Knowledge Management Handbook: Collaboration ...

12 A Framework for Fostering Multidisciplinary Research Collaboration and Scientific Networking within University Environs Francisco J. Cantú and Héctor G. Ceballos

IntRoductIon Socializing and collaboration are in the very nature of human beings. Aristotle, in the fourth century B.C. defines humans as a kind of “social animal” identifying socializing as a core attribute of the entity humans, posing this attribute at the same level as reasoning the sine qua non feature of humankind. Since early ages, humans organized and gathered together to cooperate in order to survive and assure the continuation of the species, and we observe this behavior in the various eras of history as well as in modern times. With the advent of experimental science, the industrial revolution, and the revival of Kantian philosophy and positivism in the XIX century, we witness the emergence of social sciences and in particular, sociology, led by scholars such as Émile Durkheim, Ferdinand Tönnies, and Georg Simmel, who studied social phenomena from a philosophical and scientific standpoint introducing concepts and background theory, and contributing in this way to the establishment of social science as a discipline by the end of the nineteenth century and laying down the concepts for what is now known as social networks. In the first half of the twentieth century different approaches to social networks theory and practice were developed in the United States and the United Kingdom. With the advent of computers in the 1960s and 1970s and as a result of 207

208 • Knowledge Management Handbook the work of a growing number of social science scholars, new methods and analytical tools for social networks appeared in various universities including Harvard, California, Chicago, and others. In parallel, advances in computer science and electronics led to the establishment of information and communication technologies in the last few decades, which have become enablers for new means of communication and collaboration among persons and groups in contemporary society. Research during the 1970s in the Defense Advanced Research Projects Agency (DARPA) project laid down the foundations for the establishment of the Internet during the 1980s and means of communication such as electronic mail and interactive chatting sessions among remote individuals. The growth of public electronic sites in the 1990s was supported by tools such as Mosaic and others, which became predecessors of the World Wide Web. Navigators such as Netscape™ and Explorer™ also played an important role in facilitating virtual traveling through the Web of sites around the world. The need for searching mechanisms that would assist users in finding useful information among the myriads of data stored in millions of Web sites around the world soon became evident, and the solutions appeared without delay with the invention of search engines such as Google™, Yahoo™, and others with built-in intelligence implemented in sophisticated computer algorithms. Finally, to satisfy the inherent need of humans to communicate and socialize, theories and methods for social networking were conceived to play such a function, leading to the inventions of tools such as Facebook™ and Twitter™, among others (Sudeshna, 2010). Thus, the world is communicated in various ways, and people are using such means to share values, faith, beliefs, and hopes, in organizing themselves for achieving aspirations based on values of freedom, justice, and fraternity, such as recent events in North Africa and the Middle East have shown. Nations collaborating for exchange and improvement in economic development is another social networking with initiatives such as the one from the Organization for Economic Cooperation and Development (OECD) and other initiatives (The Royal Society, 2011). What happens at national or regional levels may also be mirrored at institutional levels when organizational culture, traditions, and practices may not adapt as quickly as needed to respond appropriately to new challenges in business, public management, and education in a changing and communicated world. We are well communicated and networked with external parties, but the same is not necessarily true when we look inside our organization and realize that we do not know what my officemate, factory partner, or academic

A Framework for Fostering Research Collaboration • 209 department colleague does. I may know well what a colleague in my same discipline who lives on another continent is working on, but ignore what projects my university colleagues from other departments are doing. Concepts such as the intranet and extranets were developed to notice and become conscious of the need for internal communication, collaboration, and socialization. In this chapter, we address the importance of inner collaboration and social networking within an institution to better achieve their goals and objectives and present a model and experience in fostering inner collaboration and networking among research groups from multiple disciplines in a university environment (Byrne, 2010).

bacKgRound Social networks theory, practice, and tools developed particularly in the last few decades, including what is known as social network analysis (SNA). SNA is a formal approach for the study of social networks using theory, methods, and tools from Graph Theory for the mathematical analysis of social processes. It uses graph theory, pattern recognition, and data mining methods to discover and measure properties and patterns on graphs and networks. A network is stated as a collection of entities and their interactions in which entities are called nodes and interactions are called arcs or edges. Nodes represent individuals, classrooms, workplaces, families, countries, and other kinds of entities. The arcs or edges represent interactions or relationship between nodes of the network. Properties of networks include concepts such as centrality (how well connected is a node with respect to other nodes), betweenness (how well a node connects sets of nodes), closeness (how distant a node is with respect to other nodes), and some others (Wasserman and Faust, 1994). SNA software tools assist in doing quantitative or qualitative analysis of social networks by finding properties of a network which are shown either as tables of numerical attributes or by displaying visual representations. Among popular SNA software tools are C-IKNOW, a Web-based software tool for numerical and visualization analysis; Commetrix, a framework for dynamic network analysis and visualization that is applied to study coauthorship, e-mail, and newsgroups; UCI-Netdraw; and CFinder, for finding and visualizing overlapping dense communities in social networks using the clique percolation method (Freeman, 2006).

210 • Knowledge Management Handbook SNA was applied to study patterns of collaboration in scientific fields including physics and other disciplines (Newman, 2001). Newman analyzes the structure of scientific collaboration networks in terms of coauthorship and shows that any randomly chosen pairs of scientists are typically separated by only a short path of intermediate colleagues. Newman et al. (2002) demonstrate the presence of clusters in networks and the patterns of collaboration between the scientific fields (Newman et al., 2002; Newman, 2004). David Liben-Nowell and Jon Kleinberg propose the link-prediction problem to infer new interactions among members of a social network which are likely to occur in the near future based on measures of proximity of nodes and present experiments on large publications networks to predict future coauthorship (Liben-Nowell and Kleinberg, 2007). In the following section, we present a model and case study in research collaboration and multidisciplinary scientific networking in a university in which we apply some of the principles of SNA and scientific collaboration following Newman´s concepts and methods for establishing patterns of behavior in networks of scientific research and collaboration.

a case study In ReseaRch collaboRatIon and scIentIfIc netwoRKIng We now present a case study in research collaboration and multidisciplinary scientific networking conducted at Tecnológico de Monterrey, a comprehensive teaching and research university located in Monterrey, Mexico. The academic staff is composed of around 800 research professors and around 1,600 research students at the doctoral and master level from disciplines in engineering, information technologies, social sciences, arts and humanities, natural sciences, and health sciences. This conglomerate of 2,400 researchers are organized in groups called research chairs with a principal investigator as the group leader as well as various adjunct professors and graduate students for an average of 20 researchers per chair. Thus, a research chair is a kind of collaborative scientific social network integrated by multidisciplinary researchers. For instance, the research chair in medical engineering congregates researchers from mechanical engineering, electrical engineering, and medicine. A research chair in border studies gathers researchers from economics, demography, and international relations. Similarly, a research chair in student learning

A Framework for Fostering Research Collaboration • 211 20 Researchers per Chair 125 Research Chairs +2,400 Researchers

5 Adjunct Professors

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fIguRe 12.1 (see color insert.)

The research chair model.

integrates researchers from education, information technologies, and psychology. Today, there are 125 research chairs in the various disciplines that followed the model depicted in Figure 12.1 since 2003 when they started (Cantú et al., 2009).

a netwoRKIng and collaboRatIon database Scientific publications from research chair members are registered in a database administered by the university. An information system called SIIP (Sistema de Información para la Investigación y el Posgrado [Information System for Research and Graduate Programs]) was implemented in 2004 to register their scientific publications including journal articles, conference papers, patents, books and book chapters, theses, and technical reports. It also stores information about research professors and graduate students, research chairs, research centers, and graduate programs in a relational database. When registered, publications are associated explicitly to a research chair by their respective authors. A publication can be associated to several research chairs as long as at least one of its authors belongs to that chair. Sharing credit in publication stimulated collaboration among

212 • Knowledge Management Handbook groups and attained acquaintance with the work of groups in all the disciplines. SIIP is a type of multiagent system with learning capabilities and data mining facilities as described in Cantú and Ceballos (2010). Studies to analyze social behavior for networking and collaboration over publications of Tecnológico de Monterrey researchers were conducted by Valerio and collaborators (Treviño et al., 2007). Research professors’ academic profile can be obtained by any internal user from the SIIP database in the form of a short curriculum vitae or as a full one with the main scientific publications organized by journal, conference, patents, books, theses, technical reports, and other products. Professor expertise is given in terms of keywords and industrial sectors, which narrows the search of specialists in certain areas. Professors also register scientific stays on other universities, indicating the visited department and a contact on the other university. This information is used for developing a catalog of universities with which there exists collaboration agreements. This catalog is used for quantifying the level of exchange and collaboration with these universities. SIIP classifies the list of participants of research chairs in three categories: internal researchers, students, and foreign researchers. The profile of internal researchers is used for characterizing the specialization of the research group. In the case of students and foreign researchers, their profile is more limited. For students, we import information like their background, the program in which they are enrolled, thesis advisor, and dissertation topic. For foreign researchers, the university and department they belong to are registered in the system. This information along with publications registered in the SIIIP allow for characterizing the research group and its specialization area which must be aligned with one of the strategic areas of the Tecnológico de Monterrey. Coauthorship in publications allows for measuring the level of participation of students and foreign researchers in the group. In the opposite direction, we also measure the relationship between graduate programs and research chairs based on the number of master and PhD theses aligned with some line of the research chair. This alignment is made explicit when the thesis is registered.

a guIde foR students Prospective and enrolled students may consult the profile of professors attached to a graduate program in order to select a thesis advisor or

A Framework for Fostering Research Collaboration • 213 reviewer. Professors’ profiles include scientific production, thesis advised, patents, expertise (given in keywords and industrial sectors), research projects, and research chairs. This information is used for identifying other professors with similar interests or developments. Related professors are ranked based primarily on their keywords, journals in which they publish, and collaboration on research chairs and projects. Students additionally must join a research chair. In order to select one aligned with their interests they can browse the catalog of research chairs by area, specialty, participants, projects, publications, and so forth. Students can address chairs or participants directly through the system. Students can also identify universities with whom Tec de Monterrey has collaboration in order to plan a research stay or choosing an external advisor. This collaboration is given by coauthorship on publications, research stays, and research projects. These elements are used for calculating an index of collaboration that in turn is used for ranking universities in certain areas.

collaboRatIon netwoRKs In order to measure the impact of research chairs on the collaboration of our professors, we used the concept of collaboration network proposed by Newman (2004). This refers to the list of distinct coauthors of publications in the last 5 years for a given professor. We observed a constant increment on both scientific production and collaboration among researchers since 2003 when the research chairs program started. We observed that the average collaboration network size passed from 5 in the year 2000 to 11.5 in the year 2010, where the collaboration network size is the average number of coauthors for a given publication. It is worth noticing that in the case of professors participating in research chairs, the increment on the network size passed from 6.3 to 19.1 in the same period. That is to say, the research chair position creates collaboration by its very nature. This pattern of behavior is illustrated in Figure 12.2. Similarly, the number of journal and conference researcher publications passed from 729 in the year 2000 to 1,779 in the year 2010. This increment is mostly attributed to professors participating in research chairs, as can be seen in Figure 12.3. Figure 12.4 shows the variations in different types of publications considered in this study: articles in journals, articles presented at conferences,

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articles in divulge journals, books, and book chapters. As can be seen, the number of books and articles in divulge journals fluctuates on the same range, meanwhile the number of articles in refereed journals, articles presented at conferences, and book chapters show a relatively constant increment. On the other hand, we distinguish three types of collaborations in publications: with students, with professors of our university, and with professors at other universities. The first two represent internal collaboration, whereas the last represents external collaboration. Figure 12.5 shows the

A Framework for Fostering Research Collaboration • 215 800 700

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Type of collaborations in research chairs.

number of collaborations with students, colleagues of the same university, and colleagues of other universities on average for professors participating in research chairs, as well as the total internal collaboration (the sum of the first two). As can be seen in Figure 12.5, during earlier years external collaboration prevailed over internal collaboration. Nevertheless, in 2003 internal collaboration increased drastically, passing from 3.5 to 10.5 in 2010, even surpassing external collaboration. Growth of internal collaboration has the

216 • Knowledge Management Handbook same pattern for both students and university colleagues. Finally we can observe that for 2010 the collaboration with colleagues of the same university and from other universities is practically the same (McDonald, 2003).

multIdIscIplInaRy ReseaRch netwoRKs By design, a research chair gathers researchers from various disciplines. If collaboration among researchers really happens, this must be reflected in the number of coauthored publications within a research chair as well as among research chairs. We analyzed both parameters and found that many of the chairs have at least one publication with authors from at least two disciplines and that a good number of the publications have one or more authors from at least two research chairs.

conclusIons and lessons leaRned We presented a model for multidisciplinary collaborative research and scientific networking within a university based on the concept of a research chair and presented evidence that this model is fostering collaboration in the form of coauthorship between the various types of researchers within a chair and between disciplines. The support of an intelligent multiagent system called SIIP proved useful in providing a mechanism to record scientific publications and in attaining acquaintance on the work of other researchers within the university. Thus SIIP stimulated coauthorship among professors and graduate students of various disciplines. SIIP has other uses that were not mentioned here such as the distribution of performance indicators, the automatic distribution of alerts and notifications to authors, the quality of publications according to citations and impact factor, and the periodic evaluation of research.

futuRe woRK Further studies along the ideas outlined by Newman, Liben-Nowell, Kleinberg, and others will be conducted in the near future on the SIIP

A Framework for Fostering Research Collaboration • 217 database to learn more about patterns of publications, correlations of various types, inner and external connectivity, and distance on network paths of authorship among researchers and disciplines (ChincillaRodriguez et al., 2008).

RefeRences Byrne, Tony. (2010). How to use internal collaboration and social networking technology. Sales and Marketing Newsletter, INC.Com. p. 209. Cantú, F., Bustani, A., Molina, A., and Moreira, H. (2009). A knowledge-based development model: The research chairs strategy. Journal of Knowledge Management, 13(1): 154–170. Cantú, F., and Ceballos, H. (2010). A multiagent knowledge and information network approach for managing research assets. Expert Systems with Applications; An International Journal, 37: 5272–5284. Chinchilla-Rodríguez, Z., de Moya-Anegón, F., Vargas-Quesada, B., Corera-Álvarez, E., and Hassan-Montero, Y. (2008). Inter-institutional scientific collaboration: An approach from social network analysis. Proceedings of the Prime-Latin America Conference, G. Dutrenit, Ed. Mexico City: UAM-UNAM. pp. 1–24. Freeman, Linton. (2006). The Development of Social Network Analysis. Vancouver: Empirical Press. Liben-Nowell, David, and Kleinberg, Jon. (2007). The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58(7): 1019–1031. McDonald, David W. (2003). Recommending collaboration with social networks: A comparative evaluation. Proceedings of the ACM Conference on Human Factors in Computing Systems. G. Cockton and P. Korhonen, Eds., Fort Lauderdale, FL. Newman, M.E.J. (2001). Scientific collaboration networks: I. Network construction and fundamental results. Journal of Physical Review E, American Physical Society, 64(1): 016131. Newman, M.E.J., Watts, D.J., and Strogatz, S.H. (2002). Random graph models of social networks. Proceedings of the National Academy of Sciences of the USA, 99(1): 2566–2572. Newman, M.E.J. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences of USA, 101: 5200–5205. Sudeshna, D., Rogan, M., Kawadler, H., Brin, S., Corlosquet, S., and Clark, T. (2010). PD Online: A case study in scientific collaboration on the Web. Proceedings of the FWCS, E. Prudhommeaux, Ed. Raleigh, NC, 1–7. The Royal Society. (2011). Knowledge, Networks, and Nations: Global Scientific Collaboration in the 21st Century, London. Treviño, Ana, Valerio, Gabriel, and Ramírez, Pablo. (2007). Social knowledge networks at ITESM, Proceedings of the Fifth International Conference on Formal Concept Analysis. S. Kuznetsou and S. Schmidt. Clemont-Ferrand, Eds. France: Springer. pp. 25–32. Wasserman, S., and Faust, K. (1994). Social Networks Analysis: Methods and Applications. Cambridge: Cambridge University Press.