{"url":"/dataset/ulm-tsst","name":"Ulm-TSST","full_name":"Ulm-Trier Social Stress Dataset","description_markdown":"**Ulm-TSST** is a dataset continuous emotion (valence and arousal) prediction and `physiological-emotion' prediction. It consists of a multimodal richly annotated dataset of self-reported, and external dimensional ratings of emotion and mental well-being. After a brief period of preparation the subjects are asked to give an oral presentation, within a job-interview setting.  Ulm-TSST includes biological recordings, such as Electrocardiogram (ECG),  Electrodermal Activity (EDA), Respiration, and Heart Rate (BPM) as well as continuous arousal and valence annotations. With 105 participants (69.5% female) aged between 18 and 39 years, a total of 10 hours were accumulated.","description_withheld":null,"homepage":"https://www.muse-challenge.org/data","introduced_date":"2021-04-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/the-muse-2021-multimodal-sentiment-analysis","title":"The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress","first_author":"Lukas Stappen","url":null},"license":{"name":"Custom (non-commercial)","url":null},"modalities":[],"tasks":[{"name":"Emotion Recognition","url":"/task/emotion-recognition","datasets_with_task":"/datasets/task/emotion-recognition"}],"languages":[],"variants":["Ulm-TSST"],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}