Research and partnership in studies of sugarcane using molecular markers: a scientometric approach

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Abstract

In this study, we applied scientometric methods and performed a science mapping analysis to investigate and describe the application of molecular markers in genetic studies for sugarcane. We analyzed 344 papers retrieved from Web of Science Thomson Reuters database, considering the time frame from 1974 to 2017, allowing the analysis of the produced scientific knowledge, temporal trends and research partnership in the subject. To our knowledge, a scientometric evaluation of the use of molecular markers for genetic studies in sugarcane has not yet been reported. Our main finding shows that dominant markers are more commonly used than co-dominant markers, and that when co-dominant markers are applied, most of the retrieved information is underexploited, due to lack of knowledge for the use of dosage information. Our results also show that despite the development of high-throughput sequencing technologies, for sugarcane only 13.33% of the studies reported the use of more than 1000 markers. We also found connectivity among researchers and institutions publishing worldwide, but only few authors represented a bridge between different research groups. Thus, the international research partnership is still low. Our findings show dominance in country publication. Six countries (India, Brazil, USA, Australia, China, and France) were responsible for 80% of the published papers and received most citations. We suggest special reinforcement in building international partnership, since collaborations occur mostly within countries. International collaboration can improve research performance and eventually lead to a better exploitation and more development of knowledge for sugarcane.

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APA

de Bem Oliveira, I., Nunes, R., Mattiello, L., Barros-Ribeiro, S., de Souza, I. P., Coelho, A. S. G., & Collevatti, R. G. (2019). Research and partnership in studies of sugarcane using molecular markers: a scientometric approach. Scientometrics, 119(1), 335–355. https://doi.org/10.1007/s11192-019-03047-6

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