{'fraction_non_alphanumeric': 0.030207608655852952, 'fraction_numerical': 0.016367031598989418, 'mean_word_length': 5.271412169919633, 'pattern_counts': {'":': 0, '<': 1, '<?xml version=': 0, '>': 1, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Research Leadership Flow Determinants and the Role of Proximity in Research Collaborations Networks

Characterizing the leadership in research is important to revealing the interaction pattern and organizational structure through research collaboration. This research defines the leadership role based on the corresponding author's affiliation, and presents, the first quantitative research on the factors and evolution of five proximity dimensions (geographical, cognitive, institutional, social and economic) of research leadership. The data to capture research leadership consists of a set of multi-institution articles in the life sciences and biomedical during 2013-2017 from Web of Science Core Citation Database. Our sample consists of 484,903 articles from 244 Chinese institutions, which have been the primary affiliation of the corresponding author for at least one paper (with multiple institutions) in each year. A Tobit regression-based gravity model indicates that research leadership mass of both the leading and participating institutions and the geographical, cognitive, institutional, social and economic proximity are important factors of the flow of research leadership among Chinese institutions. In general, the effect of these proximity for research leadership flow has been declining recently. The outcome of this research sheds light on the leadership evolution and flow among Chinese institutions, and thus can provide evidence and support for grant allocation policy to facilitate scientific research and collaborations.


Introduction
Research collaboration is highly valued by academic communities for that it combines and diffuses complementary knowledge expertise of different background (Padial, Nabout et al. 2010). Research collaborations have been increasingly prevalent in academia (Sun, Wei et al. 2017). Research collaboration is defined as the working together of researchers to achieve a common goal of producing new scientific knowledge (Katz and Martin 1997). It has been recognized that the research collaborations among researchers/institutions/countries form a task-oriented complex systems with various interaction and organizational patterns (Newman 2004).
Characterizing the collaboration patterns have been a focus of scientometrics (Nguyen, Ho-Le et al. 2017). In particular, identifying key proximity factors that promote research collaboration can inform actionable policy making to facilitate effective collaborations that carry out good research, which could eventually enhance the prospects of science as a whole (Scherngell and Hu 2011). Existing research mainly focuses on examining the geographic, socioeconomic, and cognitive proximity as potential determinants of research collaboration using a gravity model (Hoekman, Frenken et al. 2010, Plonikova and Rake 2014, Sidone, Haddad et al. 2017. To summarize, the intensity of research collaboration is greater if researchers are closer to each other geographically (physical distance between the two researchers/institutions/countries) (Fernandez, Ferrandiz et al. 2016, Jiang, Zhu et al. 2018; socioeconomic factors such as educational R&D are positively correlated with research activities (Wagner, Park et al. 2015); the cognitive factors such as the similarity in research topics also influence the intensity of research collaboration (Gilsing, Nooteboom et al. 2008). Therefore, understanding the proximity effect is important to characterize the research collaborations.
The previous studies on research collaboration and its proximity factors have three limitations. First, the collaboration relationship was homogeneously assigned among authors for the same article, without considering the research leadership. However, the collaboration relationship between two non-corresponding authors is different from the relationship between a co-author and the corresponding author, who is the leader of the research (Jarneving 2010, Wang, Xu et al. 2013 . Typically, the first author is usually an early-career scientist who undertakes the research (e.g. a PhD student or postdoc); the corresponding author is usually the senior person who supervises and shapes the study (e.g. supervisor) (Sekara, Deville et al. 2018). Other co-authors are usually the researchers who assist the first and corresponding authors in the carrying out the work or writing the paper (not necessarily in the same group) (Hu, Rousseau et al. 2010).
Therefore, the common practice is to define the first and corresponding author as the research leaders. The relationship between co-authors and research leaders are stronger than those among non-leaders (Wang, Xu et al. 2013). With the recent trend of the increasing size of multi-institution authorship, the effect of research leadership is becoming even more significant (Wang and Wang 2017). Second, although Boschma (2005) identified five notions of proximity (geographical, cognitive, institutional, organizational, and social), most studies failed to systematically and comprehensively examine the relationships between these key identified factors and research collaboration (Ferná ndez, Ferrá ndiz et al. 2016, Jiang, Zhu et al. 2018). There are, to the best of our knowledge, only two studies involving all five proximity dimensions, but these two studies used static data without revealing the evolution of these five proximities (Plotnikova andRake 2014, Fernandez, Ferrandiz et al. 2016). Third, existing studies were mainly based on regional/country-level data, which is with a relatively low resolution of depicting the research collaborations and leadership (Hoekman, Frenken et al. 2010, Gui, Liu et al. 2018. Studying the collaborations at the institutional-level can provide high-resolution of collaboration patterns and inform actionable policy making, and thus is critically needed for characterizing the flow of research leadership. To fill these research gaps, we extend the literature in several ways. First, we introduce a new measure, research leadership (RL), where we only account for the collaborative relationship between a co-author and the first author or corresponding author.
Second, we focus on proximity as one of the main determinants of research collaboration (Gui, Liu et al. 2018, Plonikova andRake 2014). Following Boschma (2005), we examine the effect of all the four proximity dimensions (geographical, cognitive, institutional, and social proximity) on research collaboration using a gravity model. Note that we do not have organizational proximity since we are focusing on the RL among institutions in specific domains. In addition, we also examine the effect of economic proximity, which has been found to be associated with research performance and collaborations (Hwang 2008, Acosta, Coronado et al. 2011. Third, we examine the research collaboration between Chinese institutions. Because the corresponding authorship is explicitly set to be the primary criterion in the research evaluation system in China (for example, promotion practice and grant review) (Hu, Rousseau et al. 2010).
The paper is organized as follows. Section 2 reviews the literature. Section 3 describes the methods and data. Section 4 and Section 5 present the results and robustness check, respectively. The paper is concluded in Section 6 with discussions of the limitations and future work.
2 Literature review

Research collaboration
Research collaboration is a form of intense interaction that allows for effective communication and sharing of capabilities and other resources (Heffner 1981). Research collaboration may lead to various outcomes, such as co-authored paper, patents, deepened personal contact, or nothing at all. Among these outcomes, journal papers have been widely adopted as a reliable proxy to measure the intensity of research collaboration (Newman 2004, Acosta, Coronado et al. 2011. Therefore, the volume of co-authored journal papers is adopted to reflect the degree of intensity of research collaboration.

Research leadership in research collaboration
It has been recognized that the first author and corresponding author often lead the research collaboration in most fields such as biological, engineering, and management (Sekara, Deville et al. 2018). Typically, the person who carries out the research and writes the paper is the first author (Hu, Rousseau et al. 2010). The corresponding author, on the other hand, is responsible for leading the research team, securing grant to sustain the study, shaping the research ideas, designing the roadmap of a project and corresponding with editors, coordinating the collaboration among other co-authors (Wang and Wang 2017). The other co-authors are usually with complementary contributions to assist in undertaking the research. Recently, there is a trend of the increasing size of authorship and more frequent cross-institutional collaborations (Osorio 2018), and thus makes the leadership role of the corresponding author more pronounced (Wang, Wu et al. 2014).
At institution-level, the corresponding author's affiliation is commonly recognized as the leading institution in the project (Alvarez-Betancourt and Garcia-Silvente 2014).
Therefore, the corresponding authorship is widely adopted to be a key measure of research contribution and the leadership role (Hu, Rousseau et al. 2010). In many countries/regions (such as China), the corresponding authorship is set to be the primary criterion for research evaluations (for example, promotion and grant review) (Hu, Rousseau et al. 2010). Although the first authorship is also a key indicator of leadership, the first and corresponding authors usually belong to the same institution (for example, the first author is the student/postdoc of the corresponding author) (Wang, Wu et al. 2014). Therefore, we set the corresponding author's affiliation as the leader in cross-institution collaborations.

Proximity of research collaboration
The dynamic interplay of various proximity factors influences the outcome of research collaboration (Hoekman, Frenken et al. 2010). Proximity is an underpinning theoretical framework in investigating the determinants of research collaboration (Berge 2017, Hoekman, Frenken et al. 2010, Gui, Liu et al. 2018, Plonikova and Rake 2014. Boschma (2005) identified five commonly adopted proximities dimensions: geographical, cognitive, institutional, organizational, and social. Recent research has highlighted the significant role that the economic proximity plays during research collaboration.
Since we focus on the research leadership between institutions in specific domains, the organizational proximity is not applicable here. The detailed descriptions of selected proximities are as follows.
Geographical distance refers to the physical distance between researchers, which can be measured by absolute metrics such as kilometers, or relative metrics such as travel times (Plotnikova and Rake 2014). There is plenty of evidence showing that the geographical distance between researchers is negatively associated with the intensity of collaborations between them, indicating that researchers/institutions that are close to each other are more likely to collaborate (Hoekman, Frenken et al. 2010).
Cognitive proximity refers to the similarity between researchers' research interests and background. The cognitive proximity partly determines the researchers' absorptive capacity in collaborative projects. The cognitive proximity has been recognized to be positively associated with the intensity of collaborations (Scherngell and Barber 2011).
There is also evidence showing that close cognitive proximity does not always benefit research collaboration because of the lack of complementary backgrounds in the research team (Gilsing, Nooteboom et al. 2008, Balland 2012. Institutional proximity is defined as the similarity between institutions, such as cultural norms and economic development. High institutional proximity could reduce the uncertainty and costs of interactions, and facilitate the establishment of mutual trust (Boschma 2005, Boschma and. It is usually modeled as a dummy variable to capture whether the collaborators belong to the same administrative division (such as province and state) or the same country. It has been recognized that research collaboration is more frequent among institutions within the same administrative division/ country/ linguistic area (Hoekman, Frenken et al. 2010).
Social proximity refers to the social embeddedness based on the more intangible closeness, such as friendship or prior collaborations between researchers, which is expected to stimulate knowledge interactions and research collaborations owing to the established trust and commitment (Coenen, Moodysson et al. 2004, Boschma 2005.
Social proximity is usually measured by the existing/prior collaborations and has been found to be positively associated with future collaborations between individuals, institutions, and countries (Niedergassel and Leker 2011, Hoekman, Scherngell et al. 2013, Plotnikova and Rake 2014 . Economic proximity, also named as the socioeconomic proximity, refers to the degree of closeness of academic-related economic resource between institutions (Fernandez, Ferrandiz et al. 2016). According to the center-periphery hypothesis, the difference in academic economic resources between institutions could influence the research collaborations (Schubert and Sooryamoorthy 2009). Economic proximity has been found to have a mixed effect on the collaboration intensity at country level (Hwang 2008, Parreira, Machado et al. 2017, Ling, Zhu et al. 2018. There is few research on examining the influence of economic proximity on the collaboration between institutions. Absolute difference in real GDP per capita It measures research collaboration at country level (Jiang, Zhu et al. 2018) Absolute difference of gross regional product It measures research collaboration at regional level in China (Scherngell and Hu 2011).

Impact Measures using Gravity model
Spatial interaction patterns, such as inter-regional/international trade, population commuting, and migration, can be modeled by the analogy to Newton's law of universal gravitation (Burger, Van Oort et al. 2009). The basic idea of the gravity model stems from the law of universal gravitation, hypothesizing that the gravitational force between two objects is directly related to the mass of the objects, and is inversely proportional to the physical distance between them. Initially, the gravity model has been successfully used in predicting trade flows between countries (Isard 1954, Anderson 1979). The model has been applied to a variety of fields. In particular, because the gravity model can capture the joint-effect of both the mass (for example, the number of publications) and distance/proximity (for example, physical distance) of entities, it has been extensively applied to modeling research collaborations (Acosta, Coronado et al. 2011, Parreira, Machado et al. 2017. In this paper, we will integrate the five measures of proximity to measure their impact on RL flows using a gravity model.

Measurement of research leadership
The we defined the role of leadership, there exists the relationship between leaders and others being led to conduct the research. Therefore, the RL is directed (from the leading institution to non-leading participating institutions) and weighted (on the frequency of prior collaborations). We define that a paper possesses a total leadership mass of 1, and the RL flows from the leading institution (the affiliation of the corresponding author) to all other participating institutions (the affiliations of co-authors). Since we focus on the flow of RL, we do not consider a complete network of coauthors; instead, we consider a star-like network (Figure 1) with the leading institution in the center and participating institutions connecting to it (including a self-loop from the leading institution to itself). If there are multiple leading institutions (a paper with multiple corresponding authors affiliated with multiple institutions), the RL mass contributed by them is evenly distributed (Figure 1). In our data, papers with multiple leading institutions account for 3%. The RL flow intensity , from the leading institution to institution in the paper is expressed as where is the number of leading institutions in paper . And is the number of institutions in paper . Thus, the RL mass contributed by the leading institution is the total RL flow minus the self-loop: Therefore, the more institutions involved in paper , the more RL mass contributed by the leading institution . And the total RL flow intensity from institution a to institution is calculated as where is the number of papers where is the leading institution and is a participating institution. And institution 's total RL mass, total RL flow intensity to all other institutions is calculated as where is the number of institutions that institution has led.

Model and variables
To analyze the determinants of research leadership among different institutions, we adopt a gravity model. In its elementary form, the gravity model can be expressed as where is the collaboration intensity (i.e., the number of co-publication) between institutions and ; K is a proportionality constant; and are the numbers of publications of institutions and ; is the geographical distance between the two institutions; 1 and 2 reflect the potential to collaboration and 3 is an impedance factor reflecting the distance decaying factor for research collaboration. Taking logarithms of both sides and adding a random disturbance term, the multiplicative form of gravity model (1) can be converted into a testable linear stochastic model: ln = ln + 1 ln + 2 ln − 3 ln + , And there has been a substantial body of literature involving the subject of which regression model are suitable to estimate (6) (Burger, Van Oort et al. 2009). Because RL of an institution reflects the extent to which it leads collaborations, and the aforementioned proximity measures quantify the distance between institutions, the gravity model is an appropriate modeling framework for our study. To explore the roles and their dynamic evolutions of multiple proximities in shaping RL flows, we first conduct a cross-section estimate by the pooling data of 2013-2017, and then we perform cross-section estimates using two sub-period data. The equation that will be estimated is: The dependent variable in our model is the RL flow intensity from institution Institutional proximity ( ) is defined by the similarity on both formal and informal laws and practice. China's research resource and policy is distributed at the provincial level. In this paper, institutional proximity is a dummy variable (Lander 3 Supplementary material provides a details of measurement of cognitive proximity.

2015)
, which equals to 1 if institutions and are in the same province, and 0 otherwise. Note that in our data, there is little collinearity concern between and .
Social proximity ( ) draws on the social embeddedness based on the more intangible closeness, e.g., friendship or prior collaboration experience between researchers. Here, social proximity is a dummy variable, which equals to 1 if >0 or

Results
For simplicity, we mainly introduce the results in the field of "Life sciences & biomedicine". We introduce the difference of the results in other fields in sub-section 4.4.

Descriptive analysis
For contextualization, we present the top 15 institutions by RL mass ( ) and top 15 institution pairs by RL flow intensity ( ) in Table 3 and Table 4, respectively.
And Figure 2 and 3 show the spatial pattern and the dynamic pattern of RL flows, respectively. Table 3 Table 4 shows the top-15 institution pairs by RL flow intensity from the leading institution to participating institution. A clear reciprocal pattern presents. Except for the RL flow from "China Agr Univ" to "Chinese Acad Agr Sci", each RL flow its reciprocal form among top 15. For example, the RL flow intensity from "Fudan Univ" to "Shanghai Jiao Tong Univ" is the largest, and the RL flow intensity from "Shanghai Jiao Tong Univ" to "Fudan Univ" is the second largest. Furthermore, all the top 15 institution pairs consist of institutions in the same city.

Figure 2 Spatial pattern of RL flows at the province level
Because of the existence of the proximities, the intensity of RL could be heterogeneously distributed. For example, institutions in the same city usually have higher chance to collaborate (Table 4). We adopt the disparity metric to measure the heterogeneity of RL intensity distribution. The disparity of institution is calculated as follows, where is the number of institutions that institution has led. If institution 's RL is evenly distributed to all its participating institutions, → 0. The more unevenly the institution 's RL is distributed, the larger the is. In the extreme case, when almost all of 's RL flow to one participating institution, → 1. There exist a few institutions with very high disparity. Over these five years, the kernel density distribution changed steadily. The disparity of many institutions decreased, resulting in higher density around 0.1 and lower density over 0.2. This indicates that the RL flow intensity has become more evenly distributed. A possible explanation is that the hindering effect of proximities has declined over time. We will discuss this in Section 4.3 Estimation for different sub-periods. Table 5 presents the descriptive statistics and the correlation matrix between varia-

bles. All variables variance inflation factors (VIFs) are lower than 3, indicating that
there is no significant multicollinearity in the data.

Estimation result
In order to measure the determinants of research leadership, we also used Tobit gravity model to estimate the impacts. Table 6  research. In addition, we found that the coefficients of ( ) are larger than that of ( ) in all models, indicating that there is a larger influence of the leading institution's RL mass on RL flow intensity. The negative and significant coefficient of suggests that RL flow intensity decays with longer geographical distance. This is in line with previous literature on The positive and significant coefficient of economic proximity suggests that RL flows are more likely to occur between institutions with a diverse academic economic resources. This is consistent with the center-periphery hypothesis (Schott 1998) that researchers in peripheral regions are willing to collaborate with those in core regions to gain access to research resources, while core region researchers are also willing to seek complementarities to participate in their research (Hwang 2008).

Estimation for different sub-periods
To analyze the determinants of RL flows from a dynamic perspective, we estimate the full model for two sub-periods (2013-2014 and 2016-2017) and compare their parameters over time. We take 2-year lagged independent variables to address the endogeneity and reverse causality concerns (Fernandez, Ferrandiz et al. 2016, Zhang 2016, Gui, Liu et al. 2018). Table 7 presents the estimation results for different sub-periods. To compare the fitted model in different sub-years, we adopt the Chow test (Chow 1960) to determine whether the independent variables have significant differences in the subperiods. The Chow test result rejects no difference specification (p<0.01), indicating a clear difference between the two models for two sub-periods. ) are both positively significant in both sub-periods. More specifically, although the coefficient of the ( ) have been larger than that of ( ), the coefficient of ( ) decreased, while the coefficient of ( ) increased over time.
This indicates that the RL of the leading institution has a more prominent effect than that of the participating institution on the future RL flow, but the difference has been becoming smaller over time.
The geographical proximity remains negatively significant, while its absolute value of the negative coefficient decreased, indicating that the negative effect of geographical proximity has declined over time. On the other hand, the cognitive proximity, and institutional proximity remain positively significant and their coefficients increased, indicating that RL flows have become more likely to occur between institutions that have a similar research background and are located in the same province. The social proximity is positively significant in both sub-periods, but its coefficient declines, indicating that although the prior collaborations enhance the chance of future RL flows, its influence is decreasing over time.
It's interesting that the economic proximity was statistically

The role of proximity on RL in multiple research fields
To have a comprehensive understanding of RL, we also exam the role of proximity on RL in different research fields. We combined "Humanities & Arts" and "Social Sciences" into "Humanities, Arts and Social Sciences" (HASS) because they have been found to have similar collaboration pattern, outcome form and collaborative scale (Engels, Ossenblok et al. 2012). Existing literature usually combined these two fields to explore the collaboration patterns (Sidone, Haddad et al. 2017). In addition, the number of publications in these two fields are far fewer than that of natural sciences and medicine by Chinese institutions. Combining them can avoid unstable results caused by the unbalanced data. Table 8 Figure 3, indicating that the RL flow intensity has become more evenly distributed. It is worth noting that the change of HASS is more pronounced, which is consistent with the discussions above.

Robustness check
To further test the robustness of our results, we apply the "full counting" method (i.e. the count of participating institutions) to measure the RL flow intensity and then estimate a negative binomial regression for the data of "Life sciences & biomedicine". For example, in a paper with three institutions, the leading institution's RL flow to each of the two participating institutions is 1. The RL mass obtained by the leading institution is, therefore 2. Since the dependent variable is count data with over-dispersion (i.e. its variance is greater than its mean) and there exist a large number of zeros, a zeroinflated negative binomial regression is adopted (Cameron and Trivedi 2013). The rest of the independent variables remain the same as the main model. The results of the zero-inflated negative binomial regression (Table 10) leads to the same conclusions and confirms the robustness of the main model in Table 6.

Conclusions
In this paper, we proposed the concept and measurement of "research leadership" These results remain robust to several sensitive checks. In particular, the leading institution's research leadership mass has a higher influence than that of the participating institution, though the gap is narrowing. The constraining effect of geographical and social barriers have become less significant. But cognitive proximity is playing increasingly important roles in science and technology fields. Notably, we also obtain evidence that economic proximity has recently become an important factor of research leadership flows in science and technology fields.