  Recently, data exchange platforms have emerged in the digital economy to
enable better resource allocation in a data-driven society, which requires
cross-organizational data collaborations. Understanding the characteristics of
the data on these platforms is important for their application; however, the
structures of such platforms have not been extensively investigated. In this
study, we apply a network approach with a novel variable-based structural
analysis to the metadata of datasets on two data platform services. It was
noted that the structures of the data networks are locally dense and highly
assortative, similar to human-related net-works. Even though the data on these
platforms are designed and collected differently, depending on the use
objectives, the variables of heterogeneous data exhibit a power distribution,
and the data networks exhibit multi-scaling behavior. Furthermore, we found
that the data collection strategies of the platforms are related to the variety
of variables, density of the networks, and their robustness from the viewpoint
of sustainability and social acceptability of the data platforms.
