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

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.Keywords: observations; information model; data management; interoperability; cyberinfrastructure IntroductionThe magnitude and diversity of Earth science observations is increasing exponentially. This data richness is fueling new and novel studies that advance our understanding of Earth and environmental processes; however, tools for integrating, efficiently managing, properly documenting, and making such data accessible to diverse groups of collaborating scientists are still in early stages. The need for such tools is clearly shown by large collaborative projects such as the Critical Zone Observatories (e.g., Brantley et al., 2007). These projects include many investigators that produce many different types of data, much of which is often stored and managed in its own domain-specific data system or by idiosyncratic, custom approaches. While domain-specific data systems may provide advanced functionality for particular data types, working with them requires disciplinary knowledge for navigation and data access. The specificity of data types within these systems has commonly led to challenges in integrating data across systems, domains, and research groups. To achieve the science goals of these collaborative projects, however, the different data types must be compared, integrated, and analyzed across sources. Hsu et al: Enhancing Interoperability and Capabilities of Earth ScienceData using the Observations Data Model 2 (ODM2) Art. 4, page 2 of 16This need for interdisciplinary data interoperability across scientif...

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