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This article describes a range of features that are useful in data visual analysis. The language and examples are useful.

Great discussion of some of the ways that Cliff's research engages students in thinking about error, variation, repeated measures, and the relationships between models and data.

This is an interesting look at how representations can influence perception. The claim is that by making it easy to move from a structurally accurate model to an intermediate level of abstraction viewers can more easily make quantitative comparisons. 

Part of data literacy my involve dealing with very large (and very small) number. This intriguing animation deals with the largest and the smallest objects known to humans.