« ISTL Summer 2008 n54 Now Available | Main | Reinventing Science Librarianship: Models for the Future, October 2008 »

Science 2.0 Gains Another Search Engine: Q-Sensei From Lalisio

.: Barbara Quint reports in the 21 August 2008 InfoToday about another new search engine that has joined the ranks of other like Scirus and Google Scholar, to be used for searching topics in science and technology. Excerpt from her article:

Another sci-tech search engine has joined others to serve the needs and tastes of scientists. This one comes from a small company whose main service is the Lalisio social network for scientists. While the 2 million-plus article content nowhere near reaches the size and scope of behemoths such as Elsevier’s Scirus or Google Scholar, the Q-Sensei search engine (http://literature.lalisio.com/oai.html) has a metadata orientation that offers some interesting search capabilities. It can suggest alternative search strategies and allows searchers to narrow and focus their search results in a manner familiar to traditional searchers. At this point, it only searches open access content from ArXiv and PubMed Central, but parallel services also reach IngentaConnect and a series of book citation sources.

The arXiv database focuses on papers in physics, mathematics, nonlinear science, computer science, quantitative biology, and statistics. PubMed Central, from the National Library of Medicine, archives biomedical and life science journals. Under recent regulations, NIH-funded research must emerge—in time—into open access on PubMed Central. The National Institutes of Health are among the largest funders of medical research worldwide. In handling PubMed Central content, Lalisio uses MeSH thesaurus headings.

In addition to suggesting search strategies and terms, Q-Sensei lets users search within the search suggestions. It structures searches within categories, e.g., author, keyword, publisher, language, and year of publication. Users can remove search suggestions as well as adding them to focus search results. The service analyzes search results into different metadata categories, such as author, keyword, or document type, and displays terms in these categories that appear most often.

Post a comment

(If you haven't left a comment here before, you may need to be approved by the site owner before your comment will appear. Until then, it won't appear on the entry. Thanks for waiting.)