NFDI4Ing – National Research Data Infrastructure for Engineering Sciences
Co-Applicant: |
TIB – Leibniz Information Centre for Science and Technology |
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My role: |
Project member & contact person on the part of TIB |
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Funding: |
Deutsche Forschungsgemeinschaft (DFG) |
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Duration: |
Since 2020 |
Engineering sciences play a key role in developing solutions for the technical, environmental, and economic challenges imposed by the demands of our modern society. The associated research processes and the solutions themselves will only be sustainable if being accompanied by a proper research data management (RDM) that implements the FAIR data principles: data has to be findable, accessible, interoperable, and re-usable. NFDI4Ing brings together the engineering communities to work towards that goal. As part of the German National Research Data Infrastructure (NFDI), the consortium aims to develop, disseminate, standardize and provide methods and services to make engineering research data FAIR. As one of the first consortia funded as part of the NFDI, NFDI4Ing has actively engaged engineers across all engineering research areas as well as experienced infrastructure providers since 2017. It now has more than 50 active members and participants and continues to grow.
TA ELLEN – Task Area ELLEN
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 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spatial, temporal or content resolution, has diverging references regarding the object of investigation or is simply outdated.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 recognized 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.