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.
@article{arxiv.2101.09567,
title = {Scaling Scientometrics: Dimensions on Google BigQuery as an infrastructure for large-scale analysis},
author = {Daniel W Hook and Simon J Porter},
journal= {arXiv preprint arXiv:2101.09567},
year = {2021}
}