Key components of data publishing: using current best practices to develop a reference model for data publishing

  • Claire C. Austin
  • Theodora Bloom
  • Sünje Dallmeier-Tiessen
  • Varsha K. Khodiyar
  • Fiona MurphyEmail author
  • Amy Nurnberger
  • Lisa Raymond
  • Martina Stockhause
  • Jonathan Tedds
  • Mary Vardigan
  • Angus Whyte


The availability of workflows for data publishing could have an enormous impact on researchers, research practices and publishing paradigms, as well as on funding strategies and career and research evaluations. We present the generic components of such workflows to provide a reference model for these stakeholders. The RDA-WDS Data Publishing Workflows group set out to study the current data-publishing workflow landscape across disciplines and institutions. A diverse set of workflows were examined to identify common components and standard practices, including basic self-publishing services, institutional data repositories, long-term projects, curated data repositories, and joint data journal and repository arrangements. The results of this examination have been used to derive a data-publishing reference model comprising generic components. From an assessment of the current data-publishing landscape, we highlight important gaps and challenges to consider, especially when dealing with more complex workflows and their integration into wider community frameworks. It is clear that the data-publishing landscape is varied and dynamic and that there are important gaps and challenges. The different components of a data-publishing system need to work, to the greatest extent possible, in a seamless and integrated way to support the evolution of commonly understood and utilized standards and—eventually—to increased reproducibility. We therefore advocate the implementation of existing standards for repositories and all parts of the data-publishing process, and the development of new standards where necessary. Effective and trustworthy data publishing should be embedded in documented workflows. As more research communities seek to publish the data associated with their research, they can build on one or more of the components identified in this reference model.


Data publishing Open data Open Science World Data System Research Data Alliance 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Claire C. Austin
    • 1
    • 2
  • Theodora Bloom
    • 3
  • Sünje Dallmeier-Tiessen
    • 4
  • Varsha K. Khodiyar
    • 5
  • Fiona Murphy
    • 6
    Email author
  • Amy Nurnberger
    • 7
  • Lisa Raymond
    • 8
  • Martina Stockhause
    • 9
  • Jonathan Tedds
    • 10
  • Mary Vardigan
    • 11
  • Angus Whyte
    • 12
  1. 1.Research Data CanadaTorontoCanada
  2. 2.Carleton UniversityOttawaCanada
  3. 3.BMJLondonUK
  4. 4.CERNGenevaSwitzerland
  5. 5.Nature Publishing GroupLondonUK
  6. 6.University of ReadingReadingUK
  7. 7.Columbia UniversityNew YorkUSA
  8. 8.Woods Hole Oceanographic InstitutionWoods HoleUSA
  9. 9.German Climate Computing Centre (DKRZ)HamburgGermany
  10. 10.University of LeicesterLeicesterUK
  11. 11.University of Michigan/ICPSRLeicesterUK
  12. 12.Digital Curation CentreEdinburghScotland, UK

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