semantic-scholar

To find highly cited published papers, search by topic query or identifiers like DOI to retrieve metadata. Results are prioritized using citation counts and filtered by venue type, allowing you to quickly locate impactful journal and conference literature.

Yes, you can use DOI lookup to fetch specific research paper metadata. By querying with direct identifiers such as DOI, Semantic Scholar ID, or CorpusId, the skill returns full details including authors, venue, and available TLDR or abstract.

Discovering peer-reviewed literature beyond arXiv involves querying the Semantic Scholar database for journal and conference papers. The process applies de-duplication against arXiv via externalIds to ensure you receive reliable, published work with citation counts and venue metadata.

Yes, literature search results are filtered by applying default fields-of-study and publication-type parameters. This ensures that returned journal and conference papers match your research scenario, whether you need broad topic exploration or venue-focused filtering.

Prioritize research discovery results by citation ranking by applying citationCount metadata and optional filters for minimum citations or recent years. This surfaces high-impact, peer-reviewed papers at the top of your search results for better grounding.

Yes, you need Python and urllib to perform Semantic Scholar paper discovery. These dependencies are required to handle the HTTP requests for querying topics or direct identifiers and retrieving the published paper metadata from the API.