Category: Search Engines and Discovery Systems

"Affiliation Information in DataCite Dataset Metadata: a Flemish Case Study"

http://doi.org/10.5334/dsj-2021-013

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"Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI:A Multidisciplinary Comparison of Coverage via Citations"

https://arxiv.org/abs/2004.14329v3

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"Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: A Multidisciplinary Comparison of Coverage Via Citations"

https://arxiv.org/abs/2004.14329

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"Google Scholar, Web of Science, and Scopus: a Systematic Comparison of Citations in 252 Subject Categories"

Alberto Martín-Martín et al. have self-archived "Google Scholar, Web of Science, and Scopus: a Systematic Comparison of Citations in 252 Subject Categories."

Here's an excerpt:

Despite citation counts from Google Scholar (GS), Web of Science (WoS), and Scopus being widely consulted by researchers and sometimes used in research evaluations, there is no recent or systematic evidence about the differences between them. In response, this paper investigates 2,448,055 citations to 2,299 English-language highly-cited documents from 252 GS subject categories published in 2006, comparing GS, the WoS Core Collection, and Scopus. . . . Despite the many unique GS citing sources, Spearman correlations between citation counts in GS and WoS or Scopus are high (0.78-0.99). They are lower in the Humanities, and lower between GS and WoS than between GS and Scopus. The results suggest that in all areas GS citation data is essentially a superset of WoS and Scopus, with substantial extra coverage.

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Search Results Ranking Using Machine-Learning Algorithms: "Best Match: New Relevance Search for PubMed"

Nicolas Fiorini et al. have published "Best Match: New Relevance Search for PubMed" in PLOS Biology.

Here's an excerpt:

PubMed is a free search engine for biomedical literature accessed by millions of users from around the world each day. With the rapid growth of biomedical literature—about two articles are added every minute on average—finding and retrieving the most relevant papers for a given query is increasingly challenging. We present Best Match, a new relevance search algorithm for PubMed that leverages the intelligence of our users and cutting-edge machine-learning technology as an alternative to the traditional date sort order. The Best Match algorithm is trained with past user searches with dozens of relevance-ranking signals (factors), the most important being the past usage of an article, publication date, relevance score, and type of article. This new algorithm demonstrates state-of-the-art retrieval performance in benchmarking experiments as well as an improved user experience in real-world testing (over 20% increase in user click-through rate). Since its deployment in June 2017, we have observed a significant increase (60%) in PubMed searches with relevance sort order: it now assists millions of PubMed searches each week. In this work, we hope to increase the awareness and transparency of this new relevance sort option for PubMed users, enabling them to retrieve information more effectively.

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