Data on How Science Is Made Can Make Science Better

Abstract

Science is an engine of innovation and economic growth and a pathway to prosperity for countries around the world. The increasing availability of scientific publications today poses a data-driven opportunity to better understand and improve science. Scientific publications contain data on the content of published research and metadata on the context that gave rise to that research. Here we discuss and demonstrate the power of constructing, archiving, and analyzing links between scientific data and metadata to construct massive computational observatories of and for modern science. We show how these can be constructed using modern graph databases, and suggest some methods of analysis with potential to unleash sustained value for science and society. These scientific observatories would allow us to diagnose the health of the scientific workforce and institutions, and track the rate of scientific advance. They could enable us to better guide science policy and build portfolios of supported research that balance our societal commitments to diverse participation and prosperity. Moreover, they could enable scientists to surf the deluge of published research to open the scientific frontier in directions that do not follow the current, but open up new views and opportunities for others to follow. Linked scientific data can also enable the construction of artificial intelligence agents designed to complement the disciplinary focus of human scientific attention by proposing possibilities overlooked or underfunded by contemporary scientific institutions. Finally, we argue for the importance of ongoing political and legal support for the promotion of open, linked data to facilitate widespread benefit.

Alexander Belikov

Researcher, AI Manager

My research interests include graph neural networks, science of science and NLP.