"Traditional" open data is concerned mostly with the open publication and sharing of data after it is deemed "finished". In this session, we focus on the practices and processes for open collaboration on research data during the development phase, i.e. before the data is finished and published. What works, which barriers do we find?

What are the opportunities and challenges of conducting and communicating research in multiple languages?

The Research Data Act (Forschungsdatengesetz, FDG) is a planned federal law in Germany, which aims to improve and simplify data access by researchers, both from public and private entities.

In March 2024, the Federal Ministry for Education and Research (BMBF) published a paper outlining the planned key aspects of the Research Data Act: Better access to public sector data, a "German Micro Data Center", harmonized data privacy rules and improved findability of data sets (the full paper can be found here: https://www.bmbf.de/SharedDocs/Downloads/de/2024/240306_eckpunktepapier-forschungsdaten.html ). The complete draft of the act is expected to enter the parliamentary procedure in October/November.

After a short summary on the current state of the legislative process, we will discuss whether the planned measures will actually help to reach the stated goals and if not, what should - or should not - be done instead.

Library and Information Science, especially in Germany, seems to have a conformism bias when it comes to shadow libraries. Statements from main actors in the field (e.g. most recently: https://annas-archive.org/blog/critical-window.html) don’t find a counterpart in independent research on the topic.

Although the challenge at hand could hardly be more pressing, given e.g.

* German research institution collectively to hand over large parts of their acquisition budget (hence, their bargaining power) to the science publishers’ oligopoly, tellingly calling *this* their “transformation” strategy, the DEAL.

* Over and over, blanket permissions to process and redistribute large parts of human knowledge are claimed by big publishers and big tech, nurturing their LLMs, while libraries like the Internet Archive are even denied to allow individual readers to lend ebooks (https://blog.archive.org/2024/09/04/internet-archive-responds-to-appellate-opinion/)

Though by now, almost all governments officially agree to the goal of the Open Access transformation, chances are, we are collectively missing this goal.

In this session, we will check:

* If shadow libraries might turn out as the most likely plan B for OA transformation and

* if yes, what does that mean in practice – especially, in relation to (official, government funded) library work in a country like Germany.

Join us for an interactive hands-on workshop on the dynamic intersection of Artificial Intelligence (AI) and Open Science. We will focus on the Open Research Knowledge Graph (ORKG) Ask—a cutting-edge, community-based research discovery infrastructure powered by an open-source Large Language Model (LLM). We will explore how Open Science practices can be enhanced by AI-driven discovery services and, conversely, how AI and LLMs can significantly improve research workflows and Open Science practices.

In this context, we will also critically examine the challenge of maintaining rigor of semantic content while embracing openness and participation. This workshop aims to equip participants with practical skills in using ORKG Ask for advanced research discovery, while also fostering a deeper understanding of the challenges and opportunities in integrating AI with Open Science to maintain both rigor and inclusivity.

Learning Goals:

Practical Skills: Gain hands-on experience with ORKG Ask for advanced research discovery.

Critical Insight: Learn to integrate open-source LLMs into your research workflow to enhance efficiency and insight generation while promoting Open Science.

Balanced Perspective: Develop strategies to navigate the tension between openness and rigor in the evolving landscape of participatory science.

Materials Needed: Laptop/PC/tablet with internet access.

This session will not only provide you with the tools to enhance your research workflows but will also encourage a thoughtful discussion on the future of Open Science. Join us in shaping a more participatory yet rigorous scientific future.

In this session we talk about:

Which successes have you achieved by practicing Open Science in your career that you would not have achieved otherwise?

Which success stories do you know from others?

I would like to collect success stories that we can all share across our networks and use to illustrate the concrete benefits of OS practices.