Hsu et al., 2017

Paper/Book

Enhancing Interoperability and Capabilities of Earth Science Data using the Observations Data Model 2 (ODM2)

Hsu, Leslie, Emilio Mayorga, Jeffery S. Horsburgh, Megan R. Carter, Kerstin A. Lehnert and Susan L. Brantley (2017)
Data Science Journal, 16: 4, pp. 1–16  Cross-CZO National

Abstract

Earth Science researchers require access to integrated, cross-disciplinary data in order to answer critical research questions. Partially due to these science drivers, it is common for disciplinary data systems to expand from their original scope in order to accommodate collaborative research. The result is multiple disparate databases with overlapping but incompatible data. In order to enable more complete data integration and analysis, the Observations Data Model Version 2 (ODM2) was developed to be a general information model, with one of its major goals to integrate data collected by in situ sensors with those by ex-situ analyses of field specimens. Four use cases with different science drivers and disciplines have adopted ODM2 because of benefits to their users. The disciplines behind the four cases are diverse – hydrology, rock geochemistry, soil geochemistry, and biogeochemistry. For each case, we outline the benefits, challenges, and rationale for adopting ODM2. In each case, the decision to implement ODM2 was made to increase interoperability and expand data and metadata capabilities. One of the common benefits was the ability to use the flexible handling and comprehensive description of specimens and data collection sites in ODM2’s sampling feature concept. We also summarize best practices for implementing ODM2 based on the experience of these initial adopters. The descriptions here should help other potential adopters of ODM2 implement their own instances or to modify ODM2 to suit their needs.

Citation

Hsu, Leslie, Emilio Mayorga, Jeffery S. Horsburgh, Megan R. Carter, Kerstin A. Lehnert and Susan L. Brantley (2017): Enhancing Interoperability and Capabilities of Earth Science Data using the Observations Data Model 2 (ODM2). Data Science Journal, 16: 4, pp. 1–16 . DOI: 10.5334/dsj-2017-004

This Paper/Book acknowledges NSF CZO grant support.