Arctic Data Systems in the ROADS Process
The crafting of the Sustaining Arctic Observing Networks’ (SAON) Roadmap for Arctic Observing and Data Systems (ROADS) has made significant progress. It sets out a multi-phase process and governance structure for defining Shared Arctic Variables (SAV) that will provide benefits across a wide range of stakeholders and scales. However, the focus so far has been on observing systems and work remains to define the data systems portion of ROADS. This presentation will examine how the ROADS guiding principles the have evolved for Arctic Observing Systems might be applied to Arctic Data Systems. Elements of those principles include: Indigenous Peoples’ equitable partnership and funding for their active participation is critical to ROADS from its inception through its implementation; All aspects of the ROADS process should support broadly shared benefit from the observing and data systems; The ROADS process should complement and integrate, without wasteful duplication, the current planning approaches used by existing networks (regional to global), activities and projects; ROADS should support stepwise development through a flexible, federated and evolving structure that allows grassroots identification of themes, infrastructures and regional foci. Challenges for Arctic Data Systems that will considered in this context include: Improving coordination among funders. Enhancing global data communities and governance structures. Supporting data community building, coordination, and engagement. Ensuring long-term support for data management and curation. Engaging with and enhancing existing activities rather than creating new initiatives. Facilitating a change in attitude from proprietary data to data as a common good. Improving education and training in data science. Building on interoperable standards and ethically open and FAIR data principles. Involving and respecting the perspective of Indigenous peoples in data collection and management. Embracing cloud platforms and new analytical techniques (e.g., AI).