AGENT4SC focuses on the systems foundations required to operationalize agent decisions that directly affect allocations, system health, data movement, energy consumption, and scientific outcomes. This demands integrated data and metadata infrastructures spanning scalable databases, vector stores, caching layers, provenance services, and knowledge graphs, all designed to remain performant and auditable under high concurrency. At scale, even minor errors, hallucinations, or blind spots can escalate into megawatt-hour waste, irreproducibility, and compromised scientific validity. Therefore, this workshop provides a forum to advance scalable architectures, cross-continuum coordination, evaluation frameworks, verification and mitigation strategies, provenance and observability mechanisms, performance analysis, and lessons from real deployments. The workshop builds on active efforts in agentic AI for science across national laboratories, academia, and industry, reflecting an emerging and rapidly growing community.
Graph-based technologies, including knowledge graphs, ontologies, graph databases, graph analytics, and graph machine learning, are emerging as powerful unifying abstractions across all stages of the scientific research lifecycle. This workshop explores how these technologies can be applied to represent and integrate knowledge, model workflow dependencies, enable semantic interoperability across heterogeneous datasets, and support resource discovery, reasoning, and orchestration in distributed computing environments, including HPC, Cloud, Edge, and IoT infrastructures spanning the computing continuum. Beyond infrastructure and middleware concerns, the workshop welcomes contributions from any domain of science that employs graph-based methods as a primary tool for data integration, analysis, or knowledge discovery.
Scientific automation is at the forefront of enabling faster, more reproducible discoveries in AI, exascale computing, and distributed environments. The WISDOM workshop provides a platform for researchers, developers, and practitioners to discuss advances in autonomous workflow design, intelligent data management, and scalable and agentic automation techniques. We are especially interested in innovative methods that integrate AI/ML to enhance the flexibility, efficiency, and reliability of scientific processes across heterogeneous environments.
The Global Research Platform (GRP) is an international scientific collaboration that aims to create one-of-a-kind advanced ubiquitous services that integrate resources around the globe at speeds of gigabits and terabits per second. GRP focuses on design, implementation, and operation strategies for next-generation distributed services and infrastructure to facilitate high-performance data gathering, analytics, transport, computing, and storage, at 100 Gbps or higher. GRP actively works with partners in North America, Asia, Europe, and South America to customize international fabrics and distributed cyberinfrastructure to support data-intensive scientific workflows.
AI and data capability programs are increasingly central to eScience, but many fail to create durable change because they focus on tools rather than the interaction of technological, human, and organisational conditions that make adoption stick. This workshop brings together practitioners, experts, and researchers from academia, government, industry, and faith-based organisations to share real-world case studies of AI capability uplift, with attention to what works, what fails, and why. Through invited talks, peer-reviewed extended abstracts, posters, and an interactive closing session, participants will examine governance, trust, human–AI collaboration, and the cultural and technological narratives that shape transformation. The workshop will also produce a shared AI Capability Uplift Playbook to capture transferable lessons for the broader eScience community.
The workshop focuses on the integration of data-driven methodologies, high-performance computing (HPC), and physics-based models for atmospheric science applications, with particular emphasis on air quality, climate interactions, and anthropogenic impacts. In recent years, atmospheric modelling has undergone a paradigm shift driven by the availability of large datasets (satellite observations, sensor networks), advances in machine learning, and the development of scalable computing infrastructures. These developments have enabled the emergence of hybrid approaches that combine numerical modelling, statistical inference, and artificial intelligence, leading to improved predictive capabilities and new scientific insights. The workshop aims to explore this transition from traditional modelling frameworks to integrated eScience approaches, emphasizing reproducibility, scalability, and interoperability.
The development of computational models for simulating the behaviour and evolution of natural and engineered systems continues to play a central role in advancing science and engineering, and is currently being reshaped by the convergence of High Performance Computing (HPC), data-intensive methodologies, and artificial intelligence. The advent of heterogeneous and exascale architectures, coupled with rapid progress in GPU computing and distributed workflows, is transforming the way simulations are designed, executed, and integrated with large-scale data. In this context, traditional numerical approaches such as Finite Element, Finite Difference, and Particle-In-Cell methods are increasingly complemented by data-driven and hybrid techniques, including machine learning, physics-informed neural networks, surrogate models, and bio-inspired algorithms, enabling new capabilities for addressing complex multi-scale and multi-physics problems. These advances are also fostering the development of digital twins and real-time simulation frameworks, while raising new challenges in terms of scalability, portability, performance, and energy efficiency. The HPCMS Workshop at eScience 2026 aims to bring together a multidisciplinary community of researchers and practitioners to discuss recent advances and emerging trends in modelling and simulation, with particular emphasis on the integration of HPC and AI, scalable and heterogeneous computing, data-driven workflows, and sustainable computing practices. The workshop will also explore software ecosystems and programming models, including MPI, OpenMP, SYCL, and CUDA, as well as performance modelling, optimization techniques, and novel hardware approaches such as FPGAs, neuromorphic computing, and quantum computing. By fostering cross-disciplinary interaction across domains ranging from engineering and physical sciences to life sciences and socio-economic systems, the workshop seeks to promote innovative solutions to complex real-world challenges and to advance the next generation of computational science methodologies.
The INSTIL workshop focuses on ICT-enabled citizen science and its role in supporting participatory and data-driven scientific research. The workshop brings together researchers and practitioners working on digital platforms, sensor networks, and collaborative tools that enable citizens to actively contribute to scientific processes. A particular emphasis is placed on applications related to environmental monitoring, climate science, and climate change, where citizen-generated data and participatory sensing can complement traditional scientific infrastructures. In this context, emerging approaches such as citizen observatories are gaining increasing relevance. INSTIL aims to foster interdisciplinary dialogue across computer science, environmental sciences, and social sciences, highlighting how digital technologies can support citizen engagement and contribute to addressing complex societal challenges.
Modern scientific discovery depends on complex computational infrastructure composed of research software, dependencies, and distributed computing environments. These interconnected artifacts and actors form the research software supply chain (RSSC), a critical yet poorly understood component of the scientific cyberinfrastructure. While software supply chain security has received growing attention in industry, its implications for scientific infrastructure — where usability, openness, and rapid experimentation are essential — remain largely unexplored. This workshop will convene research software engineers, scientists, and cybersecurity researchers to examine the RSSC as a component of scientific infrastructure. Through peer-reviewed papers, panels, and interactive sessions, participants will share operational experiences, discuss usability–security tradeoffs, and identify emerging challenges such as autonomous scientific agents and trust in computational results. The workshop will generate a roadmap identifying technical, organizational, and community interventions needed to secure research software supply chain across the scientific ecosystem.
