Data curation during a pandemic and lessons learned from COVID-19

被引：0
|
作者：
Moritz U. G. Kraemer
Samuel V. Scarpino
Vukosi Marivate
Bernardo Gutierrez
Bo Xu
Graham Lee
Jared B. Hawkins
Caitlin Rivers
David M. Pigott
Rebecca Katz
John S. Brownstein
机构：
[1] University of Oxford,Department of Zoology
[2] Northeastern University,Network Science Institute
[3] Santa Fe Institute,Department of Computer Science
[4] University of Pretoria,School of Biological and Environmental Sciences
[5] Universidad San Francisco de Quito,Department of Earth System Science
[6] Tsinghua University,Research Software Engineering Group
[7] University of Oxford,Institute for Health Metrics and Evaluation, Department of Health Metrics Sciences
[8] Boston Children’s Hospital,undefined
[9] Harvard Medical School,undefined
[10] Johns Hopkins Center for Health Security,undefined
[11] University of Washington,undefined
[12] Georgetown University,undefined
来源：
Nature Computational Science | 2021年 / 1卷
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摘要：
Detailed, accurate data related to a disease outbreak enable informed public health decision making. Given the variety of data types available across different regions, global data curation and standardization efforts are essential to guarantee rapid data integration and dissemination in times of a pandemic.
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页码：9 / 10
页数：1
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