Gaps, Needs, and Potential Solutions for Cloud-Native Data in Natural and Engineering Sciences

This publication presents the consolidated outcomes of an interactive breakout session held during the Symposium on Cloud-Native Data in Natural and Engineering Sciences on 1 April 2026 in Utrecht, the Netherlands. The symposium brought together stakeholders from research, data infrastructures, and policy domains to explore challenges and opportunities in adopting cloud-native approaches for data-intensive research in Natural and Engineering Sciences (NES). As part of the program, participants engaged in a structured breakout session designed to identify and prioritize key gaps, needs, and potential solutions across three thematic areas: (a) cloud-native data formats and standards,(b) cloud-native data infrastructure, and(c) training and capacity development. Contributions were captured as individual post-it notes, which were subsequently reviewed and endorsed by participants through a lightweight voting mechanism (+1 marks indicating agreement). The resulting dataset reflects both the diversity of perspectives in the community and a collective prioritization of critical issues. This publication contains a curated virtual board that faithfully represents the original contributions and their relative support. The material provides a transparent and community-driven snapshot of the current state of cloud-native data practices in NES, highlighting areas where coordinated action is needed. All symposium participants who contributed to the breakout session are included as authors of this record, reflecting the collaborative nature of the exercise. The activity was conducted as part the CLOUD-NES project, which aims to stimulate the adoption of cloud-native methods for publishing, accessing, and processing research data. This output serves as both a reference for the CLOUD-NES project and a resource for the broader community, supporting ongoing efforts to align national initiatives with international best practices and to strengthen the ecosystem for cloud-native data in science.