http://www.dagstuhl.de/15433

October 18 – 23 , 2015, Event 15433

DDI Moving Forward: Facilitating Interoperability and Collaboration with Other Metadata Standards

Organizers

Arofan Gregory (Open Data Foundation – Tucson, US)
Wendy Thomas (University of Minnesota – Minneapolis, US)
Mary Vardigan (University of Michigan – Ann Arbor, US)
Joachim Wackerow (GESIS – Mannheim, DE)


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For support, please contact

Heike Clemens

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Background

The Data Documentation Initiative (DDI) metadata standard describes data that result from observational methods in the social, behavioral, economic, and health sciences. DDI development has progressed from a codebook-oriented specification to a version that supports the full lifecycle of research data. Beginning in 2012, work began at Dagstuhl on a model-driven version of the DDI standard, advanced through a series of “sprints”. Last year’s sprint brought together experts in other metadata standards to collaborate on developing comprehensive data citation information to be included in the standards.

Goals

The intent of the model-driven endeavor is a robust and sustainable solution for the future and a solid foundation for expansion of the standard. Therefore it is important that DDI is developed carefully and in alignment with best practices in metadata development. The DDI metadata standard should not exist in isolation but should participate with other standards in a larger framework characterized by interoperability and collaboration. To realize these goals, this year’s workshop will bring together representatives from other metadata standards to provide an external review of current DDI work, with an emphasis on the model-driven approach, the production framework, and the substantive content of the standard. Progress made so far will be examined in light of the high-level goals and design principles for the model-driven specification.

Results

It is anticipated that the other standards will in turn provide information about their development procedures and goals. There are several benefits of this type of cross-fertilization:

  • The metadata standards for research data have much in common and will learn from each other about successful practices
  • Groups will avoid producing solutions that are potentially not interoperable with other domains/standards
  • Improved communication across disciplines will be enabled

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