Misconduct and Misbehavior Related to Authorship Disagreements in Collaborative Science is a research paper published in Science and Engineering Ethics (2019). On theSindex it has a DataRank of 0.604. It has been cited 55 times.
Scientific authorship serves to identify and acknowledge individuals who "contribute significantly" to published research. However, specific authorship norms and practices often differ within and across disciplines, labs, and cultures. As a consequence, authorship disagreements are commonplace in team research. This study aims to better understand the prevalence of authorship disagreements, those factors that may lead to disagreements, as well as the extent and nature of resulting misbehavior. Methods include an international online survey of researchers who had published from 2011 to 2015 (8364 respondents). Of the 6673 who completed the main questions pertaining to authorship disagreement and misbehavior, nearly half (46.6%) reported disagreements regarding authorship naming; and discipline, rank, and gender had significant effects on disagreement rates. Paradoxically, researchers in multidisciplinary teams that typically reflect a range of norms and values, were less likely to have faced disagreements regarding authorship. Respondents reported having witnessed a wide range of misbehavior including: instances of hostility (24.6%), undermining of a colleague's work during meetings/talks (16.4%), cutting corners on research (8.3%), sabotaging a colleague's research (6.4%), or producing fraudulent work to be more competitive (3.3%). These findings suggest that authorship disputes may contribute to an unhealthy competitive dynamic that can undermine researchers' wellbeing, team cohesion, and scientific integrity.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.604
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.