Characterizing domain-specific open educational resources by linking ISCB Communities of Special Interest to Wikipedia
Makaleler > Tam Makale · 2022
Yazarlar: Alastair M Kilpatrick, Farzana Rahman, Audra Anjum, Sayane Shome, K M Salim Andalib, Shrabonti Banik, Sanjana F Chowdhury, Peter Coombe, Yesid Cuesta Astroz, J Maxwell Douglas, Pradeep Eranti, Aleyna D Kiran, Sachendra Kumar, Hyeri Lim, Valentina Lorenzi, Tiago Lubiana, Sakib Mahmud, Rafael Puche, Agnieszka Rybarczyk, Syed Muktadir Al Sium, David Twesigomwe, Tomasz Zok, Christine A Orengo, Iddo Friedberg, Janet F Kelso, Lonnie Welch
Bioinformatics, Uluslararası, Wos, 2022, cilt 38, ss i19-i27
DOI: 10.1093/bioinformatics/btac236
Özet
MOTIVATION: 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. RESULTS: 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.