Research institutions across Europe face a critical shift in how data is collected, managed, analysed, and shared. Funders including NWO and the European Commission expect open, FAIR-compliant data practices. The Netherlands Reproducibility Network reflects a growing recognition that reproducibility and data governance are foundational to credible science. Yet the practical infrastructure remains fragmented for most research groups: data scattered across local drives, pipelines that exist only in someone’s head, collaboration via emailed spreadsheets, and analysis that cannot be re-run six months later when a journal reviewer asks.
These workshops address the infrastructure layer of that problem – not by claiming technology alone solves the challenge, but by showing how cloud platforms can make good research data practices significantly easier to implement and sustain. Attendees leave with a concrete understanding of how Microsoft Fabric and Azure AI support the full research data lifecycle: from ingestion and governance through to AI-assisted analysis, reproducible pipelines, and systematic AI-enhanced research workflows, illustrated through live demonstrations using real research datasets.
The three workshops provide an in-depth look at how cloud based AI can support advanced research
14 May 2026 12:00 CEST Developing AI-Enhanced Research Workflows with Azure AI Foundry
This module introduces Azure AI Foundry as a platform for structuring AI-driven research initiatives. Participants examine how experiments are defined, monitored, and compared through demonstrations, while ensuring methodological consistency and traceability throughout several study iterations. The programme emphasises the shift from informal model utilisation to systematic, verifiable research processes.
A particular focus addresses EU data residency: which AI models are hosted within European data zones, how to verify data processing guarantees, and practical guidance for researchers operating under GDPR and institutional data governance requirements. Azure AI Foundry is presented as a research workbench where models are selected like instruments, prompts designed like protocols, and AI agents built to autonomously analyse datasets with full traceability.
The session culminates in a live demonstration of an AI agent using Code Interpreter to perform statistical analysis on a research dataset, showing how natural language instructions can drive reproducible, auditable computational research workflows.
Register Here29 May 2026 12:00 CEST Practical Application of Azure AI Services in Research Settings
This module explores integrating prebuilt Azure AI Services into research workflows for tasks such as text analysis and image comprehension. Illustrative use cases emphasise the appropriateness and constraints of these services, examining instances where they enhance research and situations that require bespoke methodologies or more rigorous validation to uphold scientific integrity.
Participants work through practical scenarios: extracting entities and sentiment from research text, building searchable knowledge bases from document collections, processing instrument logs and field data sheets at scale, and detecting PII in human-subjects data. Each capability is framed within the context of research methodology – when pre-built services are sufficient and when custom approaches are warranted.
This module serves as an application-focused extension to the workshop series, connecting core cloud literacy with everyday research practices while highlighting scientific rigour, reproducibility, and ethical use of AI.
Register Here29 June 2026 12:00 CEST Experimental Design in Machine Learning for Research
This module emphasises machine learning as a research methodology rather than as an implementation assignment. Participants examine the translation of research objectives into machine learning problem formulations, the selection and evaluation of models, and the interpretation and reporting of results in a scientifically rigorous manner. Scenarios address common research issues, including data leakage, overfitting, and misinterpretation of performance metrics.
A dedicated focus addresses distributed patterns for data confidentiality: how Azure ML can train models on data that cannot leave the customer’s on-premises environment. Azure Confidential Computing, Azure
Arc-enabled ML, and federated learning patterns are presented as practical solutions for institutions bound by GDPR, health data directives, or institutional data governance policies.
This module serves as an application-focused extension connecting core cloud literacy with everyday research practices, strengthening programme objectives around scientific rigour, reproducibility, and ethical use of AI while establishing Azure as an integrated research ecosystem.
Register HereWHO SHOULD ATTEND
- Academic researchers at any career stage working with substantial datasets or multi-institution collaborations
- Research data managers, data stewards, and research support staff
- Research IT professionals, infrastructure teams supporting research groups
- Graduate students & postdocs involved in data-intensive research
The sessions will be hosted by Prepared by Mario Pilija – Microsoft Certified Trainer (MCT) from Algebra Bernays University
