{"id":194,"date":"2025-12-10T12:40:39","date_gmt":"2025-12-10T17:40:39","guid":{"rendered":"https:\/\/pressbooks.bccampus.ca\/openscholarship\/chapter\/reproducible-environments\/"},"modified":"2026-02-12T15:11:06","modified_gmt":"2026-02-12T20:11:06","slug":"reproducible-environments","status":"publish","type":"chapter","link":"https:\/\/pressbooks.bccampus.ca\/openscholarship\/chapter\/reproducible-environments\/","title":{"raw":"Reproducible Environments","rendered":"Reproducible Environments"},"content":{"raw":"At this point, we have covered quite a bit. Open as a principle of software development, open as a principle of human and machine interpretability, and open as a facet of reproducible workflows.\r\n\r\nIf we think back to the content covered in Open Workflows, and the discussion of reproducibility and replicability, it\u2019s worth considering that reproducibility is really about internal validation while replicability is about external validation. Reproducibility confirms the same data and processing methods will produce the same result. Replicability contributes to the evidence base by conducting a new study modeled on a previous study.\r\n\r\nIn the spirit of open as it relates to the digital environment and reproducibility, one of the gold standards when we\u2019re looking at a single study is computational reproducibility; that is to say, if I pass off all of my inputs (data, scripts, etc.) to someone else, can they, on their computer, reproduce what I did exactly. Unfortunately, the answer is frequently no, because computers are complex environments, and no two machines are going to have exactly the same environment; hardware and software differences will exist and these will impact how data is processed by a program. In complex food production, like brewing beer, it\u2019s often said that making a great beer once is easy, but making it a second time is much harder. Small variations \u2014 such as precise temperatures, ingredient sources, and even the weather \u2014 can subtly change the flavour. The same challenge exists in computational reproducibility \u2014 unless we use a container and apply the concept of containerization.\r\n\r\nThe full details of how containerization is deployed are really beyond an introductory section on open research. But the principles being addressed by containerization are critical to navigating a digital environment when we think about the ability of work, embedded within a piece of software, to be validated by others.\r\n

A Recipe for Understanding Containers<\/h2>\r\nThe thing about any piece of software or any script is that it is never fully self-contained. We always rely on dependencies or pre-existing bundles of code, usually called libraries. Think of it like baking muffins.\r\n\r\nWhen you write your R or Python script, you\u2019re writing out your recipe; a set of instructions with particular steps that need to be followed. Your recipe requires certain things to be fully executed though. It requires:\r\n