The COVID-19 crisis is driving substantial research. Consequently, many articles pertaining to research being conducted on COVID-19 have already been published. Among them, quite a few publish COVID-19 R0 estimates. However, as the estimates are scattered across literature, it is imperative to organize R0 information, their value, confidence intervals, study location, and time frame. A way to overcome the challenge of organizing the R0 estimates is by leveraging the Open Research Knowledge Graph (ORKG).
The COVID-19 crisis is driving substantial research. Consequently, many articles pertaining to research being conducted on COVID-19 have already been published. Among them, quite a few publish COVID-19 R0 estimates. However, as the estimates are scattered across literature, it is imperative to organize R0 information, their value, confidence intervals, study location, and time frame. A way to overcome the challenge of organizing the R0 estimates is by leveraging the Open Research Knowledge Graph (ORKG).
The R0 estimate, a 95% confidence interval, the time frame, and location of the study and (optionally) the methods used to determine the R0 are published as natural text in the articles. Natural text makes the (human or machine) extraction of this information difficult, as it is unstructured and is hardly machine-actionable.
With the ORKG, the COVID-19 R0 estimates can be published in a structured manner thus making the estimates machine-actionable and ultimately FAIR. Machine actionable information opens a range of very interesting possibilities. For instance, it is possible to create literature overviews (or literature comparisons) automatically. The automatic creation of such overviews is facilitated by ORKG’s capability to represent information in a structured manner.
Another interesting possibility is that, contrary to review articles, ORKG overviews can evolve. As new literature on R0 research is published, it becomes simple to extend such an overview with ORKG, which helps reflect the current state of knowledge.
The real power of such ORKG overviews, however, can be seen if they are taken as data sources. Indeed, it is possible to link the ORKG and overviews, specifically, with downstream data science thanks to the machine actionability of both the data and the data exchange protocol (REST API).
Click here to read the original article published by the TIB BLOG.
Please give your feedback on this article or share a similar story for publishing by clicking here.
