Program

Tutorial descriptions

1st slot: September 23, 13:00-15:00

Tutorial A: Best Practices for FAIR Annotation: From Humans to LLMs 

Instructors: Raia Abu Ahmad (DFKI Berlin) & Ekaterina Borisova (DFKI Berlin)

This tutorial will introduce best practices for data annotation with a focus on FAIR principles and real-world applications. We will discuss existing tools and different approaches to annotation, including manual and semi-automatic LLM-assisted labelling. The session will be concluded with a hands-on exercise to learn how to conduct an annotation project in practice. 

Tutorial B: A Hands-on Tutorial to Explore Methods Hub

Instructors: Fakhri Momeni (GESIS) & Taimoor Khan (GESIS)

The Methods Hub is an open-access platform designed to support social science researchers in discovering, accessing, understanding, and applying computational methods. This session introduces the Methods Hub as a portal for computational methods and demonstrates its core functionalities, guiding participants through the process of finding suitable methods, accessing them, and understanding their purpose. The session also highlights the utility of tutorials that it contains, serving as a step-by-step guide to demonstrate the application of a method for a concrete task. As an interactive session, the participants will select from predefined use case scenarios and corresponding tasks based on their interests. Participants will be grouped by the selected use case scenario and collaboratively explore relevant methods on the Methods Hub. They will be encouraged to discuss methodological options, make informed decisions, and carry out the assigned task using the available resources. Throughout the activity, the Methods Hub team will provide facilitation, technical support, and time management to ensure productive engagement. The session will conclude with a plenary discussion involving all participant groups and the Methods Hub team, reflecting on the rationale behind methodological choices, challenges encountered, and collaborative decision-making processes. This feedback on participants' experiences with the portal, its usability, strengths, limitations, and perceived relevance to their research needs, will shape the subsequent iterative refinements of the platform. 

2nd slot: September 23, 15:30-17:30

Tutorial C: Applied AI Ethics – Identifying Ethical Opportunities and Risks in AI Innovation

Instructor: Johannes Katsarov (Leuphana University Lüneburg)

Short Description: Many researchers and developers are still unaware of ethical opportunities and risks revolving around innovative AI applications. In this workshop, we will playfully train our skills to identify and address ethical opportunities and risks early in the process of designing innovative AI solutions. Because… the sooner you address them, the better your outcome will be later, and the less time will be wasted on corrections. The workshop will begin with a short presentation on AI ethics, introducing key principles, opportunities, and challenges. The remainder of the time, we will play a new “Responsible AI Innovation Game” in small groups, and reflect on the experience.

Tutorial D: FAIR Digital Objects (FDOs) for Scholarly Data & AI Readiness

Instructors: Hajira Jabeen (Uniklinik Köln) & Zeyd Boukhers (Fraunhofer)

FAIR Digital Objects (FDOs) are emerging as a foundational mechanism for making scholarly research artifacts, such as datasets, software, and research papers, FAIR (Findable, Accessible, Interoperable, and Reusable) and machine-actionable.

As research increasingly relies on automation, machine learning, and AI-based systems, there is a growing need to represent digital artifacts in a way that machines can interpret, connect, and process without human intervention. FDOs directly address this need by offering a standardized, structured format that encapsulates any digital object using persistent identifiers (PIDs), rich metadata, and type information. In this tutorial, we explore how FDOs are not just about good data management—they are about creating a truly AI-ready infrastructure for science.

This tutorial introduces the architecture and logic of FDOs, then demonstrates how they can be applied in practice using the FDO Manager.

Through a hands-on session, participants will learn how to create and register an FDO, define its metadata, and inspect how machines interact with it. By showcasing the creation of FDOs for different research artifacts—data, papers, and software. Ultimately, the tutorial emphasizes the transformative potential of FDOs: they are not just containers for metadata, but building blocks for a future where all digital research outputs are connected, reusable, and machine-operable by design.

This makes them indispensable for the next generation of open science and AI-powered discovery.