Science's greatest discoverers: a shift towards greater interdisciplinarity, top universities and older age

The study investigates the distinctive traits of the scientists responsible for major scientific breakthroughs, addressing a long-standing question in the sociology and history of science. Prior classic and contemporary work has profiled Nobel laureates and influential scientists, yet most studies have focused on limited samples by time, field, or country, limiting generalizability. This paper adopts a comprehensive, global approach to compile representative data on all Nobel-prize-winning and major non-Nobel-prize discoveries to characterize discoverers’ demographic, institutional, and economic features. The purpose is to identify patterns—such as education level and interdisciplinarity, institutional affiliation, age at discovery, gender, geography, and broader socioeconomic context—and to track how these vary across fields and over time. Understanding these features is important for informing hiring, funding, and policy decisions, for recognizing systemic closures or inequalities (e.g., declines in contributions from outside North America, persistent gender disparities), and for fostering environments that enable future major discoveries. The authors find that breakthrough science has transformed in recent decades toward greater interdisciplinary training, concentration in top universities, and older age at discovery, with implications for how scientific systems are organized and rewarded.

Classic work includes Prus (1873), Znaniecki (1923), Galton (1874), and especially Zuckerman’s Scientific Elite (1977), which profiled Nobel laureates’ backgrounds and social characteristics. Subsequent studies often examined narrow samples by period, field, or nation (e.g., Li et al., 2020; Chan & Torgler, 2015; Leroy, 2003; Schlagberger et al., 2016; Sherby, 2002; Thompson, 2012; Ye et al., 2013; Breit & Hirsch, 2004; Bjork et al., 2014), limiting broader claims. Research has assessed specific factors such as age (Wang & Barabási, 2021; Sinatra et al., 2016; Jones et al., 2014), education (Chan & Torgler, 2015), interdisciplinarity (Szell et al., 2018), gender (Zeng et al., 2016; Lunnemann et al., 2019), geography and mobility (Lepori et al., 2019; Danús et al., 2023; King, 2004; Scellato et al., 2017), institutional ranking (Schlagberger et al., 2016; Ioannidis et al., 2007; Krauss et al., 2023), and country conditions (King, 2004). This study extends the literature by jointly analyzing a comprehensive set of these factors across all Nobel and major non-Nobel discoverers over the history of science.

Scope and sample: The dataset includes all 533 Nobel-prize-winning scientific discoveries (1901–2022) and 228 additional major non-Nobel discoveries identified from seven cross-field science textbooks listing the greatest 100 scientists and their discoveries. After deduplication, the final sample comprises 761 major discoveries made by 982 discoverers. The non-Nobel set serves as an independent control/robustness check. Data sources: Core biographical and discovery attributes (age, education level, gender, birth and residence country) were compiled primarily from Encyclopaedia Britannica (2023) and official Nobel Prize documentation (2023), supplemented by five science encyclopedias and the seven textbooks. University ranking at discovery used QS World University Rankings (2021). Country income per capita and population used the Maddison Project Database (2018). Religious affiliation primarily used Sherby (2002) with additional publications and emails to living Nobelists. Year of discovery, institutional affiliation at discovery, and methods/instruments used were extracted from the discovery publication. Data structure and timing: Over two dozen variables were collected per discovery, totaling 20,000+ data points over 15 months. Data reflect the year of discovery (not award) to capture conditions influencing the discovery. The average Nobel award lag is 21 years, so award-year characteristics can differ substantially from discovery-year characteristics. Analysis: Descriptive statistics track evolution over time and across fields for demographic (age, gender, religion), institutional (university ranking, mobility), and economic (income, population) factors, and methodological tool use. Cautions are noted for historical university ranking comparisons due to limited historic ranking data. Robustness checks compare Nobel vs. non-Nobel findings across the same periods and fields.

• Education: Since 1600, 88% of all major discoveries were made by researchers with a PhD at the time of discovery; for Nobel-prize discoveries, 96%. Ten Nobel-prize discoveries (≈2%) were made by researchers with only a Bachelor’s degree. Since 2000, all discoveries were made by professors (with PhDs). Field variation: in medicine/biology all had MD/PhD but ~60% were professors; in chemistry, astronomy, and economics/social sciences over 75% were professors.
• Interdisciplinarity: 54% of Nobel-prize discoveries were made by scientists with 2+ degrees in different fields; 42% for major non-Nobel discoveries in the same period. Highest in medicine/biology (69%), lowest in physics (39%). Since 2000, over 70% of discoveries were made by scientists with two different degrees; by comparison, ~25% of US doctorate recipients had a master’s in a different field (NSF 2021).
• Age: The modal “golden” range is 35–45 years; 50% of Nobel laureates made their discovery in this range. Average age at discovery is 39 (median 38) and at award 60. Only 7% (Nobel) and 15% (non-Nobel) of discoveries were made after age 50; 1% and 3% after age 60, respectively. Average age at discovery increased: 38 (1901–1950), 40 (1951–2000), 50 (2001–2022). Economists are youngest at discovery (avg 36) and wait longest for the prize (avg 31-year lag). The discovery–award gap has increased over time across fields.
• Institutions: 30% of all Nobel and 30% of major non-Nobel discoverers were at a top-25 university at discovery; expanding to top-50 yields 38% (Nobel) and 34% (non-Nobel). Only ~0.6% of researchers globally are at top-25 universities. Astronomy and economics/social sciences show the highest concentration at top-50 institutions and ~15% post-discovery moves into top-50 prior to award; 55% changed institutions between discovery and award. Five universities (Cambridge, Harvard, Berkeley, Chicago, Columbia) account for 16% of Nobel discoveries; top 10 account for 25%.
• Gender: Women constitute 5% of all major-discovery scientists and 3% of Nobel laureates. By field among Nobel discoveries: physics 2% female, astronomy 6%, medicine 7%. Over half of all female Nobelists received the prize since 2000.
• Collaboration crediting: Nobel awards average 1.4 laureates per discovery, despite discoveries typically being collective and methodologically cumulative, highlighting a mismatch between practice and recognition.
• Geography and mobility: Pre-1900, >90% of discoveries were made by scientists residing in Europe. Between 1900–1999 Europe’s share fell to 41%, and to about one-third since 2000. Since 2000, East Asia accounts for ~6% of discoverers. Economics is most concentrated in North America. Germans produced ~24% of Nobel discoveries up to 1930 (UK ~16%). From 2000–2022, 61% of discoveries were made by scientists living in North America, but over half were born outside North America. Net mobility flows show movement into North America before award; Europe also attracted some flows.
• Religion: Share religious declined from 100% (1600s) to 72% (late 20th century) and 59% (2000–2022). Astronomers are about twice as likely to be atheist/agnostic compared to other fields.
• Economic context and tools: For Nobel discoveries since 1975, scientists in the bottom two income quintiles (among countries where discoverers resided) did not systematically use fewer common methods/instruments (e.g., computers, centrifuges, electrophoresis, electron microscopes) than those in richer quintiles. Once a minimum income threshold is met, constraints on using common scientific tools appear limited.

By assembling a comprehensive dataset of all Nobel-prize and major non-Nobel-prize discoveries, the study directly addresses what distinguishes scientists who make the greatest breakthroughs. The results show that broad, interdisciplinary training is strongly associated with major discoveries, likely by expanding methodological repertoires and enabling novel combinations across fields. The increasing concentration of discoverers at top-ranked universities suggests institutional environments—with access to advanced facilities, resources, and elite networks—facilitate high-impact work, though many breakthroughs still rely on widely accessible, low-cost methods. Age patterns indicate that peak discovery propensity occurs between 35 and 45, reflecting the time required to master growing bodies of knowledge and complex methods. The rising discovery–award lag underscores the time needed for recognition and validation of breakthroughs. Persistent gender disparities and geographic concentration in North America reveal systemic closures, even as substantial international mobility fuels US-based discovery. Declining religiosity among discoverers and field differences (e.g., astronomy) reflect broader cultural shifts in explanations of natural phenomena. Overall, the findings illuminate how training, institutional context, life-cycle timing, and broader social conditions interplay to shape who makes major discoveries and when, informing policies to foster inclusive, high-impact science.

The study profiles the scientists behind major discoveries and documents structural shifts: increasing interdisciplinarity among discoverers, growing concentration at top universities, and older ages at discovery relative to past eras. Approximately half of Nobel discoveries are made by researchers with degrees in multiple fields; around one-third of all (Nobel and non-Nobel) discoveries are made at top-25 institutions; and very few discoveries occur after age 50. Policy implications include fostering interdisciplinary training, reconsidering disciplinary boundaries in journals and awards, and potentially targeting some innovation-oriented grants to the 35–45 age range. The authors argue for reforming the Nobel structure to better recognize teams and the methodological–experimental–theoretical constellation underpinning discoveries. To promote a more inclusive and globally distributed scientific system, incentives should support underrepresented groups (e.g., women, researchers outside North America) and interdisciplinary work that bridges fields. Future work will detail drivers of new discoveries across fields; psychological traits could not be assessed here but may be important.

• Causality is not established; analyses are descriptive.
• University ranking comparisons over earlier centuries are limited; QS 2021 rankings are used as a common reference and should be interpreted cautiously for historical cases.
• Some variables (e.g., religious affiliation) rely on secondary sources and, for living Nobelists, self-reports via email.
• Psychological traits (e.g., motivation, drive) could not be systematically measured.
• Data reflect conditions at the year of discovery (not award), which improves relevance but may differ from commonly reported award-year characteristics.
• Instrument usage and economic analyses post-1975 are constrained to periods with comparable data; generalizability to earlier eras of science is limited.