Greg Wilson

Morgan Taschuk, Greg Wilson

Software produced for research, published and otherwise, suffers from a number of common problems that make it difficult or impossible to run outside the original institution, or even off the primary developer's computer. We present ten simple rules to make such software robust enough to run anywhere, and inspire confidence in your reproducibility, and thereby delight your users and collaborators.

Greg Wilson, Jennifer Bryan, Karen Cranston et al.

We present a set of computing tools and techniques that every researcher can and should adopt. These recommendations synthesize inspiration from our own work, from the experiences of the thousands of people who have taken part in Software Carpentry and Data Carpentry workshops over the past six years, and from a variety of other guides. Unlike some other guides, our recommendations are aimed specifically at people who are new to research computing.

Jory Schossau, Greg Wilson

We studied scientists who attended two-day workshops on basic software skills to determine which tools and practices they found most useful. Our pre- and post-workshop surveys showed increases in self-reported familiarity, while our interviews showed that participants found learning Python more useful than learning the Unix shell, that they found pointers to further resources very valuable, and that background material---the "why" behind the skills---was also very valuable.

Marian Petre, Greg Wilson

We describe two pilot studies of code review by and for scientists. Our principal findings are that scientists are enthusiastic, but need to be shown code review in action, and that just-in-time review of small code changes is more likely to succeed than large-scale end-of-work reviews.

Marian Petre, Greg Wilson

PLOS and Mozilla conducted a month-long pilot study in which professional developers performed code reviews on software associated with papers published in PLOS Computational Biology. While the developers felt the reviews were limited by (a) lack of familiarity with the domain and (b) lack of two-way contact with authors, the scientists appreciated the reviews, and both sides were enthusiastic about repeating the experiment.

Greg Wilson, D. A. Aruliah, C. Titus Brown et al.

Scientists spend an increasing amount of time building and using software. However, most scientists are never taught how to do this efficiently. As a result, many are unaware of tools and practices that would allow them to write more reliable and maintainable code with less effort. We describe a set of best practices for scientific software development that have solid foundations in research and experience, and that improve scientists' productivity and the reliability of their software.