Knowledge discovery via multidimensional science maps: the case of the Species Problem

TL;DR

This paper introduces a multidimensional science mapping approach that integrates various bibliometric indicators to better understand the development and causal-historical connections in scientific fields, demonstrated through a case study on the species problem.

Contribution

It proposes a novel multidimensional science map framework that combines multiple bibliometric relations to analyze knowledge dynamics more comprehensively.

Findings

Effective integration of different bibliometric indicators

Enhanced understanding of knowledge evolution and causal links

Successful application to the species problem case study

Abstract

Science mapping (SM), the study of the organization and development of science and technology, is a rapidly developing field within information science. The volume of available data allows this methodology to empirically address such issues as the historical development of topics, discourses, fields or the entire science system. Based on the pool of related methods, we are proposing an integration of various maps to obtain a novel kind of science map we call multidimensional. The basic idea behind is to combine the most informative relations available from various maps based on different bibliometric indicators, in order to produce a rich structrue for the study of knowledge dynamics, with special emphasis on causal-historical connections. As a proof of concept, we deploy the proposed framework in an extensive case study on a historical topic from the life sciences, namely, the debate on the species concept in biosystematics.