# The structure and evolution of scholarly interests from antiquity to the eighteenth century

Charles de Dampierre

Institut Jean Nicod, Département d'études cognitives,
ENS, EHESS, PSL University, CNRS
charlesdedampierre@gmail.com
ORCID: 0009-0008-2687-7210

Hugo Mercier

Institut Jean Nicod, Département d'études cognitives,
ENS, EHESS, PSL University, CNRS
hugo.mercier@gmail.com
ORCID: 0000-0002-0575-7913

## Abstract

Human societies reap tremendous benefits from acquiring scientific knowledge, which provides an unparalleled understanding of our universe, along with economic growth and increased life expectancy. Science is fueled by scientists' curiosity. To better understand this curiosity and its structure, we rely on a database, Cultura 1.0, which lists all scholars found in online catalogs, in order to look for patterns in their interests. To limit the effects of Western influence, we circumscribe our study to scholars born before 1700. Since many scholars have more than one interest (e.g. mathematician and astronomer), we can construct a network of co-occurrences of interests with the 2,317 'polymaths,' defined here as scholars who have at least two interests (out of the 13,556 scholars of the database). While attempting to account for the overrepresentation of European scholars, our results suggest some above chance commonalities in the co-occurrences of interests, suggesting an underlying structure in scholars' interests and curiosity. For instance, in Europe and in the rest of the world, philosophy and mathematics co-occur relatively frequently. Clustering the network of interests, three broad domains emerge: Human (e.g. history), Natural (e.g. zoology), and Abstract (e.g. mathematics). We find that every world region in the database (e.g. ancient Mediterranean) contains a similar portion of scholars devoted to each of these domains, and that the Human and Natural domains appear substitutable for each other at the margin. We believe that our findings shed light on long term historical trends in scholarly interests, and also help us better understand the structure of human curiosity.

## Keywords

Science, Curiosity, Networks, History

## Introduction

Science plays a momentous role in modern societies, providing an unrivaled understanding of the world, extending life expectancy (Cutler et al., 2006), and enabling economic growth (Jones, 2002). If these successes require substantial financial investment, they are also fueled by scientists' intrinsic motivation. Epistemic curiosity—the appetite for knowledge for its own sake—is one of these motivations. If progress has been made in understanding the fundamental mechanisms of epistemic curiosity, very little is known about its structure: Are there patterns in the topics triggering people's curiosity? Is someone interested in history also more likely to be interested in anthropology, or in philosophy? Is a mathematician more likely to also be a philosopher, or a political scientist? A better knowledge of the structure of scientific curiosity could help us understand curiosity in general, and what drives scientists. We briefly
review work on the psychology of curiosity, which does not shed much light on the question at hand. We then analyze
a large dataset of scholars (N = 13,556, born between 800 BCE and 1700 CE, focusing on 2,317 polymaths, defined
here as scholars who have at least two interests), which suggests that their interests might form a relatively stable
network. Finally, we speculate on the explanation for the structure of curiosity observed.

Sociologists and philosophers have long emphasized that scientists, and scholars more generally, are not motivated purely by financial incentives (Kuhn, 1994; Merton, 1979). Throughout history, scholars have made substantial material sacrifices, including risking their lives, in order to pursue their research. For instance, when the Inquisition started curtailing scholarship in 16th century Italy, instead of switching careers, many scholars preferred a risky migration abroad where they could pursue their studies (Drago et al., 2022). Less dramatically, but still consequentially, contemporary scientists are willing to accept significant pay cuts in order to be able to publish their articles, and to choose their topics (Stern, 2004).

What are the non-pecuniary motivations of scientists and scholars? Prestige has been pointed out as an important factor. For instance, it has been suggested that the priority rule, by which the first discoverer gets all the credit, effectively motivates scientists through the promise of prestige (Karabegovic et al., 2024; Merton, 1957; Partha & David, 1994; Strevens, 2003). The awarding of a prestigious prize to a scientific domain is associated with a substantial growth in attractiveness for that domain (Jin et al., 2021). If the importance of prestige is hard to deny, specialists of the psychology of science point out that curiosity also plays a crucial role (Feist, 2006; Feist & Gorman, 2012). Moreover, the awarding of prestige is also likely influenced by curiosity, as prestige should be granted most easily to scientists who answer questions the community is curious about.

In the psychology literature, there are two main definitions of curiosity. One is very broad, encompassing any drive to acquire information (e.g., Pathak et al., 2017). Most commonly, however, curiosity is defined more precisely as a drive to acquire information that doesn't serve any immediate instrumental goal (such that, e.g., looking for the address of a restaurant we are going to would not be defined as curiosity) (e.g. FitzGibbon et al., 2020). It is this narrow meaning that we use here. Multiple studies (including in non-human animals, see, e.g. Bromberg-Martin & Hikosaka, 2009) have demonstrated the motivating power of curiosity. Participants in experiments are willing to wait (Kang et al., 2009) and to pay (Rodriguez Cabrero et al., 2019) in order to obtain information with no instrumental value. Researchers have developed models to better understand the features of information that trigger curiosity—e.g. whether we are more interested in information about novel objects, or about already known objects (Dubey & Griffiths, 2020)— and they have probed the neural bases of the desire for information (Kang et al., 2009; Meliss et al., 2022).

An important dimension of research on curiosity consists in drawing distinctions between different forms of curiosity, distinguishing for instance perceptual (attention to novel stimuli), sensory (experience-seeking), and epistemic (knowledge seeking) curiosity, with the latter being the most relevant for science (Collins et al., 2014; Litman, 2005; Litman & Spielberger, 2003; for a related distinction, see Reio Jr., Petrosko, Wiswell, & Thongsukmag, 2006; for a review of the many different measures of curiosity, see Wagstaff, Flores, Ahmed, & Villanueva, 2021). Within epistemic curiosity, further distinctions have been drawn, for instance between its diverse and specific components (Litman & Spielberger, 2003), and between its interest (liking information) and deprivation (wanting information) components (Litman, 2008; Litman et al., 2010). Researchers have also developed scales for specific forms of curiosity, from morbid curiosity (Scrivner, 2021), to curiosity for sports (Park et al., 2010). Most relevant here, scales have also been developed for science curiosity (Motta et al., 2019), and curiosity for mathematics (Peterson, 2019; Ufer et al 2016). These scales have established the existence of robust interpersonal differences (e.g. in the trait of epistemic curiosity Litman, 2008; Litman et al., 2010; Litman & Spielberger, 2003; Piotrowski et al., 2014, on extreme forms of curiosity, see DeLoache, Simcock, & Macari, 2007).

It has been suggested that curiosity should be distinguished from interests, defined as “disposition[s] or predisposition[s] to engage and re-engage with contents of a specific domain” (Ainley, 2019, p.789). Since other
scholars have investigated interests under the label of curiosity (see the scales for specific domains of curiosity above for instance), we will also use the terms curiosity and interest interchangeably here. Relevant research has been conducted under the label of ‘interest,’ in particular research on vocational interests: “the degree to which individuals prefer certain career choices or activities/behaviors which may be common for various positions” (Schermer, 2016, p.1). We then look for a structure in these interests. Practicing a science, like any other occupation, involves interest in different activities, not merely different informational contents: reading about zoology vs. reading about political science involves different contents, but a similar activity (reading), while practicing zoology vs. practicing political science involves different contents as well as different activities. The literature on vocational interests is thus relevant. The structure of people’s vocational interests has been extensively mapped, often by relying on the Strong Interest Inventory (e.g., Einarsdóttir, 2000). However, the most common topology of interest types—which distinguishes Realistic, Investigative, Artistic, Social, Enterprising, and Conventional interest types (Holland, 1997)—appears too broad and, to some extent, orthogonal to the more specific question of scholarly interests. Still, we note that studies comparing different populations suggest a common structure in these interests (Fouad, 2002; Fouad & Dancer, 1992; Kantamneni & Fouad, 2011).

Several strands of research show that scholars are attached to their domain of specialization, which is suggestive of curiosity. One study revealed that in order to entice scientists to work on a topic not of their choosing, significant funding has to be offered (Myers, 2020). A study on the effect of censorship in Counter-Reformation Italy showed that many scholars were forced to abandon their favorite domain of study and, as a result, produced less interesting material (Blasutto & de la Croix, 2023). Research on the patterns of scholars’ interests could thus give us some insight into the structure of scholarly curiosity. For instance, if a scholar conducts research in mathematics and in biology, it suggests that they are curious about both of these domains. We do not believe, by contrast, that the number of domains of specialization is indicative of the amount of curiosity (being curious about more things doesn’t necessarily mean being more curious on the whole).

A previous study has mapped the research space of contemporary science, creating networks of subfields of science whose proximity depends on the odds that scientists publish in both fields (Guevara et al., 2016; Hou et al., 2008; Miao et al., 2022). We would argue that this research space is partly a result of the structure of scientists’ curiosity. For instance, the research space can be used to predict which new fields a scientist is going to explore. However, the research space could also be highly constrained by institutional factors, such as the structure of modern universities (all the data in that study comes from 1971 and 2014), and the large knowledge base now required to contribute to a field (Fortunato et al., 2018), which contributes to the increased specialization of scientists (Wray, 2005), and which might constrain scientists’ natural curiosity.

To have a potentially more direct window into scholars’ curiosity, we turn to historical data: a database of all scholars who belong to at least one online catalog (e.g. Library of Congress, Freebase ID, Encyclopedia of China ID) and have an entry in Wikidata (Erxleben et al., 2014). We restrict the database to scholars born before 1700, for several reasons. First, the Western university system mostly crystallized and shaped universities worldwide after the end of this period, which reduces the potentially uniformizing effect of this institutional constraint (Perkin, 1991). Second, conducting research has become increasingly expensive, making it potentially more difficult for people to pursue their interests (Stephan, 2012). This effect is particularly distorting since the increase in costs has affected some domains of research (e.g. physics) much more than others (e.g. philosophy). Research conducted when the costs of research were lower, and more uniform across fields, might thus more directly reflect scholars’ curiosity. Third, prior to the second industrial revolution, and the advent of modern medicine in the late 19th century, the practical impact of research on economic growth and on life expectancy was greatly reduced (Mokyr & Voth, 2010). This means that scholars might have felt freer to pursue their interests, instead of being compelled, for personal or institutional reasons, to favor more applied research (although, obviously, past scholars might also have believed their research would have practical consequences; on the importance of applications for current research orientations, see Miao et al., 2022). Finally, as noted above, most contemporary scholars have little choice but to specialize. Such requirements were weaker when
the stock of available knowledge was vastly smaller than it is now, making scholars freer to pursue a variety of interests if that suited their tastes. Given the time range, we talk about scholars rather than scientists, as our sample includes interests such as philosophy. Even if we believe that there is a better match between scholars' interests and their curiosity in the present dataset than in datasets of more recent scholars, the mapping is far from perfect, as scholars have always had constraints on the domains they can actively pursue. However, we believe that this imperfect mapping will result in noise rather than in systematic distortions, and thus that the size of our dataset can to some extent compensate for it.

Even if our dataset of historical scholars has, arguably, some advantages, it might raise the question of its relevance to studies of curiosity in contemporary populations. First, we note that, by contrast with studies of curiosity that have tended to focus on so-called WEIRD populations (populations in Western, Educated, Industrialized, Rich, Democratic countries, see Henrich et al., 2010, for some exceptions, see, e.g. Bluemke et al., 2024; Fouad, 2002), our dataset comprises scholars from very different cultural backgrounds, and we attempt to compensate for the over-representation of European scholars. Second, the current analyses should be able to either reveal, or fail to reveal, common patterns in curiosity. If there are patterns across such a wide range of societies in time and space, it will be quite reasonable to believe that the same patterns are likely to be found in contemporary societies. If, by contrast, the current analyses fail to reveal any patterns, this will suggest that any pattern found in (mostly WEIRD) contemporary societies should be interpreted as likely reflecting local contextual factors rather than more universal mechanisms. Either way, our results would be relevant to the study of curiosity in contemporary societies. Finally, irrespective of the application to contemporary populations, we believe that the scholars' interests we study constitute a kind of 'cognitive fossil' that can give us some insight into the minds of past populations, which is intrinsically interesting (Baumard et al., 2024).

After briefly explaining how the sample was constructed, we turn to the main question of interest: is there a structure in scholarly curiosity through history? To this end, we rely on the scholars who had more than one interest, conducting a network analysis to show how these interests tend to co-occur (i.e. is a scholar who specializes in mathematics also more likely to specialize in philosophy or in zoology). To balance the overrepresentation of European scholars in the database, we create a network which gives equal weight to scholars from the rest of the world. We also show that, although regions or time periods with smaller numbers of scholars in the database appear to deviate from this network, these deviations are lower than those expected by chance. Finally, we show that across world regions, and across time periods, scholars divide their interest for three broad domains of science (human, natural, and abstract) in a mostly similar manner. In conclusion, we speculate on the possible origins of these robust patterns.

## Methods

**Extracting scholars from authoritative sources.** We used the Cultura 1.0 database (de Dampierre, 2023). This Database contains information about cultural producers in international and national online catalogs (e.g., Library of Congress, VIAF, GND). Although the database contains up to scholars born in 1850, we restrict the current analyses to those born before 1700, for reasons exposed above. The database contains meta-information such as nationality, gender, date of birth and, crucially, an ontology of occupations (Appendix Table S1). Each ontology (for example the creation of a category such as Philosopher or Geographer) must be validated by the Wikidata community. The Wikidata ontology system consists of a hierarchy of categories and sub-categories (Appendix Figure S1). The Cultura 1.0 database contains cultural producers who have at least one occupation (feature P106 in the Wikidata ontology) that is a subclass (P279) of Scientist (Q901), Writer (Q36180), or Artist (Q483501). We focused here on the Scientist ontology (see below for more details). The database is built around geographical areas that have historically contained a sufficient number of scholars, chiefly in Asia, the Middle-East and Northern Africa, and Europe. This excludes areas in which the historical tradition of scholarship, as found in modern databases, is too sparse, in particular the Americas, Sub-Saharan Africa, and Oceania. To define areas, the database mostly relies on linguistic areas, such as those based
on Arabic, Persian, Chinese, Sanskrit, or Italian. These linguistic areas evolve over time in accordance with the geographical shifts in language distribution. For instance, during the Hellenistic and Roman eras, Egypt is categorized within the Greek cultural sphere, but not during the Arab period. Areas can be aggregated to form regions, allowing for analyses with larger numbers of scholars in each region (see Appendix Table S2 for the list of (modern) countries within each area, and of areas within each region). If an individual is born in a location associated with a particular area, their contributions are attributed to that area. The final database comprises 13,556 scholars (scholars with death or birth date, and a place of birth or death, born before 1700) of which 11,386 (84%) are Europeans (defined here as Europe after 500 CE, excluding ancient Greece and Rome, see below and Appendix Table S2).

**Cleaning up procedure of occupations in Cultura 1.0.** All the scholars in the present database possess at least one occupation that is a subcategory of Scientist, but many also have non-scientific occupations, leading to a total of 273 occupations. Of the occupations that were not subcategories of Scientist, we only included Philosopher and Theologian, as we deemed those to also be scholars, by comparison with, for instance, Composer or Explorer (see Appendix Table S1). Crucially for the analyses below, we needed occupational categories that were not subordinate or superordinate categories of other categories, as subcategories would have created artificial clusters (all Myrmecologists are Zoologists, who are all Biologists, which makes the fact that an individual has all three occupations uninformative). Overly specialized categories (e.g. Myrmecologist) were transformed into their superordinate level (e.g. Zoologist). In the case of overly broad categories, such as Biologist or Naturalist, we proceeded as follows. When accompanied by another, more specific, occupation, such as Zoologist we only retained this more narrow category. In cases where Naturalist and Biologist occurred together, we retained Naturalist. For scholars who were identified solely as Biologist or Naturalist, we imputed missing data by examining the distribution of the top co-occurring occupations. For instance, 47% of Biologists for whom a narrower category was specified were also Zoologists and 36% were also Botanists. We then assigned 36% of scholars who were identified as Biologist, but not also as a subcategory, to the occupation of Botanist. The cleaning up process reduced the number of occupations from 273 to 25 (see Table 1 for the final list of interests and Appendix Table S3 for the number of individuals per occupation per region). The final dataset contains 13,556 scholars among which 2,317 polymaths (scholars with two or more interests) and 11,239 specialists (scholars with only one interest).
<table><caption>**Table 1.** Number of scholars by interest in Europe (defined as European countries after 500 CE) and the rest of the world (including European countries before 500 CE). The number of polymaths is indicated in brackets. Note that the polymaths appear in at least two categories of interests. Total is the number of individual specialists and polymaths.</caption><thead><tr><th>Interest</th><th>Europe</th><th>Non-Europe</th><th>Total</th></tr></thead><tbody><tr><td>Theologian</td><td>4676 (768)</td><td>259 (65)</td><td>4935 (833)</td></tr><tr><td>Historian</td><td>2692 (548)</td><td>692 (141)</td><td>3384 (689)</td></tr><tr><td>Philosopher</td><td>1514 (569)</td><td>863 (220)</td><td>2377 (789)</td></tr><tr><td>Mathematician</td><td>1171 (636)</td><td>277 (201)</td><td>1448 (837)</td></tr><tr><td>Astronomer</td><td>653 (255)</td><td>246 (183)</td><td>899 (438)</td></tr><tr><td>Geographer</td><td>669 (425)</td><td>103 (51)</td><td>772 (476)</td></tr><tr><td>Linguist</td><td>425 (145)</td><td>157 (44)</td><td>582 (189)</td></tr><tr><td>Botanist</td><td>466 (150)</td><td>42 (22)</td><td>508 (172)</td></tr><tr><td>Musicologist</td><td>277 (60)</td><td>36 (27)</td><td>313 (87)</td></tr><tr><td>Physicist</td><td>283 (206)</td><td>28 (25)</td><td>311 (231)</td></tr><tr><td>Anatomist</td><td>246 (68)</td><td>7 (4)</td><td>253 (72)</td></tr><tr><td>Zoologist</td><td>196 (109)</td><td>11 (9)</td><td>207 (118)</td></tr><tr><td>Chemist</td><td>164 (77)</td><td>13 (9)</td><td>177 (86)</td></tr><tr><td>Archeologist</td><td>143 (54)</td><td>4 (4)</td><td>147 (58)</td></tr><tr><td>Economist</td><td>136 (39)</td><td>8 (4)</td><td>144 (43)</td></tr><tr><td>Geologist</td><td>54 (34)</td><td>11 (4)</td><td>65 (35)</td></tr><tr><td>Anthropologist</td><td>46 (36)</td><td>8 (7)</td><td>54 (43)</td></tr><tr><td>Logician</td><td>15 (11)</td><td>6 (4)</td><td>21 (15)</td></tr><tr><td>Political scientist</td><td>15 (15)</td><td>6 (6)</td><td>21 (21)</td></tr><tr><td>Meteorologist</td><td>17 (14)</td><td>3 (3)</td><td>20 (17)</td></tr><tr><td>Art theorist</td><td>17 (17)</td><td>1 (1)</td><td>18 (18)</td></tr><tr><td>Pharmacologist</td><td>13 (8)</td><td>3 (2)</td><td>16 (10)</td></tr><tr><td>Paleontologist</td><td>12 (9)</td><td>0 (0)</td><td>12 (9)</td></tr><tr><td>Sociologist</td><td>7 (4)</td><td>2 (2)</td><td>9 (6)</td></tr><tr><td>Epidemiologist</td><td>3 (3)</td><td>1 (1)</td><td>4 (4)</td></tr><tr><td><strong>Total</strong></td><td>11386 (1888)</td><td>2170 (429)</td><td>13556 (2317)</td></tr></tbody></table>

**Creation of network of interests.** In order to understand the structure of interest of scholars i.e. what interest tends to co-occur more often than others, we use network algorithms (for related uses, see Miao et al, 2022; Guevara et al 2016; Fraiberger et al., 2018). The nodes are the interests and the edges are the number of times scholars possess both interests in Wikidata. By grouping interests in networks of co-occurrence, we construct groups of interests that may correspond to domains of curiosity. To construct this network, we can only rely on polymaths, which represent 17% of the overall dataset (2,317 scholars) (see Appendix Table S3 for a detailed breakdown of scholars per region). In constructing networks of scholarly interest, we initially confirmed that the associations between scholars' interests were not random by performing a chi-squared test on the global matrix of co-interests network (Chi-squared = 5627, p < 0.001; see, e.g. Kim et al., 2007).

To filter rare occurrences that might reflect noise, we only kept the top three edges for each node. Edge filtering is done to filter noise from Wikidata (some associations can be the consequences of mistakes by contributors) and to help better represent the data visually. By doing so, we ensure that we only keep the most significant relationships between interests. We chose three as a balanced approach between robustness and granularity. Using too many neighboring edges would add excessive noise, while too few would eliminate some relevant co-interests. We decided
on a local number of neighbours rather than a global number of neighbours to avoid removing less frequent interests. We use top neighbors rather than a fixed threshold because the latter would vary significantly across regions, influenced by the differing numbers of scholars in each region. Overall, the structures of networks are rather similar irrespective of the number of edges used, from one to 10 (see Appendix Figure S2). To visualize the networks, we used the ipysigma Python module (Plique, 2023) (see Figure 1). The size of the nodes represents the overall prominence of interests with the dataset (scaled from 1 to 10). The edges represent the number of co-occurrences between interests (scaled from 1 to 10). Using that method, we created networks of interests for each region for two time steps (see Appendix Figure S3).

In order to get a clear, unique representative network of the interest of scholars, we must address the overrepresentation of modern European scholars in our dataset (see Appendix Figure S4): early modern European scholars dominate the database (82% born after 1500). Due to the relative ease with which early modern European scholars could communicate with each other and travel from one country to the next, data from these regions is less independent than that from other regions. As a result, for the main analyses, those European regions are treated as a single European entity. For convenience, we call this entity Europe, even though it excludes ancient European scholars from classical Greece and Rome. If Europe is defined that way, it becomes by far the most populated region, with approximately six times as many scholars as all the other regions combined. To create a more balanced network, we generated 100 random selections of European scholars to match with the number of polymaths from the other regions (N=429 and combined them to reach 100 datasets of 858 scholars (the non-European scholars are always the same by construction and are associated with a random sample of European scholars). We then created 100 networks of interest and combined them by isolating each co-occurrence matrix and computing the average pairwise occurrence between interests to create a unified network, creating a Global Network (see Figure 1a) (see Appendix Figure S5 for a more detailed description of the method). This approach was suitable given that we are comparing networks with identical subsets of interests; interests absent in one network were excluded from the comparison network. We also created networks for non-European scholars only (N=429 polymaths, Figure 1b), and for European scholars only (N=1888 polymaths, Figure 1c).

**Broad-grained network comparisons.** To test how much the structure of scholarly interests varies between different large geographical zones, we compared the structures of the European, non-European, and Global networks using the Weighted Jaccard similarity (WJAC) (Tantardini et al., 2019). The WJAC quantifies the similarity between two sets of nodes (interests) based on the intersection and union of their weighted connections (interests' co-occurrences in Wikidata profiles). We standardized the weights (representing the number of co-occurrences between interests) across the various networks, scaling them to range between 0 and 1 to account for the difference in the number of scholars across the regions (there are six times more scholars in Europe than in the non-European regions). When two networks have different sets of nodes, we keep the intersection of nodes.

**Fine-grained network comparisons.** The broad network comparison above was complemented by a finer grained analysis in which the networks of areas (which compose the regions), as well as the networks of different periods are compared. We created a network for every area and every timestep (of 100 years, throughout the world) and compared them with the Global Network. Any difference we might observe, however, could reflect noise given the small size of many of the samples. To ignore the noisiest data points, we only include areas with at least 10 polymaths in the following analyses and in the figures. Moreover, to test whether low WJACs are higher than chance, we compared the WJAC between the networks of the areas and periods and the Global Network, on the one hand, to the WJAC between randomized versions of these same networks and the Global Network, on the other hand. The randomized versions were generated by creating an equal number of scholars, with an equal number of interests, but with their interests drawn at random (from the same pool as those of the actual scholars). For each network comparison, 20 simulations were performed and the average value of the WJAC was computed.
**Variations of interest in space and time.** Finally, we wanted to test whether the content of the interests—and not the structure of the network—varied with time and space. However, considering the interests one by one—for instance asking if there are more scholars interested in mathematics in different places—would introduce too much noise. Instead, we used the Global Network to delineate broad clusters of interest. More specifically, we used the Unsupervised Leiden Method with 100 iterations for cluster stabilization (Traag et al., 2019) on our network to extract the clusters of co-occurrence. As explained below, this yielded three main clusters, and it was thus possible to count how many scholars belonged to each. Polymaths were counted in each cluster one of their interests belongs to, as a proportion of the number of their interests. Specialists were counted in the cluster of their interest. We could then compare the relative share of scholars belonging to those clusters of interests in time and space. For comparative analysis, we employed relative rather than objective metrics due to network structure characteristics. While the overall networks demonstrate stability, cluster composition exhibits substantial variability, particularly for peripheral interests that may shift between clusters depending on the specific clustering algorithm applied. Moreover, the exact number of clusters could vary, and some of the three large clusters observed could be split into smaller clusters (finding the best number of clusters is typically difficult, see Jung et al., 2003; Mur et al., 2016; Zhou et al., 2016). As a result, we do not believe that the absolute share of scholars in each cluster is very informative. We then examined whether the proportion of scholars in each domain remained consistent across regions and time periods by comparing each region's domain-specific scholar percentages to the average distribution (and the same would apply if the clusters were subdivided in smaller clusters).

## Results

**Network of interests.** Network analysis reveals consistent co-occurrence patterns of scholarly interests across regions (see Figure 1). The Global Network analysis (the network of reference) reveals a distinct structural organization of interests. Central positions are occupied by broad interests such as Philosophy and Astronomy, which co-occur with numerous other interests, indicating their foundational role in knowledge networks. Conversely, more specialized interests such as Logic, Musicology, Pharmacology, and Sociology appear at the network periphery, demonstrating more restricted co-occurrence patterns. Some relationships appear robust, such as that between Philosophy and Mathematics for instance, represented in our dataset with scholars such as Nicolaus Copernicus, Blaise Pascal, Giordano Bruno, and Giuseppe Scala in Europe, Al-Khwarizmi and Omar Khayyám in MENA, and Xu Yue in China.

**Broad-grained network comparisons.** The goal of this analysis is to test whether the large-scale networks—in particular those of Europe and of the rest of the world—are similar to each other, and to the Global Network. To establish meaningful similarity thresholds for network comparison, we conducted an intra-regional analysis of scholarly interest structures. We compared four distinct, randomly drawn samples of scholars within the German World (Europe's largest represented region, N=942 scholars per sample) and the Arab world (the largest non-European region, N=144 scholars per sample). The self-similarity of scholars from the same regions provides a useful high benchmark against which to compare other measures of similarity. Analysis revealed WJAC values of 0.58[0.05] between German scholar samples and 0.43[0.06] between Arab World scholar samples. Based on these reference points, we established that WJAC values between 0.4 and 1 (identical networks) indicate substantial network similarity. The European and non-European networks were, by design, quite similar to the Global Network (both WJACs = 0.63), but they were also quite similar to each other (WJAC = 0.40). To provide a medium-grained analysis, we also present a comparison of the networks of all the regions to each other (Appendix Figure S6), which mostly suggests geographical proximity effects, with a cluster of similar interests centered around Western Europe.

A)
<figure><img src="image_1.png" /></figure>

B)
<figure><img src="image_2.png" /></figure>

C)
<figure><img src="image_3.png" /><figcaption>**Figure 1.** A. Global network of co-occurrence of interests within the same individual for scholars born before 1700 CE. The network represents a composite of 100 samples of Europeans (i.e. individuals born in Europe after 500 CE), as well as all the non-European scholars from the Cultura 1.0 database (de Dampierre, 2023). B. Network constructed only on the basis of the non-European scholars. C. Network constructed only on the basis of the European scholars.</figcaption></figure>

**Finer-grained network comparisons.** The goal of this analysis is to appreciate the degree to which smaller-sized networks, from different areas or centuries differ from the Global Network. Networks from the different areas demonstrate significantly higher similarity to the Global Network (average WJAC = 0.45 [Standard Deviation:0.11]) than simulated random networks (average WJAC = 0.05 [0.02], t = -13.15, p < 0.001). Similarly, temporal networks (by century) also display greater resemblance to the Global Network (average WJAC = 0.45 [0.10]) than their simulated counterparts (WJAC = 0.09 [0.049], t = -14.57, p < 0.0001), as illustrated in Figures 2A and 2B. The networks of scholars born before 1600 and of those born between 1600 and 1700 can be found in Appendix Figure S3, displaying the strong similarity between these two networks.
<figure><img src="image_4.png" /><figcaption>B)</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Measures of similarity (WJAC, higher values indicate more similarity) between the Global Network and, blue lines, the network of every area (A) and time period (100 years starting with the date labeled) (B), and, green lines, the scholars from the same population with randomized interests (average of 20 draws with 95% confidence interval).</figcaption></figure>

**Variations of interest in space and time.** We found three main clusters of interests (see Appendix Table S4 for the complete list of interests in each cluster). The three clusters broadly correspond to three domains of knowledge: Human Domain (three most common interests: Theologian, Philosopher, Historian), Natural Domain (three most common interests: Geographer, Botanist, Anatomist), and Abstract Domain (three most common interests: Astronomer, Mathematician, Musicologist). We find that, depending on the clustering parameters, Philosopher can also be assigned to the Abstract Sciences cluster, justifying a relative approach (i.e. comparing the share of scholars belonging to each cluster across places and times) rather than an absolute approach. We observe modest variations at the regional level (Figure 3), with at most a 10% difference from the average shares of the three domains (Standard Deviation (SD) Abstract Domain = 2.47; Human Domain = 6.03; Natural Domain = 5.84), but these variations are larger than those expected at chance (SD based on the average of 10 simulations; scholars are randomly drawn from the different domains: Abstract Domain = 0.64 [0.27]; Human Domain = 0.91 [0.25]; Natural Domain = 0.81 [0.32]). We also find smaller, but still higher than chance variation in time (SD, Abstract Domain = 2.99; Human Domain = 4.31; Natural Domain = 3.18; SD based on the average of 10 simulations: Abstract Domain = 1.54[0.67]; Human Domain = 2.17[0.75]; Natural Domain = 2.21[0.97]).

A)
B)

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

**Figure 3.** Share of scholars in each of the three broad domains of interests (Abstract, Human, and Natural), across regions (A) and time (B). The zero line represents the average share of scholars with at least one interest in this domain, across the whole sample. This means for instance that, in the Ancient Mediterranean, there were more scholars interested in the Human Domain than the average of all the regions. Regarding the timeline, we chose a timestep of 200 years to ensure a sufficient number of individuals at every step and make the quantities of individuals across time comparable to those across regions. For the same reason, we start the timeline at 600 BCE.
Discussion

Using a database containing 2,317 polymaths (scholars with at least two interests) born before 1700, we generated a network of co-interests which might reveal the structure of scholarly interests. In order to compensate for the large number of European scholars (defined here as having been born in Europe after 500 CE) in the database, we created a Global Network which contained all the non-European scholars, as well as an equal number of European scholars (Figure 1A). Using a measure of network similarity (Weighted Jaccard, WJAC), we found that the Global Network was (unsurprisingly) similar to both a network created only with European and with non-European scholars, but also that the latter two were reasonably similar to each other. We also found that the networks created using data from each geographical area (e.g. MENA) or time period (e.g. 13th century) were somewhat similar to the Global Network (average WJAC, for areas: 0.45[0.11]; for time periods: 0.45[0.10]), similarities that were much greater than those expected at chance. This suggests that, although there exists interesting variation in time and place, there might be an underlying universal or quasi-universal (see, Norenzayan & Heine, 2005) structure of scholarly interests. Visual inspection of the networks, as well as cluster analyses, suggests that the network of co-interests can be divided in different domains: Human (e.g. history), Natural (e.g. zoology), and Abstract (e.g. mathematics). Since some interests could be classified in different domains (e.g. philosophy could fall within the Human or the Abstract Domain), we do not believe that much can be inferred from the absolute share of scholars whose interests fall within each of the three domains. However, we were able to test whether interest for each of the domains is equally distributed across regions and across time, finding that, in spite of very strong regularities in time and space, there was more variation across regions than could be expected by chance (with less variation across time by comparison with regions) (11,239 specialists, scholars with only one interest, were added to these analyses).

What explains these regional variations, both in the structure of the networks and in the relative weight of different
domains? The variations could be historically contingent, or they could reflect environmental affordances. For
instance, the largest deviation from the average level of interest is that of the low countries, with an overrepresentation
of the natural sciences. This might be related to the fact that scholarship in the low countries flourished in the 17th
century, which coincides with the European age of discovery. During this time, we observe in Europe a general shift
towards the natural sciences (deviation from mean of Natural Domain in Europe = + 3.5%, rest of the world = -3.5%
for the 17th century), which is likely related to the opportunities offered by the age of discovery to make novel
contributions to European scholarship in zoology, botany, or geography. Such variations thus suggest that scholars'
interests can find different venues as a function of the possibilities afforded by their environment. This is also
suggested by the fact that between regions, the relative interest in the Human Domain and the Natural Domain are
nearly perfectly negatively correlated (Pearson correlation = -0.91), while the interest in the Abstract Domain is mostly
unrelated to either (correlation with Human Domain = -0.28; with Natural Domain = -0.13). One interpretation is that,
at the margin, interest in the Human and Natural Domains are substitutable, and that which one is preferred varies
based on local affordances.

Overall, our analyses suggest that there might be deep-seated patterns in the structure of scholarly interests, both in
terms of which interests tend co-occur, and in the fact that scholars in different regions display a broadly similar
interest in each of the three domains of knowledge. Our results thus suggest that the structure of scholarly interests is
not purely the result of historical accidents. For instance, the frequent co-occurrence of the interests mathematics and
musicology is unlikely to stem from the idiosyncratic organization of the quadrivium (with arithmetic, geometry,
music, and astronomy) in medieval European universities, since it is also observed in the non-European network
(Figure 1B). Still, some relationships are likely historically contingent. For instance, the very strong link between
theology and linguistics in Europe, which is weaker in the rest of the world, might reflect the challenges raised by
biblical exegesis.
When it is not due to historical contingency, we believe there are two main explanations for the structure revealed by the networks, which are not mutually exclusive. The first explanation is grounded in practical constraints: once a scholar has mastered a given field, branching out to another might be easier, thanks to shared knowledge. For instance, mathematical knowledge, compared to knowledge of history, tends to make transitioning to physics easier. Relatedly, being able to travel makes it easier to be a geographer, but also a botanist. Although this explanation might account for part of the structure, it is not clear why it would predict, for instance, the strong link between philosophy and mathematics, since most of philosophy uses very little or any mathematics. Instead, as we suggest below, such links might be reflective of an affinity, in some scholars, for formal arguments.

The second explanation for the structure observed relies on psychology: different interests might tap into different cognitive mechanisms, and different scholars might more or less enjoy using these cognitive mechanisms. For instance, some scholars might have an affinity with abstract reasoning, which might push them to study both mathematics and physics (on the cognitive science of reasoning, and differences in the propensity of people to engage in reasoning, see, e.g., ). Other scholars might enjoy classifying things and finding patterns in sets, which might drive them towards zoology, botany, or geology (on the psychology of categorization, see, e.g., Goldstone et al., 2003; in the specific case of biological species, see, e.g., Medin & Atran, 2004).

An argument in favor of the importance of psychological drives is that, in every region we examined, there were similar shares of scholars devoting themselves to each of the three broad domains (Abstract, Natural, and Human). Moreover, there was little or no temporal auto-correlations in the share of each of these three domains (see Appendix Figure S7), suggesting a limited role of historical contingencies (e.g. areas that devote more resources to one domain at some point in time is not particularly likely to maintain this specialization). One interpretation of these findings is that, everywhere, there are some people who are inclined towards pursuing scholarship in each of these three domains and that, when the opportunity arises (e.g. thanks to economic development, see Baumard, 2019), then it will be seized. We also note that even outside of the realm of scholarship that can be found in databases, we seem to find similar inclinations: in small-scale societies, it is not uncommon to have people with what appears to be a natural inclination towards more abstract reasoning (e.g., Gluckman, 1967; Netz, 2022), towards understanding the natural world (e.g., Lightner, Heckelsmiller, & Hagen, 2021), or towards historical knowledge (e.g., Vansina, 1985).

This study has a number of limitations. First, it rests on necessarily incomplete datasets, which might reflect contemporary biases in their classification (e.g. who counts as an astronomer). Second, the number of scholars in many time periods or regions is too small to enable fine-grained comparisons. In particular, errors in the databases might exert a strong influence on the analysis of time periods and regions with small Ns. This is why we mostly restricted our conclusions to the broad patterns observed, instead of speculating on more minute differences. Hopefully future studies, maybe with more curated data, will be able to overcome these limitations. Third, men make up the overwhelming majority of the database (144 women for 13,407 men). Since, in some cultures at least, there appears to be variations in the interest of women and men (e.g. DeLoache et al., 2007; Su et al., 2009), it would be fascinating to be able to explore in greater depth women's scholarly interests through history. The limited data available here suggests some commonalities between the structure of men and women's interests (see Appendix Figure S8). Fourth, the use of Wikidata occupations as a proxy for interests and curiosity, when in some cases they might reflect purely pecuniary motives. The meaning of those interests might also not be stable over time. Fifth, more work could be done regarding fine-grained analysis of the subfields within every interest. For instance, future studies could map scholars' interests directly by assessing the content of their produced outputs (books, types of paintings, etc.), providing a more fine-grained analysis.

Studies such as this could be used to start building bridges between different fields, from psychology to the history of science, in order to better understand how human curiosity is structured, and what passions have driven scholars throughout history. They could also have applied consequences, for instance by helping us understand what makes
students interested in science, and whether there might be ways of triggering curiosity towards different domains in
those students (see, e.g., Jirout, Vitiello, & Zumbrunn, 2018).

Author contributions

Both authors contributed to the study conception and design. Material preparation, data collection and analysis were
performed by Charles de Dampierre. The first draft of the manuscript was written by Hugo Mercier and Charles de
Dampierre and they both commented on previous versions of the manuscript. Both authors read and approved the
final manuscript.

Statements and Declarations

Competing Interests and Funding

This study was funded by ANR grants ANR-17-EURE-0017 to FrontCog, ANR-10-IDEX-0001-02 to PSL, and ANR-21-CE28-0016-01 to HM, as well as by a CNRS-University of Tokyo grant to HM ("Science communication for and by citizens").

Charles de Dampierre and Hugo Mercier declare they have no financial interests.

Classification Code

MSC: 91C20 (Clustering in the social and behavioral sciences)

JEL: O31 (Innovation and Invention: Processes and Incentives)

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Appendix

<table><thead><tr><th>Meta Interest</th><th>Interest</th><th>Meta Interest</th><th>Interest</th><th>Meta Interest</th><th>Interest</th></tr></thead><tbody><tr><td>anatomist</td><td>anatomist</td><td>geographer</td><td>geographer</td><td>historian</td><td>romanist</td></tr><tr><td>anatomist</td><td>medical researcher</td><td>geographer</td><td>glaciologist</td><td>historian</td><td>sinologist</td></tr><tr><td>anatomist</td><td>neuroscientist</td><td>geographer</td><td>hydrographer</td><td>linguist</td><td>classical philologist</td></tr><tr><td>anatomist</td><td>physiologist</td><td>geographer</td><td>hydrologist</td><td>linguist</td><td>grammarian</td></tr><tr><td>anthropologist</td><td>anthropologist</td><td>geographer</td><td>oceanographer</td><td>linguist</td><td>lexicographer</td></tr><tr><td>anthropologist</td><td>ethnographer</td><td>geographer</td><td>topographer</td><td>linguist</td><td>linguist</td></tr><tr><td>anthropologist</td><td>ethnologist</td><td>geologist</td><td>crystallographer</td><td>linguist</td><td>philologist</td></tr></tbody></table>
<table><tr><td>archeologist</td><td>archaeologist</td><td>geologist</td><td>geologist</td><td>logician</td><td>logician</td></tr><tr><td>archeologist</td><td>classical archaeologist</td><td>geologist</td><td>geophysicist</td><td>mathematician</td><td>mathematician</td></tr><tr><td>archeologist</td><td>numismatist</td><td>geologist</td><td>mineralogist</td><td>meteorologist</td><td>meteorologist</td></tr><tr><td>art theorist</td><td>art critic</td><td>geologist</td><td>seismologist</td><td>musicologist</td><td>ethnomusicologist</td></tr><tr><td>art theorist</td><td>art theorist</td><td>geologist</td><td>volcanologist</td><td>musicologist</td><td>music critic</td></tr><tr><td>art theorist</td><td>literary theorist</td><td>historian</td><td>architectural historian</td><td>musicologist</td><td>music theorist</td></tr><tr><td>astronomer</td><td>astronomer</td><td>historian</td><td>art historian</td><td>musicologist</td><td>musicologist</td></tr><tr><td>astronomer</td><td>astrophysicist</td><td>historian</td><td>assyriologist</td><td>naturalist</td><td>zoologist or botanist</td></tr><tr><td>biologist</td><td>zoologist or botanist or anatomist</td><td>historian</td><td>byzantinist</td><td>paleontologist</td><td>paleontologist</td></tr><tr><td>botanist</td><td>agronomist</td><td>historian</td><td>chronicler</td><td>pharmacologist</td><td>pharmacologist</td></tr><tr><td>botanist</td><td>bacteriologist</td><td>historian</td><td>Church historian</td><td>philosopher</td><td>philosopher</td></tr><tr><td>botanist</td><td>botanist</td><td>historian</td><td>classical scholar</td><td>physicist</td><td>physicist</td></tr><tr><td>botanist</td><td>bryologist</td><td>historian</td><td>cultural historian</td><td>political scientist</td><td>political scientist</td></tr><tr><td>botanist</td><td>forestry scientist</td><td>historian</td><td>egyptologist</td><td>sociologist</td><td>criminologist</td></tr><tr><td>botanist</td><td>lichenologist</td><td>historian</td><td>epigrapher</td><td>sociologist</td><td>sociologist</td></tr><tr><td>botanist</td><td>microbiologist</td><td>historian</td><td>folklorist</td><td>taxonomist</td><td>taxonomist</td></tr><tr><td>botanist</td><td>mycologist</td><td>historian</td><td>genealogist</td><td>theologian</td><td>theologian</td></tr><tr><td>botanist</td><td>parasitologist</td><td>historian</td><td>hellenist</td><td>zoologist</td><td>arachnologist</td></tr><tr><td>botanist</td><td>phycologist</td><td>historian</td><td>historian</td><td>zoologist</td><td>carcinologist</td></tr><tr><td>botanist</td><td>pomologist</td><td>historian</td><td>historian of mathematics</td><td>zoologist</td><td>coleopterist</td></tr><tr><td>botanist</td><td>pteridologist</td><td>historian</td><td>legal historian</td><td>zoologist</td><td>entomologist</td></tr><tr><td>chemist</td><td>biochemist</td><td>historian</td><td>literary historian</td><td>zoologist</td><td>herpetologist</td></tr><tr><td>chemist</td><td>chemist</td><td>historian</td><td>local historian</td><td>zoologist</td><td>ichthyologist</td></tr><tr><td>ecologist</td><td>zoologist or botanist</td><td>historian</td><td>military theorist</td><td>zoologist</td><td>lepidopterist</td></tr><tr><td>economist</td><td>economist</td><td>historian</td><td>military writer</td><td>zoologist</td><td>malacologist</td></tr><tr><td>economist</td><td>taxonomist</td><td>historian</td><td>music historian</td><td>zoologist</td><td>mammalogist</td></tr></table>
<table><tr><td>epidemiologist</td><td>epidemiologist</td><td>historian</td><td>museologist</td><td>zoologist</td><td>marine biologist</td></tr><tr><td>geneticist</td><td>geneticist</td><td>historian</td><td>mythographer</td><td>zoologist</td><td>ornithologist</td></tr><tr><td>geographer</td><td>cartographer</td><td>historian</td><td>prehistorian</td><td>zoologist</td><td>zoologist</td></tr><tr><td>geographer</td><td>demographer</td><td>historian</td><td>regional historian</td><td></td><td></td></tr></table>

**Table S1.** Correspondence between the occupations found on Wikidata (with more than 10 occurrences, left columns, bold) and the interests used in our study (right columns). *Reason for not adding jurists.* There are over 15,000 jurists in the Wikidata Database. Given this number, compared to other scientific professions, it is plausible that most are not scholars (e.g. people who have made a contribution to legal theory). The issue is that it would be very difficult to distinguish the scholars from the others, as many jurists who are legal scholars are only jurists and do not have any other specialty belonging to the our existing categories (see the examples of roman jurists like Gaius [jurist and writer], Papinian [jurist], Ulpian [legal counsel, writer], Paulus [jurist], and Modestinus [legal counsel]) (those five are cited in Bod, 2022). *Grouping of occupations into broader interests.* We removed occupations that had fewer than 10 occurrences. This helped reduce the noise given the fact that occupation is a free-text entered by Wikipedia contributors and some might be too specific. We then manually aggregated occupations into broader ones. We removed occupations that were too broad and not specific enough regarding the field of research (such as professor, university teacher, explorer or scientist). We also removed all occupations related to arts (painting, music composer, poet, etc.) given the fact that we are interested in scholarly and not in artistic work.

<figure><img src="image_8.png" /></figure>

**Figure S1.** Example of the subclasses of Astronomer within the Wikidata Ontology System. This hierarchical structure extends further upwards, ultimately leading to the more abstract concept of 'Entity,' passing through intermediate categories like 'academic professional' or 'Creator'. The hierarchical structure is not linear. (Source: Wikidata Graph Builder: https://angryloki.github.io/wikidata-graph-builder/?item=Q11063&property=P279&iterations=10&size_property=P106&sc_width=3&graph_direction=righ t)

<table><thead><tr><td>Europe / non-Europe</td><td>Region</td><td>Area</td><td>Countries</td></tr></thead><tbody><tr><td>Europe (After 500 CE)</td><td>Eastern Europe (After 500 CE)</td><td>Balkans (After 500 CE)</td><td>Bulgaria, Greece, Albania, Montenegro, Serbia, Bosnia and Herzegovina, Croatia, North Macedonia, Kosovo</td></tr><tr><td>Europe (After 500 CE)</td><td>Eastern Europe (After 500 CE)</td><td>Central Europe (After 500 CE)</td><td>Latvia, Estonia, Slovakia, Lithuania, Czechia, Poland, Hungary</td></tr><tr><td>Europe (After 500 CE)</td><td>Eastern Europe (After 500 CE)</td><td>East Slavic (After 500 CE)</td><td>Belarus, Russian Federation, Ukraine</td></tr><tr><td>Europe (After 500 CE)</td><td>British Islands (After 500 CE)</td><td>British Islands (After 500 CE)</td><td>Ireland, United Kingdom</td></tr><tr><td>Europe (After 500 CE)</td><td>France</td><td>France</td><td>France</td></tr></tbody></table>
<table><caption>**Table S2.** List of areas in the Cultura 1.0 Database, and the regions created from these areas.</caption><tr><td>Europe (After 500 CE)</td><td>German World (After 500 CE)</td><td>German World (After 500 CE)</td><td>Germany, Switzerland, Austria</td></tr><tr><td>Europe (After 500 CE)</td><td>Iberian Peninsula (After 500 CE)</td><td>Iberian Peninsula (After 500 CE)</td><td>Spain, Portugal</td></tr><tr><td>Europe (After 500 CE)</td><td>Italy</td><td>Italy</td><td>Italy</td></tr><tr><td>Europe (After 500 CE)</td><td>Low countries (After 500 CE)</td><td>Low countries (After 500 CE)</td><td>Netherlands, Belgium</td></tr><tr><td>Europe (After 500 CE)</td><td>Nordic countries (After 500 CE)</td><td>Nordic countries (After 500 CE)</td><td>Netherlands, Belgium</td></tr><tr><td>Non-Europe</td><td>Middle-East and Africa (MENA) (After 500 CE)</td><td>Arab World</td><td>Tunisia, Algeria, Morocco, Libya, Egypt, Palestine, Israel, Lebanon, the Syrian Arab Republic, Jordan, Iraq, Kuwait, Oman, the United Arab Emirates, Saudi Arabia, Bahrain, Yemen</td></tr><tr><td>Non-Europe</td><td>Middle-East and Africa (MENA) (After 500 CE)</td><td>Persian World</td><td>Iran, Afghanistan, Kyrgyzstan, Uzbekistan, Turkmenistan, Azerbaijan</td></tr><tr><td>Non-Europe</td><td>Asia</td><td>Chinese World</td><td>China, Mongolia, Taiwan</td></tr><tr><td>Non-Europe</td><td>Asia</td><td>Indian World</td><td>India, Pakistan, Bangladesh, Sri Lanka, Nepal</td></tr><tr><td>Non-Europe</td><td>Asia</td><td>Japan</td><td>Japan</td></tr><tr><td>Non-Europe</td><td>Ancient Mediterranean</td><td>Greek World (800 BCE to 500 CE)</td><td>Ukraine, Albania, Montenegro, Kosovo, Turkey, Greece, Bulgaria, Romania, France (until 500 BCE), Italy (until 500 BCE), Spain (until 500 BCE), Libya, Egypt, Israel, Palestine, Lebanon, Syrian Arab Republic, Jordan, Cyprus, Iraq</td></tr><tr><td>Non-Europe</td><td>Ancient Mediterranean</td><td>Latin World (300 BCE to 500 CE)</td><td>Tunisia, Algeria, Morocco, Romania, Croatia, Serbia, Bosnia and Herzegovina, Slovenia, France, United Kingdom, Germany, Switzerland, Austria, Spain, Portugal, Italy</td></tr></table>
<table><caption>**Table S3.** Number of scholars by interest for all regions of the database. The number of polymaths is indicated in brackets. Europe corresponds to the grouping of all the European regions after 500 CE. Non-Europe corresponds to all the other regions combined.</caption><thead><tr><th></th><th>Ancient M.</th><th>Asia</th><th>British Is.</th><th>Eastern Eur.</th><th>France</th><th>German W.</th><th>Iberian P.</th><th>Italy</th><th>Low C.</th><th>MENA</th><th>Nordic C.</th><th>Europe</th><th>non Europe</th><th>Total N</th></tr></thead><tbody><tr><td>Theologian</td><td>71(25)</td><td>16(4)</td><td>301(68)</td><td>449(81)</td><td>555(137)</td><td>2333(261)</td><td>146(33)</td><td>396(98)</td><td>334(61)</td><td>172(36)</td><td>162(29)</td><td>4676(768)</td><td>259(65)</td><td>4935(833)</td></tr><tr><td>Historian</td><td>209(48)</td><td>208(33)</td><td>206(43)</td><td>264(60)</td><td>486(95)</td><td>612(146)</td><td>284(42)</td><td>532(85)</td><td>201(50)</td><td>275(60)</td><td>106(27)</td><td>2692(548)</td><td>692(141)</td><td>3384(689)</td></tr><tr><td>Philosopher</td><td>363(110)</td><td>330(39)</td><td>141(84)</td><td>170(69)</td><td>259(132)</td><td>322(160)</td><td>114(33)</td><td>380(150)</td><td>93(53)</td><td>170(71)</td><td>35(18)</td><td>1514(699)</td><td>863(220)</td><td>2377(919)</td></tr><tr><td>Mathematician</td><td>80(68)</td><td>88(56)</td><td>135(72)</td><td>114(79)</td><td>192(84)</td><td>316(177)</td><td>56(25)</td><td>225(124)</td><td>89(51)</td><td>109(77)</td><td>44(24)</td><td>1171(636)</td><td>277(201)</td><td>1448(837)</td></tr><tr><td>Astronomer</td><td>52(41)</td><td>88(58)</td><td>69(43)</td><td>93(63)</td><td>86(62)</td><td>168(117)</td><td>32(20)</td><td>122(91)</td><td>50(38)</td><td>106(84)</td><td>33(21)</td><td>653(455)</td><td>246(183)</td><td>899(638)</td></tr><tr><td>Geographer</td><td>20(15)</td><td>41(15)</td><td>67(24)</td><td>55(25)</td><td>112(41)</td><td>130(47)</td><td>48(20)</td><td>87(32)</td><td>136(34)</td><td>42(21)</td><td>33(2)</td><td>669(225)</td><td>103(51)</td><td>772(276)</td></tr><tr><td>Linguist</td><td>10(0)</td><td>45(18)</td><td>75(19)</td><td>52(17)</td><td>81(23)</td><td>50(31)</td><td>45(13)</td><td>64(22)</td><td>38(14)</td><td>102(26)</td><td>20(6)</td><td>425(145)</td><td>157(44)</td><td>582(189)</td></tr><tr><td>Botanist</td><td>6(3)</td><td>17(7)</td><td>63(16)</td><td>34(10)</td><td>96(27)</td><td>93(37)</td><td>27(4)</td><td>86(29)</td><td>42(17)</td><td>19(12)</td><td>25(10)</td><td>466(150)</td><td>42(22)</td><td>508(172)</td></tr><tr><td>Musicalist</td><td>24(19)</td><td>4(2)</td><td>25(6)</td><td>14(3)</td><td>45(12)</td><td>74(18)</td><td>26(4)</td><td>73(10)</td><td>15(5)</td><td>8(6)</td><td>5(2)</td><td>277(60)</td><td>36(27)</td><td>313(87)</td></tr><tr><td>Physicist</td><td>13(12)</td><td>3(2)</td><td>34(29)</td><td>26(15)</td><td>62(46)</td><td>80(50)</td><td>3(2)</td><td>41(32)</td><td>26(22)</td><td>12(11)</td><td>11(10)</td><td>283(206)</td><td>28(25)</td><td>311(231)</td></tr><tr><td>Anatomist</td><td>4(2)</td><td>2(1)</td><td>32(13)</td><td>9(3)</td><td>43(14)</td><td>54(21)</td><td>5(1)</td><td>68(16)</td><td>27(13)</td><td>1(1)</td><td>8(7)</td><td>246(88)</td><td>7(4)</td><td>253(92)</td></tr><tr><td>Zoologist</td><td>2(1)</td><td>3(3)</td><td>31(12)</td><td>19(8)</td><td>32(21)</td><td>49(24)</td><td>3(3)</td><td>24(19)</td><td>23(14)</td><td>6(5)</td><td>15(8)</td><td>196(109)</td><td>11(9)</td><td>207(118)</td></tr><tr><td>Chemist</td><td>0(0)</td><td>0(0)</td><td>22(9)</td><td>13(2)</td><td>37(13)</td><td>56(16)</td><td>2(0)</td><td>10(4)</td><td>15(9)</td><td>13(9)</td><td>9(4)</td><td>164(57)</td><td>13(9)</td><td>177(66)</td></tr><tr><td>Archeologist</td><td>0(0)</td><td>2(2)</td><td>14(12)</td><td>0(0)</td><td>18(12)</td><td>18(11)</td><td>7(5)</td><td>62(20)</td><td>8(4)</td><td>2(2)</td><td>16(10)</td><td>143(74)</td><td>4(4)</td><td>147(78)</td></tr><tr><td>Economist</td><td>1(0)</td><td>6(3)</td><td>29(9)</td><td>13(2)</td><td>22(7)</td><td>18(5)</td><td>23(6)</td><td>23(7)</td><td>4(2)</td><td>1(1)</td><td>4(1)</td><td>136(39)</td><td>8(4)</td><td>144(43)</td></tr><tr><td>Geologist</td><td>0(0)</td><td>1(1)</td><td>11(7)</td><td>4(2)</td><td>6(4)</td><td>13(9)</td><td>1(0)</td><td>12(8)</td><td>1(1)</td><td>0(0)</td><td>6(3)</td><td>54(34)</td><td>1(1)</td><td>55(35)</td></tr><tr><td>Anthropologist</td><td>2(2)</td><td>3(3)</td><td>5(4)</td><td>7(3)</td><td>13(10)</td><td>7(6)</td><td>7(6)</td><td>6(6)</td><td>1(1)</td><td>3(2)</td><td>0(0)</td><td>46(36)</td><td>8(7)</td><td>54(43)</td></tr><tr><td>Logician</td><td>0(0)</td><td>0(0)</td><td>4(2)</td><td>2(1)</td><td>3(3)</td><td>4(4)</td><td>1(0)</td><td>0(0)</td><td>1(1)</td><td>6(4)</td><td>0(0)</td><td>15(11)</td><td>6(4)</td><td>21(15)</td></tr><tr><td>Political sci.</td><td>0(0)</td><td>3(3)</td><td>1(1)</td><td>1(1)</td><td>4(4)</td><td>4(4)</td><td>0(0)</td><td>0(0)</td><td>1(1)</td><td>3(3)</td><td>4(4)</td><td>15(15)</td><td>6(6)</td><td>21(21)</td></tr><tr><td>Meteorologist</td><td>0(0)</td><td>3(3)</td><td>2(2)</td><td>4(2)</td><td>2(3)</td><td>6(5)</td><td>0(0)</td><td>1(1)</td><td>0(0)</td><td>0(0)</td><td>2(2)</td><td>17(14)</td><td>3(3)</td><td>20(17)</td></tr><tr><td>Art theorist</td><td>0(0)</td><td>1(1)</td><td>0(0)</td><td>1(1)</td><td>3(3)</td><td>3(3)</td><td>0(0)</td><td>6(6)</td><td>4(4)</td><td>0(0)</td><td>0(0)</td><td>17(17)</td><td>1(1)</td><td>18(18)</td></tr><tr><td>Pharmacologist</td><td>1(1)</td><td>0(0)</td><td>1(1)</td><td>0(0)</td><td>0(0)</td><td>7(4)</td><td>0(0)</td><td>2(2)</td><td>3(1)</td><td>2(1)</td><td>0(0)</td><td>13(8)</td><td>3(2)</td><td>16(10)</td></tr><tr><td>Paleontologist</td><td>0(0)</td><td>0(0)</td><td>1(1)</td><td>1(1)</td><td>0(0)</td><td>5(3)</td><td>0(0)</td><td>4(4)</td><td>0(0)</td><td>0(0)</td><td>1(0)</td><td>12(9)</td><td>0(0)</td><td>12(9)</td></tr><tr><td>Sociologist</td><td>0(0)</td><td>0(0)</td><td>3(2)</td><td>1(0)</td><td>1(1)</td><td>0(0)</td><td>1(0)</td><td>1(1)</td><td>0(0)</td><td>2(2)</td><td>0(0)</td><td>7(4)</td><td>2(2)</td><td>9(6)</td></tr><tr><td>Epidemiologist</td><td>0(0)</td><td>0(0)</td><td>1(1)</td><td>0(0)</td><td>0(0)</td><td>0(0)</td><td>0(0)</td><td>2(2)</td><td>0(0)</td><td>1(1)</td><td>0(0)</td><td>3(3)</td><td>1(1)</td><td>4(4)</td></tr><tr><td>Total</td><td>657(146)</td><td>715(105)</td><td>991(198)</td><td>1090(192)</td><td>1729(324)</td><td>3768(505)</td><td>712(98)</td><td>1786(328)</td><td>880(164)</td><td>798(178)</td><td>430(79)</td><td>11386(1888)</td><td>2170(429)</td><td>13556(2317)</td></tr></tbody></table>

<figure><img src="image_9.png" /><figcaption>**Figure S2.** Pairwise weighted Jaccard similarity between networks created with a different number of edges (from 1 to 10) for European network (a) and non-European network (b).</figcaption></figure>
<figure><img src="image_10.png" /></figure>

A. Ancient Mediterranean

<figure><img src="image_11.png" /></figure>

B. Asia
<figure><img src="image_12.png" /></figure>

C. France

<figure><img src="image_13.png" /></figure>

D. Nordic countries
<figure><img src="image_14.png" /></figure>

E. Eastern Europe

<figure><img src="image_15.png" /></figure>

F. Low countries
<figure><img src="image_16.png" /></figure>

### G. Middle-East and North Africa (MENA)

<figure><img src="image_17.png" /></figure>

### H. British Islands
<figure><img src="image_18.png" /></figure>

## I. German World

<figure><img src="image_19.png" /></figure>

## J. Iberian Peninsula
<figure><img src="image_20.png" /><figcaption>K. Italy</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>L) Global Network for scholars born before 1600</figcaption></figure>
<figure><img src="image_22.png" /></figure>

### M) Global Network for scholars born between 1600 and 1700

**Figure S3.** From A to K: Relatedness networks of scholars' interests belonging to the 11 different regions (see Appendix Table S1). L and M: Relatedness networks of scholars' interests for scholars born before 1600, and those born in the 17th century.

<figure><img src="image_23.png" /><figcaption>B)</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>C)</figcaption></figure>
<figure><img src="image_25.png" /><figcaption>**Figure S4.** A) Number of specialists and polymaths for every area. There are 13,556 scholars in the database: 2,172 (16%) from non-European areas and 11,386 (84%) from European areas. Among those scholars, there are 11,239 (83%) specialists and 2,317 are polymaths. B) Evolution of the number of scholars in time in Europe C) Evolution of the number of scholars in non-European regions.</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>**Figure S5.** Global Network Methodology</figcaption></figure>
<figure><img src="image_27.png" /><figcaption>**Figure S6.** Weighted Jaccard Similarity (WJAC) between networks of interest from different regions. The darker the area at the intersection of regions is, the higher the resemblance in the structures of the network of interest of scholars belonging to these regions. Given that most regions contained too few scholars to produce meaningful comparisons, we aggregated them into larger regions.</figcaption></figure>

<table><thead><tr><th>Domain</th><th>Interests</th></tr></thead><tbody><tr><td>Abstract Domain</td><td>Astronomer (371)<br>Mathematician (498)<br>Physicist (82)<br>Musicologist (88)<br>Meteorologist (6)<br>Epidemiologist (1)</td></tr><tr><td>Natural Domain</td><td>Geographer (231)<br>Zoologist (49)<br>Botanist (131)<br>Anatomist (54)<br>Chemist (44)<br>Pharmacologist (5)<br>Geologist (11)<br>Paleontologist (2)<br>Art theorist (4)</td></tr><tr><td></td><td>Philosopher (1152)<br>Theologian (1151)</td></tr></tbody></table>
<table><caption>**Table S4.** Three clusters of interests in the Global Network with the number of occurrences for each interest.</caption><tr><td>Human Domain</td><td>Historian (1210)<br>Linguist (238)<br>Archeologist (31)<br>Anthropologist (17)<br>Economist (34)<br>Logician (9)<br>Political scientist (9)<br>Sociologist (3)</td></tr></table>

<figure><img src="image_28.png" /><figcaption>**Figure S7.** For every domain, we count the number of scholars every 100 years. We then plot the Autocorrelation Function (ACF) in time on the number of scholars with a lag of 4 (the number of scholars is correlated to itself in a period from t+100 to t+400). When the autocorrelation is high, it means for instance that a high value in the series is likely to be followed by another high value and thus is reflective of a path-dependence over time. Domain auto-correlation with all scholars. Autocorrelation closer to 1 or -1 is considered high. Correlations close to 0 are considered low. Between t = 0 and t = t + 100, the correlation is over 0.5 for the Natural Domain indicating an effect of temporal persistence in this domain.</figcaption></figure>
<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /><figcaption>**Figure S8.** Network of relatedness of women scholars' interest, before 1700. The WJAC with the Global Network is 0.33.</figcaption></figure>

## Code availability

**Github repository:** https://github.com/charlesdedampierre/science_history

## Data Availability

**OSF Data repository:** https://osf.io/2euxr/