Ten simple rules for being a co-author on a many-author non-empirical paper

Friederike E Kohrs, Natascha Drude, Anita Bandrowski and Tracey L Weissgerber

PLOS Computational Biology, 2025, vol. 21, issue 8, 1-10

Abstract: Many-author non-empirical papers include “how to” articles, recommendations or consensus statements, roadmaps for future research, catalogs of ideas, or calls to action. These papers benefit the research community and broader academic ecosystem by addressing unmet needs or introducing new perspectives and approaches. Large, diverse authorship teams that examine an issue from many different perspectives can create valuable resources that individual co-authors could not develop independently, or in smaller groups. Realizing the potential of many-author non-empirical papers, however, requires very different strategies than researchers would typically use to write papers with fewer authors. In our process, a core team of lead writers typically works together to lead the content generation and writing processes, while many co-authors collaboratively create content and provide feedback on outlines and drafts. Challenges for co-authors may include learning to write a different type of paper, adapting to high-volume feedback, and understanding the very diverse perspectives shared by fellow co-authors. This paper outlines ten simple rules for being a co-author on a many-author non-empirical paper. Although the rules were developed for papers with at least 30 authors, some rules may be useful for many-author research papers or for non-empirical papers with fewer authors. Co-authors may also want to consult our companion paper on ten simple rules for leading a many-author non-empirical paper, as understanding the challenges faced by lead writers will help co-authors to contribute more efficiently and effectively.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013317 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 13317&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1013317

DOI: 10.1371/journal.pcbi.1013317

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().