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Alternative bibliometrics from the web of knowledge surpasses the impact factor in a 2-year ahead annual citation calculation: Linear mixed-design models' analysis of neuroscience journals

Context: The decision about which journal to choose for the publication of research deserves further investigation. Aims: In this study, we evaluate the predictive ability of seven bibliometrics in the Web of Knowledge to calculate total cites over a 7-year period in neuroscience journals. Settings and Design: Coincidental bibliometrics appearing during 2007, 2008, 2009, 2010, and 2011, along with their corresponding cites in 2009, 2010, 2011, 2012, and 2013, were recorded from the journal citation reports (JCR) Science Edition. This was a retrospective study. Materials and Methods: This was a bibliographic research using data from the Web of Knowledge in the neuroscience category. Statistical Analysis Used: A linear-mixed effects design using random slopes and intercepts was performed on 275 journals in the neuroscience category. Results: We found that Eigenfactor score, cited half-life, immediacy index, and number of articles are significant predictors of 2-year-ahead total cites (P ≤ 0.010 for all variables). The impact factor, 5-year impact factor, and article influence score were not significant predictors; the global effect size was significant (R2= 0.999; P < 0.001) with a total variance of 99.9%. Conclusions: An integrative model using a set of several metrics could represent a new standard to assess the influence and importance of scientific journals, and may simultaneously help researchers to rank journals in their decision-making during the manuscript submission phase.