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A framework for reproducibly managing coupled research software and data assets based on shared transformation

Patrick Kuckertz¹, Benjamin Fuchs², Julian Schönau¹

¹Forschungszentrum Jülich GmbH, Institute of Energy and Climate Research - Jülich Systems Analysis, 52425 Jülich, Germany.

Patterns (New York, N.Y.)

|May 14, 2026

Summary

This summary is machine-generated.

This study enhances the DataDesc metadata schema for computational workflows, improving data model annotation and enabling automatic comparison for better scientific reproducibility and transparency.

Keywords:

DataDesc Schema data integration data models interoperability ioProc workflow manager metadata reusability scientific workflows software coupling transformation functions

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Area of Science:

Computational Science
Scientific Workflow Management
Data Management

Background:

Coupling research software and data into workflows is crucial for reproducibility in computational science.
Existing metadata schemas lack sufficient detail on data models in software interfaces and datasets.
Current workflow tools struggle with metadata for process reproduction.

Purpose of the Study:

To refine the DataDesc metadata schema for comprehensive data model annotation.
To develop a framework for automatic data model comparison and transformation identification.
To introduce the ioProc workflow manager for transparent process documentation and reusable transformation functions.

Main Methods:

Refinement of the DataDesc metadata schema to include data models in software interfaces and datasets.
Development of an extension to DataDesc for automated data model comparison.
Introduction of the ioProc workflow manager to bundle transformation functions into adapter workflows.

Main Results:

The enhanced DataDesc schema effectively annotates data models across interfaces and datasets.
The DataDesc extension facilitates automatic identification of data transformation requirements.
The ioProc manager enables transparent documentation and reuse of transformation functions.

Conclusions:

The improved DataDesc framework and ioProc manager bridge the gap between FAIR principles and practical research analyses.
This work promotes enhanced reusability, reproducibility, and transparency in data- and software-driven scientific research.
The approach is demonstrated through use cases in energy systems analysis.