2  Background

This section provides background on the concepts of reproducibility and the tools integrated into this template.

Reproducible research is a cornerstone of good scientific practice. It ensures that findings are reliable and that the scientific process is transparent. The tools and structure provided by this template are designed to support these principles.

Make sure to look into the thought of reproducible research practice (Dogucu & Çetinkaya-Rundel, 2022; Gilroy & Kaplan, 2019; Limongi & Rogers, 2025a, 2025b; Rogers & Limongi, 2025; Sullivan et al., 2019; Vuorre & Curley, 2018; Wiebels & Moreau, 2021; Wilson et al., 2017).

2.1 Reproducibility Levels

The template is designed to support different levels of reproducibility, as illustrated in the Figure 2.1. The levels are:

• Minimal Reproducibility: Using the template structure, editing READMEs, and sharing on OSF.
• Proper Reproducibility: Adding Quarto narrative (with RStudio and Git/GitHub) development and using renv package for R package management.
• Full Reproducibility (This Template’s Strength): Incorporating Docker to containerize the entire computational environment, ensuring maximum consistency and ease of replication. This is achieved using the files in the docker/ folder.

Figure 2.1: Roadmap for Developing a Dynamic and Reproducible Research Article with ARTE workflow. Illustration available at: https://doi.org/10.5281/zenodo.16883797

2.2 The Role of Dynamic Documents

Dynamic documents, like the ones created with Quarto (.qmd files), play a crucial role. They allow the narrative of your research to be directly linked to the analysis code. When you render the document, the code is executed, and the results (tables, figures, numbers) are automatically inserted into the text. This reduces manual errors and ensures that your narrative always reflects the latest analysis.

2.3 TIER Protocol 4.0 Structure

The folder structure adheres to the TIER Protocol 4.0:

• Data/: Stores raw, intermediate, and final data files.
• Scripts/: Contains R scripts for data processing, analysis, and visualization. Complex analyses are best kept here.
• Output/: Stores generated results like tables, figures, and intermediate data files.
• docs/ (Root): Contains the final rendered output (HTML, PDF) for web publication.
• Root: Contains main narrative files (index.qmd, 01-intro.qmd, etc.), project configuration (_quarto.yml), bibliography (references.bib), and CSL style file (apa7ed.csl).

This structure promotes clarity and makes it easier for others to navigate your project.

Dogucu, M., & Çetinkaya-Rundel, M. (2022). Tools and Recommendations for Reproducible Teaching. Journal of Statistics and Data Science Education, 30(3), 251–260. https://doi.org/10.1080/26939169.2022.2138645

Gilroy, S. P., & Kaplan, B. A. (2019). Furthering Open Science in Behavior Analysis: An Introduction and Tutorial for Using GitHub in Research. Perspectives on Behavior Science, 42(3), 565–581. https://doi.org/10.1007/s40614-019-00202-5

Limongi, R., & Rogers, P. (2025a). Open Science in Three Acts: Foundations, Practice, and Implementation - First Act. BAR - Brazilian Administration Review, 22(1), e250079. https://doi.org/10.1590/1807-7692bar2025250079

Limongi, R., & Rogers, P. (2025b). Open Science in Three Acts: Foundations, Practice, and Implementation - Second Act. BAR - Brazilian Administration Review, 22(2), e250116. https://doi.org/10.1590/1807-7692bar2025250116

Rogers, P., & Limongi, R. (2025). Open Science in Three Acts: Foundations, Practice, and Implementation - Third Act. BAR - Brazilian Administration Review, 22(3), e250162. https://doi.org/10.1590/1807-7692bar2025250162

Sullivan, I., DeHaven, A., & Mellor, D. (2019). Open and Reproducible Research on Open Science Framework. Current Protocols Essential Laboratory Techniques, 18(1), e32. https://doi.org/10.1002/cpet.32

Vuorre, M., & Curley, J. P. (2018). Curating Research Assets: A Tutorial on the Git Version Control System. Advances in Methods and Practices in Psychological Science, 1(2), 219–236. https://doi.org/10.1177/2515245918754826

Wiebels, K., & Moreau, D. (2021). Leveraging Containers for Reproducible Psychological Research. Advances in Methods and Practices in Psychological Science, 4(2), 251524592110178. https://doi.org/10.1177/25152459211017853

Wilson, G., Bryan, J., Cranston, K., Kitzes, J., Nederbragt, L., & Teal, T. K. (2017). Good enough practices in scientific computing. PLOS Computational Biology, 13(6), e1005510. https://doi.org/10.1371/journal.pcbi.1005510