Abstract
We study scientific collaboration at the level of universities. The scope of this study is to answer two fundamental questions: (i) can one indicate a category (i.e., a scientific discipline) that has the greatest impact on the rank of the university and (ii) do the best universities collaborate with the best ones only? Restricting ourselves to the 100 best universities from year 2009 we show how the number of publications in certain categories correlates with the university rank. Strikingly, the expected negative trend is not observed in all cases – for some categories even positive values are obtained. After applying Principal Component Analysis we observe clear categorical separation of scientific disciplines, dividing the papers into almost separate clusters connected to natural sciences, medicine and arts and humanities. Moreover, using complex networks analysis, we give hints that the scientific collaboration is still embedded in the physical space and the number of common papers decays with the geographical distance between them.
Introduction
The idea of socalled science of science is not entirely new: 20th century is well known for its critical works of Kuhn^{1}, Popper^{2}, Lakatos^{3} and Feyerabend^{4} who tried to build models describing how science should work or, which is far more important, to show how it in fact does work. However it is only in recent times that, owing to the start of the era of overwhelming data, it is now possible to track this problem quantitatively^{5,6}. Several studies are on a journey to answer such intriguing questions like “Who is the best scientist?”, “What makes the best university” etc^{7,8,9,10,11,12,13,14}.
There are at least three separate factors that can be regarded as key components of today’s science and the way it is recognized: papers, citations and rankings. The last one is devoted rather to whole unities like universities or departments although recent studies consider it also in the scope of individuals^{14}. It has been argued that rankings still can be perceived as not enough deep measures “providing finalized, seemingly unrelated indicator values”^{15}. On the other hand it is well known that scientific impact is a multidimensional construct and that using a single measure is not advisable^{16}.
Nonetheless, rankings are clearly a derivative of the number of published papers. However apart from just raw numbers the quality of science comes often with two additional factors: specialization and collaboration. Interestingly the type of the scientific category can dramatically change both the way the paper is written and received, e.g., in the case of simple lexical factors as title length its impact on the acquired citations change significantly from one category to another^{17}. In the same manner it is possible to spot that the number of citations per paper can vary by several orders of magnitude and are highest in multidisciplinary sciences, general internal medicine, and biochemistry and lowest in literature, poetry, and dance^{18}. These studies can go even as deep as to fascinating notion of scientific meme propagating along the citation graph^{19,20}.
Collaboration has been in the scope of interest for a long time^{21,22} and it is generally considered that it leads to high impact publications^{23}. One of recognized factors affecting the level of collaboration is undoubtedly geographic proximity: usually one expects to find a decaying probability of citation as well as common papers with distance^{24,25}, however it can also be connected to such features as ethnicity or level of economic development^{26}.
In this study we perform an investigation for a selected group of 100 best universities to unravel how the scientific productivity measured in the number of published papers per scientific categories (e.g, physics, art etc) correlates with the rank of the university. Using Principal Component Analysis (PCA) we study whether scientific categories coming from different areas (natural science, humanities etc) tend to stick together. In the second part of the paper we examine the complex network^{27} of scientific collaboration among 100 best universities and study the properties of such a network using the concept of weight threshold^{28}.
Results
We use the QS World University Ranking and service Web of Science datasets to examine patterns of category and geographic separation (see Methods for details). The data describes 100 best universities in a form of two matrices P_{ ij } (100 universities by 181 categories) and C_{ ij } (100 by 100 universities). The first matrix contains information about the number of papers published by a specific university i in a given scientific category j while the second one stores the total number of common papers among universities i and j (regardless of the category).
The main text of this paper concerns absolute numbers of quantities P_{ ij } and C_{ ij } while the Supplementary Information contains some results for the scaled cases.
Rank–number correlations for categories
It is interesting to understand how the university rank correlates with the number of scientific publications and, which is even far more intriguing, to split these relations according to different scientific categories. Naively one would expect a strong negative correlation between these quantities as larger number of papers should be reflected in acquiring higher rank (thus smaller number). The results for our data analysis are shown in Tables 1, 2 and Fig. 1, where we plot correlation coefficient ρ against the total number of papers N published in the given category (an alternative and much more straightforward method would be to use regression analysis however, in this case, it brings unreliable results  see SI for details). In each case ρ was obtained by taking one of the columns j of matrix P_{ ij }, ranking it and correlating with the university rank, thus calculating Spearman’s rank correlation coefficient. The outcome clearly suggests that there are categories for which we observe even positive correlation coefficient. On the other hand, one has to take into account the fact that in these cases statistical significance of such results is usually very low (pvalue > 0.05) as depicted in Fig. 1. When treated as a whole the data points give evidence of a loglinear relationship ρ = a + b log N (blue solid line in Fig. 1) between correlation coefficient and the number of papers with a = 0.098 ± 0.056 (p = 0.08) and b = −0.0415 ± 0.0068 (p < 0.001). A similar fit performed only for the highly significant categories (red solid line in Fig. 1) yields a = −0.285 ± 0.072 (p < 0.001) and b = −0.0127 ± 0.0081 (p = 0.13). An insignificant value of b in this case means that the level of correlations for the selected group of categories is in fact constant, contrary to the previous situation where we observe a significant decrease with N. It is worth to mention here that using not absolute but relative numbers of papers (i.e., divide by the total number of papers from a given university) leads to different results where positive correlations for certain categories are significant (see Fig. S1 in Supplementary Information). Interestingly, the category of Multidisciplinary Sciences seems to be unexpectedly robust, regardless of the method used (cf Fig. 1 and S1 in SI) it yields the highest correlation value, which might suggest that interdisciplinary research has a substantial influence on university ranking.
Categorical separation
As a next step of our analysis, we check the hypothesis of categorical separation of science. In order to test this assumption we perform a Principal Component Analysis (PCA) for matrix P_{ ij } where we restrict ourselves to those categories that were identified as highly correlated ones (see Fig. 1). Figure 2 presents the results of this PCA: the main panel (Fig. 2a) shows a 3D projection of the original 44 categories onto the first three principal components. As can be seen in Fig. 2d, the first three principal components explain around 75% of data variability. Each category was marked with a color connected to its OECD classification^{29} that contains six different areas: Natural Sciences, Engineering and Technology, Medical & Health Sciences, Agricultural Sciences, Social Sciences and Humanities, marking with a different color the scientific category Multidisciplinary Sciences. The 3D plot suggests two separate bundles of categories — one connected to medical sciences combined with complementary natural sciences (such as Virology or Cell Biology) and the second identified as mainly social sciences and humanities. Interestingly, such core natural sciences like Physics and Mathematics tend to point in directions separated from these two bundles. The other intriguing fact is almost complete absence of agricultural and engineering sciences (except for one category) in this scheme. Another typical way often used to present the results of PCA is to show them in a form of socalled biplot, i.e., two dimensional projections of consecutive PCs. Figure 2b,c provides this additional information: the values of the first PC are if the same sign, while the 2nd PC differentiates between natural sciences and other. It is Fig. 2c that uncovers a very clear distinction among natural sciences, medical sciences and social sciences with humanities. This distinction comes also in a clear way from the cluster analysis — Fig. 2e provides results from kmeans algorithm used in case of the outcomes from PCA. When searching for three clusters we obtain almost perfect separation among natural sciences, medicine and humanities and social sciences.
Network analysis
Apart from the categorical point of view we can also consider university quality by analyzing the direct connections between universities i and j on the basis of the collaboration matrix C_{ ij } where the element C_{ ij } gives the number of common publications of institutions i and j. The structure of such a collaboration network is depicted in Fig. 3a where each node (vertex) is a university and links (edges) show the connections between them. The width of each link corresponds to the number of common publications between the universities. The algorithm used to obtain this structure is the following. Using 100 highest ranked universities, for each of them (u_{1}, u_{2}, …, u_{100}) we search for its publications p_{1}, p_{2}, …, p_{M(u1)}. Then, if among the coauthors of p_{1} there is any that comes from either of the universities u_{2}, …, u_{100} a link of weight w = 1 between those universities (e.g., u_{1} and u_{2}) is established. The weight is increased by one each time u_{2} is found among the following publications of u_{1}. Finally the weight of the link between nodes u_{1} and u_{2} is just the number of their common publications (as seen in the database).
Weights probability distribution
In order to examine the fundamental properties of the weighted network of collaboration we need to compute link weight probability distribution function (PDF) which can give an idea about the diversity of number of publications between universities. Figure 3b presents link weight PDF, suggesting a fattail distribution where the majority of link weights can be found between w = 1 and w = 10.
Weight threshold
In the following analysis will use the concept of weight threshold^{28} depicted in Fig. 4. Let us take the original network of 5 fully connected universities seen in Fig. 4a and assume now that we are interested in constructing an unweighted network that would take into account only the connections with weight higher than a certain threshold weight w_{ T } (w > w_{ T }). A possible outcome of this procedure is presented in Fig. 4b  all the links with w < w_{ T } are omitted and as a result we obtain a network where links indicate only connections between nodes (i.e., they do not have any value).
Using weight threshold as a parameter it is possible to obtain several unweighted networks  for each value of w_{ T } in the range 〈w_{ min }; w_{ max }〉 we get a different network NT(w_{ T }) whose structure is determined only by w_{ T }. Then, for each of these networks it is possible to compute standard network quantities: (i) number of nodes N that have a at least one link (i.e., nodes with degree k_{ i } = 0 are not taken into account), (ii) Number of edges (links) E between the nodes, (iii) the average shortest path 〈l〉, (iv) clustering coefficient C, (v) assortativity coefficient r (vi) size S of largest connected component with number n of components (see Materials and Methods for details).
Network observables as a function of weight threshold
Figure 5 depicts the above described network parameters as a function of the weight threshold w_{ T }. First, as can be seen in Fig. 5a, the number of nodes N is a linearly decreasing function of the weight threshold w_{ T }. The number edges E decreases faster, following an exponential function (Fig. 5b). On the other hand the average shortest path 〈l〉 (Fig. 5c) is a nonmonotonic function of weight threshold, reaching its peak for w_{ T } ≈ 200. Clustering coefficient C (Fig. 5d) decreases with weight threshold up to the point w_{ T } ≈ 500 where it rapidly drops down to 0. The most interesting is the behavior of r(w_{ T }) shown in Fig. 5e: the coefficient starts with r < 0, while for larger thresholds it crosses r = 0 and for w_{ T } ≈ 200 it takes its maximal value. Then once again it drops down below zero reaching r ≈ −0.4 for w_{ T } around 500. Finally it increases toward zero for larger w_{ T }. In the case of largest connected component S Fig. 5f) we observe a series of rapid decreases, e.g., for w_{ t } ≈ 100 where S drops down by 20%. These results are quantitatively different from the ones obtained by randomly reshuffling the weights of the network (see SI for details).
Network visualisation
The above described nontrivial behavior of quantities r, C and 〈l〉 and S cannot be the sole cause of the relations presented in Fig. 3b although a high number of points with w_{ T } ≈ 100 can be responsible for some of these effects. It seems that there has to be another phenomenon leading to such an effect. Using \({\mathtt{R}}\)’s^{30} package \({\mathtt{igraph}}\)^{31} we visualize connections between universities and community structure (denoted by color) for different values of w_{ T }. The results for w_{ T } = 100, 200, 300 and w_{ T } = 400, 500, 1000 are shown in Figs 6 and 7, providing an input for further analysis. For w_{ T } = 100 (Fig. 6a) the network is still percolated, i.e., it is possible to reach any node from another one; over that value a separation occurs  Chinese, Australian and Singapore, Japanese, Danish and Swedish as well as Swiss universities all form separate clusters. This observation is connected with large loss of S in Fig. 5f. The remaining giant cluster is built out of American, Canadian, British, Dutch, and German universities (Fig. 6b). This is the area where both average path length 〈l〉 and assorativity r take their maximal values. For w_{ T } = 300 we witness the separation between US and British universities and from now on (with small exceptions) different clusters can be described as connected to different countries (or even smaller administrative units as English and Scottish universities are separated). Further plots depict progressing decay of connections between the universities that form either starlike structures (Japanese, Canadian, English and American in Fig. 7a,b) or ultimately chains (Fig. 7c).
A possible explanation to this phenomenon is in the geographical distance between the universities. In fact, Fig. 8 supports partially this assumption. The number of publications between universities i and j can be fitted with a decreasing powerlaw function of the geographical distance between them. The gap around d = 5000 is most probably caused by the presence of continents. Similar results regarding the role of geographical distance in science were obtained in previous studies^{25,32}. On the other hand the error bars in Fig. 8 give evidence that for relatively short distances (d ∈ [1; 300] km) the number common papers can be considered constant. This in turn would support the hypothesis of countrydriven rather than geographicallydriven collaboration. A lower than expected value of collaboration for shorter distances could also have its origin in the fact that usually there is lack of universities of the same scientific profile in the direct vicinity.
Conclusions
Our results indicate that even such fundamental and straightforward analysis as calculation of correlation coefficient between position of the university in the ranking and the number of papers published by its employees may reveal some nontrivial relationships. Although it would be natural to expect strictly negative correlation (i.e., the more you publish the higher rank you acquire) our analysis shows several scientific disciplines such as Agricultural Engineering, Horticulture or Hospitality, Leisure, Sport & Tourism where this is not the case. For the whole set of examined scientific categories we found a loglinear relationship between correlation and the number of papers. Intriguingly this relation breaks down when the most reliable correlations (i.e., most significant statistically) are selected. This study also underlines the differences among specific science areas — our PCA results give a clear picture that the separation between natural, medical and social sciences really takes place.
The second part of the paper is devoted to network analysis of the collaboration among 100 best universities. We used the concept of weight threshold to obtain several slices of the original weighted network at different levels of collaboration intensity. Treating the threshold as a control parameter we were able to track such network observables as assortativity revealing its rich behavior. Our analysis shows that the scientific collaboration is highly embedded in the physical space  it seems that the key aspect that governs the number of common publications is the geographical vicinity of the universities which confirms previous observations^{25,32}. On the other hand the dependence of network properties on the weight threshold cannot be explained just by using geographical distance rationale suggesting rather countrydriven collaboration.
Discussion
The problem of the role of scientific categories and relations among them has intrigued the greatest minds of the past century. Lately, Dias et al.^{33} have explicitly quoted Karl Popper’s The Nature of philosophical problems and their roots in science^{34} where this great philosopher had questioned the traditional identification of scientific disciplines, convinced instead that one should rather look at cognitive and social aspect thereof. Dias et al. follow this trail by comparing coincidences among disciplines retrieved by (i) classification given by experts^{29}, (ii) Jaccardlike coefficient for citations and (iii) languagebased JensenShannon measure of dissimilarity^{35,36} in articles’ abstracts. The same aspect, although in much more indirect way, has been lately addressed by one of us, arguing that scientific segregation is visible even while examining relations between text length (or emotional content) and citation patterns^{17}. While these considerations may seem to be academic (e.g., detecting similarities among disciplines that are “obviously” similar) they earn an additional dimension when treated as a dynamical process. Given the masses of data the usage unsupervised methods that require no manual classification of documents is the best choice to track the evolution of science. In this way such phenomena as convergence and divergence of specific disciplines^{33}, life cycles of paradigms^{37} or inheritance of scientific memes^{20} can be instantly spotted. When used for temporal data, our analysis of principal components basing on the number of published papers could also serve as an index for changing relations among disciplines. In particular, one may use it as indicator of the interest a certain scientific area gains over the years. It is possible to spot the emergence of certain trends in science and, in effect, react by for example establishing a new direction of research in the university.
Geographical distances among the nodes of the network usually come in the form of Tinbergen’s gravity model^{38}. Manifestations of spatial embedding of networks^{39} are truly omnipotent, ranging from the original intercountry trade^{40,41} through intercity telecommunication flows^{42} and online friendship^{43} to active protesters^{44}. In the case of scientific collaboration Pan et al. show a clear preference for researchers to seek partners in their geographical proximity^{25}, however underlining that the very form of the gravity model (i.e., a power law) does not forbid longdistance interactions. In this study we restricted ourselves to only top universities showing which particular links break up first. Although the geographical proximity is an important factor, the results clearly show that in the case of small distances the connections are not formed distancewise but rather countrywise. Moreover it also seems that the choice of data handling method (absolute values vs. normalized one) can play a crucial role: the description as well as Figs S2 and S3 in the Supplementary Material reveal a strong clustering between continents for the normalized data.
Methods
Dataset
We used two prominent data providers: QS World University Ranking^{45} and Web of Science^{46} service. The first dataset consisted of 100 best universities ranked in the year 2009. The second dataset was obtained by querying the database of years 2008–2009 for publications coming from one of the above mentioned universities and store information about socalled subject category (i.e., the scientific category) and affiliation of coauthors. The obtained matrices P_{ ij } (100 universities by 181 categories) and C_{ ij } (100 by 100 universities) that were created onthefly without physically saving partial data contain, respectively, 1363821 and 496684 papers.
Abbreviations
The seemingly straightforward procedure of querying for a specific university name encounters some problems that could have a strong impact on the further results. Web of Science has a set of abbreviations commonly used for searching such as Univ for “University” or Coll for “College”. Moreover it is essential to notice that one has to form a very specific query in order to get rid of severe mistakes. Table 3 shows an exemplary list of the search universities together with the exact search phrase that had to be used.
Ambiguity of queries
The ‘Search’ field is a search key that we use to associate with the authors of the publications and it can consist of one of the operators: + stands for AND operator in Boolean logic and  stands for NOT operator in Boolean logic. These operators are used to clearly assess the origin of the publication. Table 2 shows that using just the names of universities from the list (first column) would lead in the case of number 98 to obtaining publications of both Technical University in Munich and University of Munich, instead of just the latter. To avoid this problem one has to insert a query Univ Munich  Tech Univ Munich that ensures achieving proper results. On the other hand for instance for the case shown as number 78, it was not sufficient to enter Washington Univ, as there are many universities with such an abbreviation; it was necessary to add St. Louis in the query text.
Network analysis
Clustering coefficient C_{ i } for node i is defined as the number of existing links among its nearest neighbors e_{ i } (i.e., nodes to which it has links) divided by the total number of possible links among them k_{ i }(k_{ i } − 1)/2
The total clustering coefficient for the whole network is calculated as the average over all C_{ i }.
Assortativity coefficient r defined by
where i goes over all edges in the network. The coefficient is in the range [−1; 1], r = 1 means that the highly connected nodes have the affinity to connect to other nodes with high k_{ i } while r = −1 happens when highly connected nodes tend to link to nodes with very low k_{ i }.
Average shortest path 〈l〉 is calculated as the average value of shortest distance (measured in the number of steps) between all pairs of nodes i, j in the network.
References
 1.
Kuhn, T. S. The Structure of Scientific Revolutions (University of Chicago Press, 1996).
 2.
Popper, K. The Logic of Scientific Discovery (Routledge, 2002).
 3.
Lakatos, I. The Methodology of Scientific Research Programmes (Cambridge University Press, 1980).
 4.
Feyerabend, P. Against method (Verso, 2010).
 5.
Merton, R. K. The matthew effect in science. Science 159, 56–63 (1968).
 6.
King, D. A. The scientific impact of nations. Nature 430, 311 (2004).
 7.
Hirsh, J. E. An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America 102, 16569 (2005).
 8.
Radicchi, F., Fortunato, S. & Castellano, C. Universality of citation distributions: Toward an objective measure of scientific impact. Proceedings of the National Academy of Sciences of the United States of America 105, 17268 (2008).
 9.
Radicchi, F., Fortunato, S., Markines, B. & Vespignani, A. Diffusion of scientific credits and the ranking of scientists. Physical Review E 80, 056103 (2009).
 10.
Petersen, A. M., Wang, F. & Stanley, H. E. Methods for measuring the citations and productivity of scientists across time and discipline. Physical Review E 81, 036114 (2010).
 11.
Radicchi, F. & Castellano, C. Rescaling citations of publications in physics. Physical Review E 83, 046116 (2011).
 12.
Mzaloumian, A., YoungHo, E., Helbing, D., Lozano, S. & Fortunato, S. How citation boosts promote scientific paradigm shift and Nobel Prizes. PLoS One 6, e18975 (2011).
 13.
Fronczak, P., Fronczak, A. & Hołyst, J. A. Analysis of scientific productivity using maximum entropy principle and fluctuationdissipation theorem. Physical Review E 75, 026103 (2007).
 14.
Sinatra, R., Wang, D., Deville, P., Song, C. & Barabási, A.L. Quantifying the evolution of individual scientific impact. Science 354, 6312 (2016).
 15.
Moed, H. F. A critical comparative analysis of five world university rankings. Scientometrics 110, 967–990 (2017).
 16.
Bollen, J., Van de Sompel, H., Hagberg, A. & Chute, R. A principal component analysis of 39 scientific impact measures. PLoS One 4, 1–11 (2009).
 17.
Sienkiewicz, J. & Altmann, E. G. Impact of lexical and sentiment factors on the popularity of scientific papers. Royal Society Open Science 3, 160140 (2016).
 18.
Patience, G. S., Patience, C. A., Blais, B. & Bertrand, F. Citation analysis of scientific categories. Heliyon 3, e00300 (2017).
 19.
Perc, M. Selforganization of progress across the century of physics. Scientific Reports 3, 1720 (2013).
 20.
Kuhn, T., Perc, M. & Helbing, D. Inheritance patterns in citation networks reveal scientific memes. Physical Review X 4, 041036 (2014).
 21.
Narin, F., Stevens, K. & Whitlow, E. S. Scientific cooperation in europe and the citation of multinationally authored papers. Scientometrics 21, 313–323 (1991).
 22.
Glänzel, W., Schubert, A. & Czerwon, H. J. A bibliometric analysis of international scientific cooperation of the European Union (1985–1995). Scientometrics 45, 185–202 (1999).
 23.
Jones, B. F., Wuchty, S. & Uzzi, B. Multiuniversity research teams: Shifting impact, geography, and stratification in science. Science 322, 1259–1262 (2008).
 24.
Börner, K., Penumarthy, S., Meiss, M. & Ke, W. Mapping the diffusion of scholarly knowledge among major US research institutions. Scientometrics 68, 415–426 (2006).
 25.
Pan, R. K., Kaski, K. & Fortunato, S. World citation and collaboration networks: uncovering the role of geography in science. Scientific Reports 2, 902 (2012).
 26.
Chen, R. H.G. & Chen, C.M. Visualizing the world’s scientific publications. Journal of the Association for Information Science and Technology 67, 2477–2488 (2016).
 27.
Barabási, A. L. & Albert, R. Statistical mechanics of complex networks. Reviews of Modern Physics 74, 47 (2002).
 28.
Chmiel, A., Sienkiewicz, J., Suchecki, K. & Hołyst, J. A. Networks of companies and branches in Poland. Physica A 383, 134 (2007).
 29.
Oecd classification. https://www.oecd.org/science/inno/38235147.pdf (Accessed on 7th July 2017).
 30.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.Rproject.org/ (2017).
 31.
Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal Complex Systems, 1695, http://igraph.org (2006).
 32.
Hennemann, S., Rybski, D. & Liefner, I. The myth of global science collaboration patterns in epistemic communities. Journal of Informetrics 6, 217–225 (2012).
 33.
Dias, L., Gerlach, M., Scharloth, J. & Altmann, E. G. Using text analysis to quantify the similarity and evolution of scientific disciplines. Royal Society Open Science 5, 171545 (2018).
 34.
Popper, K. R. The nature of philosophical problems and their roots in science. The British Journal for the Philosophy of Science 3, 124 (1952).
 35.
Gerlach, M., FontClos, F. & Altmann, E. G. Similarity of symbol frequency distributions with heavy tails. Physical Review X 6, 021009 (2016).
 36.
Altmann, E. G., Dias, L. & Gerlach, M. Generalized entropies and the similarity of texts. Journal of Statistical Mechanics: Theory and Experiment 2017, 014002 (2017).
 37.
Chavalarias, D. & Cointet, J.P. Phylomemetic patterns in science evolution—the rise and fall of scientific fields. PLoS One 8, 1–11 (2013).
 38.
Squartini, T. & Garlaschelli, D. Jan Tinbergen’s Legacy for Economic Networks: From the Gravity Model to Quantum Statistics, 161–186 (Springer International Publishing, Cham 2014).
 39.
Barthélemy, M. Spatial networks. Physics Reports 499, 1–101 (2011).
 40.
Kaluza, P., Kölzsch, A., Gastner, M. T. & Blasius, B. The complex network of global cargo ship movements. Journal of The Royal Society Interface 7, 1093–1103 (2010).
 41.
Karpiarz, M., Fronczak, P. & Fronczak, A. International trade network: Fractal properties and globalization puzzle. Physical Review Letters 113, 248701 (2014).
 42.
Krings, G., Calabrese, F., Ratti, C. & Blondel, V. D. Urban gravity: a model for intercity telecommunication flows. Journal of Statistical Mechanics: Theory and Experiment 2009, L07003 (2009).
 43.
LibenNowell, D., Novak, J., Kumar, R., Raghavan, P. & Tomkins, A. Geographic routing in social networks. Proceedings of the National Academy of Sciences of the United States of America 102, 11623–11628 (2005).
 44.
Traag, V., Quax, R. & Sloot, P. Modelling the distance impedance of protest attendance. Physica A 468, 171–182 (2017).
 45.
Top Universities. https://www.topuniversities.com/ (Accessed on 7th July 2017).
 46.
Web of Science. http://clarivate.com/scientificandacademicresearch/researchdiscovery/webofscience/ (Accessed on 7th July 2017).
 47.
Clauset, A., Newman, M. E. J. & Moore, C. Finding community structure in very large networks. Physical Review E 70, 066111 (2004).
 48.
code by Richard, A. & Becker, O. S. version by Ray Brownrigg. Enhancements by Thomas P. Minka, A. R. W. R. & Deckmyn, A. maps: Draw Geographical Maps, https://CRAN.Rproject.org/package=maps, R package version 3.2.0 (2017).
Acknowledgements
The work was supported by FP7 FET Open project Dynamically Changing Complex NetworksDynaNets EU Grant Agreement Number 233847. JAH and PMAS also acknowledge the Russian Science Foundation grant 142100137. The work was partially supported as RENOIR Project by the European Union Horizon 2020 research and innovation programme under the Marie SkłodowskaCurie grant agreement No. 691152 and by Ministry of Science and Higher Education (Poland), grant Nos W34/H2020/2016, 329025/PnH/2016 and National Science Centre, Poland Grant No. 2015/19/B/ST6/02612.
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J.A.H. and P.M.A.S. conceived the study, K.S. collected the data, K.S. and J.S. analyzed the data, J.S. wrote the manuscript. All authors reviewed the manuscript.
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Correspondence to Peter M. A. Sloot.
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Sienkiewicz, J., Soja, K., Hołyst, J.A. et al. Categorical and Geographical Separation in Science. Sci Rep 8, 8253 (2018) doi:10.1038/s41598018265114
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