FAIR data management for artificial intelligence / Upravljanje podatkov za umetno inteligenco po načelih FAIR

Monday 8 Jun 2026, 09:00 → 13:00 Europe/Ljubljana

predavalnica Elektronike na Teslovi 30 (Teslova ulica 30)

Panče Panov (Jožef Stefan Institute)

Course provider: Jožef Stefan Institute (JSI) 
Instructors: Panče Panov (JSI)

Learning objectives: 

1. Understand AI data assets across the lifecycle: how datasets, labels, dataset splits, features, and evaluation artifacts evolve from collection to reuse;
2. Apply the FAIR principles to AI work: make data and outputs easier to find, access, combine, and reuse (for teams and future projects);
3. Create an actionable DMP for AI projects: a lightweight plan that supports reproducibility, handover, and compliance; and
4. Handle constraints responsibly: recognize sensitive data, ethical considerations, access limitations, and industry vs research expectations.

Course content:

• AI data assets and data life cycle (raw/processed data, labels, splits, evaluation artefacts);
• Review of the FAIR principles in the context of AI projects;
• Finding, accessing and reusing data for AI (including access conditions and licensing);
• Data interoperability for AI (formats, metadata, label definitions and basic standards);
• Data repositories and sharing strategies for AI datasets and related artefacts;
• Dealing with confidential, personal, sensitive and private data, and ethical aspects in AI;
• Data management plan (AI-focused): structure of an AI-oriented DMP, use of FAIR principles, and examples of best practices and tools;
• Data management in research and industry: open data/open science vs. industrial constraints (governance, IP, security) in AI projects.

Learning outcomes: By the end of the training, participants can:

1. Explain the AI data lifecycle and basic good practices (structure, documentation, versioning, provenance);
2. Perform a basic FAIR check on an AI dataset/project and list concrete “quick wins” (metadata, access statement, license, formats);
3. Draft a short AI-focused DMP (1–2 pages) that a team can actually follow; and
4. Identify when extra safeguards are required (personal/confidential data, restricted access, IP) and propose sensible mitigations. 

09:00 → 09:15 Welcome and Introduction

Speaker: Dr Panče Panov

09:15 → 10:30 Block 1: AI data lifecycle & FAIR principles

Introduction to AI data assets (raw data, processed data, labels, dataset splits, features, evaluation artefacts) and how they evolve across the data lifecycle. Review of the FAIR principles and what each letter means in the context of AI projects.

Speaker: Dr Panče Panov

10:30 → 10:45 Break

10:45 → 12:00 Block 2: Finding, sharing & interoperability

Practical session on how to find, access and reuse AI datasets, including access conditions and licensing. Discussion of interoperability for AI: file formats, metadata schemas, label definitions and basic standards. Overview of data repositories and sharing strategies for AI datasets and related artefacts.

Speaker: Dr Panče Panov

12:00 → 12:10 Break

12:10 → 12:55 Block 3: Sensitive data, ethics, industry constraints & DMP

Working with confidential, personal and sensitive data in AI, and the main ethical considerations. Open data and open science versus industrial constraints (governance, IP, security). Structure of an AI-focused data management plan and supporting tools.

Speaker: Dr Panče Panov

12:55 → 13:00 Wrap-up and Q&A

Speaker: Dr Panče Panov