Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions
Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions
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将生态学定位为一门大数据科学：当前进展、挑战与解决方案
DOI：
10.1093/biosci/biy068
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发表时间：
2018-08-01
期刊：
影响因子：
10.1
通讯作者：
Williams, John W.
中科院分区：
文献类型：
--
作者：
Farley, Scott S.;Dawson, Andria;Williams, John W.
生态学已经加入了大数据的世界。两个互补的框架定义了大数据：超过个人或学科分析能力的数据，或者是数量、多样性、准确性和速度的“四个V”轴。多样性在生态信息学中占主导地位，限制了生态科学的可扩展性。数量差异很大。生态速度很低，但随着数据吞吐量和社会需求的增加而增长。生态大数据系统包括原位和远程传感器、社区数据资源、生物多样性数据库、公民科学和永久站。技术解决方案包括开发开放的代码和数据共享平台，可以处理异构数据和不确定性来源的灵活统计模型，以及将高速计算交付给大容量分析的云计算。文化解决方案包括针对早期和当前科学工作者的培训，以及加强生态学家和数据科学家之间的合作。更广泛的目标是最大限度地提高生态洞察和预测的能力、可扩展性和及时性。
Ecology has joined a world of big data. Two complementary frameworks define big data: data that exceed the analytical capacities of individuals or disciplines or the "Four Vs" axes of volume, variety, veracity, and velocity. Variety predominates in ecoinformatics and limits the scalability of ecological science. Volume varies widely. Ecological velocity is low but growing as data throughput and societal needs increase. Ecological big-data systems include in situ and remote sensors, community data resources, biodiversity databases, citizen science, and permanent stations. Technological solutions include the development of open code-and data-sharing platforms, flexible statistical models that can handle heterogeneous data and sources of uncertainty, and cloud-computing delivery of high-velocity computing to large-volume analytics. Cultural solutions include training targeted to early and current scientific workforce and strengthening collaborations among ecologists and data scientists. The broader goal is to maximize the power, scalability, and timeliness of ecological insights and forecasting.