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901 results for “Modality”
Multi-modal dataset of a polycrystalline metallic material: 3D microstructure and deformation fields
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Data for figures in: Modal petrology and mineral chemistry of Apollo 17 Drive Tube Section 73002
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mRI: multi-modal 3d human pose estimation dataset using mmwave, rgb-d, and inertial sensors
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Multi-modal characterization of rodent dental development
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Single-cell multi-modal analysis of tumor microenvironment in human non-small cell lung cancer tissues
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Data from: Multi-modal ultra-high resolution structural 7-Tesla MRI data repository
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Data from: Multi-modal defenses in aphids offer redundant protection and increased costs likely impeding a protective mutualism
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Data from: Cross-modal representation of chewing food in posterior parietal and visual cortex
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Cross-modal representation of identity in primate hippocampus
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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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Data from: Impact of hunting modality on social contacts in wild boar populations across Europe
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Data from: Dissecting abstract, modality-specific and experience-dependent coding of affect in the human brain
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Data from: Shared and modality-specific brain regions that mediate auditory and visual word comprehension
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MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities
<p><strong>Abstract:</strong></p> <p>We introduce the <strong>MLM (Multiple Languages and Modalities)</strong> dataset - a new resource to train and evaluate multitask systems on samples in multiple modalities and three languages. The generation process and inclusion of semantic data provide a resource that further tests the ability for multitask systems to learn relationships between entities. The dataset is designed for researchers and developers who build applications that perform multiple tasks on data encountered on the web and in digital archives. The second version of MLM provides a geo-representative subset of the data with weighted samples for countries of the European Union. We demonstrate the value of the resource in developing novel applications in the digital humanities with a motivating use case and specify a benchmark set of tasks to retrieve modalities and locate entities in the dataset. Evaluation of baseline multitask and single-task systems on the full and geo-representative versions of MLM demonstrate the challenges of generalizing on diverse data. In addition to the digital humanities, we expect the resource to contribute to research in multimodal representation learning, location estimation, and scene understanding. </p> <p><strong>Introduction:</strong><br> Multiple Languages and Modalities comprises data points on 236k human settlements for evaluating and optimizing multitask learning systems. MLM presents a dataset with a high level of diversity in terms of modality and language. For each entity, we have extracted text summaries, images, coordinates, and their respective triple classes. Text summaries are available in three languages (English, French, and German) with each entity having between one and three language entries. </p> <p>Human settlements from all continents are provided in the overall dataset (MLM) with 72% located in Europe. Two further versions of the dataset - MLM-irle and MLM-irle-gr - were generated for use in the benchmark evaluation for multitask systems described in the paper (see above). MLM-irle-gr (ie geo-representative) was generated to serve organizations that focus on the European Union by providing a geographically balanced coverage of human settlements in this region. MLM-irle-gr contains data on 24k human settlements across the EU weighted in relation to the population count for each of the 28 countries.</p> <p>MLM contains the following fields:</p> <pre><code>---------------------------------------------------------------------- # field-label description ---------------------------------------------------------------------- 1. id a unique identifier 2. label textual label 3. coordinates longitude, latitude geo-location value 4. summaries list of textual summaries related to the entity 5. images list of images related to the entity 6. classes list of associated triple class ----------------------------------------------------------------------</code></pre> <p>MLM - Details by Dataset Version:</p> <pre><code>----------------------------------------------------------- Num. of MLM MLM-irle MLM-irle-gr ----------------------------------------------------------- Entities 236496 218681 22501 Images 412422 314533 31621 Summaries 497899 462328 47508 Triple classes 1685 1655 452 -----------------------------------------------------------</code></pre> <p><strong>Availability:</strong></p> <p>All three versions of MLM listed in the table directly above are available for direct download and use. To support findability and sustainability, the MLM dataset is published as an on-line resource at<em> <a href="https://doi.org/10.5281/zenodo.3885753">https://doi.org/10.5281/zenodo.3885753</a></em>. A separate page with detailed explanations and illustrations is available at <em><a href="http://cleopatra.ijs.si/goal-mlm/">http://cleopatra.ijs.si/goal-mlm/</a> </em>to promote ease-of-use. The project GitHub repository contains the complete source code for the system and the generation script is available at <em><a href="http://github.com/GOALCLEOPATRA/MLM">https://github.com/GOALCLEOPATRA/MLM</a></em>. Documentation adheres to the standards of <em>FAIR Data principles</em> with all relevant metadata specified to the research community and users. It is freely accessible under the Creative Commons Attribution 4.0 International license, which makes it reusable for almost any purpose. </p> <p><strong>Updating and Reusability:</strong><br> MLM is supported by a team of researchers from the University of Bonn, the Leibniz Information Center for Science and Technology, and Jožef Stefan Institute. The resource is already in use for individual projects and as a contribution to the project deliverables of the Marie Skłodowska-Curie CLEOPATRA Innovative Training Network. In addition to the steps above that make the resource available to the wider community, the usage of MLM will be promoted to the network of researchers in this project. Use among researchers and practitioners in digital humanities will be promoted by demonstrations and presentations at domain-related events. Activities are planned for the Digital Methods Summer School run by the University of Amsterdam. The range of modalities and languages present in the dataset also extend its application to research on multimodal representation learning, multilingual machine learning, information retrieval, location estimation, and the Semantic Web. MLM will be supported and maintained for three years in the first instance. A second release of the dataset is already scheduled and the generation process outlined above is designed to enable rapid scaling.</p>
The impact of learning modality on team-based learning (TBL) outcomes in anatomical sciences education
<p>Team-based learning (TBL) is an instructional methodology that has been increasingly used in anatomy and physiology education in recent years. The appropriateness of TBL methods for students with diverse preferred sensory learning modalities has not been adequately examined. This study aimed to show the influence of students preferred sensory modality for learning on TBL and traditional academic outcome measures (tests, assignments, etc.). 157 American undergraduate Communication Sciences and Disorders students taking anatomy and physiology courses took the VARK, a questionnaire quantifying their preferred learning modality. These students traditional and TBL measures of academic success were compared by preferred learning modality. A one-way MANOVA failed to find any difference in TBL or other academic outcomes by preferred learning modality (<em>p</em> = .252). The results of this study support the effectiveness of TBL methods for undergraduate students regardless of what sensory mode they prefer to receive information</p>
Dataset for "Observations of new particle formation, modal growth rates, and direct emissions of sub-10 nm particles in an urban environment"
<p>Dataset for "Observations of new particle formation, modal growth rates, and direct emissions of sub-10 nm particles in an urban environment". Each directory contains a README.md including descriptions in Markdown format.</p>
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: Modality interactions alter the shape of acoustic mate preference functions in gray treefrogs
Sexual selection takes place in complex environments where females evaluating male mating signals are confronted with stimuli from multiple sources and modalities. The pattern of expression of female preferences may be influenced by interactions between modalities, changing the shape of female preference functions, and thus ultimately altering the selective landscape acting on male signal evolution. We tested the hypothesis that the responses of female gray treefrogs, Hyla versicolor, to acoustic male advertisement calls are affected by interactions with visual stimuli. We measured preference functions for several call traits under two experimental conditions: unimodal (only acoustic signals presented), and multimodal (acoustic signals presented along with a video-animated calling male). We found that females were more responsive to multimodal stimulus presentations and, compared to unimodal playbacks, had weaker preferences for temporal call characteristics. We compared the preference functions obtained in these two treatments to the distribution of male call characteristics to make inferences on the strength and direction of selection expected to act on male calls. Modality interactions have the potential to influence the course of signal evolution and thus are an important consideration in sexual selection studies.
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.
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.