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Data Science Journal
Vol. 13 (2014) p. 88-105




Modern science is increasingly data-intensive, multidisciplinary, and network-centric. There is an emerging consensus among the members of the academic research community that the practices of this new science paradigm should be congruent with “open science”. This entails that the bonanza of research data, the wide availability of algorithms, data tools, and data services produced by the members of the research community must be discoverable, understandable, and usable by overcoming all kinds of heterogeneity and logical inconsistencies. The main concept for coping with the many dimensions of heterogeneity and logical inconsistency is mediation. Mediation is achieved by mediators or brokers. These are software modules that exploit encoded knowledge about certain datasets, data services, and user needs in order to implement an intermediary service. A mediating environment is an environment that provides a core set of intermediary services. Mediation should be a distinct functionality of future research data infrastructures. This paper surveys the different levels of interoperability, i.e., exchangeability, compatibility, and usability, their properties and relationships, mediation concepts, functions, and intermediary services. The current interoperability landscape is also illustrated. Finally, the paper advocates the need for mediating environments to be supported by future research data infrastructures and envisions that one of the most important features of future research data infrastructures will be mediation software.

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