Abstract
AbstractAcknowledgements represent scholars’ relationships as part of the research contribution. While co-authors and citations are often provided as a well-formatted bibliometric database, acknowledged individuals are difficult to identify because they appear as part of the statements in the paper. We identify acknowledged scholars who appeared in papers published in open-access journals by referring to the co-author and citation relationships stored in the Microsoft Academic Graph (MAG). Therefore, the constructed dataset is compatible with MAG, which accelerates and expands the acknowledgements as a data source of scholarly relationships similar to collaboration and citation analysis. Moreover, the implemented code is publicly available; thus, it can be applied in other studies.
Funder
MEXT | Japan Society for the Promotion of Science
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability
Cited by
4 articles.
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