Characterizing domain-specific open educational resources by linking ISCB Communities of Special Interest to Wikipedia
FOS: Political science
Resource (disambiguation)
Challenges and Innovations in Bioinformatics Education
FOS: Law
Prediction of Peptide-MHC Binding Affinity
Mathematical analysis
Data science
Computational biology
Biochemistry, Genetics and Molecular Biology
FOS: Mathematics
Perceptions
Cluster Analysis
RNA Sequencing Data Analysis
Molecular Biology
Biology
Political science
computer
ISCB/Ismb 2022
Computer network
Domain (mathematical analysis)
4. Education
Politics
Life Sciences
Computational Biology
Computer science
Systems biology
Representation (politics)
Law
Mathematics
DOI:
10.1093/bioinformatics/btac236
Publication Date:
2022-04-14T11:10:15Z
AUTHORS (26)
ABSTRACT
AbstractMotivationWikipedia 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.ResultsWe 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.
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