Abstract
Scientific discoveries do not occur in vacuum but rather by connecting existing pieces of knowledge in new and creative ways. Mapping the relation and structure of scientific knowledge is therefore central to our understanding of the dynamics of scientific production. Here we introduce a new approach to generate scientific knowledge maps based on a machine learning approach that, starting from the observed publication patterns of authors, generates an N-dimensional space where it is possible to measure the similarity or distance between different research topics and knowledge domains. We provide an implementation of the proposed approach that considers the American Physical Society publications database and generates a map of the research space in Physics that characterizes the relation among research topics over time. We use this map to measure two indicators, the research capacity fingerprint and the knowledge density, to profile the research activity in physical sciences of more than 400 urban areas across the world. We show that these indicators can be used to analyze and predict the evolution over time of the research capacity and specialization of specific geographical areas. Furthermore we provide an extensive analysis of the relation between socio-economic development indicators and the ability to produce new knowledge for 67 countries, as measured by our approach, highlighting some key correlates of scientific production capacity. The proposed approach is scalable to very large datasets and can be extended to study other disciplines and research areas without having to rely on ad-hoc science classification schemes.
Highlights
The definition of meaningful maps of the research space is a fundamental step in the study of the emergence of scientific areas and the characterization of the drivers of knowledge production and consumption
To extract the labels that we are going to use to identify the research topics, we consider all the articles published in American Physical Society’s (APS) journals in the period 1986–2009 and we associate to each article: (a) a set of authors; and (b) a set of research topics identified using the Physics and Astronomy Classification Scheme (PACS) codes reported in each publication
2.3 Knowledge density and the prediction of scientific specialization The Revealed Comparative Advantage (RCA) in the context of the research space has been introduced by Guevara et al [47] to explore the principle of relatedness [57, 58, 76] in the process of scientific production: i.e. it is easier to specialize and work in related research areas requiring a set of common skills/knowledge
Summary
The definition of meaningful maps of the research space is a fundamental step in the study of the emergence of scientific areas and the characterization of the drivers of knowledge production and consumption. The advances in the field have opened the path to the understanding and prediction of scientific productivity and individual researchers’ careers [35,36,37,38,39,40,41] In this context, the visualization and mapping of the research space is a major tool in the study of the scientific portfolio of authors, institutions, and countries, the co-production of bibliographic items, and the quantitative characterization of similarity between scientific topics [3, 4, 42,43,44,45,46,47]
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