从生态学和进化生物学的元分析中计算复制结果,使用共享的代码和数据
Steven Kambouris1,2, David P Wilkinson1,2, Eden T Smith1,3
1MetaMelb Research Initiative, The University of Melbourne, Melbourne, Victoria, Australia.
PloS one
|March 13, 2024
概括
在生态学中计算可重现性是具有挑战性的. 虽然许多研究共享数据,但更少的研究共享代码,限制了结果的复制. 这项研究发现,即使两者都是共享的,计算复制结果也是不一致的.
科学领域:
- 生态学和进化生物学
- 计算可复制性计算可复制性
- 超级研究 (Meta-Research) 是一种研究方法.
背景情况:
- 期刊越来越多地鼓励生态学和进化生物学中的数据和代码共享.
- 确保计算可重现性对于科学完整性和进步至关重要.
- 以前对数据和代码共享实践的评估是有限的.
研究的目的:
- 评估生态学和进化生物学元分析中数据和代码共享的程度.
- 用共享数据和代码计算复制已发布结果的成功率.
- 在现场建立计算可重复性的基准.
主要方法:
- 在生态学和进化生物学杂志上发表的177个元分析中进行了调查 (2015-2017年).
- 基于数据和代码共享实践的分类文章 (仅数据,仅代码,两者).
- 试图通过计算复制来自26篇共享数据和代码的文章的目标结果.
主要成果:
- 60%的文章仅共享数据,1%仅共享代码,15%共享两者.
- 在26篇文章中,目标结果的成功计算复制率从27%到73%不等.
- 生殖成功的变化取决于所使用的标准的严格性.
结论:
- 数据/代码共享与实际计算可重复性之间存在很大的差距.
- 目前的数据和代码共享实践并不总是允许对已公布的结果进行独立验证.
- 需要进一步改进数据和代码共享,以提高生态研究的可靠性.
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