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New / Trial Databases
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The following databases are newly acquired or being evaluated for a future subscription.
Trial
Kindly provide feedback on Consensus, on trial until February 12, 2026.
Consensus is an AI-powered academic search engine that specializes in extracting and condensing scientific insights from peer-reviewed sources. This helps researchers and students find evidence-based answers quickly, making it a powerful alternative to traditional tools like Google Scholar.
Please note: Any account created with an @vt.edu domain will have unlimited enterprise access until the end of the trial.
Consensus is an AI-powered academic search engine that specializes in extracting and condensing scientific insights from peer-reviewed sources. This helps researchers and students find evidence-based answers quickly, making it a powerful alternative to traditional tools like Google Scholar.
Please note: Any account created with an @vt.edu domain will have unlimited enterprise access until the end of the trial.
Trial
Please provide feedback on Scite, currently available on a trial basis through February 12, 2026.
Scite AI is an AI-powered research platform that evaluates the credibility of scientific claims using "Smart Citations," which show whether research has been supported, mentioned, or contrasted by other scholarly articles. Its core function is to analyze citation statements from full-text articles and classify them using deep learning models as supporting, contrasting, or merely mentioning the cited work's claims. This provides crucial context, allowing researchers to quickly understand how a publication has been received by the broader scientific community beyond just the raw citation count.
Scite AI is an AI-powered research platform that evaluates the credibility of scientific claims using "Smart Citations," which show whether research has been supported, mentioned, or contrasted by other scholarly articles. Its core function is to analyze citation statements from full-text articles and classify them using deep learning models as supporting, contrasting, or merely mentioning the cited work's claims. This provides crucial context, allowing researchers to quickly understand how a publication has been received by the broader scientific community beyond just the raw citation count.
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