COVID-19++: A citation-aware Covid-19 dataset for the analysis of research dynamics

Galke, L., Seidlmayer, E., Lüdemann, G., Langnickel, L., Melnychuk, T., Förstner, K. U., Tochtermann, K., & Schultz, C. (2021). COVID-19++: A citation-aware Covid-19 dataset for the analysis of research dynamics. In Y. Chen, H. Ludwig, Y. Tu, U. Fayyad, X. Zhu, X. Hu, S. Byna, X. Liu, J. Zhang, S. Pan, V. Papalexakis, J. Wang, A. Cuzzocrea, & C. Ordonez (Eds.), Proceedings of the 2021 IEEE International Conference on Big Data (pp. 4350-4355). Piscataway, NJ: IEEE.
COVID-19 research datasets are crucial for analyzing research dynamics. Most collections of COVID-19 research items do not to include cited works and do not have annotations from a controlled vocabulary. Starting with ZB MED KE data on COVID-19, which comprises CORD-19, we assemble a new dataset that includes cited work and MeSH annotations for all records. Furthermore, we conduct experiments on the analysis of research dynamics, in which we investigate predicting links in a co-annotation graph created on the basis of the new dataset. Surprisingly, we find that simple heuristic methods are better at predicting future links than more sophisticated approaches such as graph neural networks.
Publication type
Proceedings paper
Publication date
2021

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