6.28.2009

Now leaving Era of the Mystery. All aboard for Era of the Tool.

Historically, there have been three ways to make progress within a scientific paradigm:
- Solve an outstanding mystery;
- Gather and publishing new data;
- Construct a new tool.

Gathering and publishing new data has constituted, and will constitute for the forseeable future, the majority of scientific publication. Science has a healthy and voracious appetite for data, and this isn't likely to change anytime soon. The interesting thing about progress in science today though, and the topic of this post, is the balance between the first and third sort of approach, mystery vs tool.

Era of the Scientific Mystery

By and large, the emphasis in science used to be on solving mysteries. Discovering the mechanism of genetic inheritance; decoding the structure of DNA; deciphering how viruses take over cells. Scientists were billed as detectives, and the height of scientific achievement was to find an "aha" insight that solved an outstanding mystery. But- though some scientists may voraciously deny this- we've been so successful at solving the fundamental mysteries out there that we're running out of this kind of mystery in many branches of science. In turn, science is gradually becoming less about solving foundational unknowns (like decoding the structure of DNA) and more about creating tools by which to more richly and more quantifiedly understand what is no longer mysterious but too complex to trust to our intuitions and simple equations.

Era of the Scientific Tool

Scientific progress has always had a strong tool component. Grind a better lens, see the stars better, and create a more accurate description of the galaxy; build a free-swinging pendulum, observe the shifting plane of motion, and conclude the Earth is not fixed but rotates. These sort of things were not uncommon in the history of science. But there seems to be a sea change happening that modern scientific publication is beginning to center around devising and applying tools that in turn generate interesting results.

Two examples of this from my own experience are the recent publications of a couple friends who are scientists, John Hawks (UW Madison) and Bryan W. Jones (U Utah).

Hawks made waves with a recent publication, Recent Acceleration of Human Adaptive Evolution, which applied an established genetics tool (linkage disequilibrium) to the context of the human genome and came to the conclusion that not only did human evolution not stop with the advent of civilization, but that it actually sped up over a hundredfold.

Jones just published A Computational Framework for Ultrastructural Mapping of Neural Circuitry, a work which defined a new integrative workflow which enabled, for the first time, the mapping of a large-scale neural connectome, and offered the first product of this workflow, a connectome map of a rabbit's retina.

Tools are absolutely central to both publications: the first is based on the novel application of an existing tool to a context it hadn't been applied in, and the second involved inventing a new tool to enable the generation of new datasets.

These examples are anecdotal, to be sure-- but it seems that although the meme of the scientific mystery will be with us for a long time, and though there are sporadic fundamental unknowns yet to discover, increasingly the really sexy, generative results in science involve creating or repurposing a tool to shed new light on some data, or generate data at an exponentially faster rate.

In short? Science is no longer about mysteries but about problems. And given the right tool, problems solve themselves.

Notes:

- Kevin Kelly's Speculations on the Future of Science is an interesting survey of possible tools science may grow into.

Edit, 5-13-11: Bryan W. Jones has a nice description of the problem his lab faced in building a connectome, and the tools they built to solve it. The research method and goal were less about solving a well-defined mystery, but more about building tools, datasets, and models that allow more useful ways of understanding what happens in retinal tissue under various scenarios.

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