DIMS: a no-code dashboard linking multimodal dynamic signals to communication practices

Communication research increasingly relies on multimodal time-series data spanning physiological, behavioral, audiovisual, and transcript-based measures. Yet the phenomena these data are meant to capture, such as social connection or attitude, often unfold in temporally specific ways that are lost when signals are reduced to averages or analyzed in isolation. This article introduces the Dynamic Interaction and Multimodal Signals (DIMS) Dashboard, an open-source tool that aligns multiple time series with synchronized video and transcript, allowing researchers to inspect how statistically identified patterns correspond to communicative events. We present two exploratory case studies. The first examines inter-brain synchrony during interpersonal conversation; using a dynamic dyadic systems lens, we show how synchrony peaks can be read as candidate dyadic events whose meaning depends on interactional context. The second examines coupled facial electromyography during advertisement viewing; using an emotional-flow lens, we show how orbicularis oculi activity that appears positive in isolation can signal a sustained, co-active mixed-emotion state when paired with corrugator activity. Across both cases, DIMS supports an iterative qualitative-quantitative workflow: quantitative signals flag moments worth inspecting, and qualitative interpretation clarifies what they mean. DIMS does not replace statistical or qualitative analysis but creates a shared analytical space for theoretically meaningful observation.