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298 results for “Multi-modal”
Data from: Multi-modal defenses in aphids offer redundant protection and increased costs likely impeding a protective mutualism
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Code for: Multi-modal screening for synergistic neuroprotection of mild extremely preterm brain injury: Cell counting code repository
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SDT Dataset | Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification
<p>The Synthetic Depth & Thermal (SDT) dataset consists of 40k synthetic and 8k real depth and thermal stereo images, depicting human behavior in indoor environments. Included samples show uniquely posed lying, sitting, and standing persons within four different room types (living room, bedroom, bathroom, and kitchen), recorded from an elevated position. Furthermore, a fourth control class with empty rooms is provided as well. Both parts of SDT are balanced sets of these four classes and room types. The synthetic part of the dataset is intended to be used as training (and validation) data for uni-/multi-modal pose classification or person detection models, while the real part can be used to assess the generalization performance. To facilitate supervised training, pose labels and person bounding boxes are provided for all images. The real images in the dataset were captured by a multi-modal stereo camera system, consisting of an Orbbec Astra depth camera and a FLIR Lepton 3.5 thermal camera, while synthetic images, which share the image characteristics of these cameras, were acquired through 3D rendering of virtual scenes within Blender and subsequent introduction of camera-specific noise.</p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].</p> <p>[1] C. Pramerdorfer, J. Strohmayer and M. Kampel, "Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification," <em>2020 IEEE International Conference on Image Processing (ICIP)</em>, Abu Dhabi, United Arab Emirates, 2020, pp. 1611-1615, doi: 10.1109/ICIP40778.2020.9191284.</p> <p>BibTeX citation:</p> <pre>@INPROCEEDINGS{9191284, author={Pramerdorfer, C. and Strohmayer, J. and Kampel, M.}, booktitle={2020 IEEE International Conference on Image Processing (ICIP)}, title={Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification}, year={2020}, volume={}, number={}, pages={1611-1615}, doi={10.1109/ICIP40778.2020.9191284}}</pre>
Prediction of cardiovascular diseases by integrating multi-modal features with machine learning methods
<p>Electrocardiogram (ECG) and Phonocardiogram (PCG) play important roles in early prevention and diagnosis of cardiovascular diseases. As the development of machine learning technique, detection of cardiovascular diseases from ECG and PCG has been attracted much attention. However, current available methods are mostly based on single data resource. It is desirable to develop efficient multi-modal machine learning methods to predict and diagnose cardiovascular diseases. In this study, we propose a novel multi-modal method for predicting cardiovascular diseases based on ECG and PCG features. By building up conventional neural networks, we extract ECG and PCG deep coding features respectively. The genetic algorithm is used to screen the combined features and obtain the best feature subset. Then support vector machine makes classification decision. Experimental results show that compared with using single-modal features ECG and PCG, the performance of this method reaches an AUC value of 0.936 when using multi-modal data resources.</p> <p>This dataset is developed from a real-world dataset which was assembled by PhysioNet/CinC Challenge in 2016. The original dataset can be downloaded from website (<a href="http://www.physionet.org/challenge/2016/">http://www.physionet.org/challenge/2016/</a>).</p>
Data from: Specificity of multi-modal aphid defenses against two rival parasitoids
Insects are often attacked by multiple natural enemies, imposing dynamic selective pressures for the development and maintenance of enemy-specific resistance. Pea aphids (Acyrthosiphon pisum) have emerged as models for the study of variation in resistance against natural enemies, including parasitoid wasps. Internal defenses against their most common parasitoid wasp, Aphidius ervi, are sourced through two known mechanisms– 1) endogenously encoded resistance or 2) infection with the heritable bacterial symbiont, Hamiltonella defensa. Levels of resistance can range from nearly 0–100% against A. ervi but varies based on aphid genotype and the strain of toxin-encoding bacteriophage (called APSE) carried by Hamiltonella. Previously, other parasitoid wasps were found to commonly attack this host, but North American introductions of A. ervi have apparently displaced all other parasitoids except Praon pequodorum, a related aphidiine braconid wasp, which is still found attacking this host in natural populations. To explain P. pequodorum's persistence, multiple studies have compared direct competition between both wasps, but have not examined specificity of host defenses as an indirectly mediating factor. Using an array of experimental aphid lines, we first examined whether aphid defenses varied in effectiveness toward either wasp species. Expectedly, both types of aphid defenses were effective against A. ervi, but unexpectedly, were completely ineffective against P. pequodorum. Further examination showed that P. pequodorum wasps suffered no consistent fitness costs from developing in even highly 'resistant' aphids. Comparison of both wasps' egg-larval development revealed that P. pequodorum's eggs have thicker chorions and hatch two days later than A. ervi's, likely explaining their differing abilities to overcome aphid defenses. Overall, our results indicate that aphids resistant to A. ervi may serve as reservoirs for P. pequodorum, hence contributing to its persistence in field populations. We find that specificity of host defenses and defensive symbiont infections, may have important roles in influencing enemy compositions by indirectly mediating the interactions and abundance of rival natural enemies.
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>
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>
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>
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>
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>
STS-Tooth: A multi-modal dental dataset for semi-supervised deep learning image segmentation
<p>In response to the increasing prevalence of dental diseases, dental health, a vital aspect of human well-being, warrants greater attention. Panoramic X-ray images (PXI) and Cone Beam Computed Tomography (CBCT) are key tools for dentists in diagnosing and treating dental conditions. Additionally, deep learning for tooth segmentation can focus on relevant treatment information and localize lesions. However, the scarcity of publicly available PXI and CBCT datasets hampers their use in tooth segmentation tasks. Therefore, this paper presents a multimodal dataset for semi-supervised deep learning in dental PXI and CBCT, named STS-2D-Tooth and STS-3D-Tooth. STS-2D-Tooth includes 4,000 images and 900 masks, categorized by age into children and adults. Moreover, we have collected CBCTs providing more detailed and three-dimensional information, resulting in the STS-3D-Tooth dataset comprising 148,400 unlabeled scans and 8,800 masks. To our knowledge, this is the first multimodal dataset combining dental PXI and CBCT, and it is the largest tooth segmentation dataset, a significant step forward for the advancement of tooth segmentation.</p>
Using Multi-Modal Path-Specific Transit Trips in Transportation Social Sustainability Analysis: Case Study in Atlanta, GA
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ADSCAN: AI-Powered Multi-modal Framework for Enhanced Ad Detection and Web Safety
<p>We published codes and datasets (SITE-D, TEXT-D and part of IMG-D) for paper "ADSCAN: AI-Powered Multi-modal Framework for Enhanced Ad Detection and Web Safety."</p>
Data from: Creating a multi-track classical music performance dataset for multi-modal music analysis: challenges, insights, and applications
We introduce a dataset for facilitating audio-visual analysis of musical performances. The dataset comprises 44 simple multi-instrument classical music pieces assembled from coordinated but separately recorded performances of individual tracks. For each piece, we provide the musical score in MIDI format, the audio recordings of the individual tracks, the audio and video recording of the assembled mixture, and ground- truth annotation files including frame-level and note-level tran- scriptions. We describe our methodology for the creation of the dataset, particularly highlighting our approaches for addressing the challenges involved in maintaining synchronization and ex- pressiveness. We demonstrate the high quality of synchronization achieved with our proposed approach by comparing the dataset against existing widely-used music audio datasets. We anticipate that the dataset will be useful for the devel- opment and evaluation of existing music information retrieval (MIR) tasks, as well as for novel multi-modal tasks. We bench- mark two existing MIR tasks (multi-pitch analysis and score- informed source separation) on the dataset and compare against other existing music audio datasets. Additionally, we consider two novel multi-modal MIR tasks (visually informed multi-pitch analysis and polyphonic vibrato analysis) enabled by the dataset and provide evaluation measures and baseline systems for future comparisons (from our recent work). Finally, we propose several emerging research directions that the dataset enables.
DravidianMultiModality: A Dataset for Multi-modal Sentiment Analysis in Tamil and Malayalam
<p>@article{dravidian_multimodality,<br> title={DravidianMultiModality: A Dataset for Multi-modal Sentiment Analysis in Tamil and Malayalam},<br> author={Bharathi Raja Chakravarthi, Jishnu Parameswaran P.K, Premjith B, K.P Soman, Rahul Ponnusamy, Prasanna Kumar Kumaresan, Kingston Pal Thamburaj, John P. McCrae},<br> journal={arXiv.org},<br> publisher={2021}<br> }</p>
A comprehensive video dataset for Multi-Modal Recognition Systems
<p>A fully-labelled video dataset will act as a unique resource for researchers and analysts in the fields such as machine learning, computer vision and deep learning. The videos contain similar text recited by 67 different subjects. The text contains digits from 1 to 20 recited by 67 different subjects within the same experimental setup.</p>
Source data of scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection
<p>The source data of the manuscript: scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection.</p>
GOhydro Multi-modal Sensor Kit - 3D Printing Files
<p>These are the files for 3D printing the housing of the GOhydro Multi-modal Sensor Kit</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.