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1Zhao, M. ; Yan, E. ; Li, K.: Data set mentions and citations : a content analysis of full-text publications.
In: Journal of the Association for Information Science and Technology. 69(2018) no.1, S.32-46.
Abstract: This study provides evidence of data set mentions and citations in multiple disciplines based on a content analysis of 600 publications in PLoS One. We find that data set mentions and citations varied greatly among disciplines in terms of how data sets were collected, referenced, and curated. While a majority of articles provided free access to data, formal ways of data attribution such as DOIs and data citations were used in a limited number of articles. In addition, data reuse took place in less than 30% of the publications that used data, suggesting that researchers are still inclined to create and use their own data sets, rather than reusing previously curated data. This paper provides a comprehensive understanding of how data sets are used in science and helps institutions and publishers make useful data policies.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23919/full.
Objekt: PLoS One
2Yan, S. ; Rousseau, R. ; Huang, S.: Contributions of chinese authors in PLOS ONE.
In: Journal of the Association for Information Science and Technology. 67(2016) no.3, S.543-549.
Abstract: Beginning with a short review of Public Library of Science (PLOS) journals, we focus on PLOS ONE and more specifically the contributions of Chinese authors to this journal. It is shown that their contribution is growing exponentially. In 2013 almost one fifth of all publications in this journal had at least one Chinese author. The average number of citations per publication is approximately the same for articles with a Chinese author and for articles without any Chinese coauthor. Using the odds-ratio, we could not find arguments that Chinese authors in PLOS ONE excessively cite other Chinese contributions.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23400/abstract.
Objekt: PLOS ONE