RESEARCH ARTICLE - Ricerca sull'intelligenza artificiale specializzata al MIT

RESEARCH ARTICLE

Reproducible science of science at scale: pySciSci

Alexander J. Gates1

and Albert-László Barabási2,3

1School of Data Science, University of Virginia, Charlottesville, VA
2Network Science Institute, Northeastern University, Boston, MA
3Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA

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

j o u r n a l

Keywords: bibliometric data, citation networks, code, research assessment, science of science,
scientometrics

Citation: Gates, UN. J., & Barabási, A.-L.
(2023). Reproducible science of
science at scale: pySciSci. Quantitative
Science Studies. Advance publication.
https://doi.org/10.1162/qss_a_00260

DOI:
https://doi.org/10.1162/qss_a_00260

Peer Review:
https://www.webofscience.com/api
/gateway/wos/peer-review/10.1162
/qss_a_00260

Received: 7 settembre 2022
Accepted: 8 Marzo 2023

Corresponding Author:
Alexander J. Gates
agates@virginia.edu

Handling Editor:
Ludo Waltman

Copyright: © 2023 Alexander J. Gates
and Albert-László Barabási. Pubblicato
under a Creative Commons Attribution
4.0 Internazionale (CC BY 4.0) licenza.

The MIT Press

ABSTRACT

Science of science (SciSci) is a growing field encompassing diverse interdisciplinary research
programs that study the processes underlying science. The field has benefited greatly from
access to massive digital databases containing the products of scientific discourse—including
publications, journals, patents, books, conference proceedings, and grants. The subsequent
proliferation of mathematical models and computational techniques for quantifying the
dynamics of innovation and success in science has made it difficult to disentangle universal
scientific processes from those dependent on specific databases, data-processing decisions,
field practices, eccetera. Here we present pySciSci, a freely available and easily adaptable package
for the analysis of large-scale bibliometric data. The pySciSci package standardizes access to
many of the most common data sets in SciSci and provides efficient implementations of
common and advanced analytical techniques.

INTRODUCTION

Science of science (SciSci) as a discipline has grown rapidly over the last century, reflecting an
increasing interest in quantitatively modeling the processes underlying science—from the nov-
elty of scientific discoveries to the interconnectivity of scientists. The increasing prevalence of
SciSci research is due in large part to the availability of large-scale bibliometric data capturing
the products of scientific discourse, including publications, patents, and funding. Jointly with
the analysis of scientific processes, such bibliometric data are used to map the evolution of
specific fields, evaluate scientific performance and eminence, and support government policy
and funding decisions (Fortunato, Bergstrom et al., 2018; Wang & Barabási, 2021; Wu, Kittur
et al., 2022). Tuttavia, bibliometric data are distributed across diverse databases, each with
its own criteria for inclusion, and varied processes to assure the data’s quality and accuracy
(Csiszar, 2017). The manifold uses and applications for bibliometric data, combined with the
call for reproducible and replicable science, has prompted the need for flexible analysis that
is reliably reproduced across multiple data sets.

Here, we introduce pySciSci, an open-source Python package for the analysis of larges-cale

bibliometric data. The pySciSci package provides

(cid:129)
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standardized preprocessing and access to many of the most common data sets in SciSci;
an extensive library of quantitative measures fundamental to SciSci; E
advanced methods for mapping bibliometric networks.

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Reproducible Science of Science at scale

The pySciSci package is intended for researchers of SciSci working from complete biblio-
metric databases or those who wish to integrate large-scale bibliometric data into other exist-
ing projects. By creating a standardized and adaptable programmatic base for the study of
bibliometric data, we intend to help democratize SciSci, support diverse research efforts
based on bibliometric data sets, and address calls for open access and reproducibility in the
SciSci literature and community (Light, Polley, & Börner, 2014).

To the best of our knowledge, our package constitutes one of the most comprehensive col-
lections of methods and data sources in scientometrics and bibliometrics. It complements and
extends the capabilities of the Bibliometrix (Aria & Cuccurullo, 2017), BiblioTools (Grauwin &
Jensen, 2011), and Citan (Gagolewski, 2011) libraries to multiple databases and more
advanced metrics. Although two of the most popular bibliometric programs, VOSviewer
(van Eck & Waltman, 2010) and CiteSpace (Chen, 2006), are designed to provide graphical
network maps of science, neither program is open sourced and modifiable. Several programs
are much more specialized than pySciSci, and focus on implementations of method families
for specific tasks (Moral-Muñoz, Herrera-Viedma et al., 2020); Per esempio, CRXexplorer ana-
lyzes a publication’s distribution of reference years (Marx, Bornmann et al., 2014), and the
open-source Python package ScientoPy offers tools specifically for topical trend analysis
(Ruiz-Rosero, Ramırez-González, & Viveros-Delgado, 2019). Our package also complements
the CADRE (Mabry, Yan et al., 2020) environment built to host bibliometric data sets. Ulti-
mately, our goal is not to supplant these other efforts to provide access to SciSci research
but to facilitate a unified and generalizable open-source environment across different data-
bases and methods of analysis.

2. THE pySciSci PACKAGE

The pySciSci package is built around Python Pandas data frames (McKinney, 2010), providing
the simplicity of Python with the increased speed of SQL relational databases. pySciSci pro-
vides a standardized interface for working with several of the major data sets in the Science of
Scienza, including the Microsoft Academic Graph (MAG), the Web of Science ( WOS), IL
American Physics Society (APS), PubMed, the DBLP Computer Science Bibliography (DBLP),
and OpenAlex (Priem, Piwowar, & Orr, 2022). Each data set is referenced in pySciSci as a cus-
tomized variant of the BibDataBase class, which handles all data loading and preprocessing.
For an example of loading and preprocessing each database, we include a Getting Started
jupyter notebook in the examples directory. The storage and processing frameworks are highly
generalizable, and can be extended to other databases not mentioned above (per esempio., United
States Patent Office, Scopus, Lens).

The pySciSci pipeline starts by preprocessing raw data into a standardized tabular format
(Figura 1). The package creates several relational data tables based on a balance between
commonly associated data fields and memory footprint. Bibliometric records are split into five
types of entities: pubblicazione, author, affiliation (institution), journal/venue, and field of study.
The primary unit of analysis in pySciSci is the publication—a catch all phrase encompassing
scientific articles, preprints, patents, books, conference papers, and other bibliometric prod-
ucts disseminated as a single entry in a database. The publication objects are stored in their
own data table, pubblicazione. As the year of publication is the most commonly used publication
property, the mapping of publications to year is also replicated in its own Python dictionary,
pub2year, for quick reference. Depending on the specific database, the author names, affilia-
tion names, and journal names may be available and are stored in their own data tables:
author, affiliation, and journal respectively. In some databases, data fields represent expert

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Reproducible Science of Science at scale

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Figura 1. Data processing overview. The pySciSci package preprocesses many of the common
bibliometric data sources (UN) into a standardized set of relational tables (B). The package also cleans
and precomputes measures that are frequent building blocks for more advanced computations (C).
The pySciSci package provides efficient implementations for many advanced metrics focusing on
publications, authors, or journals (D), as well as advanced network analysis (E).

curated entries, and in other databases, data fields may be algorithmically inferred by the data-
base curators; see the specific database references for details. Finalmente, three relational tables
are built to link between the entities: pub2ref captures reference and citation relationships
between publications; publicationauthoraffilliation links between publications, their authors,
and the author affiliations; and pub2field links publications to their field of study. The prepro-
cessing step which builds the data tables only needs to be run once for each database.

After extracting the data tables, the pySciSci package precomputes several of the most com-
mon and useful bibliometric properties that form the backbone of many more advanced
metodi. Per esempio, if the author information is available, the team size (number of authors)
is found for all publications (Wuchty, Jones, & Uzzi, 2007). When the reference/citation infor-
mation is available, the number of citations within a user defined window (default 10 years) È
also precomputed (Wang, 2013). Finalmente, when both the author and reference/citation infor-
mation is available, the pySciSci package will archive a copy of the reference/citation relation-
ships in which self-citations are removed, pub2ref_noself.

To facilitate data movement and lower memory overhead when the complete tables are not
required, the pySciSci preprocessing step chunks the data tables into smaller tables. When
loading a table into memory, the user can quickly load the full table by referencing the table
name as a database property or specify multiple filters to load only a subset of the data. IL
pySciSci also supports dask dataframes (Rocklin, 2015), which add parallelization and block
scheduling, allowing large dataframes to be processed without loading the full dataframe into
memory.

Quantitative Science Studies

Reproducible Science of Science at scale

Due to variations in data coverage between databases, the available package functionality
will vary between data sets. Per esempio, the DBLP database does not provide citation rela-
tionships between publications, and the APS database does not disambiguate author careers.
The pySciSci package supports methods to link bibliometric entities between databases and
the framework easily facilitates augmenting a database with additional data sources, allowing
for enriched analysis (Gates, Gysi et al., 2021).

Our distribution of pySciSci is accompanied by a growing library of jupyter notebooks that
illustrate its basic functionalities and usage. We also encourage the SciSci community to con-
tribute their own implementations, dati, use cases, or attempts to reproduce key results from
the Science of Science.

3. PUBLICATIONS AND CITATIONS

The coverage of bibliometric databases varies, with some focusing only on a narrow subset of
publications defined by journal or field, and others attempting to encompass all peer-reviewed
scientific communication. As shown in Figure 2A, the number of publications and temporal
coverage vary dramatically between four common databases. This variability reflects important
decisions about data quality and generalizability that a researcher must make; Per esempio, DBLP
provides user-curated author careers in computer science, but does not contain citation informa-
zione, whereas MAG contains a wide range of document types from all of science, with algorith-
mically inferred fields and author career information. With few exceptions, these databases focus
on English-language publications, offering only sparse coverage of publications in other lan-
guages. The pySciSci package facilitates restricting each database to specific document types,
fields, or years, allowing researchers more control over the publications and authors under study.

Citation analysis is the examination of the frequency, patterns, and networks of citation rela-
tionships between publications. Some citation measures have become commonplace, con
many implementations available; others are precomputed by major database portals based
on proprietary algorithms, and still others require complex processing and computational
steps that have largely inhibited their general usage (Bollen, Van de Sompel et al., 2009).
The pySciSci package facilitates the analysis of total citation counts for publications, anche
as citation time series, fixed time window citation analysis, citation count normalization by
year and field, and fractional citation contribution based on team size. Due to the package’s
modular design, the choice of citation count and normalization is made before calculating
specific metrics. The package also includes a simplified interface for fitting models to citation
time series, such as in the prediction of the long-term citation counts to a publication (Wang,
Song, & Barabási, 2013), or in the assignment of the sleeping beauty score (Ke, Ferrara et al.,
2015). Exemplar code illustrating citation metrics can be found in the examples folder.

Due to the prevalence of citation metrics as measures of scientific prominence, techniques
for “gaming the system” have flourished that inflate an author’s citation metrics for reasons
other than scientific impact. For instance, it has been found that men tend to cite themselves
more often than women, contributing to widening gender imbalances in scientific impact
(King, Bergstrom et al., 2017). Consequently, one of the primary preprocessing steps for con-
temporary citation analysis is the removal of self-citations occurring between publications by
the same author. All analysis facilitated in the pySciSci package can be run either with or with-
out the self-citations when authors are available in the database.

The comparison of citation counts between different disciplines and fields is complicated
by differing citation norms and community sizes (Radicchi, Fortunato, & Castellano, 2008).
Therefore, it is common to normalize citation counts by field or year averages to create a

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Figura 2. Growth of science across databases. The database size as measured by the number of A)
publications, B) journals, and C) authors varies over several orders of magnitude between the APS
(gold), DBLP (purple), MAG (dark green), and WOS (teal). As the APS does not include disambig-
uated author careers, it does not appear in C).

common reference point, or to rank publications to identify “top publications” in the top 1% O
5% of publications from a field. The citation_rank function facilitates the ranking of publica-
tions by different citation metrics and groups. We also provide extended normalization mea-
sures that account for a publication’s interdisciplinarity by controlling for citation patterns in
the immediate cocitation neighborhood.

The diversity of disciplines or journals reflected in a publication’s reference and citation rela-
tionships has been used to quantify the publication’s interdisciplinarity or novelty (Gates, Ke
et al., 2019; Porter & Rafols, 2009; Stirling, 2007; Uzzi, Mukherjee et al., 2013). The pySciSci
package provides several measures of interdisciplinarity, including the Rao-Stirling diversity
index, the Gini coefficient, Simpson’s diversity index, and entropy measures, which can be
computed using the distribution of publication references or publication citations. Per esempio,
consider the publication shown in Figure 3A, with five references in three disciplines: physics,

Quantitative Science Studies

Reproducible Science of Science at scale

Figura 3. Advanced career and publication metrics. (UN) The pySciSci package captures the several advanced characterizations of publica-
tion’s influence (references) and impact (citations) including the disruption index, and Rao-Stirling Interdisciplinarity. (B) The package also
facilitates the analysis of full author careers and summarizing metrics such as total productivity, h-index, and Q-factor.

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biologia, and economics. The Rao-Stirling reference interdisciplinarity, calculated using the
raostriling_interdisciplinarity function, reflects the diversity of the disciplines referenced by
the publication (left, 0.34), and the Rao-Stirling citation interdisciplinarity reflects the diversity
of the disciplines citing the publication (right, 0.37). The pySciSci package also facilitates the
computation of publication novelty and conventionality as measured by atypical combinations
of journals in the reference list using the novelty_conventionality function (Uzzi et al., 2013).
Other measures based on the local citation graph capture the disruptive influence of a publi-
cation as measured by the frequency with which the publication is cited alongside its own
references (Funk & Owen-Smith, 2017; Park, Leahey, & Funk, 2023; Wu, Wang, & Evans,
2019), calculated by the disruption_index function. Per esempio, in Figure 3A, four of the citing
publications also cite three of the references, resulting in a disruption index of 0.2. Exemplar
code for the analysis of publication interdisciplinarity can be found in the examples folder.

4. PUBLICATION GROUPS: AUTHORS, JOURNALS, FIELDS AND AFFILIATIONS

The next unit of analysis aggregates publications into groups by common author, journal,
discipline/field, or affiliation. Per esempio, the infamous journal impact factor considers the
group of all publications from the same journal over a fixed time window (typically 2, 3, O
5 years), and is found by averaging their citation counts (Bordons, Fernández, & Gómez,
2002). The pySciSci package implements over 12 citation metrics for groups of publications,
which can be easily applied to journal, author, discipline/field, or affiliation aggregations when
available in the database. Combined with the different normalization decisions for citation
conta, the pySciSci package implements nearly 200 different measures for scientific impact.

At the heart of scientific discoveries are the scientists themselves. Consequently, the soci-
ology of science has analyzed scientific careers in terms of individual incentives, productivity,
competition, collaboration, and success. The pySciSci package facilitates author career anal-
ysis through both aggregate career statistics and temporal career trajectories. We implement
more than 10 metrics for author citation analysis, including the h-index (Hirsch, 2005),
author_hindex, and Q-factor (Sinatra, Wang et al., 2016), author_qfactor. The package also
includes a simplified interface for fitting models to author career trajectories, such as identify-
ing topic switches (Zeng, Shen et al., 2019), the assessment of yearly productivity patterns
(Way, Morgan et al., 2017), or the hot-hand effect (Liu, Wang et al., 2018).

Quantitative Science Studies

Reproducible Science of Science at scale

Per esempio, consider the representation of Derek de Solla Price’s publication career as
represented in the MAG, shown in Figure 3B. It captures the citations recieved by 71 articles
and books published over 50 years (even though Dr. de Solla Price died in 1983, articles can
be reprinted or published posthumously). Using this career trajectory, we find that Dr. de Solla
Price has an h-index of 20 and a Q-factor of 14. Exemplar code for the analysis of author
careers can be found in the examples folder.

Greater scrutiny is being given to the prevalence of systematic bias in science (Saini, 2019),
supported by observations that, Per esempio, female authors have fewer publications than their
male colleagues (Larivière, Ni et al., 2013; Xie & Shauman, 1998). Although most databases
do not include author biographical information (genere, race, age, position, sexual orientation,
eccetera.), the pySciSci package facilities linking user provided biographical information to author
careers. Implementations are then available for advanced measures of inequality, including the
measurement of categorical bias in reference lists (Dworkin, Linn et al., 2020), or career
lengths (Huang, Gates et al., 2020).

Inoltre, the movement of scientists between institutions and countries requires longitu-
dinal data capturing the changes in affiliation throughout a career. When the affiliations are
disambiguated, the pySciSci package allows for collaboration and mobility networks between
affiliations. These affiliations can be aggregated to the city, state, and country level, allowing
for large-scale analysis of global patterns in scientific production and impact.

5. NETWORK ANALYSIS

Scientific discoveries and careers do not exist in isolation; Piuttosto, science evolves as a conver-
sation between scientists, empowered by links between authors, publications, istituzioni, E
other entities. Consequently, many key results from SciSci consider publications, authors, O
fields as embedded in a complex web of interrelationships. The pySciSci package provides a
flexible interface for working with networked bibliometric data. Primo, the bibliometric relation-
ships are processed to extract the edge list representation of the network. The package then
maps these edge lists to an adjacency matrix, treated internally as a scipy sparse matrix—a
memory-efficient and highly flexible network representation. All network relationships can
be further unraveled over time by considering snapshots of the network for each year. pySciSci
facilities basic network measures, including the number of connected components, extraction
of the largest connected component, threshold filtering, disparity filter (Serrano, Boguná, &
Vespignani, 2009), and analysis of degree distributions (Barabási, 2016). The scipy sparse adja-
cency matrix can also be directly imported into many of the most common packages for more
advanced network analysis and visualization.

One of the most common bibliometric networks is the coauthorship network, in which
nodes represent authors and two authors are linked if they coauthored a publication (Barabási,
Jeong et al., 2002; Gold, Gates et al., 2022; Newman, 2004). Coauthorship networks are used
to capture general patterns of collaboration including how many different people an author
publishes with, how often an author’s collaborators are also each-other’s collaborators
(network clustering), what the typical networked-based distance between authors is (average
path length), and how patterns of collaboration vary between fields and over time. Given a
subset of publication and author relations, the coauthorship_network function can build both
the static and temporal coauthorship networks. Exemplar code for the analysis of coauthorship
networks can be found in the examples folder.

The scientific community’s perception of which publications are most related to each other
is reflected in the publication cocitation network (Boyack & Klavans, 2010; Gates, Ke et al.,

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Reproducible Science of Science at scale

Figura 4. Cocitation network. The interdisciplinary impact of a publication is illustrated through
the cocitation network between citing articles. Here nodes are publications that cited Stirling
(2007). Two nodes are linked if some other publication cited both. Node color reflects the publi-
cation’s discipline: (yellow) computer science, (magenta) economics, (blue) sociology, E (green)
other. The three prominent clusters reflect the fact that Stirling (2007) impacted three distinct com-
munities of researchers.

2019). Here, nodes represent publications and two publications are linked if they are both
cited by another publication. Per esempio, consider the cocitation network shown in
Figura 4, in which nodes come from the set of publications that cite Stirling (2007). IL
cocitation network shows three distinct clusters of publications, each of which is enriched
by a subset of related fields (Computer Science, Economics, Sociology), and a fourth that features
Other publications. Infatti, modularity maximization using the Louvain heuristic (Blondel,
Guillaume et al., 2008; Newman, 2006) identifies four communities. The similarity of the
publication fields and the detected communities can be assessed using the element-centric
similarity (Gates & Ahn, 2019; Gates, Wood et al., 2019), a measure between 0 E 1, Dove
1 captures that the two network communities are identical, E 0 reflects two network com-
munities that group publications very differently. The element-centric similarity between the
publication fields and the detected communities is 0.33, reflecting a modest level of agree-
ment. Decomposing the error terms into contributions from publications in different fields,
we find the the majority of the error arises from the Other publications (0.26), whereas pub-
lications in Economics and Computer Science are more faithfully recovered (0.45 E 0.35
rispettivamente). This cocitation network analysis demonstrates how the diversity measure intro-
duced in Stirling (2007) has impacted three distinct scientific communities. Exemplar code for
the analysis of cocitation networks can be found in the examples folder.

Citation networks form the basis for collective measures of scientific impact. Per esempio,
the collective assignment of credit to a publication’s authors can be measured by the fre-
quency with which an author’s other publications are cocited alongside the focus publication
(Shen & Barabási, 2014). The pySciSci package algorithmically calculates the collective credit
allocation temporally for each year since the article’s publication.

Advance in statistical learning methods for graph embedding allow networks to be repre-
sented in high-dimensional metric spaces (Goyal & Ferrara, 2018). Such graph embedding
methodologies provide compressed representations of the original network that can be used,
Per esempio, to predict new connections based on node similarities (Martinez, Berzal, &
Cubero, 2016). The pySciSci package provides implementations of the node2vec (Grover &
Leskovec, 2016) graph embedding method and its extension for authors, persona2vec (Yoon,
Yang et al., 2021), which produces effective representations of scientific journals (Peng, Ke

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et al., 2021) and author mobility (Murray, Yoon et al., 2020). Exemplar code for graph embed-
ding can be found in the examples folder.

6. SUMMARY AND DISCUSSION

Here we introduced the open-source pySciSci Python package for bibliometric data analysis.
Due to its modular structure, the pySciSci framework is highly generalizable and can easily
accommodate many available data sets beyond the four mentioned here. The package also
provides efficient implementations of common and advanced SciSci methods, facilitating
reproducible analysis across multiple data sets. Most importantly, it is our hope that this pack-
age stimulates other researchers to add their own methods and facilitates large-scale collabo-
rations throughout the Science of Science community.

ACKNOWLEDGMENTS

Special thanks to Jisung Yoon for contributing the implementation of graph2vec and Yong-Yeol
Ahn for feedback on package structure. Thanks to two anonymous reviewers for wonderful
suggestions that improved the presentation and functionality of the package, and thanks to
the wonderful research community at the Center for Complex Network Research for helpful
discussions.

AUTHOR CONTRIBUTIONS

Alexander J. Gates: Conceptualization, Data curation, Funding acquisition, Methodology,
Software, Visualization, Writing—Original draft, Writing—Review & editing. Albert-László
Barabási: Conceptualization, Funding acquisition, Writing—Review & editing.

COMPETING INTERESTS

A.-L.B. is co-scientific founder of and is supported by Scipher Medicine, Inc., which applies
network medicine strategies to biomarker development and personalized drug selection, E
is the founder of Naring Inc. that applies data science to health and nutrition.

FUNDING INFORMATION

This material is based upon work supported by the Air Force Office of Scientific Research
under award number FA9550-19-1-0354. A.-L.B. is also supported by the Templeton Founda-
zione, contract #62452, the European Union’s Horizon 2020 research and innovation pro-
gramme under grant agreement No 810115 – DYNASNET, by The Eric and Wendy Schmidt
Fund for Strategic Innovation, Grant G-22-63228, and NSF grant SES-2219575.

CODE AND DATA AVAILABILITY

The pySciSci package and all code used to generate the figures in this publication can be
found on github: https://github.com/SciSciCollective/pyscisci. The data sets referenced are
freely or commercially available as described in the package documentation.

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