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This paper introduces CODA-19, a human-annotated dataset that codes theBackground, Purpose, Method, Finding/Contribution, and Other sections of 10,966English abstracts in the COVID-19 Open Research Dataset. CODA-19 was created by248 crowd workers from Amazon Mechanical Turk within 10 days, and achievedlabeling quality comparable to that of experts. Each abstract was annotated bynine different workers, and the final labels were acquired by majority vote.The inter-annotator agreement (Cohen's kappa) between the crowd and thebiomedical expert (0.741) is comparable to inter-expert agreement (0.788).CODA-19's labels have an accuracy of 82.2% when compared to the biomedicalexpert's labels, while the accuracy between experts was 85.0%. Reliable humanannotations help scientists access and integrate the rapidly acceleratingcoronavirus literature, and also serve as the battery of AI/NLP research, butobtaining expert annotations can be slow. We demonstrated that a non-expertcrowd can be rapidly employed at scale to join the fight against COVID-19.

CODA-19: Using a Non-Expert Crowd to Annotate Research Aspects on 10,000+ Abstracts in the COVID-19 Open Research Dataset