Best Practices in Scientific Computing

01/28/2021 ∙ by Ricardo Sanchez, et al. ∙ 0

The world is becoming increasingly complex, both in terms of the rich sources of data we have access to as well as in terms of the statistical and computational methods we can use on those data. These factors create an ever-increasing risk for errors in our code and sensitivity in our findings to data preparation and execution of complex statistical and computing methods. The consequences of coding and data mistakes can be substantial. Openness (e.g., providing others with data code) and transparency (e.g., requiring that data processing and code follow standards) are two key solutions to help alleviate concerns about replicability and errors. In this paper, we describe the key steps for implementing a code quality assurance (QA) process for researchers to follow to improve their coding practices throughout a project to assure the quality of the final data, code, analyses and ultimately the results. These steps include: (i) adherence to principles for code writing and style that follow best practices, (ii) clear written documentation that describes code, workflow and key analytic decisions; (iii) careful version control, (iv) good data management; and (iv) regular testing and review. Following all these steps will greatly improve the ability of a study to assure results are accurate and reproducible. The responsibility for code QA falls not only on individual researchers but institutions, journals, and funding agencies as well.



There are no comments yet.


page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.