longtable

# Berlin: A Quantitative View of the Structure of Institutional Scientific
Collaborations

Aliakbar Akbaritabar111German Centre for Higher Education Research and Science
Studies (DZHW), Berlin, Germany<EMAIL_ADDRESS><EMAIL_ADDRESS>ORCID = 0000-0003-3828-1533 (Corresponding Author)

###### Abstract

This paper examines the structure of scientific collaborations in a large
European metropolitan area. It aims to identify strategic coalitions among
organizations in Berlin as a specific case with high institutional and
sectoral diversity. By adopting a global, regional and organization based
approach we provide a quantitative, exploratory and macro view of this
diversity. We use publications data with at least one organization located in
Berlin from 1996-2017. We further investigate four members of the Berlin
University Alliance (BUA) through their self-represented research profiles
comparing it with empirical results of OECD disciplines. Using a bipartite
network modeling framework, we are able to 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 disciplines.
Larger divides exist in some disciplines e.g., Agricultural Sciences and
Humanities. Only Medical and Health Sciences have cohesive intraregional
collaborations which signals the success of regional cooperation established
in 2003. We explain possible underlying factors shaping the observed trends
and sectoral and intra-regional groupings. A major methodological contribution
of this paper is evaluating coverage and accuracy of different organization
name disambiguation techniques.

keywords: Berlin, Internationalization, Co-authorship Network Analysis,
Bipartite Community Detection, Disambiguation, Berlin University Alliance

##  1 Introduction

Researchers work for academic and non-academic 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., COST222https://www.cost.eu/ 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 inter-organizational collaborations with researchers of
other (inter)national organizations (e.g., ERC333https://erc.europa.eu/
starting, consolidator or advanced grants). We aim to look at the outcome of
scientific collaborations, in form of scientific publications, which is
produced through the former process or the latter. By understanding the
structure of scientific collaborations between these organizations, we aim to
find a proxy to identify possible strategic coalitions among them that in turn
could have been inspired by individual researchers.

These strategic coalitions could take different forms and lead to differing
set of outputs (Katz & Martin, 1997; Laudel, 2002). Here we are focused on co-
authorship as one of the main forms and scientific publications as the
expected output of it. We are aware that co-authorship offers only a
reductionist view, but nevertheless it is one of the highly used measures of
scientific collaborations.

Moreover, these 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 & Adler (2019) presented a
diverse array of literature that emphasizes on the effect of _space_ in
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. Formation of these ties are _facilitated_ or _hindered_ by contextual
(Sonnenwald, 2007; Small, 2017 p 154; Akbaritabar, Casnici, & Squazzoni,
2018), social (Smith-Doerr, Alegria, & Sacco, 2017; Akbaritabar & Squazzoni,
2020) and epistemic preferences of researchers and they can result in denser
or instead sparser scientific communities (Akbaritabar et al., 2020).

In addition, the increasing trends towards more collaborative work and _team
science_ is well-known. It is claimed that scientific disciplines, even social
sciences, are moving towards more intense collaborations and more
internationalization (Wuchty, Jones, & Uzzi, 2007; Araújo, Araújo, Moreira,
Herrmann, & Andrade, 2017). However, studies have highlighted the differences
in national or disciplinary contexts in the rate of internationalization
(Moed, De Bruin, Nederhof, & Tijssen, 1991; Babchuk, Keith, & Peters, 1999) or
differing rates of benefits, in terms of impact, obtained from
internationalized collaborations (Glänzel, Schubert, & Czerwon, 1999).

It is argued in the literature that scientific and complex economic activities
are concentrated in urban and metropolitan areas. In a large-scale study of
the USA, Balland et al. (2020) investigated scientific papers, patents,
employment rates and gross domestic product of 353 US metropolitan areas.
Their main hypothesis was that disproportionate spatial concentration
increases with complexity of productive activities, which was confirmed. They
used average number of authors in scientific publications as a proxy of the
complexity (due to higher coordination cost of larger scientific teams) and
they found that scientific fields with higher complexities tend to have more
urban concentration.

In the case of Europe, policies and initiatives are developed aiming at
building an “integrated European Research Area”. Hoekman, Frenken, & Tijssen
(2010) tested whether this objective is achieved. They concluded that Europe
leans towards more integration of previously dominated national contexts.
Nevertheless, they reported prevalence of geographically localized co-
authorship with high degrees of difference among disciplines in the regional,
national or Europe oriented tendencies. They found that some fields, e.g.,
physical sciences and life sciences are in a more advanced stage of
“Europeanization” while other fields, e.g., medicine, engineering, social
science and humanities present a more nationally oriented scientific
collaboration. Furthermore, they concluded that although regional
collaborations are still composing high shares of overall collaborations, the
effect of territorial and national borders seem to be decreasing over time and
Europe is moving towards a more level field of scientific collaboration.

Specific national contexts can present a higher or lower degree of scientific
production and internationalized co-authorship. Therefore it is important to
take the national context into account along with the continental and regional
views. Stahlschmidt, Stephen, & Hinze (2019) presented a view of the science
system of Germany and provided an updated version of some of their findings in
Stephen, Stahlschmidt, & Hinze (2020). They found that Germany has a stable
rate of scientific production similar to that of OECD countries with more
established science systems (e.g., the USA, the UK and France), but the growth
rate of scientific production of Germany is decreasing. Some countries (e.g.,
China and India) have higher growth rates of scientific production in recent
years. Thus the overall share of Germany from world’s scientific publications
is decreasing in the recent decades (from 6.3% in 1995 to 4.3% in 2018 based
on Web of Science (WOS)) (Stephen et al., 2020). As presented in Stahlschmidt
et al. (2019), Germany is moving towards higher rates of international
collaborations in most scientific disciplines (from 46% internationalized co-
authorships in 2007 to 55% in 2017 in Scopus and from 47% in 2007 to 59% in
2017 in WOS). The USA, the UK, France, Switzerland, Italy, the Netherlands,
China, Spain, Austria and Australia are the ten countries with the highest
shares of co-authorship with Germany in Scopus. In addition, Aman (2016)
presented evidence of increasing internationalization and also higher rates of
citations for inter-organizational and international co-authorship for the
German science system in WOS from 2007 to 2012.

There is also studies specifically focused on the Berlin metropolitan region.
Rammer, Kinne, & Blind (2020) provide a fine-grained view of the case of
Berlin and how a form of selective spatial proximity exists between knowledge
producing institutions (e.g., universities) and knowledge demanding
institutions (e.g., innovative companies and firms). They used the first wave
of panel data curated through Berlin Innovation Panel which surveys
enterprises with five or more employees in manufacturing and knowledge-
intensive services located in Berlin. They reported a micro-geographic scope
where innovative firms are surrounded by same-sector firms and they were
located closer to universities and research institutes that can signal a
selective process. They also found that change in innovative activities of a
given firm affects other firms in their vicinity. It is necessary to note that
the sample analyzed in their study was composed of 80% small firms and 70%
service sector which can affect their conclusions.

Abbasiharofteh & Broekel (2020) explored the biotechnology field in Berlin
metropolitan region. They investigated co-patenting, co-authorship and joint
R&D collaborations and provided an exhaustive view of the temporal changes in
the scientific landscape of the region in the specific case of biotechnology.
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 agreements and strategic coalitions
are being developed to support higher rates of scientific collaboration among
the actors in these contexts. A specific example is _Berlin University
Alliance_ (BUA)444https://www.berlin-university-
alliance.de/en/about/index.html. 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, 2018, 2019).
BUA claims to be established based on a long lasting record of intra-regional
collaborations between these institutions. The interaction between these
institutions have started from an era of institutional isolation after the
fall of Berlin wall during which these institutes needed to define and empower
their unique identities. Afterwards, first forms of cooperation between these
institutes emerged which lead in 2003 to establishment of a shared medical
faculty between HU and FU to be located in Charité and allow higher
collaborations in medical and natural sciences. There are examples of
competition, mutual definition of exclusive research areas and graduate
programs versus close cooperation among BUA members in the past three decades.
These are highlighted in the BUA proposal as strengths of the region. Although
the four major institutions of the region have their own unique identity and
research profiles (further detail will follow in Data and Methods section),
nevertheless, they aim at fostering previous collaboration experiences in a
new organizational form, i.e., BUA. We aim to control whether these four
institutions have a distinctive position in the structure of scientific
collaborations formed in the Berlin metropolitan region. Thus, we add specific
measures to control whether BUA members form cohesive collaboration ties among
themselves. As the research profiles presented later in text advocates, these
institutions have overlapping disciplinary focuses. We aim to investigate
whether they have prevailing roles in these overlapping disciplines or whether
we can find signs of strategic coalitions among them i.e., cohesive co-
authorship communities.

Network analysis can be used to identify the presence of communities in co-
authorship networks (Palla, Barabási, & Vicsek, 2007; Leone Sciabolazza,
Vacca, Kennelly Okraku, & McCarty, 2017; Akbaritabar et al., 2020).
Quantitative models are used to examine if collaboration patterns persist
_between_ 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.

In lower levels than continental, national or regional frameworks, scientific
organizations themselves could have strategic plans to define their
overarching identities and main research focus. This might inspire researchers
in a certain organization to prioritize research in specific disciplines and
areas (Blume, Bunders, Leydesdorff, & Whitley, 1987) to signal allegiance with
the organization’s designed identity which in turn could penalize selection of
innovative research themes (Rijcke, Wouters, Rushforth, Franssen, &
Hammarfelt, 2016). Collaboration can be affected by the goals set out by
funding agencies (Nederhof, 2006; Wagner, Park, & Leydesdorff, 2015).
Furthermore, it can be affected by the type of organization (i.e., sector)
which partially determines the type of research of an organization or expected
outcomes of it.

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.
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. This affects results of macro
studies depending on the database employed, subset of scientific publications
used, document types analyzed and the level of aggregation 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 (e.g., authors, organizations) names which might bias the quality of
results (Aman, 2018; Donner, Rimmert, & van Eck, 2019; D’Angelo & van Eck,
2020). 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.

Based on this literature, we intend to explore the interplay between different
contextual variables and space to investigate the structure of scientific
collaborations in the Berlin metropolitan region. Thus, on the one hand, we
focus on Berlin as a regional hub or context that can inspire specific
organizational arrangements, e.g., BUA. Furthermore, institutions located in
Germany and presumably true for institutions in Berlin, collaborate with many
institutions worldwide (Stahlschmidt et al., 2019), hence, we expect to see a
high degree of internationalized collaborations. On the other hand, the Berlin
metropolitan region hosts a diverse group of national and international
researchers affiliated to scientific organizations (Berlin University
Alliance, 2019 p 13), thus, we expect to see a wide array of research outputs
in multiple disciplines and disciplinary specific trends of collaboration.
Therefore, we formulate and investigate the following macro, quantitative and
exploratory research questions:

  * •

RQ1: How _collaborative_ and _internationalized_ is the scientific landscape
of the Berlin metropolitan region?

  * •

RQ2: Are there _disciplinary_ differences in the rate of collaborative and
internationalized scientific work?

  * •

RQ3: How _regionally_ or _continentally_ oriented is scientific collaboration
in the Berlin metropolitan region?

  * •

RQ4: How _sector_ oriented is scientific collaboration in the Berlin
metropolitan region?

  * •

RQ5: How does the _diversity_ and level of development of national or regional
science systems worldwide affect the collaborations with the Berlin
metropolitan region?

  * •

RQ6: Is there evidence of strategic coalitions, disciplinary, regional or
organizational _agreements_ in the structure of scientific collaborations in
the Berlin metropolitan region?

  * •

RQ7: Are there specific disciplinary, sectoral, national or continental
_cohesive subgroups_ driving the scientific collaborations in the Berlin
metropolitan region?

  * •

RQ8: How influential is the _disambiguation_ of organization names on the
structure of scientific collaborations?

Contributions of the current paper is fourfold: 1) we focus on scientific
output of the Berlin metropolitan region and trace the share of collaborative
works and identify the share of 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, by investigating
the structure of scientific collaborations in a multi-level framework. 2) we
cover six major OECD scientific disciplines and provide a comparative view of
the specificities of these disciplines and we include a sectoral view based on
the type of organizations. 3) we develop and use multiple organization name
disambiguation techniques and compare their efficiency, coverage and accuracy
and 4) we employ a bipartite network modeling and community detection approach
and present how it can be useful in co-authorship network analysis and
identification of denser groups collaborating preferably among themselves.
Since investigation on the level of entire organization can cause a high rate
of interconnectivity in the network (due to aggregation to organization level
and overlooking individual researcher or research group borders), as described
in Data and Methods Section, our bipartite modeling approach takes
specificities in _single_ publication level into account and provides a more
fine-grained view of groups of organizations contributing to each publication.

The structure of the paper is as follows: Data and Methods section presents
our data sources and modeling strategy. Results section presents our findings
which is followed by Discussion section.

##  2 Data and methods

We use Scopus 2018 data from the German Bibliometrics Competence Center
(KB)555Kompetenzzentrum Bibliometrie (KB), http://bibliometrie.info. We
extract _article_ , _review_ and _conference proceedings_ documents published
from beginning of the database in 1996 till end of 2017. To delineate _Berlin_
metropolitan region and to identify the scientific collaborations occurred in
the region, we select only publications that have at least one authoring
organization located in Germany and Berlin. Thus, co-authorship here includes
Berlin organizations and their collaborators worldwide. Our level of analysis
are _scientific organizations_ (i.e., each affiliation address mentioned in a
publication that can be academic or non-academic organizations or firms where
researchers affiliate) and we do not investigate lower levels e.g., authors,
since our goal is to identify structure of scientific collaborations among
organizations.

Our data include different meta-data for each publication such as _publication
year_ , _title_ , _affiliation addresses_ , _scientific discipline_ , _journal
name_ and _document type_. We include _conference proceedings_ in addition to
articles and reviews since 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 disciplines based on Scopus ASJC666All Science
Journal Classification. We compare the aggregate data with trends of different
OECD scientific disciplines i.e., _Agricultural Sciences_ (AS), _Engineering
Technology_ (ET), _Natural Sciences_ (NS), _Medical and Health Sciences_
(MHS), _Humanities_ (H) and _Social Sciences_ (SS). Please note that some
publications might be assigned to multiple disciplines. In the aggregate
analysis, we use the first assignment of each publication, but in a single
discipline view, we take publications with any assignment in the given
discipline, thus, interdisciplinary publications are covered separately in all
their assigned disciplines.

As described earlier, scientific organizations set goals and define strategic
paths to ensure a unique research profile and identity. In order 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
(2019) and Berlin University Alliance (2018). We expect to observe prevailing
roles of these institutions in structure of scientific collaborations of the
disciplines closer to their areas of focus. FU: “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”.
HU: “Application-Oriented Mathematics”, “Image Sciences”, “Integrative Life
Sciences”, “Integrative Natural Sciences”, “Research on Law and Society”,
“Study of Ancient Civilizations” and “Sustainability Research”. CH:
“Cardiovascular Research & Metabolism”, “Infection, Immunology &
Inflammation”, “Neuroscience”, “Oncology”, “Rare Disease & Genetics” and
“Regenerative Therapies”. 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”. Except CH which has a focus on MHS and NS, the other three
institutions are active in areas close to major OECD disciplines.

###  2.1 Organization name disambiguation

The data delivered by Scopus is not perfect. It is prone to error and there is
a strong need for _disambiguation_ of organization names (Donner et al.,
2019). Without disambiguation, co-authorship 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., _PyString_ and _Fuzzy_
matching) and compared their results with a previously established technique
(i.e., Research Organization Registry (ROR)777https://ror.org/about), as
depicted in figure 1. ROR uses data from Global Research Identifier Database
(GRID888https://www.grid.ac/pages/policies) prepared by Digital
Science999https://www.digital-science.com/, ISNI101010International Standard
Name Identifier, https://isni.org/, Crossref and
Wikidata111111https://www.wikidata.org.

In _PyString_ matching (gray shaded area on the left of figure 1), we
standardize organization names and perform a match with GRID (snapshot of
February 17, 2019). We use only the largest entity that Kompetenzzentrum
Bibliometrie extracts from the Scopus using the first part of affiliation
string before the first comma. To match, we used _string_ comparison methods
in Python (for simplicity we call it _PyString_) which matches whole and
subsets of the text strings but does not account for change in the order of
words in organization names. To remove the effect of order of organization
name parts, we split the names based on space (i.e., words) and reorder them
alphabetically 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 homonyms121212As an example, from this address string
delivered by Scopus, “Freie Universität Berlin, Department of education and
psychology, DEU”, KB extracts “Freie Universität Berlin” as the first part
which we use for PyString and Fuzzy 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”)..
For those still non-matched, we perform another PyString match with scientific
organizations in Wikidata and for the still missing ones, with Wikidata
entities which have geographical coordinates. We limit the results to the most
promising ones using Jaro Winkler distance of more than 0.85 between the two
matched names. We do this after the PyString match is done, as a control of
reliability. We chose this threshold based on manual evaluation of match
results to have the highest accuracy. At the end and to complement the results
of PyString procedure, we search organization names in an in-house database
which is previously developed by comparing organization names to Wikidata
entities by Rimmert (2018)131313This in-house data is only accessible through
KB infrastructure..

In parallel, we compare organization names with GRID by _Fuzzy_ matching the
names. This method takes differing word order and subsets of the name into
account (we standardize the names as before and add country). For Fuzzy text
matching, we use the _FuzzyWuzzy_ 141414https://github.com/seatgeek/fuzzywuzzy
library in Python. Using _fuzz.ratio_ as scorer, we set a threshold of 80
percent 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 Research Organization Registry (ROR) API (see
previous footnote for an example). We obtain further information (i.e.,
country, geographical coordinates (longitude and latitude) of the main address
and type of organization as _education_ , _non-profit_ 151515Organization that
uses its surplus revenue to achieve its goals. Includes charities and other
non-government research funding bodies. Example, the Max Planck Society
(grid.4372.2), _company_ , _government_ , _health-care_ , _facility_ 161616A
building or facility dedicated to research of a specific area, usually
contains 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),
_archive_ 171717Repository of documents, artifacts, or specimens. Includes
libraries and museums that are not part of a university. Example, New York
Public Library (grid.429888.7) and other). We used the ROR snapshot from
November 7th 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 ROR API (see
gray shaded area on the right of figure 1).

To clarify the importance and effect of the disambiguation, we define
different scenarios based on non-disambiguated and disambiguated data. We
present the results and implications of them in construction of the co-
authorship networks. However, for our disciplinary, geographical and sector
analysis and on the basis of the accuracy and coverage results presented
later, we use the third disambiguation technique described above (i.e., ROR).
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 non-disambiguated co-
authoring organizations.

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

###  2.2 Bipartite network modeling

We construct bipartite co-authorship 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 co-authorship 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 density and connectivity due to
publications with high number of authors which project to maximally connected
cliques. By adopting methods specifically developed for _bipartite networks_
we are able to resolve the shortcomings.

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

To identify communities of co-authorship, we use _bipartite community
detection_ by _Constant Potts model_ (CPM). CPM is a specific version of Potts
model (Reichardt & Bornholdt, 2004) proposed by Traag, Van Dooren, & 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 _Leidenalg_ 181818https://github.com/vtraag/leidenalg
library in Python. Community detection emphasizes the importance of links
_within_ communities rather than those _between_ them. CPM uses a resolution
parameter $\gamma$ (i.e., “ _constant_ ” in the name), leading to communities
such that the link density between the communities (external density) is lower
than $\gamma$ and the link density within communities (internal density) is
more than $\gamma$. We set different resolution parameters in case of
aggregate data (ROR = $3\times 10^{-3}$) and scientific disciplines (AS =
$7\times 10^{-4}$, ET = $6\times 10^{-3}$, NS = $6\times 10^{-3}$, MHS =
$5\times 10^{-3}$, H = $4\times 10^{-4}$ and SS = $6\times 10^{-3}$). We chose
these parameters after exploration of 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.

##  3 Results

###  3.1 Implications of organization name disambiguation

Figure 1 presents different disambiguation techniques used and the coverage of
item(publication)-author-organization links (_PyString_ 66.18%, _Fuzzy_ 56.38%
and _ROR_ 77.47%). We present author level counts of links here since
different authors from the same institution might mention different
affiliation addresses by including department names or they might report
erroneous addresses. However, in building organization level co-authorship
networks, we exclude repeated organization-publication links. Each technique
successfully disambiguates a set of unique organization names which other
techniques are unable to disambiguate (PyString 1,206, Fuzzy 8,198 and ROR
8,449). Note that not all organizations involved in authoring a publication
are successfully disambiguated (PyString 115,749 (32.43%) organizations from
239,390 (93.18%) publications, Fuzzy 194,054 (54.37%) organizations from
184,990 (72%) publications and ROR 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 PyString, 53,569 (21%) in Fuzzy
and 126,130 (49%) in ROR. Table 1 compares the networks constructed using non-
disambiguated data with the output of different disambiguation techniques. We
observed a high rate of dis-connectivity in the non-disambiguated network
(10,269 components) while this was extremely reduced through disambiguation
techniques (i.e., to 66 in PyString, 159 in Fuzzy and 100 in ROR). The share
of nodes in the giant component which was initially high in the non-
disambiguated 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 error and name order changes which can
affect the networks constructed to a high degree (see De Stefano, Fuccella,
Vitale, & Zaccarin (2013) for a discussion of possible effects). In PyString,
the ratio of disambiguated unique organizations to non-disambiguated ones were
1 to 9.8 (in Fuzzy 1 to 11 and in ROR 1 to 15). This proves the high influence
disambiguation could have on the results (RQ8). Table 2 presents the networks
in different OECD scientific disciplines using ROR results. Note that the
results which follow are based on the 49% of publications that were
successfully disambiguated by ROR technique. Each of the disciplines presented
in Table 2 covers a different share of connected components observed in the
aggregate data ranging from 13 components in Agricultural Sciences (AS) to 68
in Social Sciences (SS). Natural Sciences (NS) has both the highest number of
publications and organizations while Humanities (H) has the smallest number of
publications and organizations.

Table 1: Berlin organizations co-authorship networks using non-disambiguated
and disambiguated data (G = giant component)

Metrics | Non disambiguated | PyString | Fuzzy | ROR
---|---|---|---|---
N. of connected components | 10,269 | 66 | 159 | 100
N. of biparitite nodes | 613,827 | 135,057 | 58,547 | 133,387
N. of biparitite edges | 1,083,775 | 246,704 | 89,199 | 246,472
% of biparitite nodes in G | 95 | 100 | 99 | 100
% of biparitite edges in G | 98 | 100 | 100 | 100
N. of organizations | 356,918 | 5,244 | 4,978 | 7,257
N. of organizations in G | 337,755 | 5,176 | 4,809 | 7,153
N. of publications (%) | 256,909 | 129,813 (51%) | 53,569 (21%) | 126,130 (49%)
N. of publications in G | 245,203 | 129,657 | 53,248 | 125,949

Table 2: Berlin organizations co-authorship networks in different OECD
scientific disciplines (G = giant component, ROR organization name
disambiguation)

Metrics | AS | ET | H | MHS | NS | SS
---|---|---|---|---|---|---
N. of connected components | 13 | 56 | 34 | 48 | 55 | 68
N. of biparitite nodes | 8,528 | 33,930 | 4,835 | 46,842 | 89,032 | 16,045
N. of biparitite edges | 13,822 | 57,671 | 6,195 | 84,945 | 170,334 | 25,336
% of biparitite nodes in G | 100 | 100 | 98 | 100 | 100 | 99
% of biparitite edges in G | 100 | 100 | 99 | 100 | 100 | 100
N. of organizations | 1,687 | 2,991 | 798 | 3,843 | 5,970 | 2,091
N. of organizations in G | 1,668 | 2,933 | 763 | 3,792 | 5,910 | 2,012
N. of publications | 6,841 | 30,939 | 4,037 | 42,999 | 83,062 | 13,954
N. of publications in G | 6,828 | 30,851 | 3,988 | 42,913 | 82,989 | 13,849

###  3.2 Macro view of scientific output of Berlin metropolitan region

Figure 2 presents the raw and fractional count of publications among different
OECD disciplines. Note that it is based on publications which have at least
one collaborator from Berlin metropolitan region and for organizations that
were successfully disambiguated with ROR technique. Nevertheless, the trends
are in-line with what Stephen et al. (2020) and Stahlschmidt et al. (2019)
reported for Germany. Some disciplines show higher rates of collaborative work
(e.g., see 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. In
contrast, some disciplines that are traditionally known to be less
collaborative (Leahey, 2016) present a smaller gap on the plot (e.g.,
Humanities). Since the Y axis is on log 10 scale, the figure shows growth
rates in raw and fractional count of publications which is tripled in case of
Humanities and Social Sciences. But it can be due to higher coverage of Scopus
in recent years and not merely increase of publications (see Stahlschmidt et
al. (2019) for a discussion). To investigate internationalization of
publications, figure 3 presents the _single_ (intra-DEU) versus _multiple_
country publications. Despite the fact that it presents only the 49% of
publications which were disambiguated, but the trends observed are in line
with the case of German science system reported in Stahlschmidt et al. (2019).
It is clear that some disciplines have already reached close to 50% share of
_internationalization_ (i.e., NS) driving the aggregate trend of increasing
internationalization observed on top panel of the figure. However, there are
other disciplines with still lower than 25% share of international
collaborations (i.e., H and SS). In all these disciplines an increasing trend
towards further internationalization is evident (see increasing length of
black bars in the figure) except H and SS that do not present a clear
increasing trend and in some years the rate of internationalization decreases.
This answers our RQ1 and RQ2 signaling a high disciplinary difference in rate
of collaborative work and internationalization.

Figure 2: Raw and fractional count of Berlin publications by OECD disciplines
(1996-2017, Scopus, fractional count based on organizations, Y on log scale)
Figure 3: Share of intra-Germany versus multiple country co-authorship, (Top)
aggregate (Bottom) different disciplines (1996-2017, Scopus)

Figure 4 presents the internationalization of collaborations between OECD
disciplines divided over continental and geographical regions worldwide. It
also shows the countries where the top five percent of collaborators in terms
of number of organizations and publications are located (see country labels).
Note that the scale of X and Y axes are different among panels of figure and
they are on log 10 scale. Collaborations inside Germany prevail the aggregate
image in all disciplines. After Germany, the USA, the UK, France, China and
Russia dominate the aggregate view with highest number of publications and
organizations. However, disciplines have noticeable differences. In AS, H and
SS, Germany, the USA and the UK are the countries where most of collaborators
and prolific ones are located. In MHS, France is the only country which joins
the previously mentioned top five percent group. In NS and ET, China and
Russia join this group as well and the image becomes closest to the aggregate
level. This further improves our findings for RQ2 and RQ3.

Figure 4: Number of organizations and co-authorship with Berlin by countries
worldwide, aggregated (top) and by discipline (bottom, 1996-2017, Scopus, X
and Y on log scale, label: top 5 percent)

To investigate our RQ4 and RQ5, we focus on organization sectors. In total out
of 7,257 unique organizations in Berlin sample based on ROR, there were 2,844
from education sector, 1,667 facility, 860 healthcare, 587 company, 436
nonprofit, 429 government, 282 Other, 124 archive and 28 not available. Table
3 presents the distribution of organizations in different sectors in five
countries with highest number of organizations (i.e., China, Germany, France,
the UK and the USA, in alphabetical order). While education is the sector with
the highest number of organizations in four countries, facility has the
highest number of organizations in Germany which can be an artifact of the
disambiguation and exclusion of publications with non-disambiguated
organizations. Figure 5 presents the geographical distribution of
organizations worldwide separated by sectors and aggregated in countries. To
make the image clearer, we remove countries where no organizations from a
given sector is present and brighter colors show higher number of
organizations in a given country. It is clear that most countries have
organizations in 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 of previously mentioned regions with representation in
many sectors. The distribution of companies is another interesting observation
where many countries do not have any representatives in contrast to education
sector. Figure 6 focuses on Berlin metropolitan region providing a more fine-
grained view of the sectoral distribution of organizations and their dense
geographical proximity. Note that on this figure, name of organizations with
more than 10,000 publications is printed which are the four BUA members.
Berlin presents a science hub with high degree of densely located academic and
non-academic scientific organizations which belong to multiple sectors and
based on previous map in figure 5, they collaborate with organizations from
many sectors worldwide.

Table 3: Five countries with highest number of organizations by sector (GRID data based on Berlin sample 1996-2017) Country code | Organization sector | Count
---|---|---
CHN | Education | 193
CHN | Facility | 77
CHN | Healthcare | 39
CHN | Government | 18
CHN | Company | 7
CHN | Nonprofit | 5
CHN | Other | 3
CHN | Archive | 1
DEU | Facility | 319
DEU | Education | 215
DEU | Company | 205
DEU | Healthcare | 115
DEU | Nonprofit | 113
DEU | Other | 113
DEU | Government | 66
DEU | Archive | 35
FRA | Facility | 261
FRA | Education | 114
FRA | Healthcare | 45
FRA | Government | 35
FRA | Company | 34
FRA | Other | 15
FRA | Nonprofit | 8
FRA | Archive | 4
GBR | Education | 114
GBR | Healthcare | 91
GBR | Facility | 46
GBR | Company | 34
GBR | Nonprofit | 26
GBR | Government | 25
GBR | Other | 16
GBR | Archive | 7
USA | Education | 423
USA | Healthcare | 157
USA | Company | 129
USA | Facility | 110
USA | Nonprofit | 95
USA | Government | 44
USA | Other | 34
USA | Archive | 23

Figure 5: Countries worldwide collaborating with Berlin region by sector
(color: N. of organizations. If a country does not have presence in a sector,
it is removed) Figure 6: Organizations in Berlin (Colors: Education = red,
Nonprofit = yellow, Government = blue, Facility = orange, Healthcare = green,
Company = brown, Other = pink, Archive = gray, NA = white, Labels: > 10,000
publication, name, 1000-10,000 publication, count)

###  3.3 Structure of institutional scientific collaborations in Berlin

We focus now on communities of co-authorship identified from the giant
component using bipartite community detection (RQ6 and RQ7). This enables us
to go beyond the macro descriptive view presented thus far and investigate the
structure of collaborations in single publication level. Note that these are
communities detected from the giant component which is connected in itself,
however, these communities signal the denser areas of the collaboration
network. We are interested to know what could be the underlying factors behind
these higher densities which constructs these cohesive sub-groups. Note also
that we tested with a diverse array of resolution parameters as discussed in
the Data and methods section. We finally set the resolution parameters that
gave the most consistent number of communities.

Figure 7 presents the distribution of communities based on the number of
organizations in each community and aggregate number of publications of all
organizations in a given community (each community is represented with one
dot). The clearest observation in this figure is the discipline-based
collaboration patterns among BUA members indicated by the shape and color of
dots where green triangles show presence of one or more BUA member(s) in a
given community. In the aggregate view on top, ET, NS and SS, we see BUA
members populating the _two_ most prolific communities. MHS is the only case
where all these four members are present in _a single community_ which is due
to closer cooperation among BUA members that was formed through a shared
faculty by HU and FU located in Charité in 2003 and it seems they have been
successful in integrating TU in the collaboration structure. In case of AS and
H they populate _three_ distinct communities which signals a larger divide in
the collaboration structure despite the smaller size and lower number of
players in these two disciplines. It is in-line with the self represented
research profiles of the BUA members since they aim to have strong research
focus in these areas. However, it shows a divide between them that can be
bridged by shared research projects or new organizational forms as it was the
case in MHS. In ROR, TU is a member of community 1 while FU, CH and HU are
members of community 0. In AS, HU and FU are members of community 0 while TU
is in community 1 and CH is in community 2. This could be due to the fact that
TU, being a technical university, pursues more technical and application
oriented research. But the divide between CH from one side and HU and FU on
the other side needs further probes for underlying causes. In ET, TU is in
community 0 while FU, HU and CH are in community 1. In NS and SS, CH, FU and
HU are in community 0 and TU is in community 1. In H, HU and CH are in
community 0, FU is in community 1 and TU is in community 2. Overall, it seems
that the joint cooperation between HU and FU in form of the shared faculty
located in CH is paying off in most cases in form of a more cohesive structure
of collaborations. However, in Humanities, where HU, FU and TU have strong
research focuses, they have formed distinctive and separated collaboration
structures. Of course it can be affected by the disciplinary properties of
Humanities which is closer to the ideal type of “sole investigators” (Leahey,
2016). BUA needs to integrate TU further into the structure of scientific
collaborations in the region through shared projects or organizational forms.
It is clear from aggregate and disciplinary 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
organizations with different levels of productivity (e.g., see the difference
between communities 0, 1 and 47 on the aggregate view or communities 0, 1 and
2 in AS). Another clear finding which is in-line with the previously observed
number of connected components is that some disciplines present more groupings
(e.g., see the distribution of dots in MHS and NS) while some present a lower
rate of groupings (e.g., see H and AS). We now exclude communities with the
lowest number of organizations which were not prolific and focus instead on
the most interesting communities (those with labels in figure 7). We present a
view of the potential underlying factors based on geographical regions,
communities of co-authorship, sectors and productivity which could have lead
to the emergence of observed groupings.

Figure 7: Organizations in communities of the giant component vs publication
(label: largest and most prolific communities, color and shape: green triangle
includes BUA member(s), X and Y on log scale)

Figure 8 presents the communities in the aggregate view (i.e., ROR) consisting
of more than 30 organizations or with more than 25,000 publications (aggregate
of all community members) and Table 4 provides details on regional and
sectoral composition of these selected communities. We chose these parameters
to present the most interesting communities with highest productivity in terms
of number of publications and the most distinctive structure of
collaborations. Note that, although the most interesting communities are
chosen based on number of organizations and _raw count_ of publications, but,
the total publications presented on the bottom panel of figure 8 are
_fractional counts_ to better present the share of collaborative works. We
separate _Berlin_ and _Germany (DEU)_ to provide a better comparison of
_intra_ /_inter_ -regional collaborations. Selected communities in ROR are 16
in total (see the labels on central stacked bar, i.e., 0, 1, 2, 3, 6, 14, 25,
30, 47, 55, 57, 58, 74, 89, 103 and 177). From one hand, top and bottom panels
of this figure presents the _regional_ composition of the communities (see
left stacked bars with region names) and _sector_ of the organizations in
these communities (see right stacked bars with sector names). On the other
hand, top panel shows the size of these communities in terms of number of
organizations (see the width of ribbons outgoing from each stacked bar) and
bottom panel shows the level of productivity in terms of total number of
publications by organizations in each community (see the width of ribbons
outgoing from each stacked bar).

In the aggregate level in ROR, Europe (excluding Berlin and Germany) has the
highest share of organizations (see length of left stacked bar on top panel)
and it is followed by Americas, Asia and Germany (excluding Berlin). However,
in productivity, Berlin and Germany overtake the other regions. It is clear
that communities 0 and 1 are more prolific than other communities (see the
length of central stacked bar in bottom panel of the figure 8), despite their
smaller sizes. Although these 16 communities are composed of an international
mixture of organizations, the most prolific communities (i.e., 0 and 1) are
dominated by Berlin, Germany and other European organizations (compare the
multiple ribbons ingoing to each community in central stacked bar on top panel
and how the most prolific ones in bottom panel are dominated with few ribbons
and highest length of stacked bars). Furthermore, these two communities
include all of the four BUA members (FU, CH and HU are in community 0 and TU
is in community 1). Organizations from other countries (even those with
comparable number of organizations) populate the smaller, less prolific
communities. While education and facility have the largest share of
organizations and productivity, these communities present a diverse sectoral
composition and they are not dominated by any specific sector. Nevertheless,
it is interesting that government organizations have representatives in all
these selected communities except 6 (see Table 4).

Figure 8: Organizations by regions vs membership in communities of the giant
component aggregated in ROR (Top: size of communities, Bottom: productivity)
Table 4: Composition of the largest and most prolific communities of the giant
component by region and sector in ROR (N = community size, P = aggregate
publications)

| Region | Sector
---|---|---
cluster | N | P | Africa | Americas | Asia | Berlin | DEU | Europe | No region | Oceania | Archive | Company | Education | Facility | Government | Healthcare | No sector | Nonprofit | Other
0 | 43 | 104,247 |  |  |  | 6 | 34 | 3 |  |  |  | 1 | 28 | 5 | 1 | 8 |  |  |
1 | 15 | 36,918 |  |  |  | 6 | 6 | 3 |  |  |  |  | 8 | 6 | 1 |  |  |  |
2 | 38 | 9,862 |  | 8 | 6 | 1 | 6 | 17 |  |  |  |  | 24 | 11 | 2 |  |  | 1 |
3 | 41 | 6,074 |  | 5 | 6 | 1 | 7 | 22 |  |  |  | 4 | 20 | 11 | 2 |  |  | 2 | 2
6 | 31 | 3,193 |  | 3 | 2 | 1 |  | 24 |  | 1 |  | 1 | 19 | 11 |  |  |  |  |
14 | 47 | 1,447 | 1 | 6 | 26 | 3 | 3 | 6 |  | 2 |  | 2 | 30 | 8 | 1 | 1 |  | 4 | 1
25 | 34 | 686 | 1 | 13 | 2 | 1 | 5 | 12 |  |  | 1 | 2 | 20 | 7 | 2 | 1 |  | 1 |
30 | 43 | 497 | 4 | 7 | 16 | 1 | 5 | 9 |  | 1 | 1 |  | 19 | 13 | 3 |  | 1 | 3 | 3
47 | 49 | 624 |  | 2 | 43 |  |  | 4 |  |  |  | 7 | 30 | 9 | 1 | 1 |  | 1 |
55 | 34 | 424 |  | 23 | 3 |  |  | 8 |  |  |  | 5 | 5 | 12 | 2 | 5 |  | 5 |
57 | 38 | 398 |  | 5 | 15 |  | 1 | 17 |  |  | 1 | 3 | 16 | 12 | 3 | 2 |  |  | 1
58 | 34 | 519 |  | 4 |  | 1 | 3 | 26 |  |  | 1 | 4 | 8 | 8 | 3 | 9 |  |  | 1
74 | 37 | 356 |  | 3 | 8 | 1 | 1 | 7 |  | 17 |  | 3 | 10 | 7 | 4 | 7 |  | 5 | 1
89 | 33 | 322 | 18 | 3 |  |  | 1 | 10 |  | 1 |  | 2 | 9 | 3 | 4 | 11 |  | 2 | 2
103 | 46 | 317 |  |  | 3 |  | 1 | 42 |  |  |  | 1 | 11 | 15 | 2 | 12 |  | 2 | 3
177 | 36 | 170 | 5 | 6 | 2 |  | 1 | 19 | 1 | 2 | 1 |  | 3 | 4 | 12 | 4 | 1 | 5 | 6

Table 5 presents the communities in Agricultural Sciences consisting of more
than 50 organizations, which are 7 communities in total (i.e., 0, 1, 2, 3, 4,
5 and 6. These communities can be noted on the sub-panel of figure 7 dedicated
to AS). Similar to the composition observed in aggregate level (i.e., ROR),
these communities are composed of an internationalized mixture of
organizations. But, different from what we observed in aggregate level (i.e.,
domination of Berlin, Germany and Europe in the most prolific communities),
here we observe that highly prolific communities are composed of a mixture of
multiple regions. Considering these internationalized collaborations and the
fact that BUA members are located in communities 0 (HU and FU), 1 (TU) and 2
(CH), we can conclude that each of these highly prolific organizations has
their own specific group of international collaborators in AS. This is in line
with the self-representations of BUA members of their research profiles.
Community 0 is the largest and most prolific one in AS. While communities 1
and 2 have comparable productivity rates, the size of community 2 is about
twice that of community 1. We cannot observe a dominating pattern of sectors
in composition or productivity of the communities in AS and education,
facility and government present relatively similar productivity levels which
could be an attribute of AS. It is interesting to see the high density of
health-care organizations in community 2 where Charité is located.

Table 5: Composition of the largest and most prolific communities of the giant
component by region and sector in AS (N = community size, P = aggregate
publications)

| Region | Sector
---|---|---
cluster | N | P | Africa | Americas | Asia | Berlin | DEU | Europe | No region | Oceania | Archive | Company | Education | Facility | Government | Healthcare | No sector | Nonprofit | Other
0 | 296 | 8,386 | 32 | 68 | 22 | 12 | 78 | 76 |  | 8 | 20 | 3 | 187 | 49 | 18 | 7 | 1 | 10 | 1
1 | 127 | 1,464 | 9 | 18 | 28 | 11 | 27 | 30 | 2 | 2 |  | 5 | 81 | 26 | 6 | 2 |  | 2 | 5
2 | 210 | 1,498 | 6 | 54 | 35 | 14 | 31 | 65 |  | 5 |  | 10 | 99 | 41 | 13 | 39 | 1 | 7 |
3 | 114 | 538 | 3 | 22 | 12 | 5 | 11 | 56 |  | 5 | 2 | 5 | 51 | 32 | 6 | 5 |  | 9 | 4
4 | 71 | 238 |  | 6 | 9 | 4 | 3 | 42 |  | 7 | 2 | 2 | 37 | 13 | 7 | 4 |  | 3 | 3
5 | 53 | 237 |  | 13 | 13 | 3 | 6 | 18 |  |  | 5 |  | 29 | 13 | 2 |  | 1 | 2 | 1
6 | 53 | 168 | 4 | 8 | 2 | 2 | 5 | 32 |  |  |  | 2 | 23 | 12 | 9 | 3 |  | 2 | 2

Table 6 presents the communities in Engineering Technology consisting of more
than 18 organizations or those with more than 10,000 publications. These are
13 communities in total (i.e., 0, 1, 2, 4, 15, 20, 24, 40, 41, 50, 56, 70 and
94). On the one hand, Europe, Americas, Asia and Germany seem to have the
highest share of organizations populating these communities. On the other
hand, education, facility and companies dominate the sectoral composition. The
most prolific communities (i.e, 0 and 1 both with more than 12,000
publications) are dominated by Berlin and German organizations and community 1
has only 1 member from Europe. Community 0 includes TU from BUA members. It
can signal the specialization of TU in Engineering Technology discipline and a
locally oriented structure of collaborations which is in line with what
Hoekman et al. (2010) reported about ET in other European countries. Community
1 composed of 11 organizations from Berlin, Germany and Europe is highly
prolific (+12,000 publications) and includes FU, HU and CH from BUA members
which signals the closer collaboration ties between these three members.
However, community 2 is an interesting case here. It is rather small in size
(23 organizations), it has no BUA members and is composed of an international
group of organizations and is relatively prolific (the third most prolific
with close to +4,000 publications).

Table 6: Composition of the largest and most prolific communities of the giant
component by region and sector in ET (N = community size, P = aggregate
publications)

| Region | Sector
---|---|---
cluster | N | P | Africa | Americas | Asia | Berlin | DEU | Europe | No region | Oceania | Company | Education | Facility | Government | Healthcare | Nonprofit | Other
0 | 7 | 15,116 |  |  |  | 5 | 2 |  |  |  |  | 2 | 5 |  |  |  |
1 | 11 | 12,457 |  |  |  | 4 | 6 | 1 |  |  |  | 5 | 2 | 1 | 3 |  |
2 | 23 | 4,092 |  | 2 | 6 | 1 | 4 | 9 | 1 |  |  | 9 | 9 | 3 | 1 | 1 |
4 | 30 | 2,059 |  | 1 | 7 | 1 | 2 | 19 |  |  | 1 | 18 | 6 | 2 |  | 1 | 2
15 | 25 | 446 |  | 2 | 3 |  | 1 | 19 |  |  | 1 | 10 | 11 | 1 | 1 | 1 |
20 | 31 | 294 |  | 2 | 2 |  | 3 | 23 |  | 1 | 2 | 13 | 7 | 1 | 2 | 4 | 2
24 | 19 | 163 | 1 | 5 | 1 |  | 1 | 11 |  |  | 2 | 10 | 7 |  |  |  |
40 | 19 | 158 |  |  | 11 |  | 1 | 7 |  |  | 5 | 8 | 6 |  |  |  |
41 | 23 | 147 |  | 13 | 1 | 2 | 2 | 5 |  |  | 2 | 10 | 5 | 1 | 2 | 3 |
50 | 20 | 154 |  |  | 16 |  |  | 4 |  |  | 2 | 15 | 2 | 1 |  |  |
56 | 19 | 158 |  | 2 | 3 |  |  | 13 | 1 |  | 1 | 8 | 6 | 1 | 1 | 2 |
70 | 24 | 129 |  | 4 | 5 |  |  | 14 |  | 1 | 3 | 14 | 4 | 3 |  |  |
94 | 19 | 106 |  | 12 | 2 |  | 1 | 4 |  |  |  | 15 | 2 |  |  | 1 | 1

Table 7 presents the communities in Natural Sciences consisting of more than
25 organizations or those with more than 20,000 publications. These criteria
are satisfied only by five communities (i.e., 0, 1, 4, 18 and 71). Community 0
is the most prolific one (58,586 publications) and _only_ includes 32 members
which are all from Berlin and Germany with one member from Europe. The second
most prolific community with more than 24,000 publications is community 1 with
seven members which are all from Berlin and Germany with one organization from
Europe. Community 71 is the largest one with 45 members from Europe
(education, facility, government, health-care, non-profit and other), Americas
(education, facility and health-care sectors), Asia (education and facility),
Africa (facility) and Oceania (facility) _without_ any members from Berlin or
Germany. Note that, these communities are detected based on denser areas of
the giant component and they do not necessarily need to include Berlin
organizations. However, while being the largest community, it is not prolific
compared to other NS communities (553 publications). Community 18 is another
interesting case which is highly international with 26 members and 968
publications. Africa is only present in community 71 and Oceania is only
present in communities 18 and 71 i.e., the communities with the lowest
productivity levels. NS is showing a highly prolific community (i.e., 0, 32
members, 3 BUA members, i.e., HU, FU and CH) and a small but still prolific
community (i.e., 1, 7 members, includes TU from BUA members) with BUA members.
This can signal a divide between these two groups of BUA members which are
collaborating within Berlin, Germany and Europe but with less overlapping
collaboration ties that can be bridged and fostered through future
cooperations.

Table 7: Composition of the largest and most prolific communities of the giant
component by region and sector in NS (N = community size, P = aggregate
publications)

| Region | Sector
---|---|---
cluster | N | P | Africa | Americas | Asia | Berlin | DEU | Europe | Oceania | Company | Education | Facility | Government | Healthcare | Nonprofit | Other
0 | 32 | 58,586 |  |  |  | 6 | 25 | 1 |  | 1 | 20 | 4 | 1 | 6 |  |
1 | 7 | 24,045 |  |  |  | 4 | 2 | 1 |  |  | 2 | 4 | 1 |  |  |
4 | 27 | 4,869 |  | 2 | 6 | 1 | 2 | 16 |  | 2 | 17 | 6 | 1 |  |  | 1
18 | 26 | 968 |  | 2 | 16 | 3 | 1 | 3 | 1 |  | 20 | 1 | 1 |  | 2 | 2
71 | 45 | 553 | 1 | 3 | 2 |  |  | 38 | 1 |  | 9 | 19 | 3 | 6 | 4 | 4

Table 8 presents the communities in Medical and Health Sciences with more than
25 organizations. These are six communities (i.e., 0, 8, 29, 37, 38 and 51).
MHS is presenting a highly interesting case where community 0 is the most
prolific (55,480 publications) and it is populated by only 37 organizations
from Berlin and Germany (education, facility, health-care, in both Germany and
Berlin. One government organization from Berlin and one company from Germany
i.e., excluding Berlin). Community 0 includes all four BUA members and signals
a high level of intraregional collaboration in MHS. This shows that the
strategic cooperation between FU and HU to establish a shared MHS faculty in
Charité has paid off and they have been successful in integrating TU and three
other organizations from Berlin and 30 organizations from Germany. All other
communities, whether international or regional have much lower productivity
levels compared to community 0.

Table 8: Composition of the largest and most prolific communities of the giant
component by region and sector in MHS (N = community size, P = aggregate
publications)

| Region | Sector
---|---|---
cluster | N | P | Africa | Americas | Asia | Berlin | DEU | Europe | No region | Archive | Company | Education | Facility | Government | Healthcare | Nonprofit | Other
0 | 37 | 55,480 |  |  |  | 7 | 30 |  |  |  | 1 | 23 | 5 | 1 | 7 |  |
8 | 26 | 529 | 2 | 18 | 2 |  |  | 4 |  |  | 2 | 7 | 4 |  | 12 |  | 1
29 | 34 | 251 |  | 1 |  |  | 1 | 32 |  |  |  | 12 | 9 | 2 | 9 | 2 |
37 | 28 | 234 |  | 4 | 1 |  | 1 | 21 | 1 |  |  | 12 | 1 | 2 | 11 | 1 | 1
38 | 35 | 180 |  | 5 | 28 |  |  | 2 |  |  | 3 | 24 | 5 | 1 | 2 |  |
51 | 29 | 192 | 16 | 4 |  |  |  | 9 |  | 1 | 2 | 10 | 2 | 1 | 8 | 2 | 3

Table 9 presents the communities in Humanities consisting of more than 30
organizations. These are six communities (i.e., 0, 1, 2, 3, 4 and 5).
Community 0 is composed of a highly international group of organizations from
all regions. Other communities in Humanities are comparably international,
however they are less prolific than community 0. Similar to our expectation,
Humanities scholars are mainly affiliated to organizations in the education
sector, but, a highly inter-sectoral composition is observed as well. BUA
members are located in _three_ most prolific communities i.e., 0 (two BUA
members, HU and CH), 1 (FU) and 2 (TU) which signals a rather non-overlapping
structure of collaboration which is consisting of highly internationalized
mixture of organizations. It is in line with high focus of BUA members on
distinctive areas in H mentioned in their self-representations in BUA proposed
(Berlin University Alliance, 2018, 2019) and can be remnants of the era where
BUA members needed to have mutually exclusive areas of focus and identities.
Note that, as presented in figure 3, Humanities is not that much
internationalized and more than 75% of all publications are _single country_
ones (i.e., intra-Germany co-authorship). Nevertheless, Humanities so far is
presenting the closest image to the one observed in Agricultural Sciences
where Berlin, Germany and European organizations do not dominate the prolific
communities and we observe an internationalized mixture or organizations from
multiple sectors.

Table 9: Composition of the largest and most prolific communities of the giant
component by region and sector in H (N = community size, P = aggregate
publications)

| Region | Sector
---|---|---
cluster | N | P | Africa | Americas | Asia | Berlin | DEU | Europe | No region | Oceania | Archive | Company | Education | Facility | Government | Healthcare | Nonprofit | Other
0 | 343 | 3,319 | 11 | 92 | 23 | 20 | 60 | 120 | 3 | 14 | 1 | 4 | 260 | 43 | 13 | 14 | 4 | 4
1 | 130 | 1,294 | 2 | 28 | 20 | 5 | 24 | 46 | 1 | 4 | 1 | 2 | 85 | 22 | 12 | 4 | 3 | 1
2 | 74 | 549 | 1 | 31 | 5 | 4 | 12 | 20 |  | 1 | 2 | 1 | 52 | 9 | 1 | 2 | 5 | 2
3 | 42 | 411 | 1 | 17 | 1 | 6 | 1 | 16 |  |  | 2 | 1 | 25 | 5 | 1 |  | 6 | 2
4 | 53 | 206 |  | 3 | 11 | 3 | 4 | 32 |  |  | 3 |  | 26 | 20 | 4 |  |  |
5 | 43 | 169 |  | 4 | 2 | 4 | 10 | 22 | 1 |  | 5 | 1 | 19 | 10 | 1 | 2 | 4 | 1

Table 10 presents the communities in Social Sciences consisting of more than
20 organizations or those with more than 1,500 publications. These are six
communities in total (i.e., 0, 1, 2, 6, 8 and 47). Social Sciences are
presenting an image far from the one observed in the case of Humanities and
closer to natural and hard sciences. The two most prolific communities (i.e.,
0 and 1) are completely dominated by Berlin, German and European
organizations. Three of the BUA members are located in community 0 (FU, HU and
CH) and one in community 1 (TU). Since FU and HU have formed a closer
collaboration with CH since 2003, it would be interesting to look further into
the topics of focus in their research to investigate whether these separate
communities which are all active in SS study different subjects. Both these
communities are collaborating preferably within Berlin and Germany and they
have only three members from Europe. Community 0 with 22 members is the most
prolific (10,895 publications). Community 2 is an interesting case with one
organization from Berlin (which is not from BUA members), six organizations
from Americas, four from Germany, seven from Europe and in relative terms, it
has a high productivity. It can signal even higher disciplinary divide in
Humanities in the Berlin region. However, there are smaller, more
international communities which are not highly prolific as it was the case in
ET, NS and MHS. Americas have the largest share of organizations after Europe
and it is highly represented in community 8 which is only composed of
Americas, two organizations from Berlin and 11 organizations from Europe
(excluding Germany).

Table 10: Composition of the largest and most prolific communities of the
giant component by region and sector in SS (N = community size, P = aggregate
publications)

| Region | Sector
---|---|---
cluster | N | P | Africa | Americas | Asia | Berlin | DEU | Europe | No region | Company | Education | Facility | Government | Healthcare | Nonprofit | Other
0 | 22 | 10,895 |  |  |  | 3 | 17 | 2 |  | 1 | 14 | 2 |  | 5 |  |
1 | 8 | 2,046 |  |  |  | 4 | 3 | 1 |  |  | 3 | 4 |  |  |  | 1
2 | 19 | 1,733 |  | 6 |  | 1 | 4 | 7 | 1 |  | 17 | 2 |  |  |  |
6 | 29 | 291 | 1 | 4 | 4 | 1 | 1 | 18 |  | 1 | 16 | 9 | 1 | 1 |  | 1
8 | 31 | 353 |  | 18 |  | 2 |  | 11 |  | 2 | 24 | 2 |  |  | 2 | 1
47 | 24 | 150 | 1 | 3 | 2 |  | 2 | 16 |  | 1 | 16 | 2 | 4 | 1 |  |

##  4 Discussion

In this paper, we provide a quantitative, exploratory and macro view of the
structure of scientific collaborations in the Berlin metropolitan region. Our
main level of analysis was scientific _organizations_ (which can be academic
or non-academic organizations or firms) and we investigated the share of
collaborative work, internationalized work versus single country
collaborations. We covered all OECD scientific disciplines and presented a
comparative view of the similarities and differences of collaborations in
these disciplines.

In methodological terms, we developed two organization name disambiguation
techniques (i.e., PyString and Fuzzy matching) and compared their performance
and coverage with an established technique (i.e., Research Organization
Registry (ROR)). We presented the high impact 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 which had one
or more non-disambiguated organizations to limit our analysis to successfully
disambiguated cases.

At a first view and only based on descriptive analysis, we observed a highly
collaborative scientific landscape. Some disciplines present a high degree of
difference between _raw_ and _fractional_ count of publications. Despite the
prevalence of collaborative works and increasing trend towards
internationalization in aggregate view, we observed that some disciplines
(e.g., Natural Sciences and Agricultural Sciences) have more internationalized
collaborations while other disciplines (e.g., Medical and Health Sciences,
Humanities and Social Sciences) are less internationalized or they did not
present a steady upward trend which is in-line with observation by Moed et al.
(1991) and Babchuk et al. (1999). But our further investigation showed that
these disciplines have a more complex structure of collaboration which in some
cases is highly dominated by local (e.g., Berlin and Germany) organizations
and in some cases it is already Europeanized. In rare cases (e.g.,
Agricultural Sciences and Humanities) we observed a higher representation of
international organizations among the most prolific communities despite the
smaller size of these disciplines and in case of Humanities less than 25%
share of internationalization. Overall we observed a high degree of
collaboration between Berlin organizations and other European countries which
is in-line with the trend observed by Hoekman et al. (2010), however, we
observed that the image is more complicated once we look at cohesive subgroups
and groupings.

In geographical terms, different disciplines present various collaboration
trends, however, all of them have high degrees of regional and European
collaboration. Berlin presents a specific case. It is similar to a science hub
with a diverse sectoral composition of organizations which is in line with
Balland et al. (2020)’s observation in metropolitan regions in the USA and
Rammer et al. (2020)’s observation of Berlin metropolitan region. However, it
can 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 collaborations between the partners excluding Berlin organizations that
we do not cover here. North America is the preferred collaboration partner
outside of Europe. But this image changes in some disciplines with closer
share of collaborators from East Asia and Pacific and North America and their
shares is the closest in Engineering Technology. Intra Germany (i.e., single
country) collaborations prevail the co-authorships. The USA and the UK are
central members of the collaboration landscape in most disciplines based on
the aggregate number of publications. But, France, China and Russia join the
top five percent of most prolific collaborators in Engineering Technology and
Natural Sciences.

We provided a sectoral view of the geographical distribution of organizations
worldwide and in Berlin. 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
prevailing sector where scientific publications are produced which is not
counterintuitive. China and India have representatives in many sectors.

We modeled the scientific collaborations through _bipartite_ co-authorship
networks which treats each scientific publication as an event where
organizations interact in producing scientific texts. Our bipartite community
detection configuration was helpful in detecting the diverse 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 extracted the largest connected components in the
aggregate level and for each scientific discipline. We then looked at denser
collaborating groups. We observed that in most disciplines, with the exception
of _Humanities_ and _Agricultural Sciences_ , the most prolific communities
were composed 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
internationalized communities in all disciplines, but they were not highly
prolific.

Disciplines present interesting differences in the composition of communities
(i.e., denser collaborating groups). Not all communities were composed of
highly prolific organizations. However, in some cases we observed that all
highly prolific organizations were member of one or two communities. We
observed a highly regional oriented Medical and Health Sciences, national and
Europe oriented Natural Sciences, Engineering Technology and Social Sciences
and an internationally oriented Agricultural Sciences and Humanities. This can
be due to the restrictive criteria we set in the further investigation of the
highly prolific or the largest communities or it can signal strategic regional
coalitions.

Looking at the members of Berlin University Alliance (BUA) and their position
in these cohesive sub-groups presented interesting findings. In Agricultural
Sciences and Humanities which were the disciplines with lower aggregate
internationalization (less than 25%), we observed higher rates of
internationalized collaboration once the structure of communities were
analyzed and there was a larger divide between BUA members. They formed denser
collaboration ties within _three_ different communities. This is in line with
the self-representations of the research profiles of the BUA members (Berlin
University Alliance, 2018, 2019). Only in Medical and Health Sciences which
was dominated by high productivity of Berlin and Germany, the four BUA members
were located in _one_ community and collaborated densely. This is highly
affected by the fact that from 2003, HU and FU have jointly established a MHS
faculty located in Charité and our findings show that this strategic coalition
has been successful in integrating other organizations from Berlin and TU.
Furthermore, some of the observed disciplinary divide between BUA members
could be remnants of the east-west division in Germany and the reorganization
of research profiles and mutually exclusive definition of areas of focus to
reduce parallel work and competition that happened after the reunification.
This divide was recently observed in biotechnology field by Abbasiharofteh &
Broekel (2020). TU presents a specific case. While the other three BUA members
formed different collaboration structures based on disciplinary
specialization, TU is in most cases member of a separated community of its
own. In ET, TU’s collaboration network is dominated by other Berlin and German
organizations. It shows that BUA needs to develop further strategic
cooperations among the members to ensure a 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 publication type preferred more
by the technical universities. Since TU is the main technical university in
our sample, the collaboration structure reflected in this document type might
have affected the observed results and over inflate the divide between TU and
other BUA members. Charité from other hand seems to have built a large
collaboration network worldwide which is evident in health-care sector and
high representation of national and international organizations in the
communities where Charité was located. BUA can plan to benchmark this as a
case of successful internationalization of collaborations.

We conclude that mixing a macro and global view while keeping regional,
national and continental granularity can help in describing observed
quantitative trends. It is necessary to move beyond the macro descriptive view
presented based on yearly count of publications or increasing trends of team
science. In addition, investigation of self-representation of research
profiles of scientific organizations was helpful in interpretation of observed
trends. As our investigation proved, not all members of the community are
moving towards internationalization and some parts of the community, which are
normally the highly prolific ones, prevail and distort the aggregate images.
This methodological approach is close to what Stadtfeld (2018) suggests in
moving between micro, meso and macro levels to better explain the observed
trends.

Our paper suffers from certain limitations. When we construct the co-
authorship network at the organization level, we naturally overlook the
changes that happen in the composition of researchers affiliated to those
organizations. The same organization could have a highly different composition
of members over the time that affects the type of research carried out and
collaboration ties formed.

We only use Scopus as the main database and although it covers German speaking
publications, it is dominated by English speaking records. Bibliometric
databases, including Scopus, are regularly updated which can affect the
temporal trends we observe here. In addition, 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. Furthermore, we were unable to disambiguate all the organizations in
our sample which lead to excluding 51% of the publications which had one or
more non-disambiguated organizations. Thus, while our results seem to be
following the general trends observed in the German scientific system (see
Stahlschmidt et al. (2019), Stephen et al. (2020) and Aman (2016)), but the
specificities observed in the structure of scientific collaborations among BUA
members and the international collaborations could be highly affected if we
were able to improve the coverage of the disambiguation techniques.

Another limitation of our data and research in organization level is the
_superstar_ 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.
While in the networks constructed, these cases might seem as an international
collaboration when a single author is affiliated to multiple countries. High
quality data with disambiguated records of publications in author level would
allow a more complete investigation. Another limitation of our study could be
that our disambiguation techniques penalizes non-English speaking countries or
less known organizations which are usually less prolific. They can be more
represented among the organizations that we excluded from our analysis since
the disambiguation did not give reliable results for them. In addition,
different disambiguation techniques are effective to identify a differing set
of organizations and any choice would have implications on a subset of the
organizations while penalizing another subset.

We do not have any insight over the background of individual researchers
affiliated to these scientific organizations. We do not know about the
motivations that drive the scientific collaboration and observed trends
(Subramanyam, 1983; Katz & Martin, 1997). As an example, in case of all
disciplines, we observed small communities which were leaning more towards
internationalized collaborations. This might be groups mainly consisting of
migrant scientists who collaborate with their former scientific organizations
or they play a “boundary spanning role” among regional, national and
continental contexts. We cannot investigate these type of questions at the
organization level.

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

##  5 Acknowledgements

We would like to thank Sybille Hinze, Martin Reinhart and Paul Donner for
comments and suggestions on earlier versions of this paper. Caveats are our
responsibility.

##  6 Funding Information

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

##  7 Data Availability

Data cannot be made publicly available due to the licensing and contract terms
of the original data.

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