Description: Research Data Management (RDM) has emerged as a key component of the broader DRI (Digital Research Infrastructure) ecosystem. FAIR principles (making data Findable, Accessible, Interoperable, and Reusable) have been at the core of RDM initiatives for almost a decade now -- but how has our understanding and application of these principles evolved to address emerging technologies such as Machine Learning and AI? To answer this, we look at a recent policy document, “Enabling Global FAIR Data: WorldFAIR Policy Recommendations for Research Infrastructures”, published by CODATA and WorldFAIR in 2024. The first half of this session will provide a high-level distillation of, and reflection upon, this global policy brief, flagging areas where Canadian stakeholders can better support and promote FAIR data practices. The second half of this session will address the importance of the FAIR principles at a time when data are being suppressed or deleted, data agencies are being gutted or shuttered, and data-driven decision making is devalued and disparaged. Real-world examples will be provided to illustrate the range and impact of the data deletion chaos we are witnessing in real time, and possible responses to these actions.
Teachers: Ann Allan (Compute Ontario) and Jeff Moon (Compute Ontario)
Level: Introductory
Format: Lecture
Certificate: Attendance
Prerequisites: None