The database in TIM Open Access contains documents from the Semantic Scholar database.

Semantic Scholar is a free, AI-powered search and discovery tool that helps researchers discover and understand scientific literature that’s most relevant to their work. Semantic Scholar uses machine learning techniques to extract meaning and identify connections from within papers, then surfaces these insights to help scholars gain an in-depth understanding quickly. A variety of carefully tuned mechanisms are used to make sure only high-quality academic papers are indexed.

Semantic Scholar sources its data from a number of scientific journals and databases, the current corpus includes research publications in all fields of science. This is a complete list at the time of writing: ACL, ACM, AMiner, ArXiv, BioOne, CiteSeer, Clinical Trials Transformation Initiative, DBLP, De Gruyter, Frontiers, HighWire Press, Hyper Articles en Ligne (HAL), IEEE, Karger, Microsoft Academic, MIT Press, OdySci Academic, Papers with Code, Project MUSE, PubMed, SAGE Publishers, Science, SciTePress, SPIE, Springer Nature, Taylor & Francis, The Royal Society, Wolters Kluwer.

From wikipedia:

Semantic Scholar is a project developed at the Allen Institute for Artificial Intelligence. Publicly released in November 2015, it is designed to be an AI-backed search engine for scientific journal articles. The project uses a combination of machine learning, natural language processing, and machine vision to add a layer of semantic analysis to the traditional methods of citation analysis, and to extract relevant figures, entities, and venues from papers. In comparison to Google Scholar and PubMed, Semantic Scholar is designed to highlight the most important and influential papers, and to identify the connections between them.