计算可重复性的五大支柱:生物信息学及其他领域
Mark Ziemann1,2, Pierre Poulain3, Anusuiya Bora1
1Deakin University, School of Life and Environmental Sciences, Geelong, Australia.
Briefings in bioinformatics
|October 23, 2023
概括
在生物信息学中实现计算可重现性是具有挑战性的. 这一框架,即可重现计算研究的五大支柱,提供了一份实用指南,以确保研究可以轻松重做.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 计算可重现性对于科学严谨性至关重要,但很难在实践中实现.
- 现有的提高生物信息学可重现性的努力为新的框架提供了基础.
- 计算工作流程的复杂性需要标准化的方法.
研究的目的:
- 提出一个实际的框架,可重复计算研究的五大支柱,以解决计算可重复性的挑战.
- 为生物信息学家和数据分析师提供可操作的指南,以提高其工作的可复制性.
- 提高计算研究成果的长期可访问性和可重复使用性.
主要方法:
- 该框架整合了五个关键组成部分:识字编程,代码版本控制和共享,计算环境控制,持久数据共享和全面文档.
- 这些支柱旨在在生物信息学研究项目中系统地实施.
- 该方法强调研究人员和实习生的实际应用.
主要成果:
- 拟议的五个支柱为实现高水平的计算可重复性提供了一个结构化的方法.
- 这些实践的实施有助于研究成果的快速和简单的复制.
- 该框架适应性强,对生物信息学数据分析师和实习生有益.
结论:
- 采用可重现计算研究的五大支柱对于保持科学发现的完整性和可靠性至关重要.
- 这一框架使研究人员能够确保他们的计算工作现在和将来是可重复的.
- 该指南是提高生物信息学和相关领域的可复制性标准的宝贵资源.
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