Good enough practices in scientific computing

Abstract

Author summaryComputers are now essential in all branches of science, but most researchers are never taught the equivalent of basic lab skills for research computing. As a result, data can get lost, analyses can take much longer than necessary, and researchers are limited in how effectively they can work with software and data. Computing workflows need to follow the same practices as lab projects and notebooks, with organized data, documented steps, and the project structured for reproducibility, but researchers new to computing often don't know where to start. This paper presents a set of good computing practices that every researcher can adopt, regardless of their current level of computational skill. These practices, which encompass data management, programming, collaborating with colleagues, organizing projects, tracking work, and writing manuscripts, are drawn from a wide variety of published sources from our daily lives and from our work with volunteer organizations that have delivered workshops to over 11,000 people since 2010.

Our path to better science in less time using open data science tools

“…As we began the second global OHI assessment in 2013 we faced challenges across three main fronts: (1) reproducibility, including transparency and repeat ability, particularly in data preparation; (2) collaboration, including team record keeping and internal collaboration; and (3) commu nication, with scientific and broader communities. We knew that environmental scientists are increasingly using R because it is free, cross platform, and open source 11 , and also because of the training and support provided by developers 33 and independent groups 12,41 alike. We decided to base our work in R and RStudio for coding and visualization 42,43 , Git for version control 44 , GitHub for collabo ration 45 , and a combination of GitHub and RStudio for organiza tion, documentation, project management, online publishing, 36,37 .…”

“…Previous to this evolution, most team members with any coding experience-not necessarily in R-had learned just enough to accomplish whatever task had been before them using their own unique conventions. Given the com plexity of the OHI project, we needed to learn to code collabora tively and incorporate best 50,51 or good enough practices 12,52 into our coding, so that our methods could be co developed and vetted by multiple team members. Using a version control system not only improved our file and data management, but allowed individuals to feel less inhibited about their coding contributions, since files could always be reverted back to previous versions if there were problems.…”

“…While reproducibility means distinct things within the protocols of each sub discipline or specialty, underpinning reproducibility across all disciplines in modern science is working effectively and collabora tively with data, including wrangling, formatting and other tasks that can take 50-80% of a data scientist's time 75 . While reaching full reproducibility is extremely difficult 5,76 , incrementally incorporat ing open data science practices and tools into scientific workflows has the potential to alleviate many of the troubles plaguing science, including collaboration and preserving institutional memory 12 . Further, sharing openly is fundamental to truly expediting scien tific progress because others can build directly off previous work if well documented, re usable code is available 16,47,48,77 .…”

Software Curation in Research Libraries: Practice and Promise

“…Because many researchers lack formal training in software development practices, a recent set of guidelines advocates for "good enough" practices (Wilson et al, 2017). While packaging and containerization platforms such as ReproZip and Docker enable the tracking, bundling, and sharing of software libraries and dependencies, managing output means confronting the same curation difficulties (Emsley & De Roure, 2017).…”

General guidelines for biomedical software development

“…They include topics such as version control and software discoverable or automatic building. In a similar approach, Wilson et al 17, 18 described a set of “good enough” principles that should be followed to better organize scientific computing projects, starting at the data gathering phase and continuing up to the writing of the manuscript.…”