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1,389 results for “Multimodal”
N20EM dataset for multimodal lyric transcription
<p>N20EM dataset for multimodal lyric transcription, proposed in our ACM MM 2022 paper, MM-ALT: A Multimodal Automatic Lyric Transcription System. This dataset contains recordings of three modalities: audio, video, and IMU motion signal. </p> <p>Our paper's camera ready version: https://arxiv.org/abs/2207.06127</p> <p>Project website: https://n20em.github.io/</p> <p><strong>Note: </strong></p> <ol> <li><strong>Once you download the dataset, we assume you have read and agreed with the <a href="https://drive.google.com/file/d/1te7AxPTSGAdyqqNtkfjFbtCv4ydwgcOF/view?usp=sharing">Terms and Conditions</a>.</strong></li> <li><strong>Commercial usage is strictly prohibited.</strong></li> </ol> <p>Please cite our work as:</p> <p>@inproceedings{gu2022mm, title={MM-ALT: A multimodal automatic lyric transcription system}, author={Gu, Xiangming and Ou, Longshen and Ong, Danielle and Wang, Ye}, booktitle={Proceedings of the 30th ACM International Conference on Multimedia}, pages={3328--3337}, year={2022} }</p> <p> </p>
Gender annotations for Multimodal Opinion-level Sentiment Intensity dataset (MOSI)
<p>Annotations of perceived gender (female/male) for all files of the Multimodal Opinion-level Sentiment Intensity dataset (MOSI) [ arXiv:1606.06259]. The annotations were done by a single German and English speaking male annotator.</p>
Supplementary files for the article "A Systematic Literature Review on Multimodal Machine Learning"
<p>All included data was used for this review study. Data contains information from collected articles. Results of the analysis of each article are also available. All files are related to each other. A file named "All Articles Selected And Rejected Decision.xlsx" contains the name and ID of all articles, and that ID is used in other files as a reference for the analysis.</p>
Brain-inspired multimodal hybrid neural network for robot place recognition
<p>Brain-inspired multimodal hybrid neural network for robot place recognition</p>
Supplementary material - Non-invasive Multimodal Imaging Reveals Early Therapy-Induced Senescence in Human Cancer Cells
<p>The repository features .xls and .txt worksheets including all the data used through this work and reported in the manuscript figures and graphs. More precisely, we included the following: </p> <ul> <li>Figure 2. Raw pixel-wise signals detected in NLO images of TIS cells control cells that were used to perform the colocalization graphs and analyses reported. We describe the average colocalization of SRS and F-CARS signals, and TPEF and E-CARS signals, in both phenotypes.</li> <li>Figure 4. Raw data from image analyses of TPEF and SRS channels of multimodal NLO images, divided in 5 different time points over the therapy follow-up period. The data describe the early rearrangement of mitochondria (TPEF) and lipid vesicles (SRS) in TIS cells, with respect to control counterparts.</li> <li>Figure 6. Raw data from image analyses of QPI images, divided in 4 different time points over the therapy follow-up period. The data describe the early morphological modifications of TIS cells, with respect to control counterparts.</li> </ul>
Dataset for "Suppressing transverse mode instability through multimode excitation in a fiber amplifier"
<p>Numerical and theoretical data associated with "Suppressing transverse mode instability through multimode excitation in a fiber amplifier" (doi.org/10.1073/pnas.2217735120). </p>
A Multimodal Dataset on Stainless Steel for Electrochemical Corrosion Studies: Optical Microscopy and Linear Sweep Voltammetry
<p>The upload includes optical and electrochemical data for corrosion experiments.</p> <p>This dataset presents the results of an experimental study conducted to investigate the electrochemical behavior of electropolished Stainless Steel 316L (SS316L) samples immersed in NaCl solutions. The combination of Linear Sweep Voltammetry (LSV) and optical microscopy techniques was employed to gather comprehensive insights into the electrochemical processes occurring on the surface of the stainless steel samples.</p> <p>The samples used in the experiment were electropolished SS316L, chosen for its widely recognized corrosion resistance properties and frequent application in various industrial sectors. LSV was performed on the samples in a potential range of -0.5V to 1.35V, (vs 3.4M KCl Ag/AgCl). NaCl solutions with concentrations of 5mM, 10mM, and 50mM were prepared to simulate different electrolyte conditions.</p> <p>Two different scan rates, 50mV/s and 100mV/s, were applied during the LSV experiments to observe the effect of varying scan rates on the electrochemical behavior of the SS316L samples. The scan rates were chosen to cover a range commonly encountered in electrochemical studies.</p> <p>List of experiments:</p> <ul> <li> 5 mM solution, 100mV/s scan rate</li> <li> 10 mM solution, 50mV/s scan rate</li> <li> 10 mM solution, 100mV/s scan rate</li> <li> 50 mM solution, 50mV/s scan rate</li> <li> 50 mM solution, 100mV/s scan rate</li> </ul> <p>The dataset is accompanied by animated plots. The top left plot shows electrochemistry data, bottom left - average normalized intensity and derivative of intensity. Top right - original optical images, bottom right - normalized images.</p> <p>The scale for optical images: 1px = 480 nm. Axes on images are in pixels</p> <p>Jupyter notebook with the code, used to create videos included. We recommend opening the Jupyter notebook file in a Python 3 environment.<br> </p>
BSL-Hansard: A parallel, multimodal corpus of English and interpreted British Sign Language data from parliamentary proceedings
<p>BSL-Hansard is a novel open source and multimodal resource composed by combining Sign Language video data in BSL and English text from the official transcription of British parliamentary sessions. This paper describes the method followed to compile BSL-Hansard including time alignment of text using the MAUS (Schiel, 2015) segmentation system, gives some statistics about this dataset, and suggests experiments. These primarily include end-to-end Sign Language-to-text translation, but is also relevant for broader machine translation, and speech and language processing tasks.</p> <p>This dataset will be useful for translation between BSL and English, or for studies in BSL or English down to the phonetic level.</p>
TongueTap: Multimodal Tongue Gesture Recognition with Head-Worn Devices
<p>Please cite the primary paper at <a href="http://doi.org/10.1145/3577190.3614120">https://doi.org/10.1145/3577190.3614120</a> when referencing this dataset.</p> <p>This dataset contains multimodal tongue gesture data as a supplement for "TongueTap: Multimodal Tongue Gesture Recognition with Head-Worn Devices" published in ICMI (International Conference on Multimodal Interfaces) 2023. The data is presented in three formats (XDF, pickle and NumPy) at various stages of pre-processing. Please review the READMEs in each file before working with them. Please also review the paper at <a href="http://doi.org/10.1145/3577190.3614120">https://doi.org/10.1145/3577190.3614120</a> for more information about the data and how it was collected.</p> <p><strong>Abstract</strong></p> <p>Mouth-based interfaces are a promising new approach enabling silent, hands-free and eyes-free interaction with wearable devices. However, interfaces sensing mouth movements are traditionally custom-designed and placed near or within the mouth. TongueTap synchronizes multimodal EEG, PPG, IMU, eye tracking and head tracking data from two commercial headsets to facilitate tongue gesture recognition using only off-the-shelf devices on the upper face. We classified eight closed-mouth tongue gestures with 94% accuracy, offering an invisible and inaudible method for discreet control of head-worn devices. Moreover, we found that the IMU alone differentiates eight gestures with 80% accuracy and a subset of four gestures with 92% accuracy. We built a dataset of 48,000 gesture trials across 16 participants, allowing TongueTap to perform user-independent classification. Our findings suggest tongue gestures can be a viable interaction technique for VR/AR headsets and earables without requiring novel hardware.</p>
Multimodal Toxic Memes Detection Dataset
<p>The dataset for training and evaluating multimodal toxic memes detection models. Contains images, extracted texts and toxicity labels. Images are collected from popular Russian Telegram channels and labelled with respect to <a href="https://transparency.fb.com/policies/community-standards">Facebook Community Standards</a>.</p>
ASSIST-IoT Multimodal Fall Detection Dataset
<p>Multimodal dataset for fall detection. Includes acceleration data collected from a tag and two smartwatches, and location reported by the tag. More details about the data collection procedure can be found in <code>notes.md</code>.</p> <p><strong>Contents</strong></p> <p>The repository contains:</p> <ul> <li><code>data/location_data.csv</code> and <code>data/full_acceleration</code> – preprocessed acceleration and location data from 10 participants and mannequin simulated falls with target variable identified</li> <li><code>data/subsampled_acceleration_data.csv</code> – subsampled acceleration dataset used for training the AI model</li> <li><code>notes.md</code> – description of activities performed and notes from data collection</li> <li><code>videos</code> – reference videos for performed activities</li> </ul> <p><strong>Authors</strong></p> <ul> <li><a href="https://orcid.org/0000-0002-2543-9461">Piotr Sowiński</a> – research methodology, data collection and processing</li> <li><a href="https://orcid.org/0000-0003-3217-1050">Monika Kobus</a> – research methodology, data collection</li> <li><a href="https://orcid.org/0000-0003-4295-3005">Anna Dąbrowska</a> – research methodology, methodological supervision</li> <li><a href="https://orcid.org/0000-0003-1524-7877">Kajetan Rachwał</a> – data collection</li> <li><a href="https://orcid.org/0000-0002-7109-891X">Karolina Bogacka</a> – research methodology</li> <li><a href="https://orcid.org/0000-0002-9572-2705">Krzysztof Baszczyński</a> – research methodology, data collection</li> <li><a href="https://orcid.org/0000-0002-3080-0303">Anastasiya Danilenka</a> – research methodology, data collection and processing</li> </ul> <p><strong>Acknowledgements</strong></p> <p>This work is part of the <a href="https://assist-iot.eu/">ASSIST-IoT project</a> that has received funding from the EU’s Horizon 2020 research and innovation programme under grant agreement No 957258.</p> <p>The <a href="https://www.ciop.pl/en">Central Institute for Labour Protection – National Research Institute</a> provided facilities and equipment for data collection.</p> <p><strong>License</strong></p> <p>The dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>
MarTREC Publication forBio-Inspired Stabilization of Levee Slope on Expansive Yazoo Clay at the Maritime and Multimodal Transportation Infrastructure in Mississippi
<p>The link considers the data file for the MarTREC Project: Bio-Inspired Stabilization of Levee Slope on Expansive Yazoo Clay at the Maritime and Multimodal Transportation Infrastructure in Mississippi.</p> <p>PI: Dr. Sadik Khan</p> <p>Funding Agency: This material is based upon work supported by the U.S. Department of Transportation under Grant Award Number DTRT13-G-UTC50. The work was conducted through the Maritime Transportation Research and Education Center at the University of Arkansas.</p>
Study of Innovative Multimodal Imaging Biomarkers to Predict Anatomical Outcome in Naive Patients With wAMD Treated With Brolucizumab.
ClinicalTrials.gov study NCT04774926. IPD Sharing: YES. Countries: 1. Publications: 1.
Multimodal Exercises to Improve Leg Function After Spinal Cord Injury
ClinicalTrials.gov study NCT01740128. IPD Sharing: YES. Countries: 1. Publications: 2.
Multimodality Assessment of Ventricular Scar Arrhythmogenicity.
ClinicalTrials.gov study NCT04632394. IPD Sharing: NO. Countries: 1. Publications: 1.
Multimodal Mobile Intervention Application (App) to Address Sexual Dysfunction in Hematopoietic Stem Cell Transplant Survivors
ClinicalTrials.gov study NCT03967379. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: Multimodal in situ datalogging quantifies inter-individual variation in thermal experience and persistent origin effects on gaping behavior among intertidal mussels (Mytilus californianus)
Open the record for dataset details and reuse information.
Context-dependent multimodal behaviour in a coral reef fish: Stage 1 & 2 total duration and count data in behaviour trials
Open the record for dataset details and reuse information.
Multimodal communication of adult, subadult, and infant plains zebras (Equus quagga)
Open the record for dataset details and reuse information.
Data from: Multimodal mimicry of hosts in a radiation of parasitic finches
<p>Brood parasites use the parental care of others to raise their young and sometimes employ mimicry to dupe their hosts. The brood-parasitic finches of the genus <i>Vidua</i> are a textbook example of the role of imprinting in sympatric speciation. Sympatric speciation is thought to occur in <i>Vidua</i> because their mating traits and host preferences are strongly influenced by their early host environment. However, this alone may not be sufficient to isolate parasite lineages, and divergent ecological adaptations may also be required to prevent hybridisation collapsing incipient species. Using pattern recognition software and classification models, we provide quantitative evidence that <i>Vidua</i> exhibit specialist mimicry of their grassfinch hosts, matching the patterns, colours and sounds of their respective host's nestlings. We also provide qualitative evidence of mimicry in postural components of <i>Vidua</i> begging. Quantitative comparisons reveal small discrepancies between parasite and host phenotypes, with parasites sometimes exaggerating their host's traits. Our results support the hypothesis that behavioural imprinting on hosts has not only enabled the origin of new <i>Vidua</i> species, but also set the stage for the evolution of host-specific, ecological adaptations.</p>
ScienceDex guides
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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.