Researchers, especially those relying on computer simulations, frequently generate and analyze large amounts of data. With recent increases in computational capacity, the demand to efficiently and reliably process the generated data is growing accordingly. To address these needs, one powerful approach is to streamline the data handling, transformation, and visualization procedures into a data processing pipeline. Akin to the Continuous Integration pipelines widely used in modern software engineering, a data processing pipeline implements an automated sequence of predefined, yet dynamically configurable operations. Ultimately, using such a data processing pipeline can greatly improve the efficiency, reliability, and reproducibility of the scientific workflow.
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