Science as a Culinary Art: How Data Science and Informatics Will Change Knowledge Discovery for Everyone

There are 7.6 billion scientists on this planet. Every one of us uses the scientific method in our daily lives. We are continually forming new hypotheses—the fastest route for the morning commute, the best strategy for keeping an orchid healthy, or the appropriate cooking time for a bone-in ribeye. We then test these hypotheses against our observations, reevaluate and adjust our views, and then do it all over again. Granted, these are not the rigorous randomized experiments used by research laboratories, but not all knowledge comes from controlled studies. The example of cooking is especially interesting, as I personally think culinary science to be humanity’s most advanced. For 1.9 million years (1), nearly every human has come up with new ideas about how to prepare food. Today alone, billions will form hypotheses about the right combination of spices, temperatures,andwinepairings.Eachofthesehypotheseswillbetested,evaluatedfortheirsuccess, and accepted or rejected, ultimately contributing to the body of human culinary knowledge. Imaginehowadvancedmedicinewouldbeifeveryhumanwasequippedtoformandtestbiomedical research hypotheses the way that we do for cooking! Not only would the mass of knowledge be greater, but it would arguably be more useful as well. The knowledge generated would naturally be contextual—in other words, knowledge specific to particular regions or subpopulations would emerge. Medicine as a scientific discipline will especially benefit from contextual knowledge. The needs and risks of those living in, say, sub-Saharan Africa are much different than those of Inuits living near the Arctic Circle. The push toward precision medicine is evidence that contextual knowledge is recognized as necessary to advance human health. Contextual knowledge made possible by newly available data

• Publication year

  2018

• Venue

  Annual Review of Biomedical Data Science

• Publication date

  2018-07-20

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1146/ANNUREV-BD-01-041718-100011
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

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• No concepts are published for this paper.

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