Plaudit · Open endorsements from the academic community

“Plaudit links researchers, identified by their ORCID, to research they endorse, identified by its DOI….

Because endorsements are publisher-independent and provided by known and trusted members of the academic community, they provide credibility for valuable research….

Plaudit is built on open infrastructure. We use permanent identifiers from ORCID and DOI, and endorsements are fed into CrossRef Event Data.

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Patterns of information – clustering books and readers in open access libraries

Abstract:  Open access libraries operate in a continuum between two distinct organisation models: online retailers versus ‘traditional’ libraries. Online retailers such as Amazon.com are successful in recom-mending additional items that match the specific needs of their customers. The success rate of the recommendation depends on knowledge of the individual customer: more knowledge about persons leads to better suggestions. Thus, to optimally profit from the retailers’ offerings, the client must be prepared to share personal information, leading to the question of privacy.

In contrast, protection of privacy is a core value for libraries. The question is how open access librar-ies can offer comparable services while retaining the readers’ privacy. A possible solution can be found in analysing the preferences of groups of like-minded people: communities. According to Lynch (2002), digital libraries are bad at identifying or predicting the communities that will use their collections. It is however our intention to explore the possibility to uncover sets of documents with a meaningful connection for groups of readers – the communities. The solution depends on examining patterns of usage, instead of storing information about individual readers. 

This paper will investigate the possibility to uncover the preferences of user groups within an open access digital library using social networking analysis techniques.

Omnity search engine finds documents relevant to yours — regardless of language | TechCrunch

“With the amount of published research, patents, white papers and other written knowledge out there, it’s hard to be even reasonably sure you’re aware of the goings-on around a certain topic or field. Omnity is a search engine made to make it easier by extracting the gist of documents you give it and finding related ones from a library of millions — and now supports more than a hundred languages.

The process is simple and free, at least for the public-facing databases Omnity has assembled, comprising U.S. patents, SEC filings, PubMed papers, clinical trials, Library of Congress collections and more.

You upload a document or text snippet and the system scans it, looking for the least common words and phrases — which generally indicate things like topic, experiment type, equipment used, that sort of thing. It then looks through its own libraries to find documents with similar or related phrases that appear in a manner that suggests relevance….”