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The Open Research Knowledge Graph (ORKG) is an Open Science digital infrastructure for the production, curation, publication, and reuse of machine-actionable scholarly knowledge. Built on top of the RDF data model and extensible ontologies, the ORKG provides a common vocabulary for researchers to describe their research contributions and data, improving the discoverability and reusability of scholarly knowledge and research data. The ORKG includes tools for visualizing the relationships between different entities, making it easier to understand the connections between different pieces of research and their findings. It facilitates collaboration between researchers by providing a collaborative platform for organizing and sharing scholarly knowledge and data, reducing duplication and enabling more efficient use of resources. As research becomes increasingly data-driven, tools like the ORKG will become essential for enabling efficient, transparent, and collaborative research. [ABSTRACT FROM AUTHOR]

The NFDI4Energy consortium aims to establish new services filling a variety of needs for the energy system research community, from making FAIR research data easily accessible to promoting collaboration among community entities. Seven Task Areas (TAs) have been defined to achieve the consortium's objectives, each with a specific focus. Task Area 4 (TA4): FAIR Data for Energy System Research shall develop ontologies, metadata standards, and services to promote semantic consistency and improve interoperability of energy research projects, thereby supporting the harmonization of data management among various institutions and research fields. [ABSTRACT FROM AUTHOR]

In the past years, scientific research in Data Science and Artificial Intelligence has witnessed vast progress. The number of published papers and digital objects (e. g., data, code, models) is growing exponentially. However, not all research artefacts fulfill the criteria of being findable, accessible, interoperable and reusable (FAIR), contributing to a rather low level of reproducibility of experimental findings reported in scholarly publications and to the reproducibility crisis. In this paper, we focus on Data Science and Artificial Intelligence Open Science best practices, i. e., a set of recommendations that eventually contribute to the management and development of digital artefacts that are as FAIR as possible. While several guidelines exist, we add best practices for the FAIR collection, processing, storing and sharing of scholarly findings via Research Knowledge Graphs. The final list of recommendations will be available on the NFDI4DS website as an interactive web application. [ABSTRACT FROM AUTHOR]