Abstract
With the trend of open science, efforts have been made to openly utilize research data. Considering the use of shared research data for interdisciplinary research, developing a researcher-friendly abstract writing method in different research fields is pertinent. In this study, we focus on abstracts from Scientific Data, a journal specializing in research data. We examine the influence of each part of speech on the utilization of research data through multiple regression analysis of the number of occurrences of the part of speech, the number of words and index-keywords in the abstract, and the number of citations research data article. Based on these results, we set the explanatory variables as the number of nouns, verbs, the other parts of speech, words, and index-keywords in the abstract. Thereafter, we developed a classifier to estimate the number of citations using machine learning. An analysis of the relationship between the number of citations and index keywords was also conducted.
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Kai, N., Yoshihisa, T., Shimbaru, T., Yano, H., Tanushi, H. (2024). Citation Estimation Method Using Abstracts of Research Data Articles: A Focus on Scientific Data. In: Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing . 3PGCIC 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 189. Springer, Cham. https://doi.org/10.1007/978-3-031-46970-1_1
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DOI: https://doi.org/10.1007/978-3-031-46970-1_1
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