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Conceptions of Good Science in Our Data-Rich World

机译:数据丰富世界中的良好科学构想

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Scientists have been debating for centuries the nature of proper scientific methods. Currently, criticisms being thrown at data-intensive science are reinvigorating these debates. However, many of these criticisms represent long-standing conflicts over the role of hypothesis testing in science and not just a dispute about the amount of data used. Here, we show that an iterative account of scientific methods developed by historians and philosophers of science can help make sense of data-intensive scientific practices and suggest more effective ways to evaluate this research. We use case studies of Darwin's research on evolution by natural selection and modern-day research on macrosystems ecology to illustrate this account of scientific methods and the innovative approaches to scientific evaluation that it encourages. We point out recent changes in the spheres of science funding, publishing, and education that reflect this richer account of scientific practice, and we propose additional reforms.
机译:几个世纪以来,科学家一直在争论正确的科学方法的性质。当前,对数据密集型科学的批评正在重新激发这些辩论。但是,这些批评中的许多批评都代表着关于假设检验在科学中的作用的长期矛盾,而不仅仅是关于所使用数据量的争论。在这里,我们表明,由历史学家和科学哲学家开发的科学方法的迭代说明可以帮助理解数据密集型科学实践,并提出评估该研究的更有效方法。我们使用达尔文关于自然选择进化研究和现代宏观系统生态研究的案例研究,来说明这种科学方法及其鼓励的科学评估创新方法。我们指出了科学资助,出版和教育领域的最新变化,这些变化反映了这种对科学实践的丰富描述,并提出了其他改革措施。

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