Artificial intelligence opens up new possibilities in research data management: from searching and structuring to documentation and quality assurance, all the way to the automation of recurring processes. At the same time, new challenges are emerging regarding reliability, data protection, scientific integrity, and accountability.

In our three-part workshop series, we explore the relationship between the technical aspects, practical applications and critical reflection of the use of AI in research data management. Participants will learn about various AI tools and use cases for research data management, test, and develop criteria for the meaningful and responsible use of AI in their own work.

Target group: Researchers, teaching staff, doctoral candidates and research support staff at the Berlin University Alliance with prior knowledge of the fundamentals of research data management. Please note that there will be no introduction to the basics of research data management.

Workshop language: English

Instructor: Dr. Katarzyna Biernacka (rti-studio)

Register for the workshops

10.11.2026 | 10:00-11:30 | Understanding AI and Using It Effectively for Research Data Management

Participants will gain a basic understanding of how generative AI and large language models work and what implications this has for their use for research data management. The session will use a sample research project to explore the areas in which AI can support research data management.

Contents:

• Fundamentals of generative AI and large language models

• Capabilities and limitations of current AI systems

• Choice of suitable AI tools

• AI-assisted research based on standards, requirements, and best practices

• Critical evaluation of sources and assessment of AI-generated research result

Registration form (deadline: 01.11.2026): www.berlin-university-alliance.de/objective-5-sharing-resources/PM-Anmeldung-CARDS-ki-fdm-ws-1/index.html 

17.11.2026 | 10:00-11:30 | Working with AI in Research Data Management

This workshop focuses on the practical application of generative AI. Participants will learn how the quality of the results is influenced by context, the task at hand, data and the desired output format, and how unstructured information can be processed systematically.

Contents:

• Context Engineering Instead 

• Structured Tasks: Role, Context, Task, Format, and Constraints

• Structuring Unstructured Information

• Key-Value Extraction and Structured Output Formats

• Hallucinations and Other Common Sources of Error

• Quality Assurance Through Source-Bound AI Systems

• Human Oversight and Verification

Registration form (deadline: 10.11.2026): https://www.berlin-university-alliance.de/objective-5-sharing-resources/PM-Anmeldung-CARDS-ki-fdm-ws-2/index.html?ts=1788175326 

24.11.2026 | 10:00-11:30 | Responsibility and Application of AI to Research Data Management Practice

This workshop focuses on entire work processes with AI in research data management. Participants distinguish between AI assistants, automated workflows, and more autonomous systems, and develop criteria for determining which tasks in research data management can be automated and in which areas human oversight remains essential.

Contents:

• AI assistants, workflows, agents, and agent-based systems

• Appropriate and inappropriate automation scenarios in FDM

• Human-in-the-loop and boundaries of responsibility

• Bias and responsibility

• Data protection and handling of sensitive research data

• Copyright, scientific integrity, and the EU AI Act

• Documentation of AI usage and traceability

• Use cases

Registration form (deadline: 20.11.2026): https://www.berlin-university-alliance.de/objective-5-sharing-resources/PM-Anmeldung-CARDS-ki-fdm-ws-3/index.html?ts=1788175779