collaborative computational platform setup

This work addresses the persistent challenge of inconsistent development and execution environments faced by researchers operating across heterogeneous computing platforms—ranging from laptops and workstations to supercomputers and cloud infrastructures. To overcome this, the authors propose a modular and portable software ecosystem featuring a unified command-line interface that enables seamless orchestration and execution of scientific workflows. The system ensures cross-platform consistency, reproducibility, and scalability, thereby streamlining computational research across diverse hardware configurations. Its practical efficacy has been demonstrated through successful integration into the plan4res project under the European Union’s Horizon 2020 initiative, where it effectively supported complex, large-scale scientific workflows in varied computing environments.

In multi-stakeholder platforms, software architecture decisions often implicitly entrench conflicting requirements without systematic support for mapping governance principles to technical design. This work proposes the first governance-architecture alignment framework, explicitly linking five core governance principles to the space of architectural decisions, thereby rendering implicit governance stances identifiable and contestable. The framework also exposes how default technical choices can obscure underlying value commitments. Feasibility is preliminarily demonstrated through a constructive case study of a pig-farming knowledge platform in Rwanda. Future work will employ pre- and post-intervention user judgment studies to evaluate the framework’s impact on actual governance outcomes.

Computational workflows often fail to comply with the FAIR principles (Findable, Accessible, Interoperable, Reusable), leading to redundant development, low reusability, and poor reproducibility. Method: This work establishes the first FAIR-oriented service ecosystem for computational workflows. It innovatively integrates FAIR data and software principles, introducing a workflow-specific persistent identifier (PID) system and a machine-actionable metadata framework, alongside a cross-domain workflow reuse and adaptation paradigm. The ecosystem comprises a PID infrastructure, standardized RESTful APIs, workflow-system integration adapters, and a FAIRification toolchain to support FAIR compliance across the entire workflow lifecycle. Contribution/Results: Experimental evaluation demonstrates significant improvements in cross-disciplinary workflow reuse, substantial reduction in methodological redundancy, and enhanced reproducibility. The framework has been successfully deployed in large-scale research infrastructures, including EOSC-Life.

This study investigates design challenges of research infrastructure software—exemplified by the HERMES system—in multi-stakeholder environments, particularly under automated software release workflows, revealing significant misalignments between Research Software Engineers (RSEs) and Infrastructure Staff (IFs) regarding technical compatibility, usability, documentation quality, accountability mechanisms, and quality assurance. Method: A two-round structured survey and cross-group comparative analysis were conducted, introducing a novel hybrid analytical framework integrating organizational maturity assessment and usability evaluation to systematically identify inter-role priority differences and intra-group heterogeneity (e.g., technical experience gradients). Contribution/Results: Findings indicate IFs prioritize usability and governance assurance, whereas RSEs emphasize infrastructure compatibility; only 50% of RSEs actively perform software releases, hindered by both cultural inertia and technical barriers. The study provides empirically grounded insights and a methodological foundation for designing research software tailored to diverse stakeholder needs.

Existing software architecture frameworks inadequately model machine learning (ML) systems, as they overlook the needs of emerging stakeholders—such as data scientists and data engineers—and lack expressive support for ML-specific characteristics, including component uncertainty, heterogeneity, and collaborative behavior. Method: Through an empirical study involving interviews and surveys with 61 domain experts from 25 organizations across 10 countries, we systematically identified ML-relevant stakeholders and their concerns for the first time. Contribution/Results: We propose novel, ML-adapted architectural viewpoints and views, extending traditional frameworks to enable unified modeling of both ML and non-ML components. This yields the *ML-Enhanced Systems Architecture Framework Extension Guide*, which has been preliminarily adopted in industry for intelligent system architecture governance. Our work bridges a critical theoretical and practical gap in stakeholder modeling and viewpoint systematization for ML system architecture design.

This study addresses the challenges posed by escalating geopolitical, organizational, and technological fragmentation by proposing a multilevel, polycentric digital ecosystem framework spanning individual, organizational, inter-organizational, and global tiers. The framework integrates four key technological clusters—AI and automation, blockchain-based trust mechanisms, federated data spaces, and immersive technologies—to construct a loosely coupled, distributed network that enables cross-border coordination and innovation. By synthesizing these elements, the research extends platform theory and uncovers novel pathways through which AI-driven infrastructures can foster digital integration in an increasingly fragmented world. The proposed approach offers a systematic solution for cross-domain digital collaboration, balancing autonomy with interoperability across diverse institutional and technological contexts.

This work addresses the challenge that existing AI experimentation platforms struggle to simultaneously support rapid prototyping and governance requirements such as access control, tenant isolation, and process transparency. The authors propose and implement a governance-aware, multi-tenant AI sandbox platform featuring a layered architecture that decouples the user interface, control plane, and execution layer. The platform integrates approval workflows, audit logging, and configuration persistence mechanisms, and innovatively combines structured experimentation with cross-project reusable evaluation evidence generation, thereby establishing persistent linkages between governance decisions and experimental data. Deployed in an industry–academia collaboration setting, the platform demonstrates its effectiveness in enabling controlled collaboration, traceable experiments, and cross-project result comparison, offering a reusable reference architecture and practical insights for integrating governance into AI development environments.

Current DevOps infrastructures for blockchain applications are predominantly controlled by single entities, lacking decentralized deployment and governance mechanisms. This work proposes a decentralized deployment architecture decoupled from specific governance and upgrade schemes, integrating DAO-based governance, smart contract upgradability, and DevOps best practices. By adopting an extended registry pattern, the architecture enables deterministic deployments and, for the first time, incorporates version control, testing and validation, and user interface components into a unified decentralized framework. The project provides an open-source reference implementation that substantially lowers the barrier to practical decentralized deployment. Experimental evaluation demonstrates the effectiveness and practicality of the proposed architecture.

Community-driven scientific workflow ecosystems often struggle to sustain themselves due to ambiguous maintenance and user support mechanisms, particularly in cross-platform collaboration and heterogeneous execution environments. This study presents the first cross-platform empirical analysis of the nf-core ecosystem, systematically examining 15,760 GitHub issues, 35,411 pull requests, and 895 forum discussions. By integrating metadata and textual features into predictive models, the research uncovers significant disparities in maintenance and support activities across platforms and highlights weak explicit linkages among them. The findings reveal that issues, pull requests, and forum posts predominantly serve distinct roles—coordinating maintenance, facilitating code integration, and providing user support, respectively. Moreover, issue actionability, diagnostic evidence, and depth of interaction emerge as critical determinants of resolution efficiency.