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Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies

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Abstract & Overview

Scopus is among the largest curated abstract and citation databases, with a wide global and regional coverage of scientific journals, conference proceedings, and books, while ensuring only the highest quality data are indexed through rigorous content selection and re-evaluation by an independent Content Selection and Advisory Board. Additionally, extensive quality assurance processes continuously monitor and improve all data elements in Scopus. Besides enriched metadata records of scientific articles, Scopus offers comprehensive author and institution profiles, obtained from advanced profiling algorithms and manual curation, ensuring high precision and recall. The trustworthiness of Scopus has led to its use as bibliometric data source for large-scale analyses in research assessments, research landscape studies, science policy evaluations, and university rankings. Scopus data have been offered for free for selected studies by the academic research community, such as through application programming interfaces, which have led to many publications employing Scopus data to investigate topics such as researcher mobility, network visualizations, and spatial bibliometrics. In June 2019, the International Center for the Study of Research was launched, with an advisory board consisting of bibliometricians, aiming to work with the scientometric research community and offering a virtual laboratory where researchers will be able to utilize Scopus data.

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Jeroen Baas, Michiel Schotten, Andrew Plume, Grégoire Côté, Reza Karimi (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1). https://doi.org/10.1162/qss_a_00019

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Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1). https://doi.org/10.1162/qss_a_00019
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