Each election cycle, thousands of candidates vie for seats in the U.S. House of Representatives and the Senate. Until now, there has been no comprehensive, publicly available resource cataloging what those candidates say about who they are or what they stand for. A new open-access database called CampaignView, created by researchers at the University of Notre Dame, offers researchers, journalists and educators a powerful tool to understand congressional elections.

A new article in the peer-reviewed journal Psychedelic Medicine takes aim at one of the most influential, and controversial, narratives in comparative psychedelic culture: the idea that ancient Greek mystery rites at Eleusis were fueled by psychedelic drugs.

Just like when multiple people gather simultaneously in a meeting room, higher-order interactions—where many entities interact at once—occur across various fields and reflect the complexity of real-world relationships. However, due to technical limitations, in many fields, only low-order pairwise interactions between entities can be observed and collected, which results in the loss of full context and restricts practical use. KAIST researchers have developed the AI model “MARIOH,” which can accurately reconstruct* higher-order interactions from such low-order information, opening up innovative analytical possibilities in fields like social network analysis, neuroscience, and life sciences.  *Reconstruction: Estimating/reconstructing the original structure that has disappeared or was not observed.