TL;DR
This paper introduces a nanopublication-based semantic model for scientific publishing that enhances transparency, machine interpretability, and user accessibility of publication elements and peer reviews.
Contribution
It proposes a unified nanopublication framework for representing publication components, reviews, and processes, enabling improved access and analysis in scientific communication.
Findings
A dataset of 627 nanopublications representing publication elements and reviews.
A set of SPARQL queries for executing meta-review scenarios.
A user interface prototype that helps editors access review comments effectively.
Abstract
Scientific publishing is the means by which we communicate and share scientific knowledge, but this process currently often lacks transparency and machine-interpretable representations. Scientific articles are published in long coarse-grained text with complicated structures, and they are optimized for human readers and not for automated means of organization and access. Peer reviewing is the main method of quality assessment, but these peer reviews are nowadays rarely published and their own complicated structure and linking to the respective articles is not accessible. In order to address these problems and to better align scientific publishing with the principles of the Web and Linked Data, we propose here an approach to use nanopublications as a unifying model to represent in a semantic way the elements of publications, their assessments, as well as the involved processes, actors,…
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