Reproducibility with Jupyter Notebooks

Let's start by learning what it is meant by computational reproducibility. Research, solutions, prototypes, and even a simple algorithm that is developed is said to be reproducible if access is provided to the original source code that was used to develop the solution, and the data that was used to build any related software should be able to produce the same results. However, today, the scientific community is experiencing some challenges in reproducing work developed previously by peers. This is mainly due to the lack of documentation and difficulty in understanding process workflows.

The impact of a lack of documentation can be seen at every level, right from understanding the approach to the code level. Jupyter is one of the best tools for improvising this process, for better reproducibility, and for the reuse of developed code. This includes not just understanding what each line or snippet of code does, but also understanding and visualizing data...