  We propose tabular two-dimensional correlation analysis for extracting
features from multifaceted characterization data, essential for understanding
material properties. This method visualizes similarities and phase lags in
structural parameter changes through heatmaps, combining hierarchical
clustering and asynchronous correlations. We applied the proposed method to
datasets of carbon nanotube (CNTs) films annealed at various temperatures and
revealed the complexity of their hierarchical structures, which include
elements like voids, bundles, and amorphous carbon. Our analysis addresses the
challenge of attempting to understand the sequence of structural changes,
especially in multifaceted characterization data where 11 structural parameters
derived from 8 characterization methods interact with complex behavior. The
results show how phase lags (asynchronous changes from stimuli) and parameter
similarities can illuminate the sequence of structural changes in materials,
providing insights into phenomena like the removal of amorphous carbon and
graphitization in annealed CNTs. This approach is beneficial even with limited
data and holds promise for a wide range of material analyses, demonstrating its
potential in elucidating complex material behaviors and properties.
