English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

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

MPS-Authors
There are no MPG-Authors in the publication available
External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Supplementary Material (public)
There is no public supplementary material available
Citation

Galke, L., Seidlmayer, E., Lüdemann, G., Langnickel, L., Melnychuk, T., Förstner, K. U., et al. (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, et al. (Eds.), Proceedings of the 2021 IEEE International Conference on Big Data (pp. 4350-4355). Piscataway, NJ: IEEE.


Cite as: https://hdl.handle.net/21.11116/0000-0009-FA0B-2
Abstract
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.