The Quantitative Science Studies (QSS) research group studies science from a quantitative perspective, using methods ranging from mathematical analysis and computer simulation to statistical analysis, network analysis, text mining, and visualization. The focus of the group is on problems in the areas of research evaluation and research management. The QSS group has two main aims. On the one hand, the group aims to make innovative high-quality scientific contributions at the methodological, theoretical and empirical level. On the other hand, the group aims to contribute to the development of improved research evaluation and research management practices. The introduction of a new 'contextualized scientometrics' framework for the use of scientometrics in research evaluation and research management is a key contribution in this area.
The core areas to which the group intends to contribute are covered by research lines. Each of the research lines is led by a member of the group.
The contextualized scientometrics research line aims to develop a new framework for the use of scientometrics for supporting research evaluation and research management. Contextualized scientometrics offers an alternative approach to present approaches based on three key principles: context, simplicity, and diversity. According to these principles, scientometric indicators should be complemented with contextual information, they should preferably be simple and easy to understand, and a diversity of scientometric indicators should be made available to cover the different dimensions relevant in the evaluation of scientific research. The research line will use results from a number of other research lines in the QSS group (i.e., altmetrics, bibliometric data sources, full-text analysis, and scientometric tools), and it is likely that the research line will also benefit from results of the SES and STIS groups. Furthermore, work in the contextualized scientometrics research line will be done in close collaboration with the Responsible Metrics research theme.
The main objective of this research line is to investigate the characteristics and possibilities of altmetric data sources in order to provide a more comprehensive and contextualized understanding of science and scholarship. In particular, the research line focuses on the following questions: What is the role of social media in scholarly communication? What could be the value of altmetric data in the framework of contextualized scientometrics? And what are the practical applications of altmetrics for the study of the sciences? To answer these questions, the altmetrics research line takes three main approaches: (1) the conceptual and theoretical study of altmetric data sources and their analytical possibilities, (2) the technical analysis of the features, possibilities, and limitations of altmetric data sources and the metrics they provide, and (3) the development of applications of altmetrics for a variety of practical purposes. The altmetrics research line is expected to develop models of understanding of online activities around scholarly outputs as well as practical proposals on how these models can be framed in research evaluation and scientometric studies.
This research line is concerned with analyzing and comparing the characteristics of different bibliometric data sources, in particular Web of Science, Scopus, Google Scholar, and Microsoft Academic. The research line for instance focuses on the in-depth comparison of Web of Science and Scopus, the analysis of the coverage of conference proceedings in bibliometric data sources, and the study of the consequences of differences between bibliometric data sources on the outcomes of bibliometric analyses.
The full text of scientific publications is increasingly becoming available for text mining purposes. We will study the full text of scientific publications at three levels of analysis: the data level, the empirical level, and the application level. At the data level, the research line focuses on processing large amounts of full-text data obtained from heterogeneous data sources and on enriching the data, for instance by identifying scientific concepts and by classifying citation links. At the empirical level, full-text data is used to get a deeper understanding of important phenomena in the sciences, such as the accumulation of scientific knowledge and the emergence of new research areas. Finally, at the application level, full-text data is used to provide relevant information for supporting research evaluation and research management.
This research line focuses on building quantitative models of the research system and on analyzing these models mathematically and by using computer simulations. The models constructed in the research line aim to provide an enhanced understanding of the way in which the system works. Moreover, they aim to offer a deeper insight into possible improvements that can be made to the system. These improvements could address all kinds of issues that are commonly perceived as problematic in today’s research system, such as funding being overly competitive, review and evaluation taking too much time, and many studies not being replicable. Specifically, our modelling research line aims to focus on the following three topics: (1) funding and strategic behavior, (2) open science, and (3) knowledge accumulation. Models will be grounded in careful theoretical reasoning, but will also be based on empirical insights, both from large-scale quantitative data sources and from qualitative lab studies and fieldwork.
VOSviewer and CitNetExplorer are two popular freely available scientometric software tools developed at CWTS. The scientometric tools research line aims to continue the development of these tools and the underlying algorithms. In particular, the research line aims to develop a completely new version 2.0 of the VOSviewer tool for visualizing bibliometric networks. VOSviewer 2.0 will be able to handle very large networks and will support innovative ‘drill-down’ functionality for exploring these networks. It is expected that VOSviewer 2.0 will also play an important role in implementing the idea of contextualized scientometrics.
PhD candidate and member of the QSS research group. His research focuses on machine learning and natural language processing of papers to improve literature searches.
Researcher focusing on full-text analytics and the humanities. Giovanni is interested in computational approaches to model the use scholars make of language, in science within Wikipedia, and in understanding the social and intellectual organisation of humanities disciplines.
Senior researcher. Rodrigo's research focuses on the development of new social media metrics (altmetrics), new scientometric applications at the individual-level and the study of funding acknowledgements.
Senior researcher and head of ICT. Nees Jan's research focuses on the development of visualization tools and algorithms, mainly for analyzing the structure and development of science.
PhD candidate. Zhichao’s research interests focus on the development and application of altmetrics and usage metrics.
PhD candidate also affiliated with the Leiden Centre of Data Science. Wout’s research focuses on studying patterns in scientific knowledge accumulation using full-text analysis of publications.
Visiting researcher. Tina's research focuses on the statistical analysis and modeling of bibliometric data and citation networks.
Senior researcher and bibliometric consultant. Vincent's research focuses on complex networks and social dynamics. He holds a Master in sociology and a PhD in applied mathematics, and tries to combine the two in his work.
Researcher and PhD candidate. Martijn's research focuses on data sources for the measurement of research performance.
Professor of Quantitative Science Studies and deputy director of CWTS. Ludo leads the Quantitative Science Studies (QSS) research group. He is coordinator of the CWTS Leiden Ranking and co-developer of the VOSviewer software for bibliometric visualization.
PhD candidate. Zohreh is doing research on altmetrics (alternative metrics and tools) and investigating potential of using altmetrics for measuring research performance.