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.

翻译：本研究量化了过去三十年间女性进入科学领域的趋势。通过结构化大数据，我们得以估算女性科学家在不同学科（STEMM领域共14个学科）及不同时期（1990-2023年）对科学发展的贡献。研究发现，STEMM科学这一统一领域内部呈现出分化：在一些学科中，女性科学家强有力地推动了学科发展；而在另一些学科中，女性的作用则微乎其微。目前有四个学科中，活跃发表的科学家中有50%为女性；另有五个学科中，青年科学家的女性比例超过50%。然而，仍有四个高度数学化的学科集群（数学、计算机科学、物理学和工程学），其科学发展的主要驱动力并非来自女性。科学家在其出版物中留下的数字痕迹，这些著作被收录于全球数据集，为大规模学术职业研究开辟了两个新维度：时间与性别。欧洲科学的增长伴随着女性科学家数量的增加，但存在显著的跨学科和跨代际差异。我们研究了来自32个欧洲国家及四个对照国家（美国、加拿大、澳大利亚和日本）的十个不同年龄队列中女性科学家的比例。研究样本涵盖1,740,985名科学家（其中女性科学家占39.40%）。本文还讨论了使用文献计量型结构化大数据的三个关键方法学挑战：性别判定、学术年龄判定及学科判定。