Rise of the (learning) machines: is your eResearch data capability ready for the next generation of AI-augmented research demands?
Year: 2024
Type: article
Abstract: Significant challenges in research data management (RDM), as detailed in the November 2023 report (DOI: 10.5281/zenodo.10076883), result in research institutions struggle to identify, catalogue, value, and manage their data. Core RDM capabilities are often described as inaccessible or requiring considerable effort. The rise of Artificial Intelligence (AI) and machine learning as key data analysis technologies underscores the urgency of making access to research data machine-readable, driving a need to adopt a national research data taxonomy. However, this step, while necessary, needs to be extended to be truly usable and sustainable; it must be complemented by seamless integration with RDM infrastructure to enable automated data discovery by providing scalable, safe and performant data access. Drawing on a decade of research data management engagements, NetApp's experts have gathered a wealth of insights, comparing Australian and international experiences. These unique insights will be shared, highlighting the strategies and best practices that have enabled our partner institutions to overcome RDM challenges and build robust capabilities. The session will provide attendees with actionable knowledge, informed by a rich comparative analysis, to navigate their RDM complexities. By leveraging these insights, participants can strengthen their RDM systems, ensuring effective data management in an increasingly AI-integrated research environment.
Author: Wu, Benjamin
Institution: NetApp (United States)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6930554080
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