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901 results for “Modality”
Cross-modal matching of audio-visual German and French fluent speech in infancy
<p>The present study examined when and how the ability to cross-modally match audio-visual fluent speech develops in 4.5-, 6- and 12-month-old German-learning infants. In Experiment 1, 4.5- and 6-month-old infants’ audio-visual matching ability of native (German) and non-native (French) fluent speech was assessed by presenting auditory and visual speech information sequentially, that is, in the absence of temporal synchrony cues. The results showed that 4.5-month-old infants were capable of matching native as well as non-native audio and visual speech stimuli, whereas 6-month-olds perceived the audio-visual correspondence of native language stimuli only. This suggests that intersensory matching narrows for fluent speech between 4.5 and 6 months of age. In Experiment 2, auditory and visual speech information was presented simultaneously, therefore, providing temporal synchrony cues. Here, 6-month-olds were found to match native as well as non-native speech indicating facilitation of temporal synchrony cues on the intersensory perception of non-native fluent speech. Intriguingly, despite the fact that audio and visual stimuli cohered temporally, 12-month-olds matched the non-native language only. Results were discussed with regard to multisensory perceptual narrowing during the first year of life.</p>
scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution - Supplementary data and code
<p>Data and code to reproduce the analyses from the study: "scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution". </p>
Tri-modal single cell profiling reveals a distinct pediatric CD8αα T cell subset and broad age-related molecular reprogramming across the T cell compartment
<p>Age-associated changes in the T cell compartment are well-described. However, limitations of current single- or bi-modal single-cell assays, including flow cytometry, RNA-seq, and CITE-seq, have restricted our ability to deconvolve more complex cellular and molecular changes. Here, we profile more than 300,000 single T cells from healthy children (11–13 yrs) and older adults (55–65 yrs) using the trimodal assay, TEA-seq (protein, RNA, and chromatin accessibility), revealing that molecular programming of T cell subsets shifts toward a more activated basal state with age. Naive CD4 T cells, considered relatively resistant to aging, exhibited pronounced transcriptional and epigenetic reprogramming. Moreover, we discovered a novel CD8aa T cell subset lost with age that is epigenetically poised for rapid effector responses and displays distinct inhibitory, co-stimulatory and tissue homing properties. Together, these data reveal new insights into age-associated changes in the T cell compartment that may contribute to differential immune responses.</p>
Carnosine levels in the same individuals as measured by different modalities
<p>Carnosine is an endogenous di-peptide (b-alanine -L- histidine) involved in maintaining tissue homeostasis. It is most abundant in skeletal muscle where its concentration has been determined in biopsy samples using tandem mass spectrometry (MS-MS). Carnosine levels can also be assessed in intact leg muscles by proton magnetic resonance spectroscopy (1H-MRS) or in blood and urine samples using mass spectrometry. Nevertheless, it remains uncertain how carnosine levels from these distinct compartments are correlated with each other when measured in the same individual. Furthermore, it is unclear which measurement modality might be most suitable for large-scale clinical studies. Hence, in 31 healthy volunteers, we assessed carnosine levels in skeletal muscle, via 1H-MRS, and in erythrocytes and urine by MS-MS. While muscle carnosine levels were higher in males, there was no sex-associated difference in urinary or erythrocyte levels. In a linear regression model adjusted for age, sex, race, and diet, there was a positive association between erythrocyte and urinary carnosine. However, no association was observed between 1H-MRS and erythrocytes or urinary measures. In the relationship between muscle versus urinary and erythrocyte measures, females had a positive association, while males did not show any association. We also found that 1H-MRS measures were highly sensitive to location of measurement. Thus, it is uncertain whether 1H-MRS can accurately and reliably predict endogenous carnosine levels. In contrast, urinary and erythrocyte carnosine measures may be stable and in greater synchrony, and given financial and logistical concerns, may be a feasible alternative for large-scale clinical studies.</p>
URDU Dataset for Multi-modal Sentiment Analysis
<p>The "Multi-modal Sentiment Analysis Dataset for Urdu Language Opinion Videos" is a valuable resource aimed at advancing research in sentiment analysis, natural language processing, and multimedia content understanding. This dataset is specifically curated to cater to the unique context of Urdu language opinion videos, a dynamic and influential content category in the digital landscape.</p> <p><strong>Dataset Description:</strong></p> <ul> <li><strong>Size and Diversity:</strong> This dataset comprises an extensive collection of Urdu language opinion videos, encompassing a wide spectrum of topics and sentiments. It consists of a total of 214 videos, each of varying lengths, offering a diverse and comprehensive representation of the Urdu language content landscape.</li> <li><strong>Sentiment Annotations:</strong> The dataset is meticulously annotated with sentiment labels, providing information on the emotional tone expressed in each video. The sentiment labels include "positive," "negative," and "neutral," offering a nuanced understanding of the sentiment conveyed in these multimedia opinion pieces.</li> <li><strong>Multi-modal Approach:</strong> A unique feature of this dataset is its multi-modal approach. It combines text, audio, and visual data to enable researchers to delve into the various dimensions of sentiment analysis within the context of opinion videos. The multi-modal annotations encompass the textual content of spoken words, the auditory characteristics of the videos, and the visual cues from the video frames.</li> </ul> <p><strong>Significance and Applications:</strong></p> <p>This dataset holds significant value for both the research community and practical applications:</p> <ul> <li><strong>Research Advancement:</strong> Researchers can employ this dataset to investigate the complex landscape of sentiment analysis within the context of opinion videos. It facilitates inquiries into sentiment trends, the development of sentiment analysis models, and the creation of sentiment-aware multimedia content analysis tools.</li> <li><strong>Content Recommendation:</strong> The dataset can play a pivotal role in the development of content recommendation systems that cater to viewers' emotional preferences. Understanding sentiment in opinion videos is crucial for improving content engagement and user experience.</li> <li><strong>User Engagement Analysis:</strong> The dataset can empower studies on user engagement and interaction with multimedia content. It is an essential resource for researchers aiming to decode the factors influencing viewer reactions and engagement in multimedia.</li> </ul> <p> </p> <p>Researchers are encouraged to explore and utilize this dataset for various academic and commercial purposes, fostering innovation in sentiment analysis and multimedia understanding. The dataset is made available with open access to facilitate collaborative research and to contribute to the broader knowledge in the field.</p>
Data for "Cross-modal sensory compensation increases mosquito attraction to humans"
<p>These supplementary files accompany the manuscript by Morita et al. entitled "Cross-modal sensory compensation increases mosquito attraction to humans".</p> <p>This includes all raw data in the paper, code, and supplementary data.</p>
Multi-modality medical image dataset for medical image processing in Python lesson
<p>This dataset contains a collection of medical imaging files for use in the <a href="https://github.com/esciencecenter-digital-skills/medical-image-processing">"Medical Image Processing with Python" lesson</a>, developed by the <a href="https://www.esciencecenter.nl/">Netherlands eScience Center</a>. </p> <p>The dataset includes:</p> <ol> <li>SimpleITK compatible files: MRI T1 and CT scans (<em>training_001_mr_T1.mha, training_001_ct.mha</em>), digital X-ray (<em>digital_xray.dcm</em> in DICOM format), neuroimaging data (<em>A1_grayT1.nrrd, A1_grayT2.nrrd</em>). Data have been downloaded from <a href="https://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/Python_html/00_Setup.html">here</a>. </li> <li>MRI data: a T2-weighted image (<em>OBJECT_phantom_T2W_TSE_Cor_14_1.nii</em> in NIfTI-1 format). Data have been downloaded from <a href="../records/6467772">here</a>. </li> <li>Example images for the machine learning lesson: chest X-rays (<em>rotatechest.png, other_op.png</em>), cardiomegaly example (<em>cardiomegaly_cc0.png</em>).</li> <li>Array data: Array data for the Intro to Medical Imaging lesson. Numpy arrays were created by processing and manipulation of publicly available data i.e. from <a href="https://doi.org/10.1109/TNS.1974.6499235">the Schepp Logan phantom</a> and from the <a href="https://fastmri.med.nyu.edu/">NYU FastMRI dataset</a></li> <li>Data for the anonymization exercises: ultrasound (<em>identifiable_us.jpg</em>) dowloaded from <a href="https://www.flickr.com/photos/jcarter/2461223727">here</a>, and DICOM data (<em>our_sample_dicom.dcm</em>) shared for this course specifically by a colleague</li> <li>Histopathology data: histopathology slide images from <a href="https://openslide.org/">openslide</a> library samples in the freely distributable test data </li> </ol> <p>These files represent various medical imaging modalities and formats commonly used in clinical research and practice. They are intended for educational purposes, allowing students to practice image processing techniques, machine learning applications, and statistical analysis of medical images using Python libraries such as scikit-image, pydicom, and SimpleITK.</p>
Dataset and statistical analysis related to "Amphibian studies to investigate the endocrine disrupting properties of chemicals through the Thyroid modality: a comparison of their statistical power"
Open the record for dataset details and reuse information.
MM-Office Dataset: multi-view and multi-modal dataset in an office environment
<p>MM-office is a multi-view and multi-modal dataset in an office environment (MM-Office) that records events, e.g., 'enter' to the office room, 'sit down' on the chair, and 'take out' something from a shelf, in the room assuming the daily work. These events are recorded simultaneously using eight non-directional microphones and four cameras. The audio and video clips are divided into scenes, each about 30 to 90 seconds. The amount of data was 880 clips per point and sensor. The labels available for training are given as multi-labels that indicate which each clip contains what event. Only the test data is annotated with a strong label containing the onset/offset time of each event.</p> <p>License: see the file named LICENSE.pdf</p> <p>Further information is available at [1] and Github: https://github.com/nttrd-mdlab/mm-office</p> <p>[1] Masahiro Yasuda, Yasunori Ohishi, Shoichiro Saito, Noboru Harada “Multi-view and Multi-modal Event Detection Utilizing Transformer-based Multi-sensor fusion,” in IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), 2022.</p>
Data for 'Multi-modal microscopy imaging with the OpenFlexure Delta Stage'
<p>Image data for 'Multi-modal microscopy imaging with the OpenFlexure Delta Stage'</p>
WEEE, A Multi-Device and Multi-Modal Dataset for Wearable Human Energy Expenditure Estimation
<p>We present WEEE, a multi-device and multi-modal dataset collected from 17 participants under different physical activities.<br> WEEE contains: 1) sensor data collected using 7 wearable devices placed on 4 body locations - head, ear, chest, and wrist<br> -, 2) respiratory data collected with an indirect calorimeter serving as ground-truth information, 3) demographics and body<br> composition data (e.g., muscle or fat percentage), 4) activity type - and their corresponding metabolic equivalent of task (MET) values - and intensity level, and 5) answers to questionnaires related to physical activity level, diet, stress and sleep. Thanks to the diversity of sensors and body locations of the WEEE dataset, we envision that this dataset will enable the development of novel human energy expenditure estimation techniques for a diverse set of application scenarios. Energy expenditure (EE) refers to the amount of energy an individual uses to maintain body functions and as a result of physical activity. The ability to estimate EE allows computing systems obtaining valuable insights regarding people’s physical activity and providing personalized recommendations for promoting a healthier and more active lifestyle.</p>
Cross-modal correspondences: different modes, common codes? Investigating musical engagement with an Ecological cognitive approach
<p>This folder refers to my thesis entitled "Crossmodal correspondences: different modes, common codes? Investigating musical engagement with an Ecological cognitive approach."</p>
An example dataset for Multi-modal brain tumor data completion based on reconstruction consistency loss
<p>This is part of inputs and corresponding prediction results of the method, which is proposed in "Multi-modal brain tumor data completion based on reconstruction consistency loss" . This dataset can be only used for paper review, please do not share, thanks. The struction of this dataset is as follows:</p> <p>1.There are three folders, where pred_data stores the network image outputs. test_data stores the input images. trained model include the trained model by our network.</p> <p>2.The serial number is in a one-to-one correspondence.</p> <p>3.If you want to use the trained model, please download brats18 dataset and preprocess your dataset, which can refer to <a href="https://github.com/zhangshuang317/RAGAN/">https://github.com/zhangshuang317/RAGAN/</a>.</p>
Supplementary material for the manuscript "Transition to chaos and modal structure of magnetized Taylor–Couette flow"
<p>These data and codes are the supplementary materials for the manuscript "Transition to chaos and modal structure of magnetized Taylor–Couette flow". The description of the data is in the file "DATA_OVERVIEW.txt".</p>
Leverage Points in Modality shifts: Comparing Language-only and Multimodal Word Representations
<p>Dataset for the blind submission of Leverage Points in Modality shifts: Comparing Language-only and Multimodal Word Representations paper</p>
Replication data for "The Role of Modal Substitution in Rebound Effects Within US Freight Transportation"
<p>There are two data files corresponding to the 2012 and 2017 Commodity Flow Survey and associated auxiliary data required for replication. </p>
EMA (Experimental Modal Analysis) of Concept Car Frame
<p>Result file of EMA measurement of Concept Car frame.</p>
Exploring the Spectrum of Biometric Technologies: A Systematic Literature Review of Conventional and Unconventional Modalities
Open the record for dataset details and reuse information.
MedSegBench: A Comprehensive Benchmark for Medical Image Segmentation in Diverse Data Modalities
<p>We split the dataset into two parts due to the maximum uploaded file limit. You can find other data in Version 1.</p> <p>This dataset is an article study and is under evaluation.</p> <p><code>You can access the article on <a href="https://www.nature.com/articles/s41597-024-04159-2">Nature</a>.</code></p> <p><code>Trained model weights and detailed prediction results for each dataset (<a href="../records/13381081" target="_blank" rel="noopener">Zenodo</a>)</code></p>
Modal logic problems for the TPTP
<p>Modal logic problem files using the TPTP syntax for non-classical logics. The files were created from exercises and examples included in "First-Order Modal Logic" (1998) by Fitting & Mendelsohn.</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.