The International Journal of eScience

Computing infrastructures and systems are rapidly developing and so are novel ways to map, control and execute scientific applications which become more and more complex and collaborative.
Computational and storage capabilities, databases, sensors, and people need true collaborative tools. Over the last years there has been a real explosion of new theory and technological progress supporting a better understanding of these wide-area, fully distributed sensing and computing systems. Big Data in all its guises require novel methods and infrastructures to register, analyze and distill meaning.

FGCS aims to lead the way in advances in distributed systems, collaborative environments, high performance and high performance computing, Big Data on such infrastructures as grids, clouds and the Internet of Things (IoT).

The Aims and Scope of FGCS cover new developments in:

[1] Applications and application support:

    Novel applications for novel e-infrastructures
    Complex workflow applications
    Big Data registration, processing and analyses
    Problem solving environments and virtual laboratories
    Semantic and knowledge based systems
    Collaborative infrastructures and virtual organizations
    Methods for high performance and high throughput computing
    Urgent computing
    Scientific, industrial, social and educational implications
    Education

[2] Methods and tools:

    Tools for infrastructure development and monitoring
    Distributed dynamic resource management and scheduling
    Information management
    Protocols and emerging standards
    Methods and tools for internet computing
    Security aspects

[3] Theory:

    Process specification;
    Program and algorithm design
    Theoretical aspects of large scale communication and computation
    Scaling and performance theory
    Protocols and their verification

Special Issue on Research Software Engineering – Software-Enabled Discovery and Beyond
截稿日期: 2026-02-01

Motivation and Scope

In today’s research landscape, software is essential across various disciplines. Many researchers utilize high-performance scientific software to obtain simulation results, conduct data analysis, and visualize their findings. These codes streamline researchers’ workflows and enhance scientific progress. To make this possible, research software engineers work behind the scenes to maintain these codes or improve the user experience. Research software engineers dedicate significant time to programming, while full-time software engineers focus on writing code to address research challenges, with many professionals falling somewhere in between. This special issue, titled “Code, Practices, and People”, will focus on the interactions among those who develop code and applications, as well as the practices they engage in, such as code reviews. Additionally, it will explore leadership and career paths for research software engineers.

Topics of interest include (but are not limited to):

    Discovery enabled by software
    Architectures, frameworks, libraries, and technology trends
    Research data management
    Support for scalability and data-driven methods
    Improving the reproducibility of research
    Usability, portals, workflows, and tools
    Sustainability, security, and stability
    Software engineering approaches supporting research
    Community engagement
    Training and workforce development
    Building a research software engineer (RSE) profession
    Accessibility and belonging for RSEs

Guest editors:

Patrick Diehl, Los Alamos National Laboratory, Los Alamos, New Mexico, USA. diehlpk@lanl.gov 
Miranda Mundt, Sandia National Laboratories, Albuquerque, New Mexico, USA. mmundt@sandia.gov 
Chen Zhang, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA. zhangc@ornl.gov 

Manuscript submission information:

Important Dates

    Submission portal opens: November 17, 2025
    Deadline for paper submission: February 1, 2026
    Latest acceptance deadline for all papers: May 30th, 2026

Special Issue on Emerging Technologies in Distributed Intelligence for Natural Disaster Management
截稿日期: 2026-02-27

Motivation and Scope

The increasing complexity and unpredictability of environmental phenomena and emergency situations demand advanced sensing, analysis, and response capabilities. Distributed intelligent systems --- networks of interconnected sensors, edge devices, and AI-powered analytics --- are transforming how we monitor natural and human-built environments and manage emergencies in real time. As climate change and urbanization intensify the frequency and impact of natural and human-induced hazards, there is a growing need for integrated, real-time sensing and decision-making frameworks for Natural Disaster Management (NDM). This special issue aims to explores the transformative role and to showcase cutting-edge research and innovative applications of distributed intelligent sensing and decision-making systems in environmental monitoring, disaster prediction and response, public safety, and critical infrastructure resilience. A particular emphasis is placed on cutting-edge developments in Distributed Artificial Intelligence, enabling decentralized and privacy-aware model training across sensor networks; Distributed Remote Sensing, integrating satellite, aerial, and in-situ data sources for real-time environmental monitoring; and Structural Monitoring systems, which ensure the safety and resilience of critical infrastructure through intelligent sensing and analytics.
We welcome contributions that present theoretical advances, system architectures, algorithmic innovations, and real-world applications that leverage distributed intelligence for resilience, sustainability, and safety in complex environments.

Contributions are invited on topics including, but not limited to:

    Real-time distributed sensing networks for environmental hazard detection (e.g., floods, wildfires, air and water quality)
    AI-driven distributed decision support for emergency response coordination
    Integration of IoT, edge computing, and 6G communications in emergency management systems
    Federated Learning for privacy-preserving data analysis and model training across distributed sensing nodes
    Distributed remote sensing architectures combining satellite, UAV (Unmanned Aerial Vehicles), mobile robots and ground sensor data for environmental awareness
    Structural Health Monitoring using intelligent sensor networks and AI for infrastructure resilience
    Case studies on distributed systems for urban resilience and smart city emergency preparedness
    Novel algorithms for anomaly detection, event prediction, and adaptive system reconfiguration in distributed sensing environments
    Ethical, privacy, and security considerations in distributed intelligent emergency systems

By bringing together interdisciplinary perspectives and state-of-the-art technologies, this issue seeks to advance the development of robust, scalable, and intelligent distributed systems that enhance situational awareness, enable timely interventions, and ultimately save lives and protect ecosystems.

Guest editors:

Roberto Marino, Ph.D. - University of Messina, roberto.marino@unime.it
Lorenzo Carnevale, Ph.D. - University of Messina, lorenzo.carnevale@unime.it
Daniel Balouek, Ph.D. - Inria, Nantes, daniel.balouek@inria.fr
Manish Parashar, Ph.D. - University of Utah, manish.parashar@utah.edu

Manuscript submission information:

Important Dates

Submission portal opens: December 1st, 2025
Deadline for paper submission: February 27th, 2026
Latest acceptance deadline for all papers: May 31th, 2026

Special Issue on MLOps Advancements: Improving Development, Management, and Interpretability in AI and Machine Learning
截稿日期: 2026-03-31

Motivation and Scope 

The rapid advancement of data-driven artificial intelligence has accelerated the integration of machine learning (ML) models into real-world applications across diverse domains. This transition from experimental ML to production-ready AI systems has unveiled significant operational challenges in model development, deployment, monitoring, and maintenance. MLOps has emerged as a critical framework of practices and tools designed to streamline the entire ML lifecycle, ensuring automation, reproducibility, and scalability while bridging the gap between data science experimentation and the reliable operation of ML components in production. 

As AI adoption continues to expand, the need for robust, scalable, and transparent MLOps practices has become increasingly vital to guarantee the reliability and trustworthiness of deployed ML models. This Special Issue seeks to gather cutting-edge research, case studies, and insights from both academia and industry that address the key challenges and emerging trends in MLOps, with particular emphasis on practical solutions for improving AI and machine learning workflows. 

We invite high-quality submissions that explore, but are not limited to, the following topics: 

    MLOps Frameworks and Best Practices 
    ML pipelines orchestration 
    ML model reproducibility, traceability, and explainability
    Continuous integration/continuous delivery (CI/CD) practices for ML models
    ML model monitoring and observability
    MLOps practices to ensure Interpretability and Explainability in AI systems
    Application of MLOps principles to Knowledge and Semantic Representation
    Application of MLOps principles to large language models (LLMOps)
    ML-specific architecture design and patterns
    Experience reports on real-world MLOps applications
    Challenges in applying MLOps to specific domains (e.g., healthcare and finance) 
    Ethics and Accountability in MLOps
    AutoML applications in MLOps
    Collaboration and team dynamics in MLOps
    Regulatory and policy aspects of MLOps
    MLOps strategies for Green AI
    Security and data privacy in MLOps

The proponents of this Special Issue are also the organizers of the Workshop on Machine Learning Operations – MLOps'25, which will be held at ECAI 2025, one of the premier conferences in artificial intelligence. As part of this initiative, we aim to provide an opportunity for authors of papers accepted at the workshop to submit an extended version of their work to this Special Issue. This will enable further development of innovative research contributions presented at MLOps'25, fostering deeper discussions and broader dissemination of advancements in MLOps methodologies, tools, and applications.

Guest Editors

Antonella Carbonaro
University of Bologna, Bologna, Italy
antonella.carbonaro@unibo.it 

Luigi Quaranta
University of Bari, Bari, Italy
luigi.quaranta@uniba.it

Giulio Mallardi
University of Bari, Bari, Italy
giulio.mallardi@uniba.it

Fabio Calefato
University of Bari, Bari, Italy
fabio.calefato@uniba.it

Important Dates

Submission portal opens: January 7th, 2026 
Deadline for paper submission: March 31st, 2026
Latest acceptance deadline for all papers: June 15th, 2026