The Author-Level Metrics Study: An Analysis of the Traditional and Alternative Metrics of Scholarly Impact for Neurosurgical Authors

Abstract

Background and objectiveThere is a paucity of information regarding the concordance of traditional metrics across publicly searchable databases and about the correlation between alternative and traditional metrics for neurosurgical authors. In this study, we aimed to assess the congruence between traditional metrics reported across Google Scholar (GS), Scopus (Sc), and ResearchGate (RG). We also aimed to establish the mathematical correlation between traditional metrics and alternative metrics provided by ResearchGate. MethodsAuthor names listed on papers published in the Journal of Neurosurgery (JNS) in 2019 were collated.Traditional metrics [number of publications (NP), number of citations (NC), and author H-indices (AHi)] and alternative metrics (RG score, Research Interest score, etc. from RG and the GS i10-index) were also collected from publicly searchable author profiles. The concordance between the traditional metrics across the three databases was assessed using the intraclass correlation coefficient and Bland-Altman (BA) plots. The mathematical relation between the traditional and alternative metrics was analyzed. ResultsThe AHi showed excellent agreement across the three databases studied. The level of agreement for NP and NC was good at lower median counts. At higher median counts, we found an increase in disagreement, especially for NP. The RG score, number of followers on RG, and Research Interest score independently predicted NC and AHi with a reasonable degree of accuracy. ConclusionsA composite author-level matrix with AHi, RG score, Research Interest score, and the number of RG followers could be used to generate an "Impact Matrix" to describe the scholarly and real-world impact of a clinician's work.

Measuring the Impact of Data Sharing: From Author-Level Metrics to Quantification of Economic and Non-tangible Benefits

“…Despite certain concerns about equity and fairness, author-level metrics are a commonly utilized gauge of personal scientific achievement [ 11 , 12 ]. Quantitative tools such as the h-index and its derivatives are engineered to measure both the volume of publication output, indicated by the total count of published materials, and the influence of research, denoted by the number of citations garnered [ 12 ].…”