Search (4 results, page 1 of 1)

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Abstract

This paper introduces a keyword map of the labels used by the scientists registered in the Google Scholar Citations (GSC) database from December 2011. In all, 15,000 random queries were formulated to GSC to obtain a list of 26,682 registered users. From this list a network graph of 6,660 labels was built and classified according to the Scopus Subject Area classes. Results display a detailed label map of the most used (>15 times) tags. The structural analysis shows that the core of the network is occupied by computer science-related disciplines that account for the most used and shared labels. This core is surrounded by clusters of disciplines related or close to computing such as Information Sciences, Mathematics, or Bioinformatics. Classical areas such as Chemistry and Physics are marginalized in the graph. It is suggested that GSC would in the future be an accurate source to map Science because it is based on the labels that scientists themselves use to describe their own research activity.

Object

Google scholar

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Abstract

This article offers a comparative analysis of the personal profiling capabilities of the two most important free citation-based academic search engines, namely, Microsoft Academic Search (MAS) and Google Scholar Citations (GSC). Author profiles can be useful for evaluation purposes once the advantages and the shortcomings of these services are described and taken into consideration. In total, 771 personal profiles appearing in both the MAS and the GSC databases were analyzed. Results show that the GSC profiles include more documents and citations than those in MAS but with a strong bias toward the information and computing sciences, whereas the MAS profiles are disciplinarily better balanced. MAS shows technical problems such as a higher number of duplicated profiles and a lower updating rate than GSC. It is concluded that both services could be used for evaluation proposes only if they are applied along with other citation indices as a way to supplement that information.

Object

Google Scholar

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Abstract

This study analyses the coverage of seven free-access bibliographic databases (Crossref, Dimensions-non-subscription version, Google Scholar, Lens, Microsoft Academic, Scilit, and Semantic Scholar) to identify the potential reasons that might cause the exclusion of scholarly documents and how they could influence coverage. To do this, 116 k randomly selected bibliographic records from Crossref were used as a baseline. API endpoints and web scraping were used to query each database. The results show that coverage differences are mainly caused by the way each service builds their databases. While classic bibliographic databases ingest almost the exact same content from Crossref (Lens and Scilit miss 0.1% and 0.2% of the records, respectively), academic search engines present lower coverage (Google Scholar does not find: 9.8%, Semantic Scholar: 10%, and Microsoft Academic: 12%). Coverage differences are mainly attributed to external factors, such as web accessibility and robot exclusion policies (39.2%-46%), and internal requirements that exclude secondary content (6.5%-11.6%). In the case of Dimensions, the only classic bibliographic database with the lowest coverage (7.6%), internal selection criteria such as the indexation of full books instead of book chapters (65%) and the exclusion of secondary content (15%) are the main motives of missing publications.

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Date

20. 1.2015 18:30:22