CCC Service to Include STM Journal Content from the World’s Largest University Press
Danvers, Mass. – Copyright Clearance Center, Inc. (CCC), a global licensing and content solutions organization, announces that Oxford University Press (OUP), the world’s largest university press, is participating in its RightFind™ XML for Mining solution.
RightFind XML for Mining allows publishers to offer life science companies controlled access to full-text articles in XML format for import into their preferred text mining software. Participating publishers receive usage reports that help them make decisions related to text mining and data mining (TDM) and their content-development strategy.
“By enrolling in CCC’s XML for Mining service, OUP can more efficiently expose TDM researchers to its content,” said Emily Sheahan, GM and Executive Director, CCC. “This helps strengthen OUP’s existing subscription business by enabling its customers to derive more value.”
”At this point it’s clear we’re living in the Age of Big Data,” said Casper Grathwohl, Director of Business Development, Oxford University Press. “Through mining deep collections of academic content, researchers are discovering exciting connections between ideas and gleaning insights never before possible. Given OUP’s broad range of scholarly publishing, CCC’s XML for Mining is an ideal service for us. We’re excited to see how researchers use our content in this setting to drive scholarship forward and develop real-world solutions for today’s pressing issues.”
Other publishers participating in the offering include Springer Nature, Wiley, BMJ, the Royal Society of Chemistry, Taylor & Francis, SAGE, Cambridge University Press, American Diabetes Association, American Society for Nutrition, and Future Medicine.
XML for Mining is built on the RightFind platform, CCC’s unique suite of cloud-based workflow solutions that offer immediate, easy access to a full range of Scientific, Technical, and Medical (STM) peer-reviewed journal content.
Using RightFind XML for Mining, commercial life science researchers create sets of full-text XML articles from more than 4,000 peer-reviewed journals produced by over 30 STM publishers and import these sets into their preferred third-party text mining software. Then they can identify articles associated with their research from publications to which they subscribe and discover articles that fall outside of company subscriptions, providing the most complete article collection for mining.
Text mining and data mining are methods of using appropriate software to discover knowledge from text materials (unstructured data) and databases (structured data), respectively. During text mining, researchers use software systems to identify not only areas of interest such as genes, chemicals, pharmaceutical products, and diseases but also relationships between them. As a result, they can discover new hypotheses, or validate old ones, with unprecedented ease.