RESEARCH ARTICLE

Subdivisions and crossroads: Identifying hidden
community structures in a data archive’s
citation network

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

j o u r n a l

Sara Lafia1

, Lizhou Fan2

, Andrea Thomer2

, and Libby Hemphill1,2

1ICPSR, University of Michigan, Ann Arbor, MI
2School of Information, University of Michigan, Ann Arbor, MI

Citation: Lafia, S., Fan, L., Thomer, A., &
Hemphill, l. (2022). Subdivisions and
crossroads: Identifying hidden
community structures in a data
archive’s citation network. Quantitative
Science Studies, 3(3), 694–714.
https://doi.org/10.1162/qss_a_00209

DOI:
https://doi.org/10.1162/qss_a_00209

Received: 16 May 2022
Accepted: 22 Giugno 2022

Corresponding Author:
Sara Lafia
slafia@umich.edu

Handling Editor:
Ludo Waltman

Keywords: archival science, community detection, data citation, data reuse, network analysis

ABSTRACT

Data archives are an important source of high-quality data in many fields, making them ideal
sites to study data reuse. By studying data reuse through citation networks, we are able to learn
how hidden research communities—those that use the same scientific data sets—are
organized. This paper analyzes the community structure of an authoritative network of data
sets cited in academic publications, which have been collected by a large, social science data
archive: the Interuniversity Consortium for Political and Social Research (ICPSR). Through
network analysis, we identified communities of social science data sets and fields of research
connected through shared data use. We argue that communities of exclusive data reuse form
“subdivisions” that contain valuable disciplinary resources, while data sets at a “crossroads”
broadly connect research communities. Our research reveals the hidden structure of data
reuse and demonstrates how interdisciplinary research communities organize around data sets
as shared scientific inputs. These findings contribute new ways of describing scientific
communities to understand the impacts of research data reuse.

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INTRODUCTION

1.
Data are essential resources for social science research, and data creators’ contributions
should be rewarded (Alter & Gonzalez, 2018). In addition to ensuring credit, measures of data
reuse such as downloads and citations can reveal a data set’s role in a research community
and provide insights into how researchers engage with data (Cousijn, Feeney et al., 2019).
Analyzing data citations reveals data citation practices and provides a way to quantify the
analytical utility and disciplinary reach of data collections (Buneman, Dosso et al., 2021).
Tuttavia, it has typically been challenging to find these measures because download data
is not widely available, and researchers inconsistently cite data (Buneman, Christie et al.,
2020; Lowenberg, Chodacki et al., 2019). Incomplete or opaque research data citations fail
to include persistent identifiers, which create obstacles to tracking data use and fail to give
appropriate credit to data creators (Moss & Lyle, 2018).

Data archives—particularly domain-specific archives with robust curation services—are
ideal sites to study data reuse. They provide data services that make reuse easier, making them
sites of research convergence. Archives anticipate data sets that have high analytical potential
for long-term preservation and community impact as “topical collections” (Fenlon, 2017;
Palmer, Weber, & Cragin, 2011). Additionally, some maintain bibliographies of papers that

Copyright: © 2022 Sara Lafia, Lizhou
Fan, Andrea Thomer, and Libby
Hemphill. Published under a Creative
Commons Attribution 4.0 Internazionale
(CC BY 4.0) licenza.

The MIT Press

Subdivisions and crossroads

reuse data from the archive, therefore tracking “citations” even when they are not formally
included in a paper (per esempio., NASA’s Data Archive Centers: DAACs1; biodiversity data aggrega-
tors such as Global Biodiversity Information Facility: GBIF2; and Data Observation Network
for Earth: DataONE3). There has been relatively little analysis of the intercitation networks
resulting from research data reuse in academic literature, Tuttavia.

Citations of data in these archives create networks of data sets with attributes that help us
understand data reuse and its implications. For instance, understanding the context of data
discovery and reuse may help us understand the distribution of ideas or topics within and
between research domains, and identify data sets that exhibit exceptional long-term analytical
potential (Palmer et al., 2011). Like “hibernators” among research papers (Eh & Rousseau,
2019), valuable data sets may lay dormant for years until they are discovered and “awakened”
through reuse. Identifying the different functions that data serve within knowledge communi-
ties can help us ensure data creators receive appropriate credit for their contributions.

Additionally, looking for new patterns of data couse or reuse would help identify hidden
communities that use archived data in novel ways. Data reuse can be viewed as an indirect
form of cooperation and collaboration between researchers (Sands, Borgman et al., 2012;
Thomer, Twidale, & Yoder, 2018; Zimmerman, 2008). Data archives promote research by pro-
viding access to data sets, and some of these data sets function as “boundary objects” (Star &
Griesemer, 1989) or parts of shared information spaces (Bannon & Schmidt, 1989). The visi-
bility of data reuse depends on the vantage point; while data reuse may be visible to those
directly involved, larger patterns of reuse may remain invisible, both to the data archive
(per esempio., data managers) and to prospective data users from different disciplines. Revealing hidden
reuse communities and their structures helps us understand what roles data play in knowledge
production and how they function as boundary objects between fields of research.

Despite recent data-sharing mandates, securing data deposits is still a challenge for data
archives. Researchers are often wary of sharing data because they fear being “scooped” or
are unsure how other researchers might use their data (Borgman, Scharnhorst, & Golshan,
2018; Cragin, Palmer et al., 2010). Mapping the network of data citations provides evidence
of data reuse that will help data producers and archives better assess the collaborative utility of
data and demonstrate different types of secondary use to researchers and potential depositors.

in questo documento, we inspect an authoritative bibliography of social science data sets cited in
academic publications from the Inter-University Consortium for Political and Social Research
(ICPSR) Bibliography of Data-related Literature4. Specifically, we analyzed its citation graph to
uncover hidden community structures and identified the different roles data sets play in net-
worked communities. By linking citations to metadata from a scholarly database, Dimensions,
we were able to include attributes such as “fields of research”5 in our analysis (Hook, Porter, &
Herzog, 2018). We then used community detection algorithms to identify hidden communities
within the network of data citations and identified two types of data sets that unite scientists
involved in social science knowledge production: subdivision data sets and crossroads data
sets. Subdivisions exclusively function as disciplinary resources used by a narrow set of fields.

1 https://lpdaac.usgs.gov/resources/publications/.
2 https://www.gbif.org/resource/search?contentType=literature&relevance=GBIF_USED.
3 https://search.dataone.org/profile.
4 https://www.icpsr.umich.edu/web/pages/ ICPSR/citations/.
5 According to Dimensions, the fields of research (FoR) is a hierarchical classification applicable for catego-

rizing all research and development activity.

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Subdivisions and crossroads

Crossroads, by contrast, enable interdisciplinary research. The network structures we identify
and name acknowledge the variation in reuse and help us recognize the myriad functions that
data sets serve in scientific communities.

2. BACKGROUND AND RELATED WORK

2.1. Data Archives as a Site for Understanding Scholarly Communication Practices

Data archives support data-intensive research by providing long-term data stewardship,
access, and high-quality data curation. Notable examples of data archives with high levels
of curation include GenBank, a rich repository of genetic sequence data; SESAR, a repository
of metadata describing physical samples in the earth sciences, as well as links to derived data
sets; and PANGAEA, a publisher for georeferenced data sets linked to earth system studies.
Data sharing through archives enables researchers to find and reuse data that they did not
collect. In other words, data created for one purpose can be used by new audiences to answer
new questions (Brown, 2003; Wilkinson, Dumontier et al., 2016). Researchers can use existing
data to validate previous findings, extend their data collections, or form the basis for new stud-
ies via integration or independent reuse (Gregory, Groth et al., 2020; King, 1995; Pasquetto,
Randles, & Borgman, 2017; Thomer, 2022). Additionally, as more funders and journals man-
date that data from grants and papers be shared openly, data archives are only growing in
importance as sites of scholarly communication.

The data held in these repositories often have untapped reuse potential across disciplinary
boundaries (Hey, Tansley, & Tolle, 2009; Palmer et al., 2011). Such interdisciplinary research
using archived data can lead to breakthrough discoveries (National Academy of Sciences,
2005; Tenopir, Allard et al., 2011). Fields of research may share an interest in explaining dif-
ferent aspects of the same phenomenon, giving rise to interfield theories that bridge fields of
science (Darden & Maull, 1977). “Borderland disciplines” sometimes form where fields of
research collide over shared resources, such as instruments or data, leading to the evolution
of new techniques (Gökalp, 1987). Data sets that facilitate interactions between research areas
therefore function as “boundary objects,” carrying multivalent analytical potential across
research communities (Star & Griesemer, 1989) and facilitating knowledge exchange across
boundaries. Tuttavia, there has been little research on the prevalence of such data sets-as-
boundary-objects. We know little about which features of data sets promote boundary crossing,
or how to measure their collaborative potential.

2.2. Data Citation Standards and Emerging Data Citation Networks

One way of exploring interdisciplinary data reuse—and therefore, the extent to which data sets
function as boundary objects between communities—is by studying data citation networks.
Efforts to promote data citation over the last 20 years have led to the adoption of new data
citation practices in many communities. Milestones formalizing data citation include the Joint
Declaration of Data Citation Principles (Data Citation Synthesis Group, 2014), Data Citation
Roadmap for Scholarly Data Repositories (Fenner, Crosas et al., 2016), and Data Citation
Roadmap for Scientific Publishers (Cousijn, Kenall et al., 2018). Data citation counts provide
a foundation for studying the scholarly impact of scientific data and the value of data curation
efforts.

The adoption of data citation principles makes it possible to analyze emerging data reuse
behavior and structures of hidden research communities in data citation networks. Citation
networks generally represent documents as vertices and citations of one document by another

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Subdivisions and crossroads

as edges (Leicht, Clarkson et al., 2007). Citation networks can highlight central nodes such as
influential institutions; heavy edges between nodes indicate important connections and pro-
cesses, such as the diffusion of ideas (Chen, 2017). Prior studies of citation networks have pro-
vided insights into ties between individual researchers and collaborations between research
disciplines (Tomasello, Vaccario, & Schweitzer, 2017). Studies of publication citation networks
(per esempio., papers or journals) have also identified novel papers, measured the impact of papers and
their authors, and attributed discoveries to authors (Newman, 2004).

Whereas publication citations broadly enable lineage retrieval for ideas, data citations indi-
cate the origins and processing history of the data sets that have been used in an analysis (Bose
& Frew, 2005). Data citation networks reflect connections between disciplinary literature and
the research data that they draw from. They reveal the reach of research data and support the
computation of bibliometrics that show the relationships and impacts of scientific products
(Buneman et al., 2021). The interactional context of data production and citation also reflects
relationships between data producers and consumers in a broader data economy (Vertesi &
Dourish, 2011).

Quantifying the scholarly impact of data archives and other research infrastructures relies on
proxy measures for data usage, such as downloads and citations (Mayernik, Hart et al., 2017).
Tuttavia, a number of recent studies have highlighted the limitations of studies that rely on cur-
rent data citation tracking infrastructures. Platforms such as DataCite have the potential to
enable large-scale studies of data production and its scholarly impact (per esempio., citations); Tuttavia,
a lack of consensus on the definition of “data” and alignment of metadata across providers limits
DataCite’s analytical potential (Robinson-Garcia, Mongeon et al., 2017). An analysis of
publication-data set networks constructed from GenBank and Figshare found that authors tend
to cite publications over data sets, suggesting that historically, data sets have not been regarded
as first-class research objects and that data use inferred from citation networks may undercount
data use (Zeng, Wu et al., 2020). We avoid concerns about data and metadata quality by focus-
ing exclusively on a curated bibliography linking social science studies to publications held by a
single data archive. A recent study of ICPSR’s metadata records described the thematic and
temporal dimensions of social science data sets and their citing literature separately (Lee & Jeng,
2019). We jointly analyze data and publications by constructing an interdisciplinary cocitation
rete. To tap the potential of shared data sets, we examine the role that data citations play in
the production and dissemination of knowledge in the social sciences.

2.3. Exclusive and Inclusive Communities in Knowledge Organizations

The analysis of citation networks can reveal hidden organizational structures. Cocitation anal-
ysis studies the structure of science and the emergence of specialties in bibliometric networks
by examining how frequently pairs of documents are invoked (Small, 1973). Author cocitation
analysis reveals individual contributions to specialty areas and paradigm shifts in the research
landscape (White & McCain, 1998). Citation analysis can be used to identify exclusionary
community structures, such as “invisible colleges” (Price & Beaver, 1966)—in-groups that con-
trol scientific discourse, which are defined by strong ties and informal communication (Crane,
1977). Similar analyses can also detect “citation cartels” of authors who cite each other exclu-
sively, and effectively shut out other authors who work on the same subject (Franck, 1999). In
addition to exclusionary practices, citation analysis can also identify convergence in research
communities. Studies of cross-field citation networks have found that fields of science tend to
become more integrated, rather than exclusive, over long periods of time (Varga, 2019), albeit
incrementally across neighboring disciplines (Porter & Rafols, 2009).

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Subdivisions and crossroads

While the notion of “community” is central to these analytical methods, it is a difficult con-
cept to operationalize (Orthia, McKinnon et al., 2021); communities may take many forms,
and may play many roles. Identifying communities via data citation is further complicated
by the interdisciplinary nature of data analysis and citation (Heidorn, 2008). Tuttavia, we take
inspiration from prior work showing that data reuse can be viewed as an indirect form of coop-
eration and collaboration between researchers—and groups that commonly reuse the same
data might be considered communities-at-a-distance (Sands et al., 2012; Thomer et al.,
2018; Zimmerman, 2008). Research data is a primary input for scientific knowledge produc-
zione, making data archives important sites for identifying nascent research communities. Noi
use community detection to reveal patterns of data reuse and examine the structure of research
communities that use data as shared scientific inputs.

3. DATA AND METHODS

We analyzed the ICPSR Bibliography, an authoritative source of high-quality, manually
curated links between 8,071 social science studies and 101,674 publications that have cited
them. An additional 2,420 studies (23%) do not have any data-related publications and so are
not represented in the Bibliography. At ICPSR, each study consists of one or more data files and
metadati. Tavolo 1 provides an example of available metadata for a highly cited ICPSR study.

Curation of the ICPSR Bibliography is labor-intensive, so the current coverage of the ICPSR
Bibliography is uneven6. Bibliography staff search broadly for academic literature that refer-
ences ICPSR studies and add literature to the Bibliography only if it analyzes ICPSR data or
includes an extensive discussion of data-related methodology. Publications in the Bibliography
are a mixture of materials published by the original data creator and publications that analyze
existing data. The majority of materials are journal articles, reports, conference proceedings,
theses, books, and book chapters. We restricted our analysis to materials published since the
inception of ICPSR as an archive in 1962.

We analyzed citations for all of ICPSR’s currently available studies. Many ICPSR studies
have institutional principal investigators (PIs) including U.S. government agencies (per esempio., NOI.
Census Bureau, Department of Justice, Department of Education, Department of Health and
Human Services), news outlets (CBS News, the New York Times), and university research cen-
ters (per esempio., University of Michigan’s Survey Research Center). Teams of individual researchers
also deposit data with ICPSR. Studies in our analysis included both restricted and public data
files. The terms of use for restricted data prohibit linking it to other data, so studies that include
restricted data may be undercounted in terms of their potential use.

The majority of ICPSR’s studies (62%) are also part of a series, meaning that they are part of
a recurring collection with new data archived over time (per esempio., repeated cross-sectional studies
or longitudinal studies). ICPSR provides access to 278 series. We used a natural breaks clas-
sificazione (Jenks, 1963) to find highly cited series, which are reported in Table 2.

3.1. Network Definitions

We constructed citation networks from the ICPSR Bibliography, which are summarized in
Tavolo 3. Given that studies from the same series have been created intentionally to be

6 The process of retrieving citations for all studies is ongoing. Because staff are actively searching for publi-
cations that reference ICPSR data sets, these measures are minimum counts, which likely underestimate the
number of papers and their relationships.

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Tavolo 1.

Example of available metadata for an ICPSR study

Study name
Monitoring the Future:
A Continuing Study
of American Youth
(12th-Grade Survey), 1996

Series title
Monitoring the Future (MTF)
Public-Use Cross-Sectional
Datasets

Release
1998-10-05

Citations
251

Subject terms

attitudes, demographic characteristics,
drug use, family life, high school
students, life plans, lifestyles, social
behavior, social change, values, youths

analyzed together (per esempio., across years), we grouped studies by their series and referred to the
resulting unit as a “data set”—either one series with multiple studies or one study that is not
part of a series. Grouping studies into ICPSR-defined series allowed us to distinguish data that
were designed to be used together (per esempio., by their project sponsor, funder, archive) from data
that have been discovered to be useful together (per esempio., by researchers who cocite them in
literature).

Because publications and data sets are two different classes of objects in the ICPSR Bibli-
ography, we modeled the connections between them in a bipartite network (B), consisting of
publication nodes, data set nodes, and edges linking publications to the data sets that they cite.
Citations are based on the total number of publications that use data from a study or series.
From network B, we projected data set nodes to create a weighted data set cocitation network
(S ). Edge weight in S indicates the total number of times that a pair of data sets have been used
together in publications. We removed low-frequency data cocitations from our analysis to
focus on data sets that were used together across multiple publications; we removed edges
from S with a weight less than 2, meaning that those data sets were only used together once.
This reduced edges by 87% (from 24,942 A 3,208) and nodes by 70% (from 3,363 A 998).

Tavolo 2.

Features of highly cited ICPSR series data

Series title
American National Election Study (ANES)

Lead investigators
Warren E. Miller et al. and the National Election

Series

Studi

Studi
in series
92

Combined
citations
16,771

Uniform Crime Reporting Program Data

Federal Bureau of Investigation

Series

Monitoring the Future (MTF) Public-Use

Lloyd D. Johnston et al.

Cross-Sectional Datasets

Current Population Survey Series

US Bureau of the Census

National Health and Nutrition Examination
Survey (NHANES) and Followup Series

Kathleen Mullan Harris et al.

National Survey on Drug Use and Health

United States Department of Health and Human

(NSDUH) Series

National Electronic Injury Surveillance

System (NEISS) Series

Services; National Institutes of Health;
National Institute on Drug Abuse

United States Department of Health and Human
Services; Centers for Disease Control and
Prevention; National Center for Injury
Prevention and Control

National Crime Victimization Survey

Bureau of Justice Statistics

(NCVS) Series

263

76

296

3

29

38

85

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13,041

11,808

11,012

6,951

5,893

5,255

4,472

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Subdivisions and crossroads

Network

Nodes

Edges

N (nodes)

N (edges)

Node size

Edge weight

Components

Density

Transitivity

Degree assortativity

Tavolo 3.

Summary of network definitions and metrics

B

S

Publications, set di dati

Data sets

Publication cited
ICPSR data set

ICPSR data sets cited in
the same publication

F

Fields of research

Publication tagged with
both fields cited one
ICPSR study

90,922 publications;
3,363 set di dati

998 set di dati

129 research fields

102,580

Constant

n/a

1,687

2.3e−5

n/a

n/a

3,208

Constant

1 for each publication in

which the pair of ICPSR
data sets is cited

4,238

log(Npapers)

1 for each ICPSR study
a publication cites

80

6.4e−3

0.28

−0.02

1

0.51

0.74

−0.30

Prossimo, we used a similar process to define a field of research network (F ) (Cunningham,
Smyth, & Greene, 2022). We gathered supplementary publication metadata for a subset of
44,639 publications in the ICPSR Bibliography (45% of the total) that were available in the
Dimensions database (Hook et al., 2018). We retrieved field of research (FoR) codes for each
pubblicazione. FoR codes consist of 22 high-level divisions and their subgroups (per esempio., Curriculum
and Pedagogy is a subgroup of Education). We linked FoR codes to ICPSR data sets through
their corresponding publications in an unweighted bipartite network (B0). We then projected
the FoR nodes to create a weighted cocitation network (F ). In F, edges are data sets that are
cocited between fields of research. Because each study could be cited by many different com-
binations of fields of research, we did not group studies by their series, allowing for the obser-
vation of different cocitation patterns in the same series of studies. Edge weight indicates the
total number of times a pair of data sets have been used together in publications. We simplified
F by removing low-frequency FoR cocitations, which correspond to edges with a weight less
than five.

3.2. Community Detection

We applied community detection algorithms to each network as summarized in Table 4. Com-
munity detection identifies nodes that have a high probability of interacting based on the net-
work structure (Fortunato & Hric, 2016). We selected detection approaches based on the
desired representation of communities in each type of network (Lancichinetti & Fortunato,
2009; Yang, Algesheimer, & Tessone, 2017). We allowed communities to overlap in the data
set cocitation network because we wanted to identify data sets with multiple roles. Tuttavia,
we did not allow overlap in the field of research network because we wanted to find commu-
nities defined by members with the strongest ties.

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Tavolo 4.

Summary of community detection approaches

Network
S

F

Definition
Data sets (studies or series)

Community detection
method
k-clique (k = 3)

Fields of research (FoR)

Louvain

in papers

Community definition
Data sets used in the same paper

Fields of research that use the

same study-level data

Communities
detected
41

4

We applied a k-clique percolation method to the data set cocitation network (S ) using the
corresponding implementation from the NetworkX Python library (Hagberg, Swart, & S Chult,
2008). A clique is a complete subgraph of a defined size (k) that can be reached from the
cliques of the same community through a series of adjacent cliques, meaning that the cliques
share k − 1 nodes (Palla, Derényi et al., 2005). Each node may belong to more than one clique,
resulting in overlapping communities. We selected a minimum clique size of three and labeled
each community with the three most common ICPSR subject terms for all studies in each
clique. Subject terms uniformly describe topics covered by the data and are defined by a con-
trolled vocabulary of social science concepts in the ICPSR Subject Thesaurus, which are
assigned during data curation.

We then selected an aggregation-based method to represent communities in our field of
research network. We applied the Louvain algorithm to the FoR network (F ) using the corre-
sponding implementation from the Louvain Python library (Hagberg et al., 2008). The algo-
rithm uses modularity to discover communities in large networks by moving nodes locally to
create a network aggregation; communities are merged until the resulting modularity of the
overall partition can no longer increase (Blondel, Guillaume et al., 2008). This method results
in nonoverlapping communities that show the most densely connected fields of research that
cocite ICPSR data sets. The networks (S, F ) were then arranged with a spring layout, Quale
places nodes with high degrees at the center of the graph.

4. RESULTS
We used two network measures—centrality and betweenness—to interpret the importance of
data sets and fields of research in their respective cocitation networks (Newman, 2003). Primo,
we calculated each node’s degree as the number of connections it shares with all other nodes
in the network. High-degree nodes are prominent in the network because they are highly con-
nected. We also calculated each node’s betweenness centrality by measuring all shortest paths
passing through a given node. Nodes with high betweenness function as hubs and connect
disparate parts of the network.

We also assessed structural features of the network—number of components, assortativity,
density, and transitivity—to compare the data set and field of research cocitation networks
(Tavolo 3). The FoR network is connected, meaning that all of its nodes are in the same com-
ponent, while the other two networks have multiple components or disconnected subgraphs.
This suggests that the FoR network is less complex than the data set cocitation network. Both S
and F exhibit negative degree assortativity, meaning that their nodes are less likely to be con-
nected to nodes in the network with a similar degree value. This pattern is stronger in F (−0.30)
than in S (−0.02). Finalmente, networks B and S have low density (2.3e−5 and 6.4e−3, rispettivamente),
while network F is far denser (0.51), indicating that B and S have comparatively fewer edges
linking nodes and are not as easily traversed as F.

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4.1. Data Set Cocitations

The data set cocitation network (S ) has a periphery of data sets that have been used together
only a few times and a denser core of highly connected data sets, which are often used
together. Figura 1 highlights important, central data sets, which are all found in the largest
subgraph at the core of the network. We used natural breaks to determine six data sets with
high betweenness and degree centrality, which play important roles in the network (Tavolo 5).

The important data sets we identified are long-running series made up of multiple studies.
Of these, the Uniform Crime Reporting Program Data Series has the highest degree and
betweenness. It has been used with 115 other data sets from studies or series across the citation
rete. The other data sets have strong ties to many other data sets and connect components
of the network. Half of these data sets are highly cited, with more than 10,000 citations each;
the others are less cited, yet play an important role in connecting the network. Finalmente, the lead
investigators for these important data sets include institutional PIs, meaning that one of the
study’s principal investigators or depositors is an institution (per esempio., the US Bureau of the Census),
and noninstitutional PIs.

To find collections of data sets that are often used together in publications, we performed
community detection on the data set cocitation network (Figura 2). Not all studies belong to a
cocitation community. Only a fraction of data sets in the analysis (N = 632; 63%) belong to
cliques of size three or larger; these data sets are often analyzed with at least two additional
ICPSR data sets. The data sets that fell out of our analysis were used independently and were
not combined with other data sets. We labeled each community with the three most common
ICPSR subject terms for all data sets within it. The largest clique has 461 data set members and
is topically broad (per esempio., “demographic characteristics, employment, income”) while smaller
cliques tend to have narrower focuses (per esempio., “terrorism, terrorists, radicalism”).

We also identified 20 set di dati (3% of all nodes in the network) that belong to more than
one community, which may facilitate analyses across topics. Of these, we summarized data

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Figura 1. Overview of data set cocitation network featuring data sets functioning as hubs. Inset: High degree (red), high betweenness (blue),
and high degree and betweenness (purple) nodes.

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Tavolo 5. Data sets with high betweenness and degree centrality in cocitation network

Data set name
Uniform Crime Reporting Program

Investigators
Federal Bureau of Investigation

Betweenness
0.17

Degree
115

Data Series

Studies in
series
263

Combined
citations
13,041

General Social Survey Series

National Opinion Research

Center; Davis et al.

American National Election

Study (ANES) Series

Miller et al.; National Election

Studi

Current Population Survey Series

US Bureau of the Census

Census of Population and Housing,
1790–1950 [stati Uniti] Series

Haines et al.; US Bureau

of the Census

National Health Interview Survey

National Center for Health

Series

Statistics

0.12

0.11

0.11

0.10

0.05

113

109

117

72

80

15

92

296

2

155

1,551

16,771

11,012

818

4,448

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Figura 2. Result of community detection (41 communities detected at k = 3) with labels generated from the three most frequent subject terms
for the data sets in each community. An interactive graph with detailed node information is available in Tableau7.

sets that belong to more than two communities, along with examples of other data sets that
they have been cocited with, and a representative publication that has cited the same data
Tavolo 6. Per esempio, the Census of Population and Housing, 1790–1950 [stati Uniti]
Series appears in three different data set communities. It has been used with other ICPSR data
sets to study topics such as industrial development and urbanization in the United States; con-
flict and international trade; and social movements and elections.

7 https://public.tableau.com/app/profile/ lizhou/viz/Study_communities_v2/Study_Communities_2

_Dashboard.

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Tavolo 6. Data sets in more than two communities, their cocited data sets, and publications

Data set
American National
Election Study
(ANES) Series

Community label terms
demographic characteristics,

employment, income

Example of cocited data sets

Example of citing publication

National Black Politics Study,

Wiegand, UN. W. (1999). Differences in public

[stati Uniti], 1993

public opinion, political

attitudes, political behavior

Swedish Election Test-Data Series:
Swedish Election Study, 1979

opinion between blacks and whites: A social
psychological perspective. University of
California, Santa Cruz.

Granberg, D., & Holemberg, S. (1991). Election
campaign volatility in Sweden and the United
States. Electoral Studies, 10(3), 208–230.

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candidates, foreign policy,

American Representation Study,

Hill, K. Q., & Hurley, P. UN. (1979). Mass

national elections

1958: Candidate and
Constituent, Incumbency

Census of Population

demographic characteristics,

and Housing,
1790–1950 [United
States] Series

employment, income

United States Agriculture Data,

1840–2012

international conflict, war,

Direction of Trade

nazioni

Participation, Electoral competitiveness,
and issue-attitude agreement between
congressmen and their constituents. British
Journal of Political Science, 9(4), 507–511.

Kitchens, C. T., & Rodgers, l. P. (2020). IL
impact of the WWI agricultural boom and
bust on female opportunity cost and fertility
(No. w27530). National Bureau of Economic
Research.

McKeown, T. J. (1991). A liberal trade order? IL
long-run pattern of imports to the advanced
capitalist states. International Studies Quarterly,
35(2), 151–172.

census data, historical data,

19th century

National Samples from the Census
of Manufacturing: 1850, 1860,
E 1870

Dobis, E. UN. (2016). The evolution of the American
urban system: history, hierarchy, and contagion.
Doctoral dissertation, Purdue University.

demographic characteristics,

Federal Justice Statistics Program

Bureau of Justice Statistics. (2021). Tribal crime

employment, income

Data Series

federal courts, sentencing,

defendants

Court Workforce Racial Diversity
and Racial Justice in Criminal
Case Outcomes in the United
States, 2000–2005

data collection activities. Technical Report. NCJ
301061, Washington, DC: Bureau of Justice
Statistics.

Ward, G., Farrell, A., & Rousseau, D. (2009). Fa
racial balance in workforce representation yield
equal justice? Race relations of sentencing in
federal court organizations. Legge & Society Review,
43(4), 757–806.

sentencing, federal courts,

offenses

Impact of Sentencing Guidelines
on the Use of Incarceration in
Federal Criminal Courts in the
stati Uniti, 1984–1990

Tonry, M. (1991). Mandatory minimum penalties

and the US Sentencing Commission’s mandatory
guidelines. Federal Sentencing Reporter, 4(3),
129–133.

Monitoring of Federal
Criminal Sentences
Series

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Figura 3. Results of community detection in the field of research network (F, with nodes connected by edges of size ≥ 5). The interactive
graph with detailed node and edge information in size and study numbers is available in Tableau8.

8 h t t p s : / / p u b l i c . t a b l e a u . c o m / a p p / p r o f i l e / l i z h o u / v i z / C o m m u n i t i e s i n F i e l d s o f R e s e a r c h F o R

/CommunitiesinFieldsofResearch.

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4.2. Fields of Research

To find fields of research (FoR) that often use the same data sets, we performed community
detection on the FoR cocitation network (F ). Nodes in F are color-coded by their parent-level
divisions and labeled by their child-level code. We detected four large communities, Quale
are summarized in Figure 3(UN). The primary fields of research in each community are Human
Società, Philosophy and Education (Comunità 0); Economics, Commerce and Management
(Comunità 1); Engineering, Earth and Environment, Information and Computer Science
(Comunità 2); and Medical and Health, Biology (Comunità 3).

Fields in the center of F have more cocitations, meaning that they are highly connected to
other fields. The central red frame in Figure 3(UN) shows the major domains of research that cite
ICPSR data sets: Human Society, Philosophy and Education (Comunità 0). These central
domains are consistent with the idea that most items in the ICPSR Bibliography are social sci-
ence publications. Infatti, social science (per esempio., Study of Human Society) and methodological
research fields (per esempio., Statistics) are found in the core of the network while humanities and other
fields (per esempio., Creative Writing, Performing Arts) exist mostly on the periphery.

Figures 3(B)–(e) show the composition of each of the four communities in greater detail. Noi
found that the communities tend to divide along disciplinary lines. Per esempio, members
within each community are similar, in that they tend to share the same parent-level field of
research. Per esempio, “Human Geography” and “Sociology” share the same parent-level
field of Human Society and are grouped into the same community (Comunità 0).

To examine the extent to which similar fields of research use the same data sets, we calcu-
lated citation statistics based on network F. We consider fields “similar” if they belong to the
same parent-level field (per esempio., “Civil Engineering” and “Environmental Engineering” are both
classified under Engineering) or the same community. We found that similar fields of research
cocite a limited range of data sets. The distribution of the aggregated numbers of data sets for
cocitation frequency by parent-level fields of research roughly follows a Poisson distribution
with λ = 1, indicating that as the number of parent-level fields citing the data set increases, IL
number of cocitations decreases (Figura 4(UN)). More than half (2,943 Di 5,712) of the data sets
in F are cocited by only one community, further suggesting that data set use tends not to cross
community boundaries (Figura 4(B)).

We also observed core and periphery structures in the FoR network shown in Figure 3(UN).
Tavolo 7(UN) shows examples of fields of research located at the core of each community

Figura 4. Data sets cited by parent-level fields of research. The y-axis indicates how many data sets were cited by the number of parent-level
fields on the x-axis. Most data sets are cited by a single parent-level field of research.

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Tavolo 7.

Examples of nonsocial science fields of research with core and periphery structures

(UN) Fields in the core of each community subgraph

Comunità
membership
0

0

0

1

2

2

3

3

Field of research

Psychology

Cognitive Sciences

Legge

Applied Mathematics

Library and Information Studies

Information Systems

Statistics

Artificial Intelligence and Image

in lavorazione

(B) Fields in the periphery of each community subgraph

Comunità
membership

0

1

2

3

Field of research

Performing Arts and Creative

Writing

Curriculum and Pedagogy

Transportation and Freight

Services

Archaeology

Number of connected fields of
research—degree centrality of nodes
1,833

543

1,308

26

66

138

363

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Example of frequently cocited data set and
corresponding fields of research

“National Crime Victimization Survey: School Crime Supplement,
2011", cocited by fields including Policy and Administration,
Criminology, Sociology, Specialist Studies in Education,
Psychology, Public Health and Health Services, Cognitive
Scienze

“Midlife in the United States (MIDUS 2), 2004–2006”, cocited
by fields including Applied Mathematics, Banking, Finance
and Investment, Economic Theory, Communication and
Media Studies, Business and Management, Political Science,
Econometrics, Applied Economics, Commercial Services

“American Time Use Survey (ATUS): Arts Activities, [stati Uniti],
2003–2018”, cocited by fields including Environmental Science
and Management, Other Economics, Religion and Religious
Studi, Urban and Regional Planning, Information Systems,
Library and Information Studies

“National Health and Nutrition Examination Survey III, 1988–1994”,
cocited by fields including Anthropology, Demography, Clinical
Scienze, Statistics, Artificial Intelligence and Image Processing,
Human Movements and Sports Science, Neurosciences, Nutrition
and Dietetics, Other Medical and Health Sciences, Biochemistry
and Cell Biology

subgraph. They include a wide range of subfields such as Psychology, Statistics, and Library
and Information Studies, which often advance methodological practices and make data-
related contributions. These nodes are highly connected to other fields of research and have
a much higher degree centrality compared to the average degree of nodes in F, che è 9.

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Fields of research in the periphery of each community subgraph (Tavolo 7(B)) reveal hidden
connections among disciplines through the data sets that they cocite. Per esempio, Archaeology
was cocited by 10 fields—while some of the cocitations are from social science disciplines such
as Anthropology and Demography, many others are related to biological and physical sciences,
including Clinical Sciences, Neurosciences, and Nutrition and Dietetics, which are found in
Comunità 3.

5. DISCUSSION

In this article we have applied metaphors from the built environment to interpret the hidden
research communities that we detected, and labeled the structures subdivisions and cross-
roads. These metaphors remind us that these communities of data use have emerged through
patterns of interaction in the research landscape and can be reshaped through intentional
progetto. We refer to data sets in research as subdivisions if they are inward-facing, exclusive,
and not well connected to other data sets or fields. Conversely, we refer to data sets that are
often traversed by communities and fields as crossroads. We find 632 research data sets in
subdivisions that function as disciplinary resources; 20 research data sets at crossroads in
the network that function as boundary objects by facilitating interdisciplinary research; E
nonsocial science fields that engage with social science data.

5.1. Subdivisions: Disciplinary Research Community Resources

We refer to data sets that serve a single disciplinary community as subdivisions because they
are inward-facing, exclusive, and not well connected. The largest data communities we
detected focus on international conflict, substance abuse, victimization, and public opinion
polls. Despite the topical breadth of the data set network (S ), it partitioned into coherent cli-
ques with a structure better described as a patchwork of subdivisions than a melting pot. By
comparison, the FoR network (F ) had high density and high transitivity, suggesting that its
nodes tended to be clustered together. Given its cohesive structure, we partitioned F into a
small number of meaningful communities.

To understand the communities that function as subdivisions, we drew from a combination
of metrics computed for each network, which are summarized in Table 3. Overall, the data set
cocitation network (S ) isn’t well connected. It has low density and low transitivity, is nonas-
sortative based on degree, and contains many components. By comparison, the field of
research network (F ) has a negative degree assortativity, meaning that high-degree fields of
research nodes tend to attach to low-degree nodes. The network is not fractured compared
with the data set cocitation network (S ) and has only one component.

In the data set network (S ) shown in Figure 2, we found instances of isolated cliques with
data sets that were exclusively used together. Per esempio, we detected a clique of three data
sets described by the terms “Antebellum South (USA), slavery, slave labor.” These data sets
(“Southern Farms Study, 1860"; “Mortality in the South, 1850"; and “New Orleans Slave Sale
Sample, 1804–1862”) have different investigators and were produced for different purposes,
yet have been used together numerous times in academic articles. These three studies function
like a collection even though ICPSR did not designate them as one (cioè., by naming them a
series). Generalmente, the analytic utility of data sets in subdivisions is limited to specific areas
of research. The notion of “thematic research collection”—a set of materials on a related
theme (Fenlon, 2017; Palmer, 2004)—may be useful for data archives to adopt; finding groups
of data used together is one way to identify candidate collections.

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We also found examples of cliques that shared topics, yet were disconnected from each other
(per esempio., “domestic violence, offenders, recidivism” and “domestic violence, offenders,
victimization”). While these data sets may be topically similar, researchers have not yet used
these data together. Cliques may be exclusive or disjointed for discovery reasons (cioè.,
researchers outside of the user group are not aware of these data) or their data may be discov-
erable but unsuitable (per esempio., due to variables, geography, or other properties). Per esempio, one
community with data about “drug treatment” is composed of studies funded by the U.S. Depart-
ment of Health and Human Services, while a separate community of “substance abuse” data sets
is funded by the U.S. Department of Justice. These distinct communities may have stances
toward a research topic that are not interoperable and may even conflict.

In the field of research network (F ) in Figure 3, we observed a subdividing tendency and an
in-group cocitation pattern for similar fields of research. These patterns of connection suggest that
each field of research cites a limited range of ICPSR data sets and supports the idea that ICPSR data
use divides along disciplinary boundaries (per esempio., social science disciplines such as economics and
education tend to cite the same data sets, but this is less common across nonsocial science fields,
such as engineering or nursing). Data sets in subdivisions have high analytic potential for narrow
communities of research; surfacing them and increasing their visibility may also help unlock
hidden potential for new uses beyond those narrow communities.

5.2. Crossroads: Engagement Across Research Communities

Data sets that facilitate interdisciplinary research are crossroads because they are often tra-
versed in connecting communities; in comparison to subdivision data sets, they are rare.
For instance, ICPSR is well known for large series data sets (per esempio., American National Election
Study [ANES]), which attract data users to the archive. We found several of these series in the
largest clique (Vedi la tabella 5), which overlaps with the largest subgraph of the network. These
series are well known and have high engagement across multiple research communities. In
particular, the ANES Series and the Uniform Crime Reporting Program Data Series are institu-
tionally funded, highly cited, and connect a network of researchers who use them.

Prior work found a correlation between data sets with at least one institutional PI and higher
data reuse (Hemphill, Pienta et al., 2022). When we examine data reuse based on citations
rather than downloads, Tuttavia, the relationship between data sets with institutional PIs and
reuse is less clear. Some institutional data sets already link multiple data sources into a single
data set and are useful on their own; they may not need to be combined with other data sets to
be analytically powerful. Among the crossroads data sets we found, the Census of Population
and Housing data set is unique because the individual investigator who constructed the data
set combined multiple years and data sources into a single data set, which has been broadly
useful across many applications.

In addition to the three connective data sets described in Table 6, we found 17 additional
data sets that function as crossroads between research communities. Many of these data sets
were often used with less cited data sets, explaining the negative associativity observed in net-
work F. Per esempio, the less cited “Vietnam Longitudinal Survey, 1995–1998,” is used with
the highly cited “India Human Development Survey (IHDS) Series” and “Chitwan Valley
[Nepal] Family Study Series” to study education, families, and family planning. Researchers
who seek data from a well-known study may traverse the citation network to find complemen-
tary data sets from lesser-known studies. While a single data series such as the IHDS might
meet only some users’ needs, given its limited geographic coverage, the data sets linked
through its connections offer opportunities for comparative analysis.

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In the field of research network (F ), we found two dominant patterns of cocitation, sum-
marized in Table 7. Fields in the core of the network are highly connected and operate at an
interdisciplinary crossroads; they tend to use more data sets in common with other fields.
These fields, such as Statistics and Applied Mathematics, are not in the social sciences. Piuttosto,
the data sets that they use function as crossroads, activating sites for research convergence. In
Comunità 3 (Figura 3(e)) Per esempio, Statistics cocites many of the same data sets as
Biology, Neuroscience, and Medical Sciences. Statistical methods are often applied in data
analysis and can advance the development of methodologies in these areas. Fields on the
periphery of the network also seem to indicate new forms of engagement with social science
dati. Per esempio, the field of Transportation and Freight Services uses data from “American
Time Use Survey (ATUS): Arts Activities, [stati Uniti], 2003–2018,” along with Environ-
mental Science and Management, Economics, Religion and Religious Studies, Urban and
Regional Planning, Information Systems, Library and Information Studies. Connections
between fields on the periphery of each community subgraph appear to maintain weak ties
among fields of research (Granovetter, 1973).

5.3. The Role of Research Data in Scientific Communities

These two structures suggest unique roles for data in scientific communities. Data sets in sub-
divisions and crossroads are two types of essential resources supporting social science
research; subdivisions may have high disciplinary impact for the specific research domains
that use them, while data sets at a crossroads may provide connectivity across domains. For
instance, data at crossroads enable a kind of “arm’s length” cooperative work where the work
is loosely coupled, but depends on a “shared information space” that includes common data;
much like community efforts to maintain taxonomies across time and space, the shared
analysis of data sets contributes to cooperative “conceptual infrastructures” of scientific knowl-
edge (Bannon & Schmidt, 1989, P. 361; Thomer et al., 2018).

While most ICPSR data is used by many disciplines within the bounds of social science,
data reuse outside of the social sciences tends to engage with data in two main ways. Primo,
fields such as statistics and artificial intelligence are central in the field of research cocitation
rete; these fields may reuse social science data to develop new research and analytic
metodi. Secondo, fields such as performing arts and creative writing are peripheral in the
rete; while they tend to reuse ICPSR’s data less overall, they may provide novel inroads
for “awakening” cross-disciplinary data reuse in new application areas (Eh & Rousseau,
2019).

Identifying hidden communities and their structures within the data citation graph helps us
understand how data promotes knowledge production (Buneman et al., 2021; Lowenberg
et al., 2019). It is likely that data sets occupying these different structures offer different types
of “analytical potential.” Palmer et al. (2011) describe “analytic potential” as “possible analytic
contributions for the range of possible user communities” (P. 4), and our method exposes those
possible communities and their structure. Research communities are beginning to recognize
the importance of contributing to data resources, and the citation graph enables us to assign
credit for different kinds of contributions (Alter & Gonzalez, 2018; Cousijn et al., 2019).
Naming these different structures provides an accessible, extensible language for discussing
the functions of data and assigning credit for their creation. Creating and sharing data that
are used widely within one’s discipline ought to afford researchers credit among their peers,
sometimes for facilitating disciplinary depth—subdivisions—and at other times for creating
multidisciplinary resources—crossroads.

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5.4. Limitations and Outlook

Our analysis relied on a hand-curated resource, the ICPSR Bibliography of Data-Related
Letteratura, which limits the generalizability of our findings. While other data archives also
maintain bibliographies, few are as comprehensive and cross-cutting as ICPSR’s, che è
maintained by dedicated staff who capture instances of data reuse across a wide variety of
media types and scientific disciplines. Tuttavia, our network analysis method is generally
applicable to study data reuse and highlights incentives for data archives to maintain compre-
hensive bibliographies, which support the long-term study of data impact.

We also used the Dimensions database’s existing classification scheme for fields of
research. This was a pragmatic choice given that codes were assigned at the level of publica-
tions rather than journals. Tuttavia, the granularity of fields of research may be too coarse for
interpreting finer disciplinary patterns of data use within domain archives. Adopting other
domain analysis approaches could enhance our understanding of scientific knowledge pro-
duction (Hjørland & Albrechtsen, 1995). Inoltre, we could compare the reuse of curated
social science data at ICPSR to self-archived data (per esempio., from the Dutch National Centre of
Expertise and Repository for Research Data: DANS).

We were able to identify data sets that served different purposes within scientific commu-
nities, but our data do not allow us to comment on how credit for creating different types of
data resources ought to be awarded to data creators and providers. Future research should
examine the relationship between data creation, reputation, and careers to understand how
to recognize data creators’ contributions. Because of the different roles they play in connecting
and supporting scholarly communities, data creators who produce subdivisions or crossroads
likely deserve different types of credit for their contributions. For instance, creating a data set
that operates as a subdivision should afford data creators substantial credit within their
discipline, while creating crossroads may award creators a broader reputation that is less
well-recognized within a single discipline. Data creators’ academic careers depend on how
they receive credit for their work and could impact the types of data resources they create
and share.

Our data is essentially a snapshot in time, and they do not enable us to investigate the
processes of community formation. The ICPSR Bibliography is a dynamic database; new cita-
tions are added continuously as they are discovered. The fact that a study does not have any
citations, or has very few, does not mean that its data have never been used; Piuttosto, it may
mean that any existing records of its use have not yet been discovered. More exhaustive
searches for references to ICPSR data are under way. It is unclear whether data sets men-
tioned in literature (per esempio., “Data from the American National Election Survey (ANES) È
restricted in its geographic coverage but contains valuable direct questions on the subjective
evaluation of racial groups …”) imply that the authors have analyzed the data or are men-
tioning the data for other purposes. Finer distinctions between types of data set references
will enable future studies of factors that contribute to the analytical potential and end-users’
decision to use data.

Given that data citation is a dynamic process, we are also interested in studying community
formation to better understand how social ties, data curation, or other factors shape data
citation networks. Per esempio, temporal citation dynamics provide rich insights into the
formation of research communities (Chubin, 1976). Extending the idea of “hibernation” to
research data sets that have not yet been “awakened” through reuse (Eh & Rousseau,
2019) and detecting bursts of citations following long periods of dormancy would allow us
to detect discovery events in the network. Understanding factors associated with novel data

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reuse would provide evidence to recommend underutilized research data and prioritize fund-
ing and credit for specific data curation activities.

6. CONCLUSION

Data citation networks contain hidden information about communities of data users and the
roles data play as primary inputs for scientific knowledge production. Through network anal-
ysis, we revealed these communities and identified 41 communities of social science data sets,
along with four interdisciplinary research communities that use these data. Six important data
series connect the cocitation network. Data sets that are used together exclusively form
research subdivisions, which are valuable data collections for particular disciplines. Other
data sets or fields that connect research communities are crossroads and have high topical
or analytical versatility. Research data sets that are produced for different purposes, ad esempio
long-running series data and single-purpose study data, are often used together. Similar fields
of research also tend to use the same combinations of data. In conclusion, these findings
contribute new ways of seeing scientific communities and make the impacts of research data
reuse visible.

ACKNOWLEDGMENTS

Many thanks to Elizabeth Moss and the ICPSR Bibliography staff (Homeyra Banaeefar, Sarah
Burchart, and Eszter Palvolgyi-Polyak), David Bleckley, Elizabeth Yakel, Amy Pienta, E
Dharma Akmon of the MICA team at the Inter-university Consortium for Political and Social
Research (ICPSR) for their support of this research. We are also grateful to Sagar Kumar and
Andrew Schrock for providing feedback on our earlier drafts.

AUTHOR CONTRIBUTIONS

Sara Lafia: Analysis and interpretation of data, Conceptualization, Methodology, Visualization,
Writing—original draft, Writing—review & editing. Lizhou Fan: Analysis and interpretation of
dati, Methodology, Visualization, Writing—original draft. Andrea Thomer: Conceptualization,
Funding acquisition, Supervision, Writing—original draft. Libby Hemphill: Conceptualization,
Funding acquisition, Methodology, Supervision, Writing—original draft.

COMPETING INTERESTS

The authors have no competing interests.

FUNDING INFORMATION

This material is based upon work supported by the National Science Foundation under grant
1930645.

DATA AVAILABILITY

Citation data were derived from the ICPSR Bibliography in February 2022. Code for this
article’s analysis is available in a GitHub repository (Lafia, 2022B) and data is available on
openICPSR (Lafia, 2022UN). Access to licensed metadata from Dimensions was granted to
subscription-only data sources under a license agreement with Digital Science through the
University of Michigan.

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