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Abstract/Summary
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Scientific research involves various digital objects including publications, software, data, workflows and tutorials, all key to FAIRness, reproducibility and transparency. The research lifecycle, from questions and hypotheses to results and conclusions, requires data production, collection, and transformation, a process commonly supported by software and workflows. For this cycle to prosper, we require Research Data and Software Management Plans (DMPs and SMPs), Research Objects packing things together, and metadata supporting the FAIR (data) principles and its extensions (e.g., software, workflows) as well as coverage for reproducibility, transparency, trustability, explainability, i.e., *ilities. Furthermore, despite playing an important role, data on its own is not enough to establish Linked Open Science, i.e., Open Science plus Linked Open Data (LOD). LOD principles, aka LOD 5 stars, follow objectives overlapping with FAIR and Open Science (e.g., LOD includes “openness” and usage of “non-proprietary open formats”). In DaMaLOS we will explore requirements for research digital objects and their corresponding management plans to effectively instantiate an integrated layer supporting Linked Open Science. DaMaLOS welcomes contributions aligned to the following topics: machine-actionable DMPs and SMPs; machine/deep learning approaches around rich metadata; FAIRification; FAIR by design; FAIR tooling; recognition, publication and citation for data, software and other research digital objects, and scientometrics beyond the scholarly publication (i.e., combining the different digital objects playing a role in the research cycle).
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