This article provides a quantitative analysis of peer review as an emerging field of research by revealing patterns and connections between authors, fields and journals from 1950 to 2016. By collecting all available sources from Web of Science, we built a dataset that included approximately 23,000 indexed records and reconstructed collaboration and citation networks over time. This allowed us to trace the emergence and evolution of this field of research by identifying relevant authors, publications and journals and revealing important development stages. Results showed that while the term 'peer review' itself was relatively unknown before 1970 ('referee' was more frequently used), publications on peer review significantly grew especially after 1990. We found that the field was marked by three development stages: (1) before 1982, in which most influential studies were made by social scientists; (2) from 1983 to 2002, in which research was dominated by biomedical journals, and (3) from 2003 to 2016, in which specialised journals on science studies, such as Scientometrics, gained momentum frequently publishing research on peer review and so becoming the most influential outlets. The evolution of citation networks revealed a body of 47 publications that form the main path of the field, i.e., cited sources in all the most influential publications. They could be viewed as the main corpus of knowledge for any newcomer in the field.
COBISS.SI-ID: 35190621
The evaluation of research performance increasingly relies on quantitative indicators determined by national science policies. We focus on two dimensions of research performance - productivity and excellence - as defined in the evaluation methodology of the Slovenian Research Agency. Our analysis focuses on the effects of two science policy factors - co-authorship collaboration and researcher funding - on the productivity and excellence of Slovenian researchers at the level of research disciplines. A multilevel analysis using a hierarchical linear model with regression analysis was applied to the data with several nested levels. As many variables have a semi-continuous distribution, a statistical model was used to address them. The results show a very strong positive effect of international co-authorship collaboration on productivity and excellence, while fragmentation of funding shows a negative impact only on excellence. We also include interviews with excellent Slovenian researchers regarding their views on scientific excellence and quantitative indicators.
COBISS.SI-ID: 34302813
Python is a high-level general-purpose programming language and easy to understand and learn. To deal with networks, different representations of networks in Python were proposed. On their basis, several Python libraries were developed to support programming of network analysis tasks.
COBISS.SI-ID: 18269785