Abstract
Numerical algorithms and computational tools are essential for managing and analyzing complex data processing tasks. With ever increasing availability of meta-data and parameter-driven simulations, the demand and the need for reliable and automated workflow frameworks to reproduce computational experiments has grown. In this work, we aim to develop a novel computational workflow framework, namely MaRDIFlow, that describes the abstraction of multi-layered workflow components. Herein, we plan to enable and implement scientific computing data FAIRness into actionable guidelines for FAIR computational experiments.
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