EconPapers    
Economics at your fingertips  
 

Ten simple rules for leading a many-author non-empirical paper

Tracey L Weissgerber, Natascha Drude, Rima-Maria Rahal and Friederike E Kohrs

PLOS Computational Biology, 2025, vol. 21, issue 8, 1-16

Abstract: Many-author non-empirical papers include recommendations or consensus statements, catalogs of ideas, roadmaps for future research, calls to action, or “how to” articles. These papers have great potential to change the conversation or address unmet needs within research communities. Large, diverse authorship teams can create valuable resources that no individual co-author could create independently. Achieving these goals, however, requires a very different approach than researchers typically use to prepare papers with fewer authors. In the process we describe, a small team of lead writers typically leads the content generation and writing processes. Many co-authors collaborate to create content and provide feedback throughout the writing process. Lead writers face many challenges, including defining the content and structure of the paper, coordinating complex logistics, preparing themselves and co-authors for a unique writing experience, and managing high-volume feedback. Here, we outline ten simple rules for leading a many-author non-empirical paper. These rules guide readers through the content generation and writing processes and highlight practical solutions to common challenges. While these rules were developed by preparing non-empirical papers with at least 30 authors, some rules may apply to research papers or non-empirical papers with fewer authors. Lead writers can also use our companion paper, which shares ten simple rules for being a co-author on a many-author non-empirical paper, to prepare co-authors for an efficient and effective collaborative process.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013283 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 13283&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:pcbi00:1013283

DOI: 10.1371/journal.pcbi.1013283

Access Statistics for this article

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

 
Page updated 2025-08-16
Handle: RePEc:plo:pcbi00:1013283