Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge.
Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge.
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DOI：
10.1177/2378023119849803
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发表时间：
2019-01
期刊：
影响因子：
4.5
通讯作者：
Salganik, Matthew J
中科院分区：
文献类型：
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作者：
Liu, David M;Salganik, Matthew J
再现性是科学的基础，再现性的一个重要组成部分是计算再现性：研究人员使用原作者的原始数据和代码重新创建已发表研究结果的能力。虽然大多数人都认为计算再现性很重要，但在实践中仍然很难实现。在这篇文章中，作者描述了他们的方法，使计算再现性的12篇文章在这个特殊的问题社会关于脆弱家庭的挑战。该方法利用了专业软件工程师常用但学术研究人员不广泛使用的两种工具：软件容器（例如，Docker）和云计算（例如，Amazon Web Services）。这些工具使得围绕每个提交的计算环境标准化成为可能，这将在今天和未来简化计算再现性。根据他们的成功和奋斗，作者总结了对研究人员和期刊的建议。
Reproducibility is fundamental to science, and an important component of reproducibility is computational reproducibility: the ability of a researcher to recreate the results of a published study using the original author’s raw data and code. Although most people agree that computational reproducibility is important, it is still difficult to achieve in practice. In this article, the authors describe their approach to enabling computational reproducibility for the 12 articles in this special issue of Socius about the Fragile Families Challenge. The approach draws on two tools commonly used by professional software engineers but not widely used by academic researchers: software containers (e.g., Docker) and cloud computing (e.g., Amazon Web Services). These tools made it possible to standardize the computing environment around each submission, which will ease computational reproducibility both today and in the future. Drawing on their successes and struggles, the authors conclude with recommendations to researchers and journals.