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International recognition of CORE in the Best of Business Web

Nancy Pontika, Monday 13 July 2015 | Annotate
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KMi’s project COnnecting REpositories (CORE) was included in the June issue of the Best of Business Web newsletter.

According to the editor's, Robert Berkman, comment, CORE ... is a real gold mine of a research site. You can perform precision searches by using the advanced search to quickly search via phrase or Boolean; limit by author, publisher and year; choose to only return articles available in fulltext; and search the entire text or limit to those found in the title and abstract. After the list of initial results are returned, you can further refine the list by publication type, language, journal and other fields. CORE also will suggest similar articles and displays these via a visually impressive interactive graph. While only 10% of the items are available in PDF fulltext, even for those that are not, full bibliographic information and an abstract are provided. Consider this site if you are looking for academic and scholarly papers from around the world, including those in languages other than English.

The Best of Business Web is a monthly newsletter addressed to market researchers, information professionals, entrepreneurs and business librarians. The newsletter is run by the New School of Public Engagement, New York City, USA.

This demonstrates the international attention that CORE receives and its important role in promoting open access to scholarly scientific results.

Connected

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Lucas Anastasiou

Project Officer - Open Access Publishing

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Matteo Cancellieri

Project Officer - Open Access Publishing

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Drahomira Herrmannova

PhD Research Student

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Petr Knoth

Senior Research Fellow

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Nancy Pontika

Project Officer - Open Access Aggregation

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Samuel Pearce

Technical Asst - Open Content Development

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CORE

Connecting REpositories by automatically linking Open Access research publications

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CORE - COnnecting REpositories

Linking semantically similar publications from Open Access repositories using text mining from full-text and representing the relations as Linked Data

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