Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail
Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail
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DOI：
10.1007/s10827-018-0702-z
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发表时间：
2018-12-01
影响因子：
1.2
通讯作者：
Hohol, Mateusz
中科院分区：
文献类型：
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作者：
Milkowski, Marcin;Hensel, Witold M.;Hohol, Mateusz
计算模型的可复制性和再现性在某种程度上被复制运动所忽视。在本文中，我们借鉴的方法论研究心理实验的可复制性和解释的机械帐户，分析模型复制和模型再现在计算神经科学中的功能。我们认为，模型的可复制性，或独立研究人员使用原始代码和数据获得相同输出的能力，以及模型的可重复性，或独立研究人员在没有原始代码的情况下重新创建模型的能力，具有不同的功能，并因不同的原因而失败。这意味着旨在提高模型可复制性的措施可能不会提高（在某些情况下，实际上可能会损害）模型的可复制性。我们认为，尽管两者都是不可取的，但低模型可重复性对长期科学进步的威胁比低模型可重复性更大。在我们看来，低模型再现性主要源于作者在科学论文中省略提供关键信息，我们强调共享所有计算机代码和数据不是解决方案。计算研究的报告应该保持选择性，并包括所有和只有相关的代码位。
Replicability and reproducibility of computational models has been somewhat understudied by the replication movement. In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that model replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without original code, serve different functions and fail for different reasons. This means that measures designed to improve model replicability may not enhance (and, in some cases, may actually damage) model reproducibility. We claim that although both are undesirable, low model reproducibility poses more of a threat to long-term scientific progress than low model replicability. In our opinion, low model reproducibility stems mostly from authors' omitting to provide crucial information in scientific papers and we stress that sharing all computer code and data is not a solution. Reports of computational studies should remain selective and include all and only relevant bits of code.