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Emotion expression encompasses various types of information, including face and eye movement, voice and body motion. Most of the studies in automated affective recognition use faces as stimuli, less often they include speech and even more rarely gestures. Emotions collected from real conversations are difficult to classify using one channel. That is why multimodal techniques have recently become more popular in automatic emotion recognition. Multimodal databases that include audio, video, 3D motion capture and physiology data are quite rare. We collected The Russian Acted Multimodal Affective Set (RAMAS) the first multimodal corpus in Russian language. Our database contains approximately 7 hours of high-quality closeup video recordings of subjects faces, speech, motion-capture data and such physiological signals as electro-dermal activity and photoplethysmogram. The subjects were 10 actors who played out interactive dyadic scenarios. Each scenario involved one of the basic emotions: Anger, Sadness, Disgust, Happiness, Fear or Surprise, and some characteristics of social interaction like Domination and Submission. In order to note emotions that subjects really felt during the process we asked them to fill in short questionnaires (self-reports) after each played scenario. The records were marked by 21 annotators (at least five annotators marked each scenario). We present our multimodal data collection, annotation process, inter-rater agreement analysis and the comparison between self-reports and received annotations. RAMAS is an open database that provides research community with multimodal data of faces, speech, gestures and physiology interrelation. Such material is useful for various investigations and automatic affective systems development.