Authorship criteria define who may be listed as an author on a research paper and who may not. The most widely adopted standard comes from the International Committee of Medical Journal Editors (ICMJE), which sets out four conditions that must all be satisfied. Getting authorship right is both an ethical obligation and a practical necessity: disputes over who is named, and in what order, are among the most common and most bitter conflicts in research, and most journals now require authors to confirm they meet the criteria at submission.
This guide explains the ICMJE authorship conditions, the conventions for author order, the abuses to avoid, and how to prevent and resolve disputes.
The Four ICMJE Criteria
Under the ICMJE definition, an author must meet all four of the following:
- Substantial contribution to the conception or design of the work, or the acquisition, analysis, or interpretation of data.
- Drafting the work or revising it critically for important intellectual content.
- Final approval of the version to be published.
- Accountability for all aspects of the work, including ensuring that questions about accuracy or integrity are investigated and resolved.
The crucial point is that contribution alone is not sufficient. Someone who collected data but had no part in drafting, approving, or standing behind the work does not qualify as an author under this standard. They should instead be named in the acknowledgements. This is why "I did the lab work, so I am an author" is not automatically true, a frequent source of confusion among early-career researchers.
Contributorship and CRediT
Because the binary author-or-not model hides who actually did what, many journals now use contributorship statements built on the CRediT taxonomy (Contributor Roles Taxonomy). CRediT records specific roles, conceptualisation, methodology, formal analysis, writing, supervision, and more, so the published paper states each person's exact contribution. This transparency reduces disputes and gives proper credit for work, such as statistical analysis or data curation, that might otherwise be invisible. Our biostatistics and statistical consulting teams, for instance, are routinely credited through CRediT roles when their analysis is central to a study.