SIAcast Preview

As governments tighten research policies, datasets disappear from public repositories, and artificial intelligence consumes unprecedented volumes of scientific literature, a fundamental question is emerging: how open should science be? In this episode of SIAcast, Jonny Coates and co-host Katharina Miller bring together experts in open science, privacy, licensing, research integrity, and research security to explore whether openness and security are truly opposing forces — or whether both depend on something deeper: trust.Through discussions on Creative Commons licensing, health data governance, AI training, research sovereignty, and scientific collaboration, the guests challenge the idea that science must choose between openness and protection. Instead, they argue for a more nuanced approach — one that recognizes the need for secure, trustworthy systems that enable sharing while safeguarding people, knowledge, and institutions. From privacy-by-design principles to the future of AI and international collaboration, this conversation offers a thoughtful roadmap for navigating one of the most important debates in modern research.Key TakeawaysOpen science and research security are not inherently opposites. Effective innovation requires both openness and appropriate safeguards, including cybersecurity, data integrity protections, and trusted collaboration networks.Trust is the foundation of both privacy and openness. Without trust, patients, researchers, companies, and institutions become less willing to share data and collaborate, ultimately weakening research outcomes.Creative Commons licenses provide flexibility, not unlimited openness. They allow creators to determine how their work can be reused while preserving agency over sharing decisions.Anonymization alone is no longer sufficient. AI-driven inference and data linkage create new privacy risks, making context, governance, and layered safeguards increasingly important.Overly restrictive research security policies can backfire. Limiting international collaboration may isolate researchers from cutting-edge developments and reduce national scientific capacity.AI is reshaping what “open” means. The large-scale use of openly available content for AI training has raised new questions about attribution, reciprocity, and the sustainability of the commons.BiographiesCo-hostKatharina Miller is a lawyer, ethicist and foresight strategist who advises companies, public institutions, universities and international organizations on AI governance and responsible innovation through her firm, Miller International Knowledge. She also teaches at IE University and serves as an ethics expert for the European Commission.GuestsMaría Bardají Cruz is an independent global privacy and AI governance advisor focused on the responsible use of data in research and innovation. She examines how privacy, trust and robust governance frameworks can support the responsible advancement of research and the use of data for societal benefit.Richard Gold is a legal scholar and innovation policy expert whose research focuses on knowledge sharing, intellectual property, and the role of openness in scientific progress. He advocates for collaborative approaches that strengthen both innovation and research sovereignty.Monica Granados works with Creative Commons on issues related to open knowledge, licensing, and the future of sharing in the age of AI. Her work focuses on creator agency, attribution, and sustaining the commons through effective governance.Leslie McIntosh is a research integrity and scientometrics specialist with a background in biomedical data systems and research governance. Her work examines trust, knowledge security, and the institutional responsibilities required to maintain trustworthy science.Chapter Timestamps00:00 Introduction: Why openness, security, and trust matter now01:00 Richard Gold: Why open science and research security need each other04:23 Monica Granados: How Creative Commons licenses balance openness and control07:08 María Bardají Cruz: Privacy, trust, and health data governance09:20 AI, anonymization, and the limits of privacy protection12:30 Where should the limits of openness be for health data?15:07 Leslie McIntosh: Rethinking research security as knowledge security17:00 Misconceptions and resistance to research security policies22:02 National security, scientific leadership, and international collaboration24:17 The risks of overly restrictive research security measures26:51 Protecting datasets with Creative Commons licenses28:06 AI, data sharing, and the future of openness30:51 How AI challenges traditional ideas of open licensing34:28 Creative Commons Signals and attribution in AI37:36 Who is responsible for research security?40:19 Practical steps for universities balancing openness and protection41:45 Looking ahead: openness, privacy, security, and trust in 10 years43:35 Final reflections: trust as the foundation of science and innovationMusic: Upbeat Corporate by JP Bianchini. Produced by Rippling Ideas for the Science Integrity Alliance.

What happens when scientists cannot reproduce each other's results, and what does that mean for public trust in research? In this episode of SIAcast, Jonny Coates and co-host Emmanuel Boakye trace how concerns about unreliable findings grew into a global movement for better science. From psychology's replication debates to laboratory biology and open science reform, the episode looks at why reproducibility became one of the defining conversations in modern academia.Featuring voices from the UK Reproducibility Network, the African Reproducibility Network and the Brazilian Reproducibility Network, the discussion goes past the headlines to ask a harder question: is science broken, or simply adapting? Guests discuss transparency, incentives, research culture, AI, academic pressure and community-driven reform. Rather than a story about failure, this is a story about scientists working together to improve the systems they work within.Key TakeawaysNetworks connect the isolated: Reproducibility networks were built to link researchers who were each working alone on the same problems.The UKRN model has three parts: Grassroots researchers, university leadership and stakeholders such as funders and publishers, coordinated in one structure."Crisis" is contested: Several guests question the word, arguing the issue is tied to incentives, transparency and research culture rather than to fraud.Disciplines differ: Reproducibility means different things in psychology, lab biology and qualitative research, and there is no single fix.Open practices are becoming standard: Data sharing, documentation, better statistics and transparent workflows are increasingly central to research quality.Community may be the strongest safeguard: Collaborative fields such as the Drosophila research community appear to support reproducibility and sharing on their own.BiographiesCo-hostEmmanuel Boakye is the Founder and Executive Director of the African Reproducibility Network (AREN), which he started in 2022 through the eLife Community Ambassadors program. AREN works across the continent on open science practices, research culture and reproducibility, running training and building local communities of practice at universities and research institutes.GuestsMarcus Munafò is Professor of Biological Psychology at the University of Bristol and a co-founder of the UK Reproducibility Network, launched in 2019. His work covers research integrity, reproducibility and open research, and he chairs UKRN's supervisory board.Olavo Amaral is Professor at the Leopoldo de Meis Institute of Medical Biochemistry at the Federal University of Rio de Janeiro. He coordinates the Brazilian Reproducibility Initiative, a multi-laboratory replication project, and is a founding member of the Brazilian Reproducibility Network.Bruno Lemaitre is a researcher in Drosophila immunity and a co-author of ReproSci, a retrospective analysis of 1,006 claims from 400 papers published between 1959 and 2011. He has also written on how scientific communities respond to replication efforts.Eduarda Centeno is co-Executive Director of the Brazilian Reproducibility Network, where she leads community engagement, training and advocacy. Her work focuses on culture change, education and widening access to reproducible research practices.Chapter Timestamps00:00 – Introducing the reproducibility crisis01:23 – What is the UK Reproducibility Network?03:30 – Why convening researchers, institutions and funders matters04:45 – Why reproducibility networks exist06:42 – How UKRN was created13:52 – The global spread of reproducibility networks14:04 – Building the African Reproducibility Network (AREN)17:44 – Is reproducibility really a "crisis"?20:57 – Measuring reproducibility vs measuring quality24:36 – Reproducibility challenges in bioscience27:11 – Bruno Lemaitre's Drosophila reproducibility project 31:31 – Concerns about the future of academia 32:36 – What reproducibility networks actually do 34:48 – Inside the Brazilian Reproducibility Network 36:27 – MOOCs, training and changing research culture 39:45 – Is science improving? 42:26 – AI, universities and the future of research 46:15 – Why community matters in science 48:10 – What changes academia still needs 51:41 – How researchers can get involved 52:14 – Can we still trust scientific knowledge? 54:41 – Final reflections: improving science togetherMusic: Upbeat Corporate by JP Bianchini Produced by Rippling Ideas for the Science Integrity Alliance

What happens when the system designed to safeguard scientific truth starts to crack? In this episode of SIAcast, Jonny Coates and co-host Yagmur Ozturk dig into a growing crisis in research integrity, from AI-generated “papers” and hallucinated citations to peer-review overload and stealth edits in published work. With real-world examples (including a famously impossible lab rat image that passed peer review), the episode pulls back the curtain on how flawed science can slip through and why it’s happening more often.But this isn’t a doom-and-gloom story. Featuring insights from research integrity experts Joanna Diong and René Aquarius, the conversation explores the human, institutional, and systemic forces shaping modern science. From perverse incentives and publication pressure to grassroots sleuthing communities fighting back, this episode asks a critical question: can we still trust science, and if so, what will it take to protect that trust?Key TakeawaysThe scale of the problem has changed: Scientific errors and fraud aren’t new, but AI and paper mills are amplifying them dramatically.Peer review is under strain: Overworked reviewers and overwhelmed editors are struggling to maintain quality control.AI is a double-edged sword: It helps detect fraud (e.g., image duplication tools) but also enables large-scale fabrication.Incentive systems are broken: Metrics like publication count and H-index can encourage questionable research practices.“Stealth corrections” undermine trust: Papers can be altered post-publication without any formal notice to readers.Responsibility is diffuse: No single actor can fix the system; change requires collective effort across the ecosystem.BiographiesCo-hostYagmur Ozturk is a PhD candidate in the ERC Nanobubbles project based at Grenoble Alpes University, France. Within this project, she works on building semi-automatic systems to help with post-publication peer review. She also serves as a maintainer for the Collection of Open Science Integrity Guides (COSIG), working on curating accessible guides for everyone to do forensic peer review. GuestsRené Aquarius is a biomedical scientist working at the Neurosurgery department of the Radboud University Medical Center in Nijmegen, The Netherlands. Besides his regular work, which focuses mainly on systematic reviews, as well as in vivo, in vitro, and clinical research in the field of vascular neurosurgery, he has also been active as a so-called "science sleuth" for the last two years. As a science sleuth, he is focusing mostly on finding image-related issues in scientific articles.Dr. Joanna Diong is a Senior Lecturer at The University of Sydney. Her research program aims to improve the value of health and medical research by using epidemiological methods to find and test ways to improve research quality. She serves as Research Integrity Advisor for the Faculty of Medicine and Health, champions good research practices through the Association for Interdisciplinary Meta-Research and Open Science (AIMOS), and has been awarded for her contributions to peer review and improving research quality. Chapter Timestamps00:00 – The “impossible rat” and why trust in science is under pressure01:44 – AI in research: tool or threat?03:04 – AI-generated fraud and hallucinated citations05:17 – Peer review under strain & publishing incentives07:43 – Why research integrity matters (historical context)08:53 – René’s origin story: spotting widespread issues11:20 – How institutions handle research integrity15:28 – What happens when misconduct is reported18:05 – Why researchers cut corners21:23 – Introducing COSIG and open integrity guides24:24 – What are stealth corrections?27:48 – Risks and backlash faced by sleuths29:05 – Can we tell fraud from honest mistakes?31:28 – How the research community responds35:45 – Why integrity tools need to be accessible36:32 – Who is responsible for fixing science?41:20 – Rethinking peer review and trust systems46:24 – What listeners can do to help48:39 – Final reflections: the future of trust in scienceMusic: Upbeat Corporate by JP Bianchini. Produced by Rippling Ideas for the Science Integrity Alliance.