Open Code has gained considerable popularity in various areas such as software development, research and data analysis. It promotes transparency, innovation, knowledge sharing and collaboration and ensures that researchers are recognised for their research work.

Open Code makes source code written for scientific research such as data analyses, statistical models and simulations accessible to others. If provided with a corresponding Open Source licence, it can be reused and referred to as Open Source code or Open Source software. Code published under an Open Source licence allows third parties to access, view, use, modify and redistribute it for various purposes without restrictions. In this respect, the topic of Open Code has major overlaps with the Open Source principle.

However, it is not only the licence used that is decisive for the openness of the code. The use of open programming languages and associated software, the quality assurance of the code and a fitting publication strategy that also allows the code to be cited are also important. Concerning data analyses, the programming language R is very well suited, for example, because it can be used free of charge and is itself Open Source, including all programme libraries (collection of already written programme routines) and the compiler (translates source code into an executable form). Stata and Excel are often used in data analysis – however, these software solutions are commercial and not Open Source. They also use proprietary file formats, which is not ideal from an Open Science perspective and restricts reusability.

Incidentally, Open Code has nothing to do with “open coding”, a step in the so-called “grounded theory“. This is a qualitative research approach that involves an iterative data analysis process with several steps.