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2 changes: 0 additions & 2 deletions index.md
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Expand Up @@ -37,8 +37,6 @@ There is also a strong benefit for AI research.
Open datasets have driven some of the most productive advances in AI: the Multimodal Universe dataset, ImageNet, and AlphaFold's structural archive were all released openly from the start, and each catalyzed a research community that built on it for years afterward.
A fully public EDF-S would be precisely the kind of open, scientific dataset that programs such as NSF's AI-Ready Datasets solicitation ([NSF 26-512](https://www.nsf.gov/funding/opportunities/ai-datasets-unlocking-dataset-value-ai-enabled-scientific-discovery/nsf26-512/solicitation)) are designed to advance: rich enough for AI-driven discovery, and open enough for the community to build the enhancement pipelines and cross-survey harmonization tools that such discovery requires.

A public EDF-S also benefits the community directly: Science Collaborations would no longer need to require data rights as a condition of membership, broadening participation and, in turn, the range of science they can pursue.

EDF-S, rather than COSMOS, is proposed for public release because Euclid does not plan to observe COSMOS as one of its primary dedicated deep fields ({cite}`esa_euclid_deepfields`).
Open, non-proprietary fields have historically become the community's most productive resources: the COSMOS field remains a cornerstone of astrophysics decades after its inception, precisely because it was kept open from the start.
But with nearly twenty years of deep imaging from HST, Spitzer, Chandra, and the VLA, its remaining discovery potential is limited, and for this reason, Euclid has not chosen it as a deep field.
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