Abstract
While women are generally underrepresented in STEM fields, there are noticeable differences between fields. For instance, the gender ratio in biology is more balanced than in computer science. We were interested in how this difference is reflected in the interdisciplinary field of computational/quantitative biology. To this end, we examined the proportion of female authors in publications from the PubMed and arXiv databases. There are fewer female authors on research papers in computational biology, as compared to biology in general. This is true across authorship position, year, and journal impact factor. A comparison with arXiv shows that quantitative biology papers have a higher ratio of female authors than computer science papers, placing computational biology in between its two parent fields in terms of gender representation. Both in biology and in computational biology, a female last author increases the probability of other authors on the paper being female, pointing to a potential role of female PIs in influencing the gender balance.
Highlights
There is ample literature on the underrepresentation of women in STEM fields and the biases contributing to it
What about computational biology? As an interdisciplinary STEM field, would its gender balance be close to one of its “parent” fields, or in between the two? To investigate this question, we examined authorship data from databases of scholarly publications in biology, computational biology, and computer science
We found that computational biology lies in between computer science and biology, as far as female representation goes
Summary
There is ample literature on the underrepresentation of women in STEM fields and the biases contributing to it Those biases, though often subtle, are pervasive in several ways: they are often held and perpetuated by both men and women, and they are apparent across all aspects of academic and scientific practice. Articles authored by women are cited less frequently than articles authored by men [5, 14], which might in part be due to men citing their own work more often than women do [8] Inferring bias in these studies is difficult, since the cause of the disparity between male and female authorship cannot be readily determined. The way in which evidence for gender bias is received is in itself biased: Male scientists are less likely to accept studies that point to the existence of gender bias than are their female colleagues [11]
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