
This deliverable addresses the modelling of the LUMEN Use Cases (UCs) and the definition of a structured and operational execution plan. It provides the formal definition, modelling approach, and planning framework for the project UCs, with the objective of establishing a robust and actionable basis for the subsequent real-life testing of LUMEN solutions. The implementation and validation activities are explicitly out of scope of this deliverable and will be carried out under Task 6.6 (and its respective Deliverable D6.4). Accordingly, D3.2 focuses on defining testing scope and methodology, involved actors, resource requirements, evaluation procedures, and associated risk and mitigation measures. The deliverable builds on the outcomes of early user research conducted in Task 3.1 and translates identified requirements into three well-defined Use Cases, aligned with LUMEN objectives and the European Open Science Cloud (EOSC) ecosystem. A common methodological framework is applied across all Use Cases, combining structured modelling, scenario definition, service planning, evaluation strategies, and systematic risk analysis. For each Use Case, risks are identified, their potential impact assessed, and mitigation and contingency measures defined to reduce the likelihood of adverse effects during real-life testing. The User Experience Quality Use Case (UC1), addresses the adaptation and validation of “white-label” discovery solutions, notably through the extension of the GoTriple platform beyond its original SSH focus to additional scientific domains. This Use Case plans the assessment of usability, accessibility, and feasibility of white-labelling in real-life contexts, with the aim of producing guidelines and best practices for wider uptake across disciplines. The Advanced Tools for Resources Production Use Case (UC2), focuses on planning the enhancement of existing platforms and the development of new services that strengthen the production, quality, and interoperability of scientific outputs. Particular attention is given to improving links between publications, software, and data, as well as supporting collaborative research practices. The deliverable defines the actors involved, required resources, and expected impacts on transparency, reproducibility, and trust in research outputs. The Enhanced Discovery Experiences Use Case (UC3), focuses on piloting innovative, cross-disciplinary discovery solutions that integrate advanced visualisations, meta-search functionalities, and AI-based technologies such as Large Language Models (LLMs) and chatbots. The planning addresses how these tools can support more inclusive, efficient, and interdisciplinary discovery workflows, while also identifying potential challenges in human–AI interaction and increased system complexity. Across all three Use Cases, D3.2 defines testing boundaries, target user groups, resource allocations, evaluation metrics, and risk mitigation measures that will guide the implementation and validation activities in Task 6.6. By embedding risk management within Use Case planning, this deliverable ensures that LUMEN’s testing phase is methodologically sound, resilient to uncertainty, and fully aligned with EOSC principles, including interoperability and FAIR data access.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
