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Enhancing Research Classification Standards for Improved Collaboration and Comparison

Explore the complexities of research classifications across various data sources, the importance of consensus within the community, and the challenges associated with sharing paper-level classifications. Learn about key organizations and resources shaping the classification landscape.

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Enhancing Research Classification Standards for Improved Collaboration and Comparison

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  1. Science Classifications Current situation Eachresearch group/data provider/governmentagency has itsown classification; Different data sources (funding, publications, PhD programs) use different classifications; For bibliometric data, journal-level classifications, which have been the norm for decades, are not as precise as paper-level classifications (such as PubMed’sMeSH and other classifications recentlydevelopped); Classifications standards shows the consensus in the community and allows for variousstatistics to be comparable and reproducable. However, paper-level classifications are potentialytougher to sharethan journal-level classifications.

  2. Many sources for many purposes Bibliographicdatabases Thomson Reuters Scopus Governmentalagencies NSF – The Patent Board OECD IES: Classification of Instructional Programs Firms / research groups Science-Metrix CWTS CNS-IU SciTechStrategies

  3. Vincent Larivière is the Canada Research Chair on the transformations of scholarly communication, Assistant Professor of Information Science at the Université de Montréal, regularmember of the Centre interuniversitaire de recherche sur la science et la technologieand associateresearcherat the Observatoire des sciences et des technologies. He canbereachedat: vincent.lariviere@umontreal.ca Websites: http://lariviere.ebsi.umontreal.ca http://www.ost.uqam.ca http://www.cirst.uqam.ca

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