Jakub Jirka’s Post

Last month, a data science lead told me something that stuck: “We’ve been doing FAIR for two years — but I still can’t find half the datasets I need.” Sound familiar? After watching dozens of FAIR initiatives sputter, one truth became clear: FAIR isn’t about ticking boxes. It’s about creating a shared language that humans and machines can understand. That’s what drove us to go back to basics and build a FAIR ontology — our Rosetta Stone between data and the tools that need to use it. Real IDs that resolve. Governed vocabularies. Licenses + provenance baked in. As a new member of the Pistoia Alliance, we’re bringing these ideas into open collaboration — helping pharma and biotech teams turn FAIR principles into something computable, measurable, and AI-ready. The result? FAIR maturity scores you can defend in a boardroom, not just describe in theory. 👉 Where does FAIR break down most often in your experience — Findability, Accessibility, Interoperability, or Reusability? Next week, we will share what this looks like in action. #FAIRData #Ontologies #LifeSciences #CompassFAIR #PistoiaAlliance

FAIR maturity and alignment shouldn't be a debate or disagreement between your data team and business users, but too often it becomes just that. So we moved from checklist and manual inspection FAIR to ontology backed FAIR evaluation (IDs that resolve, controlled vocabularies, licenses + provenance) that turns FAIR into something computable — a single source of truth. #FAIRdata #Ontology

In the latest issue of our Bibliotech newsletter, Peace Ossom, MIT Libraries’ Director of Research Data Services, shares how campus collaborations are meeting a clear need across MIT. https://lnkd.in/epdZ7bgb

At Living Data 2025 in Bogotá, our colleagues Marie Alavi and Vanessa-Ariane Guzek Hernando presented “Ensuring continuous open data flow on the state of Open Science – GATE’s service for reliable research and optimum Open Science capacity building, practice and policies.” Their presentation showcased how GATE acts as a collaborative ecosystem — connecting knowledge creators, research communities, data infrastructures, and policy actors — to strengthen Open Science through trustworthy data practices, FAIR(-R) standards, and responsible conduct of research. Through its GATE Lifecycle, the platform continuously integrates community-contributed knowledge into actionable guidance, shared via the GATE Report, helping institutions align their Open Science materials, training, and policies for real-world impact. 👏 A heartfelt thank you to all participants of #LivingData2025 for engaging in this dialogue on sustainable and responsible data openness — and to our partners #NERQ, Kiel University, Pensoft Publishers, and MILLER INTERNATIONAL KNOWLEDGE (MIK) for their support. 🔗 Discover more: www.openscienceGATE.com

🔬 Our new paper is out! “Messengers: breaking echo chambers in collective opinion dynamics with homophily” with Mohsen Raoufi and Pawel Romanczuk Now published in npj Complexity and available open access.  📘 What the paper is about In collective decision-making, agents exchange information locally to converge on a shared estimate. But when agents preferentially interact with like-minded peers (homophily), echo chambers can emerge and block consensus. Our work addresses how to overcome that obstacle in spatially embedded systems. 🧠 Key ideas & contributions > We model agents moving through a continuous information landscape, sampling local signals while interacting only within a limited communication radius.   > As homophily guides interactions, networks can become fragmented: clusters of agents reinforce their own views and fail to integrate information across groups.   > To counteract that, we introduce a new behavioral role: Messengers. These agents stop updating their opinion and instead carry their last known opinion as they roam the space, acting like mobile bridges across disconnected clusters.   > We let agents probabilistically switch between being normal "Exploiters" and Messengers via a Dichotomous Markov Process. The switching dynamics (how often, for how long) critically shape performance.   > Through simulations and analysis, we map out regimes where a balanced mix of Messengers and Exploiters helps the collective break free from echo chambers and reach high‐precision consensus. Too few or too many Messengers degrade performance.   > We validate that these benefits hold across different spatial information landscapes.  🚀 Why it matters & where it applies > The idea of embodied "data ferries" (Messengers) suggests a decentralized, scalable way to mitigate fragmentation in collective systems. > Our work gives insights into how to design robust collective intelligence in contexts where connection is limited or interactions are selective. > The echo‐chamber dynamics we simulate have conceptual echoes in social or information networks (e.g. polarization), and the Messenger mechanism offers a metaphorical tool for thinking about bridging disconnected communities. If you’re interested, here’s the open-access link to the full article: https://lnkd.in/esBGbKay

What does the A.I.-enabled golden age of qualitative mean for quantitative research - when rigorous, statistically significant data collection and analysis still matters? On Oct 15, our Leigh Kessler joins Smith Geiger Group's Dan Reines at Greenbook #iiex.ai to examine how quant-focused researchers are adding the power and insights of Qual-at-Scale to survey work. https://lnkd.in/eDCCR-rY

As we continue celebrating Open Access Week 2025 (Oct. 20–26), we’re shining a light on how the Library helps make research data open, citable and accessible to all. Through our Research Data Digital Collections, UC San Diego researchers can deposit, preserve and share their datasets. This ensures their work remains transparent, reproducible and globally impactful. Many of these datasets are linked directly to peer-reviewed publications, expanding access to the data behind the discoveries. The Library’s Research Data Curation services provides tools to help you: - Create Digital Object Identifiers (DOIs) for your research outputs - Develop compliant Data Management Plans (DMPs) - Share and preserve data and code - Build open and reproducible research skills 📖 Read the full article: https://lnkd.in/e7F5rgSm #OAweek

Authors can seamlessly deposit big datasets to Dryad from the EM submission workflow, saving time and making open research more discoverable and citable: https://bit.ly/4nTCkfi

🔗 What stories do networks tell? Networks aren’t just diagrams—they reveal patterns, relationships, and hidden structures in data. This one-day conference brings together scholars and practitioners to discuss cutting-edge developments in two key areas: Visual Network Analysis and the use of network metrics and models for statistical analysis. Through a series of short talks and panel discussions, the event explores how visual, computational, and statistical methodologies complement each other in network analysis. ✔ Discover how network analysis transforms research ✔ Learn visualization techniques ✔ Explore statistical approaches 📅 Date: November 11, 2025 📍Location: Aalborg Universitet in Copenhagen ✅ Sign up here: https://lnkd.in/dXQuE9uU If you’re curious about computational methods and visual storytelling, this event is for you! Thanks to the organisers Mathieu Jacomy Kristian Gade Kjelmann & Matilde Ficozzi #computationalSSH #machinelearning #networks #MASSHINE

📊 FAIR ≠ FREE-for-ALL FAIR DATA is not about giving everything away. It is about giving a structure to the data, thus it can be found, understood and reused. Too often, we confuse FAIR DATA with: - full openness - data dumps - uploading for the sake of uploading and forgetting But SMART science is not just OPEN but also ORGANIZED and what makes data FAIR is its clarity, consistency, and context such as: ✅ Reliable and understood (humans or machines) metadata. ✅ Persistent IDs and versioning. ✅ Clear links between samples, methods, results, datasets and code. ✅ Meaningful user permission layers Want to boost reuse, trust, and collaboration? Start by making your data make sense to both HUMANS and MACHINES. Let’s build a DATA MINDSET where data is not just available, but also actionable. #FAIRdata #Reproducibility #DigitalScience #MetadataMatters #RDM #SmartDataSharpMinds