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. 2016 Nov 4;14(11):e1002573.
doi: 10.1371/journal.pbio.1002573. eCollection 2016 Nov.

Differences in Collaboration Patterns across Discipline, Career Stage, and Gender

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Differences in Collaboration Patterns across Discipline, Career Stage, and Gender

Xiao Han T Zeng et al. PLoS Biol. .

Abstract

Collaboration plays an increasingly important role in promoting research productivity and impact. What remains unclear is whether female and male researchers in science, technology, engineering, and mathematical (STEM) disciplines differ in their collaboration propensity. Here, we report on an empirical analysis of the complete publication records of 3,980 faculty members in six STEM disciplines at select U.S. research universities. We find that female faculty have significantly fewer distinct co-authors over their careers than males, but that this difference can be fully accounted for by females' lower publication rate and shorter career lengths. Next, we find that female scientists have a lower probability of repeating previous co-authors than males, an intriguing result because prior research shows that teams involving new collaborations produce work with higher impact. Finally, we find evidence for gender segregation in some sub-disciplines in molecular biology, in particular in genomics where we find female faculty to be clearly under-represented.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Lower number of publications by female scientists results in lower total number of distinct co-authors.
Survival curve of the total number of co-authors over careers of females (orange) and males (purple). We test the null hypothesis that there is no gender difference in the total number of distinct co-authors for females and males with similar number of publications. The grey shaded region indicates the 95% confidence interval obtained under the null hypothesis. To construct the confidence interval, we generate samples of NF males, where NF is the number of females in our dataset. For a female with nF publications, we select a male whose number of publications falls in the range of [0.8 nF, 1.2 nF] (see Materials and Methods). Note that the curve for females falls inside the confidence interval, indicating that after correcting for number of publications, females and males have comparable numbers of distinct co-authors over their careers. The curve for males falls outside the confidence interval because some male researchers in the dataset have very large numbers of publications (see Fig 7 of [30]). Data for this figure are in S1 Data.
Fig 2
Fig 2. Gender differences in the propensity to co-author with prior collaborators.
Probability distribution of the fraction of total coauthors who are repeated for all females (orange) and males (purple) in the dataset with at least 10 publications. We exclude single-author publications. Orange and purple lines are kernel density estimation of the distributions for females and males with bandwidth given by Scott’s Rule [34]. We obtain p-values for the validity of the null hypothesis that the samples were drawn from the same distribution using the Kolmogorov-Smirnov test. For all disciplines, we find δ=2(f¯r,F-f¯r,M)/(f¯r,F+f¯r,M)<0, where f¯r,F and f¯r,M are the average fr of the female and male faculty, respectively. Females have fr smaller than those of males, suggesting that, except for materials science, female faculty have a lower propensity than male faculty to repeat collaborations. Data for this figure are in S2 Data.
Fig 3
Fig 3. Male and female faculty have similar number of co-authors per publication for five other disciplines, but not for molecular biology.
Probability of females having greater number of co-authors per publication in a given year of her career than a male peer at the same career stage (red lines). We use z-scores to account for the increasing size of research teams and the fluctuations over career stage (see Materials and Methods). We indicate the 99% confidence intervals by the grey areas, and the medians of the probabilities obtained from random ensembles by black lines. The p-values are obtained under under the null hypothesis that there is a 99% probability of any value being outside the confidence interval. Note that although the difference in the average size of teams appears to be statistically significant, it is not consistent along the career stage, except for chemistry for the first few years, and for molecular biology in later career stages (dark horizontal bars). Data for this figure are in S3 Data.
Fig 4
Fig 4. Female faculty in molecular biology departments publish more in journals and sub-disciplines where typical team size is smaller.
We show correlation between the average number of co-authors corrected for the annual average versus the fraction of publications authored by females, grouped by journal. We only consider publications authored after the tenth year mark in an author’s career. We restricted the publication types to "article", "letter", and "note." The size of the circle is proportional to the logarithm of the number of publications in that journal or sub-discipline. We use journal category in the ISI Journal Citation Report as the sub-disciplines. Journals with multiple categories are plotted as concentric rings. The purple line indicates the total average fraction of publications by females for all the publications authored by faculty in molecular biology in our cohort, fM (17.3%). The blue line is a weighted linear regression, in which we assign to each journal a weight equal to the number of publications. We only include data points within the range of [0.5fM, 2fM]. Data for this figure are in S4 Data.
Fig 5
Fig 5. Topic dependence of female representation in publications in the six disciplines.
We show the average number of co-authors corrected for the annual average for male faculty versus that for female faculty. Note for molecular biology most of the data points fall above the line y = x, indicating that for most topics females work in smaller teams than males. We label the seven topics which fall outside the 99% confidence region (brown ellipse) (see Table 2 for topic details). Data for this figure are in S5 Data.

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