Usage of OpenAlex for creating meaningful global overlay maps of science on the individual and institutional levels

TL;DR

This paper introduces a method to create global overlay maps of science using OpenAlex, enabling visualization of scientific performance at individual and institutional levels with normalization techniques.

Contribution

It proposes a procedure for generating overlay maps with OpenAlex, provides multiple base maps, and discusses normalization methods and their effects.

Findings

Normalized overlay data reduces bias in visualizations

Overlay maps highlight specific research areas more clearly

Methodology is applicable to individual and institutional analysis

Abstract

Global overlay maps of science use base maps that are overlaid by specific data (from single researchers, institutions, or countries) for visualizing scientific performance such as field-specific paper output. A procedure to create global overlay maps using OpenAlex is proposed. Six different global base maps are provided. Using one of these base maps, example overlay maps for one individual (the first author of this paper) and his research institution are shown and analyzed. A method for normalizing the overlay data is proposed. Overlay maps using raw overlay data display general concepts more pronounced than their counterparts using normalized overlay data. Advantages and limitations of the proposed overlay approach are discussed.