MaRDIFlow: A CSE workflow framework for abstracting meta-data from FAIR computational experiments

TL;DR

MaRDIFlow is a novel workflow framework that automates metadata abstraction from computational experiments, enhancing reproducibility and FAIR compliance across platforms.

Contribution

It introduces a new framework for automating metadata extraction from mathematical objects and environmental dependencies, integrating FAIR principles into computational workflows.

Findings

Prototype demonstrates effective metadata abstraction

Addresses environmental dependencies in workflows

Enhances FAIR compliance in computational experiments

Abstract

Numerical algorithms and computational tools are instrumental in navigating and addressing complex simulation and data processing tasks. The exponential growth of metadata and parameter-driven simulations has led to an increasing demand for automated workflows that can replicate computational experiments across platforms. In general, a computational workflow is defined as a sequential description for accomplishing a scientific objective, often described by tasks and their associated data dependencies. If characterized through input-output relation, workflow components can be structured to allow interchangeable utilization of individual tasks and their accompanying metadata. In the present work, we develop a novel computational framework, namely, MaRDIFlow, that focuses on the automation of abstracting meta-data embedded in an ontology of mathematical objects. This framework also effectively addresses the inherent execution and environmental dependencies by incorporating them into multi-layered descriptions. Additionally, we demonstrate a working prototype with example use cases and methodically integrate them into our workflow tool and data provenance framework. Furthermore, we show how to best apply the FAIR principles to computational workflows, such that abstracted components are Findable, Accessible, Interoperable, and Reusable in nature.