Wilson Greg, Bryan Jennifer, Cranston Karen, Kitzes Justin, Nederbragt Lex, Teal Tracy K
Software Carpentry Foundation, Austin, Texas, United States of America.
RStudio and Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada.
PLoS Comput Biol. 2017 Jun 22;13(6):e1005510. doi: 10.1371/journal.pcbi.1005510. eCollection 2017 Jun.
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.
计算机如今在科学的各个领域都至关重要，但大多数研究人员从未接受过与研究计算基础实验室技能相当的培训。因此，数据可能会丢失，分析可能会比必要的时间长得多，而且研究人员在使用软件和数据时的工作效率也会受到限制。计算工作流程需要遵循与实验室项目和笔记本相同的做法，要有条理的数据、记录步骤以及为可重复性构建的项目结构，但刚接触计算的研究人员往往不知道从哪里开始。本文提出了一套每个研究人员都可以采用的良好计算实践，无论他们当前的计算技能水平如何。这些实践涵盖数据管理、编程、与同事协作、组织项目、跟踪工作以及撰写手稿，它们来自于我们日常生活中各种各样已发表的资料，以及自2010年以来我们与为超过11000人举办过研讨会的志愿者组织合作的经验。
