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Draft recommendations for making VREs FAIR and FAIR-enabling

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  • #133956

    Kerry Levett

    Collaborative session notes:…

    0-10 min: Welcome and introductions 

    10-30min: Presentation of the draft recommendation: Guidance for making VREs FAIR and FAIR-enabling 

    30-70 min: Obtaining feedback on the draft recommendations – issues, additions, edits.   

    70-80 min: Call for further case studies of FAIR and FAIR-enabling VREs, and examples of guidance materials for VRE developers 

    80-90 min: Assigning Actions and wrap up

    Additional links to informative material
     FAIR4VREs WG​ RDA page
     FAIR4VREs RDA WG folder 
    FAIR4VREs Scope of work
    FAIR for VREs_FAIR slides
    FAIR4VREs Work Plan_draft

    Avoid conflict with the following group (1)
    FAIR for Machine Learning (FAIR4ML) IG

    Brief introduction describing the activities and scope of the group
    One of the major challenges of data-driven research is to facilitate knowledge discovery by assisting humans and machines in their discovery of, access to, integration and analysis of data and their associated research objects, e.g., algorithms, software, and workflows. The FAIR data principles strongly contribute to addressing this challenge with regard to research data. The principles, at a high level, are intended to apply to all research objects; both those used in research and those that form the outputs of research. Here we focus on the adaptation and adoption of the FAIR principles for VREs (Virtual Research Environments, also called science gateways, research platforms or virtual labs).
    Digital objects such as data, software and workflows cannot be made FAIR in isolation – digital infrastructure is needed to store, manage, analyse and share the digital objects, and to make them discoverable. VREs are increasingly used as the vehicle for collecting or generating digital objects, processing, analysing, annotating and visualising these, then sharing the research outputs. How infrastructure such as a VRE is developed, and the functions it supports, therefore have a large impact on the FAIRness of digital objects themselves. 
    VREs should enable FAIRness in the digital objects that they create or produce, and at the very least should not make digital objects that they process less FAIR. VREs themselves should also be FAIR, in that they should be easily discoverable and accessible; should interoperate with other digital research infrastructures; and their technical architecture, components and services should be reusable to improve development efficiency. 
    The FAIR4VREs WG will enable coordination between existing communities working with VREs, science gateways, platforms and virtual labs, to define what it means for VREs to be and enable FAIR, and provide guidance to VRE developers in achieving this.
    The working group will:

    Investigate how the existing application of the FAIR principles to data, software, workflows, computational notebooks, training materials, AI and machine learning enable VREs to enable FAIR digital objects, and themselves be FAIR, and identify any gaps in the existing work.

    Produce guidance on and examples of how VRES can and should be FAIR.

    Produce guidance on and examples of how VREs can and should enable FAIRness for other digital objects.

    Group chair serving as contact person
    Kerry Levett

    Meeting objectives
    The FAIR for Virtual Research Environments (FAIR4VREs) WG has produced recommendations for making VREs FAIR and FAIR-enabling, and wishes to gain feedback from VRE users and developers. 
    The working group also wishes to crowdsource case studies on how VREs contribute to FAIRness of other digital objects and examples of guidance materials.

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