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CORE hosts the world\'s largest collection of open access research outputs, which are used and referenced by people globally, including researchers, libraries, software developers, funders and many more. CORE delivers a number of key measurable benefits to institutions, repositories, and researchers through its services. The value of CORE is not only provided by its services, but mostly by helping others in the delivery of their use cases. This makes CORE an enabling infrastructure, allowing for text mining, business intelligence, compliance monitoring and research analytics.
The CORE services:
* provide real-time machine access to metadata and full texts of research papers in CORE.
* help to download CORE data and run processes in your own infrastructure, access data across all of our data providers, prototype new methods, data analysis and text mining
* recommend papers to read based on users\' interests;support users in discovering articles of interest from across the network of open access repositories
* increase the visibility of content in open access repositories and journals
* assist users in finding freely accessible copies of research papers that are often behind a paywall
* provide an online interface offering valuable technical information and statistics to content providers
We aim to:
* Support the right of citizens to access the results of research towards which they contributed by paying taxes
* Provide support to both content consumers and content providers by working collaboratively with them
* Contribute to a cultural change by promoting open access, a fast-growing movement for good
* Make use of artificial intelligence and machine learning techniques to enrich and organise research content and support users in discovering knowledge of their interest


Kunnath, Suchetha N., Pride, David, Gyawali, Bikash, Knoth, Petr, Overview of the 2020 WOSP 3C Citation Context Classification Task

Pride, David, Knoth, Petr, An Authoritative Approach to Citation Classification

Pride, David, Knoth, Petr, Peer review and citation data in predicting university rankings, a large-scale analysis

Shearer, Kathleen, Rodrigues, Eloy, Bollini, Andrea, Cabezas, Alberto, Castelli, Donatella, Carr, Les, Chan, Leslie, Humphrey, Chuck, Johnson, Rick, Knoth, Petr, Manghi, Paolo, Matizirofa, Lazarus, Perakakis, Pandelis, Schirrwagen, Jochen, Smith, Tim, Van de Sompel, Herbert, Walk, Paul, Wilcox, David, Yamaji, Kazu, Next generation repositories: Scaling up repositories to a global knowledge commons


Lucas Anastasiou Valeriy Budko Matteo Cancellieri Bikash Gyawali Jozef Harag Drahomira Herrmannova Alexander Huba Catherine Kuliavets Samuel Pearce Nancy Pontika David Pride Svetlana Rumyanceva Maria Tarasiuk Viktor Yakubiv Petr Knoth


Big Scientific Data and Text Analytics Group (BSDTAG)


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