MaRDIFlow: A Workflow Framework for Documentation and Integration of FAIR Computational Experiments

Authors

DOI:

https://doi.org/10.52825/cordi.v1i.323

Keywords:

FAIR, Reproducibility, MaRDIFlow, Computational Workflows

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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References

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C. Goble, S. Cohen-Boulakia, S. Soiland-Reyes, et al., “FAIR Computational Workflows,” Data Intelligence, vol. 2, no. 1-2, pp. 108–121, 2020.

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MaRDI. “Mathematic research data initiative.” (2021), [Online]. Available: https://www.mardi4nfdi.de.

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Published

2023-09-07

How to Cite

Veluvali, P. L., Heiland, J., & Benner, P. (2023). MaRDIFlow: A Workflow Framework for Documentation and Integration of FAIR Computational Experiments. Proceedings of the Conference on Research Data Infrastructure , 1. https://doi.org/10.52825/cordi.v1i.323
Received 2023-04-26
Accepted 2023-06-29
Published 2023-09-07