Earth and Space Science Informatics Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

• Published in: Earth and space science (Hoboken, N.J.) N.J.), 2022-04, Vol.9 (4), p.n/a
• Main Authors: Hills, D. J., Damerow, J. E., Ahmmed, B., Catolico, N., Chakraborty, S., Coward, C. M., Crystal‐Ornelas, R., Duncan, W. D., Goparaju, L. N., Lin, C., Liu, Z., Mudunuru, M. K., Rao, Y., Rovetto, R. J., Sun, Z., Whitehead, B. P., Wyborn, L., Yao, T.
• Format: Article
• Language: English
• Subjects: Artificial intelligence; citizen science; Cloud computing; Collaboration; crowdsourcing; Data management; Datasets; Funding; geoinformatics; GEOSCIENCES; Informatics; Information sharing; Interdisciplinary aspects; interoperability; Machine learning; Observatories; Open data; Remote sensing; Satellites; Scale models; Science; Software; sustainability
• ISSN: 2333-5084; 2333-5084
• DOI: 10.1029/2021EA002108

This article is composed of three independent commentaries about the state of Integrated, Coordinated, Open, Networked (ICON) principles (Goldman, et al., 2021b, https://doi.org/10.1029/2021EO153180) in Earth and Space Science Informatics (ESSI) and includes discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (Section 2) Global collaboration, cyberinfrastructure, and data sharing; (Section 3) Machine learning for multiscale modeling; (Section 4) Aerial and satellite remote sensing for advancing Earth system model development by integrating field and ancillary data. ESSI addresses data management practices, computation and analysis, and hardware and software infrastructure. Our role in ICON science therefore involves collaborative work to assess, design, implement, and promote practices and tools that enable effective data management, discovery, integration, and reuse for interdisciplinary work in Earth and space science disciplines. Networks of diverse people with expertise across Earth, space, and data science disciplines are essential for efficient and ethical exchanges of findable, accessible, interoperable, and reusable (FAIR) research products and practices. Our challenge is then to coordinate the development of standards, curation practices, and tools that enable integrating and reusing multiple data types, software, multi‐scale models, and machine learning approaches across disciplines in a way that is as open and/or FAIR as ethically possible. This is a major endeavor that could greatly increase the pace and potential of interdisciplinary scientific discovery. Plain Language Summary We present commentaries on the state of “Integrated, Coordinated, Open, Networked (ICON) principles” in Earth and Space Science Informatics. ICON principles are meant to improve the research experience for all. Ultimately, data standardized according to community conventions and formats lead to more effective and efficient collaboration, data discovery, integration, and analyses. Data standards, tools, and machine learning developed using ICON principles enhance our understanding of Earth processes. Using ICON principles improves model results and efficacy, fosters interdisciplinary research, and provides a framework by which non‐experts can confidently contribute volunteered data and findings. Standardized data also provides reliable common resources to help train and benchmark machine learning algorithms. When