Why a Scientist’s Big Break May Be Just Around the Corner
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Careers Nov 2, 2016

Why a Scientist’s Big Break May Be Just Around the Corner

Researchers, have hope: your most suc­cess­ful paper can occur at any point in your career.

Successful scientists hope their next paper will be a hit.

Lisa Röper

Based on the research of

Roberta Sinatra

Dashun Wang

Pierre Deville

Chaoming Song

Albert-László Barabási

Con­ven­tion­al wis­dom holds that a scientist’s best work is usu­al­ly pub­lished mid-career, in the sweet spot after they have learned the ropes, but before admin­is­tra­tive duties or thoughts of retire­ment encroach upon research. So is an aging aca­d­e­m­ic with an under­whelm­ing research career a lost cause?

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That was a moti­vat­ing ques­tion behind a recent study by Kellogg’s Dashun Wang. Some­times when I give talks, I say this is the hope project,’” says Wang, an asso­ciate pro­fes­sor of man­age­ment and orga­ni­za­tions. It is hope­ful because Wang and col­leagues find that a scientist’s most-cit­ed paper is equal­ly like­ly to pop up at any point in her career.

It may occur in your first work, or it may be the last work that you pub­lish,” Wang says. This was a very sur­pris­ing finding.”

There is more than just researcher ego at stake. The suc­cess of sci­en­tif­ic research has major impli­ca­tions for both indi­vid­ual sci­en­tists and the uni­ver­si­ties that employ them, since weighty mat­ters of tenure and research fund­ing depend on a sci­en­tists’ abil­i­ty to make a splash in their field.

Dis­cov­er­ing Ran­dom Impact

The paper — coau­thored with Rober­ta Sina­tra of Cen­tral Euro­pean Uni­ver­si­ty, Pierre Dev­ille of Swan Insights, Chaom­ing Song of Uni­ver­si­ty of Mia­mi, and Albert-László Barabási of North­east­ern Uni­ver­si­ty — is a seri­ous con­tri­bu­tion to what Wang calls the sci­ence of sci­ence.” This is a rapid­ly expand­ing field that seeks to turn the micro­scope back on the sci­en­tif­ic world itself to answer fun­da­men­tal ques­tions about how research is produced.

The team used the research data­bas­es Google Schol­ar and Web of Sci­ence to com­pile a list of more than 10,000 sci­en­tists who had pub­lished for at least 20 years in the dis­ci­plines of biol­o­gy, chem­istry, cog­ni­tive sci­ence, ecol­o­gy, eco­nom­ics, and neuroscience.

Some­times when I give talks, I say this is the hope project.’”

Suc­cess in the world of aca­d­e­m­ic pub­lish­ing is often equat­ed with how often a paper is cit­ed by oth­er aca­d­e­mics. So the researchers pin­point­ed the most-cit­ed paper for each sci­en­tist and looked care­ful­ly at the papers pre­ced­ing and fol­low­ing that big hit.

That is where they noticed some­thing sur­pris­ing: A typ­i­cal scientist’s pub­li­ca­tions did not tend to ramp up in cita­tion counts lead­ing to the big hit — nor did papers pub­lished after the big hit receive a cita­tion boost. In the aggre­gate, the trend line for cita­tions before and after the most-cit­ed paper lied com­plete­ly flat.

So flat, in fact, that Wang’s team want­ed to know if there was any pat­tern at all. Was the tim­ing of suc­cess entire­ly random?

We said, OK, with­in a career, what if we just shuf­fle all the work you pub­lished — as if we’re obliv­i­ous to which one gets pub­lished first and which one gets pub­lished sec­ond?’” Wang says. They ran a sim­u­la­tion that ran­dom­ized the order in which each sci­en­tist pro­duced their papers.

The sim­u­la­tion, it turned out, was indis­tin­guish­able from the real-world data.

To make sure this was not a fluke, Wang’s team cut the data into dif­fer­ent seg­ments — look­ing only at sci­en­tists from a par­tic­u­lar decade, for exam­ple, or in a par­tic­u­lar dis­ci­pline. Every time they ran the sim­u­la­tion, the same result held.

The tim­ing of a big sci­en­tif­ic hit, it seemed, was tru­ly — and unex­pect­ed­ly — ran­dom. For the new­ly mint­ed Ph.D. and the long-tenured pro­fes­sor alike, this meant a big break could be just around the corner.

Mod­el Behavior

The research also pre­sent­ed an oppor­tu­ni­ty to study more than just the great­est hits. Since they now under­stood that, across an indi­vid­ual career, impact was occur­ring ran­dom­ly, the researchers could try to pre­dict how cita­tions would accu­mu­late over an entire sci­en­tif­ic career. Specif­i­cal­ly, they want­ed to under­stand why some sci­en­tists were more suc­cess­ful than oth­ers. Was suc­cess sim­ply a mat­ter of increased pro­duc­tiv­i­ty — with more pub­li­ca­tions upping the chances for a break­away hit? Or was some oth­er fac­tor at play?

From the ini­tial analy­sis, Wang’s team cre­at­ed a sin­gle list that com­bined all of the cita­tion counts received by every paper pub­lished by the sci­en­tists in his sam­ple. That dis­tri­b­u­tion con­tained lots of the low and medi­um cita­tion counts that the aver­age paper reached, as well as a few high num­bers that the occa­sion­al hit paper had achieved.

To pre­dict how a career would unfold, the team built a mod­el that drew repeat­ed­ly from the dis­tri­b­u­tion. You just ran­dom­ly pick a num­ber every time some­one pub­lish­es a paper,” explains Wang. By putting those ran­dom draw­ings togeth­er, they could approx­i­mate an individual’s entire career.

But this time when they first ran their mod­el, the out­put did not quite match the real-world data. While the mod­el pre­dict­ed that a scientist’s aver­age cita­tion count increased as they pro­duced more papers (and upped their odds of get­ting a hit), the real data showed that this increase was steep­er than pre­dict­ed. The mod­el also failed to cap­ture the fact that sci­en­tists whose hit papers had been par­tic­u­lar­ly big hits tend­ed to pro­duc­er high­er-impact papers all through­out their careers.

In oth­er words, each sci­en­tist was indeed draw­ing ran­dom­ly from a dis­tri­b­u­tion — but they were not draw­ing from the same distribution.

This sug­gest­ed there was some intrin­sic qual­i­ty that allowed cer­tain sci­en­tists to pro­duce more citable work than their peers. To account for that qual­i­ty, the team added anoth­er para­me­ter — which they called Q” — to their model.

When they ran the mod­el again, account­ing for Q, its out­put matched the real-world data almost perfectly.

Mak­ing Sense of Q

A high Q score does not make some­one a bet­ter researcher, nec­es­sar­i­ly. It just means they are more adept at turn­ing a research top­ic into an atten­tion-grab­bing pub­li­ca­tion, Wang says. A high-Q sci­en­tist can draw from the same knowl­edge pool as his peers, but mul­ti­ply it into a much high­er-cita­tion paper.”

Fur­ther­more, the Q para­me­ter cap­tures con­sis­ten­cy over an entire career — so even a high-Q sci­en­tist will strike out occa­sion­al­ly, draw­ing a low num­ber from the dis­tri­b­u­tion of pos­si­ble impacts. But with time, as you draw more and more, as long as you have a high Q, most of the work you do will have high cita­tions,” says Wang.

The more that Wang and coau­thors looked into Q, the more impor­tant it appeared to be. Q scores pre­dict­ed which sci­en­tists would win major prizes, includ­ing the Nobel, bet­ter than any oth­er fac­tor. And Q val­ues cal­cu­lat­ed at var­i­ous stages of a scientist’s career were found to be remark­ably sta­ble over time, mean­ing it was more than just a proxy for luck.

If we know someone’s Q para­me­ter ear­li­er in the career, we’ll have a much bet­ter under­stand­ing of what will hap­pen going for­ward,” Wang says.

The exis­tence of Q rais­es some crit­i­cal ques­tions. Sure­ly researchers will want to know if a sci­en­tist can cul­ti­vate a high­er Q score — and if so, how. Wang has already begun research on this question.

He is also curi­ous to see if his results hold beyond the realm of acad­e­mia, and how they might help orga­ni­za­tions or coun­tries pre­dict and nur­ture tal­ent. So many deci­sions are based on this abil­i­ty to fore­see a super­star,” he says.

Of course, Q’s pre­dictabil­i­ty may have a dark side. For a low-Q sci­en­tist, even their biggest-hit paper would be doomed to a rel­a­tive­ly low num­ber of citations.

Wang, how­ev­er, prefers a more opti­mistic inter­pre­ta­tion of his results. (This is his hope project, after all.)

No mat­ter how dis­ap­point­ing your past work, he says, the ran­dom order of impact means your bright­est days may be ahead of you yet. As long as you pub­lish, you’re draw­ing from a dis­tri­b­u­tion,” he says. And that means there is hope.”

Featured Faculty

Dashun Wang

Associate Professor of Management & Organizations

About the Writer

Jake J. Smith is a freelance writer and radio producer in Chicago.

About the Research

Sinatra, Roberta, Dashun Wang, Pierre Deville, Chaoming Song, and Albert-László Barabási. 2016. “Quantifying the Evolution of Individual Scientific Impact ” Science. Vol. 354, Issue 6312.

Read the original

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