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The growing complexity and interdisciplinarity of research and applied science questions requires the developments of standards to exchange data within continuously growing communities as well as across domains. In most domains, geo-spatial data is the fundamental base layer for data science and analysis, as the vast majority have some spatial characteristics or apply to elements in space. Using the available standards, a good level of interoperability can already be realized. Nevertheless, the increasing complexity of research questions, the growing number of available data, and the increasing range of data providers, ranging from citizen scientists to fully automated sensor networks making their data directly available at the Internet, require even richer models that need to be developed to enhance the level of interoperability.