Deconstructing the collaborative impact: Article and author characteristics that influence citation count

Abstract

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.

Authorship and Content Analysis of Engineering Education Research: A Case Study

“…This phenomenon has also been studied by other researchers [53][54]. Sooryamoorthy in fact states that it is now commonly accepted that co-authorship leads to higher citation rates [55].…”

The relationship between the number of authors of a publication, its citations and the impact factor of the publishing journal: Evidence from Italy

“…An analysis by Skilton (2009), based on a sample of works in top-WoS natural science journals, stresses the fundamental role of 'diversity' in disciplinary backgrounds within the co-author team, and identifies the dominance of 'intellectual' over 'social' capital in citation behavior. Recently, scholars have illustrated how the effect of collaboration on citations tends to diminish if the analysis controls for subtle effects in the composition of the co-author networks and the articles themselves (Hurley et al, 2013;Didegah and Thelwall, 2013b). Finally, we note the correlation between citations received and author numbers could be traced in part to the natural increase in self-citation when works are by more authors (Leimu & Koricheva, 2005) and potentially from distinct institutions (Herbertz, 1995).…”

A review of author name disambiguation techniques for the PubMed bibliographic database

“…In related works [37–39], the authors have invented new similarity metrics to compare biomedical literature; they can be used to enhance the accuracy of the Author-ity database. Although this work has been highly influential and has been used to prepare datasets to analyse collaboration networks of scientists [40–42], scientists’ research strategy [43], global mobility patterns of researchers [44] and a country’s scientific output [45], there are some shortcomings in their approach: (1) name synonymy problem is not adequately addressed; (2) the training corpus is not manually generated and hence, not error-free; (3) up-to-date disambiguated dataset is not available online (Author-ity 2009 is the latest version which disambiguates author names across all citations in PubMed and MEDLINE till ~ September 2008); (4) it is unclear how to adapt it for efficient incremental disambiguation as PubMed keeps growing fast; and (5) there are many magic numbers in the AND algorithm that are heuristically set and may not generalise well as database is updated.…”