Research Data Management in Data Science and AI - Avoiding a Replicability Crisis (RDM4AI)
Würzburg
09:08
Ricardo Usbeck (Leuphana), Angelie Kraft (Leuphana) and Leyla Jael Castro (ZB MED)
This tutorial, supported by NFDI4DataScience, addresses the replicability crisis in Artificial Intelligence, with a particular focus on machine-based learning approaches. It covers the research data life cycle, emphasizing best practices for data/software management, metadata, documentation, versioning, and sharing practical examples on how to achieve such practices. Additionally, it introduces model and dataset cards for comprehensive reporting, and advocates for the adoption of FAIR Data Principles to transform research outputs into FAIR Data Objects (FDOs). Aimed at academics across all domains working on AI fields, the tutorial provides practical guidance to enhance transparency and accountability, fostering a more reliable and impactful AI landscape.