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

• Published in: IEEE computer graphics and applications 2023-03, Vol.43 (2), p.89-100
• Main Authors: Reina, Guido, Basole, Rahul C., Ferrise, Francesco
• Format: Magazinearticle
• Language: English
• Subjects: Collaboration; Complexity; Complexity theory; Data visualization; Graphics; Reproducibility; Reproducibility of results
• ISSN: 0272-1716; 1558-1756
• DOI: 10.1109/MCG.2023.3241819

Reproducibility is a cornerstone of good scientific practice; however, the ongoing “reproducibility crisis” shows that we still need to improve the way we are doing research currently. Reproducibility is crucial because it enables both the comparison to existing techniques as well as the composition and improvement of existing approaches. It can also increase trust in the respective results, which is paramount for adoption in further research and applications. While there are already many initiatives and approaches with different complexity aimed at enabling reproducible research in the context of visualization, we argue for an alternative, lightweight approach that documents the most relevant parameters with minimal overhead. It still complements complex approaches well, and integration with any existing tool or system is simple. Our approach uses the images produced by visualizations and seamlessly piggy-backs everyday communication and research collaborations, publication authoring, public outreach, and internal note-taking. We exemplify how our approach supports day-to-day work and discuss limitations and how they can be countered.