Establishing Open Science as the default practice in the academic community is critical. Reporting a research result is more than just the manuscript. It is the actual process undertaken, the data (and metadata), the implemented software (and environment), and more. It is also important to ask questions pertaining to transparency and reproducibility, such as those regarding their reviews, publishing metadata, and, in particular, Where are the data and software that underlie their reported results? By making the connections between these artifacts through Open Science practices, it democratizes research by lowering the barrier of entry to understanding (and replicating) cutting edge research, while simultaneously accelerating novel research by reducing the need to re-implement software or re-collect data. Connecting these components and data in a useful and understandable way is a core mission of Semantic Web research.
In this project we leverage the leadership and reputation of the Semantic Web journal by IOS Press to demonstrate that the open sharing of data underlying publications can be done without significant overhead and provides significant added value. We will, for this journal, require authors to sustainably publish the data and software underlying their results, include software and data provision as part of the peer review assessment, and publish rich metadata for the supplementary data and software. We will develop technology in support of open science and will work with the community to refine the software and policies that enhance open science for the Semantic Web journal. The approach can serve as a model for other journal editors who seek a blueprint for adopting similar Open Science Data practices.