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  • × author_ss:"Yang, B."
  1. Yang, B.; Rousseau, R.; Wang, X.; Huang, S.: How important is scientific software in bioinformatics research? : a comparative study between international and Chinese research communities (2018) 0.00 
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    Abstract
    Software programs are among the most important tools in data-driven research. The popularity of well-known packages and corresponding large numbers of citations received bear testimony of the contribution of scientific software to academic research. Yet software is not generally recognized as an academic outcome. In this study, a usage-based model is proposed with varied indicators including citations, mentions, and downloads to measure the importance of scientific software. We performed an investigation on a sample of international bioinformatics research articles, and on a sample from the Chinese community. Our analysis shows that scientists in the field of bioinformatics rely heavily on scientific software: the major differences between the international community and the Chinese example being how scientific packages are mentioned in publications and the time gap between the introduction of a package and its use. Biologists publishing in international journals tend to apply the latest tools earlier; Chinese scientists publishing in Chinese tend to follow later. Further, journals with higher impact factors tend to publish articles applying the latest tools earlier.
    Type
    a
  2. Jiao, H.; Qiu, Y.; Ma, X.; Yang, B.: Dissmination effect of data papers on scientific datasets (2024) 0.00 
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    Abstract
    Open data as an integral part of the open science movement enhances the openness and sharing of scientific datasets. Nevertheless, the normative utilization of data journals, data papers, scientific datasets, and data citations necessitates further research. This study aims to investigate the citation practices associated with data papers and to explore the role of data papers in disseminating scientific datasets. Dataset accession numbers from NCBI databases were employed to analyze the prevalence of data citations for data papers from PubMed Central. A dataset citation practice identification rule was subsequently established. The findings indicate a consistent growth in the number of biomedical data journals published in recent years, with data papers gaining attention and recognition as both publications and data sources. Although the use of data papers as citation sources for data remains relatively rare, there has been a steady increase in data paper citations for data utilization through formal data citations. Furthermore, the increasing proportion of datasets reported in data papers that are employed for analytical purposes highlights the distinct value of data papers in facilitating the dissemination and reuse of datasets to support novel research.
    Type
    a

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