Please use this identifier to cite or link to this item: http://hdl.handle.net/11366/2008
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dc.contributor.authorEzra, Tamien_US
dc.date.accessioned2022-04-13T13:15:26Z-
dc.date.available2022-04-13T13:15:26Z-
dc.date.issued2022-05-13-
dc.identifier.urihttp://hdl.handle.net/11366/2008-
dc.descriptionExtended abstract presented at the CRIS2022 conference in Dubrovnik.-- Event programme available at https://cris2022.srce.hr/#section-programen_US
dc.descriptionPoster presented within the poster session on May 13th-
dc.description.abstractA key element of any CRIS is the tracking of research output in the form of publications - and associating them to the researchers who authored them. Creating a comprehensive list – retrospectively and for ongoing new research - is not a simple task and often institutions rely on manual work. There are multiple challenges from lack of or inconsistent metadata to missing identifiers. In this session we are discussing the use of algorithms, developed with machine learning methodologies, for automating the author-work associating at scale. Using the example of Smart Harvesting in Esploro, we outline the challenges and the successes of such an approach.en_US
dc.language.isoenen_US
dc.publishereuroCRISen_US
dc.relation.ispartofseriesCRIS2022: 15th International Conference on Current Research Information Systems (Dubrovnik, Croatia, May 12-14, 2022)-
dc.subjectcurrent research information systemsen_US
dc.subjectresearch outputen_US
dc.subjectmachine learningen_US
dc.subjectauthor matchingen_US
dc.subjectEsploroen_US
dc.titleConnecting authors to their work – the challenges and successesen_US
dc.typePosteren_US
dc.relation.conferenceCRIS2022 – Dubrovniken_US
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairetypePoster-
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