{"id":1824,"date":"2018-09-23T16:28:02","date_gmt":"2018-09-23T07:28:02","guid":{"rendered":"http:\/\/163.180.4.222\/lab\/?p=1824"},"modified":"2019-10-15T19:46:41","modified_gmt":"2019-10-15T10:46:41","slug":"predicting-scientific-success","status":"publish","type":"post","link":"https:\/\/biochemistry.khu.ac.kr\/lab\/?p=1824","title":{"rendered":"Predicting scientific success"},"content":{"rendered":"

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Even sophisticated, data-driven models of academic careers have trouble forecasting the highs and lows.<\/h5>\n

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Physicist, Frank Wilczek, whose Nobel-winning work on the forces acting on quarks was published when he was just starting out.\u00a0<\/span>Credit: Bertil Ericson\/AFP\/Getty Images<\/p>\n<\/figcaption><\/figure>\n

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When Frank Wilczek was a graduate student in his early twenties, he published work on the forces holding quarks together that later won him a Nobel Prize.<\/p>\n

At the other end of a career span, John Fenn, a retired analytic chemist in his seventies, developed the award-winning technique for analysing large proteins using mass spectrometry.<\/p>\n

From early starters to late bloomers, the timing of a researcher\u2019s career high is largely dependent on chance. This was the conclusion of a 2016 study, in which researchers developed a mathematical model to describe publication and citation trends based on the records of thousands of people.<\/p>\n

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