نبذة مختصرة : Foundation Models are pivotal in advancing arti ficial intelligence, driving notable progress across diverse areas. When merged with dataspace, these models enhance our capabil ity to develop algorithms that are powerful, predictive, and honor data sovereignty and quality. This paper highlights the potential benefits of a comprehensive repository of Foundation Models, contextualized within dataspace. Such an archive can streamline research, development, and education by offering a comparative analysis of various models and their applications. While serving as a consistent reference point for model assessment and fostering collaborative learning, the repository does face challenges like un biased evaluations, data privacy, and comprehensive information delivery. The paper also notes the importance of the repository being globally applicable, ethically constructed, and user-friendly. We delve into the nuances of integrating Foundation Models within dataspace, balancing the repository’s strengths against its limitations. ; We would like to acknowledge Science Foundation Ireland (SFI/12/RC/2289 P2) for funding this research.
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