Scholarly work and communication encompass the entire system in which research and creative works are created, evaluated for quality, disseminated to the academic community and beyond, used, and preserved for future use. It includes formal publications, such as journal articles and books, as well as informal sharing through preprints, conference presentations, data sharing, and broader engagement with scholarly works and research outputs. Scholarly knowledge serves as the primary engine of progress, shaping our world and guiding our collective future. It forms the backbone of technological advancement, public health systems, and sustainable environmental practices. Obtained through rigorous methods of observation, experimentation, and validation, it is a reliable resource that helps societies solve complex problems and improve the quality of life by achieving sustainable development goals (SDGs) [6]. To be truly useful, scholarly knowledge must first be systematically extracted and organized. However, the scholarly community of today faces the problem of an overload of scientific papers in their respective domains. There is an increasing number of papers published every year (currently, 3 million), in addition to more than 200 million papers that have already been published . This gives rise to the research question: "How can we provide a reliable and living scholarly knowledge base that empowers researchers to query, synthesize, and analyze the vast body of scholarly knowledge?"

This short paper highlights the growing importance of information retrieval (IR) engines in the scientific community, addressing the inefficiency of traditional keyword-based search engines due to the rising volume of publications. The proposed solution involves structured records, underpinning advanced information technology (IT) tools, including visualization dashboards, to revolutionize how researchers access and filter articles, replacing the traditional text-heavy approach. This vision is exemplified through a proof of concept centered on the ``reproductive number estimate of infectious diseases'' research theme, using a fine-tuned large language model (LLM) to automate the creation of structured records to populate a backend database that now goes beyond keywords. The result is a next-generation IR method accessible at https://orkg.org/usecases/r0-estimates.

Noninvasive brain stimulation (NIBS) encompasses transcranial stimulation techniques that can influence brain excitability. These techniques have the potential to treat conditions like depression, anxiety, and chronic pain, and to provide insights into brain function. However, a lack of standardized reporting practices limits its reproducibility and full clinical potential. This paper aims to foster interinterdisciplinarity toward adopting Computer Science Semantic reporting methods for the standardized documentation of Neuroscience NIBS studies making them explicitly Findable, Accessible, Interoperable, and Reusable (FAIR). In a large-scale systematic review of 600 repetitive transcranial magnetic stimulation (rTMS), a subarea of NIBS, dosages, we describe key properties that allow for structured descriptions and comparisons of the studies. This paper showcases the semantic publishing of NIBS in the ecosphere of knowledge-graph-based next-generation scholarly digital libraries. Specifically, the FAIR Semantic Web resource(s)-based publishing paradigm is implemented for the 600 reviewed rTMS studies in the Open Research Knowledge Graph.

In the last decade, we experienced an urgent need for a flexible, context-sensitive, fine-grained, and machine-actionable representation of scholarly knowledge and corresponding infrastructures for knowledge curation, publishing and processing. Such technical infrastructures are becoming increasingly popular in representing scholarly knowledge as structured, interlinked, and semantically rich Scientific Knowledge Graphs (SKG). Knowledge graphs are large networks of entities and relationships, usually expressed in W3C standards such as OWL and RDF. SKGs focus on the scholarly domain and describe the actors (e.g., authors, organizations), the documents (e.g., publications, patents), and the research knowledge (e.g., research topics, tasks, technologies) in this space as well as their reciprocal relationships. These resources provide substantial benefits to researchers, companies, and policymakers by powering several data-driven services for navigating, analysing, and making sense of research dynamics.