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| Towards an Appropriate Scale of Datasets for Domain Bibliometrics: Empirical Study under Multiple Tasks |
| Chen Guo1,2, Wang Panting1, Wang Yuefen1 |
1.Department of Information Management, School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094
2.Jiangsu Science and Technology Collaborative Innovation Center of Social Public Safety, Nanjing 210094 |
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Abstract It is impossible to construct a complete dataset for domain bibliometrics owing to the centralized and decentralized distribution of literature, which raises an essential question about the appropriate scale of datasets. This problem should be resolved under specific bibliometric analysis tasks. In this study, we comprehensively consider typical task scenarios including domain scale, elements for analysis (such as subject classification, country, institution, keyword, reference, author, and their co-occurrence relationship), top N values of elements, and whether to sort or not. Based on this, we designed a corresponding experimental scheme. We selected “artificial intelligence” as an example domain, constructed various subsets with different sampling scales, and obtained 4,800 indices showing the imitative effect of those subsets to the full dataset. The results show that, in bibliometrics, when analyzing subject classifications or countries, a small dataset is sufficient for a reliable result. Analysis tasks on authors should be conducted with as large a dataset as possible, because author analysis is quite sensitive to data scale. When analyzing institutions, keywords, or references, a certain scale that corresponds to the specific task scenarios can also achieve reliable results. Additionally, for the co-occurrence analysis, more top elements—or sorting elements—and a larger dataset are necessary.
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Received: 21 July 2019
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