RESEARCH ARTICLE
The structural shift and collaboration capacity in
GenBank Networks: A longitudinal study
Jian Qin
, Jeff Hemsley
, and Sarah E. Bratt
School of Information Studies, Syracuse University, Syracuse, NY
a n o p e n a c c e s s
j o u r n a l
Keywords: collaboration capacity, collaboration networks, GenBank metadata analysis, impact
assessment, longitudinal study of collaboration networks
Citation: Qin, J., Hemsley, J., & Bratt,
S. E. (2022). The structural shift and
collaboration capacity in GenBank
Networks: A longitudinal study.
Quantitative Science Studies, 3(1),
174–193. https://doi.org/10.1162
/qss_a_00181
DOI:
https://doi.org/10.1162/qss_a_00181
Peer Review:
https://publons.com/publon/10.1162
/qss_a_00181
Supporting Information:
https://doi.org/10.1162/qss_a_00181
Received: 25 May 2021
Accepted: 22 December 2021
Corresponding Author:
Jian Qin
jqin@syr.edu
Handling Editor:
Ludo Waltman
Copyright: © 2022 Jian Qin, Jeff
Hemsley, and Sarah E. Bratt. Published
under a Creative Commons Attribution
4.0 International (CC BY 4.0) license.
The MIT Press
ABSTRACT
Metadata in scientific data repositories such as GenBank contain links between data submissions
and related publications. As a new data source for studying collaboration networks, metadata in
data repositories compensate for the limitations of publication-based research on collaboration
networks. This paper reports the findings from a GenBank metadata analytics project. We used
network science methods to uncover the structures and dynamics of GenBank collaboration
networks from 1992–2018. The longitudinality and large scale of this data collection allowed us
to unravel the evolution history of collaboration networks and identify the trend of flattening
network structures over time and optimal assortative mixing range for enhancing collaboration
capacity. By incorporating metadata from the data production stage with the publication stage,
we uncovered new characteristics of collaboration networks as well as developed new metrics
for assessing the effectiveness of enablers of collaboration—scientific and technical human
capital, cyberinfrastructure, and science policy.
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1.
INTRODUCTION
Data repositories, software tools, and high-performance computing constitute key components
of cyberinfrastructure (CI), which is established to facilitate and support data-intensive science.
Data repositories store and manage scientific data and provide data submission, curation, and
discovery services for sharing and reusing scientific data. Since the 1980s, the U.S. federal
government has invested significant resources into building cyberinfrastructure, including data
repositories and research data services. In parallel with the advancement of CI and growth of
data repositories is a paradigm shift in science from empiricism, theory, and simulation to data
(i.e., the fourth paradigm), as envisioned by Jim Gray (Gray, 2007; Gray, Liu et al., 2005) and
subsequently articulated by Szalay and Blakeley (2009). Science today, small or large scale, is
increasingly carried out through the distributed global collaborations enabled by CI.
The rapid increase in science data is attributable in no small part to the support provided by
CI-enabled tools and services. The large number of tools for using the vast biomedical data
available on the National Center for Biotechnology Information (NCBI)’s website underlines
the importance of CI-enabled tools and services in data-driven science. GenBank is one of
NCBI’s key data repositories and stores “massive amounts of genetic sequence data generated
from evolving high-throughput sequencing technologies,” serving “more than 30 terabytes of
biomedical data to more than 3.3 million users every day” (NLM, 2015). What is unclear in
this grand picture of data-driven science is how this changing climate of science research has
The structural shift and collaboration capacity in GenBank Networks
affected scientific capacity and the aggregation of the knowledge, skills, abilities, and techni-
cal facilities of individual scientists (referred to here as Scientific and Technical (S&T) Human
Capital), as well as their networks of collaborative relationships (Bozeman, Dietz, & Gaughan,
2001). More broadly, there is also an unanswered question of how CI-enabled data services
have impacted the increment of scientific capacity at individual, project, and institutional
levels, and if there is any impact, how much it has affected the extent and rate at which
scientists turn data into knowledge. Understanding these questions will require data beyond
publication metadata to enable novel insights into the grand picture of data-driven science and
CI-enabled research.
This paper reports the findings from a longitudinal study that uses the metadata from
GenBank (Sayers, Cavanaugh et al., 2019) as the data source. We will first review previous
research related to scientific collaboration networks and address the limitations of publication-
based data sources in past research. As metadata from a data repository is a novel data source
for studying collaboration networks, this paper attempts to provide the background of
GenBank and its metadata and articulate on the suitability, feasibility, and possible issues in
using this new data source to study data-intensive collaboration networks. Following the
methods of data collection and processing, the analyses focus on the network structures
and dynamics as well as their implications for the assessment of knowledge production and
diffusion.
2. RELATED RESEARCH
Past research on scientific collaboration networks has generated a large body of literature
that is scattered across scientometrics/bibliometrics, social studies of science, mathematics,
physics (complex networks), information science, and science policy. Empirical collaboration
network research has used almost exclusively publication metadata with varying sources and
sizes, and with limited longitudinal time coverage. Theoretical research has also explored the
statistical and mathematical mechanics of complex networks (Albert & Barabási, 2002; Costa,
Rodrigues et al., 2007). Complex network theory has found wide applications in natural and
social phenomena, including scientific collaboration networks (Barabási, 2009; Butts, 2009).
This literature review section will focus on the complex collaboration networks research and
rationalize the need for data-intensive study of collaboration networks and its implications to
science policy research and research data practices.
2.1. Complex Collaboration Networks
Collaboration in research is typically measured by coauthorship in publications. Researchers
in a collaboration network are called nodes or vertices and the relationships (i.e., coauthor-
ship) between nodes are edges. Collaboration networks with very large numbers of nodes and
edges together with variant weights of edges and other factors are highly complex, as nodes
have uneven numbers of edges and the edges may vary in length between nodes. Such net-
works consist of clusters or communities of researchers, which are self-organized, may be
interconnected in some ways, and evolve over time. Over the last 50 years, since de Solla
Price’s work Little Science, Big Science (1963), scientific collaboration networks have been
studied extensively from a wide range of disciplines. Newman (2001) collected and analyzed
publication author data from MEDLINE, e-Print Archive, and NCSTRL, which represented the
biomedical, physics, and computer science fields respectively. He found that these collabora-
tion networks formed small worlds and the randomly selected nodes were typically separated
by a short path of intermediate acquaintances. Scientific collaboration networks are essentially
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The structural shift and collaboration capacity in GenBank Networks
a kind of social network in which communities form through tightly knit groups (Girvan &
Newman, 2002). Such a social aspect can be reflected in whom a researcher chooses to col-
laborate with and how such collaborations may enhance their S&T human capital (Bozeman
& Corley, 2004). Barabási, Jeong et al. (2002) give an excellent summary of the research on
collaboration networks, which include: Most networks have the “small world” property;
real networks have an inherent tendency to cluster, more so than comparable random
networks; and the distribution of the number of edges for nodes (degree distribution)
“contains important information about the nature of the network, for many large networks
following a scale-free power-law distribution” (p. 591).
The CI-enabled research environment led to a shift to what has been called the fourth par-
adigm of science, an era that is characterized by distributed global collaboration, data-
intensiveness, and reliance on high-performance computing (Szalay & Blakeley, 2009). Large
data repositories have been built in the last three decades for researchers to submit, manage,
share, and reuse data. For many scientific disciplines, submitting to a repository has become
part of the regular research process and been made as policy mandates (NIH, 2021; NSF,
2020). As the science paradigm shifts and data management and sharing policy mandates
blurred the boundaries between data professionals and researchers, researchers have been
devoting more time to data processing and analyses. The cause of this blurred division of labor
stems from the work needed to make raw data clean. That is, data usually cannot be directly
fed into algorithms without preprocessing, transformation, and sometimes meshing with
other data sources (Kamath, 2009). The impact of such paradigm shift on collaboration net-
works is largely unknown and publication coauthorship alone would be insufficient to
address. The CI-enabled links between publications and data sets have created a ripe condi-
tion for studying complex collaboration networks on a large scale by integrating metadata
from data submissions.
2.2. Theories and Models
The study of complex networks has traditionally used graph theory, but in the last 50 years
statistical methods have gained increasing significance in this research field. Questions of
interest for complex network researchers include the typologies and properties of complex
networks, interaction between these two components in a network, and the tools and mea-
surements for capturing “in quantitative terms” the underlying organizing principles of real
networks (Albert & Barabási, 2002). Well-known theories include those of random graph, per-
colation, small-world networks, scale-free networks, networks with community structure, and
evolving networks, for which Albert and Barabási (2002) and Costa et al. (2007) provided
exhaustive surveys.
Three of the theories/models among those reviewed by Albert and Barabási (2002) and
Costa et al. (2007) are the Watts-Strogatz model of small-world networks (Watts & Strogatz,
1998), the Barabási-Albert model for scale-free networks (Barabási & Albert, 1999), and the
theory of evolving networks (Albert & Barabási, 2002). In the discussion of each of these
theories and models, Albert and Barabási (2002) used the average path length, clustering coef-
ficient, and degree distribution, among others, to explain the statistical mechanics of these
theories and models, which are considered as three robust measures of a network’s topology.
Network theories and models have been applied in studying collaboration networks in biol-
ogy, ecology, and physics, as mentioned in the previous section. Several properties of scientific
collaboration networks have been identified in these studies: Small worlds are common in
scientific communities; the networks are highly clustered; and biomedical research appears
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The structural shift and collaboration capacity in GenBank Networks
to have a much lower degree of clustering compared to other disciplines such as physics
(Newman, 2001). The evolution of scientific collaboration networks shows that the degree
of distribution follows a power law and key network properties (diameter, clustering coeffi-
cient, and average degree of the nodes) are time-dependent; that is, the average separation
decreases in time and clustering coefficient decays with time (Barabási et al., 2002).
2.3. The Data Gap
Studies of scientific collaboration are abundant in scientometrics and information science
scholarly journals. Many of them are often limited in that the data used are filtered by
discipline and period from a single database and almost exclusively use publication-based
authorship data, as seen in the studies cited above. The limitations of data source and variant
timescales make it very difficult, if not impossible, to generate data sets that can be meaning-
fully reused or integrated with other data sources for understanding the complexity of scientific
collaboration networks. Metadata in scientific data repositories offer a new breed of data
source for studying research networks. Their large scale and continuous time coverage provide
a rich testbed not only for developing models and theories but also for meshing other related
data sources to examine and interpret complex collaboration networks from more dimensions.
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3. THE BACKGROUND OF GENBANK
GenBank was conceived in 1979 by a group of biologists and computer scientists at a meeting
held at the Rockefeller University in New York. The meeting participants agreed on “the neces-
sity to create a national, computerized database” (Strasser, 2008, p. 537). Three years later the
Los Alamos Sequence Library became the cutting-edge repository—GenBank—for curating
nucleic acid sequence data (Cinkoski, Fickett et al., 1991). Soon after, the sequence data
started to grow exponentially as the computer technology and network availability rapidly
advanced in the second half of the 1980s. Meanwhile, nucleotide sequencing methods and
technologies have evolved from the first generation represented by “Sanger sequencing” to
Next Generation Sequencing (NGS), which allowed many parallel sequencing reactions at
a much lower cost, namely high-throughput sequencing (Heather & Chain, 2016). During this
period, the sequence data processed by GenBank grew from 1.38 million nucleotides in 1984
to 14.1 million in 1990 (Cinkoski et al., 1991).
Early data entry into GenBank relied on curation staff who performed extraction of nucle-
otide sequences from published articles and made them available in electronic form to
researchers. The rapid increase in the volume of nucleotide sequence data soon made it clear
that this model could not keep up with the growth of sequence data, as it was labor intensive,
and the publishing of these data lagged far behind their generation. In addressing this problem,
GenBank worked with journal editors to develop policies to make direct submission of
sequence data to GenBank a requirement for publishing a paper. This policy mandate,
together with automated data processing, not only reversed the data flow, which was originally
from journal articles to GenBank (Cinkoski et al., 1991), but also pioneered the incentive
mechanisms for data sharing. Another significant driving force for GenBank’s data growth is
the Human Genome Project (HGP, https://www.genome.gov/human-genome-project), which
started in 1990 and was completed in 2003. Six years into the HGP, countries participating in
this international effort reached consensus on the timely release of sequence data through the
Bermuda Principles, which established policies on sequence data quality standards, sequence
submission and annotation, and sequence claims and etiquette to ensure the prepublication
sharing and rapid disclosure of sequence data (BERIS, 2019; Cook-Deegan & McGuire, 2017;
Quantitative Science Studies
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The structural shift and collaboration capacity in GenBank Networks
Maxson Jones, Ankeny, & Cook-Deegan, 2018). If the development of NGS technology accel-
erated the increment of the volume and kinds of sequence data and shifted data generation
toward more analyses (Alekseyev, Fazeli et al., 2018), then the journals’ requirement for data
submission before manuscript submission and the Bermuda Principles cultivated the data shar-
ing culture, an impact that goes far beyond GenBank.
The GenBank records are acquired in two ways: direct submission by individual
researchers using tools such as BankIt (https://www.ncbi.nlm.nih.gov/ WebSub/) and Submis-
sion Portal (https://submit.ncbi.nlm.nih.gov/), and batch deposit from sequencing centers by
sequence types (Benson, Karsch-Mizrachi et al., 2011). The author field in these tools is
designed to support multiple author entries in an annotation record. The public display of
metadata section in GenBank annotation (Figure 1) does not show all the data authors, but
they are in the released files on the FTP server.
Although the advances in sequencing technology liberated researchers from performing
sequencing work themselves, the researchers themselves continued to act as authors of the
data submissions. In one of the data sets we created by matching the NIH funding records
with the GenBank records related to infectious diseases, we randomly selected 55 GenBank
records. We used this sample to examine the authors who submitted the sequences to
GenBank and how they were related to the principal investigators (PI). The funding data set
was extracted from the NIH RePORT database, which contains information on PIs, publica-
tions, and affiliations. We mapped the funding records to GenBank records by PubMed article
ID (PMID), which allowed us to track submission author’s affiliations and roles by triangulating
with multiple sources of information, including affiliation and acknowledgment in the article,
institutional and personal websites, LinkedIn, and researcher’s curriculum vitae/resumé. The
records examined represent only a small fraction of the GenBank records; hence we do not
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Figure 1. The metadata section in a GenBank annotation record.
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The structural shift and collaboration capacity in GenBank Networks
have the generalization power of the whole data collection. Nevertheless, they offer some
insights into who the submission authors are and what roles they may have played. Table 1
presents the summary of the findings from the manual checking of 55 records at three different
time intervals.
We observed that many submission authors in this sample were also publication authors,
while the PI was listed in publications more than half of the time. Through triangulation among
the multiple sources mentioned above, we found that when the submission authors and the PIs
appeared in both submissions and publications, they were more likely than not in a PhD
advisee–advisor or postdoc–mentor relationship. In this context, the first author in data sub-
mission and publication was usually the doctoral student or postdoc. When the PIs were not
included in the publication or submission, it seemed that they often held a position such as a
director of a large laboratory or a government research staff position that did not allow them to
engage in the project enough to be given the credit. In some cases, the submission authors
were visiting scientists with their own grant and project but needed to use the research facility
of a given PI. Although we observed in several acknowledgments that the sequencing was
performed outside of the submission authors’ labs, this did not change the fact that submission
authors were mostly researchers themselves who were also actively engaged in publication
activities.
Sequence data submitted to GenBank will be assigned an accession number and reviewed
by GenBank staff for quality assurance purpose. A GenBank annotation record contains meta-
data for identifying and describing the creators and characteristics of the sequence data,
including authors who are included in the direct submission field, date of submission, data
of public release, and publications associated with the sequence submission, as well as the
molecular attributes of the sequences, such as locus, taxon lineage, and features (Figure 1).
It is worth pointing out that the time between the date of submission and date of public release
provides an important piece of information about the data-to-knowledge production. A
GenBank record has two sets of authors: those in publications (references) and those in direct
submissions of molecular sequences (i.e., the data authors). An author may or may not appear
in both spaces, though it is likely that many authors reside in both the publication and
sequence submission metadata. Because the act of data submission represents a stage in a
research life cycle earlier than publication, examining the metadata about sequence data sub-
missions and subsequent publications provides an opportunity to uncover how collaboration
networks evolved “in action” and gain insights into research collaboration that publication
authorships alone would have been unable to offer.
One caveat in using metadata from GenBank to study collaboration networks is that the
publications associated with data submissions are not representative of the full publication
productivity of researchers because GenBank is not a publication repository. Therefore,
Table 1.
GenBank records.
Summary of observations on the submission authors, publication authors, and principal investigators in infectious disease-related
Year
1997
2006
2012–2014
Number of
observations
19
19
17
Category 1: Submission
author in publication
Yes
No
16
3
1
19
16
Category 2:
PI in publication
Yes
No
11
8
8
2
11
15
Category 3:
PI in submission
Yes
No
8
11
8
9
11
8
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three categories
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11
0
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The structural shift and collaboration capacity in GenBank Networks
metadata for data submissions are more suitable for studying relationships between publica-
tion and data submission networks than publication productivity. The data about sequence
submissions, for example, the dates of sequence submission and public release, as well as
related dates of patent applications and publications, allow for the creation and testing of
new metrics for evaluating the impact of cyberinfrastructure, science policy, and S&T human
capital on the biomedical research enterprise.
4. METHODS
4.1. Data
GenBank data is hosted on an FTP server at NCBI. The GenBank flat file release 229 (cutoff
date December 15, 2018) consists of 3291 files in compressed format, each of which ranges
between single digit to three-digit megabytes. We downloaded all the annotation records from
1982 to 2018 and extracted the metadata section in January 2019. The extracted metadata
were then parsed into a relational database (we excluded the genetic sequence data, which
comprised about 80% of the data volume). The data download and processing workflow
included the following steps:
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(cid:129) Download one compressed sequence file from the FTP server.
(cid:129) Decompress the file.
(cid:129) Extract the metadata section from each record in the file.
(cid:129) Save the metadata records to a buffer space.
(cid:129) Delete the downloaded file.
(cid:129) Parse the metadata into a database.
(cid:129) Repeat the workflow for the next compressed file on the FTP server.
A computer program was created to automatically complete these steps in a batch style. We
set up a data server with the necessary software and storage space for the GenBank metadata
extractions. This process resulted in 227,905,057 annotation records minus the sequence data,
in which 44,480,172 publications were referenced. This data collection also includes
42,511,832 patent references.
Author names in this GenBank metadata collection were disambiguated by using the Kag-
gle solution from Chin, Zhuang et al. (2014) and by cross-checking the results with author
metadata from Web of Science, SCOPUS, and Microsoft Academic Graph. After the disambig-
uation process, the data collection resulted in 877,134 unique author names (nodes), of which
519,719 are in the publication network, 523,013 in the submission network, and 214,197 are
unique scientists in the patent network.
We grouped the data by year and then, for each year, we constructed two networks: a pub-
lication coauthor network and data submission coauthor network. For each network, we built
a data set that included information such as the year, if it was a publication or data submission
network, how many publications (data submission) there were, and the number of authors, as
well as network statistics such as degree centrality and clustering coefficient. We also looked
at the distribution of degree centrality for each network. The degree centrality of all these net-
works, except the first few years, follows a power law. Research has shown that the shape of a
power law distribution can be a useful signal that reflects information about the network
(Hemsley, 2016). As such, we use the power-law shape parameter in iGraph (Csardi &
Nepusz, 2006), which is an R package devoted to social network analysis, and stored that
in our data as well. This data collection went through parsing, name disambiguation, slicing
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The structural shift and collaboration capacity in GenBank Networks
by year, and edge list generation and was used to compute the statistical properties for screen-
ing and analysis. For additional analysis, the publication and submission networks for each
year were merged and the calculations rerun.
4.2. Measure for Collaboration Capacity
The inclusion of data submission metadata created an opportunity for examining a new aspect
of collaboration networks: Collaboration Capacity (CC). In the context of this paper, we define
CC as the ability of an individual, group, or institution to assemble and effectively use the S&T
human capital in collaborative research. We assume that the greater the S&T human capital a
researcher can accumulate or assemble, the more opportunities and resources they can garner
to collaborate with other researchers and the more likely the S&T human capital will be used
more effectively. This means that CC measures not only how much S&T human capital a per-
son may accrue but more importantly, how effectively they can utilize the S&T human capital
as well as the support provided by cyberinfrastructure and science policy to increase produc-
tivity and innovations. Because collaborative research starts well ahead of a coauthored pub-
lication, the trace data that document collaboration prior to publication, namely, the data
submission records in science data repositories, can provide insights into the assessment of
research performance and impact.
One of the measures we tested for CC is the number of new collaborators an author added
to their coauthor list in a period. To compute the value of CC for individual authors, a sample
of authors was selected by following two criteria: Authors eligible to be selected should be
located in the elbow section of the L-shaped distribution (which is the pattern for all years;
see Figure S1 in Supplementary Materials); that is, not those with extremely high number of
publications or in the long tail, which was determined as between 1–50 publications; and an
author must have published at least once in a 3-year window starting from 1997, namely,
1997–1999, 2000–2002, 2003–2005, etc., to be selected. A random selection of authors with
these two criteria generated a sample of 6,503 authors in 10 3-year windows between 1997
and 2017. The computation of CC was performed on all 6,503 authors. The following steps
were taken to calculate the value of CC:
1. Find all coauthors of an author who had collaborated each year during 1997–2017. If
an author was inactive in a given year, they would not have any coauthors that year.
2. Collapse this timeframe into windows of 3 years each. Now each window has a list of
all authors with whom an author collaborated in that 3-year window.
3. Remove any duplicate authors that may have appeared in the list. For example, if an
author collaborated with an author twice in one window, they will be counted just once.
4. CC values were calculated in two ways:
(a) Noncumulative CC: this value is obtained by counting how many new authors an
author added as compared to previous window. For example, if an author collabo-
rated with three authors A, B, and C in window 1 and three authors A, D, and F in
window 2, this author would have two new authors (D, F) in window 2. Therefore,
the CC value for window 2 is 2. The resulting CC value is the average of all windows,
hence noncumulative collaboration capacity for that author.
(b) Cumulative CC: this value measures how many new coauthors an author added in a
given window as compared to all previous windows. For example, suppose an author
collaborated with two authors A and B in window 1, two authors B and C in window
2, and 2 authors A and D in window 3. The CC value for window 3 will be just one
Quantitative Science Studies
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The structural shift and collaboration capacity in GenBank Networks
because the author added only one new author (D) in window 3 as they had already
collaborated with author A in window 1. If it were noncumulative collaboration, the
value for window 3 would be 2, as both A and D are new authors as compared to
window 1. The average of all windows is used as the average cumulative collabora-
tion capacity value for that author.
5. RESULTS
5.1. Collaboration Networks in Time
GenBank started operation in 1984. It took about 8 years for the growth in data submissions
to take off. Data before 1992 were merged into 1992 due to the sporadic nature of direct
submissions. Figure 2 shows that the mean degree (average number of connections an author
has) for the GenBank publication network doubled from a mean of 3 to 6 by 2018. At the same
time, the mean degree for sequence data submission networks almost tripled (Figure 2).
The publication network displayed a scale-free property from 1999 onward while the data
submission network showed a scale-free property earlier in 1997 (Figure S1 and Table S1). A
Kolmogorov-Smirnov test (see Table S1) confirms that the degree distribution of GenBank net-
works fits a power law distribution (Clauset, Shalizi, & Newman, 2009). A further examination
of the data reveals that when we merge the GenBank publication and data submission net-
works, the result also has a power law distribution after 1998 (Table S2). Analysis of the com-
bined publication and data submission networks displays a trend of increasing percentages of
nodes belonging to the giant components throughout the whole 27-year span. A giant com-
ponent is a set of nodes in a graph that are connected directly or indirectly and an indicator for
the connectedness of nodes in a graph. The size of the giant component in GenBank (publi-
cation and submission networks together) grew from 43.7% in 1992 to 82.2% in 2012, the
highest point in all years, before dropping off its peak by 15% by 2018 (Table S2), an indica-
tion that the networks became more interconnected over time.
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Figure 2. Distributions of alpha values and mean degrees for both publication and sequence data
submission networks in GenBank 1992–2018. The alpha values for both networks appear to be
almost identical, while the mean degree values for publication network have been consistently
higher than that of the submission network. (The data used to generate this chart are in Table S1.
In this paper, table and figure numbers with an S mean they are in Supplementary Materials).
Quantitative Science Studies
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The structural shift and collaboration capacity in GenBank Networks
A prominent property in scale-free networks is that they follow an 80/20 rule (Barabási,
2016). In the case of GenBank combined networks, the degree distribution of authors clearly
presents this property. In Figure S1, the red colored points represent authors only in the data
submission network, blue points represent authors only in the publication author network, and
purple points represent authors who were in both publication and submission networks. The
degree distribution in these plots appears highly skewed, following an L-shape. That is, a very
small number of authors had very high degree centrality in the publication or submission
networks or both, while the majority of authors tended to have a very low number of connec-
tions. As time went on, the number of authors only in the data submission networks (red) and
in both networks (purple) grew, while the number of authors only in the publication network
grew much more slowly.
However, three strata of degree distribution among the authors can also be seen in Figure
S1: a majority of authors remained at the bottom level (<10 links), the middle group ranged
roughly between 10 and 500 links, a very small number of authors had over connec-
tions. Also, red tail on plot suggests that those in data network only tended to have
the smallest while nearer top, or with most connections,
tended work both networks. In fact, plots shift from mainly blue (publication only) to
mainly purple (both networks), long tail, time, implying more activity and
people were engaged work. It also actors who publishing
were work.
5.2. Structural Shift
As noted above, shown Figure 3, we observed percentage nodes net-
works giant component increase. However, 2018 it decreased to
near 1998–1999 (67%) levels. The edges remained high
and slight decline.
Even though quantitatively 2018
dropped, structure was quite different 1998 and
Figure 3. Giant size changes 1992–2018 have been steadily growing. The
growth has outpaced nodes. See Table S2 for used
to draw this plot.
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