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A delineating procedure to retrieve relevant publication data in research areas: the case of nanocellulose

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Abstract

Advances concerning publication-level classification system have been demonstrated striking results by dealing properly with emergent, complex and interdisciplinary research areas, such as nanotechnology and nanocellulose. However, less attention has been paid to propose a delineating method to retrieve relevant research areas on specific subjects. This study aims at proposing a procedure to delineate research areas addressed in case nanocellulose. We investigate how a bibliometric analysis could provide interesting insights into research about this sustainable nanomaterial. The research topics clustered by a Publication-level Classification System were used. The procedure involves an iterative process, which includes developing and cleaning a set of core publication regarding the subject and an analysis of clusters they are associated with. Nanocellulose was selected as the subject of study, but the methodology may be applied to any other research area or topic. A discussion about each step of the procedure is provided. The proposed delineation procedure enables us to retrieve relevant publications from research areas involving nanocellulose. Seventeen research topics were mapped and associated with current research challenges on nanocellulose.

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Acknowledgments

The authors are grateful to the São Paulo Research Foundation (Process Number 2012/16573-7) and comments from researchers of CWTS, NIT/Materiais, and ISSI Conference. We are thankful to the Graduate Program in Materials Science and Engineering at the Federal University of São Carlos for supporting this work. We also acknowledge the nanocellulose researchers for their valuable opinion on the research areas and on the map of topics.

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Correspondence to Douglas Henrique Milanez or Ed Noyons.

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Milanez, D.H., Noyons, E. & de Faria, L.I.L. A delineating procedure to retrieve relevant publication data in research areas: the case of nanocellulose. Scientometrics 107, 627–643 (2016). https://doi.org/10.1007/s11192-016-1922-5

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