Research data management across borders and cultures: Working globally to find local solutions

As a global organisation with over 15,000 members across 150+ countries, the Research Data Alliance's (RDA)[1] strength lies in its ability to connect researchers, policymakers, and infrastructure providers to address research data management and open science challenges in an open and community driven way. For over 10 years, the global RDA community has been developing and producing recommendations for research data management in disciplinary and organizational contexts. During this time, we have seen major shifts in technology, such as the explosion of AI, and growth in international connectivity and collaboration. Artificial Intelligence (AI) has rapidly transformed various sectors, further driven by mainstream adoption of generative AI technologies like ChatGPT. This swift progress has sparked both high expectations and uncertainties about AI's impact. AI relies on data, software, and computational resources to generate outputs like predictions and decisions. Significant investments are being made globally, both by public and private entities. The RDA has identified AI as a strategic topic of global importance for the community. Challenges like data quality, legal issues, and the digital divide must be addressed to harness AI responsibly. Global collaboration to address these challenges is essential. It is critical that we develop standards and practices on an international scale and have the frameworks to implement them at a local and / or disciplinary level. Since 2019, a global cohort has been working on different elements of so called "Open Science Commons" or "Data commons providing a shared virtual space or platform that offers a marketplace for data and services. Examples include the European Open Science Cloud[2], the Australian Research Data Commons (ARDC)[3], open government portals and initiatives outside traditional research contexts. Each of these initiatives provides the local research infrastructure to address the challenges presented by AI's data-intensive nature and far-reaching impact across research disciplines. Coordinating across these initiatives to enable a network of interoperable data commons is the goal of the RDA Global Open Research Commons[4] work under the umbrella of the RDA. This session will outline the huge progress of the activity, the model developed and examples of implementation of the model in new commons, existing networks, and interoperability analysis. A new activity demonstrates how the model will be instrumental in supporting FAIR research within the health data spaces and data commons[5]. [1] Research Data Alliance (RDA – https://www.rd-alliance.org/ (11 April 2025) [2] European Open Science Cloud - EU Node https://open-science-cloud.ec.europa.eu/ (11 April 2025) [3] Australian Research Data Commons (ARDC) https://ardc.edu.au/ (11 April 2025) [4] RDA GORC International Implementations Working Group https://www.rd-alliance.org/groups/gorc-international-implementations-working-group-gorc-ii-wg/activity/ (11 April 2025) [5] RDA Health Data Commons GORC Profile WG https://www.rd-alliance.org/groups/health-data-commons-gorc-profile-wg/activity/ (11 April 2025)