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Data Granularity Guidance – Going Once, Going Twice,…

  • Creator
  • #133954

    Reyna Jenkyns

    Collaborative session notes:…

    Introduction – contextualization and motivation

    Methodology – how did we reach our conclusion

    Survey with Re3Data

    Use Case collection and scheme

    Constraints collection

    Present draft outputs: guidance paper & use cases

    Interactive activities for participants to:

    Provide feedback on draft outputs

    Discuss potential adoption cases

    Conclusion/next steps

    Additional links to informative material
    Draft WG outputs will be provided when available
    Podcast about the WG:

    Avoid conflict with the following group (1)
    Data Discovery Paradigms IG

    Brief introduction describing the activities and scope of the group
    The RDA Data Granularity Working Group (WG) addresses issues of data granularity in data discovery, access, interoperability, analysis, citation, and more. More efficient and effective reuse of data requires that users can find and access data at various levels of granularity. The WG explores key questions and collects and shares valuable information for how to best support data granularity, providing guidance to help data professionals to determine the best level of granularity for user discovery, access, interoperability and citability. The activities and final recommendations of the Data Granularity WG build upon and complement existing and ongoing work of several RDA Working and Interest Groups that touch upon the subject of data granularity. The final deliverable for the WG is a set of collected use cases and a guidance document of data granularity approaches for prioritized use cases, including terminology, methods to evaluate approaches, and a summary of community feedback.

    Group chair serving as contact person
    Brigitte Mathiak

    Meeting objectives

    Contextualize the big picture and scope of data granularity 

    Present draft outputs: guidance paper & use cases

    Gain feedback on draft outputs

    Recruit early adopters

    Privacy Policy

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