A key characteristic of computational sciences are their enormous data requirements. Information from many heterogeneous disciplines has to be compiled. The satisfaction of information needs usually take up a significant amounts of time and has to be repeated in regular intervals. Often, the required information is not available at all in sufficient spatial, temporal or content resolution, has diverging references regarding the object of investigation or is simply outdated. The more of the required data is not available, the more often scientists are forced to resort to inexact estimates and assumptions, which limit the reliability and legitimacy of their research outcomes.

The aim of this task area is to support engineers in their search for data by facilitating established research methodologies as potential data sources, raising their level of integration and reducing the amount of time required for their application. To this end, in the case of unavailable data sets, scientifically recognised methodological concepts and their software implementations will be made available to generate the missing data. Since neither journal articles nor software codes are suitable to be used as a guide to the implementation of a methodology, conceptual and machine-interpretable workflow descriptions will serve this purpose within the research data landscape.

The major objectives of Ellen are to:

• support scientists’ data retrieval processes by providing the methodological knowledge and the technical means to generate sought data in case it is not found to be available or well suited.


• develop a semantic framework to enable the representation and reuse of:
  a) scientifically recognized methodological knowledge in form of semantically enriched and machine-interpretable concepts.
  b) software implementations, software workflows and data sets.


• enable the engineering community as well as infrastructure service providers to provide and utilize knowledge-based data storage and retrieval services.