Data Without Borders: Open and FAIR Data Services for Fusion Science
Fusion experiments generate large amounts of complex data that hold significant potential for advancing fusion science. Ensuring these data are openly accessible and adhere to the principles of FAIR (Findable, Accessible, Interoperable, and Reusable) data management is critical for fostering collaboration, reproducibility, and innovation in the field. However, achieving these goals presents significant technical and organizational challenges.
This presentation will focus on the development of an open data service for MAST (Mega Amp Spherical Tokamak) at Culham, illustrating how FAIR principles can be practically implemented to enhance the usability and accessibility of fusion experiment data. The challenges of standardization, interoperability, and designing a service for broad accessibility will be discussed, alongside the strategies employed to address them.
By showcasing the integration of FAIR practices in the context of MAST, this talk highlights the broader implications for advancing open data initiatives across the fusion research community. Future directions for scaling these solutions to other fusion experiments and fostering a global ecosystem of open fusion data will also be explored.