Based on our findings and best practices we proposed, we designed Julynter, a Jupyter Lab extension that identifies potential issues in notebooks and suggests modifications that improve their reproducibility. We discuss patterns we discovered, which provide additional insights into notebook reproducibility. Finally, we mined association rules from the notebooks. We report how these factors impact the reproducibility rates. Third, we conducted a more detailed analysis by isolating library dependencies and testing different execution orders. Second, we sampled notebooks from the full dataset for an in-depth qualitative analysis of what constitutes the dataset and which features they have. First, we separated a group of popular notebooks to check whether notebooks that get more attention have more quality and reproducibility capabilities. In this paper, we extended the analysis in four different ways to validate the hypothesis uncovered in our original study. We presented a detailed analysis of their characteristics that impact reproducibility, proposed best practices that can improve the reproducibility, and discussed open challenges that require further research and development. To better understand good and bad practices used in the development of real notebooks, in prior work we studied 1.4 million notebooks from GitHub. At the same time, there has been growing criticism that the way in which notebooks are being used leads to unexpected behavior, encourages poor coding practices, and makes it hard to reproduce its results. The self-documenting aspects and the ability to reproduce results have been touted as significant benefits of notebooks. They support the creation of literate programming documents that combine code, text, and execution results with visualizations and other rich media. Having evolved from its origins, today the application supports over 40 programming languages and is an open source project, based on the IPython Notebook project.Jupyter Notebooks have been widely adopted by many different communities, both in science and industry. Apart from research projects that involve visualizations of data or formulas it is used for documentation of processes with code, sharing of code or interactive visualizations. Nowadays Jupyter is the environment of choice for several use cases. The name Jupyter is an acronym which stands for the three languages it was initially designed for: JUlia, PYThon, and R, as the application was developed originally for data science needs. The application allows to combine code, comments, multimedia contents, and visualizations in a single interactive document - called a notebook, which runs in a web browser. Jupyter Notebook is a client-server application that allows to edit and run Notebook documents in a web browser.
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