updates speakers and abstract section

Author: astruck

content/RSdays/2026/Berlin/_index.md

@@ -27,8 +27,8 @@ The aim of the Research Software Day is to **learn, share and strengthen local c
 |**13:45-14:30**| **Networking and Coffee** <br/>with booths by <br/>- [FuturRSI](https://www.futursi.de/)<br/> - [Research Software Engineering Award](https://rse.gi.de/rse-award) der [Klaus Tschira Stiftung](https://klaus-tschira-stiftung.de/)<br> - [Open Research Office Berlin](https://www.open-research-berlin.de/) <br> - Wikimedia & Research Software Discovery|
 |**14:30-15:30**| **Session 2: Workshops, Talks and Meetups**
 | |**(2.1) Unveiling the Iceberg: Enhancing the Quality and Visibility of Research Software at Helmholtz** <br>*Antonia Schrader (Helmholtz Open Science Office)*|
-| |**(2.2) Connecting the Dots: Publications, Data, Models, and Code in the GenAI Era** <br>*Prof. Sonja Schimmler (TU Berlin, Fraunhofer FOKUS, Weizenbaum-Institut)*|
-| |**(2.3) RSE Education: Into the Light** <br>*Julian Dehne (GI), Magnus Hagdorn (Charité), Nick Del Grosso (ibehave.nrw)*|
+| |**(2.2) Connecting the Dots: Publications, Data, Models, and Code in the GenAI Era** <br>*Prof. Sonja Schimmler (TU Berlin, Fraunhofer FOKUS, Weizenbaum-Institut), Dr. Angelie Kraft (Weizenbaum-Institut)*|
+| |**(2.3) RSE Education: Into the Light** <br>*Julian Dehne (GI), Magnus Hagdorn (Charité), Nick Del Grosso (University of Bonn, ibehave.nrw)*|
 | |**(2.4) Reproducible Computational Analyses** <br>*Jochen Knaus (Weizenbaum-Institut)*|
 |15:30-16:00| Coffee Break |
 |**16:00-17:00**| **Session 3: Workshops, Talks and Meetups**
@@ -93,9 +93,12 @@ Open research software is central to transparent and reproducible science: only
 Generative AI is reshaping the research landscape, making transparency and reproducibility more critical than ever. As AI-augmented research cycles rise, this shift concerns all digital artifacts: publications, data, models, and code. This one-hour session explores the essential competencies and infrastructures required for this new era, featuring insights into the NFDI (National Research Data Infrastructure) and Data Competency Centers. Designed for both beginners and experts, the session opens with lightning talks on how GenAI reshapes science and redefines the necessary competencies and infrastructures. This transitions into a hands-on session, where participants engage in peer-to-peer discussions to navigate complex transparency and reproducibility challenges. The session culminates in a collaborative synthesis, drafting an outline for a joint paper to ensure a tangible community contribution.
 
 
-**(2.3) RSE Education: Into the Light** <br>*Julian Dehne (GI), Magnus Hagdorn (Charité), Nick Del Grosso (ibehave.nrw)*
+**(2.3) RSE Education: Into the Light** <br>*Julian Dehne (GI)*
 RSE Education has started out in some dark corners. PhD students may have had some practical tips from their supervisor. Colleagues meet online, and Stackoverflow has been relevant in building communities of interest. With the rise of the field, RSE education has stepped more and more into the light. First, mentoring and consulting was professionalized. Magnus Hagdorn talks about the Capentries and how they have shaped the RSE community as educators. Next, RSE education as a service was invented to move RSE workshops away from the central IT units toward a more method and research-centric support infrastructure. Nick del Grosse is going to talk about his experience leading such as service group and how he perceives RSE education from that angle. Finally, RSE education is stepping into the light fully by getting recognized and institutionalized as university classes. Julian Dehne talks about his experience coordinating the effort for guidelines for an RSE master as well as his two first RSE-classes taught in 2026 at RWTH.
 
+**Building Research Software Capacity with Training-Focused RSE Infrastructure: Experiences from the iBOTS Project** <br>*Nick Del Grosso (University of Bonn, ibehave.nrw)* 
+The iBehave Open Technology Support (iBOTS) group at the University of Bonn builds research software engineering capacity for neuroscience through embedded consulting, practical training, and reusable teaching materials. Using a training-driven philosophy, we support more than 40 labs to help researchers move from fragile scripts to reproducible workflows with Python, Git and GitHub, workflow automation, and data management practices. This talk introduces our service model, teaching and support formats, and the lessons we have learned about scaling community, improving software quality, and making open, sustainable research computing visible in academic work across diverse teams and projects.
+
 **(2.4) Reproducible Computational Analyses** <br>*Jochen Knaus (Weizenbaum-Institut)*
 The replication and reproduction of scientific results and methods constitute good scientific practice. Despite the fact that software and research software are now deeply embedded in mostly all processes throughout the scientific lifecycle in many scientific domains, this technical aspect is often overlooked: the results of software, despite their nominally deterministic nature of computer programs, is stable, but reproducing them is not guaranteed per se, but require adjustments to methodology and design. This becomes all the more evident when considering the entire class of research software, from a single statistical analysis to infrastructure components, over an extended period of time: can computational results performed with software still be reproduced with reasonable effort even years later? And if not, why not? This talk provides an overview of various challenges and solutions: from individual aspects in the programs, using data formats and data preparation, the use of software libraries and (commercial) third-party software, to approaches using dynamic documents and reproducible workflows, all the way to the technical preservation of entire execution environments. Current topics such as the integration of external AI systems, agents, and Vibe Codings will also be addressed from the view of reproducibility, as well as the status and practices of computational reproducibility within the scientific publishing system will be examined.