Active GO FAIR Implementation Network

The key driver for the Rare Diseases (RDs) Global Open FAIR Implementation Network (RDs GO FAIR) is the need to speed up substantially the progress towards earlier diagnosis and new treatments of rare diseases. This is much more likely when data that are collected are Findable, Accessible (under well-defined conditions), Interoperable, Reusable (FAIR), for analysis by computer.

The RD community is motivated to make the accessibility and analysis of RD data as efficient as possible for all potential users, but is still far from having an established FAIR-minded culture in support of this goal. A GO FAIR Implementation Network for the RD community is needed because the current standards and tools, as well as the pathways to their adoption, are insufficiently mature for RD resources to become FAIR at source and by design to establish a functioning FAIR ecosystem. Adoption will be supported by community-tailored guidelines, standards, and tools.

Main purpose and objectives
Within the context of supporting the goals of the international rare diseases research consortium (IRDiRC), the main purpose of RDs GO FAIR is to establish a culture in the RD community where members help each other choose, adopt, and tailor guidelines, standards, and tools to implement FAIR principles. RDs GO FAIR will first aim towards reaching a tipping point for change of culture by supporting the RD community stakeholders to engage in the implementation of FAIR principles. Following successful progress, it will aim at developing RD community-specific FAIR metrics.

Targeted Objectives of RDs GO FAIR IN:

  1. Foster and strategically oversee the expansion of the adoption of FAIR data principles by the RD community.
  2. Enable support for the RD community in FAIRifying the data and metadata that stakeholders collect and curate, defining strategies and sustainable service models for adoption.
  3. Ensure that RD patients and patient representatives are actively engaged in each phase of planning and implementation.
  4. Ensure that FAIR sharing in the RD community is respectful and responsible towards RD patients.
  5. Collect existing and specifying novel metrics to formally and quantitatively assess FAIRness of rare disease data resources; commit to their regular application to monitor improvement and success.
  6. Ensure that information standards relevant for implementing FAIR in the domain of rare diseases are identified, applied, aligned, improved, extended to fit to purpose, and shared following the FAIR Principles. New standards should only be developed when existing standards are not sufficient and cannot be extended or adapted.
  7. Identify overlaps/duplications of effort towards implementation of FAIR in the RD community, and work with the identified groups to help foster collaborations and funding initiatives, thus minimizing duplication and maximizing harmonization.
  8. Foster examples demonstrating how a critical mass of FAIR data facilitates information retrieval and federated integrative data analysis to answer key questions for rare and ultra-rare diseases beyond current capabilities, and support the dissemination of these examples.
  9. Ensure that the analytics developed for rare and ultra-rare diseases are increasingly capable of exploiting FAIR data and guarantee their applicability to automated, distributed analysis and distributed learning (the Personal Health Train paradigm). Work towards a data and tool sharing infrastructure for distributed analysis.
  10. Ensure that RD analytical tools (software, workflows, training materials, and other digital objects) are available through public FAIR repositories, and can adequately address the requirements for rare disease data.

Main tasks

  1. Complete the execution plan & roadmap as part of the process of becoming a GO FAIR Implementation Network.
  2. Organise and maintain the network.
  3. Foster the definition of requirements, criteria and metrics for FAIR data and FAIR standards, in compliance with the RDs GO FAIR objectives.
  4. Engage with software solution providers to foster production of FAIR data generating and FAIR data analysing software in line with RDs GO FAIR objectives.
  5. Foster the definition and implementation of a service model that can be sustained by the rare disease community to make and keep all their valuable data sources FAIR.
  6. Monitor and advocate engagement of patient representatives in FAIR implementation activities that RDs GO FAIR engages with.
  7. Foster example applications of a critical mass of FAIR data and services, and support their reuse.

Download the Manifesto here.

Marco Roos

Are you interested in joining Rare Diseases IN? Please express your interest by filling in the form below. Your request will be forwarded to the IN Coordinator who will get in touch as soon as possible.

Virginie Bros-Facer, EURORDIS-Rare Diseases Europe
Claudio Carta, National Centre for Rare Diseases, Istituto Superiore di Sanità
Ronald Cornet, Amsterdam UMC, University of Amsterdam, Medical Informatics, Amsterdam
Public Health Research Institute
Esther van Enckevort, UMCG, University of Groningen, NL
Gulcin Gumus, EURORDIS-Rare Diseases Europe
Marc Hanauer, INSERM US14 Orphanet, FR
Ian Harrow, Ian Harrow Consulting, UK
Victoria Hedley, Newcastle University, UK
Annika Jacobsen, Leiden University Medical Center
Dipak Kalra, University College London
Veronica Maria Popa, President Andreas-Rares Association, chair MCT8-AHDS Foundation
Ana Rath, INSERM US14 Orphanet, FR
Marco Roos, Leiden University Medical Centre, NL
Yaffa Rubinstein, Special volunteer, National Library of Medicine, NIH
Domenica Taruscio, National Centre for Rare Diseases, Istituto Superiore di Sanità
Rachel Thompson, Newcastle University, UK
Mark D Wilkinson, CBGP UPM-INIA, Madrid, Spain
Peter-Bram ‘t Hoen, Radboudumc Nijmegen, The Netherlands