Identification of Successful Mentoring Communities using Network-based Analysis of Mentor-Mentee Relationships across Nobel Laureates

Skills underlying scientific innovation and discovery generally develop within an academic community, often beginning with a graduate mentor’s laboratory. In this paper, a network analysis of doctoral student-dissertation advisor relationships in The Academic Tree is used to identify successful mentoring communities in high-level science, as measured by number of Nobel laureates within the community. Nobel laureates form a distinct group in the network with greater numbers of Nobel laureate ancestors, descendants, mentees/grandmentees, and local academic family. Subnetworks composed entirely of Nobel laureates extend across as many as four generations. Successful historical mentoring communities were identified centering around Cambridge University in the latter 19th century and Columbia University in the early 20th century. The current practice of building web-based academic networks, extended to include a wider variety of measures of academic success, would allow for the identification of modern successful scientific communities and should be promoted.

whether they have a greater number of Nobel laureate academic family members than non-Nobel 48 laureates have. We restricted our analysis to doctoral student-advisor relationships and assessed 49 academic family structure in several ways. We examined the number of Nobel laureate ancestors encompassed an individual's mentor, grandmentor, great-grandmentor, mentees, grandmentees, 56 great-grandmentees, sibling, aunts, and uncles. We compared the outcomes of this analysis to 57 results obtained from many topologically identical networks in which Nobel status was randomly 58 assigned across all individuals in each of the networks.    For all four analyses, negative binomial models were chosen to adjust for greater than 125 expected dispersion in the data (i.e., a high variance to mean ratio). Spearman's correlations (see 126   Table 1) indicated that the number of Nobel laureate family members was positively related to 127 the size of the academic family. Therefore, in each case, the size of the academic family was 128 entered along with Nobel status as a predictor of the size of the Nobel laureate academic family.

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As described in the method, the significance level for each analysis was adjusted by comparing 130 the observed test statistics with a distribution of expected test statistics, derived from 1,000 131 topologically identical networks, each with a random permutation of Nobel status. The 132 regression model coefficients and the distributions of random coefficients used to adjust the 133 significance levels of predictors in the models are available in Table S1  In contrast to the previous two results, the number of mentees/grandmentees did serve as 142 a significant predictor of number of Nobel laureate mentees/grandmentees (adjusted p < 0.001).

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Still, after controlling for family size, Nobel laureates had a greater number of Nobel laureate 144 mentees and grandmentees than did non-Nobel laureates (adjusted p < 0.001). Finally, Nobel laureates also had a greater number of local Nobel Laureates in their academic family than did 146 non-Nobel laureates (adjusted p < 0.001). The number of local academic family members did not 147 significantly predict the number of Nobel laureates (adjusted p < 0.964).   It is significant that many of the successful communities identified by this network 281 analysis existed at a time when travel and communication were much more difficult than they are 282 today. Ernest Rutherford (chemistry, 1908) traveled from New Zealand to attend Cambridge as 283 one of the first students admitted from outside the university 17,18 . This occurred in the latter half 284 of the 19th century prior to the invention of the airplane and intercontinental telephone service.

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At this point in history, physical proximity was critical to the transmission of ideas and expertise.

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In modern science, however, virtual meetings, video lectures, online courses, and online 287 databases (e.g., PubMed 19 , Google Scholar 20 ) provide remarkably easy access to current, 288 innovative ideas in science. It seems likely that the mentoring patterns among scientists are being 289 radically altered by greater accessibility to information and each other. Still, for many scientists, 290 it is difficult to imagine that virtual proximity could ever be a satisfying replacement for the day-  To accomplish this, the C++ program described earlier had options available for 422 generating 1,000 networks with Nobel status randomly assigned to nodes across the network in 423 the same proportion as the true data, each time recomputing outcome measures for each node. As 424 can be seen in Fig. 4, this produced alternate networks with equivalent topology (i.e., the same 425 number of family members and academic structure for each node) but randomly distributed 426 Nobel laureates and thus, random outcomes.