Challenges and Opportunities of Real FAIR-by-design

Federica Bazzocchi, Area Science Park GenOA Week 2025 18/11/2025 CHALLENGES AND OPPORTUNITIES OF REAL FAIR-BY-DESIGN Master in Data Management and Curation The Master in Data Management and Curation was launched in 2024–2025 as part of a pilot initiative by Area Science Park, CNR-IOM, and SISSA, supported by projects funded under Italy’s National Recovery and Resilience Plan (PNRR). Master in Data Management and Curation Pilot edition 2024-2025 had well defined and clear goals Training new experts in FAIR Data Management devoted to dissemination of FAIR principles, techniques and tools in the experimental laboratories of the PRP and NFFA-DI Research infrastructure Realizing FAIR-by-design pipelines in the laboratories of the involved Research Infrastructures Funded by the National Recovery and Resilience Plan (“PNRR”) within “Missione 4, Istruzione e Ricerca - Componente 2, Dalla ricerca all’impresa - Linea di investimento 3.1, Fondo per la realizzazione di un sistema integrato di infrastrutture di ricerca e innovazione”, with funds from the European Union –NextGenerationEU. Turn to be a favorable opportunity for our format: mixing frontal lessons and practical internship Why do we need MDMC? 6 Data are growing faster than ever. The real challenge is not their quantity, but our capacity to manage them responsibly MDMC aims at preparing participants with the theory and practice to succeed in this mission, providing the skills to turn this challenge into opportunity Mission : To train professionals capable of transforming data challenges into value, innovation, and collective knowledge. DIGITAL INFRASTRUCTURES and DATA SPACES will provide the technological and policy framework, people provide the practice. We need a change of perspective from ‘FAIR data’ to ‘FAIR-by-design’ behaviors across labs, repositories, and services. We cannot have a FAIR ecosystem without FAIRskilled people. MDMC is designed to The fundamental principles of reproducibility FAIR DATA MANAGEMENT is not a bureucratic burden FAIR can be seen as the thecnical implementation of the scientific method Training the next generation of FAIR & Data professionals •Open Research Infrastructures •Data Center and HPC ORFEO •Laboratory of Data Engineering – LADE •Technology Park • Theoretical and Scientific Data Science Group •Pioneer in OA (hosted ArXive servers, OA journals) •Other successful Masters programs (MSC, MHPC) Our ecosystem combines infrastructures, research experience, innovation and a strong Open Science culture MASTER IN DATA MANAGEMENT AND CURATION FAIR-BY-DESIGN METHODOLOGY FAIR data management by design is an approach that ensures the application of FAIR principles from the inception of a project, making the data Findable, Accessible, Interoperable, and Reusable (FAIR) throughout all stages of research data lifecycle. The next generation of researchers should be trained and supported to do transparent and reproducible science from day one — that is, to be “open science natives”. Open By Design — Open By Design at Stanford (dsi-cores.github.io) and DOI 10.5281/zenodo.3332807 BY DESIGN!!! WE HAVE A DREAM…. •analyzing the content and structure •defining and choosing a semantic model, •enriching the recoverable metadata • transforming it into an open and linkable format •assigning licenses. FAIRification GO FAIR framework 0 20 40 60 80 100 120 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 time spent Produce the data Images generated by AI LEGO® Data Classification Games for FAIR Data Training https://doi.org/10.5281/zenod o.17183871 •Planning the structure •Planning the ingestion •defining and choosing a semantic model, •Planning how to collect metadata • how to transform data into an open and linkable format •choosing licenses FAIR-by-design Implement the pipeline Images generated by AI 0 20 40 60 80 100 120 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 time spent ‘‘ the planning and the implementation of a data journey such that, for every data movement from site to site, data and metadata are produced and transformed in a way compatible to the FAIR principles. Ideally, every movement of the journey outputs a machine-actionable data object.’’ Fair-by-design data journey: a blueprint , F.Vasone and F.Bazzocchi, in preparation Data repository OFED (datalake) SEP The Context: Elements of the Research Digital Infrastructure Catalogue management/ Facility access manangement Data & Metadata Productors FAIR-by-design data&metadata collectors Data services DB alignment Example: CNR-IFN.TN AND CNR-ISMN.BO Questa foto di Autore sconosciuto è concesso in licenza da CC BY Questa foto di Autore sconosciuto è concesso in licenza da CC BY-SA-NC TN BO FABLIMS CAMS JSON FORMAT , OWN VOCABULARY WORD FORMAT OWN VOCABULARY BOTTOM –UP APPROACH •Creation of synergies among laboratories of different institutions that had same techniques (and different instrumentations) •Comparison among different data production workflow to reach a common layer •Acquisition of the same metadata standards and data formats •Sharing of skills and competencies, creation of a community Example: CNR-IFN.TN AND CNR-ISMN.BO TN BO FABLIMS CAMS JSON FORMAT , SHARED VOCABULARY JSON FORMAT SHARED VOCABULARY NEW MODULE MAPPING UNIQUE PLUGIN REPOSITORY Data produced at CNR-IFN.TN and CNR.ISMN.BO are NOW fully interoperable! common semantic and syntactic Questa foto di Autore sconosciuto è concesso in licenza da CC BY Questa foto di Autore sconosciuto è concesso in licenza da CC BY-SA-NC We learnt and we are learning a lot … Passion drives better science When students learn the principles of Open Science and Data Curation, they don’t just acquire new skills, they embrace a new way of doing science. They feel engaged, motivated, and aware that by taking care of data, they are also taking care of the integrity and future of research. And that passion makes them better scientists. Image generated by AI STRATEGIES LEARNT FROM THE MASTER ➢FAIR-by-design mindset: integrating data care from the very beginning. ➢Interdisciplinary language: learning to communicate across technical, legal, and ethical domains. ➢Hands-on learning: through real-world internships and applied problemsolving. ➢Community approach: learning together, sharing experiences, building trust. ➢Data as a shared value: understanding that caring for data means caring for science itself. STRATEGIES THAT COULD MAKE THE DIFFERENCE ✓Recognise and reward data care: include stewardship efforts in research assessment. ✓Invest in professional roles: support for data stewards, engineers, and curators. ✓Encourage community engagement: foster active participation in open science networks. ✓Highlight success stories and champions: make best practices visible and inspiring. ✓Strengthen the bridge with institutions and companies: align scientific and innovation value. ✓Teach data care early: embed Open Science and FAIR principles in every stage of education. ✓Reward collaboration and openness: celebrate shared processes, not only results. [email protected] [email protected] OUR CONTACTS ✓Website: MDMC - SISSA ✓Available OPEN material: MDMC - Zenodo ✓Git repository: MDMC – GitHub ✓Email : [email protected] MDMC INFORMATION and LINKS