RESEARCH ARTICLE
Investigating the division of scientific labor using
the Contributor Roles Taxonomy (CRediT)
Vincent Larivière1,2
, David Pontille3
, and Cassidy R. Sugimoto4
1École de bibliothéconomie et des sciences de l’information, Université de Montréal, Montréal, Québec (Canada)
2Observatoire des sciences et des technologies, Université du Québec à Montréal, Montréal, Québec (Canada)
3Centre de sociologie de l’innovation, Mines ParisTech – CNRS UMR 9217, Paris (France)
4School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, Indiana (USA)
Keywords: authorship, contributorship, CRediT, gender, Public Library of Science
ABSTRACT
Contributorship statements were introduced by scholarly journals in the late 1990s to provide
more details on the specific contributions made by authors to research papers. After more than a
decade of idiosyncratic taxonomies by journals, a partnership between medical journals and
standards organizations has led to the establishment, in 2015, of the Contributor Roles Taxonomy
(CRediT), which provides a standardized set of 14 research contributions. Using the data from
Public Library of Science (PLOS) journals over the 2017–2018 period (N = 30,054 papers), this
paper analyzes how research contributions are divided across research teams, focusing on the
association between division of labor and number of authors, and authors’ position and specific
contributions. It also assesses whether some contributions are more likely to be performed in
conjunction with others and examines how the new taxonomy provides greater insight into the
gendered nature of labor division. The paper concludes with a discussion of results with respect to
current issues in research evaluation, science policy, and responsible research practices.
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1.
INTRODUCTION
Scientific authorship is regularly considered as the primary currency in academia, whether for
hiring, promotions or priority disputes (Biagioli & Galison, 2003; Cronin, 2001; Pontille,
2004). Yet, from the 1950 onwards, issues have been progressively raised about the use of
authorship for attributing scientific capital (Bourdieu, 2001). These issues can be grouped into
three categories. The first one relates to the increasing number of authors per article (Larivière,
Sugimoto et al., 2015; Zuckerman, 1968). In some domains, such as clinical research, genomics,
and high-energy physics—where articles often bear several hundreds or thousands of names in
the byline—identifying respective contributions and, thus, assessing individual researchers’
contributions, is increasingly difficult. Second, with the rise of multidisciplinary projects, the
meanings attributed to authorship—and to name ordering—have multiplied, with unintended
consequences for authorship (Paul-Hus, Mongeon et al., 2017; Smith, Williams-Jones et al.,
2020a, 2020b). The frictions of conventions have sown discord among the participants in
research projects (Wilcox, 1998) and greater confusion has also prevailed among gatekeepers
(Bhandari, Guyatt et al., 2014). Third, scientific research has regularly—and some may argue
increasingly (Azoulay, Furman et al., 2015)—been shaken by cases of fraud. In some alleged
cases, all authors on a work under investigation have asked journals to remove their names from
a n o p e n a c c e s s
j o u r n a l
Citation: Larivière, V., Pontille, D., &
Sugimoto, C. R. (2020). Investigating
the division of scientific labor using the
Contributor Roles Taxonomy (CRediT).
Quantitative Science Studies, 2(1),
111–128. https://doi.org/10.1162
/qss_a_00097
DOI:
https://doi.org/10.1162/qss_a_00097
Received: 12 June 2020
Accepted: 22 November 2020
Corresponding Author:
Vincent Larivière
vincent.lariviere@umontreal.ca
Handling Editor:
Ludo Waltman
Copyright: © 2020 Vincent Larivière,
David Pontille, and Cassidy R.
Sugimoto. Published under a Creative
Commons Attribution 4.0 International
(CC BY 4.0) license.
The MIT Press
Investigating the division of scientific labor using CRediT
publications. Such a systematic denial of responsibility has gone to the point that certain articles
have found themselves “orphaned” (Rennie & Flanagin, 1994).
Considering these different aspects as undermining factors in the fair attribution of scientific con-
tributions, researchers, journal editors, research administrators, and members of funding bodies have
been looking for alternative ways to assign authorship. This issue has been of particular concern in the
biomedical sciences, given the immediate public concerns that occur when research lacks transpar-
ency. Over the last few decades, discussions and debates have taken place in major journals, several
workshops have been organized, and an “authorship task force” group was formed to imagine
better ways of attributing credit for scholarly publications (Davidoff, 2000). This collective explo-
ration has resulted in at least two concomitant phenomena: the development of more precise
vocabulary for authorship malpractice and the development of new authorship attribution devices.
A new vocabulary has progressively emerged to characterize controversial authorship practices
(Pontille, 2016; Sismondo, 2009). Omission of a researcher who contributed significantly to the
project—ghost authorship—is one of the most frequent of transgressions and also one of the most
difficult to count (given that omission is often of junior scholars or more technical contributors who
may lack capital in science). This is particularly problematic in some disciplines; for example, a
survey of biomedicine suggests that about one-fifth of all papers exhibit ghost authorship (Wislar,
Flanagin et al., 2011). Ghost authorship is also fairly common in industry-initiated trials, where most
ghost authors are statisticians (Gøtzsche, Hróbjartsson et al., 2007). This may be less malicious than
other forms of authorship misconduct and more of a reflection of differing forms of capital exchange
between industry and academe. However, the more pernicious relative of ghost authorship is ghost
management of research by pharmaceutical companies (Sismondo & Doucet, 2010). These prac-
tices demonstrate the flip side of authorship: Where ghost authorship calls attention to the lack of
rewards for the author, ghost management highlights the issues that arise when there is no transpar-
ency in accountability. Honorary authorship falls on the other side of the coin: providing reward
where there was no labor. Two forms of this have been identified: guest and gift authorship.
Guest authorship designates already recognized names that stand as a sign of quality and potentially
increase the chances for the article to be published (Haeussler & Sauermann, 2013). Gift authorship
sets up a principle of reciprocal exchange between colleagues, resulting in the inclusion of people as
authors regardless of their actual contribution (Smith, 1994; Street, Rogers et al., 2010). Levels of
honorary authorship on scholarly papers have been reported as between 20% and 40% (Flanagin,
Carey et al., 1998; Hardjosantoso, Dahi et al., 2020; Mowatt, Shirran et al., 2002).
To mitigate instances of misconduct, new attribution devices have been proposed. For instance,
Richard Horton, editor in chief of the Lancet, suggested that the relationship between journal editors
and researchers be conceived as a legal contract, each of the parties being held up to mutual engage-
ments (Horton, 1997). The proposal that received the most attention, however, was the systematic
description, in scholarly articles, of each author’s contribution (Rennie, Yank, & Emanuel, 1997). This
approach allows both readers and editors to identify precisely which work was done by individual
researchers. Explicitly based on suggestions made during the previous decade (Moulopoulos, Sideris,
& Georgilis, 1983; Saffran, 1989), the concept of “contributorship” was aimed at better distinguishing
credit and responsibility, two interrelated features of authorship (Birnholtz, 2006). Such contributor-
ship statements were the focus of experiments before they were finally introduced in the “instructions
to authors” of several biomedical journals (Northridge, 1998; Rennie, Flanagin, & Yank, 2000; Smith,
1997) and the recommendations of regulatory authorities, such as the International Committee of
Medical Journal Editors (ICMJE) and Committee on Publication Ethics (COPE).
Linked to a conception of research activity heavily influenced by accountability, these contri-
butorship statements allowed for both finer recognition of and responsibility for the specific tasks
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Investigating the division of scientific labor using CRediT
performed, but also assumed that the research process can be segmented into different acts that can
be properly ascribed to individual contributors. The segmentation of scientific contributions was
not introduced by contributorship, but rather emerged from researchers who have proposed taxon-
omies in response to Moulopoulos et al.’s (1983) work. These idiosyncratic taxonomies differed in
the number of contributions listed (from six to 15) and their degree of accuracy. For example,
“writing up the paper” was sometimes considered as one contribution, while in other taxonomies
it was supplemented with “critical revision of manuscript,” or even split into “writing the first
draft of the paper,” “writing later draft(s),” and “approving final draft” (Goodman, 1994).
Biomedical journals were the main drivers of new taxonomies. Two peculiarities have resulted
from this. First, these taxonomies are characterized by research task contributions clearly specific
both to the biomedical sciences (“collecting samples or specimens,” “providing DNA probes”) and
clinical research (“referred patients to study,” “provision of study materials or patients”). Second,
there are significant differences in not only the number of contributions from one journal to another
but also the variations in contribution taxonomies and their organization (Bates, Anic(cid:1) et al., 2004;
Baerlocher, Gautam et al., 2009; McDonald, Neff et al., 2010). Journals request contributions in
free-text form, organized as a predefined list of research tasks to choose from, or even as hierarchi-
cal items that make some contribution roles a prerequisite for others. As these taxonomies evolve,
studies have investigated the relationship between the structure of these forms, the number of con-
tributions described, and the differences in perception among coauthors of the same article
(Ilakovac, Fister et al., 2007; Ivaniš, Hren et al., 2008, 2011; Marušic(cid:1), Bates et al., 2006).
Early taxonomies paved the way for large-scale empirical studies of authorship practices in
science. For instance, Larivière, Desrochers et al. (2016) analyzed contributorship statements—
divided into five contributions—for 87,002 papers published in all Public Library of Science
(PLOS) journals, focusing on labor distributions across disciplines, authors’ order, and seniority.
They showed that the division of scientific labor is higher in medical research than in natural sci-
ences, and that in all domains but medicine, the most common task among authors was drafting
and editing of the manuscript. Results of this and subsequent analyses (Macaluso, Larivière et al.,
2016) also showed strong distinction between tasks performed and author characteristics: Younger
researchers and women were more likely to perform technical contributions, whereas older, male
researchers were more often associated with conceptual contributions. Authors’ order was also
strongly associated with number of contributions: First authors were generally associated with
the vast majority of contributions, followed by last authors—who generally were not involved
in technical work—and then by middle authors, whose contributions were fewer and more likely
to be technical (Larivière et al., 2016).
These findings were confirmed by Sauermann and Haeussler (2017), who analyzed more than
12,000 articles published between 2007 and 2011 in PLOS ONE. As with Larivière et al. (2016),
they found that first and last authors were associated with more contributions than middle authors.
In an examination of team size, they demonstrated that the number of contributions per author
decreases with the number of authors, but remains stable for last authors. They complemented this
analysis with a survey of 6,000 corresponding authors from these papers. Their findings suggest
that a majority of corresponding authors believe that contributorship statements provided more
information about the contribution, but only a minority think that contributorship provides more
information on the importance of contributions. Furthermore, they found that in one-fifth of
papers, contributorship statements were determined by the corresponding authors alone.
Sauermann and Haeussler (2017) suggested that it was difficult to predict the contribution based
on author order alone. Corrêa, Silva et al. (2017)—also using the PLOS ONE data set—confirmed
this uncertainty between authors’ order and contributions made. Using a network-based approach,
Quantitative Science Studies
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Investigating the division of scientific labor using CRediT
they found that the relationship becomes increasingly random as the number of authors per
paper increases. They also provided evidence of how division of labor increases as the number
of authors increases and showed that contributions can be grouped into three categories: those
who write, those who perform data analysis, and those who conduct experiments.
These studies provided novel insight on the relationship between authorship and one coarse-
grained contributorship taxonomy. However, the previously used five contributorship categories fail
to account for the complexity of contemporary science. To address the need for a more refined
taxonomy, an International Workshop on Contributorship and Scholarly Attribution was organized
at Harvard in May 2012 at the initiative of the Wellcome Trust (IWCSA, 2012). One outcome was a
pilot project involving publishers, funders, and scientists to design a cross-disciplinary standardized
taxonomy for contributor roles and contribution types, which would be practicable for all scientific
fields. The goal was to be interoperable with different databases and to reduce the many ambiguities
that remain with earlier contributorship typologies. In the eyes of its promoters, this standardized
taxonomy would not only codify the contributions of each researcher with fine granularity, allowing
for specific skills to be easily identified, but also rely on an infrastructure to manage the complex
relationships between the information, its archiving, and its consultation in real time.
An initial prototype comprised of 14 types of contribution roles was designed and tested among
corresponding authors of work published in various (mostly biomedical) journals (Allen, Scott et al.,
2014). Based on the positive result of this experiment, a partnership with two information industry
standards organizations (Consortia Advancing Standards in Research Administration Information
(CASRAI) and the U.S.-based National Information Standards Organization (NISO)) was established
to achieve broader consultation and to refine the preliminary taxonomy. An updated version of the
taxonomy was made public in 2015 under the name CRediT (Contributor Roles Taxonomy) to
provide “a controlled vocabulary of contributor roles” (Brand, Allen et al., 2015) for published
research outputs.
The introduction of CRediT provides more details on the division of scientific labor than was
given with previous contributorship taxonomies. First, not only may a given role be assigned to
multiple contributors, but when this is the case, a degree of contribution may optionally be
specified as “lead,” “equal,” or “supporting.”1 The granularity of contribution roles is thus more
precise and the same contribution role can be prioritized among contributors. Second, the 14 con-
tribution roles go beyond the commonly identified research tasks in traditional authorship. They
notably include various roles related to research data, such as “resources” (provision of study ma-
terials, reagents, materials, patients, laboratory samples, animals, etc.), “data curation” (annotation,
scrubbing, and maintenance), “software” (programming, software development, designing com-
puter programs, etc.), or “visualization” (preparation, creation and/or presentation of the published
work, specifically visualization/ data presentation). Third, the writing process is divided into two
main roles, “original draft” and “review and editing,” introducing nuance in this primary contribu-
torship role. With these improvements, CRediT is suited to account for both the division of scientific
labor and the allocation of individual contributions.
PLOS adopted CRediT in 2016 (Atkins, 2016). By the end of 2018, more than 30,000 articles
had employed this new taxonomy. In this paper, we provide an examination of these articles to
investigate whether the more fine-grained analysis provides a more nuanced portrait of division of
labor than was possible with previous taxonomies. More specifically, we examine how research
contributions are divided across research teams, focusing on the association between number of
authors and division of labor, and on the relationship between authors’ position and specific tasks
1 Although this is included in CRediT, these distinctions were not given in the data provided by PLOS for our
analysis.
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Investigating the division of scientific labor using CRediT
Table 1. Definition of each contribution found in the Contributor Roles Taxonomy (CRediT)2
Contribution
Conceptualization
Ideas; formulation or evolution of overarching research goals and aims.
Definition
Data curation
Management activities to annotate (produce metadata), scrub data and maintain research data (including
software code, where it is necessary for interpreting the data itself ) for initial use and later reuse.
Formal analysis
Application of statistical, mathematical, computational, or other formal techniques to analyze or synthesize
study data.
Funding acquisition
Acquisition of the financial support for the project leading to this publication.
Investigation
Conducting a research and investigation process, specifically performing the experiments, or data/evidence
collection.
Methodology
Development or design of methodology; creation of models.
Project administration Management and coordination responsibility for the research activity planning and execution.
Resources
Provision of study materials, reagents, materials, patients, laboratory samples, animals, instrumentation,
computing resources, or other analysis tools.
Software
Programming, software development; designing computer programs; implementation of the computer code
and supporting algorithms; testing of existing code components.
Supervision
Oversight and leadership responsibility for the research activity planning and execution, including
mentorship external to the core team.
Validation
Verification, whether as a part of the activity or separate, of the overall replication/reproducibility of
results/experiments and other research outputs.
Visualization
Preparation, creation and/or presentation of the published work, specifically visualization/data presentation.
Writing—original
draft
Writing—review
& editing
Preparation, creation and/or presentation of the published work, specifically writing the initial draft
(including substantive translation).
Preparation, creation and/or presentation of the published work by those from the original research group,
specifically critical review, commentary or revision—including pre- or postpublication stages.
performed. We also consider the association between each of the 14 contributions, to assess
whether some contributions are more likely to be performed in conjunction with others.
In their review of the taxonomy, Allen, O’Connell, and Kiermer (2019) identify how CRediT can
be a useful tool in science of science. As they state: “If we can understand how collaborations work
and when, or how to optimize the best team mix, then we may be able to incentivize the sorts of
behaviours and activities that can bring about and accelerate discovery” (p. 74). They particularly
draw attention to the issues of diversity in team composition and how contributorship studies can
provide insights into how to best support women and early career researchers as they progress in
science. Therefore, we also explore how the new taxonomy provides greater insight into the gen-
dered nature of science, comparing this with the earlier PLOS typology (Macaluso et al., 2016).
2. DATA SET AND METHODS
Launched in 2014, CRediT categorizes contributions made to scholarly papers into 14 categories
(Table 1). Several journals—such as eLife, Cell, F1000—and publishers—PLOS, Elsevier, Springer,
2 https://casrai.org/credit/
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Investigating the division of scientific labor using CRediT
Table 2. Number of papers published with CRediT contributions, mean number of authors and mean number of CRediT contributions per
paper, by PLOS journal
Journal
PLOS Biology
PLOS Computational Biology
PLOS Genetics
PLOS Medicine
PLOS Neglected Tropical Diseases
PLOS ONE
PLOS Pathogens
All journals
No. papers
13
763
786
250
1,144
27,057
757
30,770
No. papers
in WoS
13
% papers
in WoS
100
Mean No.
authors
7.2
Mean No.
contributions
11.8
754
778
249
1,115
26,398
747
30,054
98.8
99.0
99.6
97.5
97.6
98.7
97.7
4.9
8.5
14.2
9.1
6.8
9.4
7.0
11.1
11.1
10.8
11.1
10.6
11.0
10.6
BMJ—have adopted it or, in the case of major publishers, have seen some of their journals adopt
it. By early 2019, more than 120 journals had implemented the taxonomy (Allen et al., 2019),
a number that increased substantially at the end of 2019 with the adoption of the typology by
1,200 journals from Elsevier (Elsevier, 2019). Our analysis is based on one of these publishers—
PLOS—which provided us with all of its contributorship information for papers published be-
tween June 15, 2017 and December 31, 2018 (N = 30,770). The data covered all PLOS journals
and included publication date, Digital Object Identifier (DOI), journal name, author name as it
appears on the paper, and associated CRediT contributions for each author3.
Table 2 presents the characteristics of the data set. The bulk of the papers were published in
the megajournal PLOS ONE (87.9%), which is the second largest megajournal (Siler, Larivière, &
Sugimoto, 2020). Our data set contains comprehensive data for all journals with the exception of
PLOS Biology, for which contributorship information could only be obtained for 13 papers4.
Important differences are observed in terms of mean number of authors per paper, with PLOS
Computational Biology having, on average, slightly less than five authors per paper, while PLOS
Medicine has almost three times the rates of PLOS Computational Biology. However, the mean
numbers of contributions per paper are quite constant across journals, with a maximum of 11.8
in PLOS Biology and a minimum of 10.6 in PLOS ONE. Given the strong focus on medical sci-
ences of the multidisciplinary journal PLOS ONE (Siler et al., 2020) and of other PLOS journals,
the results need to be interpreted as illustrative of the use of the CRediT taxonomy in those
disciplines.
Contribution information provided by PLOS did not, however, contain author order; to
obtain this information we had to match each PLOS paper with its record in our in-house version
of Clarivate Analytics’ Web of Science based on the DOI; this was feasible for 30,054 papers
(97.7% of the PLOS data set; see Table 2 for percentages by journal), which included 222,938
3 This made the processing of contributorships much more straightforward than what is provided through the
bulk download of the full text of papers in XML format (http://api.plos.org/text-and-data-mining/). See, for
instance, Larivière et al. (2016). In this case, the full names of authors were provided, along with each con-
tribution role, thereby facilitating the author-matching process.
4 A different editorial system for PLOS Biology made it difficult for PLOS to provide us with the data for this
journal. Therefore, while the PLOS Biology contributorship data is included in the global analysis, individual
data for the journal is not provided (i.e., Figures 1 and 2).
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Investigating the division of scientific labor using CRediT
Gender
Gender assigned
Female
Male
Initials
Unisex
Unknown
Total
Table 3. Number of authorships with gender assigned, by author order
First
Middle
Last
Any order
N
26,005
12,094
13,911
382
704
5,462
32,553
%
79.9
37.2
42.7
1.2
2.2
16.8
100.0
N
129,198
52,106
77,092
2,085
3,899
21,847
157,029
%
82.3
33.2
49.1
1.3
2.5
13.9
100.0
N
27,064
8,600
18,464
447
911
3,633
32,055
%
84.4
26.8
57.6
1.4
2.8
11.3
100.0
N
182,267
72,800
109,467
2,914
5,514
30,942
221,637
%
82.2
32.8
49.4
1.3
2.5
14.0
100.0
authorships. Once the papers were matched with the WoS, we matched each author in both data
sources to obtain their individual order in the authors’ list. This was first based on a perfect match of
the full name string (e.g., Derek John de Solla Price = Derek John de Solla Price). However, as
several names could not be matched because they were written in different manners in both da-
tabases (e.g., Derek de Solla Price, Derek J. Price, Derek Price), we performed additional matching
focusing on specific parts of the name string. More specifically, we iteratively focused on the first
and last 2–5 characters of the names; this allowed us to match 221,637 authorships (99.4% of the
sample).
For this subset of authors who could be attributed an author order, we assigned a gender based on
their given names. Such gender assignation of researchers has become a relatively standard practice
and was shown to obtain relatively high precision and recall (Karimi, Wagner et al., 2016;
Santamaría & Mihaljevic(cid:1), 2018). In this paper, we used the algorithm developed in Larivière, Ni,
et al. (2013), which was created using several country-level lists of given names along with their
gender. The algorithm has been tested for precision, and was found to be 98.3% precise for men
and 86.7% for women (see the supplementary material in Larivière et al. (2013) for more details). The
algorithm assigned a gender to 82.2% of the authorships covered in this analysis (Table 3). This
percentage varies by author order, however, with a higher proportion of last authors assigned a
gender, and a lower proportion of first authors. The percentage of female authorships in the
PLOS data set represents 39.9% of authorships to which a gender could be assigned, which is
slightly greater than the percentage of female authorships found in the WoS for disciplines of
the medical sciences (about 35%).
3. RESULTS
Figure 1 presents, for each PLOS journal, the percentage of papers on which each contribution
appears. This provides an indication of importance of each task across the spectrum of PLOS jour-
nals and, conversely, of the tasks that are not performed by any of the authors on a given paper.
Nearly all papers had an author writing the original draft (99%), as well as authors reviewing and
editing (96%) and conceptualizing (95%) them. This suggests that these remain essential research
acts—all papers are conceptualized and written. The percentages of papers with at least one
author contributing to formal analysis (91%), methodology (90%), and investigation (86%) are also
very high, suggesting that empirical papers are the bulk of those published in these journals. The
supervision task is contained in 84% of papers; the 16% of papers without such a task likely do not
include trainees as coauthors. Data curation is present in 79% of papers—although this
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Figure 1. Percentage of papers with specific CRediT contribution, by journal (30,054 papers published in 2017 and 2018).
percentage is higher in journals like PLOS Medicine—and 70% of papers contain project admin-
istration and funding acquisition, with the latter task accounting for a higher percentage in PLOS
Pathogens and PLOS Genetics. Resources, validation, and visualization are present in about half
of all papers. Software contribution appears in less than 40% of papers, except in PLOS
Computational Biology, where it is found in almost three-quarters of papers.
To assess division of labor across authors, we compiled, for each journal, the percentage of
authors who performed a given contribution. As shown in Figure 2, the majority of authors con-
tribute to writing—review and editing (68%), as well as methodology (55%), investigation (53%),
and conceptualization (51%). Worth mentioning is the fact that 95% of authors from PLOS
Medicine have contributed to the review and editing of the manuscript; this is likely due to the
second criterion of the ICMJE which states that all authors should have “[drafted] the work or
[revised] it critically for important intellectual content” (International Committee of Medical
Figure 2. Percentage of authors who performed a given CRediT contribution (when contribution appears on the paper), by journal (30,054
papers published in 2017 and 2018).
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Journal Editors, 2019, p. 2). All other CRediT contributions were, on average, performed by a
minority of authors. Formal analysis, data curation, and validation were, on average, performed
by 42–45% of authors across all PLOS journals, with higher percentages of authors contributing to
formal analysis at PLOS Computational Biology and PLOS Genetics, as well as a higher share of
authors contributing to validation at PLOS Computational Biology. Contrary to what was observed
in the previous typology used by PLOS (Larivière et al., 2016), where more than half of authors (and
as much as 80% in social sciences and physics, among others) had “written the paper,” the writing
of the original draft is a contribution done by a much narrower percentage of authors (39% across
all PLOS journals). Tasks typically performed by principal investigators (resources, supervision,
project administration, and funding acquisition), as well as contributions than can be considered
to be more specialized (visualization and software) are performed by a minority of authors
(between 31% and 38%), with higher percentages of visualization and software for PLOS
Computational Biology.
Figure 3 shows the percentage of men and women, respectively, who have performed a
specific CRediT contribution. The newly adopted taxonomy reinforces some of the initial findings
for gender, particularly the gendered divide between conceptual and empirical work: Although
57% of women contributed to the investigation, this percentage is of 49% for men. A similar gap is
also observed for data curation. Men, on the other hand, are more likely to conduct tasks asso-
ciated with seniority, such as funding acquisition and supervision (30% more likely than women),
contributing resources, software, conceptualization, and project administration. Although such
differences are likely influenced by the fact that women academics are on average younger than
men (McChesney & Bichsel, 2020), other studies have shown that gender differences in contri-
butions remained constant with age as well as with the number of authors per paper (Macaluso
et al., 2016).
A striking feature of CRediT compared to previous studies based on the PLOS typology
(Macaluso et al., 2016) is in the writing of the manuscript. Using the previous PLOS typology,
it appeared that men dominated in the writing of the manuscript. However, the nuanced division
between writing the original draft and doing reviewing and editing demonstrated a delineation
Figure 3. Percentage of male and female authors who have performed a specific CRediT contri-
bution (30,054 papers published in 2017 and 2018).
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between labor roles for men and women: Women are 6% more likely to have written the original
draft, whereas men are 8% more likely to review and edit the manuscript. While those differences
are not necessarily sizeable, the fact that we observe a clear inversion of leading genders in the
two contributions associated with writing is quite striking. This also demonstrates that the original
finding obtained in Macaluso et al. (2016) was skewed by the ubiquity of the “review” portion of
writing. Once the taxonomy isolated original drafting of the text, the contribution of women as
more likely to write the original draft emerges. This suggests that the more nuanced taxonomy
lends greater insight into contrasted divisions of labor.
Division of labor, furthermore, varies as a function of numbers of authors. Figure 4 presents the
percentage of authors who have performed a given task, for papers between 1 and 20 authors (N =
29,689 papers, 96.5% of the data set). Obviously, for single-authored papers, 100% of tasks are
performed by a single author. As the number of authors increases, tasks are increasingly divided—
although the extent to which they are varies as a function of the tasks involved. In other words,
while some tasks are performed by a smaller proportion of authors as the number of authors
increases, other tasks remain relative stable once a certain threshold is met. For instance, the
writing—review and editing task remains performed by a high percentage of authors (i.e., more
than half of authors), even when there are 20 authors on a paper. In a similar manner, the propor-
tion of authors who contribute to investigation stabilizes once 10 authors are reached, with, again,
about half of authors contributing to the task. Other tasks, however, are increasingly divided as the
number of authors increases. For instance, the proportion of authors who perform supervision and
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Figure 4. Percentage of authors who performed a given CRediT contribution, by number of authors,
for papers between 1 and 20 authors (N ¼ 29,689 papers). Inset: mean number of authors who per-
formed a subset of CRediT contributions (writing—review and editing, investigation, writing—original
draft, and project administration).
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writing of the original draft—among others—decreases steadily as the number of authors in-
creases, which suggests, as shown in the inset, that these tasks remain performed by a few authors.
More specifically, even in papers by 20 authors, between three and four authors have been in-
volved in those two tasks.
As shown with the previous PLOS typology, there is a strong relationship between authors’
order and tasks performed (Larivière et al., 2016; Sauermann & Haeussler, 2017). Figure 5 presents
the percentage of authors who have performed a given CRediT contribution, as a function of their
order on the byline of the article (first, middle, last). Taken globally, the figure shows an inverse
relationship between the tasks performed by first authors and the tasks performed by last authors.
More specifically, first authors are much more likely to write the original draft of the manuscript,
curate the data, and perform the formal analysis, visualization, and investigation, as well as
contribute to the methodology. Globally, the mean number of tasks to which first authors
contribute is higher for first authors, followed by last authors, and then by middle authors. Last
authors, on the other hand, are much more likely to have contributed to supervision, funding,
resources, and project administration. Conceptualization, and reviewing and editing of the man-
uscript, are performed by both first and last authors in relatively similar proportions, although last
authors are slightly more likely to have performed the tasks. There are no tasks that middle authors
are more likely to perform than first and last authors. However, there are a few tasks where their
participation is relatively more important: They are more likely to contribute to supervision and to
resources than first authors, and more likely to contribute to data curation, investigation, and soft-
ware than last authors.
Figure 6 presents the contributions that are the most likely associated with each other (i.e.,
performed by the same authors), as well as the asymmetry of these relationships. More specifi-
cally, it shows the percentage of authors who have performed contribution A who have also
performed contribution B. For example, the figure shows that, although 93% of authors who
have contributed to the funding acquisition have reviewed and edited the manuscript, only
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Figure 5. Percentage of authors who performed CRediT contribution, by authors’ order.
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Figure 6. Percentage of authors who have performed contribution A who also have performed contribution B.
46% of authors who reviewed and edited the manuscript have acquired funding. This relation-
ship is among the most asymmetrical, along with software, project administration, visualization,
resources, and supervision, on the one hand, and their relationship with reviewing and editing
the manuscript. That is not surprising: Writing and editing the manuscript is a task that most au-
thors perform, irrespective of their other contributions to the manuscript. At the other end of the
spectrum, funding acquisition is the contribution that has the lowest relationship with other
tasks, except with supervision and project administration. A similar phenomenon is observed
for supervision, project administration, and resources. Software also has little relation with other
tasks, except for visualization.
4. DISCUSSION
Our analysis has delved into the ways in which scientific labor is accounted using a more refined
contributorship taxonomy than was previously available. While confirming several previous
findings (Corrêa et al., 2017; Larivière et al., 2016; Sauermann & Haeussler, 2017), the research
has provided novel information on the composition and distribution of labor across teams. For
example, contributorship information reveals the types of labor that are critical for producing
scientific research: Almost all research articles include conceptualization, operationalization,
and communication through writing. Deviations by discipline, however, reveal the importance
of other more niche tasks, such as visualization and software, acknowledged in certain domains.
These findings suggest greater heterogeneity in evaluation processes to attend to the importance
of tasks by discipline. Privileging one type of labor will inevitably lead to inequities across disci-
plines, where specific tasks performed remain either nonperformed or unacknowledged through
authorship and contributorship. Furthermore, both the heterogeneity of labor types and the
number of contributions per paper suggests that mentoring and doctoral education may need
to be reconfigured to address the changing composition of team science (Sugimoto, 2016).
The bureaucratization of science can be considered as an inevitable consequence of the ubiq-
uity of collaborative science (Larivière et al., 2015). As team size increases, the mean number of
authors contributing to investigation, for instance, also increases, which suggest that the expansion
of teams is largely a function of the increasing number of researchers who contribute to technical
tasks, and of the acknowledgment that this contribution warrants authorship (Shapin, 1989). This is
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not associated with a concomitant rise in those who have written papers’ first drafts or supervisors:
There can only be a few supervisors and original authors, but there is a constant expansion in other
forms of labor, recognized through authorship (Pontille, 2016). As Shapin (1989) observed:
“Scientists’ authority over technicians typically means that it is the former who decide how offi-
cially to arrange the relationship, whether to ‘make them’ authors or coauthors, what counts as
genuine knowledge as opposed to mere skill, and what technicians’ work signifies in scientific
terms” (p. 562). Our research suggests that, despite the steep increase in number of authors, the
number of scientific leaders remains small (Robinson-Garcia, Costas et al., 2020). Such division of
labor and capital reinforces scientific hierarchies and cumulative advantages (Merton, 1968). Our
investigation of the current multiple authorship practices and contributorship distributions illumi-
nates the selective attribution process among coauthors, wherein having one’s name in an article
byline does not equate to or result in leadership positions. Consequently, the growing proportion
of “supporting authors” (Milojevic, Radicchi, & Walsh, 2018) has strong implications for the
composition of the scientific workforce.
The high proportion of data curation—present in 79% of papers—draws attention to a heavily
overlooked labor role in science. The majority of articles involve this task, but there is relatively
little training provided to doctoral students, nor are many scientists prepared to engage in this.
With the increasing prevalence of calls for open science (e.g., McKiernan, Bourne et al., 2016), it
is essential that data be properly curated for better sharing and transparency. For example, several
countries have established policies requiring the sharing of data created through funded research.
Interviews with scientists, however, have revealed strong social and technical challenges to
fulfilling these mandates (e.g., Borgerud & Borglund, 2020). Data curation work continue to be
widely underresourced, despite increasing calls for data transparency (Leonelli, 2016) and the
overwhelming importance of this work, as demonstrated by our analysis. Future work should
ensure that data curation is both valued and supported in research environments.
Women are more likely to be associated with this data curation, as well as other technical work,
such as investigation, which confirmed results obtained in previous analyses (Macaluso et al.,
2016). However, CRediT provided a much more nuanced way to evaluate the conceptual vs. tech-
nical divisions identified in earlier research (Macaluso et al., 2016). Furthermore, and perhaps
more importantly, the taxonomy elucidated a key difference in one of the main contribution types:
writing. Whereas the original five categories contained a single writing category, where men
dominated, the new classification distinguished between the editing and reviewing and the much
more labor-intensive writing of the first draft. In this distinction, the role of women emerged starkly.
Given that they are underrepresented in first and last authorships, this is particularly striking and
speaks to some of the underlying injustices in the division of labor and calculation of production
(Penders & Shaw, 2020; Rossiter, 1993). This can be critical for the career of women and other
underrepresented minorities. As sociologist Mary Frank Fox (2005) observed: “…until we under-
stand factors that are associated with productivity, and variation in productivity by gender, we can
neither assess nor correct inequities in rewards, including rank, promotion, and salary […]
because publication productivity operates as both cause and effect of status in science […] pro-
ductivity reflects women’s depressed rank and status, and partially accounts for it.” It is no surprise,
therefore, that junior scholars were the most concerned about their representation in contributor-
ship statements and expressed the greatest desire for broad participation in these discussions
(Sauermann & Haeussler, 2017). There is a considerable need for greater transparency about
the career lifecycles and interoperability between systems (Cañibano, Woolley et al., 2019).
The integration of CRediT and ORCID is a useful start to this.
It is clear from the data that contributorship provides a lens to add greater transparency in the
capital exchange for authorship. In addition to providing greater accountability for research,
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contributorship also sheds greater light on the flaws in the current system. Our work demonstrates
a clear division of labor as team size increases and the corresponding isolation of certain contri-
bution types. While this facilitates efficiency and may be necessary for certain types of research, it
inevitably increases the chances of potential misconduct, mistake, or fraud, given that several team
members provide their contributions without direct oversight5. One critical role, therefore, may be
validation. However, this was present in only 55% of papers (performed by 42% of authors). One
may argue that this is merely idiosyncratic interpretations of the contributorship roles, where some
authors may consider validation a part of the “investigation” or “formal analysis.” However, the
task definition is clear: “verification, whether as a part of the activity or separate, of the overall
replication/reproducibility of results/experiments and other research outputs.” The lack of valida-
tion in the PLOS papers reinforces the concerns of the “reproducibility crisis” (Baker, 2016). To
address this, journals could require validation as a mandatory contribution type for empirical
work. Contributorship statements are not without limitation. One strong concern at present is the
assumed relationship between the actual labor and the indicator of this labor in contributorship
statements. Undoubtedly, when scholars mutually ascribe the different tasks of CRediT to
themselves, they maintain the opacity necessary to favor good working relationships
between colleagues and teams. As criteria for authorship vary considerably across disciplines
(Paul-Hus et al., 2017; Pontille, 2004, 2016), so too might the interpretation of contribution
roles. More research is necessary to understand whether CRediT provides a valid representation
of the work.
Another related general concern has simultaneously been raised by some clinical re-
searchers and regulatory bodies regarding these expansive categories: if contributorship re-
moves “much of the ambiguity surrounding contributions, it leaves unresolved the question
of the quantity and quality of contribution that qualify for authorship” (International
Committee of Medical Journal Editors, 2019). As with any system tied to capital, there is likely
to be goal displacement as the taxonomy gains wider acceptance and use. For example, the
disproportionately high degree of PLOS Medicine authors associated with writing and editing
may be less a disciplinary difference and more an adherence to the ICMJE criteria. And, as
some critically emphasized, the contributorship procedure favors pharmaceutical firms that,
without having to pretend to intervene intellectually by figuring in an article byline, could
now become “contributors” and thus avoid allegations of conflicts of interest (Matheson,
2011). This suggests that authors may modify their behavior in order to meet certain require-
ments, norms, or incentives. Further investigations are thus needed to explore such issues.
Despite the accountability it aspires to, any description of scientific contributions, even the
fine-grained provided by CRediT, can never be complete. As Sauermann and Haeussler (2017)
noted, contributorship statements may reduce misconduct while simultaneously leading scien-
tists to avoid association with those tasks with a greater potential for risk. Scientists may also begin
to adopt similar practices of ghost, guest, and gift authorship to contributorship. The systematic
description of work does not, therefore, preclude invisibility, but only displaces it elsewhere. As a
consequence, it leaves ghostwriting of articles and potential honorary contributorship in the
backrooms of scientific research. Contributorship statements are not a panacea for the problems
of authorship misconduct; however, they do contribute to clarifying the contributions that are
sufficiently important to warrant authorship from those that are not. Issues with authorship are
not an indication of problems inherent with the contributorship model, but symptomatic of a larger
5 Some journals (e.g., BMJ ) identify a role for a “guarantor,” who will take responsibility for the entire man-
uscript. This is also the implied role taken by many corresponding authors. It is not, however, made explicit
and is not easily defensible in misconduct cases.
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structural problem in the contemporary scientific community, which is the demand, by both
policymakers and researchers themselves, for procedural ways of assessing excellence and
scientific performance.
5. CONCLUSION
Over the last few decades, transparency in authorship and scholarly publishing have become
increasingly discussed in academe. This is due to several interrelated phenomena. First, biblio-
metric evaluations have become widespread across all countries, and have been applied to the
promotion of individual researchers (Quan, Chen, & Shu, 2017) and to institutions, mostly through
the ever-expanding university rankings (Debackere & Glänzel, 2004). Secondy, the rise in the
number of PhD graduates, linked with the relative stability of faculty positions, is increasing the
competition among new graduates, who are ever more aware—as this is often made explicit—that
publications are the currency that will allow them to find a position. The pressures wrought by this
system have led to several authorship malpractices. There are flagrant acts of “civil disobedience” in
authorship, such as adding humorous fictional coauthors, pets, or celebrities to a paper (Penders &
Shaw, 2020). However, some new authorship issues are more pernicious, such as adding children
as coauthors so that they can begin to build their publication record (Zastrow, 2019), and the
growth of predatory publishing (Grudniewicz, Moher et al., 2019) and publication bazaars
(Hvistendahl, 2013). These latter actions demonstrate how critical authorship is for the reward
structure of science and the misconduct that can arise as a result of these pressures to publish.
By fragmenting scientific production process into clearly distinct tasks, CRediT was designed
to transcend the customary rules specific to name orderings in scientific publications. Information
about the conditions of production of research is being made available in each scientific article,
and the systematic description of contributions according to CRediT is not limited to the author-
ship practices of a particular discipline. On the contrary, it can easily be adjusted to various kinds
of division of scientific labor and their specific hierarchical principles across research teams (e.g.,
a team led by a leader, a project carried out among peers, a multicenter research project). In other
words, CRediT is not at odds with the distinct authorship practices in place across disciplines.
Rather, based on the traceability of individual performance, it provides additional information
on the attribution process. Simultaneously, as other accounting devices (Strathern, 2000), the sys-
tematic description of contributions, especially through CRediT, comes with ambivalence. While
it undoubtedly introduces greater transparency in both reward and accountability related to the
division of labor involved in a published article, it simultaneously fuels a regression of trust at the
root of scientific relations (Pontille, 2015). Put differently, the beneficiaries of the information
made available—especially women and junior scholars—may become the potential victims of
devices that facilitate monitoring and surveillance at the heart of scientific activity.
All of these elements have one point in common: the (sole) emphasis on scholarly publications
as the criterion for research excellence. It seems that, along the way, we have forgotten what drives
researchers to do what we do, and why our societies have made the choice to support us in this
endeavor, which is to discover new things. We have replaced a “taste for science” by a “taste for
publication” (Osterloh & Frey, 2014). As per Gingras (2018), the meaning of scholarly publications
has changed from a unit of (new) knowledge produced, to an accounting—or accountability—unit.
Directly related to CRediT, “the systematic description of contributions leads toward accounting
management for scientific activity. […] As a divisible, accounting unit, each scientific act may even
be associated with a specific amount” (Pontille, 2016: 122). In this way, contributorship does not
dismantle performance-based rewards (Debackere & Glänzel, 2004; Sivertsen, 2010), but rather
serves to bring greater precision in accounting.
Quantitative Science Studies
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COMPETING INTERESTS
The authors have no competing interests.
FUNDING INFORMATION
This research was funded by the Canada Research Chairs program (950-231768).
DATA AVAILABILITY
The PLOS data set can be downloaded from https://doi.org/10.6084/m9.figshare.13277168.v1.
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Quantitative Science Studies
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