输入 | The unprecedented outbreak of COVID-19 is one of the most serious global threats to public health in this century. During this crisis, specialists in information science could play key roles to support the efforts of scientists in the health and medical community for combatting COVID-19. In this article, we demonstrate that information specialists can support health and medical community by applying text mining technique with latent Dirichlet allocation procedure to perform an overview of a mass of coronavirus literature. This overview presents the generic research themes of the coronavirus diseases: COVID-19, MERS and SARS, reveals the representative literature per main research theme and displays a network visualisation to explore the overlapping, similarity and difference among these themes. The overview can help the health and medical communities to extract useful information and interrelationships from coronavirus-related studies. |
输出 |
1)如何作图?
1,准备作图数据;2,用excel打开数据,调整为示例格式;3,将调整后的数据粘贴到输入框;4,选择参数;5,提交出图
2)为什么不出图?
对输入数据格式有严格要求。请观看输入框上面的视频介绍,并仔细阅读右侧说明,示例数据。
3)如何引用?
3000+篇google学术,2400+篇知网学术引用
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Cite: Tang D, Chen M, Huang X, Zhang G, Zeng L, Zhang G, Wu S, Wang Y. SRplot: A free online platform for data visualization and graphing. PLoS One. 2023 Nov 9;18(11):e0294236. doi: 10.1371/journal.pone.0294236. PMID: 37943830.
Method: Heatmap was plotted by https://www.bioinformatics.com.cn (last accessed on 20 Feb 2024), an online platform for data analysis and visualization.
Acknowledgement: We thank Shanghai NewCore Biotechnology Co., Ltd. (https://www.bioinformatics.com.cn, last accessed on 20 Feb 2024) for providing data analysis and visualization support.
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