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1000
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Abstract/Summary
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Research data is core to data-driven research as it complements scientific publications and expose results from experimental work. Most research activities follow the research data cycle, where data is continuously used, modified and produced, transitioning from one research group to another. For this cycle to prosper, we require Research Data Management plans supporting the findable, accessible, interoperable and reusable (FAIR) principles. Despite playing an important role, data on its own is not sufficient to establish Open Science nor Linked Open Science, i.e., Open Science plus Linked Open Data (LOD) principles. To get the full picture, in addition to data, we also need other digital objects playing a role in research, e.g., software and workflows. In this workshop we will explore what is required for Research Data (and other digital objects) Management Plans to effectively instantiate Linked Open Science, including effective support for LOD, automation by, e.g., machine/deep learning approaches, and innovations to include supporting data elements such as the software used to produce/consume it or the tutorials showcasing usage and fostering further developments.
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