Ensuring Quality, Usability, and Integrity in Research Systems Through AI, Governance, and Open Standards
In the evolving landscape of higher education and research, the reliability, accessibility, and usability of research information systems are becoming increasingly vital. This session brings together four synergistic presentations that showcase innovative approaches and practical experiences in designing and improving tools for research management..
We’ll begin with the use of cutting-edge technologies such as Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) pipelines to support semantic search and advanced analysis of institutional publications. This highlights how AI can be used to unlock insights from complex research data. The second research addresses a fundamental yet often overlooked issue in research software development: the importance of quality assurance. It advocates for prioritizing software testing to ensure reliability, reproducibility, and trust in research outputs..
Next, a case study from Qatar will take us into the world of research data preservation, showing how an OAIS-compliant open archive can help institutions retain the value of research grants long after the projects are complete. Finally, the session will explore the role of data governance and data quality in enhancing the usability of the Ludzie Nauki CRIS system in Poland. It underscores how legal compliance, user-centric design, and AI-enhanced services can elevate research information systems..
Together, these presentations provide a comprehensive view of how institutions can improve research infrastructure through AI integration, sound software practices, sustainable archiving models, and data governance. The session will appeal to research managers, IT professionals, and policy-makers seeking to strengthen the digital foundation of their research ecosystems..
Moderators: Anna Pacholak and Laurence Desnos
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“It works on my research”: An argument for prioritized software testing in research software
