Quantitative analysis of human behavior is critical for objective characterization of neurological phenotypes, early detection of neurodegenerative diseases, and development of more sensitive measures of disease progression to support clinical trials and translation of new therapies into clinical practice. Sophisticated computational modeling can support these objectives, but requires large, information-rich data sets. This work introduces Neurobooth, a customizable platform for time-synchronized multimodal capture of human behavior. Over a two year period, a Neurobooth implementation integrated into a clinical setting facilitated data collection across multiple behavioral domains from a cohort of 470 individuals (82 controls and 388 with neurologic diseases) who participated in a collective 782 sessions. Visualization of the multimodal time series data demonstrates the presence of rich phenotypic signs across a range of diseases. These data and the open-source platform offer potential for advancing our understanding of neurological diseases and facilitating therapy development, and may be a valuable resource for related fields that study human behavior.