The HaMMon project is the outcome of an industrial partnership that includes many Italian research institutions and private companies. It is led by UnipolSai and Leitha, and funded by the ICSC, the Italian National Research Center for High Performance Computing, Big Data and Quantum Computing.The ambition of HaMMon is to build a flexible and scalable platform to analyze the hydrogeological and atmospheric balance of the Italian territory. The project aims to expand the current knowledge in hazard mapping, monitoring, and forecasting from an industrial perspective by leveraging innovative technologies and the interdisciplinary activities carried out by the ICSC.In this work, we present the cloud-HPC infrastructure deployed in the High-Performance Computing for Artificial Intelligence (HPC4AI) green data center of the University of Turin which supports the testing and development of HaMMon’s applications and services. We describe the current activities and preliminary results related to the integration of Photogrammetry techniques, Data Visualization and Artificial Intelligence technologies, applied on aerial images, to assess extreme natural events and evaluate their impact on risk-exposed assets.

Modern Astrophysics and Cosmology (A&C) projects produce immense data volumes, necessitating advanced software tools for data access, storage, and analysis. Visualization Interface for the Virtual Observatory (VisIVO) is one such tool enabling multi-dimensional data analysis and knowledge discovery across complex astrophysical datasets. Leveraging containerization and virtualization, VisIVO has been deployed on various distributed computing platforms. Additionally, Blender, an open-source 3D suite, provides robust tools for rendering and processing volumetric data, making it suitable for visualizing complex datasets. At the SPACE Center of Excellence these tools are being adapted for high-performance visualization of cosmological simulations performed with GADGET and ChaNGa on pre-exascale systems. However, implementing high-performance visualization on diverse HPC platforms presents several challenges, including hardware and software compatibility, data management, scalability, performance portability, and efficient resource allocation. This paper outlines strategies to integrate VisIVO with workflow frameworks and streaming platforms to address these challenges. Workflow frameworks enhance portability, scheduling, and reproducibility of visualization workflows on pre-exascale systems used in A&C simulations. We also discuss the use of streaming platforms to enable concurrent (i.e. in-situ) analysis and visualization of simulations, reducing the need to store full simulation data by leveraging distributed databases that stream the output data in real time. Lastly, we present an adaptation of Blender to handle large-scale particle-based astrophysical data, offering high-quality visualization with interactive exploration capabilities.

As the Square Kilometre Array (SKA) radio telescopes approach full operation, they are set to generate hundreds of petabytes of data annually, offering unprecedented resolution and detail in astrophysical observations. This massive influx of data presents significant challenges in terms of storage, processing, analysis and visualization. Scientific visualization becomes essential in this context, enabling researchers to interpret complex, high-dimensional datasets and extract valuable insights. This paper presents the latest developments of VisIVO Visual Analytics, an interactive visualization tool that is evolving to meet the needs of the SKA and similar large-scale astrophysical projects such as the James Webb Space Telescope and the Atacama Large Millimeter/submillimeter Array (ALMA). Originally designed as a desktop application, VisIVO is transitioning to a client-server architecture, allowing it to efficiently run remote visualization pipelines on remote servers. This shift significantly enhances the tool’s ability to handle large scale datasets, facilitating real-time, interactive analysis of complex data. This work also explores how VisIVO Visual Analytics can distribute computational workloads across multiple nodes in high-performance computing (HPC) clusters for parallel visualization pipelines, enabling advanced and interactive techniques to derive meaningful insights from the vast volumes of data generated by next-generation observatories.

Abstract NEANIAS is a research and innovation action project funded by the European Union under the Horizon 2020 program. The project addresses the challenge of prototyping novel solutions for the underwater, atmospheric and space research communities, creating a collaborative research ecosystem, and contributing to the effective materialization of the European Open Science Cloud (EOSC). NEANIAS drives the co-design, implementation, delivery, and integration into EOSC of innovative thematic and core services, derived from state-of-the-art assets and practices in the target scientific communities. We present the overall NEANIAS ecosystem architecture, with an emphasis on its core visualization services, detailing their specifications and software development plan, and focusing on the underpinning service-oriented architecture for their delivery. We report on the underlying ideas and guiding principles for designing such visualization services, outlining their current release status and future development roadmaps towards Technological Readiness Level (TRL) 8 maturity and EOSC integration.

This paper presents a novel immersive Virtual Reality platform, named ARGO3D, tailored for improving research and teaching activities in Earth Sciences.The platform facilitates the exploration of geological environments and the assessment of geo-hazards, allowing reaching key sites of interest (some of them impossible to be reached in person) and thus to take measurements and collect data as it can be done in the real field.The target audience of ARGO3D encompasses students, teachers and early career scientists, as well as civil planning organisations and non-academics.The overall workflow for real ambient reconstruction, processing and rendering of the virtual ambient is presented, as well as a detailed description of the VR software tools and hardware devices employed.

This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Nebula, to integrate their existing services and make them available through AAI services compliant with GEANT interfederation policies, thus guaranteeing transparency and trust in the provisioning of such services. Our middleware facilitates the execution of applications using containers on Cloud and Grid based infrastructures, as well as on HPC clusters. Our developments are freely downloadable as open source components, and are already being integrated into many scientific applications.

The Grid and Cloud User Support Environment (gUSE) enables users convenient and easy access to grid and cloud infrastructures by providing a general purpose, workflow-oriented graphical user interface to create and run workflows on various Distributed Computing Infrastructures (DCIs). Its arrangements for creating and modifying existing workflows are, however, non-intuitive and cumbersome due to the technologies and architecture employed by gUSE. In this paper, we outline the first integrated web-based workflow editor for gUSE with the aim of improving the user experience for those with industrial data workflows and the wider gUSE community. We report initial assessments of the editor's utility based on users' feedback. We argue that combining access to diverse scalable resources with improved workflow creation tools is important for all big data applications and research infrastructures.

Summary The availability of large‐scale digital surveys offers tremendous opportunities for advancing scientific knowledge in the astrophysics community. Nevertheless, the analysis of these data often requires very powerful computational resources. Science gateway technologies offer Web‐based environments to run applications with little concern for learning and managing the underlying infrastructures that execute them. This paper focuses on the issues related to the development of a science gateway customized for the needs of the astrophysics community. The VisIVO Science Gateway is wrapped around a WS‐PGRADE/grid User Support Environment portal integrating services for processing and visualizing large‐scale multidimensional astrophysical data sets on distributed computing infrastructures. We discuss the core tools and services supported including an application for mobile access to the gateway. We report our experiences in supporting specialized astrophysical communities requiring development of complex workflows for visualization and numerical simulations. Further, available platforms are discussed for sharing workflows in collaborative environments. Finally, we outline our vision for creating a federation of science gateways to benefit astrophysical communities by sharing a set of services for authentication, computing infrastructure access and data/workflow repositories. Copyright © 2014 John Wiley & Sons, Ltd.

Summary Nowadays, collaborative applications are valuable tools for scientists to share their studies and experiences, for example, by interacting simultaneously with their data and outcomes giving feedback to other colleagues on how the data are processed. This paper presents a mobile application connected to a workflow‐enabled framework to perform visualization and data analysis of large‐scale, multi‐dimensional datasets on distributed computing infrastructures. In particular, the usage of workflow‐driven applications, through science gateway technologies, allows the scientist to share heavy data exploration tasks as workflows and the relative results in a transparent and user‐friendly way. Copyright © 2015 John Wiley & Sons, Ltd.

Workflow management has been widely adopted by scientific communities as a valuable tool to carry out complex experiments. It allows for the possibility to perform computations for data analysis and simulations, whereas hiding details of the complex infrastructures underneath. There are many workflow management systems that offer a large variety of generic services to coordinate the execution of workflows. Nowadays, there is a trend to extend the functionality of workflow management systems to cover all possible requirements that may arise from a user community. However, there are multiple scenarios for usage of workflow systems, involving various actors that require different services to be supported by these systems. In this paper we reflect about the usage scenarios of scientific workflow management based on the practical experience of heavy users of workflow technology from communities in three scientific domains: Astrophysics, Heliophysics and Biomedicine. We discuss the requirements regarding services and information to be provided by the workflow management system for each usage profile, and illustrate how these requirements are fulfilled by the tools these communities currently adopt. This paper contributes to the understanding of properties of future workflow management systems that are important to increase their adoption in a large variety of usage scenarios.

Nowadays visualization-based knowledge discovery can play an important role in astrophysics. Collaborative visualization can enable multiple users to share visualization experiences, e.g. by interacting simultaneously with astrophysical datasets giving feedback on what other participants are doing/seeing. Further, workflow-driven applications allow reproduction of specific visualization results, a challenging task as selecting suitable visualization parameters may not be a straightforward process. This paper presents VisIVO Science Gateway, a web-based workflow-enabled framework integrating large-scale, multidimensional datasets and applications for visualization and data filtering on Distributed Computing Infrastructures (DCIs). Advanced users are able to create, change, invoke, and monitor workflows while standard users are provided with easy-to-use customised web interfaces hiding all technical aspects of the visualization algorithms and DCI configurations.

The aim of this work is to compare some deterministic optimization algorithms and evolutionary algorithms on parameter estimation in a biological circuit design problem: the negative feedback loop between the tumor suppressor p53 and the oncogene Mdm2. We compared deterministic optimization algorithms and evolutionary algorithms in terms of robustness of the resulting parameters including all sources of uncertainty into the statistical representation of reference data and evaluating the obtained solutions in terms of confident limits. The experimental results obtained show as evolutionary algorithms are more robust with respect of deterministic optimization algorithms in particular the algorithm Differential Evolution (DE) showed the best performance over the minimization of the fitting function.

Developing complex models of cellular function requires the collaboration of multiple teams of researchers remotely distributed worldwide. A challenge of computational systems biology is to find easy and accessible mechanisms to enable such collaboration to construct higher level models of cellular function. This paper presents the development of an on-line Web portal for enabling open access to Cytosolve, an existing, proven and scalable computational architecture for integrating quantitative molecular pathways. The developed graphical user interface allows ease-of-use for developers of quantitative molecular pathway models to remotely collaborate to build larger and more complex models using the Cytosolve infrastructure. The on-line Web portal will be accessible and it will allow users to remotely collaborate with the existing Cytosolve computational environment that supports integration of models in a parallel manner without geographical restrictions. A creator of a model will be able to integrate their model from their local location to an ensemble of distributed models through this on-line Web portal.

In this work, we show a software able to collect all the required information for the process simulation of a semiconductor technology directly from the clean room equipments by using net CAM systems, and gives as output a script in the simulator software syntax, giving a useful verification and validation tool to process engineering.