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dc.contributor.authorMartin, Jose
dc.contributor.authorHan, Lee Yen
dc.date.accessioned2019-07-07T11:08:13Z
dc.date.available2019-07-07T11:08:13Z
dc.date.issued2019-06-19
dc.identifier.urihttp://hdl.handle.net/10754/655933
dc.description.abstractA systems specialist and a liaison librarian worked together in this project to analyze resource usage, Open Access coverage, library holdings coverage and citation patterns from a collection of doctoral theses in a graduate research university. The extracted citations and some basic metadata about the theses and their authors were processed using a workflow created with KNIME, an open source data-mining software. The workflow uses Summon and Crossref APIs for library holdings coverage, CORE and Unpaywall APIs for Open Access coverage and an SQLite database to store the output and enable detailed analysis. This tool provides an insight into the resources that have been effectively used to produce doctoral theses. It would be useful for academic libraries interested in evaluating the impact of Open Access resources and how they contribute to their scholarly output, and to evaluate the coverage provided by their holdings to the research activity in their institutions beyond the usual usage reports provided by publishers or third parties.
dc.subjectCitations, Open Access, KNIME
dc.titleData mining of Citations in Theses: a workflow for automated analysis of Open Access and library holdings coverage
dc.typePoster
dc.contributor.departmentUniversity Library
dc.conference.date19 - 21 June, 2019
dc.conference.nameOAI 11 - Workshop on Innovations in Scholarly Communication
dc.conference.locationGeneva, Switzerland
refterms.dateFOA2019-07-07T11:08:14Z


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