Abstract
The acceleration in the field of data science is well known [see, e.g., D. Donoho, J. Comput. Graph. Stat. 26(4), 745-766 (2017) and references therein]. Improvements in technology for acquisition, storage, and processing have made unheard of amounts of data available to scientists; in parallel with that, the pace of methodological advance has also been rapid; with new techniques and packages becoming available, it seems, every day. With these affordances come many challenges, notably the volume and variety of the data [Fan et al., Natl. Sci. Rev. 1(2), 293-314 (2014)]. In this Perspective piece, we examine a different challenge-how to choose and use the right analysis method-and make an argument for the sharing of raw data.
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