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Data Always Getting Bigger—A Scalable DOI Architecture for Big and Expanding Scientific Data

Author

Listed:
  • Giri Prakash

    (Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37831, USA)

  • Biva Shrestha

    (Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37831, USA)

  • Katarina Younkin

    (Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99354, USA)

  • Rolanda Jundt

    (Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99354, USA)

  • Mark Martin

    (DOE Office of Scientific and Technical Information, 1 Science.gov Way, Oak Ridge, TN 37830, USA)

  • Jannean Elliott

    (DOE Office of Scientific and Technical Information, 1 Science.gov Way, Oak Ridge, TN 37830, USA)

Abstract

The Atmospheric Radiation Measurement (ARM) Data Archive established a data citation strategy based on Digital Object Identifiers (DOIs) for the ARM datasets in order to facilitate citing continuous and diverse ARM datasets in articles and other papers. This strategy eases the tracking of data provided as supplements to articles and papers. Additionally, it allows future data users and the ARM Climate Research Facility to easily locate the exact data used in various articles. Traditionally, DOIs are assigned to individual digital objects (a report or a data table), but for ARM datasets, these DOIs are assigned to an ARM data product. This eliminates the need for creating DOIs for numerous components of the ARM data product, in turn making it easier for users to manage and cite the ARM data with fewer DOIs. In addition, the ARM data infrastructure team, with input from scientific users, developed a citation format and an online data citation generation tool for continuous data streams. This citation format includes DOIs along with additional details such as spatial and temporal information.

Suggested Citation

  • Giri Prakash & Biva Shrestha & Katarina Younkin & Rolanda Jundt & Mark Martin & Jannean Elliott, 2016. "Data Always Getting Bigger—A Scalable DOI Architecture for Big and Expanding Scientific Data," Data, MDPI, vol. 1(2), pages 1-9, August.
  • Handle: RePEc:gam:jdataj:v:1:y:2016:i:2:p:11-:d:77138
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