Trends of μ^ on career stage for the seven disciplines considered.

We divide each researcher’s chronologically-ordered publication records into three sets with equal number of publications (start, middle, and end) and fit the model to each set of publications to obtain μ^s, μ^m, and μ^e. We then used ordinary-least-squares to perform a linear regression on the time dependence of (μ^s,μ^m,μ^e). We then calculate the fraction of researchers whose μ exhibits a statistically significant dependence on career length, by performing a two-tailed significance test on the slope of the regression. We use a randomization test (1,000 samples), combined with a multiple hypothesis correction [50] (false discovery rate of 0.05) to calculate a p-value: for each researcher, we randomly re-order his or her publications, divide them into three sets with equal number of publications and fit the model to each set of publications, and calculate the new slope; we obtain a p-value by comparing the original slope of the fit with the distribution of the randomized slopes.Trends of μ^ on career stage for the seven disciplines considered.