Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, UK.
Faculty of Science, Engineering and Computing, Kingston University, London, UK.
Bioinformatics. 2022 Jun 24;38(Suppl 1):i19-i27. doi: 10.1093/bioinformatics/btac236.
Wikipedia is one of the most important channels for the public communication of science and is frequently accessed as an educational resource in computational biology. Joint efforts between the International Society for Computational Biology (ISCB) and the Computational Biology taskforce of WikiProject Molecular Biology (a group of expert Wikipedia editors) have considerably improved computational biology representation on Wikipedia in recent years. However, there is still an urgent need for further improvement in quality, especially when compared to related scientific fields such as genetics and medicine. Facilitating involvement of members from ISCB Communities of Special Interest (COSIs) would improve a vital open education resource in computational biology, additionally allowing COSIs to provide a quality educational resource highly specific to their subfield.
We generate a list of around 1500 English Wikipedia articles relating to computational biology and describe the development of a binary COSI-Article matrix, linking COSIs to relevant articles and thereby defining domain-specific open educational resources. Our analysis of the COSI-Article matrix data provides a quantitative assessment of computational biology representation on Wikipedia against other fields and at a COSI-specific level. Furthermore, we conducted similarity analysis and subsequent clustering of COSI-Article data to provide insight into potential relationships between COSIs. Finally, based on our analysis, we suggest courses of action to improve the quality of computational biology representation on Wikipedia.
维基百科是公众科学传播的最重要渠道之一，经常作为计算生物学的教育资源被访问。国际计算生物学学会（ISCB）和维基百科分子生物学专题小组（一群专业的维基百科编辑）的计算生物学工作组近年来共同努力，大大提高了维基百科上计算生物学的代表性。然而，与遗传学和医学等相关科学领域相比，其质量仍亟待进一步提高。促进 ISCB 特别利益社区（COSIs）成员的参与将改善计算生物学这一重要的开放教育资源，同时允许 COSIs 为其特定子领域提供高质量的教育资源。
我们生成了大约 1500 篇与计算生物学相关的英文维基百科文章列表，并描述了开发二进制 COSI-Article 矩阵的过程，该矩阵将 COSIs 与相关文章联系起来，从而定义了特定于领域的开放教育资源。我们对 COSI-Article 矩阵数据的分析对维基百科上的计算生物学代表性进行了定量评估，与其他领域以及 COSI 特定水平进行了评估。此外，我们进行了相似性分析和随后的 COSI-Article 数据聚类，以深入了解 COSIs 之间的潜在关系。最后，根据我们的分析，我们提出了改善维基百科上计算生物学代表性的行动方案。
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