts nested as a special case in catalogues of real-world data sources. Moreover, we list recommendations to create an integrated community of metadata catalogues and an open catalogue ecosystem to sustain these efforts and maximise impact.Conclusions: We propose to embrace the autonomy of motivated catalogue teams and invest in their collaboration via minimal standardisation efforts such as clear data licensing, persistent identifiers for linking same records between catalogues, minimal metadata ‘common data elements’ using shared ontologies, symmetric architectures for data sharing (push/pull) with clear provenance tracks to process updates and acknowledge original contributors. And most importantly, we encourage the creation of environments for collaboration and resource sharing between catalogue developers, building on international networks such as OpenAIRE and research data alliance, as well as domain specific ESFRIs such as BBMRI and ELIXIR. OBJECTIVES: Existing individual-level human data cover large populations on many dimensions such as lifestyle, demography, laboratory measures, clinical parameters, etc. Recent years have seen large investments in data catalogues to FAIRify data descriptions to capitalise on this great promise, i.e. make catalogue contents more Findable, Accessible, Interoperable and Reusable. However, their valuable diversity also created heterogeneity, which poses challenges to optimally exploit their richness. METHODS: In this opinion review, we analyse catalogues for human subject research ranging from cohort studies to surveillance, administrative and healthcare records. RESULTS: We observe that while these catalogues are heterogeneous, have various scopes, and use different terminologies, still the underlying concepts seem potentially harmonizable. We propose a unified framework to enable catalogue data sharing, with catalogues of multi-center cohorts nested as a special case in catalogues of real-world data sources. Moreover, we list recommendations to create an integrated community of metadata catalogues and an open catalogue ecosystem to sustain these efforts and maximise impact. CONCLUSIONS: We propose to embrace the autonomy of motivated catalogue teams and invest in their collaboration via minimal standardisation efforts such as clear data licensing, persistent identifiers for linking same records between catalogues, minimal metadata 'common data elements' using shared ontologies, symmetric architectures for data sharing (push/pull) with clear provenance tracks to process updates and acknowledge original contributors. And most importantly, we encourage the creation of environments for collaboration and resource sharing between catalogue developers, building on international networks such as OpenAIRE and research data alliance, as well as domain specific ESFRIs such as BBMRI and ELIXIR.