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Challenges and Opportunities of real FAIR-by-design: The experience of the Master in Data Management and Curation (MDMC)

Posted in Individual Presentations.

  • 2025-09-16 14:00
  • 15:00
  • Room: 81/R-003C - Science Gateway Auditorium C
  • Speaker:
    • Mariarita de Luca, , With a background in Bioengineering, I initially focused on mathematical modelling applied to biological systems. My research later evolved into the numerical simulation of large deformations in active materials, such as nematic liquid crystals and swelling polymers. Motivated by a strong attitude for collaboration and knowledge sharing, I became passionate about Open Science to advance research and foster collective progress. In recent years, my work has focused on Research Data Management (RDM) and the implementation of FAIR-by-design approaches, especially in the field of materials science. I consider the training of young researchers not only a responsibility, but a mission. I am lecturer on Open Science and FAIR Data at the University of Trieste for PhD students, because they are the future researchers and the ambassadors of an open and ethical science. I am currently a Research Technologist at the Laboratory of Data Engineering at Area Science Park, where I support research and training activities in data-intensive science. Since the launch of the pilot edition in 2024, I have served as the Coordinator of the Master in Data Management and Curation (MDMC), a joint initiative by Area Science Park and SISSA , Area Science Park, https://www.areasciencepark.it/en/, Italy
    • Federica Bazzocchi, , I studied physics at the University of Trieste and obtained a PhD in elementary particle physics at SISSA in Trieste. As a physicist, I worked on phenomenological Standard Model extension to explain flavour physics or dark matter. I gained experience as an analyst in a private company, where I started dealing with large databases and problems related to knowledge representation. Both experiences converged into what I do now: designing workflows for scientific data management in a FAIR-compliant manner, addressing interoperability issues related to scientific data, and identifying methodologies and tools for their optimal management. I am interested in the interplay between high-level data management and its technical implementation, as well as AI applications in the field of materials physics. I am currently working as research technologist at the Laboratory of Data Engineering in Area Science Park and I am lecturer at the University of Trieste for the course Advanced Data Management. I am deeply involved as a lecturer, student supervisors and support to the coordination of the activity of the Master in Data Management and Curation organised by Area Science Park and SISSA. , Area Science Park, https://www.areasciencepark.it/en/, Italy

Nowadays, data fuels discovery, innovation, and decision-making; therefore, the ability to manage and curate data responsibly is crucial.
The Master in Data Management and Curation (MDMC) is a pioneering educational program that embraces the “FAIR-by-design” paradigm, going beyond theory to train professionals in the practical implementation of FAIR principles across the entire research data lifecycle. Rather than retrofitting datasets to meet FAIR criteria, MDMC students learn to embed Findability, Accessibility, Interoperability, and Reusability from the earliest stages of data planning, within the wider context of Open Science.
This forward-thinking approach is made possible through a well-established collaboration between Area Science Park and SISSA and benefits from its dynamic research and innovation ecosystem. Designed for a new generation of data professionals, MDMC fosters a strategic understanding of the research process, combined with a unique mix of technical, ethical, and communication skills essential for real-world FAIR implementation.
The training structure consists of eight intensive weeks of in-person lectures and hands-on exercises, followed by a six-month internship in cutting-edge research laboratories or data-intensive institutions, during which students implement FAIR-by-design workflows and pipelines in real scientific contexts. This model offers a rare opportunity to work closely with researchers, develop tailored data strategies, and engage with the practical challenges of semantic interoperability, metadata standards, and sustainable infrastructure.
By shaping versatile and practice-oriented data professionals, MDMC contributes to building a new generation of researchers who can transform data from a research byproduct into a powerful strategic asset—crucial for both academic excellence and data-driven innovation.

Parallel Session 2, Open Science for All: Skills & Community