Figure 1.
Percentage awarded to females of the total number of bachelor (green lines), master (blue lines) and doctoral (purple lines) degrees in the period 1966–2008. We obtained these data from [40]. We also show the percentage of female faculty in our datasets (orange lines). We could not obtain separate data for molecular biology, so we show the data for biology instead. The grey shaded areas indicate values lower than 50%. The gender ratio of the faculty members given by our data is close to that reported elsewhere. For example, in our data, the percentage of female faculty members in chemistry is 16.2%, and according to the report of Chemical & Engineering News, this percentage is 17% [43].
Table 1.
Female and male cohorts in study.
Figure 2.
Career lengths of faculty members.
Career length distribution of female (red) and male (blue) current faculty members for a selected set of U.S. universities (Table 1). Data is binned into two year intervals. Currently, females hold about 16% of faculty positions in chemistry and in material science departments, and about 25% of faculty positions in molecular biology departments.
Table 2.
Requirement for research resources and risk of academic career choice.
Figure 3.
Average number of annual publications per author.
Average number of publications authored by females (red) and males (blue) as a function of time. Data is smoothed using moving averaging over a -year time window. Note the increasing trend in all disciplines. Because of these trends, we must account for the different starting years and career stages of authors when comparing publication rates.
Figure 4.
Gender difference in publication rate.
Average z-score of number of publications for females (red) and males (blue) as a function of career stage. Shaded areas indicate the standard errors. See Fig. S10 for the statistical significance of the gender difference in publication rate.
Figure 5.
Lower publication rates of female faculty is correlated with higher requirements for research resources.
Effects of the magnitude of the resource requirements on the difference in publication rates between genders. Ecology is not included as we could not obtain data for resource requirements. The difference in publication rates is measured by the average z-score of number of publications by females in each year, and the error bars indicate the standard errors. The resource requirements is defined as the average annual research expenditure per principal investigator in the departments studied (Table 2, [40]). The trend line (black dashed line) indicates a negative correlation (coefficient of determination ). These data suggest that higher resource requirements lead to greater differences in the publication rates between females and their male peers.
Figure 6.
Fraction of publications in which a faculty member is the last author (purple diamonds) and the fraction of publications in which a faculty member is the first author (green squares). In many disciplines, the senior author of a study is listed last. Looking at the change in the fraction of times a faculty member in our dataset is a first or last author can thus be used as a proxy for change in seniority-level of an individual in these disciplines. We order publications, excluding single-author publications, by years after first publication and aggregate within each discipline. We fit the data to generalized logistic functions (green/purple lines) and define career independence (grey shaded areas) as the mid-point of the logistic function for fraction of last-author publications (Methods, Table S11, S12, S13, S14, S15, S16, S17). While we do not observe gender effects (Figs. S11, S12), we do observe differences between fields.
Figure 7.
Relation between impact and number of publications.
Dependence of the h-index on number of publications for faculty with at least 30 publications that are at least 10 years old (Table S18). We consider only publications at least 10 years old in order to ensure that they all have accrued close to their ultimate number of citations [42]. Blue and red dots show values for individual male and female faculty members in our cohorts. The solid black line shows a maximum likelihood power-law fit to under the assumption of Poissonian fluctuations (Methods). Shaded areas indicate one standard deviation (dark grey areas) and two standard deviations (light grey areas) from the mean.
Figure 8.
Comparison of publication impact for authors with different numbers of publications.
The z-score of the -index as a function of number of publications. We use the mean and standard deviation obtained from the parameters in the model to determine the z-scores.
Table 3.
Linear models predicting the gender difference in publication impact.
Figure 9.
Higher publication impact of female faculty is correlated with higher relative risk of academic career choice.
Risk in academic career choice and difference in publication impact. We quantify the risk of academic career choice according to Eq. (10). We show results for two alternative measures of difference in publication impact. In (A), we defined the gender difference in publication impact as the average h-index z-scores of females. The error bars indicate standard errors. See Fig. S13 for the statistical significance of the gender difference in publication impact. The trend line (black dashed line) indicates a significant positive correlation (coefficient of determination ). In (B), we defined the gender difference in publication impact as the probability that female authors have larger h-index z-scores than male authors, as depicted in Fig. S13. The trend line (black dashed line) indicates a significant positive correlation (coefficient of determination
). Note that the values of the risk of academic career choice in (A) and (B) are different for each discipline because the coefficients in the linear regression are different. The data suggest that in disciplines where it is risky to pursue an academic career, female faculty have publications with higher impact than male faculty.