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324
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ShareScore release 0.9.0
Dataset results
324 results for “humor”
MuSe-Humor: Cross-Cultural Humor Detection Sub-Challenge (MuSe 2023)
<p><strong>Description</strong>: Predicting the presence of humor in football press conference recordings. Available modalities: audio and video, transcriptions. The dataset offers a cross-cultural setting: all press conferences in the training and development partition are held in German, all videos in the test partition are taken from English Premier League press conferences.</p> <p><strong>Important</strong>: <strong>this package does not include the recordings themselves, please find them here</strong>: <a href="https://zenodo.org/deposit/7843460">https://zenodo.org/record/7843460</a></p> <p><strong>Labels</strong>: windows of 2 seconds are given a binary label, indicating presence and absence of humor. At least 7 human annotators labelled each video.</p> <p><strong>Dataset</strong>: MuSe-Humor is based on the Passau-Spontaneous Football Coach Humor (Passau-SFCH) dataset (<a href="https://arxiv.org/pdf/2209.14272.pdf">paper</a>). It includes press conference recordings of 10 different German Bundesliga football coaches collected in 2017. Moreover, for the MuSe 2023 challenge, recordings of 6 Premier League coaches have been added. The data provided here only includes segments in which the respective coach is speaking. The training data set contains recordings of 7 German-speaking coaches, development data consists of videos of 3 German-speaking coaches; and the test data set comprises recordings of 6 English-speaking coaches (one native speaker).</p> <p><strong>General</strong>: The <a href="http://muse-challenge.org">4th Multimodal Sentiment Analysis Challenge and Workshop (MuSe) 2023</a> adresses research questions that are of interest to affective computing, machine learning and multimodal signal processing communities and encourages a fusion of their disciplines. The goal of the MuSe workshop and challenge is to gain new insights into the merits of each of the core modalities and to serve as a stimulating environment for the development and evaluation of multimodal affect recognition approaches.</p>
MuSe-Humor: Cross-Cultural Humor Detection Sub-Challenge (MuSe 2024) - Videos
<p><strong>Description</strong>: Predicting the presence of humor in football press conference recordings. Available modalities: audio and video, transcriptions. The dataset offers a cross-cultural setting: all press conferences in the training and development partition are held in German, and all videos in the test partition are taken from English Premier League press conferences.</p> <p><strong>Important</strong>: <strong>This package only contains the video recordings, please find labels, audio files, transcripts and features here</strong>: <a href="../records/11211840">https://zenodo.org/records/11211840</a></p> <p><strong>Labels</strong>: windows of 2 seconds are given a binary label, indicating the presence and absence of humor. At least 7 human annotators labeled each video.</p> <p><strong>Dataset</strong>: MuSe-Humor is based on the Passau-Spontaneous Football Coach Humor (Passau-SFCH) dataset (<a href="https://arxiv.org/pdf/2209.14272.pdf">paper</a>). It includes press conference recordings of 10 different German Bundesliga football coaches collected in 2017. Moreover, for the MuSe 2024 challenge, recordings of 6 Premier League coaches have been added. The data provided here only includes segments in which the respective coach is speaking. The training data set contains recordings of 7 German-speaking coaches, development data consists of videos of 3 German-speaking coaches, and the test data set comprises recordings of 6 English-speaking coaches (one native speaker).</p> <p><strong>General</strong>: The <a href="http://muse-challenge.org/">5th Multimodal Sentiment Analysis Challenge and Workshop (MuSe) 2024</a> addresses research questions that are of interest to affective computing, machine learning and multimodal signal processing communities and encourages a fusion of their disciplines. The goal of the MuSe workshop and challenge is to gain new insights into the merits of each of the core modalities and to serve as a stimulating environment for the development and evaluation of multimodal affect recognition approaches.</p>
microRNA profiling in the aqueous humor of highly myopic eyes by next generation sequencing
GEO Series GSE142359. Homo sapiens. 10 samples. Type: Non-coding RNA profiling by high throughput sequencing.
miRNA profiles of natural recovery in vitreous humor after NAION induction in rat
GEO Series GSE154312. synthetic construct; Rattus norvegicus. 5 samples. Type: Non-coding RNA profiling by array.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.