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生物医学研究中的分析代码共享实践.

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此摘要是机器生成的。

大多数生物医学研究缺乏可重复的研究成果,如代码和数据. 加强代码共享实践对于科学透明度和加速发现至关重要.

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科学领域:

  • 生物医学研究生物医学研究
  • 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:

  • 生物医学研究中不断增加的数据量需要强大的计算分析.
  • 缺乏共享的研究成果 (数据,代码,方法) 阻碍了研究的透明度和可重复性.
  • 由于文件,代码和数据的共享不足,许多已发表的研究无法复制.

研究的目的:

  • 分析生物医学研究出版物中代码和数据共享的程度.
  • 识别与增加代码可用性相关的因素.
  • 提出提高生物医学研究可复制性的策略.

主要方法:

  • 综合分析了2016-2021年间发表的453份生物医学手稿.
  • 评估代码可用性,数据共享和代码组织.
  • 统计分析以确定代码可用性和其他因素之间的关联.

主要成果:

  • 在分析的手稿中,50.1%未能分享分析代码.
  • 大多数共享代码的研究没有共享数据等额外的研究成果.
  • 只有10%的论文以结构化,可重复的方式组织代码.
  • 代码可用性声明与增加的代码可用性有显著的相关性 (p=2.71×10-9).
  • 二级分析比初级分析更有可能共享代码 (p=1.15*10-07).

结论:

  • 需要立即采取措施,以提高代码的可用性和提高生物医学研究中的可复制性.
  • 优先考虑开放科学实践,包括共享代码和数据,对于科学严谨性和协作至关重要.
  • 提高透明度和可重复性将加速科学发现,并使科学界能够在现有研究基础上建立.