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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.

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Through broad and inclusive partnerships, AGU aims to advance discovery and solution science that accelerate knowledge and create solutions that are ethical, unbiased and respectful of communities and their values. Our programs include serving as a scholarly publisher, convening virtual and in-person events and providing career support. We live our values in everything we do, including through our net zero energy renovated building in Washington, D.C. and our Ethics and Equity Center, which fosters a diverse and inclusive geoscience community to ensure responsible conduct.

<p>In Earth and Biological sciences, data are often preserved and publicly available in data repositories where the data are citable by DOIs and published under a Creative Commons CC-BY license. Researchers combine many datasets across disciplines, repositories, and regions to better understand processes, patterns, and drivers. Citing these many datasets is difficult as the large number does not fit into the references section of a paper but the licenses of the datasets require that credit is given to their creators.</p><p> </p><p>The Data Citation Community of Practice (CoP) was formed to target such challenges in data citation and other scholarly work that will support indexing and measuring the impact. The CoP identified a container as a solution for large numbers of data citations that holds the citations and its internal format, which is referred to as a 'reliquary'. The existing dataset collection methods have been gathered and evaluated using concrete citation use cases. Requirements for the reliquary content have been identified and applied to the use cases. In this presentation, we will report on the current progress on an approach to building a reliquary.</p><p> </p><p>Reliquaries are an important part of enabling cross-disciplinary analysis of large amounts of data stored in many repositories. The challenge with a reliquary will be to design a method that works across diverse repositories and domain citation practices and to enhance the indexing system to direct credit to the reliquary content and authors. The CoP is in the process of setting up a Research Data Alliance (RDA) Working Group on Complex Citations in the Earth, Space, and Environmental Sciences to broaden the discussion and to find further use cases for evaluation and interested early adopters.</p>