Many studies in big data focus on the uses of data available to researchers, leaving without treatment data that is on the servers but of which researchers are unaware. We call this dark data, and in this article, we present and discuss it in the context of high-performance computing (HPC) facilities. To this end, we provide statistics of a major HPC facility in Europe, the High-Performance Computing Center Stuttgart (HLRS). We also propose a new position tailor-made for coping with dark data and general data management. We call it the scientific data officer (SDO) and we distinguish it from other standard positions in HPC facilities such as chief data officers, system administrators, and security officers. In order to understand the role of the SDO in HPC facilities, we discuss two kinds of responsibilities, namely, technical responsibilities and ethical responsibilities. While the former are intended to characterize the position, the latter raise concerns—and proposes solutions—to the control and authority that the SDO would acquire.

EMS Magazine Article from: Tobias Boege, René Fritze, Christiane Görgen, Jeroen Hanselman, Dorothea Iglezakis, Lars Kastner, Thomas Koprucki, Tabea H. Krause, Christoph Lehrenfeld, Silvia Polla, Marco Reidelbach, Christian Riedel, Jens Saak, Björn Schembera, Karsten Tabelow, Marcus Weber

The European Materials and Modelling Ontology (EMMO) has recently been advanced in the computational molecular engineering and multiscale modelling communities as a top-level ontology, aiming to support semantic interoperability and data integration solutions, e.g., for research data infrastructures. The present work explores how top-level ontologies that are based on the same paradigm ­ the same set of fundamental postulates ­ as the EMMO can be applied to models of physical systems and their use in computational engineering practice. This paradigm, which combines mereology (in its extension as mereotopology) and semiotics (following Peirce's approach), is here referred to as mereosemiotics. Multiple conceivable ways of implementing mereosemiotics are compared, and the design space consisting of the possible types of top-level ontologies following this paradigm is characterized.

In computational engineering, numerical simulations produce huge amounts of data. To keep this research data findable, accessible, inter-operable and reusable, a structured description of the data is indispensable. This paper outlines the genesis of EngMeta – a metadata model designed to describe engineering simulation data with a focus on thermodynamics and aerodynamics. The metadata model, developed in close collaboration with engineers, is based on existing standards and adds discipline-specific information as the main contribution. Characteristics of the observed system offer researchers important search criteria. Information on the hardware and software used and the processing steps involved helps to understand and replicate the data. Such metadata are crucial to keeping the data FAIR and bridging the gap to a sustainable research data management in computational engineering.