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The lack of development environments for interdisciplinary research conducted on large-scale datasets hampers research at every stage. Projects incur large startup costs as disparate infrastructure is assembled; experimentation slows when software components and environment are mismatched for specific research tasks; and findings are disseminated in forms that are hard to examine, learn from, and reuse. Behind these problems is a common cause - the lack of good tools. When large, heterogeneous and distributed data is added to the equation, further frustration, at the least, ensues. As a result using existing platforms, the programmers of 21st century interactive visualizations are reduced to working in the same fashion with the same tools as 20th century database programmers. Our contribution is to bring the tools of digital artists to bear on the aforementioned data analysis and visualization challenges. Here we report on the current state of progress in adapting Field for large-scale, web-based scientific data analysis and visualization with an emphasis on Linked Open Data  and especially the current data hosted by RPI .