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Application Note | Open Access

gcCov: Linked open data for global coronavirus studies

Wenyu Shi1, Guomei Fan1, Zhihong Shen2, Chuan Hu2, Juncai Ma1,3, Yuanchun Zhou2, Zhen Meng2, Songnian Hu1,3, Yuhai Bi4, Liang Wang4, Haiying Yu1,3, Siru Lin1, Xiuqiang Sun1, Xinjiao Zhang1, Dongmei Liu1, Qinlan Sun1( ), Linhuan Wu1,3 ( )
Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
Computer Network Information Center, Chinese Academy of Sciences, Beijing, China
State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
CAS Key Laboratory of Pathogenic Microbiology & Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China

Edited by Fangqing Zhao, Beijing Institutes of Life Science, Chinese Academy of Sciences, China

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mLife
Volume 1 Issue 1,
March 2022
Pages 92-95
DOI: 10.1002/mlf2.12008

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Cite this article:
Shi W, Fan G, Shen Z, et al. gcCov: Linked open data for global coronavirus studies. mLife, 2022, 1(1): 92-95. https://doi.org/10.1002/mlf2.12008

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Received: 05 June 2021
Accepted: 25 November 2021
Published: 16 March 2022
© 2022 The Authors. mLife published by John Wiley & Sons Australia, Ltd. on behalf of Institute of Microbiology, Chinese Academy of Sciences.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.