Semantic Search Engine for Scientific Studies
Most academic search engines match the exact words you type and rank papers by how often they are cited. That works when you already know the right terminology — but it buries relevant work that describes the same idea differently, and it favors the most-cited papers over the most relevant ones. SciRadar compares the meaning of your query against each study, so closely related research surfaces even when it shares none of your exact words.
Matches literal words. It misses papers that use different terminology for the same concept, and can rank unrelated papers highly just because they happen to share a word.
Compares the underlying meaning of your query and each study using language-model embeddings, so conceptually related work is ranked by relevance — not by exact wording or citation count.
Type your research question as a natural sentence, in plain language — not just keywords.
Your description is turned into a vector embedding and compared against scientific studies by semantic similarity.
Open any result for its details, or jump directly to the publisher through its DOI.
Traditional engines match the exact words you type and rank results by citation count. SciRadar ranks by semantic similarity, so it surfaces conceptually relevant work even when the wording differs from your query, and it is not biased toward only the most-cited papers.
Bibliographic metadata and abstracts are indexed from the U.S. National Library of Medicine (PubMed/MEDLINE). SciRadar is an independent project and is not affiliated with or endorsed by the NLM.
Yes, it is free to use. There is no account, no sign-up, and no advertising.
Describe your research topic as a natural sentence rather than a few isolated keywords. The model compares the meaning of your description against scientific studies, so more context generally produces better matches.
The index covers a growing subset of the scientific literature and is expanded continuously. Results are not exhaustive, and a study you are looking for may not be indexed yet.