Good enough practices in scientific computing
By G. Wilson, J. Bryan, Karen A. Cranston, Justin Kitzes, Lex Nederbragt, Tracy K. Teal. PLoS Comput. Biol., 2016
428 citations recorded by the source corpus.
Author summary Computers 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.
Subfields: Information Systems and Management
View Paper: https://www.semanticscholar.org/paper/f0e432ffd9040b38ec5d6b238b9b5ad91134b093
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- N. Gaudio
- F. Gérard
- A. Escobar-Gutiérrez
- W. van der Werf
- P. Hinsinger
- J. Evers
- P. Casadebaig
- Marie Launay
Citations in the corpus
- Exploration and Explanation in Computational Notebooks (2018, neutral): “As the organizers of the Software Carpentry workshops note, “most researchers are never taught the equivalent of basic lab skills for research computing” [38].”
- Applications for deep learning in ecology (2018, background): “As many others have before, we also strongly encourage sharing datasets and code whenever possible to make ecological research faster, easier and directly replicable in the fu‐ ture, especially when using complex tools such as deep learning (Lowndes et al., 2017; Wilson et al., 2017).”
- Garbage in, garbage out?: do machine learning application papers in social computing report where human-labeled training data comes from? (2019, neutral): “The reproducibility movement is deeply linked to the open science movement, focusing on getting researchers to release everything that is necessary for others to perform the same tasks needed to get the exact same results [29, 68].”
- Current knowledge and future research opportunities for modeling annual crop mixtures. A review (2019, positive): “These perspectives could be viewed as good practice for model use and sharing (Wilson et al. 2017).”