Data Reproducibility

Expanding the Lifespan of Software for Demographic Analysis with Containers: An Application of Spatial Sampling

Egor Kotov, Max Planck Institute for Demographic Research
Headshot of Esther Denecke
Esther Denecke, Max Planck Institute for Demographic Research
Figure 1. Reproducibility struggles. Left: Three laptops showing different software configurations, symbolizing challenges in reproducing results. Right: A single laptop displaying multiple virtual containers with distinct software setups, highlighting improved reproducibility.
Figure 1. Reproducibility struggles. Left: failing to reproduce the exact software setup on multiple computers. Right: running countless software setups on a single laptop with the help of containers. Image source: Microsoft Image Creator powered by DALL-E 3. Generated with AI ∙ 25 October 2023

Introduction Software, such as specific R packages, evolve over time, which may prevent older analysis code from working as expected. For example, default values for arguments in a function can change. Therefore, for computational reproducibility, knowing which specific R and package versions were used to run the analysis is crucial. One popular solution in R… Read More