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Detecting structure in data is the first step to arrive at meaningful representations for systems. This is particularly challenging for evolving dislocation networks evolving as a consequence of plastic deformation of crystalline materials. Our study employs Isomap, a manifold learning technique, to show the intrinsic structure of high-dimensional dislocation density field data of dislocation structures resulting from different compression axes. Our maps provide a systematic framework for quantitatively comparing dislocation structures and offer unique fingerprints based on dislocation density fields. It represents a novel, unbiased approach that contributes to the quantitative classification of dislocation structures, which can be systematically extended using different representations of dislocation systems.