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2,863 results for “Challenge”
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>
MuSe-Personalisation: Personalisation Sub-Challenge (MuSe 2023)
<p><strong>Description</strong>: Predicting valence and arousal of individuals in a stressed disposition, as induced by the Trier Social Stress Test (TSST) protocol. Available modalities: audio, video, text and physiological signals (respiratory rate, ECG, BPM).</p> <p><strong>Labels</strong>: The valence labels are obtained by fusing the ratings of three human annotators. For arousal, in contrast, two human annotations have been fused with the TSST subject's electrodermal activity (EDA) signal in order to obtain a more objective arousal gold standard. Hence, this sub-challenge is <strong>not</strong> identical to the 2021 MuSe-Stress sub-challenge.</p> <p><strong>Dataset</strong>: MuSe-Stress is based on the Ulm-TSST data set as introduced in the MuSe 2021 challenge. It contains 69 recordings of individuals during a Trier Social Stress Test (TSST). <strong>In order to facilitate personalisation, parts of the test data labels are provided</strong>. Overall, about 6 hours of recordings are provided. The data is split in a speaker-independent manner with the training data set comprising 41 individuals, development and test each comprising 14 individuals. This split is identical to the split used in the MuSe 2022 challenge.</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>
Datasset to the publication: Application and challenges of TCR and BCR sequencing to investigate T and B cell clonality in abdominal aortic aneurysm
<p>Title: Application and challenges of TCR and BCR sequencing to investigate T and B cell clonality in abdominal aortic aneurysm</p> <p>Abstract:<br>Background: Abdominal aortic aneurysm (AAA) is a life-threatening cardiovascular disease.32<br>Although its pathogenesis is still poorly understood, recent evidence suggests that AAA displays33<br>characteristics of an autoimmune disease. Particularly T cells responding to AAA-related antigens in34<br>the aortic wall may contribute to the initial immune response. Single-cell RNA (scRNA) T- and B- cell35<br>receptor (TCR and BCR) sequencing is a powerful tool to investigate clonality. However, difficulties36<br>such as limited numbers of isolated cells must be considered during implementation and data analysis,37<br>making biological interpretation challenging. Here, we perform a representative single cell immune38<br>repertoire analysis in experimental murine AAA and show a reliable bioinformatic processing pipeline39<br>highlighting opportunities and limitations of this approach.40<br>Methods: We performed scRNA TCR and BCR sequencing of isolated lymphocytes from the41<br>infrarenal aorta of male C57BL/6J mice 3, 7, 15, and 28 days after AAA induction via elastase42<br>perfusion of the aorta. Sham-operated mice at day 3 and 28 as well as non-operated mice served as43<br>controls.44<br>Results: Comparison of complementarity-determining region (CDR3) length distribution of 179 B45<br>cells and 796 T cells revealed neither differences between AAA and control nor between the disease46<br>stages. We found no clonal expansion of B cells in AAA. For T cells, we identified expanded clones47<br>in 11 of 16 AAA samples and in 1 of 8 control samples. Immune receptor repertoire comparison48<br>indicated that only few clones were shared between the individual AAA samples. The most frequently49<br>used V-genes in the TCR beta chain in AAA were TRBV3, TRBV19, and the splicing variant50<br>TRBV12-2+TRBV13-2.51<br>Conclusion: We found no clonal expansion of TCRs or BCRs in elastase-induced AAA in mice at52<br>different disease stages. Our findings imply that a more precise characterization of TCR and BCR53<br>distribution requires a more extensive number of lymphocytes to prevent undersampling and to54<br>potentially detect rare clones. In summary, this paper examines TCR and BCR sequencing results,55<br>identifies limitations and pitfalls, and offers guidance for future studies.</p> <p> </p> <p> </p> <p>The Raw Data can be found here: https://zenodo.org/doi/10.5281/zenodo.10725979</p> <p> </p>
Effector cell signatures in peripheral blood after nasal allergen challenge: biomarkers of the allergic response
GEO Series GSE43497. Homo sapiens. 190 samples. Type: Expression profiling by array.
Expression data from peripheral blood - blood draws at Pre and Post time points of Methacholine (MCh) inhalation challenge
GEO Series GSE32198. Homo sapiens. 28 samples. Type: Expression profiling by array.
Escherichia coli global gene expression due to ciprofloxacin challenge in bloodstream infection like conditions
GEO Series GSE68107. Escherichia coli CFT073; Escherichia coli. 28 samples. Type: Expression profiling by array.
Lungs from ovalbumen sensitized and challenged C57 mice
GEO Series GSE447. Mus musculus. 6 samples. Type: Expression profiling by array.
Effect of dietary immunostimulation in the portals of entry of rainbow trout after LPS challenge
GEO Series GSE12961. Oncorhynchus mykiss. 8 samples. Type: Expression profiling by array.
affy_riz_2011_7-: Transcriptomic analysis of rice leaves challenged with virulent and avirulent Xoo strains
GEO Series GSE34192. Oryza sativa. 18 samples. Type: Expression profiling by array.
Ferulic Acid Challenge of Lactobacillus brevis
GEO Series GSE22826. Levilactobacillus brevis. 6 samples. Type: Expression profiling by array.
Lungs from ovalbumen sensitized and challenged A/J mice
GEO Series GSE450. Mus musculus. 6 samples. Type: Expression profiling by array.
Gene expression profiling of porcine skeletal muscle satellite cells after challenge with poly I:C
GEO Series GSE112527. Sus scrofa. 21 samples. Type: Expression profiling by high throughput sequencing.
Peripheral blood mononuclear cell gene expression pre-/post- oral glucose challenge
GEO Series GSE56607. Homo sapiens. 46 samples. Type: Expression profiling by array.
Aspirin Challenge–Induced Genome-Wide DNA Methylation Profile of Peripheral Blood Lymphocytes in Aspirin-Exacerbated Respiratory Disease
GEO Series GSE235569. Homo sapiens. 24 samples. Type: Methylation profiling by genome tiling array.
Immunomodulatory effects of dietary seaweeds in LPS challenged Atlantic salmon Salmo salar as determined by deep RNA sequencing of the head kidney transcriptome
GEO Series GSE101545. Salmo salar. 9 samples. Type: Expression profiling by high throughput sequencing.
RNA-Seq in HG-challenged H9C2 cells with or without FGFR1 knockdown
GEO Series GSE195733. Rattus norvegicus. 6 samples. Type: Expression profiling by high throughput sequencing.
Gene Ontology map used in GO Test Challenge of QfO benchmark
<p>Gene Ontology map file used in the 2018 Quest for Ortholgs benchmarking workflow (https://github.com/qfo/benchmark-webservice/tree/elixir)</p>
Vocalisation Challenge Corpus (VOC-C)
<p> </p> <p><strong>2022 ComParE Vocalisations Sub-Challenge</strong></p> <p>This repository contains the Vocalisation Challenge Corpus (VOC-C) for the 2022 Vocalisations Sub-Challenge of the Computational Paralinguistics ChallengE (ComParE).</p> <p>The corpus features human non-speech vocalisations, such as laughter, cries, moans, or screams, expressing different emotions, varying from mild to extreme affective intensity. The emotion classes include achievement/triumph, anger, fear, pain, sexual pleasure, and positive surprise. The data from the female speakers have been made openly available (see publication below); the male speakers are so far unseen. The data are partitioned into the female vocalisations in train (6 speakers, 625 items) and development (5 speakers, 460 items), and the male vocalisations (2 speakers, 276 items) in test. </p> <p> </p> <p>Please find information on the published female speaker data in <strong>Holz, N., Larrouy-Maestri, P., & Poeppel, D. (2022). <a href="https://doi.apa.org/record/2022-30295-001?doi=1">The variably intense vocalizations of affect and emotion (VIVAE) corpus prompts new perspective on nonspeech perception.</a> <em>Emotion, 22</em>(1), 213–225. </strong></p> <p><em>Additional information on male speaker data: </em>Male speakers were undergraduate students at the Berklee College of Music in Boston. None of them underwent formal acting training or had professional acting experience. In difference to the female speakers, none of them reported stage experience as singers. Recording procedures were the same across all speakers; no technical validation was performed on the male speaker data.</p> <p> </p> <p> </p>
The intracranial tumor segmentation challenge: ICTS 2021-image labels
<p>This dataset was retrieved from clinical work of radiosurgery. It contains brain tumor labels of 150 patients.</p>
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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.