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2,863 results for “Challenge”
Effects of OVA challenge in mouse model of asthma
GEO Series GSE41665. Mus musculus. 123 samples. Type: Expression profiling by array.
Mouse model of severe asthma: ovalbumin challenge
GEO Series GSE27066. Mus musculus. 36 samples. Type: Expression profiling by array.
BCG vaccination of mice elicits transcriptional changes in blood that predict protection against M.tb. challenge
GEO Series GSE212586. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Time-dependent changes in gene expression after endotoxinic challenge followed by LR12-scrambled or LR12 treatment.
GEO Series GSE48311. Macaca fascicularis; Homo sapiens. 56 samples. Type: Expression profiling by array.
Profiling gene expression of the host response to an irradiated sporozoite immunization and Plasmodium vivax malaria challenge
GEO Series GSE85263. Homo sapiens. 55 samples. Type: Expression profiling by high throughput sequencing.
Escherichia coli global gene expression due ciprofloxacin challenge in human whole blood
GEO Series GSE68106. Escherichia coli CFT073; Escherichia coli. 10 samples. Type: Expression profiling by array.
Expression data from Arabidopsis thaliana Col-0 challenged with Ralstonia solanacearum race 4 (roots)
GEO Series GSE92631. Arabidopsis thaliana. 6 samples. Type: Expression profiling by array.
Dataset related to article: "Psychosocial Cardiological Schedule-Revised (PCS-R) in a Cardiac Rehabilitation Unit: Reflections Upon Data Collection (2010–2017) and New Challenges"
<p><strong>Introduction: </strong>The Psychosocial Cardiological Schedule (PCS) was developed as a screening tool for patients undergoing cardiac rehabilitation (CR) to detect clinically relevant psychosocial/cognitive problems requiring psychological assessment/intervention. Filled out by a trained nurse, it classifies patients according to their need or not for a psychological interview and intervention provided by the psychologist (PCS-Yes vs. PCS-No).</p> <p><strong>Aims: </strong>The main aim was to compare PCS data collected, respectively, in 2010 and 2017, regarding patients' socio-demographic characteristics, clinical variables, and the inclusion criteria for psychological counseling. Subsequently, the original Italian PCS was revised and an English version of the schedule was provided [PCS-Revised (PCS-R)].</p> <p><strong>Results: </strong>28 patients (aged 53.5 + 12.6 years, M = 20) of the 87 recruited in 2010 vs. 35 (aged 64.9 + 12.7 years, M = 28) of the 83 recruited in 2017 met the criteria for PCS-Yes: age < 55 years, social problems (living alone, no social support), manifest psychological/behavioral problems, suspected neuropsychological disorders, low prescription adherence, inadequate disease awareness. Comparing the two samples (2010 vs. 2017), clinical variables were similar, and the need for a psychological interview did not differ substantially (32.2 vs. 42.2%), but age increased significantly (PCS-Yes: 53.5 ± 12.6 vs. 64.9 ± 12.7 years, <em>p</em> = 0.001; PCS-No: 68.3 ± 8.0 vs. 75.0 ± 7.7 years, <em>p</em> = 0.0001). A significant increase was observed in the recommendation for neuropsychological assessment (3.6 vs. 25.7%, <em>p</em> = 0.02) to confirm eventual cognitive deficits. These results, the clinical experience, and the recent evidences from literature led to the PCS-R, incorporating a psychosocial screening, a psychological/neuropsychological deeper assessment, and a recommendation for a specific intervention to be carried out either during rehabilitation or in outpatient services.</p> <p><strong>Conclusion: </strong>The data comparison highlight changes in the cardiac population, which is aging and more frequently requires neuropsychological assessment. The PCS-R could be considered in clinical practice as a useful screening tool to implement a timely coordinated interdisciplinary intervention, comprehensive of specific and tailored psychotherapeutic techniques.</p>
MuSe-Topic: Multimodal Emotion-Target Sub-challenge (MuSe2020)
<p><strong>[Post-challenge] MuSe-Topic of MuSe2020: </strong>Predicting 10-class domain-specific topics as the target of 3-class (low, medium, high) emotions of valence and arousal. This package includes only MuSe-Topic features (all partitions) and annotations of the training and development set (test scoring via the MuSe website). </p> <p><strong>General: </strong>The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge and Workshop (MuSe) is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), and the sentiment analysis community (symbol-based). </p> <p>We introduce the novel dataset MuSe-CAR that covers the range of aforementioned desiderata. MuSe-CAR is a large (>36h), multimodal dataset that has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity.</p> <p>We have designed MuSe-CAR to be of high voice and video quality, as informative video social media content, as well as everyday recording devices have improved in recent years. This enables robust learning, even with a high degree of novel, in-the-wild characteristics, for example as related to: i) Video: Shot size (a mix of closeup, medium, and long shots), face-angle (side, eye, low, high), camera motion (free, free but stable, and free but unstable, switch, e.g., zoom, fixed), reviewer visibility (full body, half-body, face only, and hands only), highly varying backgrounds, and people interacting with objects (car parts). ii) Audio: Ambient noises (car noises, music), narrator and host diarisation, diverse microphone types, and speaker locations. iii) Text: Colloquialisms, and domain-specific terms.</p>
MuSe-Wild: Multimodal Sentiment in-the-Wild Sub-challenge (MuSe2020)
<p><strong>MuSe-Wild of MuSe2020: </strong>Predicting the level of emotional dimensions (arousal, valence) in a time-continuous manner from audio-visual recordings. <strong>This package includes only MuSe-Wild features (all partitions) and annotations of the training and development set </strong>(test scoring via the MuSe website). </p> <p><strong>General: </strong>The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge and Workshop (MuSe) is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), and the sentiment analysis community (symbol-based). </p> <p>We introduce the novel dataset MuSe-CAR that covers the range of aforementioned desiderata. MuSe-CAR is a large (>36h), multimodal dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity.</p> <p>We have designed MuSe-CAR to be of high voice and video quality, as informative video social media content, as well as everyday recording devices have improved in recent years. This enables robust learning, even with a high degree of novel, in-the-wild characteristics, for example as related to: i) Video: Shot size (a mix of close-up, medium, and long shots), face-angle (side, eye, low, high), camera motion (free, free but stable, and free but unstable, switch, e.g., zoom, fixed), reviewer visibility (full body, half-body, face only, and hands only), highly varying backgrounds, and people interacting with objects (car parts). ii) Audio: Ambient noises (car noises, music), narrator and host diarisation, diverse microphone types, and speaker locations. iii) Text: Colloquialisms, and domain-specific terms.</p> <p> </p>
MuSe-Trust: Multimodal Trustworthiness Sub-challenge (MuSe2020)
<p><strong>MuSe-Trust of MuSe2020:</strong> Predicting the level of trustworthiness of user-generated audio-visual content in a sequential manner utilising a diverse range of features and (optional) emotional (arousal and valence) predictions. This package includes only MuSe-Trust features (all partitions) and annotations of the training and development set (test scoring via the MuSe website). </p> <p><strong>General:</strong> The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge and Workshop (MuSe) is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), and the sentiment analysis community (symbol-based). </p> <p>We introduce the novel dataset MuSe-CAR that covers the range of aforementioned desiderata. MuSe-CAR is a large (>36h), multimodal dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity.</p> <p>We have designed MuSe-CAR to be of high voice and video quality, as informative video social media content, as well as everyday recording devices have improved in recent years. This enables robust learning, even with a high degree of novel, in-the-wild characteristics, for example as related to: i) Video: Shot size (a mix of closeup, medium, and long shots), face-angle (side, eye, low, high), camera motion (free, free but stable, and free but unstable, switch, e.g., zoom, fixed), reviewer visibility (full body, half-body, face only, and hands only), highly varying backgrounds, and people interacting with objects (car parts). ii) Audio: Ambient noises (car noises, music), narrator and host diarisation, diverse microphone types, and speaker locations. iii) Text: Colloquialisms, and domain-specific terms.</p>
Addressing infrastructure challenges posed by the Harwich Formation through understanding its geological origins - dataset
<p>The dataset contains borehole records and laboratory test results from PhD research titled 'Addressing infrastructure challenges posed by the Harwich Formation through understanding its geological origins' by J. Edgar.</p>
MuSe-Stress: Emotional Stress Sub-Challenge (MuSe 2022)
<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). 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 2021 challenge.</p> <p><strong>General</strong>: The 3rd Multimodal Sentiment Analysis Challenge and Workshop (MuSe) 2022 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>
"Postural analysis reveals persistent vigilance in paper wasps after conspecific challenge" Videos.
<p>Videos of field assays demonstrating persistent effects of a simulated social challenge on the vigilance behavior of wild northern paper wasp foundresses, <em>Polistes fuscatus</em>. Untracked and tracked ("predictions.cleaned.slp") videos included. Please refer to the corresponding manuscript and the associated online supplementary materials for more information.</p>
MuSe-Humor: Humor Detection Sub-Challenge (MuSe 2022)
<p><strong>Description</strong>: Predicting the presence of humor in football press conference recordings. Available modalities: audio and video, transcriptions.</p> <p><strong>Labels</strong>: windows of 2 seconds are given a binary label, indicating presence and absence of humor. 9 human annotators labelled each video.</p> <p><strong>Dataset</strong>: MuSe-Humor is based on the novel Passau-Spontaneous Football Coach Humor (Passau-SFCH) dataset. It includes press conference recordings of 10 different German Bundesliga football coaches collected in 2017. The data provided here only includes segments in which the respective coach is speaking. The training data set contains recordings of 4 coaches, development and test data each contain recordings of 3 coaches.</p> <p><strong>Change in Version 1.1</strong>: we added the manually corrected transcriptions and BERT features based on them.</p> <p><strong>General</strong>: The 3rd Multimodal Sentiment Analysis Challenge and Workshop (MuSe) 2022 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>
Studying in Challenging Times (SICT) – A Longitudinal Study on Social Work Students' Situations During the Covid-19 Pandemic
<p>The aim of this quantitative study was to examine challenges and attitudes towards studying, social support, and personal lives of social work students during the Covid-19 pandemic, and to analyze how their situations have changed over the past few semesters under pandemic conditions. Drawing on a longitudinal research design, 99 students enrolled in the bachelor’s and master’s programs of social work at the Eastern Switzerland University of Applied Sciences (OST) were surveyed over four points in time, starting in the spring semester 2020 and ending in the fall semester 2021. The SICT dataset is available on demand.</p>
Supplementary data: "Challenges in automated high-throughput ab initio thermodynamics of magnetic high-entropy alloys"
<p>These supplementary data contain input and output files of EMTO density-functional theory (DFT) calculations and IPython/Jupyter notebooks that were used in analyzing the data for the paper "Challenges in automated high-throughput ab initio thermodynamics of magnetic high-entropy alloys".</p> <p>The data are organized as follows:</p> <ul> <li><em>alloy_discovery</em> folder contains the analysis notebooks and the processed EMTO DFT output data in a h5 database. <ul> <li>An installation of Python and Jupyter notebooks is required to run the notebooks. A recommended way of installing them is the Anaconda Python distribution: https://www.anaconda.com/download/</li> <li>It is recommended to first run the Requirements notebook, which checks what Python dependencies are needed to be downloaded.</li> <li>DFT output data is stored as an HDF5 database is located in the <em>results</em> subfolder.</li> <li>Figures generated with the notebooks are located in the <em>figures </em>subfolder.</li> </ul> </li> <li><em>output_files </em>folder contains raw output files of EMTO DFT calculations.</li> <li><em>input_files </em>folder contains raw input files that were used in the EMTO DFT calculations.</li> </ul>
The intracranial tumor segmentation challenge: ICTS 2021-unlabeled images
<p>This dataset was retrieved from clinical work of radiosurgery. It contains contrast-enhanced T1-weighted images (MRI) of 1500 patients with various brain tumors. Please see <a href="https://ntuh.ml/">https://ntuh.ml/</a> .</p>
Datasets used in Venice et al., "An evolutionary perspective on fungal human pathogens: the genomes of Scedosporium between environmental challenges and opportunism"
<p>This datasets collection includes:</p> <p>- For <em>Scedosporium aurantiacum</em> MUT6114 and <em>S. minutisporum </em>MUT6113: fasta files with assembled genome sequences, and gff files with coordinates of both genes and putative TEs insertions. All these files were generated by the authors of this study.</p> <p>- For <em>S. apiospermum</em> IHEM 14462 and <em>Lomentospora prolificans</em> JHH-5317: gff files containing putative TEs insertions only, since the genomic sequences and the gene models were produced by other authors and deposited in the NCBI database under accession numbers GCF_000732125.1 and GCA_002276285.1, respectively.</p> <p>- For <em>S. apiospermum </em>HDO1<em>, S. aurantiacum</em> WM09.24, <em>S. boydii</em> IHEM23826, <em>S. dehoogii</em> 120008799-01-4,<em> Scedosporium</em> sp. IMV00882 and <em>Scopulariopsis brevicaulis</em> LF580: gff files with coordinates of both genes and putative TEs insertions, since the genomic sequences were produced by other authors and deposited in the NCBI database under accession numbers GCA_002158515.1, GCA_000812075.1, GCA_002221725.1, GCA_002812735.1, GCA_001931805.1 and GCA_017657125.1, respectively.</p> <p> </p>
Computational models of immunity for pertussis boost (CMI-PB) multiomics datasets used for the first challenge
<p><strong>Systems vaccinology is increasingly generating massive datasets to characterize the immune state of individuals before and after vaccination, which require data science to interpret them. These studies are used to draw conclusions on what drives different individuals to have different vaccine responses. </strong></p>
ScienceDex guides
Understand access before you commit
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.