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Scientists and Engineers have been happily performing research and Analyses for hundreds of years without the Semantic Grid. What’s changing in their world now that would motivate them to look to the Semantic Grid? Which of their problems can it solve? And how can we recognize the low-hanging fruit – the combinations of communities and issues where introducing the Semantic grid now will create the most scientific value? Traditional science is being done faster and community-level discovery-based science and systems approaches are emerging. Semantic Grid technologies can provide a critical capability to reuse data, software, and services while evolving the underlying grid and science models involved. While not often mentioned by name, SG technologies – exposing and reasoning over model-level descriptions of resources within and on the Grid – are directly relevant to problems of managing large amounts of heterogeneous data in a fluid scientific and technological environment. This presentation will attempt to map between language of science and that of grids and the semantic web to identify use cases where deploying a "Semantic Grid" could have significant scientific value.