dc.contributor.author	Pannabecker, Virginia	en
dc.date.accessioned	2016-09-27T07:07:32Z	en
dc.date.available	2016-09-27T07:07:32Z	en
dc.date.issued	2016-09-27	en
dc.description.abstract	When discussing project planning for systematic reviews and meta-analyses with faculty and graduate students, librarians sometimes hear wistful inquiries about automated approaches. Systematic reviews (SRs) require management, analysis, and synthesis of large amounts of data, perhaps particularly those including numerous studies with qualitative text-based data. To investigate how text and data mining approaches might be used in SRs to increase project efficiency, the author conducted, and reports on the results of a literature review.	en
dc.identifier.uri	http://hdl.handle.net/10919/73042	en
dc.language.iso	en_US	en
dc.rights	Creative Commons Attribution 3.0 United States	en
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/us/	en
dc.subject	systematic review	en
dc.subject	text mining	en
dc.subject	data mining	en
dc.subject	software	en
dc.title	Text and Data Mining for Systematic Reviews: Investigating Trends to Update Collaboration Services	en
dc.type	Presentation	en

Text and Data Mining for Systematic Reviews: Investigating Trends to Update Collaboration Services

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