Hampton Stephanie E, Jones Matthew B, Wasser Leah A, Schildhauer Mark P, Supp Sarah R, Brun Julien, Hernandez Rebecca R, Boettiger Carl, Collins Scott L, Gross Louis J, Fernández Denny S, Budden Amber, White Ethan P, Teal Tracy K, Labou Stephanie G, Aukema Juliann E
Stephanie E. Hampton (
Bioscience. 2017 Jun 1;67(6):546-557. doi: 10.1093/biosci/bix025. Epub 2017 May 3.
The scale and magnitude of complex and pressing environmental issues lend urgency to the need for integrative and reproducible analysis and synthesis, facilitated by data-intensive research approaches. However, the recent pace of technological change has been such that appropriate skills to accomplish data-intensive research are lacking among environmental scientists, who more than ever need greater access to training and mentorship in computational skills. Here, we provide a roadmap for raising data competencies of current and next-generation environmental researchers by describing the concepts and skills needed for effectively engaging with the heterogeneous, distributed, and rapidly growing volumes of available data. We articulate five key skills: (1) data management and processing, (2) analysis, (3) software skills for science, (4) visualization, and (5) communication methods for collaboration and dissemination. We provide an overview of the current suite of training initiatives available to environmental scientists and models for closing the skill-transfer gap.
复杂且紧迫的环境问题的规模和严重程度,使得通过数据密集型研究方法来进行综合且可重复的分析与综合变得十分迫切。然而,近期技术变革的速度之快,导致环境科学家缺乏开展数据密集型研究所需的适当技能,而他们比以往任何时候都更需要获得更多的计算技能培训和指导。在此,我们通过描述有效处理异构、分布式且快速增长的可用数据所需的概念和技能,为提升当前及下一代环境研究人员的数据能力提供了一份路线图。我们明确了五项关键技能:(1)数据管理与处理,(2)分析,(3)科学软件技能,(4)可视化,以及(5)协作与传播的沟通方法。我们概述了目前可供环境科学家使用的一系列培训举措以及缩小技能转移差距的模式。