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Recent developments and discussions concerning the SARS-Cov-2 virus and the development of a vaccine illustrate once again the necessity to assume “that scientific claims are supported by solid evidence” (Branco et al. 2017). In recent years, however, we see increasing evidence that casts doubts on this assumption (e.g. Fanelli 2009, Ioannidis 2011, Prinz et al. 2011, Begley, Ellis 2012, Fokkens et al. 2013, Open Science Collaboration 2015). “As a result, there is an increasingly urgent call for validation and verification of published research results, both within the academic community and the public at large (e.g. Naik 2011, Zimmer 2012, Begley 2012, Editorial 2013a,b, Branco 2012)” (Branco et al. 2017, p. 1). This is particularly important at a time when the scale and complexity of scientific studies grow, and replicability and reproducibility of scientific research has gained salience (Peng 2011). Recent developments in software and web browser technology may help in solving this problem. They have enabled exciting and fast-moving developments in many areas of research, among them tools that can help validate and verify research as well as stimulate knowledge transfer among researchers. Computational notebooks, particularly Jupyter Notebooks, represent a major advance for scientific research. A Jupyter Notebook is an open-source web application which enables creating and sharing documents containing live code, equations, visualisations and narrative text (Jupyter Project 2019). A Jupyter notebook comprises a series of ‘cells’ containing executable code, or markdown, along with the popular HTML markup language for prose descriptions and LaTeX for mathematical equation write up. Jupyter Notebooks have enabled a new type of programming which emphasises a prose-first approach where exposition with human-friendly narrative is threaded with code blocks. The Jupyter Notebook was originally developed by Fernando Perez and Brian Granger in the Python programme language in 2011 and known as IPython Notebooks. In 2013, the technology was expanded to allow for additional programming languages and renamed ‘Jupyter’. The interactive and narrative nature of computational notebooks provide unique opportunities for sharing computational results, enabling reproducibility and publishing scientific research. Traditionally, code, data, results, and their exposition are stored in separate files, which is a source of disconnect and easy misalignment. Computational

The Potential of Notebooks for Scientific Publication, Reproducibility and Dissemination