FAIR Guidance

This Paper provides interpretations and implementation considerations (choices and challenges) for each FAIR principle: 

• FAIR Principles: Interpretations and Implementation Considerations 

This paper describes a generic step-by-step FAIRification workflow to be performed in a multidisciplinary team guided by FAIR data stewards: 

• A Generic Workflow for the Data FAIRification Process 

The papers above are from a special issue dedicated to experiences in implementing FAIR principles: 

• Special Issue: Emerging FAIR Practices. 

VASCA FAIRification workflow 

• This paper describes the setting up of the VASCA FAIR registry (VASCA, part of VASCERN ERN), includes a more general description of the de novo FAIRification process which makes all data FAIR automatically and in real-time upon collection and explains how all these steps contribute to FAIR, also including the lessons learned: 
  • The de novo FAIRification process of a registry for vascular anomalies
    “Existing methods to make clinical trial and registry data (more) machine-readable and FAIR are usually carried out after a project is conducted and data are collected (post hoc). This is typically more expensive than building in FAIRification into the data collection process.
    In practice, this means that all the hands-on work for the FAIRification is conducted before data collection. Subsequently, data is made FAIR through entering them into the Electronic Data Capture (EDC) system. This has the advantage that clinical data is made FAIR without any intervention from data management and data entry personnel. Due to the generic approach and developed tooling, the VASCA working group believes that the method can be used in other registries and clinical trials as well.” 

•  This paper focuses more on the technical implementation of the de novo FAIRification in an electronic data capture system: 
  • De-novo FAIRification via an Electronic Data Capture system by automated transformation of filled electronic Case Report Forms into machine-readable data 

FAIRification experience – the (on-going) example of ERKReg, a centralised registry

• ERKReg FAIRification steps 
  • The European Rare Kidney Disease registry (ERKReg) was established in 2018 for all patients with a rare kidney disease. It collects disease and treatment related information from all the patients with a rare kidney disease that are followed in an expert centre. ERKReg is a centralised registry, meaning that all the data entered in the registry is transferred and stored on one secured server. The registry was implemented before the beginning of the EJP RD project and did not consider the FAIR principles from start. By design though, the registry recorded the rare disease diagnosis as an Orphacode and the genetic diagnosis was encoded using HGVS. The use of such ontologies allows for better findability, interoperability and reusability and is a simple first step to take towards making resources more FAIR. While most of the Common Data Elements (CDE) defined by the JRC were already collected by ERKReg, work was conducted to integrate the missing data elements and fully align. In order to make the registry findable and the registry metadata searchable, ERKReg was then registered on ERDRI.dor and ERDRI.mdr. The training material developed by EJP RD and help from the EJP RD experts was then needed to deploy the CDE in a box and the metadata semantic model. The training material was particularly useful to understand what needed to be done, why it was needed to be done and how. At this date (11/2021), a local deployment of the CDE in a box has been achieved, and it is hoped to achieve soon a global deployment. It is planned to FAIRify all or part of the Domain Common Data Elements (DCDEs) collected in ERKReg at a later stage (a decision is still pending on the extent of the data ERKReg wishes to share). It is envisioned to make automatic regular updates of the database to our triple store via CDE in a box. 

• Take home message & advice from the ERKReg Data Steward 
  • ERKReg is in the process of making its data and metadata FAIR retrospectively. While it is possible to achieve FAIR after a registry was built, we would strongly advise to start building any new registry with the FAIR principles in mind. It includes, but is not limited to, use the training resources developed by EJP RD to gain knowledge on the FAIRification process, get help from your FAIRification steward whenever you feel stuck or if anything is unclear (this is completely normal, FAIR is NOT obvious), use ontologies in your registries wherever possible, deploy the CDE in a box and the EJP RD metadata model. One very important aspect that it often overlooked (and that we indeed overlooked), is the identification of the FAIR objectives. Without clear objectives in mind, and without real knowledge of which data the registry owner would like to share in the future and therefore make FAIR, time and resources are lost. Finally, bear in mind that FAIRification is a flexible process, you always stay in control of which data you share, in which format, with which stakeholder. It “just” makes the data easy to find, access, interoperate with other resources and reuse. 

Patient organizations

• Patient organizations are key drivers of the FAIRification process in practice and dialogue with stakeholders is critical to success: 
  • How Patient Organizations Can Drive FAIR Data Efforts to Facilitate Research and Health Care: A Report of the Virtual Second International Meeting on Duchenne Data Sharing, March 3, 2021 
  • Patient organisations are creating their own network within the ‘Rare Disease Global Open FAIR implementation network’: https://www.go-fair.org/implementation-networks/overview/rare-diseases/