Can Image Data Facilitate Reproducibility of Graphics and Visualizations? Toward a Trusted Scientific Practice

Area of Science:

• Scientific Visualization
• Research Methodology

Background:

• Reproducibility is critical for scientific advancement, enabling comparison, composition, and trust in research findings.
• The current "reproducibility crisis" highlights the need for improved research practices.
• Existing complex solutions for reproducible research in visualization may present integration challenges.

Purpose of the Study:

• To propose a lightweight, minimal-overhead approach for documenting visualization parameters.
• To enhance the reproducibility of scientific research through accessible documentation methods.
• To complement existing complex reproducibility initiatives.

Main Methods:

• Developing a lightweight documentation strategy for visualization parameters.
• Integrating parameter documentation into everyday communication and publication workflows.
• Utilizing visualization outputs (images) as a medium for capturing relevant metadata.

Main Results:

• Demonstrated a simple, low-overhead method for improving reproducibility in scientific visualization.
• Showcased how the approach integrates into daily scientific activities like collaboration and authoring.
• Provided examples of practical application and discussed potential limitations.

Conclusions:

• A lightweight approach to documenting visualization parameters can significantly enhance scientific reproducibility.
• Seamless integration into existing workflows makes this method broadly applicable.
• This strategy supports trust and facilitates the adoption of research findings.