Scaling Scientometrics: Dimensions on Google BigQuery as an infrastructure for large-scale analysis

TL;DR

This paper explores how cloud computing, specifically Google BigQuery, can scale scientometric analysis by democratizing data access and computational resources, enabling large-scale research evaluation and decision-making.

Contribution

It introduces a novel visualization technique and demonstrates the potential of cloud infrastructure to enhance large-scale scientometric analysis.

Findings

Cloud technologies enable scalable scientometric analysis

Democratization of data access and compute capacity is feasible

Visualization techniques facilitate large-scale data exploration

Abstract

Cloud computing has the capacity to transform many parts of the research ecosystem, from particular research areas to overall strategic decision making and policy. Scientometrics sits at the boundary between research and the decision making and evaluation processes of research. One of the biggest challenges in research policy and strategy is having access to data that allows iterative analysis to inform decisions. Many of these decisions are based on "global" measures such as benchmark metrics that are hard to source. In this article, Cloud technologies are explored in this context. A novel visualisation technique is presented and used as a means to explore the potential for scaling scientometrics by democratising both access to data and compute capacity using the Cloud.