Researchers can now discover and explore research from a broad range of Springer Nature publications from chemistry to public health through the free, AI-based search engine Semantic Scholar. The expanded collaboration between Semantic Scholar’s creators, AI2, and Springer Nature builds on a pilot project started in 2016 that originally included publications in computer science and biomedicine. By incorporating articles and book chapters in a wider range of research areas, scientists and scholars can now more easily find and assess relevant Springer Nature content to pursue and advance scientific discovery.
Semantic Scholar draws on the metadata of over 170 million scientific research papers to help researchers discover publications most relevant for them. Through the expanded partnership, Springer Nature now delivers the metadata of over 3.4 million content items to AI2. By using artificial intelligence, Semantic Scholar creates a comprehensive literature graph that allows scientists and scholars to navigate links between articles, authors and topics, stay up-to-date with the latest developments in their field, identify new areas for investigation, aid hypothesis generation and investigate new research methodologies.
Users have full access to open access publications, and excerpts of text from subscription content. A link to Springer Nature’s content platforms is provided for the full-text version. The publications now included in Semantic Scholar cover chemistry and materials science, earth and environmental sciences, geography and public health.
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