It is well known that collaborative papers tend to receive more citations than solo-authored papers. Here we try to identify the subtle factors of this collaborative effect by analyzing metadata and citation counts for co-authored papers in the biomedical domain, after accounting for attributes known to be strong predictors of citation count. Article-level metadata were gathered from 98,000 PubMed article records categorized with the term breast neoplasm, a topic offering longevity and relevance across biomedical subdisciplines, and yielding a relatively large sample size. Open access citation data was obtained from PubMed Central (PMC). Author-level attributes were encoded from disambiguated author name data in PubMed and appended as article-level attributes of collaborations. A logistic regression model was built to assess the relative weights of these factors as influences on citation counts. As expected, the journal and language of the paper were the strongest predictors. The significance of the number of authors diminished after accounting for other attributes. Some of the more subtle predictors included the group's highest hindex, which was positively correlated, while the diversity of author h-indices, minimum professional age, and author's total unique collaborators were negatively correlated. These observations indicate that smaller collaborations composed of early superstars-young, rapidly successful researchers with relatively high and similar h-indices-may be at least as influential in biomedical research as larger collaborations with different demographics. While minimum h-index was important, the first author's h-index was insignificant, underscoring the importance of the middle authors' publishing history. The gender diversity outcomes suggest that mixed groups may be ideal, and further research in this area is indicated.
- Citation analysis
ASJC Scopus subject areas
- Information Systems
- Library and Information Sciences