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Title

Multilevel exploration of the realities of interdisciplinary research
centers for the management of knowledge integration

Authors

Alfonso Ávila-Robinson, Shintaro Sengoku

Citation

Technovation, Volume 62‒63, pp. 22-41

Pub. date2017, 1

DOI

http://dx.doi.org/10.1016/j.technovation.2017.01.003

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Technovation: The International Journal of Technological Innovation,
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Manuscript Draft
Manuscript Number: TECHNOVATION-D-16-00108R2
Title: Multilevel exploration of the realities of interdisciplinary
research centers for the management of knowledge integration
Article Type: Research Paper
Keywords: Interdisciplinarity; knowledge integration; knowledge
structures; bibliometric mapping; university research centers; R&D
management/assessment; multilevel approach; convergence
Corresponding Author: Mr. Alfonso Avila-Robinson, Ph.D.
Corresponding Author's Institution: Kyoto University
First Author: Alfonso Avila-Robinson, Ph.D.
Order of Authors: Alfonso Avila-Robinson, Ph.D.; Shintaro Sengoku, Ph.D.
Abstract: The fostering of interdisciplinarity is increasingly requestedof research organizations. However, conventional approaches to academic
research management limit our understanding of the way interdisciplinary
research (IDR) centers integrate multiple disciplines. This paper
proposes a multilevel approach to explore the patterns of knowledge
integration and the forms of research organization emerging from thepractices and activities of IDR centers. Several bibliometric-based,
network-oriented and visualization-rich approaches are used. The cases of
two prominent IDR centers are considered: Harvard University's Wyss
Institute and Kyoto University's WPI-iCeMS. At the macro level, ourresults show similarities in the scientific positioning of both IDR
centers, which translate into differences in the nature, intensity anddrivers of their knowledge interconnections at the meso-level. At the
micro-level, we demonstrate that far from idealizations of fullconvergence, the realities of IDR centers are characterized byheterogeneous patchworks of multi-trajectory research domains—some ofthese enabling, others generating interdisciplinary knowledge.
Differences in knowledge integration occur between but also, and more
importantly, within IDR centers. Thus, tailored strategies tuned to theparticularities of organizations and topic-based forms of researchorganization appear to cope better with interdisciplinary knowledge. The
understanding of these inter- and intra-organizational differences proves
crucial for effectively fostering knowledge integration. An integrated
model relating levels of research management and visualization approaches
is proposed for the management and assessment of knowledge integration in
IDR centers.

*Title page with author details

Multilevel exploration of the realities of interdisciplinary research centers for the
management of knowledge integration
Alfonso Ávila-Robinson1, Shintaro Sengoku2
1

Institute for Integrated Cell-Material Sciences (WPI-iCeMS), Kyoto University, Sakyo-ku, Kyoto,
Japan

2

Department of Innovation Science, School of Environment and Society, Tokyo Institute ofTechnology, Meguro-ku, Tokyo, Japan

Corresponding author:
Alfonso Ávila-Robinson, Ph.D.
Kyoto University
Institute for Integrated Cell-Material Sciences (iCeMS)
Yoshida Ushinomiya-cho, Sakyo-ku, Kyoto 606-8501, Japan
Phone: +81-(0)75-753-9766
E-mail: aavilarobinson@icems.kyoto-u.ac.jp

Highlights

Highlights

A multilevel approach — macro, meso, and micro levels — is proposed for the exploration ofinterdisciplinary research (IDR) centers.



We study the patterns of knowledge integration and the forms of research organization
emerging in the practices of IDR centers in Japan and the US.



Our results demonstrate that the realities of knowledge integration in IDR centers are far fromtheir typical idealizations of full convergence.



We provide cues about the management, organization, and assessment of knowledge integrationin IDR centers.

An integrated and systematic understanding of knowledge integration proves crucial for
fostering interdisciplinarity.

Graphical Abstract

Graphical abstract

*Manuscript without author identifiers
Click here to view linked References

Multilevel exploration of the realities of interdisciplinary research centers
for the management of knowledge integration
The fostering of interdisciplinarity is increasingly requested of research organizations. However, conventional
approaches to academic research management limit our understanding of the way interdisciplinary research (IDR) centers
integrate multiple disciplines. This paper proposes a multilevel approach to explore the patterns of knowledge integrationand the forms of research organization emerging from the practices and activities of IDR centers. Several bibliometricbased, network-oriented and visualization-rich approaches are used. The cases of two prominent IDR centers are
considered: Harvard University’s Wyss Institute and Kyoto University’s WPI-iCeMS. At the macro level, our results
show similarities in the scientific positioning of both IDR centers, which translate into differences in the nature, intensityand drivers of their knowledge interconnections at the meso-level. At the micro-level, we demonstrate that far from
idealizations of full convergence, the realities of IDR centers are characterized by heterogeneous patchworks of multitrajectory research domains—some of these enabling, others generating interdisciplinary knowledge. Differences inknowledge integration occur between but also, and more importantly, within IDR centers. Thus, tailored strategies tuned
to the particularities of organizations and topic-based forms of research organization appear to cope better withinterdisciplinary knowledge. The understanding of these inter- and intra-organizational differences proves crucial for
effectively fostering knowledge integration. An integrated model relating levels of research management andvisualization approaches is proposed for the management and assessment of knowledge integration in IDR centers.

1. Introduction
It is widely recognized that the production of knowledge and the various institutions involved in the sciencesystem are constantly transforming (Hessels and Van Lente, 2008). These changes are largely due to theincreasingly complex scientific, technical, and societal problems facing research institutions (Anzai et al.,2012; Siedlok and Hibbert, 2014). Novel conceptions of and solutions to these challenges are believed to more
likely arise from integrative or synthetic approaches cutting across multiple and disparate disciplines (NRC,2014; Repko, 2008; Stehr and Weingart, 2000). Several labels are used to describe this phenomenon, such as“interdisciplinarity”, “transdisciplinarity”, “fusion”, “convergence”, “hybridization”, “cross-disciplinarity”,“anti-disciplinarity”, and “cross-fertilization”, among others (Battard, 2012; Islam and Miyazaki, 2010; Lauto
and Sengoku, 2015; Moss, 2011). Despite their differences, these terms all imply the significance of theintegration of different strands of expertise, theories, methods, or data (Repko, 2008; Wagner et al., 2011). To
emphasize this common ground, the remainder of this paper uses the terms “knowledge integration” and“interdisciplinarity” interchangeably.
Knowledge integration is believed to lead to new knowledge (Huutoniemi et al., 2010). It has also been
regarded as a potential source of competitive advantage and innovation (Siedlok and Hibbert, 2014; Siedlok etal., 2015). Several authors have expressed caution and skepticism about the promises of interdisciplinary
approaches (Frodeman, 2011; Jacobs, 2013). Nevertheless, the increased interest in knowledge integration has
led to its continuing and accelerating support in science and technology policy programs throughout the world(Anzai et al., 2012). New modes of production of integrative knowledge have emerged through the creation ofresearch centers, programs, and courses with explicitly interdisciplinary aims (Hessels and Van Lente, 2008;
Siedlok and Hibbert, 2014). These activities have embraced multiple fields of science and technology.
Interdisciplinarity has been particularly influential on the life sciences (Burggren et al., 2010). Building on
advances in molecular and cellular biology and genomics, interdisciplinary, high-impact life sciences researchis expected to lead to innovative solutions and sustainable new technologies (Sharp and Langer, 2011). Recent
reports have proposed the convergence of life sciences with physical, mathematical, computational,
engineering, and social sciences as a way to accelerate innovation (MIT, 2016; NRC, 2014). Examples of
convergent, interdisciplinary initiatives in the US include the Brain Research through Advancing Innovative
Neurotechnologies (BRAIN) Initiative, the Precision Medicine Initiative, and the National Cancer Moonshot
Initiative (MIT, 2016).
1

Over the years, numerous research efforts have been undertaken to elucidate the determinants (Siedlok andHibbert, 2014; Stokols et al., 2008; Su, 2014; Van Rijnsoever and Hessels, 2011), processes (Lee et al., 2015;
Siedlok et al., 2015), outcomes (Anzai et al., 2012; Bishop et al., 2014; Gowanlock and Gazan, 2013; Jensen
and Lutkouskaya, 2014), or combinations of these aspects (Wooten et al., 2014) of interdisciplinary research.
Other studies have approached interdisciplinarity more theoretically, such as in definitions of typologies(Huutoniemi et al., 2010; Siedlok and Hibbert, 2014), or more practically, such as in studies of its barriers andfacilitators (Aldrich, 2014; CFIR, 2005; NRC, 2014). Although no consensus on the definition of“interdisciplinarity” has yet been established, all these studies have clarified its characteristic features: itsscientific domain-dependence (Sanz Menéndez et al., 2001; Van Rijnsoever and Hessels, 2011), the coexistence of multiple forms of interdisciplinarity (Huutoniemi et al., 2010; Klein, 2008; Siedlok and Hibbert,2014), its close complementarity with disciplinary knowledge (Jacobs, 2013; Stehr and Weingart, 2000), andits cognitive and social duality (Klein, 2008; Wagner et al., 2011). As knowledge accumulates, the needbecomes urgent for research stakeholders to facilitate and foster interdisciplinary research in theirorganizations. Despite these calls, we know little about how interdisciplinary research centers integrate
multiple disciplines in practice. It is hypothesized that knowledge integration in IDR centers is likely
influenced by the features of interdisciplinarity mentioned above. However, no empirical research has yet
demonstrated pragmatically how these features translate into the patterns of integration emerging from thepractices and activities of IDR centers. Several assessment and measurement approaches have been proposed
for this purpose (Anzai et al., 2012; Bishop et al., 2014; Gowanlock and Gazan, 2013; Jensen andLutkouskaya, 2014; Kaplan et al., 2014; Rafols, 2014), but they have not been able to properly address themulti-dimensionalities, complexities, multiple levels of aggregation and granularity, and different perspectives
inherent in interdisciplinary research (Cambrosio et al., 2006; Klein, 2008; Rafols et al., 2012; Rafols andMeyer, 2010; Sanz Menéndez et al., 2001). There is thus a clear need for empirical approaches to study thepractices of knowledge integration in interdisciplinary research centers in a holistic, integrated, and multilevel
manner.
Within this context, this paper addresses the following research questions: What patterns of knowledge
integration emerge from the practices and activities of IDR centers?, and how do these patterns relate to theirforms of research organization? To answer these questions, this paper uses the empirical cases of twoconvergent, life sciences-oriented research centers explicitly established with interdisciplinary aims: Kyoto
University’s Institute for Integrated Cell-Material Sciences (WPI-iCeMS) in Japan and Harvard University’s
Wyss Institute for Biologically-inspired Engineering (Wyss Institute) in the US. A three-level (macro, meso,
and micro) analytical framework is proposed. Each level comprises a series of research activities that visuallyand quantitatively capture, from different degrees of granularity and perspectives, the cognitive structures
underpinning research centers. For that purpose, this paper uses several bibliometric-based, network-oriented
and visualization-rich approaches, including research landscape maps, science overlays (Leydesdorff andRafols, 2009), density maps (Van Eck and Waltman, 2011), cluster mapping approaches, and heatmaps. The
properties and dynamics of these cognitive structures are used as proxies for the patterns of knowledge
integration and the forms of organization emerging in the practices of IDR centers. Knowledge integration ismeasured through the analysis of published scientific papers. The limitations of this method will be discussed
in subsequent sections. Our results demonstrate that the realities of knowledge integration in IDR centers arefar from their typical idealizations of full convergence. The similar scientific positionings of both IDR centersat the macro level translate into differences in the nature, intensity and drivers of their knowledge
interconnections at the meso-level. At the micro level, IDR centers are characterized by heterogeneous
patchworks of multi-trajectory research domains—some of these indirectly enabling, others directly
generating interdisciplinary knowledge to different degrees. We argue that the exploration of the inter- andintra-organizational differences of IDR centers proves crucial for effectively fostering knowledge integration.
An integrated model relating the levels of research management and visualization approaches is proposed forthe management and assessment of knowledge integration in IDR centers.
The rest of this paper is structured as follows. Section 2 provides an overview of the relevant literature
highlighting interdisciplinary research and approaches for its assessment in research centers. Section 3
2

continues with a description of the analytical framework and the case studies of this paper. Section 4
enumerates the data and research methods used. In Section 5, we report the findings of this study. Section 6
lists some of the main implications drawn from the study. Finally, Section 7 briefly concludes the paper.
2. Relevant literature
We first describe interdisciplinary research and knowledge integration, followed by a discussion of the rolesof research centers established with explicitly interdisciplinary aims. This section finalizes with a review ofstudies assessing IDR centers, with a focus on studies using bibliometric approaches.
2.1. Interdisciplinary knowledge and research centers
The dynamics of science and technology are closely related to the generation, testing, and modification ofknowledge (Loasby, 2002). Studies have described the evolution of knowledge as highly cumulative and pathdependent, featuring uncertain, open-ended, collective, and dynamically uneven processes (Consoli and
Ramlogan, 2008; Nelson, 2003). The advancement of knowledge can take several routes; of these, knowledge
that cuts across multiple and disparate disciplines has recently increased in importance (NRC, 2014; Repko,
2008). Such interdisciplinary knowledge is believed to be a potential source of competitive and innovativeadvantage (Huutoniemi et al., 2010; Siedlok and Hibbert, 2014; Siedlok et al., 2015), yet some researchers are
skeptic (Frodeman, 2011; Jacobs, 2013). They plead for the dynamism, breadth, openness, and flexibility of
disciplines, away from their prevailing view as isolated “silos” in the interdisciplinary studies literature(Jacobs, 2013; Repko, 2008).
Interdisciplinary research involves converging “data, techniques, tools, perspectives, concepts, and/or theories
from two or more disciplines or bodies…” (CFIR, 2005). Integration is a defining characteristic ofinterdisciplinary research (Repko, 2008). It involves the (re-)combination of knowledge from disciplines,
interdisciplines, and schools of thought through processes of knowledge transfer and creation (Repko, 2008;
Siedlok and Hibbert, 2014). Knowledge integration has typically been characterized by its diversity (i.e. thedisparity, variety, and (im)balance of given bodies of knowledge) and its coherence (i.e., the degree ofinterconnection between these bodies of knowledge) (Porter et al., 2007; Rafols, 2014).
There is still no clear consensus on the definition of “interdisciplinarity” (Wagner et al., 2011). However, aseries of characteristics are repeatedly reported in the literature. Due to the intense context-dependence andmulti-dimensionality of interdisciplinarity, we should expect multiple “interdisciplinarities” to coexist(Huutoniemi et al., 2010; Klein, 2008). This has led to explorations of the different modes of conducting
interdisciplinary research through definitions of typologies and taxonomies (Huutoniemi et al., 2010; Siedlok
and Hibbert, 2014). Other studies have examined the differences in interdisciplinarity across scientificresearch contexts (Frodeman, 2011; Sanz Menéndez et al., 2001; Van Rijnsoever and Hessels, 2011). Most
have associated interdisciplinary research with application-oriented and problem-solving research (Van
Rijnsoever and Hessels, 2011). For Heimeriks (2013), co-evolutionary processes among research, science, andsociety play a crucial role in these field-dependent differences of interdisciplinarity. Another importantcharacteristic of interdisciplinarity is its high complementarity with disciplinary domains. This implies thatboth disciplines and interdisciplines are parallel, mutually reinforcing research strategies (Jacobs, 2013;
Repko, 2008; Siedlok and Hibbert, 2014; Stehr and Weingart, 2000). Finally, the literature has described thecognitive and social duality of interdisciplinary research (Klein, 2008; Wagner et al., 2011). For Klein (2008),
intellectual integration is tightly coupled socially through learning and other joint activities. Certainly, thesefeatures should impact the way interdisciplinary research centers integrate multiple disciplines. Nevertheless,
we argue that there is still a need to pragmatically assess how these characteristics translate into the patterns ofknowledge integration emerging from the practices and activities of interdisciplinary research centers.
Research centers are key vehicles for the convergence of scientists from multiple scientific backgrounds andthe advancement of integrated scientific and technical knowledge (Battard, 2012; Bishop et al., 2014; Youtie
et al., 2006). Research centers with explicitly interdisciplinary aims are regarded as a new kind of institutionalinnovation in the science system (Su, 2014). Interdisciplinary research centers provide both the internal3organization frameworks and the external interfaces that support knowledge integration (Anzai et al., 2012;
Bozeman and Boardman, 2003). Their existence traces back to the early 1980’s with the establishment of the“multipurpose, multidiscipline university research centers” in the US (Bozeman and Boardman, 2003). Recent
examples include research centers moving into the convergence of the life sciences with mathematical,
physical, engineering, and even social sciences (MIT, 2016; NRC, 2014). Over the years, the diffusion ofinterdisciplinary research centers has advanced through different trajectories (Thorp and Goldstein, 2013): (a)the creation of research centers housing scholars from disparate disciplines; (b) the organization of research
centers focusing on emerging knowledge domains; (c) the creation of new departments or hybrid disciplines;and (d) the establishment of research structures with fundamentally different organizational principles.
Interdisciplinary research centers encompass organizational, institutional, geographical, social, and cognitive
dimensions, among others (Boschma, 2005; Rafols, 2014; Stokols et al., 2008). As these dimensions and theirinterplay can provide information for fostering and facilitating interdisciplinary knowledge, numerous
assessment approaches have been proposed in the literature. The next section describes some of theseassessment approaches, particularly those focusing on research centers and using of bibliometric approaches.
2.2. Assessment of interdisciplinarity in research centers and the role of bibliometric approaches
Over the years, research centers have been forced to find alternative approaches to demonstrate their successin achieving their missions. The research has highlighted that decision makers have tended to rely excessively
on intuitive judgments (Porter, 2007; Porter and Newman, 2011). Hence, there is an urgent need toincorporate a richer base of empirical information into R&D management processes (Porter, 2007).
Interdisciplinary research centers are not an exception, particularly since their assessment is still
misunderstood (Bishop et al., 2014; Klein, 2008) and heavily dependent on the qualitative judgment of peer
review (Anzai et al., 2012). This area of research is still in development. The assessment of interdisciplinary
research still faces several hurdles (Anzai et al., 2012): (a) a lack of methodologies for the evaluation ofresearch institutions, (b) the need to develop practical measuring for quantitatively and objectively measureinterdisciplinary and collaboration, and (c) the need for methods of evaluating the effect of managerial
approaches in organizations.
Numerous research efforts have attempted to assess interdisciplinary research centers. Some approaches havebeen restricted to the delineation of guidelines for the systemic evaluation of interdisciplinary research (Strangand McLeish, 2015). Others have focused on the assessment of transdisciplinary teams through researcher
surveys (Mâsse et al., 2008) or mixed methods encompassing outcome-based, process, and developmental
evaluations (Wooten et al., 2014). Bishop et al. (2014) corroborated the positive impact of interdisciplinarity
on the productivity and collaboration of faculty affiliated with an interdisciplinary research center in the fields
of mathematical and biological sciences. Focusing on the case of nanotechnologies, Battard (2012) proposed
the “technological hub” concept to describe how scientists use multidisciplinary knowledge to create newscientific outcomes. Similarly, using the case of a nanotechnology research center, Kaplan et al. (2014)
studied the day-to-day efforts of researchers to coordinate across disciplines. Juanola-Feliu et al. (2012)
examined the challenges of multidisciplinary teams and organizations involved in the research on andcommercialization of a nano-enabled biomedical device. Other studies have provided more qualitativedescriptions of the barriers to and facilitators of interdisciplinarity in research centers (Aldrich, 2014; CFIR,
2005; NRC, 2014).
Many other studies have used tech-mining and bibliometric-based approaches to assess interdisciplinarity(Wagner et al., 2011). Anzai et al. (2012) measured the impact of interdisciplinarity on two large academic
research projects in Japan. They proposed a series of key performance indicators (KPIs) for the measurementof the strategic fitness academic research projects on the basis of their interdisciplinarity and collaboration.
Others have used bibliometric techniques combined with machine learning algorithms to assess theinterdisciplinarity of an astrobiology research center (Gowanlock and Gazan, 2013), as well as its patterns ofcollaboration through social network approaches (Taşkın and Aydinoglu, 2015). Jensen and Lutkouskaya(2014) proposed a set of six quantitative indicators to measure the interdisciplinarity of 600 CNRS
laboratories in France. These indicators are mainly based on typical numerical indicators used ininterdisciplinary research studies, such as balance, variety, disparity, and diversity. At the researcher level, Pei
and Porter (2011) assessed the research efforts of 21 leading nanobiomedical scientists on the basis of4interdisciplinarity and collaboration. These and other studies have relied on the science overlay approachdeveloped by Leydesdorff and Rafols (2009) and Rafols et al. (2010), and later enhanced by Rafols (2014).
Thus, despite the numerous studies examining interdisciplinary research centers, few studies, if any, haveattempted to explore interdisciplinarity from more integrated, holistic perspectives. Despite their drawbacks
and limitations, which will be mentioned later, we believe that bibliometric mapping approaches are usefultools for the practical and systematic assessment of interdisciplinary research as they enable (a) the study ofknowledge interactions from different perspectives (Shiffrin and Börner, 2004); (b) the use of bibliometricmaps as powerful metaphors shaping the way we view, organize and classify the world (Milojević et al.,2012); (c) the revelation of structures from patterns visualized in the data (Fekete et al., 2008); and (d) the
possibility for new knowledge to emerge from their visualizations (Heymann and LeGrand, 2014). The
following section describes the analytical framework underlying such an approach.
3. Analytical framework and case studies
3.1. Analytical framework
The core of this paper is the integrated and multilevel framework shown in Fig. 1. The analytical activities
underpinning Fig. 1 are based on a series of bibliometric-oriented, network-based, and visually-intensive
approaches. These will be described in detail in the sections below.
[Figure 1 here]
Previous research has highlighted the benefits of a multilevel systems perspective for the study of scientificteamwork (Börner et al., 2010). Fig. 1 makes use of such an approach for the study of team science. In theanalytical framework shown in Fig. 1, each of the three levels of analysis (macro, meso, and micro)
approaches interdisciplinarity/knowledge integration from different perspectives and granularities:(a) Macro level: This activity locates research centers within their landscape of comparable organizations on
the basis of their scientific knowledge bases. This “scientific positioning” relies on the construction ofscientific research landscape maps.
(b) Meso level: This activity characterizes and operationalizes the bodies of knowledge underpinning a given
research center. In using the “science overlay maps” approach (Leydesdorff and Rafols, 2009; Rafols etal., 2010), the meso level aims to position these scientific knowledge bases within the whole of science.
By doing so, it is possible to estimate the knowledge diversity and interconnectivity of research centers.
(c) Micro level: At deeper levels of analysis, this activity seeks to explore the dynamics, structure andcontents of the cognitive networks underlying the scientific production of research centers. In contrast to
the previous activities, this level of analysis evaluates knowledge integration at the level of specificresearch topics.
As complex organizations, IDR centers can be dissected into the geographical, organizational, institutional,social, and cognitive layers that determine their behavior and performance (Battard, 2012; Boschma, 2005;
Rafols, 2014; Stokols et al., 2008). Each of these layers provide the driving forces for the formation of
networks, be it between countries or regions, organizational arrangements, institutions, people, or knowledge,
respectively (Boschma, 2005). The analytical framework of Fig. 1 is mostly cognitive in nature as it relies on
the production of scientific knowledge in IDR centers. As shown in Fig.1, the cognitive structures emerging
from IDR centers are assessed from multiple levels of granularity and understanding. Depending on the levelof analysis, these are regarded as scientific research landscapes, science overlays, or cognitive maps. In thispaper, the dynamics, structure and contents of these cognitive structures are used as proxies for exploring thepatterns of knowledge integration observed in the practices and activities of IDR centers. We hypothesize thatthese patterns of knowledge integration are likely influenced by the characteristic features ofinterdisciplinarity described in Section 2.1: scientific context-specificity, co-existence of multiple forms ofinterdisciplinarity, complementarity between disciplinary and interdisciplinary knowledge, and its cognitivesocial duality.

5The layers do not operate in isolation but interact with each other (Boschma, 2005). In fact, relating these
dimensions allows an understanding of the dynamics of scientific research (Rafols, 2014). Accordingly, thispaper relates the patterns of knowledge integration observed from the cognitive structures mentioned above tothe forms of research organization in IDR centers. Both aspects provide crucial cues about the management
and organization of their knowledge integration efforts. As shown later, the analytical framework of Fig. 1
will prove to be a practical and systematic approach for the understanding and assessment of the context andeffects of managerial approaches in interdisciplinary research organizations.
3.2. Case studies
The empirical analysis of this study examines two leading-edge, interdisciplinarity-dedicated researchorganizations: the Institute for Integrated Cell-Material Sciences (WPI-iCeMS) at Kyoto University in Japanand the Wyss Institute for Biologically-inspired Engineering at Harvard University in the US. Both research
centers are regarded as archetypes of organizations established exclusively to advance interdisciplinary
research. Table 1 provides a general comparison of these research centers. They are fairly similar in terms ofsize and general fields of research. The rest of this section provides more detailed information on theseresearch centers.
[Table 1 here]
3.2.1. WPI-iCeMS
The WPI-iCeMS (http://www.icems.kyoto-u.ac.jp/en/) was established in 2007 as part of the World PremierInternational Research Center Initiative (WPI), a high-end funding program led by the Ministry of Education,
Culture, Sports, Science and Technology (MEXT) in Japan. The WPI initiative strives to build “globally
visible” research centers in Japan with four basic objectives: the advancement of leading-edge research, thecreation of interdisciplinary domains, the establishment of international research environments, and thereformation of research organizations. As part of Japan’s Third Science and Technology Basic Plan, the WPIinitiative allocates selected institutes 0.5 to 2 billion yen per year (over US$ 4 to 16 million) for a period of 10
years with the possibility of a five year-extension. The WPI-iCeMS seeks to create a new field of integrated
cell-material sciences through the fusion of chemistry, physics, and cell biology. Two main knowledgeintegrative approaches are derived from these interactions: materials for cell control and cell-inspired
materials; the former refers to the creation of compounds to control processes in cells and the latter to theharnessing of the cellular processes to create chemical materials. Both approaches are supported by theunderstanding of synthetic methods and cellular processes and mechanisms.
3.2.2. Wyss Institute
The Wyss Institute (http://wyss.harvard.edu/) was founded in 2009 at Harvard University with a US$125
million donation, doubled to US$250 million in 2013, from Swiss entrepreneur Hansjörg Wyss. The Wyss
Institute’s mission is to “uncover nature’s design principles and harness these insights to create new bioinspired materials and devices that will revolutionize health care and create a more sustainable world” (Ingber,2011). The Wyss Institute’s innovation model is centered on disruptive change targeting early-stage scientificresearch as well as its translation and commercialization into marketable products. This dual stress on scienceand technology has resulted in the development of a novel research model for approaching science (Ingber,
2011, 2013). The Wyss Institute’s focus on biologically-inspired engineering takes place along three main
trajectories of interaction based on the understanding of nature’s design principles: the development of bioinspired engineering approaches, materials and devices, and applications in living systems, environment, andconstruction.
4. Data and research methods
For this study, the publications of both research centers were harvested from Thomson Reuters’ database Webof Science (WoS). This study is restricted to articles and conference proceedings (except for the methods ofSection 4.1.1), as it is believed that they are associated with scientific advances more directly than are review
6articles and editorial materials. Additionally, only those publications coauthored by the principal investigators(PIs) or adjunct faculty members are assessed. The collected documents total 1,067 and 716 for the WPIiCeMS and the Wyss Institute, respectively. These scientific publications underwent the series of bibliometricmapping approaches described below.
4.1. Bibliometric mapping approaches
The construction of the bibliometric maps of this study follows the general methods described in Cobo et al.
(2011) and the references therein. This section describes the specific methods we used to construct the
mapping approaches of the different levels of the conceptual framework shown in Fig. 1. High resolution
imagesofthemapping
approachesof
this
section
are
found
at:
https://sites.google.com/site/technovation2016idrmapping/.
4.1.1. Macro level of analysis: Scientific research landscape maps
The macro level of analysis relies on the construction of scientific research landscape maps. This mapping
approach uses undirected two-mode networks (i.e. networks with two different types of nodes), relating
research institutions (mode-1) with their respective fields of scientific research (mode-2). Following theliterature, the scientific disciplines of research centers are approximated by the journal subject categories (SC)allocated to the total of their scholarly articles. SCs refer to the categorization scheme used by the WoS
database to classify the scientific content of their journals. Typically, one or more SCs of the more than 250
categories are assigned to a given journal.
The scientific research landscape maps were constructed as follows. First, we harvested the publicationsindexed in the WoS database of the 17 research centers, including the WPI-iCeMS and the Wyss Institute,listed in Table 2.
[Table 2 here]
These organizations are regarded as being comparable to the research center to which one of the authors
belongs. In the selection of these organization, the support of experts was sought. Articles, proceedings, andreviews from the year of their establishment up to 2014 were collected for each of these research centers. To
focus on relevant SCs, a threshold ≥ 0.02 was set on the frequency of the SCs normalized by the total of the
publications of each research center. This threshold represents the average of the third quartile of thenormalized SCs of the research centers. Moreover, to get around the generality of the SC “Multidisciplinary
Sciences,” articles with this SC were allocated two to four relevant SCs from their list of cited references.
Then, the Pajek software (De Nooy et al., 2011) was used to visualize the two-mode network that arises fromthe interconnections between the research centers and their relevant SCs. The network layout was rearranged
with the Kamada-Kawai layout algorithm with circular starting positions. Finally, the nodes of the network
were colored according to the classification of the field of science and technology proposed by the OECD
(OECD, 2007). In this classification scheme, science and technology fields are classified into 42 specificcategories. The mapping of the classification of the WoS’ SCs into the OECD’s specific categories was usedin this paper.
4.1.2. Meso level of analysis: Science overlay maps
Science overlay maps describe the meso-level in the analysis of knowledge integration. Developed by
Leydesdorff and Rafols (2009) and Rafols et al. (2010), this bibliometric technique locates the bodies ofresearch of organizations within the global structure of science. This method relies on a basemap built by thecosine-normalization of the cross-citation relationships between journals in terms of their subject categories.
These normalized cross-citation interactions are regarded as approximations of the cognitive distances
between scientific disciplines. In this approach, visual representations of the diversity and coherence of bodies
of knowledge are obtained by the superimposition of the basemap with a layer of nodes depicting thescientific disciplines of a given research center (i.e. diversity), as well as the interactions between these
disciplines (i.e. coherence).
7

The research methods for the construction of the science overlay maps are based on Rafols (2014) and Rafols
et al. (2012). Rafols and colleagues have made a toolkit for building science overlay mappings publicly
available (http://www.leydesdorff.net/overlaytoolkit/). Following these procedures, the diversity andcoherence of the bodies of research for the WPI-iCeMS and the Wyss Institute are visually and quantitatively
captured. For the case of diversity, this approach results in the superimposition on the basemap of nodes withsizes that vary according to the frequency of the relevant scientific disciplines of a given research center.
Similar to the previous section, the journal subject categories are used as proxies for the scientific disciplines.
By assessing differences in the intensity, number, and distribution of these scientific disciplines, insights into
the diversity of the institute’s research bodies can be gained. To focus on relevant disciplines, normalized
frequencies of SCs to the total of articles greater than or equal to 0.30% are used following the literature(Rafols, 2014; Rafols et al., 2012). In this study, the commonly used Integration Index (I), also referred to“Rao-Stirling diversity,” is used as a measure of diversity (Rafols et al., 2010):[1]

where pi and pj refer to the relative share of references citing the subject categories (SC) i and j, respectively,
and dij defines the degree of relatedness between the SC i and j as given by their cosine similarity measure.
Here, higher I values reflect greater levels of diversity. It is well-known that the I index captures a broader
picture of the diversity phenomenon than do other measures by simultaneously considering variety, balance,and disparity-related issues in their calculation (Rafols, 2014). Additionally, Shannon’s entropy was used as
an alternative measure of knowledge integration (Hinze and Grupp, 1992):[2]

in which, similar to Equation 1, pi refers to relative share of references citing a given SC.
For the case of coherence, the cross-citations (i.e. the citing-cited relationships between publications) based on
their SCs are used. For this purpose, SCs are extracted from the source publications and their list of references.
To do so, a thesaurus relating the list of journals indexed in the WoS database with their SCs was applied tothe data. This procedure was conducted using VantagePoint software (Porter and Cunningham, 2004). As
proposed by Rafols et al. (2012), the evaluation of coherence attempts to capture the level of interaction
between the relevant scientific disciplines of a given research center. Quantitatively, coherence (C) is defined
as follows (Rafols, 2014):[3]

where pij defines the proportion of citations between the subject categories i and j, and dij defines the degree ofrelatedness between the SC i and j as given by their cosine similarity measure. Graphically, coherence can beassessed by comparing the cross-citations, as given by their SCs, observed for a particular research entity withthose that should be expected based on the scientific interrelations of the global science map (Rafols, 2014;
Rafols et al., 2012). Superimposing the basemap with those cross-citation relationships with observed-vsexpected ratios above a certain threshold (in our case 3.5) reveals the relevant interconnections between
disciplines. To focus on relevant interrelations, cross-citations with weights higher than 0.15% were assessed.
These thresholds were empirically determined to fit the data from both research centers.
4.1.3. Micro-level of analysis
The mapping approaches described so far have approached knowledge integration at a disciplinary level.
However, interdisciplinary efforts can be visualized from deeper levels of analysis by using term maps andrelated mapping approaches.
4.1.3.1. Term maps and density maps

8

Term maps, also referred to as “co-words maps,” have a long history in bibliometrics. The earliest efforts inthis field date back as far as the 1980s in the work of Callon and colleagues (Callon et al., 1983) . Term maps
refer to the two-dimensional representations of the associations that arise from the co-occurrence of terms inscholarly articles. The more often these terms appear together in a document (i.e. co-appear), the stronger theirdegree of association. The stronger the degree of association between them, the closer they tend to appear on
the term map. For us, term maps represent the cognitive networks underlying the scientific activities ofresearch centers.
Term maps are constructed as follows. First, terms are extracted from the relevant documents of the WPIiCeMS and Wyss Institute. Although this study relied on data from the WoS database, we used the Scopus
database for the harvesting of relevant terms. The Scopus database provides a wider range of indexed terms(e.g. MESH, EMTREE terms) than does the WoS database. An exhaustive parallel procedure was conducted
to review each scholarly article manually in order to define additional relevant terms from their titles, abstract,
introduction, and conclusions. This was followed by iterative cleaning and grouping procedures on theselected terms. Terms with frequencies greater or equal to three were assessed. To consider differences in thesizes of research groups, frequencies greater or equal to two were considered for those PIs with fewer than 10
publications in the time periods considered in this study. These thresholds were empirically determined toapproximate the structures of these research centers.
After applying these thresholds, 1,498 and 947 terms were obtained for the WPI-iCeMS and the Wyss
Institute, respectively. Three time periods were considered: up to 2010, 2011 to 2012, and 2013 to 2014. For
each time period, matrices quantifying the co-occurrences of terms were built with VantagePoint software(Porter and Cunningham, 2004). Following the literature, a cosine-normalization was applied on the cooccurrence values of these matrices. To focus our attention in relevant interconnections, a threshold greater or
equal to 0.18 was set on the cosine-normalized matrices. Cosine thresholds between 0.10 and 0.20 are
common in the literature (Ávila-Robinson and Miyazaki, 2013). The visualization of these matrices was
conducted using Pajek software. Two types of layouts are used for the visualization of these matrices: graphand distance-based maps. Although graph layouts tend to be more readable and aesthetically pleasing, thedistances between nodes need not correspond to the strength of the relations between items; for this, distancelayouts are used (Borgatti et al., 2013; Van Eck and Waltman, 2010). The Fruchterman-Reingold algorithm
was used for the case of the graph layout, and the mapping approach embedded in the VOSviewer software
was used for the distance layout approach. VOSviewer was also used to extract the clusters of highlyinterconnected terms from the term maps. For the Fruchterman-Reingold graph layout, the Kamada-Kawailayout was used as a seed layout to account for their high sensitivity to the starting position of nodes. For thegraph- and distance-based layouts, we used a minimum cluster size of 15 and a cluster resolution of 2.0, which
defines the level of detail of the clustering technique (Van Eck and Waltman, 2010). Given the lower numberof publications for the Wyss Institute during the periods from 2009 to 2010 and from 2011 to 2012, minimum
cluster sizes of 8 and 10 were used, respectively. This study used the density map visualization provided by
VOSviewer. Through a red/green/blue color palette, the density map highlights important areas of the term
map based on the number of neighboring items and their weights (Van Eck and Waltman, 2011).
4.1.3.2. Cluster maps
The mapping approaches described in Section 4.1.3.1 rely on matrices relating terms to terms. However,additional dimensions can be gained by converting the terms-to-terms matrices of Section 4.1.3.1 into
matrices relating scholarly articles to scholarly articles. Following the cognitive emphasis of this study, twoapproaches were used in combination (Horlings and Gurney, 2013): paper co-word networks andbibliographic coupling networks. Paper co-word networks are derived from the matrices Tw that relate
documents to their terms. Paper co-word networks are obtained by multiplying the matrix Tw with its
transposed matrix Tw’. This gives paper-to-paper matrices relating scholarly articles with each other on thebasis of the terms they share. Similarly, bibliographic coupling networks are estimated by multiplying thematrix Tb relating documents to their references with its transposed matrix Tb’. This gives paper-to-paper
matrices relating scholarly articles with each other based on the references they share. Bibliographic coupling
networks relate scholarly articles based on the number of cited references they share. Similar to previous
approaches, both paper co-word and bibliographic coupling networks were cosine normalized. Then, the9average of both matrices was calculated for each research center. Clusters were extracted from this hybrid
matrix using VOSviewer software; 39 and 52 clusters were obtained for the Wyss Institute and WPI-iCeMS,
respectively. In a subsequent step, the hybrid matrices of section 4.1.3.2 were combined into their clusters
with the UCINET/NetDraw software leading to simplified networks relating clusters with clusters.
4.2. Bibliometric indicators
The mapping approaches of Section 4.1 were complemented with a series of bibliometric indicators.
Following the literature, four main categories of bibliometric measures were used: knowledge integration-,
cognitive-, collaboration-, and research impact-oriented bibliometric indicators. The specific indicators
included in each of these categories are described below.
4.2.1. Knowledge integration
Two indicators were used to measure the degree of integration in the bodies of knowledge of research centers:
the Rao-Stirling diversity index and Shannon’s information entropy index. Both indexes are described indetail in Section 4.1.2.
4.2.2. Nature of scientific knowledge
Knowledge is characterized by different rubrics or natures. To determine the nature of the scientificknowledge generated by both research centers, three proxies are used:▪ Cognitive classification: This indicator classifies articles according to the macro scientific disciplines they
belong to using the classification scheme proposed by (OECD, 2007). In this classification scheme, scienceand technology fields are classified into 42 specific categories. The mapping of the classification of WoS’SCs into the OECD’s specific categories was used in this paper.
▪ Cognitive group: The nature of scientific knowledge is also dependent on where the research efforts are
occurring on the cognitive networks underpinning research centers. A proxy for this measure is given by
the clusters allocated to the scholarly articles of the WPI-iCeMS and the Wyss Institute on the hybrid paper
co-word/bibliographic coupling maps described in Section 4.1.3.2,.
▪ Cognitive stage: This measure describes the stage the research efforts embodied in scholarly articles havereached in the problem-solving sequences embedded in particular fields of research. “Problem sequences”
are defined as the “recurrent patterns of problem search and solution” guiding research (Ávila-Robinson
and Miyazaki, 2013; Metcalfe et al., 2005). To operationalize this measure, we defined the taxonomies ofproblem sequences involved in the main research fields of both research centers. The cognitive stage wassimplified into three main stages: basic understanding, intermediary activities (e.g. synthesis of materials,
development of component technologies), and downstream activities (e.g. application-oriented efforts
regardless of their stage of development), as shown in the Supplemental Tables 1. Although the boundaries
between these problem stages may be blurred, we believe that the “cognitive center of gravity” of articles
tend to revolve around one of these stages. As these definitions are highly dependent on the field of study,
an in-depth understanding of the different technologies is necessary for correctly tagging articles. For this,the technical literature and expert advice were consulted, as described in Section 4.3.
4.2.3. Collaboration
In this study, several measures were used to characterize the nature of the collaboration schemes of bothresearch centers:▪ Number of affiliated countries: This measure refers to the number of countries involved in a scholarly
article. As such, it is used as a proxy for the degree of internationalization of a given article. Professorial
appointments of faculty members in multiple countries were fractionalized for this measure.
▪ Percentage of local co-authors: This indicator refers to the percentage of coauthors affiliated withorganizations geographically co-located with the research centers under study (i.e. the prefecture of Kyoto
for the WPI-iCeMS and the state of Massachusetts for the Wyss Institute) regardless of the type oforganization. In a sense, this measure defines the degree of locality of a scholarly article.
10▪ Number of PIs: This indicator measures the number of PIs coauthoring a scholarly article. For the
purposes of this measure, adjunct faculty and other faculty members under the PIs, such as assistantprofessors, associate professors, and lecturers are also included. The specifics of the calculation of thismeasure vary according to the organizational structure of each research center. For the Wyss Institute, forexample, we included the additional organizational units of “Advanced Technology Team” and “Research
Scientists & Engineers” as aggregate PIs. The “number of PIs” measure refers to the level of intracollaboration in a given scholarly article.
▪ Types of co-authoring organizations: This measure assesses the percentage of coauthors in a given article
affiliated with firms and hospitals, respectively.
4.2.4. Research Impact
The assessment of the research impact of publications relies on their normalized citation impacts. For thismeasure, the raw citation counts of articles are divided by the appropriate ESI (Essential Science Indicators)
baseline value provided by Thomson Reuters (as of September 2015). The ESI baseline values refer to theaverage performance measures of a group of articles within the same field, document type, and in a given yearof publication (Thomson-Reuters, 2014). Self-citations are included.
4.2.5. Heatmaps of bibliometric indicators
Heatmaps were built to visualize the performance of research centers across the different bibliometricindicators mentioned above. The data were normalized through standardized z-scores by z = (value – mean ofbaseline data) / standard deviation of baseline data. By forcing each data sample to have mean = 0 andstandard deviation = 1, it is possible to compare units of a different nature. To classify the datasets, ahierarchical clustering approach was conducted using SPSS software. This analysis relies on Ward’s
clustering method and squared Euclidean distances. The dendrograms obtained from this analysis were used toclassify the dataset. To color the heatmaps, a color scale ranging from green (z-scores of -2 and below) to red(z-score of +2 and above) was used. For the visualization of the data into heatmaps, the Origin 2015 software
was used.
4.3. Expert review
Qualitative review of the results of the bibliometric studies was conducted as follows. For the Wyss Institute a
member of its management committee and a former postdoctoral student were selected as reviewers. In bothcases, an hour-long semi-structured interview was conducted. The results of the interviews werecomplemented with publicly available sources of information, such as general articles about the Wyss Institutein journals and academic reports, press releases, and video media found online. For the WPI-iCeMS, theexpert review consisted of presentations and discussions of the results to an audience of PIs and postdoctoral
studies in two workshops. Additionally, progress reports, press releases, and additional internal documents
were used to complement our results.
5. Results
Here we report the results on the research activities described in the conceptual framework shown in Fig. 1.
This section presents the results of the bibliometric-based, network-oriented, and visualization-richapproaches used to explore the patterns of knowledge integration emerging from the practices and activities ofinterdisciplinary research centers.
5.1. Macro level: Positioning of research centers in the scientific research landscapeThis research activity aims to locate research centers relative to each other based on their scientific makeup(i.e. the contents of their scientific knowledge bases). These relationships are visualized in the scientificresearch landscape maps, which refer to two-mode networks that relate the research centers listed in Table 2(blue square-shaped nodes in Fig. 2) with their relevant scientific disciplines (circle-shaped nodes in Fig. 2).

11

[Figure 2 here]
As mentioned, the scientific disciplines of research centers are approximated by the journal subject categories
allocated to the total of their scholarly articles. In Fig. 2, the scientific disciplines (circle-shaped nodes) arecolored according to the OECD’s general classification of macro scientific fields. In this figure, lines connect
research centers to their relevant scientific disciplines, or scientific competencies. The thickness of these lines
varies with the value of the normalized frequencies of the scientific disciplines of research centers. By
dividing the scientific research landscape of Fig. 2 into general regions of science, we can observe two mainpoles— namely, the physical and life sciences, punctuated by patches of engineering and mathematical andcomputer sciences, to a lesser degree. The “positioning” of a research center within this map varies with thenature and intensity of its scientific competencies relative to those of other research centers. The morescientific competencies they share, the closer they appear on the map in Fig. 2.
The scientific research landscape map reveals a clear constellation of research centers. At one end, Fig. 2
shows a group of research centers with scientific competencies heavily influenced by life sciences domains,such as biochemistry and molecular biology, cell biology, biotechnology and applied microbiology,
biophysics, biochemical research methods, and (partly) genetics and heredity. These research centers are theMIT’s Whitehead Institute, Riken’s CDB, Tata Institute of Fundamental Research’s NCBS, University of
Edinburgh’s CRM, Max Planck’s CBG, Princeton University’s LSI, Tsinghua University’s Center for Life
Sciences, and MIT-Harvard University’s Broad Institute. Besides their core disciplines, some of theseresearch centers emphasize unique competencies: quantitative sciences by the LSI, biomedical domains by the
Broad Institute, general biology domains at the NCBS, cell and tissue engineering at the CRM, anddevelopmental biology by the CDB. At the other end, the scientific competencies of the Université deVersailles’ ILV and the Lawrence Berkeley National Lab’s Molecular Foundry appear to be significantly
dominated by the physical sciences and partly by engineering. These research centers share interests among a
set of core disciplines, such as physical chemistry, nanoscience and nanotechnology, multidisciplinarymaterials sciences, multidisciplinary chemistry, and condensed matter physics. Despite their similarities, theILV focuses on basic fields of chemistry such as electrochemistry, crystallography, and inorganic and organic
chemistry, while the Molecular Foundry is active in more applied disciplines such as nanotechnology, applied
physics, materials science, and analytical chemistry.
A further examination of Fig. 2 reveals a set of organizations lying between both groups of research centers.
The Scripps Research Institute’s Skaggs Institute and to a lesser degree the Max Planck’s IMP arecharacterized by scientific competencies cutting across the life and physical sciences. These research centers
have similar scientific makeups that combine both the core life sciences domains mentioned above withphysical sciences fields such as organic chemistry, medicinal chemistry, and multidisciplinary chemistry.
Finally, another group of research centers appear to be building scientific competencies at the intersection of
the life sciences, physical sciences, and engineering. These are Harvard University’s Wyss Institute, MIT’s
Koch Institute, Arizona State University’s Biodesign Institute, WPI-iCeMS, and to a lesser degree the UCLA
and UC Santa Barbara’s CNSI. The potential interdisciplinary nature of these research centers is reflected intheir missions: bio-inspired engineering, integrative cancer research, nature-inspired research, integrated cellmaterial sciences, and nanosystems, respectively. Previous reports have highlighted the potential opportunities
of research centers bridging multiple regions of science (MIT, 2016; NRC, 2014; Sharp and Langer, 2011).
Given their scientific positioning, we expect potentially high interdisciplinary and converging natures forthese research centers.
Thus, the positions occupied by research centers within the scientific research landscape provide initialinsights, at the aggregate level of groups of research centers, into the patterns of knowledge integration efforts.
This approach revealed the nature of the integrative scientific competencies of research centers. It was shownthat the scientific positioning of research centers encompassed single to multiple macro-fields of research:physical, life, computational and mathematical, and engineering sciences. However, this approach overlooked
the interconnections between the scientific disciplines of Fig. 2. These will be examined in the next section
through the science overlay mapping approach. In the rest of this paper, the cases of the Wyss Institute and the
WPI-iCeMS are emphasized.

125.2. Meso level: Positioning of knowledge bases within the whole of science and their characterization
This section uses the science overlay mapping approach, which locates the scientific competencies of research
centers within the whole of science as a way to assess their diversity and coherence (Leydesdorff and Rafols,
2009; Rafols, 2014). Whereas diversity refers to the disparity, variety, and (im)balance of given bodies ofknowledge, coherence describes the interconnections between them (Porter et al., 2007; Rafols, 2014). Fig. 3ashows the diversity of the research bodies at the WPI-iCeMS and Wyss Institute between 2007 and 2014 andbetween 2009 and 2014, respectively.
[Figure 3 here]
In Fig. 3, basemaps depicting the whole of science are superimposed with a set of nodes representing therelevant scientific disciplines of research centers as proxied by the journal subject categories of their scholarly
articles. The sizes of these nodes vary according to the frequencies of the scientific disciplines normalized by
the total of the scholarly articles of a given research center. Fig. 3 shows that both research centers display
diverse and cognitively distant scientific disciplines. Interestingly, the scientific knowledge bases of bothresearch centers are rather similar. These similarities can be further confirmed with the diversity-related
measures the Rao-Stirling diversity and Shannon’s entropy values, as shown in Table 3 (top).
[Table 3 here]
In line with the previous section, the scientific makeup of both research centers cuts across the life sciences,
physical sciences, and engineering, including cell biology, biochemistry and molecular biology, biotechnology
and applied microbiology, nanoscience and nanotechnology, multidisciplinary chemistry, physical chemistry,
applied physics, and multidisciplinary materials science. Less intensely, both research centers explore thefields of physics, computer sciences, mathematical methods, and clinical medicine. Differences also appear,
however. The Wyss Institute uniquely emphasizes engineering-oriented disciplines such as biomedical
engineering, biomaterials, and robotics, while the WPI-iCeMS is characterized by its focus on inorganic
chemistry, optics, medicinal chemistry, pharmacology and pharmacy, and, to a lesser extent, on oncology anddevelopmental biology. For both research centers, the “Multidisciplinary Sciences” subject category is
prominent, with shares of 19% and 7% for the Wyss Institute and WPI-iCeMS, respectively. As“Multidisciplinary Sciences” agglomerates multiple disciplines, it may lead to a loss of information. This isparticularly important for leading-edge research centers as they tend to emphasize the publication of articles inhigh-impact journals, such as Science, Nature, PNAS, and Scientific Reports, which are usually classified as
multidisciplinary by bibliographic databases.
Diversity does not assess the interactions between disciplines, however, which is a key feature of knowledge
integration. This is done through the evaluation of coherence, for which we follow Rafols et al. (2012)’s
approach. Basically, this method highlights higher-than-expected interactions between disciplines, which may
be regarded as relevant and unconventional interconnections between them. To do so, the cross-citations
between the source publications of a given research center and their cited references are defined in terms of
their journal subject categories. These are referred to Rafols et al. (2012) as the “observed cross-citations”. By
contrast, “expected cross-citations” embody those cross-citations that would be expected to take place basedon the interactions between disciplines drawn from the basemap. Interactions between disciplines above acertain ratio of observed vs. expected cross-citations are visualized in this figure. Fig. 3b shows these maps forboth research centers. A more detailed display of the interconnections of Fig. 3b is shown in Fig. 4.
[Figure 4 here]
Although both research centers “overexpress” interactions bridging the life and physical sciences, the intensityof their knowledge integration efforts differs significantly. As shown in Fig. 4a, the Wyss Institute displays
denser and more intense interconnections. What is more, the Wyss Institute’s interactions involve morecognitively distant disciplines in the fields of clinical medicine (cardiac and cardiovascular systems,
peripheral vascular diseases, and microbiology) and materials sciences (polymer science). In fact, the Wyss
Institute displays 2.7 times more interconnections than does the WPI-iCeMS, as inferred from its higher
13coherence values (see Table 3). Biomedical engineering and biomaterials appear to work as “connectors”
between the life sciences and the physical sciences. Both disciplines account for almost half of all the relevantinteractions in the science overlay map of Fig. 4a, signifying the pivotal intermediary role of engineering. As
the lines of Fig. 4 are directed (i.e. they flow from the citing articles to their cited references), it is possible toevaluate disciplines according to their number of out-going or incoming lines. Whereas the former may be
regarded, even if only approximately, as drivers of knowledge integration, the latter may be referred as thebuilding blocks supporting knowledge integration. For the Wyss Institute, besides biomedical engineering andbiomaterials, nanoscience and nanotechnology and chemistry (multidisciplinary) appear to be the driving
disciplines behind knowledge integration (see Table 3, bottom). Based on the top-five scientific disciplines
with the highest in-degree values (see Table 3, bottom), biology-oriented disciplines appear to be underlying
the Wyss Institute’s knowledge integration efforts.
By contrast, the WPI-iCeMS shows sparser and less intense knowledge-integrating relations (see Fig. 4b). In
line with its mission, the WPI-iCeMS appears to be stressing interconnections between biological andmaterials science-oriented disciplines. The bulk of these interactions appears to be dominated by the discipline“chemistry, multidisciplinary,” which tends to include journals approaching chemistry from aninterdisciplinary perspective. In fact, half of the relevant interconnections interact with this discipline. Besides
this, nanoscience and nanotechnology, materials science (multidisciplinary), and cell and tissue engineering
are also significant drivers of knowledge integration at the WPI-iCeMS. Similar to the Wyss Institute,knowledge integration efforts at the WPI-iCeMS rely on biology-oriented disciplines such as biochemistry
and molecular biology, cell biology, and biotechnology and applied microbiology (see Table 3, bottom).
Compared to the Wyss Institute, the WPI-iCeMS displays stronger interconnections with physics-related
disciplines such as Applied Physics and Physics, Atomic, Molecular and Chemical and with life sciencesrelated fields such as Developmental Biology and Pharmacology and Pharmacy.
Through the science overlay maps we can confirm that both research centers are actively breaking theboundaries between living and non-living systems (Ingber, 2011). However, the nature, intensity and driversof their knowledge integration efforts varied considerably. Although this approach explores in greater detailthe scientific makeup of research centers and their interconnections, it is still limited to the aggregate level ofdisciplines. There is thus a need to examine knowledge integration through the research projects and topics
behind these scientific disciplines and their interconnections. The next section presents this approach with theuse of term maps and other related methods.
5.3. Micro level: Exploration of the structure, dynamics, and contents of the cognitive maps
This section explores the patterns of knowledge integration of the Wyss Institute and the WPI-iCeMS at
higher levels of granularity. To this end, cognitive maps are built. In doing so, this section clarifies the
structure, dynamics, and contents of these cognitive maps, and reveals how these maps relate to theorganizational forms of these interdisciplinary research centers.
5.3.1. Structure and dynamics of cognitive maps
Three types of visualization approaches are used to characterize the structure and dynamics of the cognitive
maps of IDR centers (see Figs. 5a and 5b): graph layout-based term maps and their density visualizations (leftand center, respectively), and the density visualization of the distance layout-based term map (right). As
described, although graph layouts tend to be more readable and aesthetically pleasing, the distances between
nodes need not correspond to the strength of the relations between items; for this, distance layouts are used
(Borgatti et al., 2013; Van Eck and Waltman, 2010). Density visualizations are used to delineate the general
structure of the cognitive maps, as well as to highlight their important regions of interaction (Van Eck andWaltman, 2011). As shown in Figs. 5a and 5b (center and right), the intensity of the interconnections within
the cognitive structures is captured through a red/green/blue color scale: red denotes higher densities ofinterconnection between terms. For both research centers, three time periods are defined – up to 2010, 2011 to2012, and 2013 to 2014 – to evaluate their dynamics of change over time.
[Figure 5 here]
14

The colors of the nodes of the term maps of Figs. 5a and 5b (left) correspond to those regions of highinteraction between research terms, or clusters. These clusters are labeled according to the terms they contain.
Contrasting the cognitive structures shown in Fig. 5a and 5b leads to some generalizations about the patterns
of knowledge integration of both research centers. As can be observed in the visualizations of Figs. 5a and 5b,
the cognitive structures of the WPI-iCeMS and the Wyss Institute vary considerably. Particularly, strong
differences can be observed in the structures and distribution of their research contents across these maps. For
the WPI-iCeMS, the density maps of Fig. 5a (center and right) denote a bipartite cognitive map. The cognitive
contents of the WPI-iCeMS appear to be distributed as follows: a mostly physical sciences-oriented block on
the left and a mostly life sciences-oriented block on the right. The former focuses on the synthesis of adiversity of materials (e.g. metal organic frameworks, organic compounds, perovskites, nanomaterials, andglycomaterials), while the latter emphasizes the understanding and manipulation approaches in different
domains of cell biology (e.g. stem cells, neurobiology, germ cells, membrane biochemistry mechanisms).
Additionally, the latter block embraces research efforts that appear to be integrating the physical sciences, life
sciences, and engineering (e.g. cell imaging techniques, DNA nanotechnology, drug delivery approaches forchemo- and gene delivery). Interestingly, the results of Fig. 5a (center and right) reveal that both the life
sciences- and the physical sciences-oriented blocks are getting closer over time. This may be attributable to
the research efforts “bridging” the cognitive blocks.
By contrast, the cognitive structure of the Wyss Institute (see Fig. 5b, center and right) reveals a more
fragmented or modular structure characterized by a wide range of topics across its cognitive map. The Wyss
Institute’s cognitive map is dominated by a highly connected region, or core, on tissue engineering-related
topics (e.g., biomaterials and tissue scaffolds, therapies, and basic understanding) surrounded by a series ofsmaller yet intense regions on synthetic biology, antibiotic activity, cell engineering, and DNA nanostructures.
The rest of the cognitive structure is characterized by several less-intense research domains not shown on thedensity maps, as they are overshadowed by other research streams. The general structure of the cognitive
maps of the Wyss Institute can be defined as follows. The upper left region involves tissue engineering,
diagnostic devices, and robotics-oriented cognitive domains characterized by the combination of engineeringand the physical and life sciences in different degrees. The bottom left region of these maps consists mostly oflife sciences-oriented domains, such as synthetic biology, systems biology and antibiotics, cell engineeringand gene editing approaches. Finally, the right region of the map embraces physical sciences-oriented
domains mostly related to the synthesis of materials, such as nanostructured surfaces, hierarchical structures,
and adaptive materials.
5.3.2. Characterization of the contents of cognitive maps
Greater understanding of the particularities and specificities of the cognitive maps of Fig. 5 can be gained
through their conversion into document-based networks (i.e. networks that relate “papers with papers” insteadof “terms with terms”). The methods of constructing document-based networks are described in Section
4.1.3.2. For simplicity’s sake, the nodes of these paper-to-paper networks are combined into their clusters
leading to networks relating clusters with clusters (see Fig. 6). In these cluster networks, each node representsa group of highly interconnected scholarly articles on the basis of the terms and cited references they share.
These clusters represent the “elemental” research domains along which a given research center channels its
R&D efforts. In total, 39 and 52 research domains were obtained for the Wyss Institute and the WPI-iCeMS,
respectively. Similarly, the colors of the nodes in Fig. 6 denote groups of related research domains. For
visualization purposes, these are also encircled with the red dotted lines. In a sense, these “clusters of clusters”
relate to the general research fronts of these research centers. Each of these research fronts is labeledaccording to the terms extracted from the publications they contain, as shown in the list of their representative
keywords shown in Fig. 6 (right).
[Figure 6 here]
Building on the discussions of Section 5.3.1, we can relate the results of Fig. 6 to the R&D paths andorganization of these research centers. The modular approach observed in the Wyss Institute results in threemain poles: “tissue engineering and related fields”, “synthetic biology and gene editing approaches”, and“robot technologies, soft devices and non-linear dynamics/biological models”. These are punctuated by the15intermediary research fronts “adaptive materials” and “DNA technologies”. By contrast, the bipartite
approach of the WPI-iCeMS is characterized as follows. There is a large group of research domains dealing
with the “materials synthesis” (e.g. coordination, organic, inorganic, glycol-materials) on the right of Fig. 6(bottom). At the other end, we observe a group of research domains on “stem cells” surrounded at a closer
range by a series of cell-oriented research domains, such as “genetic switches”, “drug delivery approaches”,
and “nanobio- and biophysical studies”. Further away, “DNA nanotechnologies”, “plasma membrane-related
studies”, and “cell imaging technologies” are located. Finally, “terahertz technologies” resides away from therest of these research domains. Fig. 6 also reveals that despite the fragmented nature of the cognitive maps ofthe Wyss Institute, its research domains display greater levels of cognitive interaction than do those of the
WPI-iCeMS, in line with the results of Section 5.2.
The differences in the structure and contents of the networks of Fig. 6 are strongly related to the forms oforganization established by these IDR centers to carry out their R&D missions. For the Wyss Institute, Fig. 6(top) closely resembles the “enabling technology platforms” underpinning its research organization. As
described by Ingber (2011), technology platforms represent teams working on bio-inspired technologies
focusing on certain application areas of interest. Six platforms have been defined: adaptive material
technologies, living cellular devices, bio-inspired robotics, biomimetic microsystems, programmablenanomaterials, and synthetic biology. Faculty is allocated to one or more of these research platforms. By
contrast, the WPI-iCeMS follows a more PI (principal investigator)-centered approach based on theestablishment of independent research laboratories each dealing with the particular research interests of PIs(see Fig. 6, bottom). The fields to which PIs belong include nanobiotechnology, microfluidics, biophysics,
stem cell biology, membrane biology, neurosciences, chemical biology, cellular biochemistry, terahertz
optical science, germ cell biology, materials science, nanomaterial, and theoretical chemistry, among others.
The differences between these models of research organization—by topic or discipline—though apparently
subtle, are believed to significantly impact the way research centers extract value from knowledge integration(Huutoniemi et al., 2010; Siedlok and Hibbert, 2014; Stehr and Weingart, 2000), as the next section will show.
By characterizing the contents of the cognitive maps of Fig. 6, it is possible to gain deeper insights into thepatterns of knowledge integration in the practices of IDR centers. We first examine this in Fig. 7 by mapping
the levels of interdisciplinarity and the cognitive stage of the research domains of Fig. 6 for both research
centers. Whereas interdisciplinarity is measured through the Rao-Stirling index of the research domains (seeSection 4.1.2), the assessment of the cognitive stage is based on the stage of problem-solving embodied intheir scholarly articles, as described in Section 4.2.2. The maps of Fig. 7 show statistically significant
relationships between both measures, as inferred from the correlation matrices of the Supplemental Table 2.
This is line with the differences of interdisciplinarity observed across different research contexts (Frodeman,
2011; Sanz Menéndez et al., 2001; Van Rijnsoever and Hessels, 2011). However, the patterns of distributionof research domains differ widely in these maps. The Wyss Institute (see Fig. 7, left) shows higher levels ofinterdisciplinarity and a greater tendency to engage in “downstream” (application-oriented) R&D efforts thanthe WPI-iCeMS (see Fig. 7, right). This is to be expected given the engineering- and translation-oriented
nature of the Wyss Institute; yet, Fig. 7 provides the full portfolio of research domains. There are some
exceptions, such as the synthesis of inorganic materials and the development of terahertz technologies in the
WPI-iCeMS. Despite their “basic research” connotations, these fields of research show high levels ofinterdisciplinarity through the close integration of chemistry and physics.
[Figure 7 here]
Continuing with the characterization of the contents of the cognitive maps in Fig. 6, we mapped the 39 and 52
research domains obtained for the Wyss Institute and the WPI-iCeMS, respectively, across the bibliometricindicators of Section 4.2. These include cognitive-, collaboration-, research impact-, and diversity-related
measures. To visualize these data, heatmaps were built for both research centers (see Fig. 8), as described in
Section 4.2.5. The rows of these heatmaps are arranged on the basis of hierarchical clustering approaches, as
shown in the dendrograms placed at the left of both heatmaps of Fig. 8. On the right of these heatmaps, wefind the list of research domains, the nodes of Fig. 6, and the research fronts, the group of nodes encircled
with red dotted lines in Fig. 6. The colors on the list of research domains refer to the research fronts of Fig. 6.

16

[Figure 8 here]
Fig. 8 reveals distinctive patterns in the distribution of the research domains of Fig. 6 on the basis of theintensities of their bibliometric indicators across the heatmaps. Five and nine macro-groups sharing particular
combinations of bibliometric measures were obtained for the Wyss Institute and the WPI-iCeMS, respectively.
Fig. 8 shows that research domains from similar research fronts tend to group together in similar macrogroups. This suggests that research domains are not only cognitively intertwined as inferred from the networksof Fig. 6, but also share similar properties as inferred from the heatmaps of Fig. 8. These groups of researchdomains are characterized by specific patterns of common properties, (i.e. research trajectories, acrosscognitive, collaborative, impact-related, and integrative measures). For both research centers, the macrogroups of Fig. 8 show strong dependences on specific fields of science and technology: life sciences, physicalsciences, engineering, mathematical sciences, and computer sciences. For the WPI-iCeMS, Fig. 8 shows thehigh degrees of internationalization of the research domains “nanobiotechnology” and “stem cells”. On thecontrary, “genetic switches,” “organic materials,” and “terahertz technologies” rely heavily on researchers colocated in the Kyoto prefecture. Others such as “imaging technologies,” “drug delivery systems,” and “plasma
membrane studies” tend to stress intra-collaboration schemes among WPI-iCeMS’ PIs. Moreover, whereas“coordination materials (MOF/PCP)” and “terahertz technologies” tend to collaborate with firms, “stem cells”
and “drug delivery systems” do so with hospitals. For the case of the Wyss Institute, “microfluidic devices,”“tissue engineering, biomaterials,” and “tissue engineering, mechanotransduction” show the greatest levels ofinternationalization. In contrast, “adaptive materials,” “gene editing,” “soft devices,” and “tissue engineering,cardiovascular” tend to rely on collaborations with researchers co-located in the Massachusetts area. Besides“soft devices” and “tissue engineering, mechanotransduction”, the research domain “microfluidic devices”
shows the greatest levels of intra-collaboration among the Wyss Institute’s faculty. Whereas “tissue
engineering, drug delivery” is characterized by high interactions with firms, “microfluidic devices,” “tissue
engineering, biomaterial,” and “tissue engineering, mechanotransduction” denote stronger clinical orientations
through their close interactions with hospitals. These relationships can be corroborated with the correlations in
Supplemental Table 2. As inferred from these results, the practices of IDR centers reveal heterogeneous
patchworks of research domains characterized by multiple research trajectories. Regarding their propensity forknowledge integration, two general patterns can be drawn from Fig. 8: interdisciplinarity-enabling andinterdisciplinarity-generating research domains. The former tends to focus on the creation of mostly
monodisciplinary, basic understanding and intermediary knowledge, such as the synthesis of materials and thedevelopment of basic technologies, which, through subsequent recombination efforts may lead tointerdisciplinarity-oriented knowledge. The latter tends to target more downstream areas of problem solving,
mostly devices or solutions oriented to applications, with high levels of interdisciplinary knowledge.
In summary, this section analyzed the patterns of knowledge integration at greater levels of granularity. By
studying the structure, contents and dynamics of the cognitive maps of research centers, we could reveal
differences between and, more importantly, within these organizations. The realities of the practices ofinterdisciplinary research centers display heterogeneous patchworks of research domains driven by multiple
research trajectories—some enabling, and others generating interdisciplinary knowledge. As the next section
explains, an understanding of these differences has implications for the management and organization ofknowledge integration.
6. Discussions and Implications to policy, practice and theory
6.1. Key findings of this research
The complexity of the problems and challenges faced by researchers and scientists is calling for solutions thatcut across multiple and cognitively diverse disciplinary domains. We succeeded in demonstrating, at different
levels, perspectives, and granularities, the patterns of knowledge integration emerging from the practices andactivities of IDR centers. At the macro level, our approach revealed the nature of the integrative scientificcompetencies of research centers. We could see how the scientific positioning of research centersencompassed single to multiple macro-fields of research, from the physical, life, computational andmathematical, and engineering sciences. From the meso-level perspective, we demonstrated that the Wyss
17

Institute and the WPI-iCeMS were actively breaking the boundaries between living and non-living systems.
Yet, the makeup of knowledge interconnections of both research centers differed significantly on the basis of
their natures and intensities. The Wyss Institute appears to be mostly driven by application-oriented fields
such as biomedical engineering, biomaterials, and nanotechnology, while the WPI-iCeMS tends to rely on the
more basic-oriented fields of chemistry, materials sciences, and biology. The greatest differences were
captured through the micro level. Different topologies were observed for both research centers: fragmented or
modular for the Wyss Institute, bipartite for the WPI-iCeMS. The practices of both IDR centers were denotedby heterogeneous patchworks of research domains driven by multiple research trajectories. From thesefindings, implications to the policy, practice and theory of IDR centers are presented below.
6.2. Implications to theory
This study contributes to the literature on interdisciplinarity in three ways. First, we propose to study
knowledge integration in the practices and activities of IDR centers using a multilevel, integrated approach.
Indeed, this approach has been suggested previously for the science of team science (Börner et al., 2010), but
it has neither been advanced nor empirically developed in the context of IDR centers. Our findings indicatethat such approaches are appropriate for addressing the multi-dimensionalities, complexities, multiple levelsof aggregation and granularity, and different perspectives inherent in interdisciplinary research (Cambrosio etal., 2006; Klein, 2008; Rafols et al., 2012; Rafols and Meyer, 2010; Sanz Menéndez et al., 2001). Our study
also provides a more realistic conceptualization of the practices of IDR centers. Beyond the idealizations offull convergence common in the literature, we visualize IDR centers as patchworks of research domains
driven by diversified research trajectories—some indirectly enabling, others directly generatinginterdisciplinary knowledge to different degrees. These results reveal the complementary roles of disciplinary
and interdisciplinary knowledge, as inferred by others (Jacobs, 2013; Repko, 2008; Siedlok and Hibbert,
2014; Stehr and Weingart, 2000). We would expect disciplinary research efforts to catalyze into
interdisciplinary research through their recombination with additional sources of knowledge, for example, as
seen in the multi-directional chains of understanding (i.e., basic or discovery-oriented) and manipulation (i.e.,
problem- or application-oriented) in the case of science research. Studies have highlighted thatinterdisciplinarity is not an end in itself but a means to an end—namely, the advancement of knowledge
through solutions to complex problems (Frodeman, 2011; Jacobs, 2013). The need to not overlook the whys
for the hows of interdisciplinarity has also been highlighted, emphasizing the “… underlying impulses behindthe push for interdisciplinarity approaches to knowledge production” (Frodeman, 2011). Our results haveshown that the “impulses” of interdisciplinary research in science are strongly driven by the types of research
problems facing IDR centers (NRC, 2014), which in turn should be intimately related to their respective
missions. Consequently, the level of interdisciplinarity in a research center assumes a secondary role; instead,stakeholders associated with the research should question the extent to which their efforts fulfil the highlyinterdisciplinary-oriented missions of their IDR centers, the main impulse behind knowledge integration.
Second, this study provides empirical evidence on the role of the heterogeneities of knowledge integration in
the research management of IDR centers. Many authors have highlighted the social, cognitive and physicalboundaries and contexts delimiting different fields of research (Battard, 2012; Heimeriks, 2013; Sanz
Menéndez et al., 2001; Van Rijnsoever and Hessels, 2011). Differences in knowledge integration certainly
occur, not only in between but also (and more importantly) within IDR centers. Our results highlight the needfor understanding the cognitive makeup of IDR centers for the effective fostering of interdisciplinarity. We
show that the heatmaps in Fig. 8 are practical tools for understanding at a glance the particularities andspecificities of IDR centers on the basis of their disciplines, cognitive nature, collaboration schemes, impact,
and levels of knowledge diversity. Certainly, these studies should be expanded to include the greater contexts
within which IDR centers function. Given these differences, we would expect that numerous ways ormodalities of bringing about integrated knowledge exist (Battard, 2012; Huutoniemi et al., 2010; Klein, 2008).
As pointed out by NRC (2014), the larger differences in size, missions, and available budgets preclude thedefinitions of single “recipes” for effectively facilitating interdisciplinary research. Thus, in regard tointerdisciplinarity, “one size does not fit all.” Our findings show that research stakeholders should deploy
tailored and targeted strategies, instead of “all encompassing” ones, tuned to the specific characteristics of theparticularities and specificities of IDR centers. In addition, our results expand the research management of
IDR centers by redirecting their unit of analysis from disciplines to clusters, or research domains, which make18

up their cognitive structures. The results of this paper also demonstrate that observing the properties of theseresearch domains and their interconnections can improve the understanding of research stakeholders, and thus,
provide them better opportunities to channel their research efforts along their respective interdisciplinary
missions.
Third, our study empirically demonstrates the impact of organizational structures on the effectiveness of IDR
centers toward fulfilling the interdisciplinary missions for which they have been established. This is reflectedin the different bibliometric mapping approaches employed in this paper. Our findings demonstrate that the“enabling technological platform-based” approach followed by the Wyss Institute appears to foster
interdisciplinary research more effectively than the conventional principal investigator-centered approach of
the WPI-iCeMS. We regard such modular approaches as more suitable for coping with the complexities ofknowledge integration as they provide IDR centers with a sense of direction (even if at higher levels) of thepathways along which to channel their integrative efforts. This point has also featured in the NRC (2014)’sreport, which notes the need for IDR centers to organize their missions around common themes, problems, orscientific challenges so as to foster knowledge integration. The differences between these research centers are
widened by the organizational structure of the Wyss Institute, which is based on “colaboratories,” whereingroups of people from different faculties work together within the context of a project or a specific application(Ingber, 2011, 2013). This contrasts with the organizational scheme of the WPI-iCeMS, wherein faculties
have independent laboratories. We infer that the differences between the organizational structures of both IDR
centers may be due to cultural differences in their ways of approaching integrated research management(Shibayama et al., 2015).
6.3. Implications to policy and practice
The results of this study provide research stakeholders with a series of practical recommendations for planningand assessing knowledge integration in IDR centers. First, the visualization approaches of this paper are notregarded as end goals in themselves but rather as points of departure from which to enrich discussions amongscientists, analysts, policy makers, and reviewers about interdisciplinary research (Milojević et al., 2012). In
particular, the visibility provided by the bibliometric mapping approaches used in this paper offers scientists
and researchers greater opportunities for accessing, discussing, valuing, and managing the knowledge
produced by research centers (Eppler and Burkhard, 2007; Sparrow, 1998). This is of great importance, as
scientists tend to become so focused on their own research that they usually fail to grasp the “bigger picture”
within which their research is embedded.
Second, as research becomes more complex, we foresee the continued use by research stakeholders of
empirically grounded tools for R&D management, such as those presented in this paper. Of interest is theiruse as planning and assessment tools for practically and systematically understanding the effects ofmanagerial decisions on knowledge integration efforts (Anzai et al., 2012). By considering these needs for theassessment and evaluation of interdisciplinary research, our study reveals cues for effective research
management at multiple levels of the analysis, as demonstrated in the synthetic model presented in Fig. 9.
[Figure 9 here]
The macro-level visualization approach embodied in the academic research landscapes of Section 5.1 provides
an overall perspective of the portfolio of R&D projects (or institutions) within a policy program. This level ofanalysis evaluates projects against other programs or projects on the basis of their scientific positioning andthe quantity/quality of scientific outcomes according to the research program’s goals. The meso-levelvisualization approach, as depicted by science overlay maps, can be used for the selection, development andevaluation of research centers on the basis of the project’s overall mission and value proposition (see Section
5.2). Finally, the micro-level visualization approach using term maps and related methods (see Section 5.3)
provides a prospective view of the networking possible among various research topics and potential
collaborations. This level gives the heads of research labs useful insights into exploring, planning, andcalibrating their research directions in accordance with the strategic goals of their respective research centers.
Additionally, these three visualization approaches can be used for any level of research management. For
instance, the macro or meso-level visualizations may help PIs understand the overall trends of their research19programs or centers in a top-down manner. Micro-level visualizations may assist the program director or
officers at the policy-making level to pursue emerging research topics and calibrate or renew the program
design in a bottom-up manner. Notably, it becomes possible to not only acquire periodic data from affiliatedresearchers, but also to understand the current situation and discuss improvement plans during institutemanagement decision-making on these indicators. For example, initiating a PDCA (plan-do-check-act) cycleof R&D activity could contribute toward systematic and continuous improvement of processes and activities,
especially in applied research and/or interdisciplinary research, which involves the participation of numerous
researchers from different institutions or academic fields. Certainly, these discussions do not overlook the factthat interdisciplinarity may often arise from unexpected, spontaneous interactions between researchers andscientists, instead of fixed R&D plans.
Third, the approaches in this paper can also be helpful in review processes. It is well known that reviewers
often lack the necessary competencies to properly assess research centers dedicated to interdisciplinarity(Anzai et al., 2012). Moreover, the research centers under review may encounter difficulties in effectively
conveying their performance and functionality to a reviewing committee. Review processes should be
regarded as information-asymmetric situations in which research centers are certainly more capable ofeffectively assessing their own performance, including interdisciplinarity and knowledge integration, than ateam of reviewers. In this regard the provision of empirically based analytical tools to reviewers can diminish
the information asymmetries inherent in review processes.
6.4. Limitations of this study
This study has three main limitations. First, as with any research technique, bibliometric-based approaches
have inherent limitations including (a) their inability to take into account R&D efforts that do not lead to
publications; (b) their tendency to be highly sensitive to the inherent differences and specificities among
research fields; (c) their limitation to publications indexed in the WoS database; (d) the well-known
differences in referencing behaviors across research fields, such as the intensive use of conference
proceedings by computer science or robotics; and (e) the difficulty of discerning the real relevance of a given
scholarly article for a research center. Despite their imperfect nature, however, these approaches embody
reproducible, informed and evidence-based approximations of reality complementing, not supplementing,
R&D decision processes (Anzai et al., 2012). Second, given the nature of our approach, this paper is limited tothe study of interdisciplinarity from a cognitive perspective, (i.e. the diversity of the knowledge being
integrated by research centers). Although some collaboration-related measures were considered, we largely
overlooked the social dimension of interdisciplinarity, (i.e. the diversity of the team of co-investigators).
Social-oriented interdisciplinarity demands an estimation of the scientific and technological competencies ofthe set of co-authors of scholarly articles— methodologically, a daunting challenge. Social aspects appear toplay an important role in the perceptions of interdisciplinarity by researchers—in the case of the research
center of one of the authors, a qualitative self-assessment by the PIs on the level of interdisciplinarity of theirscholarly articles against the bibliometric, (i.e. quantitative,) indicators used in this paper. It was found thatscientists tended to overstress, by up to three times, social diversity (diversity of the team of co-investigators)
relative to cognitive diversity (the diversity of the knowledge being integrated) in their perceptions ofinterdisciplinarity. Third, we have considered only the cases of two research centers in this study. However,this selection relied on archetypal research centers established with explicitly interdisciplinary aims, both offairly similar sizes and working in similar fields of research. Given the generality of our approach, we believethat it can be transferred to other research centers active in different fields of science and technology.
7. Conclusions
As research becomes increasingly complex, the need to understand research centers, particularly those createdwith an interdisciplinarity spirit in mind, becomes more urgent. This study involved the use of multiple
visualization and quantitative approaches to pragmatically examine the patterns of knowledge integration
emerging and the forms of research organization from the practices and activities of interdisciplinary research
centers. A multilevel approach including three approaches was used: the scientific positioning and makeup of
research centers (macro level), the location of their scientific competencies within the whole of science (mesolevel), and the characterization of the structure, dynamics, and contents of their cognitive maps (micro-level).
20

The cases of two interdisciplinary-oriented research centers were empirically evaluated: the Wyss Institute at
Harvard University and the WPI-iCeMS at Kyoto University. The similarities in scientific positioning of both
IDR centers at the macro-level translated into differences in the natures, intensities, and drivers of theirknowledge interconnections at the meso-level. Beyond idealized conceptualizations of full convergence, therealities of IDR centers were characterized by heterogeneous patchworks of multi-trajectory research domains
at micro levels—some of these indirectly enabling, others directly generating interdisciplinary knowledge, todifferent degrees. We observed that approaches to knowledge integration vary between but also, and more
importantly, within IDR centers. The exploration and understanding of these inter- and intra-organizational
differences proves crucial for effectively fostering knowledge integration. Several implications expected tocontribute to the multilevel, pragmatic, and systematic assessment of research management, organization, andassessment of IDR centers were drawn from this study. Future efforts should be aimed at enhancing theapproaches presented in this paper on two dimensions: (i) the use of additional sources of information, such as
patent applications, internal project data, or grant applications, and (ii) the inclusion of additional case studies.
This will widen the dimensions and perspectives of this paper.
Acknowledgement
This work was financially supported by the World Premier International Research Center Initiative Program(WPI Program) of Japan’s Ministry of Education, Culture, Sports, Science and Technology (MEXT) [AAR]
and by the Japan Society for the Promotion of Science (JSPS) Kakenhi Grant No. 26285084; Project leader
Prof. Shintaro Sengoku [AAR, SS]. An earlier version of this manuscript was presented at the Portland
International Center for Management of Engineering and Technology (PICMET) 2015 conference (Portland,
US). The authors appreciate the helpful discussions with Dr. Ayis Antoniou (Harvard University, Wyss
Institute), and Dr. Yusuke Torisawa (Kyoto University). The authors also thank Dr. Ismael Rafols (INGENIO,
CSIC-UPV) for his helpful advice on the use of his ‘science overlay’ toolkit. AAR also extends his gratitude
for the comments received during his presentation at the University of Tokyo’s Innovation Policy ResearchCenter, especially to Dr. Junichiro Mori and Dr. Masahiro Sugiyama. All remaining errors are, of course, ourown.
References
Aldrich, J.H., 2014. Interdisciplinarity: Its role in a discipline-based academy. Oxford University Press, New York, NY.
Anzai, T., Kusama, R., Kodama, H., Sengoku, S., 2012. Holistic observation and monitoring of the impact ofinterdisciplinary academic research projects: An empirical assessment in Japan. Technovation 32(6), 345-357.
Ávila-Robinson, A., Miyazaki, K., 2013. Dynamics of scientific knowledge bases as proxies for discerning technologicalemergence—The case of MEMS/NEMS technologies. Technological forecasting and social change 80(6), 1071-1084.
Battard, N., 2012. Convergence and multidisciplinarity in nanotechnology: Laboratories as technological hubs.
Technovation 32(3-4), 234-244.
Bishop, P.R., Huck, S.W., Ownley, B.H., Richards, J.K., Skolits, G.J., 2014. Impacts of an interdisciplinary research
center on participant publication and collaboration patterns: A case study of the National Institute for Mathematicaland Biological Synthesis. Research Evaluation, 1-14.
Borgatti, S.P., Everett, M.G., Johnson, J.C., 2013. Analyzing social networks. SAGE Publications Limited, Thousand
Oaks, CA.
Börner, K., Contractor, N., Falk-Krzesinski, H.J., Fiore, S.M., Hall, K.L., Keyton, J., Spring, B., Stokols, D., Trochim,W., Uzzi, B., 2010. A multi-level systems perspective for the science of team science. Science Translational Medicine
2(49), 49cm24.
Boschma, R., 2005. Proximity and innovation: a critical assessment. Regional studies 39(1), 61-74.
Bozeman, B., Boardman, C., 2003. Managing the new multipurpose, multidiscipline university research center:
Institutional innovation in the academic community. IBM Center for the Business of Government.
Burggren, W., Chapman, K., Keller, B., Monticino, M., Torday, J., 2010. Biological sciences, in: Frodeman, R. (Ed.),
The Oxford Handbook of Interdisciplinarity. Oxford University Press, New York, p. 119.
Callon, M., Courtial, J.-P., Turner, W.A., Bauin, S., 1983. From translations to problematic networks: An introduction to
co-word analysis. Social science information 22(2), 191-235.
Cambrosio, A., Keating, P., Mercier, S., Lewison, G., Mogoutov, A., 2006. Mapping the emergence and development oftranslational cancer research. European Journal of Cancer 42(18), 3140-3148.
CFIR, 2005. Facilitating interdisciplinary research. Committee on Facilitating Interdisciplinary Research: National
Academies Press, Washington , DC.
21

Cobo, M.J., López‐Herrera, A.G., Herrera‐Viedma, E., Herrera, F., 2011. Science mapping software tools: Review,
analysis, and cooperative study among tools. Journal of the American Society for Information Science andTechnology 62(7), 1382-1402.
Consoli, D., Ramlogan, R., 2008. Out of sight: problem sequences and epistemic boundaries of medical know-how onglaucoma. Journal of Evolutionary Economics 18(1), 31-56.
De Nooy, W., Mrvar, A., Batagelj, V., 2011. Exploratory social network analysis with Pajek. Cambridge University
Press, New York, NY.
Eppler, M.J., Burkhard, R.A., 2007. Visual representations in knowledge management: framework and cases. Journal of
Knowledge Management 11(4), 112-122.
Fekete, J.-D., Van Wijk, J.J., Stasko, J.T., North, C., 2008. The value of information visualization, Information
visualization. Springer, pp. 1-18.
Frodeman, R., 2011. Interdisciplinary research and academic sustainability: managing knowledge in an age ofaccountability. Environmental Conservation 38(2), 105-112.
Gowanlock, M., Gazan, R., 2013. Assessing researcher interdisciplinarity: a case study of the University of HawaiiNASA Astrobiology Institute. Scientometrics 94(1), 133-161.
Heimeriks, G., 2013. Interdisciplinarity in biotechnology, genomics and nanotechnology. Science and Public Policy
40(1), 97-112.
Hessels, L.K., Van Lente, H., 2008. Re-thinking new knowledge production: A literature review and a research agenda.
Research Policy 37(4), 740-760.
Heymann, S., LeGrand, B., 2014. Exploratory Network Analysis: Visualization and Interaction in: Cherifi, H. (Ed.),
Complex Networks and their Applications Cambridge Scholars Publishing, UK.
Hinze, S., Grupp, H., 1992. Applied research and industrial development in East Germany: International comparison by
performance indicators. Technovation 12(4), 257-278.
Horlings, E., Gurney, T., 2013. Search strategies along the academic lifecycle. Scientometrics 94(3), 1137-1160.
Huutoniemi, K., Klein, J.T., Bruun, H., Hukkinen, J., 2010. Analyzing interdisciplinarity: Typology and indicators.
Research Policy 39(1), 79-88.
Ingber, D.E., 2011. The Wyss Institute at Harvard University. Pulse, IEEE 2(4), 43-46.
Ingber, D.E., 2013. Science-driven innovation: The final frontier, The Chronicle of Higher Education, Washington DC(November 04, 2013) Available online at: http://chronicle.com/article/Science-Driven-Innovation-The/142785/(accessed June 18, 2015).
Islam, N., Miyazaki, K., 2010. An empirical analysis of nanotechnology research domains. Technovation 30(4), 229-237.
Jacobs, J.A., 2013. In defense of disciplines: Interdisciplinarity and specialization in the research university. Universityof Chicago Press, Chicago, IL.
Jensen, P., Lutkouskaya, K., 2014. The many dimensions of laboratories’ interdisciplinarity. Scientometrics 98(1), 619631.
Juanola-Feliu, E., Colomer-Farrarons, J., Miribel-Català, P., Samitier, J., Valls-Pasola, J., 2012. Market challenges facing
academic research in commercializing nano-enabled implantable devices for in-vivo biomedical analysis.
Technovation 32(3-4), 193-204.
Kaplan, S., Milde, J., Cowan, R.S., 2014. Interdisciplinarity in practice: A case of a nanotechnology research center,
Working paper, Rotman School of Managment, University of Toronto, Toronto, Canada.
Klein, J.T., 2008. Evaluation of interdisciplinary and transdisciplinary research: a literature review. American journal of
preventive medicine 35(2), S116-S123.
Lauto, G., Sengoku, S., 2015. Perceived incentives to transdisciplinarity in a Japanese university research center. Futures
65, 136-149.
Lee, Y.-N., Walsh, J.P., Wang, J., 2015. Creativity in scientific teams: Unpacking novelty and impact. Research Policy
44(3), 684-697.
Leydesdorff, L., Rafols, I., 2009. A global map of science based on the ISI subject categories. Journal of the American
Society for Information Science and Technology 60(2), 348-362.
Loasby, B.J., 2002. The evolution of knowledge: beyond the biological model. Research Policy 31(8), 1227-1239.
Mâsse, L.C., Moser, R.P., Stokols, D., Taylor, B.K., Marcus, S.E., Morgan, G.D., Hall, K.L., Croyle, R.T., Trochim,
W.M., 2008. Measuring collaboration and transdisciplinary integration in team science. American journal of
preventive medicine 35(2), S151-S160.
Metcalfe, J.S., James, A., Mina, A., 2005. Emergent innovation systems and the delivery of clinical services: The case of
intra-ocular lenses. Research Policy 34(9), 1283-1304.
Milojević, S., Hemminger, B.M., Priem, J., Chen, C., Leydesdorff, L., Weingart, S.B., 2012. Information visualization
state of the art and future directions. Proceedings of the Association for Information Science and Technology 49(1),1-3.
MIT, 2016. Convergence: The future of health. Massachusetts Institute of Technology. Available online at:
http://www.convergencerevolution.net/2016-report/ (accessed June 30, 2016).

22

Moss, F., 2011. The sorcerers and their apprentices: How the digital magicians of the MIT Media Lab are creating theinnovative technologies that will transform our lives. The Crown Publishing Group, New York, NY.
Nelson, R.R., 2003. On the uneven evolution of human know-how. Research Policy 32(6), 909-922.
NRC, 2014. Convergence: Facilitating transdisciplinary integration of Life Sciences, Physical Sciences, Engineering, andBeyond. National Research Council. The National Academies Press, Washington, DC.
OECD, 2007. Revised field of science and technology (FOS) classification in the Frascati manual. Working Party of
National Experts on Science and Technology Indicators, Organisation for Economic Co-operation and Development(OECD), Paris, France.
Pei, R., Porter, A.L., 2011. Profiling leading scientists in nanobiomedical science: Interdisciplinarity and potential
leading indicators of research directions. R&D Management 41(3), 288-306.
Porter, A.L., 2007. How" tech mining" can enhance r&d management. Research-Technology Management 50(2), 15-20.
Porter, A.L., Cohen, A.S., Roessner, J.D., Perreault, M., 2007. Measuring researcher interdisciplinarity. Scientometrics
72(1), 117-147.
Porter, A.L., Cunningham, S.W., 2004. Tech mining: exploiting new technologies for competitive advantage. John Wiley& Sons, Hoboken, NJ.
Porter, A.L., Newman, N.C., 2011. Mining external R&D. Technovation 31(4), 171-176.
Rafols, I., 2014. Knowledge Integration and Diffusion: Measures and Mapping of Diversity and Coherence, in: Ding, Y.,
Rousseau, R., Wolfram, D. (Eds.), Measuring Scholarly Impact: Methods and Practice. Springer International
Publishing Switzerland, pp. 169-190.
Rafols, I., Leydesdorff, L., O’Hare, A., Nightingale, P., Stirling, A., 2012. How journal rankings can suppress
interdisciplinary research: A comparison between innovation studies and business & management. Research Policy
41(7), 1262-1282.
Rafols, I., Meyer, M., 2010. Diversity and network coherence as indicators of interdisciplinarity: case studies inbionanoscience. Scientometrics 82(2), 263-287.
Rafols, I., Porter, A.L., Leydesdorff, L., 2010. Science overlay maps: A new tool for research policy and librarymanagement. Journal of the American Society for information Science and Technology 61(9), 1871-1887.
Repko, A.F., 2008. Interdisciplinary research: Process and theory. Sage, Thousand Oaks, CA.
Sanz Menéndez, L., Bordons, M., Zulueta, M., 2001. Interdisciplinarity as a multidimensional concept: its measure inthree different research areas. Research Evaluation 10(1), 47-58.
Sharp, P.A., Langer, R., 2011. Promoting convergence in biomedical science. Science 333(6042), 527.
Shibayama, S., Baba, Y., Walsh, J.P., 2015. Organizational design of university laboratories: Task allocation and lab
performance in Japanese bioscience laboratories. Research Policy 44(3), 610-622.
Shiffrin, R.M., Börner, K., 2004. Mapping knowledge domains. Proceedings of the National Academy of Sciences
101(suppl 1), 5183-5185.
Siedlok, F., Hibbert, P., 2014. The organization of interdisciplinary research: modes, drivers and barriers. International
Journal of Management Reviews 16(2), 194-210.
Siedlok, F., Hibbert, P., Sillince, J., 2015. From practice to collaborative community in interdisciplinary researchcontexts. Research Policy 44(1), 96-107.
Sparrow, J., 1998. Knowledge in organizations: Access to thinking at work. SAGE Publications Ltd, London, UK.
Stehr, N., Weingart, P., 2000. Practising interdisciplinarity. University of Toronto Press, Toronto, Canada.
Stokols, D., Misra, S., Moser, R.P., Hall, K.L., Taylor, B.K., 2008. The ecology of team science: understanding
contextual influences on transdisciplinary collaboration. American journal of preventive medicine 35(2), S96-S115.
Strang, V., McLeish, T., 2015. Evaluating interdisciplinary research: a practical guide. Institute of Advanced Study:
Durham University.
Su, X., 2014. Academic scientists’ affiliation with university research centers: Selection dynamics. Research Policy 43(2),382-390.
Taşkın, Z., Aydinoglu, A.U., 2015. Collaborative interdisciplinary astrobiology research: a bibliometric study of theNASA Astrobiology Institute. Scientometrics 103(3), 1003-1022.
Thomson-Reuters, 2014. Thomso Reuters' Incites Indicators Handbook (available at:
http://researchanalytics.thomsonreuters.com/incites/).
Thorp, H., Goldstein, B., 2013. Engines of innovation: the entrepreneurial university in the twenty-first century. UNC
Press Books.
Van Eck, N.J., Waltman, L., 2010. Software survey: VOSviewer, a computer program for bibliometric mapping.
Scientometrics 84(2), 523-538.
Van Eck, N.J., Waltman, L., 2011. Text mining and visualization using VOSviewer. ISSI Newsletter 7(3), 50-54.
Van Rijnsoever, F.J., Hessels, L.K., 2011. Factors associated with disciplinary and interdisciplinary researchcollaboration. Research Policy 40(3), 463-472.
Wagner, C.S., Roessner, J.D., Bobb, K., Klein, J.T., Boyack, K.W., Keyton, J., Rafols, I., Börner, K., 2011. Approaches
to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature. Journal of
Informetrics 5(1), 14-26.
23

Wooten, K.C., Rose, R.M., Ostir, G.V., Calhoun, W.J., Ameredes, B.T., Brasier, A.R., 2014. Assessing and evaluating
multidisciplinary translational teams: a mixed methods approach. Evaluation and the Health Professions 37(1), 33-49.
Youtie, J., Libaers, D., Bozeman, B., 2006. Institutionalization of university research centers: the case of the National
Cooperative Program in Infertility Research. Technovation 26(9), 1055-1063.

24

Tables

Institute for Integrated Cell-Material Sciences(WPI-iCeMS)
Foundation year
Funding organization

Wyss Institute for Biologically-inspired
Engineering (Wyss Institute)2009
Private donation (Hansjörg Wyss)

Host institution

2007
Ministry of Education, Culture, Sports,
Science, and Technology (MEXT) World
Premiere International Research Center
Initiative (WPI)
Kyoto University, Japan

Core / adjunct faculty

26 / 1719 / 12

Total research staff

246 (as of 2015)
2

Harvard University, United States~350 (as of 2014)
2

Approx. room space
Mission

11,000 m
To manipulate cell fate and functions withsynthetic molecules: (1) nucleus information,(2) membrane compartments, and (3) cellcommunication

9,290 mTo discover the engineering principles thatnature uses to build living things, and harness
these insights to create biologically inspired
materials and devices that will revolutionize
healthcare and create a more sustainable world

General fields ofresearch

Cell biology, chemistry, and physics

Engineering, medicine, biology, and physicalsciences

Table 1 Comparison between WPI-iCeMS and Wyss Institute as of 2014 (based on publicly available
information)

Institute
Biodesign InstituteBroad Institute
California Nanosystems Institute (CNSI)
Center for Developmental Biology (CDB)
Center for Life Sciences (CLS)
Centre for Regenerative Medicine (CRM)
Institute for Molecular Cell Biology &Genetics
Institute for Molecular Physiology
Institute Lavoisier
Koch Institute for Integrated Cancer
Research
Lewis Stigler Institute for Integrated
Genomics (LSI)
Molecular Foundry
National Centre for Biological Sciences(NCBS)
Skaggs Institute for Chemical Biology
Whitehead Institute
WPI-iCeMS
Wyss Institute

Table 2

Host institution
Arizona State University
MIT/Harvard University
University of California Los
Angeles (UCLA) and Santa Barbara
Riken Institute
Tsinghua/Peking Universities
University of Edinburgh
Max Planck InstituteCountry
USA
USA
USA

Earliest year
2003
2004
2001

Total pubs
1,584
4,417
1,549

Japan
China
UK
Germany

2000
2011
2006
1998

1,683
548
354
1,548

Max Planck Institute
Université de Versailles
MIT

Germany
France
USA

1993
1993
2008

1,842
1,211
1,129

Princeton University

USA

2002

690

Lawrence Berkeley Natl Lab
Tata Institute of Fundamental
Research
Scripps Research InstituteMIT
Kyoto University
Harvard University

USA
India

2004
1992

697
1,039

USA
USA
Japan
USA

1996
1982
2007
2009

3,978
4,068
1,189
887

List of comparable research centers

Diversity andcoherence measures

Measures
Diversity (RaoStirling)
Shannon’s Entropy
CoherenceTop-five out-degreescientific disciplines[drivers]
In-degree/Outdegree disciplines
Top-five in-degreescientific disciplines[building blocks]

Wyss Institute
0.61

WPI-iCeMS
0.59

3.14
0.48
▪ Engineering, Biomedical▪ Materials Science,
Biomaterials▪ Multidisciplinary Sciences▪ Nanoscience &Nanotechnology▪ Chemistry, Multidisciplinary▪ Cell Biology▪ Biochem & Molecular
Biology▪ Biotech & Applied
Microbiology▪ Multidisciplinary Sciences▪ Chemistry, Multidisciplinary

3.26
0.36
▪ Chemistry, Multidisciplinary▪ Multidisciplinary Sciences▪ Nanoscience &Nanotechnology▪ Materials Science,
Multidisciplinary▪ Cell & Tissue Engineering▪ Biochem & Molecular
Biology▪ Biotech & Applied
Microbiology▪ Cell Biology▪ Physics, Condensed Matter▪ Energy & Fuels

Table 3 Comparative table of indicators extracted from science overlay maps

Figure_part 1

Fig. 1 Analytical framework for the understanding of knowledge integration

Fig. 2 Landscape of scientific research organizations

Fig. 3 Knowledge diversity and coherence for the WPI-iCeMS and the Wyss InstituteFigure_part 2

Fig. 4 Relevant scientific interconnects for a) the Wyss Institute and b) the WPI-iCeMS

Fig. 5 Cognitive maps over the years for a) the WPI-iCeMS and b) the Wyss InstituteFig. 5 Cognitive maps over the years for a) the WPI-iCeMS and b) the Wyss Institute (cont)Figure_part 3

Fig. 6

Cluster networks for the total of publications, Wyss Institute (top) and WPI-iCeMS (bottom)

Fig. 7

Relationships between the level of interdisciplinarity and the cognitive stage for the Wyss Institute (left) andthe WPI-iCeMS (right)

Fig. 8 Heatmaps of cognitive domains vs. bibliometric indicators for the Wyss Institute (left) and the WPIiCeMS (right)

Fig. 9 Strategic matrix for the assessment of knowledge integration in IDR centersSupplementary material

Supplementary Tables 1a) Wyss Instituteb) WPI-iCeMS

Supplementary Tables 2 Biserial correlation matricesa) Wyss Instituteb) WPI-iCeMS

NOTES:

 Results are based on 2-tailed Pearson’s biserial correlations.
 Abbreviations NATUR_KNOW: Cognitive contents; FIELD_COMP: computer sciences; FIELD_ENG: engineering; FIELD_MATH:
mathematical sciences; FIELD_PHYS: physical sciences; YEAR: publication year; NUM_COUNTRY: number of countries;
NUM_AUTHOR: number of co-authors; PERC_LOCAL: percentage of local collaborators; NUM_PIS: number of PIs; NUM_COMPANY:
number of firms as co-authors; PERC_COMPANY: percentage of co-authors from companies; NUM_HOSPITAL: number of hospitals as coauthors; PERC_HOSPITAL: percentage of co-authors from hospitals; IMPACT_FACTOR: normalized impact factors; NORM_CITATION:
normalized citation; INTERDISC_RAO: Rao-Stirling diversity index; INTERDISC_ENTROPY: entropy index.
 CLU variables refer to the research fronts listed in the tables of Fig. 6.