Women in Science: Measuring Participation in Europe Across Disciplines, Generations and Over Time

TL;DR

This study analyzes the participation of women in European science over three decades, revealing significant disciplinary and generational differences using large-scale bibliometric data.

Contribution

It provides a comprehensive quantitative analysis of women's contribution to science growth across disciplines, generations, and countries using structured big data.

Findings

Four disciplines have over 50% women scientists currently publishing.

Growth driven by women varies significantly across disciplines.

Large-scale data reveals cross-disciplinary and generational differences in women's participation.

Abstract

In this research, we quantify an inflow of women into science in the past three decades. Structured Big Data allow us to estimate the contribution of women scientists to the growth of science by disciplines (N = STEMM 14 disciplines) and over time (1990-2023). A monolithic segment of STEMM science emerges from this research as divided between the disciplines in which the growth was powerfully driven by women - and the disciplines in which the role of women was marginal. There are four disciplines in which 50% of currently publishing scientists are women; and five disciplines in which more than 50% of currently young scientists are women. But there is also a cluster of four highly mathematized disciplines (MATH, COMP, PHYS, and ENG) in which the growth of science is only marginally driven by women. Digital traces left by scientists in their publications indexed in global datasets open two new dimensions in large-scale academic profession studies: time and gender. The growth of science in Europe was accompanied by growth in the number of women scientists, but with powerful cross-disciplinary and cross-generational differentiations. We examined the share of women scientists coming from ten different age cohorts for 32 European and four comparator countries (the USA, Canada, Australia, and Japan). Our study sample was N = 1,740,985 scientists (including 39.40% women scientists). Three critical methodological challenges of using structured Big Data of the bibliometric type were discussed: gender determination, academic age determination, and discipline determination.