Research Data Management and Archiving – Powering the Next Wave of AI Development

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24 hours

About Course

Date: 25th – 27th February 2026

Venue: Online (MS Teams)

Training Fee: $100

Registration closes on 18th February 2026

Learning Objectives

  • To explain the link between RDM, archiving, and AI deployment.
  • To apply FAIR principles and metadata standards to structure AI-ready datasets.
  • To implement workflows for data collection, cleaning, curation, and long-term storage.
  • To evaluate ethical, legal, and policy implications of AI-driven data reuse.
  • Design institutional data governance strategies to foster AI innovation.
  • Produce a comprehensive Data Management Plan (DMP) aligned with AI deployment needs

Teaching Methods

  • Lectures: Introduce key concepts and frameworks
  • Case Studies: Highlight successes and failures in AI/data projects
  • Hands-on Labs: Apply tools like CKAN. Dataverse.
  • Group Work: Collaborative design of DMPs

What Will You Learn?

  • Understand core concepts of data management
  • Be able to draft and implement a Data Management Plan
  • Organize and document research data effectively
  • Identify storage, sharing, and preservation strategies
  • Align practices with institutional and funder requirements

Course Content

Data as the Engine of AI

Research Data Lifecycles and the FAIR Paradigm

Data Collection and Curation for AI

Archiving and Long-Term Preservation

From Data to AI Pipelines

Policy, Ethics, and Governance

Institutional and Global Perspectives

Capstone Project

Instructors

LH

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