Wout Lamers

Understanding scientific progress using full-text analysis of scholarly publications

Centre for Science and Technology Studies, Leiden University,  w.s.lamers@cwts.leidenuniv.nl

Supervisor(s): Prof. Ludo Waltman, Prof. Holger Hoos, Nees Jan van Eck

Background

Wout Lamers obtained a Bachelors Degree in Science and Innovation Management and a Masters Degree in Science and Innovation from Utrecht University. His MA-thesis was written at the Leiden Centre for Science and Innovation Studies (CWTS) on the topic of content words as measure of structure in the scientific landscape. He worked as a research assistant for CWTS on various research projects before entering a PhD candidate position created in collaboration with the Leiden Centre for Data Science.

Summary

The goal of this PhD research project is to study knowledge accumulation in academic research. We aim to develop methods for tracking the propagation of scientific topics and concepts from cited to citing publications, and to work towards a description of knowledge flows and the incremental advance of the scientific knowledge frontier. We will develop methods to leverage full text data of scientific publications for describing processes of knowledge accumulation in academia, using modern natural language processing and machine learning techniques.

Full text of scientific publications is a potent new source of data for scientometric research that has, to date, not been fully explored. Even in its current limited form the databases CWTS has acquired represent a significant subset of the global production of academic knowledge. If we develop methods for systematically assessing this full text data, this data source would open up many new potential research avenues into how individual academic publications build on existing knowledge and help propagate the frontiers of the scientific landscape.

This research will leverage modern natural language processing techniques, for instance, topic modeling, word embedding, and machine learning methods for pattern recognition and (text) classification, combined with the expertise in quantitative science studies at CWTS.