Conceptions of Good Science in Our Data-Rich World

Abstract

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.

Keywords: data-intensive science; hypothesis testing; iteration; science education; science funding.

Figure 1.

Linear account employed in many descriptions of the scientific method.

Figure 2.

A depiction of the waxing and waning of hypothesis-driven approaches.

Figure 3.

A representation of scientific practice as an iterative process, with many approaches and links (as depicted by two-way arrows). The evaluation or assessment of scientific practices is based on the importance of the knowledge generated, the importance of the gap or challenge addressed, and the alignment of the approaches and methods used to conduct the science.

Figure 4.

Two examples of iterative scientific efforts using multiple approaches.