首页    期刊浏览 2024年09月16日 星期一
登录注册

文章基本信息

  • 标题:Computational Reproducibility via Containers in Psychology
  • 本地全文:下载
  • 作者:April Clyburne-Sherin ; Xu Fei ; Seth Ariel Green
  • 期刊名称:Meta-Psychology
  • 电子版ISSN:2003-2714
  • 出版年度:2019
  • 卷号:3
  • 页码:1-9
  • DOI:10.15626/MP.2018.892
  • 出版社:LnuOpen
  • 摘要:Scientific progress relies on the replication and reuse of research. Recent studies suggest, however, that sharing code and data does not suffice for computational reproducibility —defined as the ability of researchers to reproduce “par- ticular analysis outcomes from the same data set using the same code and software” (Fidler and Wilcox, 2018). To date, creating long-term computationally reproducible code has been technically challenging and time-consuming. This tutorial introduces Code Ocean, a cloud-based computational reproducibility platform that attempts to solve these problems. It does this by adapting software engineering tools, such as Docker, for easier use by scientists and scientific audiences. In this article, we first outline arguments for the importance of computational reproducibility, as well as some reasons why this is a nontrivial problem for researchers. We then provide a step-by-step guide to getting started with containers in research using Code Ocean.
  • 其他摘要:Scientific progress relies on the replication and reuse of research. Recent studies suggest, however, that sharing code and data does not suffice for computational reproducibility —defined as the ability of researchers to reproduce “par- ticular analysis outcomes from the same data set using the same code and software” (Fidler and Wilcox, 2018). To date, creating long-term computationally reproducible code has been technically challenging and time-consuming. This tutorial introduces Code Ocean, a cloud-based computational reproducibility platform that attempts to solve these problems. It does this by adapting software engineering tools, such as Docker, for easier use by scientists and scientific audiences. In this article, we first outline arguments for the importance of computational reproducibility, as well as some reasons why this is a nontrivial problem for researchers. We then provide a step-by-step guide to getting started with containers in research using Code Ocean. (Disclaimer: the authors all worked for Code Ocean at the time of this article’s writing.)
  • 关键词:computational reproduciblity; social psychology; containers; Docker; Code Ocean
  • 其他关键词:computational-reproducibility;reproducibility;methods;containers;code-ocean
国家哲学社会科学文献中心版权所有