Beyond authorship: Analyzing disciplinary patterns of contribution statements using the CRediT taxonomy
Identificadores
URI: https://hdl.handle.net/10481/97322Metadatos
Mostrar el registro completo del ítemMateria
CRediT taxonomy Authorship Contributorship Division of labor
Fecha
2025-12-01Patrocinador
This paper is part of the COMPARE project (Ref: PID2020-117007RA-I00) funded by the Spanish Ministry of Science (Ref: MCIN/AEI/10.13039/501100011033 FSE invierte en tu futuro). Elvira González-Salmón is currently supported by an FPU grant from the Spanish Ministry of Science (Ref: FPU2021/02320). Victoria Di Césare is supported by a FPI grant from the Spanish Ministry of Science (Ref: PRE2021-097022). Aoxia Xiao is supported by a scholarship by from the China State Scholarship Fund. Nicolas Robinson-Garcia is supported by a Ramón y Cajal grant from the Spanish Ministry of Science (Ref: RYC2019-027886-I).Resumen
In this research article, we present the first cross-disciplinary descriptive analysis of the use of
contribution statements. Our main objective is to obtain further insight on contributions by a variety of
fields (Multidisciplinary, Health, Life, Physical, and Social Sciences) from the largest dataset used up
to now. We examine more than 700,000 articles published between 2018 and 2023 in Elsevier and
PLOS journals, in combination with bibliometric data extracted from the Scopus database. The
descriptive analysis of the dataset focuses on the overall coverage of the merged data, the distribution
of authorship and disciplines at paper level, and the interactions between contribution statements, author
order, and disciplines. Our two main findings indicate that, on the one hand, looking at contributions
and authorship order can enrich the way we understand science as a social endeavor. On the other hand,
delving deeper into contributorship differences by field is key. We underscore the value of the
Contributor Role Taxonomy (CRediT) in unveiling nuanced research dynamics and offering a more
equitable framework for evaluation.





