The growth of programming in the sciences has been explosive in the last decade. This has facilitated the rapid advancement of science through the agile development of computational tools. However, concerns have begun to surface about the reproducibility of scientific research in general (Baker, 2016) and the potential issues stemming from issues with analytical software (Stodden, Seiler, & Ma, 2018). Specifically, there is a growing recognition across disciplines that simply making data and software “available” is not enough and that there is a need to improve the transparency and stability of scientific software (Pasquier et al., 2018).
- code cleaning
- data provenance