Mr Andreas Rauber
In recent years data-driven science and in-silico experimentation have produced remarkable results and established e-Science as a completely new paradigm in many different disciplines. Yet, with the growing complexity of experiments it becomes increasingly difficult to reproduce the results published in scientific journals and papers, and to keep them available and accessible for future re-use. This talk will address some of the challenges regarding the capture and preservation of scientific processes. Specifically, we will focus on the need for and extent of context to capture as part of preserving a scientific process, presenting a context model capturing all process aspects down to the level of licenses, HW and SW dependencies structured as an ontology. Secondly, we will discuss means to identify the specific data (out of a potentially huge and dynamic data source) that went into a particular analysis based on the recommendations published by the Working Group on Dynamic Data Citation of the Research Data Alliance.