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

Abstract

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 SummaryWe 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 networked communities work together to standardize and share data openly, the resulting web of research products is more readily findable, accessible, interoperable, and reusable. Ongoing support is crucial to develop and sustain the people, systems, and tools necessary to embrace ICON principles in Earth and Space Science Informatics now and in the future.

Integrated, Coordinated, Open, and Networked (ICON) Science to Advance the Geosciences: Introduction and Synthesis of a Special Collection of Commentary Articles

“…The "Open" principle of ICON is also intentionally defined to include the FAIR (findable, accessible, interoperable, reusable) data principles (Wilkinson et al, 2016). ICON is often used interchangeably with ICON-FAIR to make this more explicit, because as a general concept openness does not require being FAIR and vice versa, as highlighted in the ICON Collection's Earth and Space Science Informatics article (Hills et al, 2022). Some barriers to achieving the "Open" principle are consistent across fields and some are discipline-specific.…”

“…The importance of the "Coordination" principle extends beyond physical sample collection. In the Earth and Space Science Informatics article, Hills et al (2022) describe the importance of coordinated efforts "to implement standards for effective interdisciplinary data discovery and exchange…", yet point out that there are limitations in data reuse and discovery due to the lack of consistent and transparent protocols, for example, in data and code production, and processing methods across interdisciplinary teams.…”