the interface between science and technology in …

THE INTERFACE BETWEEN SCIENCE AND TECHNOLOGY IN RESEARCH INFRASTRUCTURES

Abstract

This study aims to understand the relationship between the scientific and technological

collaboration networks of inventors in different types of research infrastructures. Thus, we

sought to understand how the organization of scientific and technological production at the

collective level affects the inventors position in both networks. The study was based on a sample

of 1,756 Brazilian research infrastructures in which 7,714 different researchers are allocated.

Firstly, a Multiple Correspondence Analysis was carried out followed by the Hierarchical

Cluster Analysis for the proposition of a typology to represent different types of infrastructures.

Next, the inventors were mapped, from which two collaborative networks were produced: one

including their co-invention links and another including their co-authorship links. From a

multinomial logit model with ordered results, we estimated the effects of the infrastructures

types to which the inventors are related based on the importance they have in both networks.

The results revealed that the characteristics of the infrastructures are associated to the

researchers’ collaboration strategies, which in turn are related not only to the levels but also to

their ability to articulate scientific and technological production.

Keywords: Science and technology. Research networks. Scientific and technological

production. Research organizations. Economics of science.

Dias, Alexandre; Kannebley, Sérgio

University of São Paulo, Brazil

1 Introduction

In the National Innovation Systems perspective, the technological advance is related to

the result of new science discoveries, that progress through development stages until turning

into marketable goods and services. This connection between science and technology (S&T)

occurs through an efficient network of public and private institutions that perform activities to

develop and diffuse new technologies (FREEMAN, 1987), through which the innovation

process is made possible. In this dynamic, we emphasize the role of companies and their

research and development (R&D) laboratories, universities, research institutes, financial system

structure and intellectual property (IP) laws (PÓVOA, 2008). Thus, each country organizes and

manages its national research system with the objective of increasing the production and

dissemination of knowledge, which is necessary for competitiveness and economic growth

(ROMER, 1990), as well as allowing the government to decide the scientific priorities and

define funding policies for research (KING, 2004).

With this in mind, the interface between S&T has been analyzed based on different

approaches. In particular, this phenomenon intensified with the Bayh-Dole Act promulgation

in 1980, which allowed US universities to exploit patent rights resulting from government-

funded research (SAMPAT; MOWERY; ZIEDONIS, 2003). From this, many studies sought to

test the effects of patenting on the scientific productivity of researchers, arguing that the

orientation to technological development would occur at the expense of the marginalization of

scientific research (BRESCHI; CATALINI, 2010). However, despite the lack of consensus,

several evidences point to the complementarity between scientific and technological outputs

(FABRIZIO; DI MININ, 2008; BRESCHI; LISSONI; MONTOBBIO, 2008; CALDERINI;

FRANZONI; VEZZULLI, 2007).

More recently, social network analysis has been applied to understand how researchers

collaborate to produce S&T. Modern science is strongly supported in a collective production

structure, in which teamwork, collaboration and interdisciplinarity are some of its main

characteristics (REY-ROCHA; MARTÍN-SEMPERE; GARZÓN-GARCÍA, 2002). Although

most applications of collaboration networks have adopted the classical formulation, that is,

contemplating a single basis of interaction, an emerging trend has sought to understand the

intersection between both scientific and technological collaboration networks. The argument is

that the knowledge production implies the collective participation of researchers with multiple

skills and competences, which results in networks capable of dealing with the complexity of

relations between S&T (DE STEFANO; ZACCARIN, 2013).

This is the panorama of this paper, whose objective is to understand the interdependence

between the scientific and technological collaboration networks of the researchers given that

they produced patents, having the characteristics of the research infrastructures in which they

work as the background. The research infrastructure is defined as the set of physical facilities

and material support conditions (equipment, resources and services) used by the researchers to

carry out R&D activities (MAPPING OF THE EUROPEAN RESEARCH

INFRASTRUCTURE LANDSCAPE (MERIL), 2013). From this, several aspects could be

explored, for example, how different are the networks characteristics, how knowledge flows

through different types of infrastructures and how would they be associated with the importance

that inventors have in both networks.

This paper can be considered a pioneering effort for some reasons. First, it involved a

significant sample of 1,756 research infrastructures, in which 7,714 different researchers are

allocated. Secondly, it took into account the collective characteristics as mediators of the

articulation between S&T networks. According to Fenwick and Edwards (2014), the human

intention governing interactions is only one dimension of the collaboration networks

characteristics, whose success also depends on the connection of a set of resources that allow

these associations to form. Thus, a major contribution to this line of studies is to consider some

collective level of organization.

2 Background

2.1 Organization of scientific and technological production

Usually the laboratory is the place where researchers work collaboratively to carry out

research activities, develop projects, or handle specific equipment, materials or animals

(STEPHAN, 2012). The laboratory environment not only facilitates the exchange of ideas but

also encourages specialization. The labor division in the research activity occurs by assigning

responsibilities among the research team, allowing the exploration of economies of scope. Thus,

the synergy created tends to increase academic productivity insofar as a common body of

knowledge is shared among several works that complement each other.

Given the need to understand the social organization of science and its fundamental unit

of analysis, it is possible to highlight the staff’s composition and size as characteristics that

impact on collective productivity. Carayol and Matt (2004) classify the laboratories’ staff into

permanent researchers, non-permanent researchers and non-researchers. Permanent researchers

are made up of professors (dedicated to teaching and research) and researchers dedicated

exclusively to research, while non-permanent researchers involve postdoctoral researchers and

graduate students. Non-researchers correspond to technicians and administrative staff. The

authors gathered evidence that the permanent researchers tend to have a strong insertion in the

international scientific community and are strongly related to the number of publications and

patents production. PhD students and postdoctoral researchers have a positive impact on

technological productivity and, to a lesser extent, on scientific productivity, which has also been

supported by Gurmu, Black and Stephan (2010) study. Non-researchers were associated with a

significant increase in scientific production, indicating the possibility of substitution between

them and non-permanent researchers in some types of laboratories.

However, these results should be interpreted with attention, since the research groups

combine inputs such as human capital in different ways, which can vary widely according to

the area (STEPHAN, 2012). This can lead to different impacts, such as the size of staff. While

the evidence produced by Rey-Rocha, Garzón-García and Martin-Sempere (2006) leads to the

conclusion that team size benefits productivity, Carayol and Matt (2006) and Braun, Glänzel

and Schubert (2001) found a negative association. Horta and Lacy (2011), for example, found

that although the research group size does not seem to influence the total outputs volume,

academics belonging to larger groups are more likely to publish in international journals.

In addition, we recognize the multidimensional nature of teaching and research

institutions in which research groups are often inserted, which implies the desing of their scope.

According to Cohn, Rhine and Santos (1989), although some private institutions are specialized

mainly in human resources training, public ones are usually larger, more complex and have to

reach a multiplicity of objectives simultaneously. The typical contrast is between teaching and

research activities, but in practice, many other activities can be carried out by them, such as

technological development, service delivery, interaction with industry and extension

(BOZEMAN, BOARDMAN, 2003).

According to Worthington and Higgs (2011), a possible long-term goal for an S&T

institution is to be able to produce the desired results at the lowest possible cost. In this view,

some seminal studies to estimate economies of scale and scope in universities were published

in the 1980s and 1990s. Cohen, Rhine and Santos (1989), for example, found that complex

higher education institutions engaged in undergraduate teaching, graduate teaching and

research activities tend to be more efficient than those specializing in one or two of these

missions. Dundar and Lewis (1995) also found that most departments of American public

research institutions benefit from efficiency while simultaneously expanding their teaching and

research outputs to the highest levels of production because of the presence of economies of

scale.

2.2 Collaboration networks and their relationship with scientific and technological

production

Several studies suggest that collaboration between researchers positively affects

scientific productivity, whether in the number of publications (BRAUN; GLÄNZEL;

SCHUBERT, 2001; HERNÁNDEZ, 2013), impact on citations (IVANOV; LIBKIND;

MARKUSOVA, 2014; BOSQUET; COMBES, 2013) or in both (GONZALEZ-BRAMBILA;

VELOSO; KRACKHARDT, 2013; LISSONI et al., 2011).

Specifically, empirical evidence suggests that co-authorship networks tend to have a

structure associated with the small world model, whose configuration has high node

connectivity and, at the same time, a small average distance between network regions (DE

STEFANO et al., 2013). Thus, the simultaneous presence of dense clusters characterized by

small distances between them, indicates a mechanism by which knowledge flows between

nodes.

However, technological collaboration networks are strongly influenced by geographical

proximity and, more than that, inventors tend to invest in social ties with individuals from their

previous location (BRESCHI; LISSONI, 2004). This may be a reflection of the need to build

and maintain trust ties that technological development requires, mainly due to the expectations

of commercial exploitation of this type of knowledge. Thus, two inventors who have worked

together on at least one patent will be predisposed to keep in touch thereafter, or will be able to

contact again to exchange information or share knowledge assets. In this way, co-invention

networks tend to be fragmented with a large number of small disconnected components

(BRESCHI; CATALINI, 2010), since inventions are made by more "closed" groups compared

to articles (DE STEFANO; ZACCARIN, 2013).

The reports that have approached the connection between the scientists’ S&T

collaboration networks sought to characterize the importance of the inventor-authors to connect

these two communities. This intersection has been captured by actors who have collaborated to

develop patents and articles simultaneously. Evidence suggests that academic authors-inventors

play a crucial role in this process. The formation of this type of connection benefits mainly from

the complementarity and non-redundancy of the technical and scientific competences. In

addition, they consider not only their own possibilities for exchange, but also the potential for

partner exchange, which involves sharing a similar resource base (DE STEFANO;

ZACCARIN, 2013).

Breschi and Catalini (2010) concluded that inventors play an essential role in connecting

the scientific and technological communities, acting as a bridge between the two domains.

These individuals occupy prominent positions in scientific and technological networks,

fulfilling the crucial function of knowledge intermediaries between the two worlds. However,

the evidence suggests that maintaining a highly central position in the scientific network occurs

at the expense of being able to assert the same importance in the technological network (and

vice versa).

When analyzing the interaction between scientific activity and technological outputs in

nanotechnology in China, Wang and Guan (2011) found that both co-authorship and co-

invention networks were fragmented separately. However, when combined in a single network,

the extent of connectivity increased not only among the inventors but also among the authors.

It was observed that most of the productive inventors and the most cited authors belong to the

group of authors-inventors, suggesting complementarity effects between patenting and

publication. Therefore, it is possible to affirm that the relationship between S&T is complex,

dynamic and non-linear. By understanding how these domains interact, it is necessary to make

explicit the complexity that governs the relations of these actors. This involves interpreting how

the collective organization which researchers are linked with is associated with their ability to

articulate S&T production. Thus, this paper sought to shed new lights about how this process

occurs.

3 The data and the proposed tipology

This study was based on a sample of 1,756 Brazilian research infrastructures, whose

primary data were obtained through the application of a questionnaire to their respective

coordinators with reference to 2012 base year. This database is a result of a set of Brazilian

public organs efforts, provided by the Applied Economic Research Institute (IPEA) which

gathered and published a wide information survey on the research infrastructures conditions

available in the country. Given that no statistics on its universe are present in literature, it is

unable to specify the sample representativity, which even motivated to perform this survey.

However, a response rate of 40% is calculated in relation to the 4,500 infrastructures identified.

The average profile of the infrastructures is reflected in the laboratories that are located

in the south-eastern region of the country - São Paulo (24%), Rio de Janeiro (19%) and Minas

Gerais (13%). The average area varies according to the region of the country in which the

infrastructure is located. Infrastructures in the north have the highest average area (1,461.97

m²), followed by the north-eastern (412.31 m²), south-eastern (344.77 m²), southern (262.60

m²) and midwest (228.49 m²) regions. In relation to the major areas of knowledge,

infrastructures are concentrated in the areas of engineering (28%), exact and earth sciences

(21%), biological sciences (18%) and agrarian sciences (12%), in addition to 18.5% with more

than one area of knowledge (multi area), which is significant.

According to Table 1, the total infrastructures’ staff of the sample is consisted of 15.52

people, on average. Regarding the structure of the staff, an average of 5.13 is formed by

permanent researchers, 7.02 by students and 3.37 by technicians. Infrastructures mainly

comprise permanent researchers (mean of 41.53%), followed by non-permanent researchers

(mean of 38.68%) and technicians (mean of 19.89%).

Table 1 – Infrastructures’ staff descriptive statistics

Staff structure Mean SD

Permanent researchers 5.13 5.00

Non-permanent researchers 7.02 10.19

Non-researchers 3.37 9.28

Total staff 15.52 16.98

According Table 2, it is possible to verify that while the research activity is carried out

continuously by 80.69% of the infrastructures, the technological activities are not developed,

or are performed sporadically, by a large part of the sample. Specifically, a rate of 48.86%,

81.61% and 68.91% stated that they do not carry out, or perform sporadically, activities related

to technological development, extension and services, respectively. Teaching activity, although

not carried out continuously by 39.35%, is performed by 31.38% of the infrastructures.

Table 2 – Activities performed by the infrastructures

Activities

Do not/

sporadically

(%)

A few days per

week/month

(%)

Continuously

(%)

Teaching 29.27 31.38 39.35

Research 7.29 12.02 80.69

Technological development 48.86 15.83 35.31

Technological extension 81.61 10.54 7.86

Technological services 68.91 13.15 17.94

From a set of variables related to staff structure and activities performed continuously

(Table 3), a typology was proposed to represent different groups of infrastructures. We adopted

the Multiple Correspondence Analysis, which allows us to analyze the relation patterns of a set

of categorical variables by the correspondence between rows and columns (ABDI;

VALENTIN, 2007). Their association is projected through points in a map of reduced

dimensions known as correspondence map (MICHAILIDIS, 2007). Therefore, it was possible

to estimate infrastructure coordinates in the selected dimensions and to group them into 5

clusters by adopting Hierarchical Cluster Analysis. According to Figure 1, by considering the

staff characteristics, the clusters 3, 4 and 5 are clearly opposed to clusters 1 and 2 when

projecting them in the respective axis. In the second dimension, related to the activities scope,

clusters 1, 4 and 5 are closer and their characteristics tend to place them in opposition to clusters

2 and 3.

Source: Results generated in Stata.

Figure 1 – Correspondence map with the five infrastructures clusters

The differences between groups can also be evidenced in Table 3, which shows the

means of each variable in each cluster. We can observe that clusters are differentiated, mainly

by the percentage of personnel with PhD, non-permanent researchers, total staff and the

activities they perform.

Table 3 –Variables averages according to the identified clusters

Variables Cluster

1

Cluster

2

Cluster

3

Cluster

4

Cluster

5

% of permanent researchers 38.11 61.57 35.00 33.85 28.37

% of PhD permanent researchers 25.59 62.09 94.76 83.19 74.27

% of non-permanent researchers 4.49 12.98 54.60 43.98 39.71

% of non-researchers 57.40 25.45 10.40 22.17 31.92

Nº of individuals in the infrastructure 14.82 7.81 14.93 22.83 31.13

% of infrastructures that performs teaching 4.05 17.63 43.15 59.11 69.83

% of infrastructures that performs research 44.59 41.72 96.95 99.36 99.14

% of infrastructures that performs technological

development

28.38 11.83 28.68 68.69 88.79


extension

8.11 0.43 0.63 12.78 73.28


services

74.32 10.54 1.90 29.07 90.52

N 74 465 788 313 116

From this, the clusters were associated with the scientific and technological production

indicator averages. The data on scientific production included the articles published in national

and international journals informed in Lattes Curriculum 1 in the period from 2009 to 2013 by

the 7,714 researchers associated to the infrastructures. The technological production was based

on the patent application filings identified at the Industrial Property National Institute (INPI2)

and Thomson Innovation in the same period. These indicators were calculated from the

effective contribution (per capita) of each researcher. We adopted the aggregate production of

the individuals associated to the same infrastructure as collective indicators. In this way, double

counting is avoided when products are shared by more than one individual of the same

infrastructure (CARAYOL; MATT, 2004). In addition, the scientific production was weighted

by the Impact Factor Point Average (IFPA), whose objective is to normalize the differences

between the impact factors and the areas of knowledge. Thus, Sombatsompop and Markpin

(2005) suggest to consider production by the relationship between the journal’s ranking and the

number of journals in each area of knowledge, as follows:

𝐼𝐹𝑃𝐴 = [𝐼𝑗

𝐼𝐴] . [1 −

𝑅

𝑁 + 1] . [𝑛]

where 𝐼𝑗 e 𝐼𝐴 correspond to the journal IF and to the aggregate IF average of all journals of the

same scientific category, respectively; R is the ranking of the journal in its category; N indicates

1 The Lattes Curriculum is administered by CNPq and has become a national standard in the record of the

researchers’ academic activities in Brazil. 2 INPI is the Brazilian IP office.

the number of journals in the same category; and n is the number of articles published by the

researcher, which in this case corresponds to the quantity per capita.

We mapped 47,269 distinct articles published by permanent researchers identified in the

database, of which 32,234 were with IFPA score greater than zero. We also identified 762

different patent application filings. Only 2.16% of the 7,714 researchers did not present

publication during the period, while 68.05% did not produce any patent. Descriptive statistics

are presented in Table 4, whose average number of articles, IFPA and patents per infrastructure

are 10.94, 51.51 and 0.22, respectively.

Table 4 – Infrastructures’ scientific and technological production descriptive statistics

Scientific and technological

production Mean SD Minimum Maximum

Number of articles 10.94 14.96 0 207.16

IFPA 51.51 148.88 0 1,486.05

Number of patent application 0.22 0.74 0 10.28

Thus, the following typology is proposed to characterize the different clusters, which

were associated to the infrastructures’ scientific and technological production indicators:

Cluster 1: Infrastructures with predominance of non-researchers oriented to technological services

It gathers 74 infrastructures, most of which are private or public independent organizations (40.54%) and a

significant share of 36.49% are technical institutes. They work mainly in Engineering (41.89%) and in Exact and

Earth Sciences (20.27%). It presents, proportionally, a greater number of infrastructures whose size of staff is

small (14.82 people, on average). The technical staff is predominant (57.4%) and maybe for this reason the

presence of PhD is 212% below the average sample standard. The activities related to the provision of

technological services predominate, representing an engagement of more than 300% compared to the sample

average. These infrastructures, of which 94.59% have collaborative relationships, have an average physical area

of 650𝐦𝟐. The average scientific output is 2.5 articles, with IFPA of 31.22 and 0.36 patent application filing per

infrastructure.

Cluster 2: Infrastructures with a small-sized staff and predominance of permanent researchers orientend to

research

It gathers 465 infrastructures, of which 50.32% are independent organizations and technical institutes. It should

be highlighted the predominance of Engineering (34.41%) and Exact and Earth Sciences áreas (21.72%). They

have the lowest staff with an average of 7.81 individuals. The participation of permanent researchers is significant

(61.57%), but little more than 60% are PhD, while the sample average is almost 80%. The technical team is also

relatively expressive (25.5%) compared to the average sample (19.89%). This group is represented by 41.72% of

infrastructures that carry out the research activity on an ongoing basis. However, the participation of

infrastructures that perform any of the activities on a continuous basis is always smaller than the average of the

sample. In addition, it brings together, proportionally, the largest number of infrastructures whose researchers do

not engage in collaborative activities (30.11%) and their average physical area is 374𝐦𝟐. The average scientific

output is 5.17 articles, with IFPA of 33.27 and 0.14 patent application filing per infrastructure.

Cluster 3: Infrastructures with predominance of non-permanent researchers oriented for teaching and research

It gathers 788 infrastructures that predominantly work in the Biological Sciences area (26.02%), and a smaller

portion linked to Engineering (22.21%) and to Exact and Earth Sciences (22.08%). They are mostly linked to

universities (83.63%) and have a staff of 14.93 individuals, on average. The participation of PhD permanent

researchers is expressive (94.76%), although the permanent team is considered small (18.66% below the sample

mean). In addition, non-permanent researchers account for 54.6% of their staff. This cluster is more related to the

research and teaching activities, with the engagement of 96.95% and 43.15% of the infrastructures, respectively.

Finally, 93.65% of the them maintain collaborative relationships. The average physical area is 197𝐦𝟐. The

average scientific output is 12.59 articles, with IFPA of 59.07 and 0.19 patent application filing per infrastructure.

Cluster 4: Infrastructures with predominance of non-permanent researchers oriented for teaching, research and

tecnological develpment

It gathers 313 infrastructures, of which 69.65% are linked to universities. Although the Engeneering and Earth

Sciences account for the most predominant areas (26.84% and 17.57%, respectively), a significant portion works

in the Agrarian Sciences area (17.25%). Staff has 22.83 individuals, on average, in which the participation of

permanent researchers is not very significant (on average, 33.85%). However, 83.2% are PhD. The participation

of non-permanent researchers accounts for the most expressive part of their staff (43.98%). This group has a

strong association with research activities (99.36%), technological development (68.69%) and teaching (59.11%).

This is the cluster which accounts, proportionally, for the second largest number of infrastructures engaged in

collabortion (98.4%). Its average physical area is 412𝐦𝟐. The average scientific output is 15.79 articles, with

IFPA of 67.02 and 0.33 patent application filing per infrastructure.

Grupo 5: Infrastructures with predominance of non-permanent researchers and non-researchers oriented for

multiple activities

It gathers 116 infrastructures, of which 68.10% are linked to universities. There are a predominancy of those that

work in multiple areas of knowledge (31.89%) and in Engineering (30.17%). They have the largest staff (31.14

individuals), which is practically twice of the sample. The characteristics that stand out are the participation of

non-permanent researchers (39.71%) and technical staff (31.92%). We highlight the association of

multidisciplinary infrastructures, that is, that operate in more than one area of knowledge, with the largest set of

activities that they perform. Group 5 presents an expressive participation of infrastructures that carry out the

various activities in a continuous character. In addition, all stated to maintain collaborative relationships and they

have the largest average physical area (974𝐦𝟐). The average scientific output is 15.13 articles, with IFPA of

44.30 and 0.45 patent application filing per infrastructure.

4 The interface between S&T in different types of research infrastructures

The analysis of the interface between S&T in the different types of infrastructures was

performed taking into consideration the connection between the scientific and technological

collaboration inventors’ networks. Of the 7,714 researchers identified in the database, only 533

were inventors of 629 patent application. Thus, the share of only 6.9% of researchers with

considered collaborative relationships were analyzed. From this, 8,787 articles were identified

from which coauthors were mapped. It is important to emphasize that the scientific and

technological production associated to the infrastructures created after 2009 was disregarded in

order to restrict the likehood of being developed without any kind of link with the infrastructure.

The co-authorship and co-invention database involved an exhaustive work of organization and

standardization of the inventors and authors’ names. After these procedures, it was found 1,917

inventors and 22,020 authors, respectively.

In order to understand how the inventors collaborate to produce S&T according to the

types of infrastructures, two networks were build using Gephi: one that included the co-

invention and one that included the co-authorship inventors’ links. The inventors that were

identified in the infrastructure database were then associated with their respective clusters.

While an infrastructure can be associated with a single cluster, an inventor can be linked to

more than one infrastructure and, therefore, to more than one cluster. They were named

multicluster. Individuals not included in the database were associated with as many clusters as

the inventors with whom they collaborated. By assuming the links among network actors, it is

assumed that they are connected by some kind of knowledge exchange, which in turn is

supported by an exchange of resources from the organizations to which they are linked

(BRESCHI; CATALINI, 2010).

Table 5 shows some statistics of the inventors’ networks associated with the clusters.

We note that the extent to which the nodes of the co-invention network are connected tends to

be greater among the inventors of cluster 1, whose largest component concentrates 28.33% of

the nodes. Among inventors of clusters 2, 3, 4 and 5, node densities fall by more than half,

being 9.42%, 6.93%, 9.97% and 10.51%, respectively. This result shows that the inventors’s

co-invention network of cluster 1 is less fragmented, while the others show a greater number of

components disconnected from each other.

Table 5 – Inventors’s S&T networks statistics

Inventors linked to infrastructures Co-invention

networks

Co-authorship

networks

Cluster 1

% of coordinators 15 3.02

% of non-coordinators 33.33 10

% of external members 51.67 86.98

Giant component (% of nodes) 28.33 57.9

Density 0.042 0.006

Inventors degree 4.35 29.4

Cluster 2

% of coordinators 13.61 4.18

% of non-coordinators 20.42 6.1



Density 0.01 0.001


Cluster 3





Density 0.003 0


Cluster 4



% of external members 67.83 91


Density 0.005 0


Cluster 5





Density 0.008 0.001


Multicluster





Density 0.008 0.001


Source: Results generated in Gephi.

In addition, the density of 0.042 of these inventors’s network can be interpreted as an

evidence that the actors establish more bonds with each other. The same happens with the

inventors’s network of cluster 2, albeit to a lesser extent. The higher cohesion of these

inventors’ networks, however, may be a result of the dependence of existing partners, which

makes it difficult to find new opportunities for collaboration (LEE et al., 2012). This is clearly

demonstrated in Figure 2, which shows how the technology production is shared among the

inventors of the different clusters. The actors’ nodes linked to clusters 1, 2, 3, 4 and 5

infrastructures were colored in dark blue, pink, green, yellow and light blue, respectively,

whereas red nodes represent individuals linked to more than one infrastructure of different

clusters. At the top right of the figure are nodes bound to clusters 1 and 2, which clearly tend to

collaborate with each other to produce technology.

On the other hand, the density decreases to 0.003, 0.005 and 0.008 in network clusters

3, 4 and 5, respectively. Hernández (2013) assigns low density to networks whose relationships

tend to focus on few actors which do not maintain a significant number of links with each other.

Such a feature makes sense for researchers from university infrastructures that predominate in

these clusters, whose structure of knowledge production would follow a model called by Zhang

(2010) of “one teacher and many students”. By being bound to infrastructures that share a

certain compatibility, it is possible to observe that inventors of clusters 3 and 4, and 4 and 5 are

more predisposed to collaborate with each other. Thus, the staff configuration and the activities

orientation in the different clusters would be associated to the dynamics of the inventors’

collaboration.


Figure 2 – Inventors’ co-invention network

Another characterization can be made regarding the actors’ bond type, which can be

classified as coordinators, researchers and external members. The coordinators and researchers

constitute the infrastructural personnel while the individuals who were not identified in the

questionnaire responses were considered as external. The participation of own personnel in the

co-invention collaboration network related clusters 1 and 2 infrastructures is 48.33% and

34.03%, in this order, suggesting that they make the most intensive use of the staff inside these

infrastructures. This was also observed in cluster 5 inventors’ network. Their degree reinforces

this finding, which are 4.35, 4.55 and 5.6 among the inventors of clusters 1, 2 and 5,

respectively. By concentrating more efforts on internal relationships, individuals would no

longer participate in more comprehensive networks (HORTA, 2013). Consequently, we may

expect fewer connections.

The components of the co-authorship network are substantially more connected and the

nodes are thickened in large groups, which favor the knowledge to flow easily among

researchers linked to the different types of infrastructures. However, the networks of the

inventors connected to cluster 3 and those linked to infrastructures of different clusters are more

likely to make this flow possible (Figure 3). In these networks, the giant components

concentrate 97.26% and 94.45% of the nodes, respectively. It is noteworthy that, contrary to

what was observed in co-invention networks, the inventors’ co-authorship networks of clusters

1 and 2 infrastructures are the most fragmented, which is a reflection of their low intensity in

performing research.

In general, co-authorship networks densities are quite low, being higher among

researchers linked to cluster 1 (0.006). The low values are the result of the greater number of

co-authors identified in the articles. In addition, the size of research groups tends to be

negatively associated with density, since the larger the group, the smaller the chances of all

relating to each other (HERNÁNDEZ, 2013). Therefore, this measure is expected to be smaller

in the networks of inventors linked to clusters 3, 4 and 5, whose infrastructures have an average

staff of 18.49 individuals, compared to 8.81 in clusters 1 and 2.

In this way, scientific production is much less endogenous than technological

production. The participation of external actors is significantly higher in all clusters networks,

being slightly higher in cluster 1 (13.02%) and lower in cluster 3 (7.64%). This fact reinforces

the findings that there are differences in how inventors collaborate to produce knowledge

according to the infrastructure they work in. The greater degree of the inventors of clusters 3,

4, 5 infrastructures and those that work in more than one type suggests that they can not only

mobilize a larger number of collaborators but also promote a greater knowledge flow within

the network. Particularly, the degree of 88.92 of the inventors linked to different clusters is

notable. By working in different infrastructures, these individuals would enjoy a certain

institutional mobility capable of substantially expanding their network (HORTA, 2013).


Figure 3 – Inventors’ co-authorship network

In order to understand how the co-authorship and co-invention networks are related

according to the different types of infrastructures, we estimated the effects of the inventors’

infrastructure clusters on the importance they have in both networks. We considered the

following measures of importance:

• Betweenness centrality: indicates how much a node acts as a connector and expresses

the control that a node exerts over the information flow and other resources (NEWMAN,

2008). It is determined by:

𝐶𝑒𝑖𝐵(𝑔) = ∑

𝑃𝑖(𝑘𝑗)/ 𝑃(𝑘𝑗)

(𝑛 − 1)(𝑛 − 2)/2𝑘≠𝑗:𝑖∉{𝑘,𝑗}

,

where 𝑃𝑖(𝑘𝑗) represents the number of geodesics (shorter paths) that connect nodes k and j

between which is i and 𝑃(𝑘𝑗) refers to the total number of geodesics between k and j and n

corresponds to the number of nodes (JACKSON, 2008).

• Eigenvector centrality: considers not only the connectivity or the density of the node

connections, but also the importance of the neighboring nodes. Therefore, the relevance

attributed to a particular node is determined by both the number of connections and the

proportion with which it is related to influential nodes (CHERVEN, 2015). It is

determined by the equation:

𝜆𝐶𝑖𝑒(𝑔) = ∑ 𝑔𝑖𝑗𝑗

𝐶𝑖𝑒(𝑔) ,

Where the node centrality is proportional to the sum of the neighbors’ centrality and λ is the

proportionality factor, also called eigenvalue. The network equation element (g) called

eigenvector is represented by 𝐶𝑖𝑒(𝑔) (JACKSON, 2008).

• Closeness centrality: expresses how much a node is close to the others and can be

interpreted as a measure of how long it takes for the information to spread in the network

(BRESCHI; CATALINI, 2010). Formally, closeness centrality is only the inverse of the

mean distance between i and any other node j: (𝑛 − 1)/ ∑ ℓ(𝑖, 𝑗)𝑗≠𝑖 , where ℓ(𝑖, 𝑗) is the

number of links in the shorther path between i and j (JACKSON, 2008). In Gephi larger

closeness indicators mean that nodes are more distant (CHERVEN, 2015).

The econometric model also included seniority and gender as control variables.

Seniority corresponds to the difference between 2013 and the year of the PhD degree obtaining.

Thus, only the nodes of the inventors whose doctoral degree was obtained in 2009 or earlier

were selected, ensuring that the centrality measures were counted only by individuals qualified

for research. The sample cutout resulted in 455 inventors, of whom 15 were linked to the

infrastructures of cluster 1, 28 of cluster 2, 171 of cluster 3, 122 of cluster 4, 72 of cluster 5 and

47 to more than one cluster.

Given the empirical distributions of the variables, three categories were created for the

inventors’ betweenness, eigenvector and closeness centralities in both co-authorship and co-

invention networks (Table 6). It should be noted that, in general, only the last 10% of the

distribution responds to centrality levels higher than the averages, which are 0.035, 0.026 and

0.266 in the co-authorship network and 0.019, 0.032 and 0.820 in the co-invention network,

respectively. The only exception was found for betweenness centrality in the co-authorship

network, whose measures surpass the average as of the 75th percentile. And also for closeness

centrality in the co-invention network, in which half of the inventors present indexes above the

average.

Table 6 – Inventors’ betweenness, eigenvector and closeness centralities in the co-authorship

and co-invention networks

Percentiles

Co-authorship network Co-invention network

Betwee-

nness

Cate-

gory

Eigen-

vector

Cate-

gory

Close-

ness

Cate-

gory

Betwee-

nness

Cate-

gory

Eigen-

vector

Cate-

gory

Close-

ness

Cate-

gory

1% 0 Low 0 Low 0.144 Low 0 Low 0 Low 0 Low

5% 0 Low 0 Low 0.161 Low 0 Low 0 Low 0.346 Low

10% 0 Low 0.001 Low 0.177 Low 0 Low 0.001 Low 0.442 Low

25% 0.005 Low 0.003 Middle 0.199 Low 0 Low 0.004 Low 0.633 Middle

50% 0.018 Middle 0.010 Middle 0.224 Middle 0 Low 0.008 Low 1 High

75% 0.039 Middle 0.024 Middle 0.247 Middle 0.002 Middle 0.026 Middle 1 High

90% 0.084 High 0.045 Middle 0.268 Middle 0.028 Middle 0.067 Middle 1 High

95% 0.123 High 0.061 High 1 High 0.068 High 0.117 High 1 High

99% 0.275 High 0.416 High 1 High 0.457 High 0.394 High 1 High

Thus, we adopted a multinomial logit model with ordered results to estimate the

probability of an individual i, which is associated with cluster z, to belong to a given distribution

category in the different centrality measures. The model is specified as follows:

𝑦𝑖∗ = 𝑥′

𝑖𝛽1 + 𝑥′𝑖𝛽2 + 𝑥′

𝑖𝛽3 + 𝑥′𝑖𝛽4 + 𝑥′

𝑖𝛽5 + 𝑥′𝑖𝛽6 + 𝑥′

𝑖𝛽7 + 𝑥′𝑖𝛽8 + 𝑢𝑖,

where:

𝛽1 = dummy for inventors linked to cluster 1





𝛽6 = dummy for inventors linked to more than one cluster

𝛽7 = dummy for gender

𝛽8 = seniority

According Cameron e Trivedi (2009), we define the following equation for an ordered

model with m categories:

𝑦𝑖 = 𝑗 𝑠𝑒 𝛼𝑗−1 < 𝑦𝑖∗ < 𝛼𝑗 , 𝑗 = 1, … , 𝑚

where 𝛼0 = −∞ e 𝛼𝑚 = ∞, so:

Pr(𝑦𝑖 = 𝑗) = Pr(𝛼𝑗−1 < 𝑦𝑖∗ ≤ 𝛼𝑗)

= Pr(𝛼𝑗−1 < 𝑥′𝑖𝛽 + 𝑢𝑖 ≤ 𝛼𝑗)

= Pr(𝛼𝑗−1 − 𝑥′𝑖𝛽 < 𝑢𝑖 ≤ 𝛼𝑗 − 𝑥′

𝑖𝛽)

= 𝐹(𝛼𝑗 − 𝑥′𝑖𝛽) − 𝐹(𝛼𝑗−1 − 𝑥′

𝑖𝛽)

where F is the cumulative distribution function of 𝑢𝑖 and 𝑢 is logistically distributed with

𝐹(𝑧) = 𝑒𝑧/(1 + 𝑒𝑧).

The marginal effect on the probability of inventor to belong to a category j when

regressor 𝑥𝑖 changes is given by:

𝜕Pr (𝑦𝑖 = 𝑗)

𝜕𝑥𝑟𝑖= {𝐹′(𝛼𝑗−1 − 𝑥′

𝑖𝛽) − 𝐹′(𝛼𝑗 − 𝑥′𝑖𝛽)}𝛽𝑟

The probability variations magnitude can be captured by the marginal effects. Taking

the multicluster inventors as reference, Table 7 presents the betweenness centrality marginal

effects against clusters, gender and seniority. In the co-authorship network, we note that the

probability of inventors linked to clusters 1, 2 and 5 belonging to the lowest category of the

distribution is 32.12%, 23.74% and 18.38% higher than in the reference group, respectively. It

falls gradually among other categories, so the probability of these inventors belonging to the

high category is 6.42%, 5.28% and 4.57% lower than that found among the multicluster

inventors. In the co-invention network, a similar pattern is observed among the inventors of

clusters 1, 3 and 5, where the probability of belonging to the low distribution category is

18.74%, 12.07% and 14.07% higher than in the reference group and 6.14%, 4.58% and 4.99%

lower when considering the high category.

When analyzing the eigenvector centrality marginal effects (Table 8), we observe a

certain similarity with the betweenness centrality, which can be expected given the positive

correlation between them. Specifically, we found a correlation between the eigenvector and

betweenness centralities of 41.61% in the co-authorship network and 37.36% in the co-

invention network. Likewise, the inventors of cluster 1, 2 and 5 are 5.39%, 6.10% and 4.33%

are less likely to belong to the high category in the co-authorship network than the multicluster

ones. In the low and middle categories, however, only individuals linked to cluster 2 have

statistically different probabilities of the multicluster inventors indicating that they effectively

have a 25.49% greater probability of having low eigenvector centrality in their co-authorship

network. This pattern is also found in the co-invention network, in which the inventors of cluster

2 present a 25.60% higher probability of being in the low category and a probability of 19.11%

and 6.49% lower to belong to the medium and high categories, respectively.

The columns referring to the co-authorship network on Table 9 show that the inventors

in cluster 1 are less likely to belong to the low category of the closeness centrality distribution.

On the other hand, it was verified that the inventors associated to cluster 5 have a 17.73% higher

probability of belonging to the low category, and it becomes 12.24% and 5.49% smaller in the

medium and high categories, respectively. It is also noted the inventors linked to cluster 2 are

much more likely to belong to the high category in the co-invention network. While they are

5.75% and 14.38% less likely to participate in the low category, it is 20.13% higher when

considering the high category.

Table 7 – Marginal effects of the betweenness centrality distribution bands against

infrastructures clusters, gender and seniority

Variables Co-authorship network Co-invention network

Low Middle High Low Middle High

cluster_1 0.3212*** -0.2570** -0.0642*** 0.1874* -0.0493* -0.0614**

(0.115) (0.100) (0.018) (0.097) (0.063) (0.028)

cluster_2 0.2374** -0.1845** -0.0528*** 0.0773 -0.0749 -0.0280

(0.105) (0.088) (0.019) (0.096) (0.045) (0.032)

cluster_3 0.0670 -0.0476 -0.0195 0.1207* -0.0199* -0.0458*

(0.079) (0.056) (0.022) (0.070) (0.046) (0.027)

cluster_4 0.0655 -0.0469 -0.0186 0.0322 -0.0908 -0.0123

(0.082) (0.060) (0.022) (0.074) (0.048) (0.028)

cluster_5 0.1838** -0.1380** -0.0457** 0.1407** 0.0248* -0.0499**

(0.085) (0.068) (0.019) (0.070) (0.030) (0.024)

gender -0.0432 0.0307 0.0125 -0.0402 0.0007 0.0153

(0.053) (0.038) (0.015) (0.049) (0.002) (0.018)

seniority -0.0227*** 0.0160*** 0.0068*** -0.0012 -0.0493 0.0005

(0.003) (0.003) (0.001) (0.003) (0.063) (0.001)

*** p<0.01, ** p<0.05, * p<0.1


Table 8 – Marginal effects of the eigenvector centrality distribution bands against




cluster_1 0.2167 -0.1628 -0.0539** 0.0360 -0.0243 -0.0117

(0.146) (0.126) (0.022) (0.143) (0.099) (0.044)

cluster_2 0.2549** -0.1938* -0.0610*** 0.2560** -0.1911* -0.0649***

(0.123) (0.108) (0.018) (0.108) (0.088) (0.022)

cluster_3 0.0095 -0.0053 -0.0042 -0.1156 0.0738 0.0418

(0.062) (0.035) (0.027) (0.081) (0.050) (0.031)

cluster_4 0.0326 -0.0190 -0.0137 -0.1030 0.0646 0.0384

(0.067) (0.040) (0.026) (0.084) (0.050) (0.035)

cluster_5 0.1309 -0.0876 -0.0433** -0.0625 0.0395 0.0230

(0.085) (0.065) (0.022) (0.092) (0.056) (0.036)

gender 0.0680* -0.0340** -0.0339* -0.0355 0.0237 0.0119

(0.036) (0.017) (0.020) (0.051) (0.035) (0.017)

seniority -0.0128*** 0.0071*** 0.0057*** 0.0049 -0.0032 -0.0017

(0.002) (0.002) (0.001) (0.003) (0.002) (0.001)

*** p<0.01, ** p<0.05, * p<0.1


Table 9 – Marginal effects of the closeness centrality distribution bands against




cluster_1 -0.1278* 0.0266 0.1012 -0.0014 -0.0027 0.0041

(0.068) (0.032) (0.095) (0.050) (0.095) (0.145)

cluster_2 -0.0400 0.0196 0.0203 -0.0575** -0.1438** 0.2013**

(0.086) (0.036) (0.050) (0.023) (0.071) (0.093)

cluster_3 0.0542 -0.0313 -0.0229 0.0212 0.0387 -0.0600

(0.065) (0.039) (0.027) (0.029) (0.051) (0.079)

cluster_4 0.0395 -0.0231 -0.0164 0.0185 0.0332 -0.0517

(0.069) (0.042) (0.027) (0.031) (0.052) (0.083)

cluster_5 0.1773** -0.1224* -0.0549*** -0.0335 -0.0715 0.1050

(0.089) (0.070) (0.021) (0.026) (0.062) (0.087)

gender 0.0456 -0.0239 -0.0217 -0.0034 -0.0063 0.0096

(0.038) (0.019) (0.019) (0.018) (0.033) (0.051)

seniority -0.0105*** 0.0059*** 0.0047*** -0.0044*** -0.0083*** 0.0127***

(0.002) (0.002) (0.001) (0.001) (0.002) (0.003)

*** p<0.01, ** p<0.05, * p<0.1


In addition, we could observe that gender practically does not affect the importance of

inventors in both networks. Men and women tend to share the same level of importance in the

co-authorship and co-invention networks. The only exception is a tendency for male inventors

to have a minor eigenvector centrality in the co-authorship network. The seniority effects are

statistically significant on all centrality measures in the co-authorship network, indicating that

the research experience positively affects the inventors’ importance. This corroborates the

researchers life-cycle theory, whose scientific productivity tends to increase over time until

reaching a peak (GONZALEZ-BRAMBILA; VELOSO, 2007). In the co-invention network,

effects are felt only by weighing the individuals’ importance by the closeness centrality.

Possibly, unobservable biographical factors, such as individual heterogeneity and the

persistence of the researcher may be more important for patent production, a feature that do not

change easily with time.

Given these considerations, it was possible to predict the average probabilities of the

inventors linked to different clusters to belong to the highest category centrality measures of

the distribution. For the ordered logit model, the prediction produces 𝑆𝑗 = 𝑥1𝑗𝛽1 + 𝑥2𝑗𝛽2 +

⋯ + 𝑥𝑘𝑗𝛽𝑘 (STATACORP, 2011). Therefore, predictions are the probabilities of 𝑆𝑗 + 𝑢𝑗 being

in the categories defined between a pair of cut-off points, 𝑘𝑖−1 e 𝑘𝑖, whose probability of the

individual to belong to the highest category is given by:

Pr(𝑆𝑗 + 𝑢𝑗 > 𝑘2) = 1 − 1/(1 + 𝑒𝑆𝑗−𝑘2 )

According to Table 10, it was found that individuals linked to more than one cluster are

most likely to be in the highest betweenness centrality category both in co-authorship (13.56%)

and co-invention (14.08%) networks. That is, the fact of moving in different types of

infrastructures would be associated with a greater capacity to control and articulate resources

and knowledge of different institutions. Individuals related to clusters 3 and 4 also have

statistically similar probabilities in the co-authorship network, and the marginal effects did not

indicate significant differences between them and the multicluster inventors indeed. This is

expected, since a significant portion of multicluster individuals (54.17%) is bound

simultaneously to infrastructures of both clusters.

Table 10 – Inventors probability to belong to the highest category of the centrality measures

Centralities Networks Cluster 1

(%)

Cluster 2

(%)

Cluster 3

(%)

Cluster 4

(%)

Cluster 5

(%)

Multicluster

(%)

Betweenness Co-authorship 2.40 4.79 10.84 11.21 8.58 13.56

Co-invention 5.49 9.53 8.42 12.19 7.67 14.08

Eigenvector Co-authorship 3.19 4.28 11.86 10.63 7.67 11.75

Co-invention 7.36 2.69 11.43 11.00 9.40 7.66

Closeness Co-authorship 18.88 13.83 9.34 10.03 6.07 11.63

Co-invention 55.96 77.40 55.08 55.70 72.43 60.99


Nevertheless, the cluster 4 inventors, with multicluster individuals, accounts for the

S&T articulation in the highest category of this measure. The probability of belonging to it is

11.21% in the co-authorship and 12.19% in the co-invention networks. Thus, the inventors

linked to cluster 3 infrastructures tend to increase their betweenness centrality in the co-

invention network only when they are linked to cluster 4 infrastructures. This result is quite

revealing, since the technological superiority of cluster 4 infrastructures possibly complements

the cluster 3 ones gaps, since they tend to share similarities in terms of personnel configuration.

By observing the probabilities for the eigenvector centrality, inventors linked to clusters

3, 4 and multicluster have a probability of 11.86%, 10.63% and 11.75% belonging to the highest

category in the co-authorship network, respectively. In the co-invention network, they represent

11.43%, 11% and 7.66%. In all these cases, there are no statistically significant differences

among them, indicating that these individuals are the most likely to belong to the highest

eigenvector centrality category in both co-authorship co-invention networks. This can be

explanained due the influence of these inventors collaborators and can be attributed as well to

the greater proportion of PhD researchers in the permanent staff who, being in a more advanced

stage in the career, would be predisposed to establish links with well connected researchers

(BORDONS et al., 2015). We should mention that in the co-invention network clusters 1 and 5

infrastructures’ inventors have probabilities statistically similar to the reference group, so their

detachable technological production can be attributed to their capacity to establish more and

influential links.

The high probabilities in the betweenness and eigenvector centralities among clusters

3, 4 and multicluster inventors indicate a collaboration strategy based on many non-redundant

connections made with several research collaborators which, according to Contandriopoulos et

al. (2016), would be associated with significant performance standards. The betweenness

centrality importance is attributed, mainly, to the fact that a researcher in this position tends to

have access to more resources than those who maintain redundant connections with a restricted

set of individuals which, in turn, proved to be a feature of inventors linked to the clusters 1 and

2 infrastructures.

Regarding the interpretation of the results related to closeness centrality, it is important

to highlight that higher probabilities of belonging to the high category mean that individuals

are more likely to be distant from all others in the network. Thus, the inventors linked to clusters

1 and 2, whose odds of 18.88% and 13.83%, respectively, are likely to be more distant from the

other actors in the co-authorship network. Although the marginal effects are not significant for

the highest category, they are for the low one. The probability of cluster 1 inventors of

belonging to it is 12.78% smaller than the reference group. Thus, these inventors are actually

less likely to be close to the other nodes.

On the other hand, inventors linked to cluster 5 infrastructures have probability of 6.07%

to belong to the high category. This suggests they tend to be less distant from the other actors.

In fact, they have a 17.73% higher probability of belonging to the low category and, therefore,

are the most likely to be close to other actors in the scientific collaboration network. Its staff,

which consists of a large number of individuals (31,13, on average) is likely to make more self-

sufficient groups, reducing the need for so many intermediaries. This could explain their

performance in research which, according to Lee et al. (2012), was considered the best

collaboration strategy for scientific production adopted by Korean research institutions. For

Bordons et al. (2015) closeness centrality would increase the opportunity to find new

partnerships by requiring less effort to reach other individuals in the network. On the other

hand, it is important to make clear that proximity to other nodes necessarily implies less

dependence on intermediate actors, resulting in shorter paths and less transmission of

information and knowledge (BRANDES; BORGATTI; FREEMAN, 2016). In this sense, the

proximity mantained by inventors linked to cluster 5 infrastructures would occur at the expense

of the redundancy of relations and knowledge that characterizes their scientific collaboration

network, which could explain why their IFPA is smaller compared to clusters 3 and 4 inventors.

Thus, the interface between S&T is made possible by different collaboration strategies, so these

inventors benefit from the proximity with other nodes to produce science and rely on the

establishment of direct and influential connections to produce technology.

In the co-invention network, it is important to consider that the probabilities are high

among the inventors in all clusters. This is a huge evidence of the low connectivity in the

technology network in which knowledge and resources do not flow easily among actors.

However, the inventors linked to cluster 2 infrastructures have a probability of 77.40%

belonging to the high category whose statistical significance shows that they are more distant

from the other nodes. This result only reinforces how difficulty is to them to build and maintain

S&T links in relation to other clusters inventors.

In summary, the results show that there are strong differences in the inventors position

in the co-authorship and co-invention networks according to different infrastructures types to

which they are linked. In addition, they indicate distinct collaboration strategies so that better

structured infrastructures that develop a larger set of activities would be made up of more

central individuals in both their S&T networks. This is a particularly important finding which

shows that S&T network positions also relies on collective aspects.

5 Conclusion

This research sought to understand relationship between the scientific and technological

collaboration networks of inventors in different types of research infrastructures. This can be

considered a pioneering effort for several reasons. First because the study involved a

considerable sample compared to the size used in other studies. Secondly because it took into

consideration the collective characteristics as mediators of S&T outputs, which according to

Carayol and Matt (2004) and Stephan (2010) should be further explored by studies in this area.

In addition, it brings an important and original contribution to the understanding of the Brazilian

research units characteristics, whose data are scarce and fragmented.

As a result, we sought to construct a typology to represent different profiles of research

infrastructures based on the characteristics of their activities and staff. In other words, this

characterization was made based on the scope and scale of the infrastructures. They were

classified into five groups that distinguished them quite well as to the variables selected to

characterize them. In general, the infrastructures belonging to clusters 1 and 2 have a staff 110%

smaller than clusters 3, 4 and 5, which proportionally includes 56% fewer doctors among

permanent researchers. What differentiates infrastrucutures from clusters 1 and 2, however, is

the predominance of technicians in the former and their orientation to technological services

activities.

In clusters 3, 4 e 5 infrastructures the participation of non-permanent researchers

represented by graduate students constitutes the main composition of their staff reflecting on a

larger scale. Thus, although the country’s research infrastructures are evaluated by Squeff and

De Negri (2014) as very low scale, there are significant differences that would even be

associated with their research performance. In addition, they presentend a greater dynamism in

the performance of teaching, research, technological development, technological services and

technological extension. However, while in cluster 3 teaching and research are the most

frequently performed activities, cluster 4 also performs technological development and in

cluster 5 all activities are carried out intensively by the infrastructures.

Once the typology was proposed, it was possible to establish the relationship between

the inventor scientific and technological collaboration networks. The unprecedented conclusion

is that the infrastructures characteristics in which they work are strongly associated with the

way in which they collaborate to produce S&T. First, we concluded that the inventors connected

to smaller infrastructures and with a low percentage of PhD permanent researchers are more

predisposed to cooperate with each other. On the other hand, the number of connections

between individuals linked to clusters 3, 4 and 5 infrastructures are more frequent. That is, the

need to share a compatible resource base is key to bringing researchers closer together and

enabling them to develop collaborative projects. Thus, even if the complementarity of resources

and knowledge is one of the presuppositions of modern scientific and technological production,

the compatibility between research structures would allow the different groups of researchers

to communicate. In other words, inventors linked to “weaker” infrastructures tend to collaborate

with peers under the same condition while those linked to the “stronger” infrastructures

collaborate more to partners linked to infrastructures equally robust.

In addition, the results allow us to conclude that infrastructures characteristics are

associated with the importance that the individuals have in the both networks. Inventors linked

to clusters 3 and 4 infrastructures are more likely to take a leading position in S&T networks

by weighing their importance by betweenness and eigenvector centralities. Inventors linked to

the cluster 3 infrastructures, specifically, tend to increase their betweenness capacity in the co-

invention network when they are linked to cluster 4 infrastructures, which probably occurs due

to their technological superiority. In this way, inventors linked to more than one cluster are also

more likely to be considered important actors in S&T networks. Thus, we can attribute that the

inventors’ collaboration strategy is strongly influenced by characteristics of these

infrastructures. Specifically, the expressive non-permanent and doctor permanent researchers

share in the staff make it more likely to adopt a collaborative strategy based on many non-

redundant connections made with several research collaborators.

On the other hand, the high levels of scientific and technological production observed

in cluster 5 infrastructures can be attributed to a strategy of hybrid collaboration. While these

inventors rely on a strategy based on more and better links to produce technology, they enjoy

proximity to produce science. Thus, when using a large staff, its co-authorship network would

imply less dependence on intermediary actors, resulting in shorter paths and less transmission

of information and knowledge (BRANDES; BORGATTI; FREEMAN, 2016). Although this

strategy is associated with high performance in research (LEE et al., 2012), the closeness

centrality in the co-authorship network would result in redundant connections, which would

explain why the IFPA of its production is comparatively smaller to clusters 3 and 4

infrastructure’s inventors. Nevertheless, it is possible to conclude that cluster 5 infrastuctures

characteristics are also associated to some S&T articulation ability, which is not experienced

by the inventors linked to clusters 1 and 2 infrastructures.

So, it can be pointed out that while collaborative knowledge production has become the

dominant and most promising way to produce high-quality research (AHRWEILER; KEANE,

2013), researchers may not be able to exploit this potential by working in infrastructures with

low scale or limited scope. Thus, their characteristics would be associated with researchers’

collaborative strategies, which in turn would be related not only to the production levels but

also to their ability to articulate S&T.

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