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Variations in Scientific Data Production: What Can We Learn from #Overlyhonestmethods?

机译：科学数据生产中的变化：我们可以从#Overlyhonest方法中学到什么？

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摘要

In recent months months the hashtag #overlyhonestmethods has steadily been gaining popularity. Posts under this hashtag-presumably by scientists-detail aspects of daily scientific research that differ considerably from the idealized interpretation of scientific experimentation as standardized, objective and reproducible. Over and above its entertainment value, the popularity of this hashtag raises two important points for those who study both science and scientists. Firstly, the posts highlight that the generation of data through experimentation is often far less standardized than is commonly assumed. Secondly, the popularity of the hashtag together with its relatively blas, reception by the scientific community reveal that the actions reported in the tweets are far from shocking and indeed may be considered just "part of scientific research". Such observations give considerable pause for thought, and suggest that current conceptions of data might be limited by failing to recognize this "inherent variability" within the actions of generation-and thus within data themselves. Is it possible, we must ask, that epistemic virtues such as standardization, consistency, reportability and reproducibility need to be reevaluated? Such considerations are, of course, of particular importance to data sharing discussions and the Open Data movement. This paper suggests that the notion of a "moral professionalism" for data generation and sharing needs to be considered in more detail if the inherent variability of data are to be addressed in any meaningful manner.
机译：在最近几个月中，＃overlyhonestmethods主题标签稳步流行。这个标签下的帖子（大概是由科学家负责的）详细介绍了日常科学研究的各个方面，这些方面与科学实验对标准化，客观和可复制的理想化解释有很大不同。除了其娱乐价值外，此主题标签的普及度对那些同时研究科学和科学家的人都提出了两个重要的观点。首先，这些帖子强调指出，通过实验生成数据的标准化程度通常远低于通常的假设。其次，主题标签的受欢迎程度及其相对应有的荣誉，受到了科学界的欢迎，这表明推文中报道的行为并没有令人震惊，实际上可以认为只是“科学研究的一部分”。这样的观察为思考提供了相当大的停顿，并且表明当前的数据概念可能会由于未能在生成活动中，因此在数据本身内无法认识到这种“内在的可变性”而受到限制。我们必须问，是否需要重新评估标准化，一致性，可报告性和可复制性等认知美德？当然，这些考虑对于数据共享讨论和开放数据运动特别重要。本文建议，如果要以任何有意义的方式解决数据的固有可变性，则需要更详细地考虑用于数据生成和共享的“道德专业性”的概念。

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