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RESEARCH ARTICLE

A quantitative view of the structure of
institutional scientific collaborations
using the example of Berlin

Aliakbar Akbaritabar1,2

1Max Planck Institute for Demographic Research (MPIDR),
Laboratory of Digital and Computational Demography, Rostock, Germany
2German Centre for Higher Education Research and Science Studies (DZHW), Berlin, Germany

Keywords: Berlin, Berlin University Alliance, bipartite community detection, coauthorship network
analysis, disambiguation, internationalization

ABSTRACT

This paper examines the structure of scientific collaborations in Berlin as a specific case with a
unique history of division and reunification. It aims to identify strategic organizational coalitions
in a context with high sectoral diversity. We use publications data with at least one organization
located in Berlin from 1996–2017 and their collaborators worldwide. We further investigate four
members of the Berlin University Alliance (BUA), as a formerly established coalition in the
region, through their self-represented research profiles compared with empirical results. Using
a bipartite network modeling framework, we move beyond the uncontested trend towards
team science and increasing internationalization. Our results show that BUA members shape
the structure of scientific collaborations in the region. However, they are not collaborating
cohesively in all fields and there are many smaller scientific actors involved in more
internationalized collaborations in the region. Larger divides exist in some fields. Only Medical
and Health Sciences have cohesive intraregional collaborations, which signals the success
of the regional cooperation established in 2003. We explain possible underlying factors
shaping the intraregional groupings and potential implications for regions worldwide. A major
methodological contribution of this paper is evaluating the coverage and accuracy of different
organization name disambiguation techniques.

INTRODUCTION

Researchers work for academic and nonacademic organizations and firms and use the resources
from these organizations to carry out scientific work and form scientific collaborations.
Coalitions and strategic ties between scientific organizations can be a cause and/or an effect
of the way scientists affiliated to them communicate with each other. An example of the former
is the top-down regional, national, or organizational policies that support specific types of
collaborations (e.g., the COST1 initiative to foster scientific networking in Europe). The latter
is driven more by the individual motivations of scientists to start bottom-up research projects
and obtain funding through interorganizational collaborations with researchers of other (inter)
national organizations (e.g., ERC2 starting, consolidator, or advanced grants).

1 https://www.cost.eu/
2 https://erc.europa.eu/

a n o p e n a c c e s s

j o u r n a l

Citation: Akbaritabar, A. (2021). A
quantitative view of the structure of
institutional scientific collaborations
using the example of Berlin.
Quantitative Science Studies, 2(2),
753–777. https://doi.org/10.1162/qss_a
_00131

DOI:
https://doi.org/10.1162/qss_a_00131

Peer Review:
https://publons.com/publon/10.1162
/qss_a_00131

Received: 2 September 2020
Accepted: 2 April 2021

Corresponding Author:
Aliakbar Akbaritabar
akbaritabar@demogr.mpg.de

Handling Editor:
Ludo Waltman

Copyright: © 2021 Aliakbar
Akbaritabar. Published under a
Creative Commons Attribution 4.0
International (CC BY 4.0) license.

The MIT Press

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A quantitative view of the structure of institutional scientific collaborations

We aim to look at the outcome of scientific collaborations, in the form of scientific publi-
cations, that are produced through either the former or latter process. By understanding the
structure of scientific collaborations between organizations, we aim to find a proxy to identify
possible strategic coalitions among them that in turn could have been inspired by individual
researchers. This dichotomy is nothing but a simplification to serve the purpose of the current
article. We are aware that large multiorganizational collaborations are not simply agreements
of individual researchers (Shrum, Genuth et al., 2007, p. 117).

Strategic coalitions could take different forms and lead to differing set of outputs (Katz &
Martin, 1997; Laudel, 2002). Here we are focused on coauthorship as one of the main forms of
collaboration and scientific publications as the expected output. We are aware that
coauthorship offers only a reductionist view of collaboration, but nevertheless it is one of
the frequently used measures of scientific collaboration (Shrum et al., 2007, pp. 7–8).

Moreover, strategic coalitions can be affected by linguistic (Avdeev, 2019), geographical (Katz,
1994), and regional proximities (Luukkonen, Persson, & Sivertsen, 1992). In an in-depth review,
Small and Adler (2019) presented a diverse array of literature that emphasized the effect of space in
the formation of social ties. Scientific organizations are populated by scientists and science is a
social enterprise (Fox, 1983). Thus, it is not counterintuitive to consider scientific collaborations
as a form of social tie oriented towards shared objectives (Shrum et al., 2007, p. viii). The formation
of these ties is facilitated or hindered by the contextual (Akbaritabar, Casnici, & Squazzoni, 2018;
Small, 2017, p. 154; Sonnenwald, 2007), social (Akbaritabar & Squazzoni, 2020; Smith-Doerr,
Alegria, & Sacco, 2017), and epistemic preferences of researchers and they can result in denser
or instead sparser scientific communities (Akbaritabar, Traag et al., 2020).

In addition, the increasing trend towards more collaborative work and team science is well
known (Araújo, Araújo et al., 2017; Wuchty, Jones, & Uzzi, 2007). It is claimed that scientific
fields, even social sciences, which tend to be nationally oriented, are moving towards more
intense collaborations and more internationalization. For hard sciences (e.g., particle physics),
scientific discovery has major reliance on multiorganizational scientific collaborations (Shrum
et al., 2007, pp. 3, 7). However, studies have highlighted the differences in national or disciplinary
contexts in the rate of internationalization (Babchuk, Keith, & Peters, 1999; Moed, De Bruin et al.,
1991) or differing rates of benefits, in terms of impact, obtained from internationalized collabo-
rations (Glänzel, Schubert, & Czerwon, 1999).

We intend to explore the interplay between different contextual variables and geographical
space to investigate the structure of scientific collaborations in the Berlin metropolitan region.
We focus on Berlin as a regional hub, with high geographical proximity, that can inspire specific
organizational arrangements. Therefore, we investigate the following research questions:

(cid:129) RQ1: How collaborative and internationalized is the scientific landscape of the Berlin

metropolitan region?

(cid:129) RQ2: Are there field differences in the rate of collaborative and internationalized scientific

work?

(cid:129) RQ3: How sector oriented is scientific collaboration in the Berlin metropolitan region?
(cid:129) RQ4: Is there evidence of strategic coalitions, or field, regional, or organizational agreements

in the structure of scientific collaborations in the Berlin metropolitan region?

(cid:129) RQ5: Are there specific field, sectoral, national, or continental cohesive subgroups driving

the scientific collaborations in the Berlin metropolitan region?

The contribution of the current paper is fourfold: (a) We focus on the scientific output of the
Berlin metropolitan region and trace the share of collaborative works and identify the share of

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A quantitative view of the structure of institutional scientific collaborations

international collaborations. We separate Berlin, Germany, Europe, and continental regions
worldwide to investigate possible groupings and we intend to move beyond the descriptive and
macro view, which advocates for increasing internationalization. (b) We cover all major OECD
scientific fields and provide a comparative view of the specificities of these fields. We also include
a sectoral view based on the type of organizations. (c) We develop and use multiple organization
name disambiguation techniques and compare their efficiency, coverage, and accuracy, and (d)
we employ a bipartite network modeling and community detection approach and present how it
can be useful in coauthorship network analysis and identification of denser groups collaborating
preferentially among themselves.

The structure of the paper is as follows: Section 2 presents the prior studies. Section 3 presents
our data sources and modeling strategy. Section 4 presents our findings, followed by discussion
and limitations in Section 5 and conclusions in Section 6.

2. LITERATURE REVIEW

Balland, Jara-Figueroa et al. (2020) argued that scientific and complex economic activities are
concentrated in urban and metropolitan areas. In a large-scale study of 353 U.S. metropolitan
areas, they found that disproportionate spatial concentration increases with complexity of
productive activities. Using the average number of authors in scientific publications as a proxy
for complexity (due to the higher coordination cost of larger scientific teams), they found that
scientific fields with higher complexities tend to have more urban concentration.

In the case of Europe, policies and initiatives are developed with the aim of building an
“integrated European Research Area.” Hoekman, Frenken, and Tijssen (2010) tested whether
this objective has been achieved. They concluded that Europe leans towards more integration
in subjects and fields that were previously national endeavors. Nevertheless, they reported
that geographically localized coauthorship was prevalent, with tendencies towards high degrees
of difference among fields in the regional, national, or European contexts. They found that some
fields (e.g., physical sciences and life sciences) were in a more advanced stage of “Europeanization,”
while other fields (e.g., medicine, engineering, social science, and humanities) present a more nation-
ally oriented profile of scientific collaboration.

Specific national contexts can present a higher or lower degree of scientific production and in-
ternationalized coauthorship. It is important to take the national context into account along with the
continental and regional views. Stahlschmidt, Stephen, and Hinze (2019) and Stephen,
Stahlschmidt, and Hinze (2020) found that Germany has a stable rate of growth in the number
of scientific publications, similar to that of OECD countries with more established science systems
(e.g., the United States, the United Kingdom, and France). Germany is moving towards higher rates
of international collaborations in most scientific fields (from 46% internationalized coauthorships in
2007 to 55% in 2017 in Scopus and from 47% in 2007 to 59% in 2017 in Clarivate’s Web of
Science ( WoS)). The United States, the United Kingdom, France, Switzerland, Italy, the
Netherlands, China, Spain, Austria, and Australia are the 10 countries with the highest shares of
coauthorship with Germany in Scopus. In addition, Aman (2016) presented evidence of increasing
internationalization and also higher rates of citations for interorganizational and international
coauthorship for the German science system in WoS from 2007 to 2012.

There are also studies specifically focused on the Berlin metropolitan region. Rammer, Kinne,
and Blind (2020) found a form of selective spatial proximity between knowledge-producing in-
stitutions (e.g., universities) and knowledge-demanding institutions (e.g., innovative companies
and firms). They reported a microgeographic scope where innovative firms were surrounded
by same-sector firms and located closer to universities and research institutes.

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A quantitative view of the structure of institutional scientific collaborations

Abbasiharofteh and Broekel (2020) explored the biotechnology field in the Berlin metropol-
itan region. They concluded that Eastern and Western organizations within Berlin are still not
cohesively collaborating with each other. The “shadow” of the Berlin wall still influences the
scientific collaborations of the region.

Organizational, regional, and continental coalitions are being developed to support higher
rates of scientific collaboration among the actors in these contexts. A specific example is the
Berlin University Alliance (BUA)3. The BUA was founded in February 2018 between the three
main universities and one university hospital located in the Berlin metropolitan region (i.e., Freie
Universität Berlin (FU), Humboldt-Universität zu Berlin (HU), Technische Universität Berlin
(TU), and Charité – Universitätsmedizin Berlin (CH)) (Berlin University Alliance, Humboldt-
Universität zu Berlin et al., 2018, 2019). The BUA claims to have been established based on
a longstanding record of intraregional collaborations between these institutions. The interaction
between these institutions began following an era of institutional isolation after the fall of the
Berlin Wall during which these institutes needed to define and empower their unique identities.
Afterwards, the first forms of cooperation between these institutes emerged, which led in 2003 to
the establishment of a shared medical faculty between HU and FU to be located in CH’s facil-
ities. There are examples of competition, mutual definition of exclusive research areas, and grad-
uate programs versus close cooperation among BUA members in the past three decades. These
are highlighted in the BUA’s proposal as dimensions of the unique history and strengths of the
region. The BUA aims at fostering previous collaboration experiences in a new organizational
form. One of our minor goals is to control whether these four institutions have a distinctive po-
sition in the structure of scientific collaborations formed in the Berlin metropolitan region.

Network analysis can be used to identify the presence of communities in coauthorship net-
works (Akbaritabar et al., 2020; Leone Sciabolazza et al., 2017; Palla, Barabási, & Vicsek,
2007). Quantitative models are used to examine whether collaboration patterns persist be-
tween or within denser areas of the network and in form of specific communities. Looking
at the composition of these communities and identifying potential factors contributing to their
cohesion helps to explain groupings in scientific collaborations.

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In levels lower than continental, national, or regional frameworks, scientific organizations
themselves can have strategic plans to define their overarching identities and main research
foci. This might inspire researchers in a certain organization to prioritize research in specific
fields and areas (Blume, Bunders et al., 1987) to show allegiance with the organization’s des-
ignated identity, which in turn could penalize researchers’ selection of innovative research
themes (Rijcke, Wouters et al., 2016). Goals set out by funding agencies could affect collab-
orations (Nederhof, 2006; Wagner, Park, & Leydesdorff, 2015). Furthermore, the type of orga-
nization (i.e., sector) partially determines the type of research that an organization conducts
and its expected outcomes.

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In addition to the themes discussed above, the type of data employed to answer the research
questions could have a large effect on the identified trends (Huang, Neylon et al., 2020).
Bibliometric databases are not perfect and any given one could be prone to specific errors. In
terms of coverage, different databases have certain policies to define what should be indexed
(Huang et al., 2020). This affects the results of macro studies depending on the database em-
ployed, subset of scientific publications used, document types analyzed, and level of aggrega-
tion and normalization applied (Stahlschmidt et al., 2019; Stephen et al., 2020). In terms of
cleanness of the data, there is a strong need for disambiguation of scientific entity names

3 https://www.berlin-university-alliance.de/en/about/index.html

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(e.g., authors, organizations) which might bias the quality of results (Aman, 2018; D’Angelo &
van Eck, 2020; Donner, Rimmert, & van Eck, 2019). Thus, one of our methodological goals is to
introduce organization name disambiguation techniques to match scientific organization names
with publicly available databases (e.g., Wikidata and Global Research Identifier Database
(GRID)) and evaluate the reliability of the results in comparison to established techniques.

3. DATA AND METHODS

We use Scopus 2018 data from the German Competence Centre for Bibliometrics (KB)4. We
extract article, review, and conference proceedings documents published from the beginning of
the database in 1996 until the end of 2017. To delineate the Berlin metropolitan region and to
identify the scientific collaborations that occurred in the region, we select only publications that
have at least one authoring organization located in Germany and Berlin. Thus, coauthorship here
includes Berlin organizations and their collaborators worldwide. Our level of analysis is scientific
organizations (i.e., each affiliation address mentioned in a publication that can be academic or
nonacademic organizations or firms with which researchers are affiliated) and we do not investi-
gate lower levels (e.g., authors).

Our data include different metadata for each publication, such as publication year, title,
affiliation addresses, scientific field, journal name, and document type. We include conference
proceedings in addition to articles and reviews as there are technical universities in the sample
for which this type of document is considered influential. We use a mapping of publications to
OECD scientific fields based on Scopus ASJC5, which reduces the number of subject catego-
ries from 33 to a more interpretable six categories (OECD, 2007). We compare the aggregate
data for trends between the different OECD scientific fields (i.e., Agricultural Sciences (AS),
Engineering Technology (ET), Natural Sciences (NS), Medical and Health Sciences (MHS),
Humanities (H), and Social Sciences (SS)). Note that some publications are assigned to multiple
fields. In the aggregate analysis, we use the first field to which a publication was assigned, but
in a single field view, we take publications with any assignment in the given field; thus, inter-
disciplinary publications are covered separately in all their assigned fields.

As described earlier, scientific organizations set goals and define strategic paths to ensure a unique
research profile and identity. To have a better understanding of how BUA members introduce their
own research goals and main areas, we use their self-representations in Berlin University Alliance
et al. (2018, 2019). We expect to observe the prevailing roles of these institutions in the structure
of scientific collaborations of the fields closer to their primary areas of focus. For FU this includes:
“Biomedical Foundations,” “Complex Systems,” “Cultural Dynamics,” “Educational Processes and
Results,” “Health and Quality of Life,” “Human-Environmental Interactions,” “In-Security and
Security Research,” “Materials Research,” and “Transregional Relations.” For HU: “Application-
Oriented Mathematics,” “Image Sciences,” “Integrative Life Sciences,” “Integrative Natural
Sciences,” “Research on Law and Society,” “Study of Ancient Civilizations,” and “Sustainability
Research.” For CH: “Cardiovascular Research & Metabolism,” “Infection, Immunology &
Inflammation,” “Neuroscience,” “Oncology,” “Rare Disease & Genetics,” and “Regenerative
Therapies.” For TU: “Materials, Design and Manufacturing,” “Digital Transformation,” “Energy
Systems, Mobility and Sustainable Resources,” “Urban and Environmental Systems,” “Optic and
Photonic Systems,” and “Education and Human Health.” Aside from CH, which has a focus on
MHS and NS, the other three institutions are active in areas close to major OECD fields.

4 Kompetenzzentrum Bibliometrie (KB), https://bibliometrie.info
5 All Science Journal Classification

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A quantitative view of the structure of institutional scientific collaborations

3.1. Organization Name Disambiguation

The data delivered by Scopus are not perfect. They are prone to error and there is a strong
need for disambiguation of organization names (Donner et al., 2019). Without disambiguation,
the coauthorship networks constructed will have multiple representations of the same actor
and an artificially higher level of (dis)connectivity.

We developed two disambiguation techniques (i.e., OrgNameString and OrgNameFuzzy
matching) and compared their results with a previously established technique (Research
Organization Registry (ROR)6), as depicted in Figure 1. ROR uses data from the Global Research
Identifier Database (GRID)7 prepared by Digital Science8, ISNI9, Crossref, and Wikidata10.

In OrgNameString matching (shown in the gray shaded area on the left of Figure 1), we stan-
dardize organization names and perform a match with GRID (snapshot of February 17, 2019). We
use only the largest entity that the KB extracts from Scopus using the first part of the affiliation string
before the first comma. To match, we used string comparison methods in Python (for simplicity we
call it OrgNameString) which matches whole and subsets of the text strings but does not account
for changes in the order of words in organization names. To remove the effect of the order of or-
ganization name parts, we split the names based on space (i.e., words) and reorder them alpha-
betically for both Scopus and GRID entities. We then add country names to the end of strings to
allow higher precision of matching and reduce the effect of organizational homonyms11. For those
still nonmatched, we perform another OrgNameString match with scientific organizations in
Wikidata and for the still missing ones, with Wikidata entities that have geographical coordinates.
We limit the results to those most promising based on a Jaro Winkler distance of more than 0.85
between the two matched names. We do this after the OrgNameString match is done, as a control
for reliability. We chose this threshold based on manual evaluation of match results to have the
highest accuracy. Finally, to complement the results of the OrgNameString procedure, we search
organization names in an in-house database that was previously developed by Rimmert (2018)12
by comparing organization names to Wikidata entities.

In parallel, we compare organization names with GRID by OrgNameFuzzy matching the
names. This method takes differing word order and subsets of the name into account (we stan-
dardize the names as before and add country). For OrgNameFuzzy text matching, we use the
FuzzyWuzzy13 library in Python. Using fuzz.ratio as the scorer, we set a threshold of 80%,
which was chosen based on empirical evaluation on some exemplar cases and proved to give
a reliable accuracy (gray shaded area in center of Figure 1).

In a third attempt, instead of the main organization names used in previous procedures, we used
the complete string of affiliation addresses delivered by Scopus to disambiguate it with the Research
Organization Registry (ROR) API (see footnote 11 for an example). We obtain further information
(i.e., country, geographical coordinates (longitude and latitude)) of the main address and type of

6 https://ror.org/about
7 https://www.grid.ac/pages/policies
8 https://www.digital-science.com/
9 International Standard Name Identifier, https://isni.org/
10 https://www.wikidata.org
11 As an example, from this address string delivered by Scopus, “Freie Universität Berlin, Department of edu-
cation and psychology, DEU” KB extracts “Freie Universität Berlin” as the first part, which we use for
OrgNameString and OrgNameFuzzy matching processes and after removing alphanumeric symbols,
lowercasing and reordering alphabetically, we add the 3-digit ISO country code to the end (e.g., “berlin freie
universität DEU”).

12 This in-house data is only accessible through KB infrastructure.
13 https://github.com/seatgeek/fuzzywuzzy

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A quantitative view of the structure of institutional scientific collaborations

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Figure 1. Organization name disambiguation techniques and comparison of coverage and accuracy.

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organization as education, non-profit14, company, government, health-care, facility15, archive,16
and others from ROR). We used the ROR snapshot from November 7, 2019. This disambiguation
takes different name spellings and misspelled words, acronyms, and multiple languages into
account. In order not to face API request limits, we have set up a local instance of the ROR API
(see gray shaded area on the right of Figure 1).

To evaluate the quality of ROR disambiguation results, we chose two random samples of
100 organization names from Scopus data. A research assistant searched online for them and
the disambiguated name from ROR to find the original address and compared the two names
to determine whether the match was correct. Through this process, we identified false matches
in 4% of one sample and 8% of the other sample, indicating that 96% and 92% of cases were
reliably matched.

To highlight the importance and effect of the disambiguation, we define different scenarios
based on nondisambiguated and disambiguated data. We present the results and implications of
each scenario in construction of the coauthorship networks. However, on the basis of the accu-
racy and coverage results, we use the third disambiguation technique described above (i.e.,
ROR) for our field, geographical, and sector analysis. It is important to note that in disambiguated
versions of the data, we include only publications for which all contributing organizations are
disambiguated and we exclude those publications with one or more nondisambiguated co-
authoring organizations. Note that due to the shared medical faculty of HU and FU from
2003, affiliations from Charité in most cases included either HU or FU (or both). We searched
each individual affiliation string; if Charité was mentioned, we assigned these to Charité. ROR
favored the affiliation appearing first and Charité was not always the first mentioned, thus this
manual correction was necessary to identify Charité’s publications.

3.2. Bipartite Network Modeling

We construct bipartite coauthorship networks (Breiger, 1974) using ties between publications
and organizations (Katz & Martin, 1997). We treat each single publication as an event where
organizations interact to produce an academic text (Biancani & McFarland, 2013). Studies on
coauthorship networks usually use a one-mode projection of these bipartite networks (Newman,
2001a, 2001b). The problem with this projection is twofold. Different structures in two-mode
networks are projected to the same one-mode structure, which causes an information loss about
the underlying structure. Second, the one-mode projection can present an artificially higher den-
sity and connectivity due to publications with a high number of authors which project to max-
imally connected cliques. By adopting methods specifically developed for bipartite networks we
are able to resolve the shortcomings. However, these methods are scarce.

To identify possible geographical, field and/or sector based coalitions between scientific
organizations, we extract the largest connected component of the network, i.e., giant compo-
nent, and investigate it further. Our aim is to see if there are cohesive subgroups of organiza-
tions preferentially collaborating among themselves. We investigate the potential underlying
factors behind these groupings.

14 Organizations that use their surplus revenue to achieve their goals. They include charities and other non-

government research funding bodies. Example, the Max Planck Society (grid.4372.2)

15 A building or facility dedicated to research of a specific area, usually containing specialized equipment.
Includes telescopes, observatories and particle accelerators. Example: member institutes of the Max
Planck Society (e.g., Max Planck Institute for Demographic Research, grid.419511.9)

16 Repository of documents, artifacts, or specimens. Includes libraries and museums that are not part of a uni-

versity. Example, New York Public Library (grid.429888.7)

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To identify communities of coauthorship, we use bipartite community detection by Constant
Potts model (CPM). CPM is a specific version of the Potts model (Reichardt & Bornholdt, 2004)
proposed by Traag, Van Dooren, and Nesterov (2011) as a resolution-limit-free method. It resolves
the resolution limit problem in modularity (Newman, 2004) which can obstruct detection of small
communities in large networks (Traag, Waltman, & van Eck, 2019). We use the implementation in
the Leidenalg17 library in Python. Community detection emphasizes the importance of links within
communities rather than those between them. CPM uses a resolution parameter γ (i.e., “constant”
in the name), leading to communities such that the link density between the communities (external
density) is lower than γ and the link density within communities (internal density) is more than γ.
We set the resolution parameter for all networks (e.g., aggregate data (ROR) and scientific fields)
−3. To ensure replicability of results, we use a seed. We chose this γ after exploration of
to 6 × 10
the number of communities detected in contrast to the number of organizations and publications
included in each bipartite community to arrive at a rather consistent distribution.

4. RESULTS

4.1.

Implications of Organization Name Disambiguation

Figure 1 presents the different disambiguation techniques used and the coverage of publication-
author-organization links (OrgNameString 66.18%, OrgNameFuzzy 56.38%, and ROR 77.47%).
We present author-level counts of links here, as different authors from the same institution might
mention different affiliation addresses by including department names or they might report erro-
neous addresses. However, in building organization level coauthorship networks, we exclude
repeated organization-publication links.

Each technique successfully disambiguates a set of unique organization names that other tech-
niques are unable to disambiguate (OrgNameString 1,206, OrgNameFuzzy 8,198 and ROR 8,449).
Note that not all organizations involved in authoring a publication are successfully disambiguated:
OrgNameString identified 115,749 (32.43%) organizations from 239,390 (93.18%) publications,
OrgNameFuzzy identified 194,054 (54.37%) organizations from 184,990 (72%) publications, and
ROR identified 227,213 (63.66%) organizations from 233,039 (90.71%) publications. We only
include publications for which all contributing organizations are successfully disambiguated and
this decreases our coverage to 129,813 (51%) publications in OrgNameString, 53,569 (21%) in
OrgNameFuzzy, and 126,130 (49%) in ROR in favor of higher accuracy of results.

Table 1 compares the networks constructed using nondisambiguated data with the output of
different disambiguation techniques. We observed a high rate of disconnectivity in the nondisam-
biguated network (10,269 components, including many organizations) while this was extremely
reduced through disambiguation techniques (i.e., to 66 in OrgNameString, 159 in
OrgNameFuzzy, and 100 in ROR). The share of nodes in the giant component, which was initially
high in the nondisambiguated network (95%), further increased and covered close to 99% in all
cases (numbers in the table are rounded up). In all these cases, disambiguation shows that many
unique organization names delivered by Scopus need to be merged due to spelling errors and name
order changes, which can affect the networks constructed to a high degree (see De Stefano,
Fuccella et al. (2013) for a discussion of possible effects). In OrgNameString, the ratio of disambig-
uated to nondisambiguated unique organizations was 1 to 9.8 (in OrgNameFuzzy 1 to 11 and in
ROR 1 to 15). This proves the high influence that disambiguation has on the results.

Table 2 presents the networks in different OECD scientific fields using ROR results. Note
that the results which follow are based on the 49% of publications for which all contributing

17 https://github.com/vtraag/ leidenalg

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Table 1.

Berlin organizations’ coauthorship networks using nondisambiguated and disambiguated data (G = giant component)

Metrics
No. of connected components

No. of biparitite nodes

No. of biparitite edges

% of biparitite nodes in G

% of biparitite edges in G

No. of organizations

No. of organizations in G

No. of publications (%)

No. of publications in G

Non disambiguated

10,269

613,827

1,083,775

356,918

337,755

256,909

245,203

OrgNameString
66

135,057

246,704

100

5,244

5,176

OrgNameFuzzy

159

58,547

89,199

100

4,978

4,809

ROR
100

133,387

246,472

100

7,257

7,153

129,813 (51%)

53,569 (21%)

126,130 (49%)

129,657

53,248

125,949

organization names were successfully disambiguated by the ROR technique. Each of the fields
presented in Table 2 covers a different share of connected components observed in the aggre-
gate data, ranging from 13 components in AS to 68 in Social Sciences (SS). NS has the highest
number of both publications and organizations, while Humanities (H) has the smallest number
of publications and organizations.

4.2. Macro View of Scientific Output of the Berlin Metropolitan Region

Figure 2 presents the raw and fractional count of publications among different OECD fields. Note
that it is based on publications that have at least one collaborator from the Berlin metropolitan
region and for organizations that were successfully disambiguated with the ROR technique.
Nevertheless, the trends are in line with what Stephen et al. (2020) and Stahlschmidt et al.
(2019) reported for Germany. Some fields show higher rates of collaborative work (e.g., see the
case of NS, blue lines, first and second from top) which is evident in the gap between the lines
presenting their raw and fractional counts and is in line with Shrum et al. (2007, p. 3)’s report of

Table 2.
disambiguation)

Berlin organizations’ coauthorship networks in different OECD scientific fields (G = giant component, ROR organization name

Metrics
No. of connected components

No. of biparitite nodes

No. of biparitite edges

% of biparitite nodes in G

% of biparitite edges in G

No. of organizations

No. of organizations in G

No. of publications

No. of publications in G

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8,528

13,822

100

1,687

1,668

6,841

6,828

33,930

57,671

100

2,991

2,933

30,939

30,851

4,835

6,195

798

763

4,037

3,988

MHS
48

46,842

84,945

100

3,843

3,792

42,999

42,913

89,032

170,334

100

5,970

5,910

83,062

82,989

16,045

25,336

100

2,091

2,012

13,954

13,849

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Figure 2. Raw and fractional count of Berlin publications by OECD fields (1996–2017, Scopus,
fractional count based on organizations, y-axis on log scale).

physical sciences. In contrast, some fields that are traditionally known to be less collaborative
(Leahey, 2016) present a smaller gap on the plot (e.g., Humanities). As the y-axis is on a log 10
scale, the figure shows that the growth over time in the raw and fractional count of publications has
tripled in the case of Humanities and Social Sciences. However, this could be influenced by the
higher coverage of Scopus in recent years and not merely an increase in publications (see
Stahlschmidt et al. (2019) for a discussion). To investigate the internationalization of publications,
Figure 3 presents the single (intra-DEU) versus multiple country publications. The trends observed
are in line with the case of the German science system reported in Stahlschmidt et al. (2019). It is
clear that some fields have already reached close to 50% of their publications involving interna-
tionalization (i.e., NS), driving the aggregate trend of increasing internationalization observed in
the top panel of the figure. However, there are other fields with still lower than 25% of publications
involving international collaboration (i.e., H and SS). In all fields except H and SS, an increasing
trend towards further internationalization is evident (see the increasing length of black bars in the
figure), while H and SS do not present a clear increasing trend and in some years the rate of
internationalization has decreased. This answers our RQ1 and RQ2, signaling large differences
between fields in the rate of collaborative work and internationalization.

To investigate our RQ3, we focus on organization sectors. In total, out of 7,257 unique
organizations, which includes Berlin organizations and their worldwide collaborators, there
were 2,844 organizations from the education sector, 1,667 facility, 860 health-care, 587 com-
pany, 436 nonprofit, 429 government, 282 other, 124 archive, and 28 with missing sectors.

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Figure 3.

Share of intra-Germany versus multiple country coauthorship: (Top) aggregate; (Bottom) different fields (1996–2017, Scopus).

Table 3 presents the distribution of organizations in different sectors in the five countries with
the highest numbers of organizations (i.e., China, Germany, France, the United Kingdom, and
the United States, in alphabetical order of ISO codes). While education is the sector with the
highest number of organizations in four countries, facility has the highest number of

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Table 3.
Berlin sample 1996–2017, percentages calculated for each country)

Five countries with the highest number of organizations by sector (GRID data based on

Country code
CHN

DEU

FRA

GBR

Organization sector

Education

Facility

Healthcare

Government

Company

Nonprofit

Other

Archive

Facility

Education

Company

Healthcare

Nonprofit

Other

Government

Archive

Facility

Education

Healthcare

Government

Company

Other

Nonprofit

Archive

Education

Healthcare

Facility

Company

Nonprofit

Government

Other

Archive

Count
193

319

215

205

115

113

261

114

%
56

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Table 3.

(continued )

Country code
USA

Organization sector

Education

Healthcare

Company

Facility

Nonprofit

Government

Other

Archive

Count
423

157

129

110

%
42

organizations in Germany, which could be an artifact of the disambiguation and exclusion of
publications with nondisambiguated organizations. Figure 4 presents the geographical distri-
bution of organizations worldwide separated by sectors and aggregated in countries. Darker
colors show higher numbers of organizations in a given country. It is clear that most countries
have organizations in the education sector. Another evident pattern is that more developed
countries (e.g., in Western Europe, North America, and Oceania) have representatives in all
sectors, which signals the higher sectoral diversity of the science systems of these countries.
However, China and India are two specific cases outside the previously mentioned regions
with representation in many sectors. The distribution of companies is another interesting ob-
servation, where many countries do not have any representatives, in contrast to the education
sector. This answers our RQ3 that there is a high diveresity of sectors collaborating with the
Berlin region, with variation depending on the specific country.

4.3. Structure of Institutional Scientific Collaborations in Berlin

We focus now on communities of coauthorship identified from the giant component using
bipartite community detection (RQ4 and RQ5). This enables us to go beyond the macro de-
scriptive view presented thus far and investigate the structure of collaborations at the individ-
ual publication level. Note that these are communities detected from the giant component,
which is connected in itself; however, these communities are the denser areas of the collab-
oration network. We are interested to know what could be the underlying factors behind these
higher densities leading to these cohesive subgroups.

Figure 5 presents the distribution of communities based on the number of organizations in
each community and the aggregate number of publications of all organizations in a given
community (each community is represented by one dot in the figure). The clearest observation
in this figure is the field-based collaboration patterns among BUA members indicated by the
shape and color of dots, where green triangles show the presence of one or more BUA mem-
ber(s) in a given community.

In most cases, there is a divide between BUA members. In the aggregate view in the top panel,
and in the NS, H, and SS fields, we see three BUA members populating the most prolific com-
munity (FU, HU, and CH) and one BUA member located in the second most prolific community
(TU). In AS, HU and TU are in the most prolific community, i.e., 0, and CH and FU are in

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Figure 4. Countries worldwide collaborating with Berlin region by sector (color: N. of organizations (ranges from 1 to 423). If a country does
not have presence in a sector, it is shown with gray color.

community 1. In the case of ET, TU is located in the most prolific community (community 0) and
HU, CH, and FU are in the second most prolific community (i.e., 1). This could be due to the fact
that TU, being a technical university, pursues more technical and application-oriented research.

MHS is the only case where all four BUA members are present in a single community,
which is perhaps due to the closer cooperation among them that was formed through a shared
faculty by HU and FU located in Charité (CH) in 2003. They have been successful in integrating
TU into the collaboration structure of MHS.

Overall, BUA needs to integrate TU further into the structure of scientific collaborations in the
region through shared projects or organizational forms. Furthermore, it is clear from aggregate
and field views that not all communities are populated with the most prolific organizations (in
terms of number of publications). There are communities of different sizes consisting of organi-
zations with different levels of productivity (e.g., see the gray circles). More detail on the sectoral
and geographical composition of the communities is presented in Table A1 in the Appendix.

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Figure 5. Organizations in communities of the giant component vs. publication (label: name of community; green triangle: includes BUA
member(s); gray circle: other communities; x and y axes on log scale).

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5. DISCUSSION

At first sight, and based only on descriptive analysis, we observed a highly collaborative sci-
ence landscape in the Berlin region. Some fields present a high degree of difference between
the raw and fractional counts of publications. Despite the prevalence of collaborative works
and increasing trend towards internationalization in aggregate view, we observed that some
fields (e.g., NS and AS) have more internationalized collaborations while other fields (e.g.,
MHS, Humanities, and Social Sciences) are less internationalized or they did not present a
steady upward trend which is in line with observations by Moed et al. (1991) and Babchuk
et al. (1999).

Berlin presents a specific case. It is similar to a science hub with a diverse sectoral composi-
tion of organizations, which is in line with Balland et al. (2020)’s observation in metropolitan
regions in the United States and Rammer et al. (2020)’s observation of the Berlin metropolitan
region. However, this could be due to our data gathering strategy, where only publications with
at least one organization located in Berlin are included. Thus, there could be other collabora-
tions between the partners, excluding Berlin organizations, that we do not cover here. Some
countries present a highly diverse science system consisting of a wide range of sectors among
those collaborating with Berlin organizations. However, in most countries, education is the pre-
vailing sector where scientific publications are produced, which is not counterintuitive.

We modeled the scientific collaborations through bipartite coauthorship networks, which
treat each scientific publication as an event where organizations interact in producing scien-
tific texts. Our bipartite community detection configuration was helpful in detecting the di-
verse composition of organizational teams contributing to scientific publications, which
could be overlooked if the network is projected to one mode due to artificially high cliquish
behavior. We observed that in most fields, with the exception of Humanities and Agricultural
Sciences, the most prolific communities were comprised of organizations located in the Berlin
metropolitan region and they were collaborating either within Berlin or with other German
organizations or exclusively with European organizations. There were of course internationa-
lized communities in all fields, but they were not highly prolific.

Looking at the members of the BUA and their position in these cohesive subgroups pre-
sented interesting findings. Only in the MHS, which was dominated by the high productivity
of Berlin and Germany, were the four BUA members located in one community and col-
laborated densely. This is likely an outcome from the efforts of the HU and FU in 2003 to
jointly establish an MHS faculty, located in Charité’s facilities, and our findings show that
this strategic coalition has been successful in integrating other organizations from Berlin,
such as TU.

Furthermore, some of the observed field division between BUA members could be remnants
of the east-west division in Germany and the reorganization of research profiles and mutually
exclusive definitions of areas of focus to reduce parallel work and competition that happened
after the reunification. This divide was recently observed in the biotechnology field by
Abbasiharofteh and Broekel (2020). TU presents a specific case and in most cases it is member
of a separate community. In ET, TU’s collaboration network is dominated by other Berlin and
German organizations. It shows that the BUA needs to develop further strategic cooperations
among the members to ensure higher integration, similar to the case of MHS. However, this
might be due to the fact that we included conference proceedings, which is a specific publica-
tion type preferred more by the technical universities. As TU is the main technical university in
our sample, the collaboration structure reflected in this document type may have affected the
observed results and overinflated the divide between TU and other BUA members.

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5.1. Limitations

Our paper has certain limitations. When we construct the coauthorship network at the orga-
nization level, we naturally overlook the changes that happen in the composition of re-
searchers affiliated to those organizations. The same organization could have a highly
different composition of members over time that affects the type of research carried out and
collaboration ties formed. In addition, the number of available algorithms for bipartite com-
munity detection limits our possiblities to provide comparative results. Nevertheless, there are
recent developments in that direction (Rossetti, Milli, & Cazabet, 2019; Taguchi, Murata, &
Liu, 2020).

We use only Scopus as the main database, and although it covers documents written in
German, it is dominated by English-language publications. Bibliometric databases, including
Scopus, are regularly updated which can affect the temporal trends we observe here. In addi-
tion, each bibliometric database covers a specific set of scientific publications (see
Stahlschmidt et al. (2019) for a comparison between WoS and Scopus), despite the similarities,
there are differences in philosophies and approaches to what should be indexed (Huang et al.,
2020). Furthermore, we were unable to disambiguate all the organizations in our sample,
which led to excluding 51% of the publications that had one or more nondisambiguated or-
ganizations. Thus, while our results follow the general trends observed in the German scien-
tific system (see Stahlschmidt et al. (2019), Stephen et al. (2020), and Aman (2016)), the
specificities observed in the structure of scientific collaborations among BUA members and
the international collaborations could be highly affected if we had higher coverage in the dis-
ambiguation techniques.

Another limitation of our data, and of research in general at the organization level, is the su-
perstar researchers with multiple affiliations. We assume that these researchers have received
resources from each of these multiple organizations. Thus, we consider these researchers as
bridges between these organizations; however, in the networks constructed, these cases might
appear as an international collaboration when really it is a single author affiliated with multiple
countries. High-quality data with disambiguated records of publications at the author level
would allow a more complete investigation of such cases. Another limitation of our study could
be that our disambiguation techniques penalize countries using languages other than English or
lesser known organizations that are usually less prolific. These organizations can be more prev-
alent among those we excluded from our analysis because our techniques likely did not accu-
rately disambiguate them. In addition, different disambiguation techniques are more or less
effective in identifying differing sets of organizations and any choice of technique would have
implications for a subset of the organizations while penalizing another subset.

We do not have any insight into the background of individual researchers affiliated with
these scientific organizations. We do not know about the motivations that form and drive
the scientific collaborations and observed trends (Katz & Martin, 1997; Shrum et al., 2007,
pp. 7–8; Subramanyam, 1983, 202, 209). In addition, we assume that scientific collaborations
are positive interactions among collaborators that successfullly led to one or more scientific
publications, which simply is not always the case and conflict arises between partners that
needs to be resolved for a collaboration to proceed (Shrum et al., 2007, pp. 197–198, 202).
As an example, for all fields, we observed small communities that were leaning more towards
internationalized collaborations. These might be groups mainly consisting of migrant scientists
who collaborate with their former scientific organizations or they may play a “boundary span-
ning role” among regional, national, and continental contexts. We cannot investigate these
type of questions at the organization level.

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Furthermore, our definition of the Berlin metropolitan region was based on the affiliation
addresses, while the literature on science geography presents a diverse array of definitions
(e.g., Abbasiharofteh & Broekel, 2020; Cottineau, Finance et al., 2019), from NUTS level to
areas covering multiple cities, which are overlooked in our data gathering strategy.

6. CONCLUSION

We provide a quantitative, exploratory, and macro view of the structure of scientific collabo-
rations in the Berlin metropolitan region. We chose Berlin because of its history of division and
reunification. This region has undergone eras of organizational self-isolation to mutually ex-
clusive definition of organizational research profiles and has a specific position in the German
science system. Our main level of analysis was scientific organization (which can be academic
or nonacademic organization or firm) and we investigated the share of collaborative work and
internationalized work versus single country collaborations. We covered all OECD scientific
fields and presented a comparative view of the similarities and differences of collaborations in
these fields. By adopting a global, regional, and organization based approach, we tried to put
the empirical results into different contexts.

In methodological terms, we developed two organization name disambiguation techniques
(i.e., OrgNameString and OrgNameFuzzy matching) and compared their performance and
coverage with an established technique (i.e., Research Organization Registry [ROR]). We pre-
sented the high impact that organization name disambiguation could have on the constructed
collaborations networks and how it can bias measures and trends. We had to exclude 51% of
the publications that had one or more nondisambiguated organizations to limit our analysis to
successfully disambiguated cases. Considering bibliometric databases as ground truth could
have implications for the results of similar studies of scientific collaborations. Future research
needs to carefully consider disambiguation and provide transparent details about the disam-
biguation procedures followed and accuracy obtained.

We conclude that mixing macro and global views while keeping regional, national, and
continental granularity can help in describing the observed quantitative trends. It is necessary
to move beyond the macro descriptive view presented based on yearly publication counts or
increasing trends of team science. As our investigation proved, not all members of the com-
munity are moving towards internationalization and some parts of the community, which are
normally those that are the most prolific, prevail and distort the aggregate images.

To provide suggestions for the Berlin region and other regions worldwide, we borrow Shrum
et al. (2007)’s conceptualization of bureaucracy and technology. To present further implications
for the structure of scientific collaborations, let us here return to the specific case of Medical and
Health Sciences (MHS) in the Berlin metropolitan region. The case of MHS evidently showed that
a new organizational form (i.e., a shared faculty with defined bureaucratic procedures) can help
to not only bridge the divide between coalition members but also form an expanded collabora-
tion network worldwide. BUA members (as one example studied here that can be extended to
other strategic coalitions formed worldwide) seem to be trying to cooperate while preserving their
distinct organizational identities, which is similar to the case of geophysics discussed in Shrum
et al. (2007, p. 200), where a highly formal structure prevented the collaboration becoming an
extension to any of the member organizations. It is also similar to the case of materials sciences
(Shrum et al., 2007, p. 201), where a more bureaucratic structure allowed higher autonomy for
the collaboration partners from diverse sectors. Therefore, clear bureaucratic procedures can
even enable brokered collaboration among previous competitors (Shrum et al., 2007, p. 192).
Although scientific collaboration cannot become a goal in itself (e.g., Shrum et al., 2007, p. 202),

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BUA and similar coalitions can use this conceptualization of bureaucracy to develop clear ob-
jectives for scientific collaboration and, while preserving the distinctive profiles of the members,
benchmark from the successful case of MHS to foster further intraregional and global collabora-
tions. The resources of a single organization are limited and multiorganizational collaborations
can enable higher achievements (e.g., Shrum et al., 2007, pp. 20, 119; Aman, 2016) and increase
the diversity of researchers, which in turn can facilitate better science (Nielsen, Alegria et al.,
2017). Nevertheless, multiorganizational scientific collaborations are complex and need a careful
design to be successful.

ACKNOWLEDGMENTS

We would like to thank Sybille Hinze, Martin Reinhart, Paul Donner, Melike Janßen, and
Dimity Stephen for comments and suggestions on earlier versions of this paper.

FUNDING INFORMATION

This research was done in the DEKiF project supported by the Federal Ministry for Education
and Research (BMBF ), Germany, grant number M527600. Data are obtained from
Kompetenzzentrum Bibliometrie (Competence Center for Bibliometrics), Germany, which is
funded by BMBF, grant number 01PQ17001.

DATA AVAILABILITY

The data cannot be made publicly available due to the licensing and contract terms of the
original data. Python scripts to replicate the organization name disambiguation techniques
and bipartite community detection may be publicly accessed at https://doi.org/10.5281
/zenodo.4657325.

COMPETING INTERESTS

The author has no competing interests.

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APPENDIX

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Table A1 provides detail about the regional and sectoral composition of the most prolific or
largest communities. We separate Berlin and Germany (DEU) from the rest of Europe to pro-
vide a better comparison of intra/inter-regional collaborations.

Except in two cases where we observe a more internationalized mixture of members i.e., AS
(6%, 12%, and 6% from Americas, Asia, and Oceania, respectively) and H (33% from
Americas), in all other cases, communities including BUA members (e.g., rows with bold font
and gray background) have no members from outside Berlin and Germany and only a small
share of European members (maximum is NS with 14%). This shows the national and regionally
oriented structure of collaborations among the most prolific actors. However, in aggregate and
all fields we observe smaller and less prolific communities with an internationalized share of
members (see percentages of members from regions outside Europe on the left side of the table).

In terms of sectoral composition of the communities, education and facility prevail and

have the highest shares of members in most of the communities.

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Quantitative Science Studies

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Table A1. Composition of the largest and most prolific communities of the giant component by region and sector in aggregate and separate fields (N = community size, P = aggregate publications,
B = number of BUA member(s))

Data cluster N

Africa Americas Asia Berlin DEU Europe Oceania

region Archive Company Education Facility Government Healthcare Nonprofit Other

Region (%)

Sector (%)

No
sector

ROR

29 98,408 3

10 35,734 1

7,356

4,002

5,285

2,414

2,822

2,157

3,441

2,110

1,571

1,230

4,308 2

3,582 2

204

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180

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Table A1.

(continued )

Q
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t
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Region (%)

Sector (%)

Data cluster N

Africa Americas Asia Berlin DEU Europe Oceania

region Archive Company Education Facility Government Healthcare Nonprofit Other

MHS

7 15,116 1

11 12,457 3

4,092

1,460

2,059

1,072

294

32 58,586 3

7 24,045 1

4,676

6,186

4,869

2,897

2,816

2,976

1,643

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31 53,817 4

1,875

1,286

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172

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