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How to Order Authors in Scientific Papers

It’s rare that an article is authored by only one or two people anymore. In fact, the average original research paper has five authors these days. The growing list of collaborative research projects raises important questions regarding the author order for research manuscripts and the impact an author list has on readers’ perceptions.

With a handful of authors, a group might be inclined to create an author name list based on the amount of work contributed. What happens, though, when you have a long list of authors? It would be impractical to rank the authors by their relative contributions. Additionally, what if the authors contribute relatively equal amounts of work? Similarly, if a study was interdisciplinary (and many are these days), how can one individual’s contribution be deemed more significant than another’s?

Why does author order matter?

Although an author list should only reflect those who have made substantial contributions to a research project and its draft manuscript (see, for example, the authorship guidelines of the International Committee of Medical Journal Editors ), we’d be remiss to say that author order doesn’t matter. In theory, everyone on the list should be credited equally since it takes a team to successfully complete a project; however, due to industry customs and other practical limitations, some authors will always be more visible than others.

The following are some notable implications regarding author order.

• The “first author” is a coveted position because of its increased visibility. This author is the first name readers will see, and because of various citation rules, publications are usually referred to by the name of the first author only. In-text or bibliographic referencing rules, for example, often reduce all other named authors to “et al.” Since employers use first-authorship to evaluate academic personnel for employment, promotion, and tenure, and since graduate students often need a number of first-author publications to earn their degree, being the lead author on a manuscript is crucial for many researchers, especially early in their career.
• The last author position is traditionally reserved for the supervisor or principal investigator. As such, this person receives much of the credit when the research goes well and the flak when things go wrong. The last author may also be the corresponding author, the person who is the primary contact for journal editors (the first author could, however, fill this role as well, especially if they contributed most to the work).
• Given that there is no uniform rule about author order, readers may find it difficult to assess the nature of an author’s contribution to a research project. To address this issue, some journals, particularly medical ones, nowadays insist on detailed author contribution notes (make sure you check the target journal guidelines before submission to find out how the journal you are planning to submit to handles this). Nevertheless, even this does little to counter how strongly citation rules have enhanced the attention first-named authors receive.

Common Methods for Listing Authors

The following are some common methods for establishing author order lists.

• Relative contribution. As mentioned above, the most common way authors are listed is by relative contribution. The author who made the most substantial contribution to the work described in an article and did most of the underlying research should be listed as the first author. The others are ranked in descending order of contribution. However, in many disciplines, such as the life sciences, the last author in a group is the principal investigator or “senior author”—the person who often provides ideas based on their earlier research and supervised the current work.
• Alphabetical list . Certain fields, particularly those involving large group projects, employ other methods . For example, high-energy particle physics teams list authors alphabetically.
• Multiple “first” authors . Additional “first” authors (so-called “co-first authors”) can be noted by an asterisk or other symbols accompanied by an explanatory note. This practice is common in interdisciplinary studies; however, as we explained above, the first name listed on a paper will still enjoy more visibility than any other “first” author.
• Multiple “last” authors . Similar to recognizing several first authors, multiple last authors can be recognized via typographical symbols and footnotes. This practice arose as some journals wanted to increase accountability by requiring senior lab members to review all data and interpretations produced in their labs instead of being awarded automatic last-authorship on every publication by someone in their group.
• Negotiated order . If you were thinking you could avoid politics by drowning yourself in research, you’re sorely mistaken. While there are relatively clear guidelines and practices for designating first and last authors, there’s no overriding convention for the middle authors. The list can be decided by negotiation, so sharpen those persuasive argument skills!

As you can see, choosing the right author order can be quite complicated. Therefore, we urge researchers to consider these factors early in the research process and to confirm this order during the English proofreading process, whether you self-edit or received manuscript editing or paper editing services , all of which should be done before submission to a journal. Don’t wait until the manuscript is drafted before you decide on the author order in your paper. All the parties involved will need to agree on the author list before submission, and no one will want to delay submission because of a disagreement about who should be included on the author list, and in what order (along with other journal manuscript authorship issues).

On top of that, journals sometimes have clear rules about changing authors or even authorship order during the review process, might not encourage it, and might require detailed statements explaining the specific contribution of every new/old author, official statements of agreement of all authors, and/or a corrigendum to be submitted, all of which can further delay the publication process. We recommend periodically revisiting the named author issue during the drafting stage to make sure that everyone is on the same page and that the list is updated to appropriately reflect changes in team composition or contributions to a research project.

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Open Access

Correspondence

Author Sequence and Credit for Contributions in Multiauthored Publications

* To whom correspondence should be addressed. E-mail: [email protected]

• Michael E Hochberg,
• Tatyana A Rand,
• Vincent H Resh,
• Jochen Krauss
• Teja Tscharntke, 
• Michael E Hochberg, 
• Tatyana A Rand, 
• Vincent H Resh, 

Published: January 16, 2007

• https://doi.org/10.1371/journal.pbio.0050018
• Reader Comments

Citation: Tscharntke T, Hochberg ME, Rand TA, Resh VH, Krauss J (2007) Author Sequence and Credit for Contributions in Multiauthored Publications. PLoS Biol 5(1): e18. https://doi.org/10.1371/journal.pbio.0050018

Copyright: © 2007 Tscharntke et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors received no specific funding for this article.

Competing interests: The authors have declared that no competing interests exist.

The increasing tendency across scientific disciplines to write multiauthored papers [ 1 , 2 ] makes the issue of the sequence of contributors' names a major topic both in terms of reflecting actual contributions and in a posteriori assessments by evaluation committees. Traditionally, the first author contributes most and also receives most of the credit, whereas the position of subsequent authors is usually decided by contribution, alphabetical order, or reverse seniority. Ranking the first or second author in a two-author paper is straightforward, but the meaning of position becomes increasingly arbitrary as the number of authors increases beyond two. Criteria for authorship have been discussed at length, because of the inflationary increase in the number of authors on papers submitted to biomedical journals and the practice of “gift” authorship [ 3 , 4 ], but a simple way to determine credit associated with the sequence of authors' names is still missing [ 4–7 ] ( http://www.councilscienceeditors.org ).

The situation in our area of research—the ecological and environmental sciences—has changed in recent years. Following informal practices in the biomedical sciences, the last author often gets as much credit as the first author, because he or she is assumed to be the driving force, both intellectually and financially, behind the research. Evaluation committees and funding bodies often take last authorship as a sign of successful group leadership and make this a criterion in hiring, granting, and promotion. This practice is unofficial, and hence not always followed, meaning that sometimes last authors “mistakenly” benefit when they actually are not principal investigators. Moreover, there is no accepted yardstick in assessing the actual contribution of a group leader to given scientific publications [ 8 , 9 ], so interpretation of author sequence can be like a lottery. Hence, one really does not know if being last author means that the overall contribution was the most or least important.

Although reducing evaluation of authors' complex contributions to simple metrics is regrettable, in reality it is already in practice in most evaluation committees. Hence, in our opinion, we need a simple and straightforward approach to estimate the credit associated with the sequence of authors' names that is free from any arbitrary rank valuation. In multiauthored papers, the first author position should clearly be assigned to the individual making the greatest contribution [ 4–6 ], as is common practice. However, authors often adopt different methods of crediting contributions for the following authors, because of very different traditions across countries and research fields, resulting in very different criteria that committees adopt to quantify author's contributions [ 8 , 9 ]. For example, some authors use alphabetical sequence, while others think that the last author position has great importance or that the second author position is the second most important. Still others detail each author's contribution in a footnote.

We suggest that the approach taken should be stated in the acknowledgements section, and evaluation committees are asked to weigh the contribution of each author based on the criteria given by the authors. This would make reviewers aware that there are different cultures to authorship order. The usual and informal practice of giving the whole credit (impact factor) to each author of a multiauthored paper is not adequate and overemphasises the minor contributions of many authors ( Table 1 ). Similarly, evaluation of authors according to citation frequencies means often overrating resulting from high-impact but multiauthored publications. The following approaches may be identified.

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Comparison of the Credit for Contributions to This Paper under the Four Different Models Suggested in the Text

https://doi.org/10.1371/journal.pbio.0050018.t001

(1) The “sequence-determines-credit” approach (SDC). The sequence of authors should reflect the declining importance of their contribution, as suggested by previous authors [ 4–6 ]. Authorship order only reflects relative contribution, whereas evaluation committees often need quantitative measures. We suggest that the first author should get credit for the whole impact (impact factor), the second author half, the third a third, and so forth, up to rank ten. When papers have more than ten authors, the contribution of each author from the tenth position onwards is then valuated just 5%.

(2) The “equal contribution” norm (EC). Authors use alphabetical sequence to acknowledge similar contributions or to avoid disharmony in collaborating groups. We suggest that the contribution of each author is valuated as an equal proportion (impact divided by the number of all authors, but a minimum of 5%).

(3) The “first-last-author-emphasis” norm (FLAE). In many labs, the great importance of last authorship is well established. We suggest that the first author should get credit of the whole impact, the last author half, and the credit of the other authors is the impact divided by the number of all authors [as in (2)].

(4) The “percent-contribution-indicated” approach (PCI). There is a trend to detail each author's contribution (following requests of several journals) [ 7 ]. This should also be used to establish the quantified credit.

The SDC approach (as a new suggestion), the EC norm (alphabetical order), the FLAE norm, and the PCI approach may be combined (e.g., FLAE and SDC), but need to be explicitly mentioned in the acknowledgements.

Our suggestion of explicit indication of the method applied, including the simple method of weighing authors' rank in publications in a quantitative way, will avoid misinterpretations and arbitrary a posteriori designations of author contributions. Multidisciplinary scientific collaboration indeed must be encouraged, but we need to avoid misinterpretations so that current and future scientific communities can evaluate author contributions.

Acknowledgments

We applied the SDC approach for the sequence of authors. We are grateful for the stimulating discussions and comments by Jan Bengtsson, Charles Godfray, Bradford A. Hawkins, Christian Körner, William F. Laurance, Bernhard Schmid, Wim van der Putten, and Louise Vet.

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Authorship: Difference Between “Contributor” and “Co-Author”

With an increasing number of researchers and graduates chasing publication opportunities under the pressure of “ publish or perish ,” many are settling for participation in multiple author projects as the first step in building a track record of publications. Over time, the trend of multiple authorship has grown from 3–4 authors of a paper to 6 or more. As those numbers grow, the potential for confusion over responsibilities, accountabilities, and entitlements grows in parallel.

The term “multiple authorship” can be misleading, since the degree to which the workload is apportioned can depend on rank, experience, and expertise. Some participants will earn a place on the team solely on the basis of rank, with the hope that their presence will improve the team’s chances of getting accepted for publication in a prestigious journal. Others will be invited because they authored the original study design, provided the dataset for the study, or even provided the institutional research facilities.

Layers of Authorship

When there are only three or four members on a research paper team, the workload should be fairly easy to divide up, with a corresponding designation of one lead author and two or three co-authors . However, when the size of the team increases, a point is reached when co-authors become contributors. The perception of these titles can vary. New researchers who aspire to official authorship status may see the title of “contributor” as a relegation or demotion in rank, but for other, more experienced researchers, it may simply be a pragmatic recognition of the fact that you may have provided valuable resources but didn’t actually contribute to the writing or editing of the research paper.

Academic Misconduct

The danger in opening up another level of authorship is that journals are now given the opportunity to stuff papers with a few extra authors.

Related: Confused about assigning authorship to the right person? Check out this post on authorship now!

If the journal’s conduct has been flagged as being questionable to begin with—charging high article processing fees (APFs) for publication, delivering suspiciously short turnaround times for peer reviews—how far can they be from colluding with editors to add on a few contributors who had nothing to do with the research paper at all?

Contributorship Statements

As the development of larger research teams or collaborative authorship teams continues, the opportunities for new researchers to get published will hopefully increase too. However, the opportunity should never be looked upon as just getting your name added to the list of collaborators because being on that list comes with responsibilities. For example, if the peer review process flags problems with the data, who will be tasked with responding to that? If the reviewers request a partial re-write and re-submission , who will be tasked with delivering on those requests?

The larger the team, the greater the need for a detailed written agreement that allocates clear responsibilities both pre- and post-submission. This would fulfill two important tasks. First, everyone would know what is expected of them and what the consequences would be for not delivering on those expectations. Second, when the paper is accepted for publication, the agreement could be summarized as a contributorship statement , so that readers are given a clear picture of who did what. In addition, as this trend of multiple authorship continues, grant and tenure committees are starting to request clarification of publication claims, and such a statement would help to delineate precisely what you contributed to the paper.

Hello, How to cite the article of Authorship: Difference Between “Contributor” and “Co-Author”? Warm regards

Thank you for sharing your query on our website. Since we are not aware of the writing style format of the research paper concerned, we would suggest you to refer to the following website : https://www.citationmachine.net/apa/cite-a-website . Accordingly you can choose the style, add the article link and create the relevant citation format for the paper. Please note that proper acknowledgement and citation of the reference is necessary to avoid any issues of plagiarism.

Let us know in case of any other queries.

An impressive share! I’ve just forwarded this onto a coworker who was conducting a little research on this. And he in fact ordered me breakfast simply because I stumbled upon it for him… lol. So allow me to reword this…. Thanks for the meal!! But yeah, thanks for spending some time to talk about this issue here on your website.

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How to Order and Format Author Names in Scientific Papers

As the world becomes more interconnected, the production of knowledge increasingly relies on collaboration. Scientific papers, the primary medium through which researchers communicate their findings, often feature multiple authors. However, authorship isn't merely a reflection of those who contributed to a study but often denotes prestige, recognition, and responsibility. In academic papers, the order of authors is not arbitrary. It can symbolize the level of contribution and the role played by each author in the research process. Deciding on the author order can sometimes be a complex and sensitive issue, making it crucial to understand the different roles and conventions of authorship in scientific research. This article will explore the various types of authors found in scientific papers, guide you on how to correctly order and format author names, and offer insights to help you navigate this critical aspect of academic publishing.

The first author

The first author listed in a scientific paper is typically the person who has made the most substantial intellectual contribution to the work. This role is often filled by a junior researcher such as a Ph.D. student or postdoctoral fellow, who has been intimately involved in almost every aspect of the project.

The first author usually plays a pivotal role in designing and implementing the research, including the formation of hypotheses, experimental design, data collection, data analysis, and interpretation of the findings. They also commonly take the lead in manuscript preparation, writing substantial portions of the paper, including the often-challenging task of turning raw data into a compelling narrative.

In academia, first authorship is a significant achievement, a clear demonstration of a researcher's capabilities and dedication. It indicates that the researcher possesses the skills and tenacity to carry a project from inception to completion. This position can dramatically impact a researcher's career trajectory, playing a critical role in evaluations for promotions, grants, and future academic positions.

However, being the first author is not just about prestige or professional advancement. It carries a weight of responsibility. The first author is generally expected to ensure the integrity and accuracy of the data presented in the paper. They are often the person who responds to reviewers' comments during the peer-review process and makes necessary revisions to the manuscript.

Also, as the first author, it is typically their duty to address any questions or critiques that may arise post-publication, often having to defend the work publicly, even years after publication.

Thus, first authorship is a role that offers significant rewards but also requires a strong commitment to uphold the principles of scientific integrity and transparency. While it's a coveted position that can be a steppingstone to career progression, the associated responsibilities and expectations mean that it should not be undertaken lightly.

The middle authors

The middle authors listed on a scientific paper occupy an essential, albeit sometimes ambiguous, role in the research project. They are typically those who have made significant contributions to the project, but not to the extent of the first author. This group often includes a mix of junior and senior researchers who have provided key input, assistance, or resources to the project.

The roles of middle authors can be quite diverse. Some might be involved in specific aspects of data collection or analysis. Others may bring specialized knowledge or technical skills essential to the project, providing expertise in a particular methodology, statistical analysis, or experimental technique. There might also be middle authors who have contributed vital resources to the project, such as unique reagents or access to a particular patient population.

In some fields, the order of middle authors reflects the degree of their contribution. The closer a middle author is to the first position, the greater their involvement, with the second author often having made the next largest contribution after the first author. This order may be negotiated among the authors, requiring clear communication and consensus.

However, in other disciplines, particularly those where large collaborative projects are common, the order of middle authors may not necessarily reflect their level of contribution. In such cases, authors might be listed alphabetically, or by some other agreed-upon convention. Therefore, it's crucial to be aware of the norms in your specific field when deciding the order of middle authors.

Being a middle author in a scientific paper carries less prestige and responsibility than being a first or last author, but it is by no means a minor role. Middle authors play a crucial part in the scientific endeavor, contributing essential expertise and resources. They are integral members of the research team whose collective efforts underpin the progress and achievements of the project. Without their diverse contributions, the scope and impact of scientific research would be significantly diminished.

The last author

In the listing of authors on a scientific paper, the final position carries a unique significance. It is typically occupied by the senior researcher, often the head of the laboratory or the principal investigator who has supervised the project. While they might not be involved in the day-to-day aspects of the work, they provide overarching guidance, mentorship, and often the resources necessary for the project's fruition.

The last author's role is multidimensional, often balancing the responsibilities of project management, funding acquisition, and mentorship. They guide the research's direction, help troubleshoot problems, and provide intellectual input to the project's design and interpretation of results. Additionally, they usually play a key role in the drafting and revision of the manuscript, providing critical feedback and shaping the narrative.

In academia, the last author position is a symbol of leadership and scientific maturity. It indicates that the researcher has progressed from being a hands-on contributor to someone who can guide a team, secure funding, and deliver significant research projects. Being the last author can have substantial implications for a researcher's career, signaling their ability to oversee successful projects and mentor the next generation of scientists.

However, along with prestige comes significant responsibility. The last author is often seen as the guarantor of the work. They are held accountable for the overall integrity of the study, and in cases where errors or issues arise, they are expected to take the lead in addressing them.

The convention of the last author as the senior researcher is common in many scientific disciplines, especially in the life and biomedical sciences. However, it's important to note that this is not a universal standard. In some fields, authors may be listed purely in the order of contribution or alphabetically. Therefore, an understanding of the specific norms and expectations of your scientific field is essential when considering author order.

In sum, the position of the last author, much like that of the first author, holds both honor and responsibility, reflecting a leadership role that goes beyond mere intellectual contribution to include mentorship, management, and accountability.

Formatting author names

When it comes to scientific publishing, details matter, and one such detail is the correct formatting of author names. While it may seem like a minor concern compared to the intellectual challenges of research, the proper formatting of author names is crucial for several reasons. It ensures correct attribution of work, facilitates accurate citation, and helps avoid confusion among researchers in the same field. This section will delve deeper into the conventions for formatting author names, offering guidance to ensure clarity and consistency in your scientific papers.

Typically, each author's full first name, middle initial(s), and last name are listed. It's crucial that the author's name is presented consistently across all their publications to ensure their work is correctly attributed and easily discoverable.

Here is a basic example following a common convention:

• Standard convention: John D. Smith

However, conventions can vary depending on cultural naming practices. In many Western cultures, the first name is the given name, followed by the middle initial(s), and then the family name. On the other hand, in many East Asian cultures, the family name is listed first.

Here is an example following this convention:

• Asian convention: Wang Xiao Long

When there are multiple authors, their names are separated by commas. The word "and" usually precedes the final author's name.

Here's how this would look:

• John D. Smith, Jane A. Doe, and Richard K. Jones

However, author name formatting can differ among journals. Some may require initials instead of full first names, or they might have specific guidelines for handling hyphenated surnames or surnames with particles (e.g., "de," "van," "bin"). Therefore, it's always important to check the specific submission guidelines of the journal to which you're submitting your paper.

Moreover, the formatting should respect each author's preferred presentation of their name, especially if it deviates from conventional Western naming patterns. As the scientific community becomes increasingly diverse and global, it's essential to ensure that each author's identity is accurately represented.

In conclusion, the proper formatting of author names is a vital detail in scientific publishing, ensuring correct attribution and respect for each author's identity. It may seem a minor point in the grand scheme of a research project, but getting it right is an essential part of good academic practice.

The concept of authorship in scientific papers goes well beyond just listing the names of those involved in a research project. It carries critical implications for recognition, responsibility, and career progression, reflecting a complex nexus of contribution, collaboration, and intellectual leadership. Understanding the different roles, correctly ordering the authors, and appropriately formatting the names are essential elements of academic practice that ensure the rightful attribution of credit and uphold the integrity of scientific research.

Navigating the terrain of authorship involves managing both objective and subjective elements, spanning from the universally acknowledged conventions to the nuances particular to different scientific disciplines. Whether it's acknowledging the pivotal role of the first author who carried the project from the ground up, recognizing the valuable contributions of middle authors who provided key expertise, or highlighting the mentorship and leadership role of the last author, each position is an integral piece in the mosaic of scientific authorship.

Furthermore, beyond the order of authors, the meticulous task of correctly formatting the author names should not be underestimated. This practice is an exercise in precision, respect for individual identity, and acknowledgement of cultural diversity, reflecting the global and inclusive nature of contemporary scientific research.

As scientific exploration continues to move forward as a collective endeavor, clear and equitable authorship practices will remain crucial. These practices serve not only to ensure that credit is assigned where it's due but also to foster an environment of respect and transparency. Therefore, each member of the scientific community, from fledgling researchers to seasoned scientists, would do well to master the art and science of authorship in academic publishing. After all, it is through this collective recognition and collaboration that we continue to expand the frontiers of knowledge.

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• Published: 03 November 2017

Percentage-based Author Contribution Index: a universal measure of author contribution to scientific articles

• Stéphane Boyer   ORCID: orcid.org/0000-0002-0750-4864 1 , 2 ,
• Takayoshi Ikeda 3 ,
• Marie-Caroline Lefort 2 , 4 ,
• Jagoba Malumbres-Olarte 5 , 6 &
• Jason M. Schmidt 7  

Research Integrity and Peer Review volume  2 , Article number:  18 ( 2017 ) Cite this article

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Metrics details

Deciphering the amount of work provided by different co-authors of a scientific paper has been a recurrent problem in science. Despite the myriad of metrics available, the scientific community still largely relies on the position in the list of authors to evaluate contributions, a metric that attributes subjective and unfounded credit to co-authors. We propose an easy to apply, universally comparable and fair metric to measure and report co-authors contribution in the scientific literature.

The proposed Author Contribution Index (ACI) is based on contribution percentages provided by the authors, preferably at the time of submission. Researchers can use ACI to compare the contributions of different authors, describe the contribution profile of a particular researcher or analyse how contribution changes through time. We provide such an analysis based on contribution percentages provided by 97 scientists from the field of ecology who voluntarily responded to an online anonymous survey.

ACI is simple to understand and to implement because it is based solely on percentage contributions and the number of co-authors. It provides a continuous score that reflects the contribution of one author as compared to the average contribution of all other authors. For example, ACI(i) = 3, means that author i contributed three times more than what the other authors contributed on average. Our analysis comprised 836 papers published in 2014-2016 and revealed patterns of ACI values that relate to career advancement.

There are many examples of author contribution indices that have been proposed but none has really been adopted by scientific journals. Many of the proposed solutions are either too complicated, not accurate enough or not comparable across articles, authors and disciplines. The author contribution index presented here addresses these three major issues and has the potential to contribute to more transparency in the science literature. If adopted by scientific journals, it could provide job seekers, recruiters and evaluating bodies with a tool to gather information that is essential to them and cannot be easily and accurately obtained otherwise. We also suggest that scientists use the index regardless of whether it is implemented by journals or not.

Peer Review reports

Deciphering the role and quantifying the amount of work provided by different co-authors of a particular paper has been a recurrent problem for the scientific community [ 1 , 2 , 3 ]. The position in the list of authors is commonly used to infer co-authors’ contribution and a number of systems have been proposed on this basis. They range from simple calculations based on the rank of the authors such as harmonic authorship credit, fractional authorship credit and inflated authorship [ 4 ] to more complex credits (e.g. [ 5 ]), some even taking into account the controversial journal impact factor [ 2 ]. However, these metrics are essentially ‘one fits all’ approaches that assume the contribution of each author based on their position in the author list and attributes subjective and unfounded values to these positions. As such, they do not attempt to represent and quantify ‘true’ contribution. Despite the growing interest in resolving the issue of authorship contributions in scientific disciplines [ 3 , 4 , 6 ], no standard ranking system has been widely recognised or adopted by scientific journals. With this lack of consensus, some journals have implemented a compulsory or recommended section dedicated to reporting authors’ contribution.

A review of the top 150 ecology journals referenced in ISI Web Of Knowledge revealed that 13.3% of them require information on author contributions (Additional file  1 ). Authors are usually asked to briefly describe which task was conducted by which co-author. Although this information is valuable, it does not provide an objective, straightforward and universal measure of author contribution. For example, ‘data collection’ for a review article may in some cases simply involve searching a database by filtering papers using specific key words, while it may be a very time-consuming task in field ecology, and a highly technical task in computational ecology. So ‘data collection’ can mean very different things depending on the field of study or the type of paper. In addition, individual tasks are often conducted by multiple authors but there is no way of knowing whether and to what extent one author has contributed more to them. Although some authorship contribution systems propose graded contributions for each task (e.g. lead, equal, supporting role in the CRedIT system [ 7 ], or the three-tier criterion proposed by the International Committee of Medical Journal Editor [ 8 ]), the lack of a quantitative value means these systems lack accuracy and produce qualitative data that is challenging to analyse or compare. The second common limitation is the complexity of the proposed metrics, which deters authors from providing data in the first place. A third major issue is the lack of fairness where often the lead or corresponding author can unilaterally decide on the order of the co-authors and the description of their contribution.

To address these shortcomings, we propose an easy to apply, universally comparable and fair tool to measure and report author contribution.

A simple and accurate measure: percentage contributions

Percentages are straightforward and can be universally applied independent of research field, the number of co-authors or the nature of the paper (e.g. experimental, review, perspective, etc.). Because the authors of a paper are the best placed to make a judgement call about the value of each contribution, it is essential that percentage contributions are determined by authors rather than by a model based solely on the authors’ rank. Although disagreement may occur between co-authors, clarifying contribution among co-authors in the early stages of the research process is likely to ease potential tension [ 9 ] and in some cases prompt ‘real collaboration’. We propose that co-authors discuss and agree on their respective contributions prior to submitting their manuscript and these figures be provided by the corresponding author at the submission stage. By confirming their authorship, all co- authors confirm their agreement with their contributions and that of all other authors. This ensures that every published paper displays percentage contributions that have been discussed and agreed upon by every co-author.

A number of guidelines and best practices have been proposed for authors’ contributions (e.g. [ 10 ]). A possible starting point is to divide 100% by the number of authors and then estimate whether and to what extent each author provided more or less work than the others. The use of author-provided percentages has been proposed before to reflect the contribution of co-authors accurately (e.g. [ 2 ]) but with limited guidance about how to implement it. Verhagen et al. [ 11 ] developed the Quantitative Uniform Authorship Declaration (QUAD) approach, where each author is attributed percentage contributions in four categories: Conception and design, data collection, data analysis and conclusion and manuscript preparation. More recently, a very similar approach was proposed, based on scores rather than percentages with the more specific aim of deciding which contributor deserves authorship and which does not [ 12 ]. Clement [ 13 ] also suggests the use of four categories, albeit slightly different ones (ideas, work, writing and stewardship). However, an overly complicated metric may deter authors from applying it, and the criteria used in calculations must be consistent or have comparable importance across research fields, which may not be applicable to every type of article. In addition, many authors suggest that contributions should be restricted to an arbitrary threshold, for example 50% of the average contribution [ 13 ], 10% of the total work [ 11 ] or a threshold chosen by the authors [ 12 ]. Such limitation is likely to introduce major inconsistencies between papers, journals and fields of research, thereby preventing comparison. In addition, these thresholds limit the number of co-authors, which may affect interdisciplinary research and act as incentives to leave out minor contributors, potentially increasing ghost authorship (i.e. the omission of collaborators who did contribute to the work).

We propose that the contribution of each co-author be summarised in one number which must be more than 0% and less than 100% in multiple-authored papers. This provides a metric that is simpler for authors to determine and for the readers to grasp. In addition, this single metric imposes no upper limit on the number of authors. The percentage contribution should be displayed on the published paper either as raw numbers or as a figure (Fig.  1 ).

Examples of a table ( a ), text ( b ) or figure ( c ) that could be used to display author contribution percentages for a given paper. Data correspond to author contributions for the current paper

A universally comparable metric: percentage-based author contribution index (ACI)

An outstanding limitation of percentage contributions is that they are difficult to compare across different papers because with more co-authors, it is mathematically difficult to obtain high percentages. As a consequence, author contributions cannot be directly compared between articles with unequal numbers of authors. To allow such comparisons, we propose a universal metric that takes into account the number of co-authors: the Author Contribution Index (ACI), calculated from the percentage contribution as per Eq. ( 1 ).

where for author i ,

Ci = contribution of author i in percentage (must be > 0 and < 1)

n = total number of authors including i (must be > 1).

ACI( i ) reflects the contribution of author i as compared to the average contribution of all other authors. It is superior to one when the contribution of author i is larger than the average contribution of all other authors and inferior to one when the contribution of author i is less than the average contribution of all other authors. For example, on a paper written by three authors, where author i contributed 60% of the paper, ACI( i ) = 3, meaning that author i contributed three times more than what the other authors contributed on average. Another useful metric is log 10 (ACI), which is positive when the author’s contribution is larger than the average contribution of all other authors and negative when the author’s contribution is less than the average. This metric is particularly useful to normalise data for further comparison and statistical analyses.

The graph in Fig.  2 displays the universe of all possible ACIs for papers written by up to 200 co-authors. The contribution profile of a particular author can be displayed in the universe of possible ACIs by adding dots, each representing one paper from the author being analysed. From these data, author profiles may appear according to a variety of criteria such as time, author’s seniority, area of research and type of institution where the author works, among others. Based on Eq. ( 1 ), it is also possible to calculate average ACI for an individual author or to plot the ACI frequency distribution of an individual author based on all or specific parts of his publications.

Universe of possible ACIs. X-axis: total number of authors including ( n  = 2 to n  = 200); Y-axis: percentage contribution of author (=0.001 to =0.999); Z-axis: author contribution index for author (see Eq. ( 1 )). Colours correspond to the value of ACI (see coloured scale on the right)

One notable feature of ACI is that it increases with the proportion of work produced but also with the number of ‘minor’ co-authors (Fig.  2 ). By giving more weight to main contributors of papers with many co-authors, ACI recognises the skills required and work involved in leading large collaborative projects. Figure  3 provides examples of how ACIs could be displayed in a paper.

Examples of table ( a ) or figures ( b, c, d ) that could be used to display the author contribution index (ACI) for a given paper. Data corresponds to author contributions for the current paper

A fair tool: assisting job seekers, recruiters and performance-based evaluations

The scientific community seems to have reached the consensus that journal impact factors are not an accurate measure of the value of a particular article or the value of its author(s) [ 14 ]. One of the main reasons is that a very highly ranked journal may publish few articles that are heavily cited, but it may also publish a number of papers that have very little impact. In recent years, article-based impact has been preferred to journal impact factor. For example, the Hirsch index (h-index), which is based on the number of citations of one’s papers, is now widely used to gauge the output of a scientist. However, the h-index can also be manipulated [ 15 ] and it does not provide a measure of the amount of work produced by each co-author, which means guest or honorary authorship (i.e. inclusion of authors who did not contribute to the work) cannot be accounted for. This issue of guest authorship has been denounced widely in medical and clinical science [ 16 ], but other research fields are not immune to the problem (e.g. in ecology [ 17 ], environmental science [ 18 ], geography [ 19 ], geology [ 20 ], etc.). Since genuine authors suffer no cost when they add co-authors, papers tend to have more and more co-authors [ 21 , 22 ]. With percentage contributions, the amount of work invested in a paper is a finite value (100%). Therefore, when more authors are added as a ‘gift’, they all need to be attributed a percentage of the work. In this zero-sum game, either it will be visible that guest authors have contributed an extremely small proportion of the work—and should receive very little recognition—or the genuine authors will have to give away large chunks of their well-deserved credit.

To ensure fair values are reported, we propose that co-authors discuss and agree on their respective contributions prior to submitting their manuscript and these figures be provided by the corresponding author at the submission stage. By confirming authorship, all co-authors confirm their agreement with their contributions and that of all other authors. This ensures that every published paper displays percentage contributions that have been discussed and agreed upon by every co-author.

The sentence-based descriptions of authors’ contribution that are used by some journals provide an indication of tasks performed by each author. Maintaining and generalising this practice as a detailed record of the role of each author is essential to increasing transparency. However, obtaining a clear idea of the amount of work a scientist is actually providing is difficult if one needs to read through all the authors’ contribution sections and weigh in the topic, the type of paper, the number of co-authors, etc. The proposed index has the potential to complement descriptive authorship information sections with a quantitative measure, which is much easier to analyse, summarise and compare across a large number of publications. For example, ACI can help in sifting through the numerous papers published by one scientist to select only those where this particular author has made a major contribution. This short list of papers could then be analysed in more details, for example using the written author contribution sections.

ACI can provide valuable information for performance-based evaluation processes and could be implemented in existing reporting systems. This includes internal evaluation for career advancement, as well as research productivity evaluation for funding purposes and national-scale ranking schemes (such as the Performance-based Research Fund (PBRF) system in New Zealand or the Research Excellence Framework (REF) in the UK). It is also in the advantage of the candidate to be able to demonstrate his/her actual contribution to a potential recruiter who may ask ‘What have you done on all these papers listed on your CV?’ One could answer such a question by analysing the distribution of a scientist’s ACI and its evolution through time or by calculating and comparing his/her average ACIs in experimental, review and perspective papers. ACI could also be used as an additional metric in network-based collaboration analyses (e.g. [ 23 ]) or to further inform composite citation indicators (e.g. [ 24 ]).

A first look: testing ACI

Here, we provide an analysis of ACI calculated from work published in the past 3 years (January 2014–December 2016) based on contribution percentages provided by scientists who volunteered to respond to an online survey (Additional file  2 ). This survey aims at (1) demonstrating that authors can provide percentage contribution for their work and are willing to do so, (2) demonstrating that there is an interest from the scientific community to provide more transparency in author contribution, (3) exemplifying basic calculations that can be easily performed using ACI values and (4) demonstrating that ACI can be used to understand the scientific production of scientists in relation to their career advancement. Because the contribution percentages were provided after publication and without discussion among co-authors, these values may not be as accurate as if they had been agreed upon by all co-authors prior to publication [ 10 ]. Hence, the aim of this exercise was not to produce a highly accurate dataset, and therefore, the following analysis should be regarded as illustrative.

Respondents were asked to provide their percentage contributions for all the articles they co-authored in the past 3 years. Additional information collected was country of origin, time since research active and job description (Additional file  2 ). The URL address and information about the online survey were distributed through electronic mailing lists, ecological society newsletters and social media. Responses were collected between September 4, 2016, and January 8, 2017. During this timeframe, 97 ecology scientists from 19 different countries completed the survey. Respondents were anonymous and no filtering of respondents was performed other than removing incomplete entries. Data with contradictory or obviously inaccurate information (for example multi-authored publications where the respondent claims 100% of the work) were removed. The final dataset comprised data for 836 publications from 97 ecology researchers. Author contribution indices (ACIs) of the respondents were calculated for each publication using Eq. ( 1 ). ACIs were log-transformed (log 10 (ACI)) to meet the assumptions of normality and all statistical analyses were conducted in R [ 25 ]. Respondents were asked to provide information about the number of years they have been research active. This was defined as the time from first year of PhD study or first published peer-reviewed paper, whichever came first. Linear regression and F statistics were used to analyse ACI in relation to the number of years as an active researcher.

Respondents were categorised in different job positions as follows: postgrad, a postgraduate student; postdoc, a postdoctoral fellow or other non-permanent staff; ECR, a tenure or permanent early-career researcher; PI, a mid-career principal investigator; prof, an associate professor of full professor; other. ACI was analysed in relation to job description using ANOVA with a priori contrasts between non-permanent supervised staff (postgraduate and postdoc) and permanent and independent researchers (ECR, PI and professors), as well as between all early career researchers (postgraduate, postdoc and ECR) and established researchers (mid-career PI and professors).

Results and discussion

ACI varied from 0.0101, which means the author claims to have produced 101 times less work than his/her co-authors have on average, to 168, which the author claims to have produced 168 times more work than his co-authors have on average (Fig.  4 a). Most researchers produced papers with a range of ACI values. Individuals with a majority of high ACI are likely to be drivers of publications, while those with a majority of medium ACI can be regarded as highly collaborative and those with a majority of low ACI may be service providers. The latter may provide assistance with a limited but potentially essential aspect of the research such as sampling, statistical treatment of the data, supervision or mentoring.

Descriptive statistics of author contribution index (ACI, see Eq. ( 1 )) Data from an online anonymous survey completed by 97 authors between 2014 and 2016. a Frequency distribution of ACI (log transformed) from survey respondents. b ACI (log transformed) in relation to research active time. c Average ACI (log transformed) in relation to job description

In our dataset, ACI varies in relation to the number of years as a researcher (F 1,835  = 87.57, p  < 0.0001); however, the correlation remains weak ( r  = 0.306), due to wide variability in ACI (Fig.  4 c). With regards to job descriptions, average ACI was higher for non-permanent supervised staff (postgraduate students and postdoctoral fellows) compared to permanent and independent researchers (early career researchers, mid-career principal investigators and professors) (ANOVA t  = 2.327, p  = 0.0229). A similar difference was observed when comparing all early career researchers (post-graduate students, postdoc and early career researchers) to established researchers (mid-career principal investigators and Professors) (ANOVA t  = 2.546, p  = 0.0132) (Fig.  4 ). As researchers become independent and establish their own research team, they probably start supervising their own students and postdocs and their ACI is likely to decrease accordingly.

It is possible to link ACI with article impact through the number of citations, altmetrics or any other article-based impact metrics, for example, by dividing ACI by the number of citations for a particular paper. By averaging ACIs, one could also summarise the output of a given scientist as a single number as suggested with other indices (e.g. [ 26 ]). However, we do not recommend such practice as it would largely mask the scientist’s output profile, thereby deceiving the purpose of ACI, and it would not be a meaningful way to compare scientist outputs as scientists with very different profiles may reach a very similar average ACI. In our dataset, there are numerous cases in which individuals at very different stages of their career reached a similar average ACI (Fig.  4 , Additional file 3 ).

Limitations and alternative applications

The proposed model requires authors to score precisely their contribution but does not provide a framework for making decisions around the value brought by each co-author. The focus of ACI is on the reporting and dissemination of a quantitative, accumulative and comparable measure of their contributions, after authors have agreed on their percentage contributions. The way these contributions are calculated and agreed upon is likely to vary greatly from one paper to another and does not equate to ‘time spent working on the paper’. For example, tasks requiring specific skills or expertise that are essential to the realisation of the work may rank highly, despite not being the most time-consuming. This apparent subjectivity should, however, be put in perspective with that of existing ranking systems. In any ranking systems, the reader can only place faith in the authors’ good judgement and ethical practices. Authors are the best placed to judge contribution to their own work and must be given the freedom, flexibility and adaptability to choose the percentage contributions that they believe are the most appropriate on a case by case basis.

It is important to note that the issues of arbitrary decisions and unethical behaviour, which may often arise from power dynamics between co-authors (e.g. between senior and junior scientists), will not be immediately resolved by using ACI. However, because percentage contributions have to be ratified by every co-author, groups are compelled to collegially discuss and score their contribution prior to submission, something that usually does not happen. It is expected that the mere fact of openly discussing contribution will reduce unilateral decisions and help to obtain thoughtful and more accurate contribution scores. With increased transparency, both among co-authors and between authors and readers, ethically questionable behaviours may not disappear, but they will become less common and easier to detect and report.

Another crucial limitation of ACI lies in the fact that a critical mass of data is required for the proposed metric to reach its true potential. Yet, its implementation by a significant number of journals or publishers will take a long time, if it is adopted at all. To circumvent this limitation and also generate ACI data from past literature and papers published in journals that will not implement the index, we propose that reference list repositories such as ResearchGate ( researchgate.net ) or ORCID ( orcid.org ) could provide an option for authors to record their percentage contributions to their publications. Because these values may not be vetted by all co-authors (as opposed to percentage contributions provided at the time of submission), several levels of verification should be displayed for each paper. Percentage contributions could be considered as (1) unverified if only one co-author provides them, (2) partially verified if at least a second co-author confirms the numbers and (3) fully verified if all co-authors of a paper confirm the numbers. In addition, we suggest authors to provide percentage contributions in their mansucripts, even if these are not specifically requested by the journal they publish in.

There are many examples of author contribution indices that have been proposed but none has really been adopted by scientific journals. Many of the proposed solutions are either too complicated, not accurate enough or not comparable across articles, authors and disciplines. The author contribution index presented here addresses these three major issues and if adopted by scientific journals, it could significantly clarify the contribution of co-authors. This index is currently implemented in the recently launched journal Rethinking Ecology [ 3 ]. We hope that ACI will be adopted by many other journals to increase transparency in co-authored work and attribute accurate credit to authors. Although the current paper uses ecology as the focus, the proposed index is readily applicable to other scientific fields. Ambiguity around co-author contribution is a systemic issue in scientific publication, and accurate reporting of author contribution would benefit a very large variety of scientists in all fields.

The current paper focuses on the reporting of one percentage contribution and one ACI per paper and per author. Another possibility is to provide percentage contributions (and calculate ACI) for a variety of specific tasks. This would provide even more detailed information by specifying ‘who did how much of what’ on a given paper. However, task-specific ACI would only be useful if the same set of tasks is systematically reported, as proposed in the QUAD or the CReDIT approach, to allow comparisons between papers and meta-analyses.

The proposed ACI index has the potential to contribute to more transparency in the science literature, and if adopted by scientists, it could provide job seekers, recruiters and evaluating bodies with a tool to gather information that is essential to them and cannot be easily and accurately obtained otherwise.

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Acknowledgements

We thank the 97 ecologists who responded to the online survey on author contribution.

No funding was associated with the preparation of this manuscript. Publication fees were funded by CNRS.

Availability of data and materials

The data produced from the online survey is available on the Figshare server (DOI: https://doi.org/10.6084/m9.figshare.5139613 ).

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Contributions

SB designed the concept and drafted the manuscript. SB and TI prepared the figures. SB, TI, JMO and JS arranged the ethical approval and gathered data from scientists in their respective country. MCL reviewed the existing journals’ policy. SB, TI, JMO, JS and MCL contributed to the writing of the final version of the manuscript. Percentage contributions are SB: 55%, TI: 15%, MCL:10%, JMO: 10%, JS: 10%. All authors read and approved the final manuscript.

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Application for human ethics were granted by the Unitec Research Ethics Committee (UREC) (application number: 2016-1062) and the University of Georgia Institutional Review Board (IRB) (application number: STUDY00004056). Our online survey was anonymous and included the following sentence: ‘By answering you agree that the data you provide is used for the above-mentioned analysis and subsequent publication’.

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Additional files

Additional file 1:.

Journal’s policy on authors’ contribution section for the top 150 ecology journals (according to ISI Web of Knowledge). (DOCX 36 kb)

Additional file 2:

Co-authorship survey used to collect data from ecology scientists. (DOCX 23 kb)

Additional file 3:

Raw data obtained from the online co-authorship survey. (CSV 77 kb)

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Boyer, S., Ikeda, T., Lefort, MC. et al. Percentage-based Author Contribution Index: a universal measure of author contribution to scientific articles. Res Integr Peer Rev 2 , 18 (2017). https://doi.org/10.1186/s41073-017-0042-y

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Plagiarism in peer-review reports could be the ‘tip of the iceberg’

• Jackson Ryan 0

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Time pressures and a lack of confidence could be prompting reviewers to plagiarize text in their reports. Credit: Thomas Reimer/Zoonar via Alamy

Mikołaj Piniewski is a researcher to whom PhD students and collaborators turn when they need to revise or refine a manuscript. The hydrologist, at the Warsaw University of Life Sciences, has a keen eye for problems in text — a skill that came in handy last year when he encountered some suspicious writing in peer-review reports of his own paper.

Last May, when Piniewski was reading the peer-review feedback that he and his co-authors had received for a manuscript they’d submitted to an environmental-science journal, alarm bells started ringing in his head. Comments by two of the three reviewers were vague and lacked substance, so Piniewski decided to run a Google search, looking at specific phrases and quotes the reviewers had used.

To his surprise, he found the comments were identical to those that were already available on the Internet, in multiple open-access review reports from publishers such as MDPI and PLOS. “I was speechless,” says Piniewski. The revelation caused him to go back to another manuscript that he had submitted a few months earlier, and dig out the peer-review reports he received for that. He found more plagiarized text. After e-mailing several collaborators, he assembled a team to dig deeper.

Meet this super-spotter of duplicated images in science papers

The team published the results of its investigation in Scientometrics in February 1 , examining dozens of cases of apparent plagiarism in peer-review reports, identifying the use of identical phrases across reports prepared for 19 journals. The team discovered exact quotes duplicated across 50 publications, saying that the findings are just “the tip of the iceberg” when it comes to misconduct in the peer-review system.

Dorothy Bishop, a former neuroscientist at the University of Oxford, UK, who has turned her attention to investigating research misconduct, was “favourably impressed” by the team’s analysis. “I felt the way they approached it was quite useful and might be a guide for other people trying to pin this stuff down,” she says.

Peer review under review

Piniewski and his colleagues conducted three analyses. First, they uploaded five peer-review reports from the two manuscripts that his laboratory had submitted to a rudimentary online plagiarism-detection tool . The reports had 44–100% similarity to previously published online content. Links were provided to the sources in which duplications were found.

The researchers drilled down further. They broke one of the suspicious peer-review reports down to fragments of one to three sentences each and searched for them on Google. In seconds, the search engine returned a number of hits: the exact phrases appeared in 22 open peer-review reports, published between 2021 and 2023.

The final analysis provided the most worrying results. They took a single quote — 43 words long and featuring multiple language errors, including incorrect capitalization — and pasted it into Google. The search revealed that the quote, or variants of it, had been used in 50 peer-review reports.

Predominantly, these reports were from journals published by MDPI, PLOS and Elsevier, and the team found that the amount of duplication increased year-on-year between 2021 and 2023. Whether this is because of an increase in the number of open-access peer-review reports during this time or an indication of a growing problem is unclear — but Piniewski thinks that it could be a little bit of both.

Why would a peer reviewer use plagiarized text in their report? The team says that some might be attempting to save time , whereas others could be motivated by a lack of confidence in their writing ability, for example, if they aren’t fluent in English.

The team notes that there are instances that might not represent misconduct. “A tolerable rephrasing of your own words from a different review? I think that’s fine,” says Piniewski. “But I imagine that most of these cases we found are actually something else.”

The source of the problem

Duplication and manipulation of peer-review reports is not a new phenomenon. “I think it’s now increasingly recognized that the manipulation of the peer-review process, which was recognized around 2010, was probably an indication of paper mills operating at that point,” says Jennifer Byrne, director of biobanking at New South Wales Health in Sydney, Australia, who also studies research integrity in scientific literature.

Paper mills — organizations that churn out fake research papers and sell authorships to turn a profit — have been known to tamper with reviews to push manuscripts through to publication, says Byrne.

The fight against fake-paper factories that churn out sham science

However, when Bishop looked at Piniewski’s case, she could not find any overt evidence of paper-mill activity. Rather, she suspects that journal editors might be involved in cases of peer-review-report duplication and suggests studying the track records of those who’ve allowed inadequate or plagiarized reports to proliferate.

Piniewski’s team is also concerned about the rise of duplications as generative artificial intelligence (AI) becomes easier to access . Although his team didn’t look for signs of AI use, its ability to quickly ingest and rephrase large swathes of text is seen as an emerging issue.

A preprint posted in March 2 showed evidence of researchers using AI chatbots to assist with peer review, identifying specific adjectives that could be hallmarks of AI-written text in peer-review reports .

Bishop isn’t as concerned as Piniewski about AI-generated reports, saying that it’s easy to distinguish between AI-generated text and legitimate reviewer commentary. “The beautiful thing about peer review,” she says, is that it is “one thing you couldn’t do a credible job with AI”.

Preventing plagiarism

Publishers seem to be taking action. Bethany Baker, a media-relations manager at PLOS, who is based in Cambridge, UK, told Nature Index that the PLOS Publication Ethics team “is investigating the concerns raised in the Scientometrics article about potential plagiarism in peer reviews”.

How big is science’s fake-paper problem?

An Elsevier representative told Nature Index that the publisher “can confirm that this matter has been brought to our attention and we are conducting an investigation”.

In a statement, the MDPI Research Integrity and Publication Ethics Team said that it has been made aware of potential misconduct by reviewers in its journals and is “actively addressing and investigating this issue”. It did not confirm whether this was related to the Scientometrics article.

One proposed solution to the problem is ensuring that all submitted reviews are checked using plagiarism-detection software. In 2022, exploratory work by Adam Day, a data scientist at Sage Publications, based in Thousand Oaks, California, identified duplicated text in peer-review reports that might be suggestive of paper-mill activity. Day offered a similar solution of using anti-plagiarism software , such as Turnitin.

Piniewski expects the problem to get worse in the coming years, but he hasn’t received any unusual peer-review reports since those that originally sparked his research. Still, he says that he’s now even more vigilant. “If something unusual occurs, I will spot it.”

doi: https://doi.org/10.1038/d41586-024-01312-0

Piniewski, M., Jarić, I., Koutsoyiannis, D. & Kundzewicz, Z. W. Scientometrics https://doi.org/10.1007/s11192-024-04960-1 (2024).

Article   Google Scholar  

Liang, W. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2403.07183 (2024).

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