Rethinking the production and publication of machine-readable expressions of research findings

Abstract

Scientific literature is the primary expression of scientific knowledge and an important source of research data. However, scientific knowledge expressed in narrative text documents is not inherently machine readable. To facilitate knowledge reuse, knowledge must be extracted from articles and organized into databases post-publication. The high time costs and inaccuracies associated with completing these activities manually has driven the development of techniques that automate knowledge extraction. Tackling the problem with a different mindset, we propose a pre-publication approach, known as reborn, that ensures scientific knowledge is born readable, i.e. produced in a machine-readable format with formal data syntax during knowledge production. We implement the approach using the Open Research Knowledge Graph infrastructure for FAIR scientific knowledge organization. With a focus on statistical research findings, we test the approach with three use cases in soil science, computer science, and agroecology. Our results suggest that the proposed approach is superior compared to classical manual and semi-automated post-publication extraction techniques in terms of knowledge accuracy, richness, and reproducibility as well as technological simplicity.