Cyberinfrastructure for sustainability sciences
Cyberinfrastructure for sustainability sciences
复制标题
可持续科学的网络基础设施
DOI：
10.1088/1748-9326/acd9dd
复制
发表时间：
2023
影响因子：
6.7
通讯作者：
Walton, Amy
中科院分区：
文献类型：
--
作者：
Song, Carol X.;Merwade, Venkatesh;Wang, Shaowen;Witt, Michael;Kumar, Vipin;Irwin, Elena;Zhao, Lan;Walton, Amy
要实现联合国的可持续发展目标(SDGs)，需要采取一种综合的科学方法，将许多学科的专业知识、数据、模型和工具结合起来，在不同的空间和时间尺度上应对可持续性挑战。这种全面的方法虽然必要，但却加剧了研究人员已经面临的大数据和计算挑战。可持续性研究中的许多挑战可以通过利用先进的网络基础设施(CI)的力量来解决。本白皮书的目的是强调CI的关键组成部分和技术，以满足SDG研究界的数据和计算需求。概述了美国的CI生态系统，特别关注了学术机构、政府机构和行业在国家、地区和地方各级所做的投资。尽管有这些投资，但本文确定了在可持续发展研究中采用CI的障碍，包括但不限于获得支持结构的机会；招聘、留住和培养灵活的劳动力；以及缺乏当地基础设施。讨论了相关的CI组件，如数据、软件、计算资源和以人为中心的进步，以探索如何解决这些障碍。该文件强调了根据几次专家会议的结果在实现可持续发展目标方面面临的多重挑战。这些问题包括数据和特定领域模型的多尺度整合、数据的可用性和可用性、不确定性量化、作出决策的时空尺度与科学分析产生的信息之间的不匹配，以及科学可再现性。我们讨论了正在进行的和未来的研究，以架设CI和可持续发展目标之间的桥梁，以应对这些挑战。
Meeting the United Nation’ Sustainable Development Goals (SDGs) calls for an integrative scientific approach, combining expertise, data, models and tools across many disciplines towards addressing sustainability challenges at various spatial and temporal scales. This holistic approach, while necessary, exacerbates the big data and computational challenges already faced by researchers. Many challenges in sustainability research can be tackled by harnessing the power of advanced cyberinfrastructure (CI). The objective of this paper is to highlight the key components and technologies of CI necessary for meeting the data and computational needs of the SDG research community. An overview of the CI ecosystem in the United States is provided with a specific focus on the investments made by academic institutions, government agencies and industry at national, regional, and local levels. Despite these investments, this paper identifies barriers to the adoption of CI in sustainability research that include, but are not limited to access to support structures; recruitment, retention and nurturing of an agile workforce; and lack of local infrastructure. Relevant CI components such as data, software, computational resources, and human-centered advances are discussed to explore how to resolve the barriers. The paper highlights multiple challenges in pursuing SDGs based on the outcomes of several expert meetings. These include multi-scale integration of data and domain-specific models, availability and usability of data, uncertainty quantification, mismatch between spatiotemporal scales at which decisions are made and the information generated from scientific analysis, and scientific reproducibility. We discuss ongoing and future research for bridging CI and SDGs to address these challenges.
登录
查看更多内容
DOI：
10.1080/13658816.2012.664276
发表时间：
2012-01-01
影响因子：
5.7
作者：
Anselin, Luc;Rey, Sergio J.
通讯作者：
Rey, Sergio J.
全球水文深度学习模型中的区域化：从物理描述符到随机向量
影响因子：
5.4
作者：
Li, Xiang;Khandelwal, Ankush;Jia, Xiaowei;Cutler, Kelly;Ghosh, Rahul;Renganathan, Arvind;Xu, Shaoming;Tayal, Kshitij;Nieber, John;Duffy, Christopher
通讯作者：
Duffy, Christopher
影响因子：
16.6
作者：
J. Willard;X. Jia;Shaoming Xu;M. Steinbach;Vipin Kumar
通讯作者：
J. Willard;X. Jia;Shaoming Xu;M. Steinbach;Vipin Kumar
影响因子：
2.7
作者：
Wang, Shaowen
通讯作者：
Wang, Shaowen
地理学中的伦理：为地理想象赋予道德形式
影响因子：
2.2
作者：
J. Proctor
通讯作者：
J. Proctor