Quantifying success in science : an overview

Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming this crucial weakness. We focus on categorizing and reviewing the current development on evaluation indices of scholarly impact, including paper impact, scholar impact, and journal impact. Besides, we summarize the issues of existing evaluation methods and indices, investigate the open issues and challenges, and provide possible solutions, including the pattern of collaboration impact, unified evaluation standards, implicit success factor mining, dynamic academic network embedding, and scholarly impact inflation. This paper should help the researchers obtaining a broader understanding of quantifying success in science, and identifying some potential research directions. © 2013 IEEE.This work was supported in part by the Liaoning Provincial Key Research and Development Guidance Project under Grant 2018104021, and in part by the Liaoning Provincial Natural Fund Guidance Plan under Grant 20180550011

Quantifying Success in Science: An Overview

Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming this crucial weakness. We focus on categorizing and reviewing the current development on evaluation indices of scholarly impact, including paper impact, scholar impact, and journal impact. Besides, we summarize the issues of existing evaluation methods and indices, investigate the open issues and challenges, and provide possible solutions, including the pattern of collaboration impact, unified evaluation standards, implicit success factor mining, dynamic academic network embedding, and scholarly impact inflation. This paper should help the researchers obtaining a broader understanding of quantifying success in science, and identifying some potential research directions

Using Bibliometrics to Evaluate Outcomes and Influence of Translational Biomedical Research Centers

Introduction. Federal grant funding to support infrastructure development of translational biomedical research centers is a form of public health intervention. Establishing rigorous methods for measuring center success and outcomes is essential to justify continued funding. Methods. Bibliometric data compiled from a 5-year funding cycle of a neurodegeneration and translational neuroscience research center was analyzed using the package bibliometrix for open source software R and the NIH-developed research tool iCite. Results. The research team and their collaborators (n=485) produced 157 grant-citing publications from 2015-2020. The science was produced by small research teams clustered around three main communities of topics: Alzheimer\u27s Disease, brain imaging, and neuropsychological testing in the elderly. Using the RCR, the publications produced by the research team were found to be influential when compared to other R01-funded publications. Conclusion. Recent developments in bibliometric analysis expand beyond traditional measurement capabilities to better understand the characteristics, outcomes and influences of research teams. These findings can be used to inform researchers and institutions about research team composition, productivity, and success. Measures of research influence may be used to justify return on investment to funders