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Research challenges and opportunities for using big data in global change biology.
Global Change Biology ( IF 10.8 ) Pub Date : 2020-08-16 , DOI: 10.1111/gcb.15317
Jianyang Xia 1 , Jing Wang 1, 2 , Shuli Niu 3
Affiliation  

Global change biology has been entering a big data era due to the vast increase in availability of both environmental and biological data. Big data refers to large data volume, complex data sets, and multiple data sources. The recent use of such big data is improving our understanding of interactions between biological systems and global environmental changes. In this review, we first explore how big data has been analyzed to identify the general patterns of biological responses to global changes at scales from gene to ecosystem. After that, we investigate how observational networks and space‐based big data have facilitated the discovery of emergent mechanisms and phenomena on the regional and global scales. Then, we evaluate the predictions of terrestrial biosphere under global changes by big modeling data. Finally, we introduce some methods to extract knowledge from big data, such as meta‐analysis, machine learning, traceability analysis, and data assimilation. The big data has opened new research opportunities, especially for developing new data‐driven theories for improving biological predictions in Earth system models, tracing global change impacts across different organismic levels, and constructing cyberinfrastructure tools to accelerate the pace of model‐data integrations. These efforts will uncork the bottleneck of using big data to understand biological responses and adaptations to future global changes.

中文翻译:

研究在全球变化生物学中使用大数据的挑战和机遇。

由于环境和生物数据的可用性大幅增加,全球变化生物学已进入大数据时代。大数据是指数据量大、数据集复杂、数据源多。最近对此类大数据的使用正在提高我们对生物系统与全球环境变化之间相互作用的理解。在这篇综述中,我们首先探讨了如何分析大数据,以确定生物对从基因到生态系统的全球变化的一般反应模式。之后,我们研究了观测网络和基于空间的大数据如何促进在区域和全球范围内发现新兴机制和现象。然后,我们通过大建模数据评估全球变化下的陆地生物圈预测。最后,我们介绍了一些从大数据中提取知识的方法,例如元分析、机器学习、可追溯性分析和数据同化。大数据开辟了新的研究机会,特别是为开发新的数据驱动理论以改进地球系统模型中的生物预测、追踪不同生物层面的全球变化影响以及构建网络基础设施工具以加快模型数据集成的步伐。这些努力将打破使用大数据了解生物反应和适应未来全球变化的瓶颈。特别是用于开发新的数据驱动理论,以改进地球系统模型中的生物预测,追踪不同生物层面的全球变化影响,以及构建网络基础设施工具以加快模型数据集成的步伐。这些努力将打破使用大数据了解生物反应和适应未来全球变化的瓶颈。特别是用于开发新的数据驱动理论,以改进地球系统模型中的生物预测,追踪不同生物层面的全球变化影响,以及构建网络基础设施工具以加快模型数据集成的步伐。这些努力将打破使用大数据了解生物反应和适应未来全球变化的瓶颈。
更新日期:2020-10-19
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