OAI14

Fostering Open Science Infrastructure Collaboration: Open for Science

The open science infrastructure has gone through three main developmental stages and has now entered a phase characterized by science clustering and collective intelligence. In alignment with the strategic goals of the “International Decade of Science for Sustainable Development (2024–2033),” this report introduces the progress in developing the open science infrastructure sharing network as part of the initiative "Open for Science."

Feynbot: Unlocking conversational access to High Energy Physics literature

Imagine having a conversation with the entire INSPIRE-HEP database—asking complex questions about particle physics research and receiving precise, contextual answers instantly. The Feynbot project aims to make this vision reality through an advanced Retrieval Augmented Generation system that transforms how researchers interact with scientific literature. This presentation unveils Feynbot's innovative approach that combines cutting-edge AI with the comprehensive INSPIRE-HEP collection. Natural language queries will unlock insights buried across thousands of research papers. Whether you're exploring theoretical frameworks, investigating experimental results, or tracking research trends, Feynbot will deliver accurate, detailed responses tailored to your specific needs. We'll explore the technical architecture behind this system, reveal proven strategies for eliminating AI hallucinations, and demonstrate how Feynbot bridges the gap between researchers' needs and RAG implementation. With tools like this, we will be able to reshape research workflow, opening new pathways for scientific exploration in particle physics.

How can researchers leverage Open Source AI with a data-centric approach

Open-source AI, particularly with open-weight models from Llama, Mistral, and DeepSeek, has shifted research focus from massive general-purpose systems toward compact, domain-specific solutions. This talk will overview the rapidly evolving open ecosystem of AI, emphasizing how researchers can adopt data-centric workflows to use AI responsibly and innovatively. By curating high-quality, rights-cleared datasets, and iteratively refining data quality, researchers can fine-tune open-weight models into transparent, reproducible, and sustainable tools. Adopting this approach democratizes AI, enhances methodological transparency, enabling labs of all sizes to build precise solutions tailored for embedded, privacy-sensitive, and energy-efficient AI applications.

Open Source AI: Propagation of Open Source Licenses in the Age of AI

Open source AI models have the potential to foster innovation and technological progress. Nevertheless, the definition of “open source” in the AI space is hotly discussed. A related – yet no less central – issue concerns the propagating effect of copyleft (or ShareAlike) clauses embedded in training data or upstream code. Do these clauses require downstream AI models, systems and their output to be released under the same open source terms, e.g. as copyright derivatives? This has major repercussions as such propagation would render entire AI projects fully open. The presentation will examine this question and find that this is presently unlikely to be the case, save for training data. Further to this finding, and in order to protect the effectiveness of ShareAlike clause, it will thus advocate for a new definition of copyright derivatives specific to the AI-context.

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Introduction

Retractions: On The Rise, But Not Enough

In 2000, there were about 40 retractions from the scholarly literature. In 2023, there were more than 10,000. That is a dramatic increase, even accounting for the growing number of papers published per year. In this talk, I will explore the reasons for the increase, why it is good news, and why the real number should be even higher. Drawing on more than a decade and a half of experience at Retraction Watch, I will tell the stories of the sleuths who are finding problems in the literature. And I will talk about how everyone can avoid ending up in the Retraction Watch Database -- or end up there for a good reason.

The virus model of research fraud: integrity requires inoculation, testing, and quarantine

It's generally thought that fraud is rare, and science is self-correcting, but is that true? Sadly, there is increasingly evidence of widespread fraud in science, ranging from individual bad actors who build a glowing reputation on faked or manipulated data, to industrial scale fraud from so-called “paper mills”, who charge for authorship and/or citations. I’ll discuss why it is urgent to tackle these, with various measures comparable to testing for a virus, tracing contact, quarantine and vaccination. I list some red flags that aid paper mill detection, and show how open science practices are important to counteract fakery in science.

How the misuse of GenAI can spoil the publishing system

The recent explosion of generative AI has had a profound impact on society as a whole, particularly through the new possibilities enabled by text generation tools, with ChatGPT being the most well-known example. The research community has not been immune to this phenomenon. In this presentation, I will discuss how this technology has been used by researchers, with a particular focus on its role in questionable research practices related to scientific publishing, and the broader questions this raises about the research ecosystem as a whole.

Q&A session

Break

Placing Humans at the Centre of Open Research

Efforts to produce standards, guidelines, technologies and infrastructures that may boost sharing practices in science have resulted in great advances over the last few decades. However, they have also unintentionally boosted a technocratic, object-oriented vision of open research that is easy to appropriate for “open washing” and does not do justice to the very efforts of the humans at the heart of such work. The result is an open research landscape that fosters, rather than mitigate, existing inequities and divides in the production and use of scientific research. This talk reflects on the requirement of keeping social agency at the heart of open research practice, and what it means for the future development of policies, tools and services.

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Q&A session