Atomic-scale detail is now possible with photons, thanks to a cooled-down silver tip and a clever use of plasmonics.

Describing and understanding personality structure is fundamental to predict and explain human behavior. Recent research calls for large personality item pools to be analyzed from the bottom-up, as item-level analysis may reveal meaningful differences often obscured by aggregation. This study introduces and applies Taxonomic Graph Analysis (TGA), a comprehensive network psychometrics framework aimed at identifying hierarchical structures in personality from the bottom-up, to an open-source 300-item IPIP-NEO dataset (N = 149,337). This framework addresses key methodological challenges that have hindered accurate recovery of hierarchical structures, including local independence violations, wording effects, dimensionality assessment, and structural robustness.
Whether or not you think humans *should* be announcing our presence to the cosmos, we’re doing it, anyway. Both intentionally, and not. And if aliens really…