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1,389 results for “multimodality”
MOCAS: A Multimodal Dataset for Objective Cognitive Workload Assessment on Simultaneous Tasks
<p><strong>Description: </strong>This MOCAS is a multimodal dataset dedicated for human cognitive workload (CWL) assessment. In contrast to existing datasets based on virtual game stimuli, the data in MOCAS was collected from realistic closed-circuit television (CCTV) monitoring tasks, increasing its applicability for real-world scenarios. To build MOCAS, two off-the-shelf wearable sensors and one webcam were utilized to collect physiological signals and behavioral features from 21 human subjects. After each task, participants reported their CWL by completing the NASA-Task Load Index (NASA-TLX) and Instantaneous Self Assessment (ISA). Personal background (e.g., personality and prior experience) was surveyed using demographic and Big Five Factor personality questionnaires, and two domains of subjective emotion information (i.e., arousal and valence) were obtained from the Self-Assessment Manikin, which could serve as potential indicators for improving CWL recognition performance. Technical validation was conducted to demonstrate that target CWL levels were elicited during simultaneous CCTV monitoring tasks; its results support the high quality of the collected multimodal signals.</p> <p><strong>Data Access:</strong> In order to protect the sensitive data and privacy of human subjects (e.g., physiological signals and facial views), only authorized researchers who consent to the End User License Agreement (EULA) are allowed to download the MOCAS. The researchers who want to access the MOCAS should visit our website and download the ELUA document;<a href="https://drive.google.com/file/d/1XKbZIlKDYH9RG7QD_v2fB7mYF3pNeA9I/view?usp=sharing"> https://drive.google.com/file/d/1XKbZIlKDYH9RG7QD_v2fB7mYF3pNeA9I/view?usp=sharing</a> After reviewing and filling the document up, they should email the signed ELUA document to <a href="mailto:info@smart-laboratory.org">info@smart-laboratory.org</a> and <a href="mailto:wonsu0513@gmail.com">wonsu0513@gmail.com </a>with Zenodo account and Reqeust Access via this repository (see the below file section). Then, our research group will review and grant their access to our Zenodo repository having the downsampled MOCAS dataset, subjective information, and supplementary codes used in this paper. For sharing the raw dataset, we will sequentially invite their email address used in the Zenodo and the EULA document to access raw dataset uploaded on an additional repository (Purdue BOX, https://purdue.box.com/v/mocas-dataset), due to huge size of the raw dataset.</p> <p><strong><a title="BibTeX">BibTeX </a>Citation:</strong></p> <div>Jo, W., Wang, R., Cha, G. E., Sun, S., Senthilkumaran, R. K., Foti, D., & Min, B. C. (2024). MOCAS: A multimodal dataset for objective cognitive workload assessment on simultaneous tasks. <em>IEEE Transactions on Affective Computing</em>.</div> <pre>or<br><br>@ARTICLE{jo2024mocas, author={Jo, Wonse and Wang, Ruiqi and Cha, Go-Eum and Sun, Su and Senthilkumaran, Revanth Krishna and Foti, Daniel and Min, Byung-Cheol}, journal={IEEE Transactions on Affective Computing}, title={MOCAS: A Multimodal Dataset for Objective Cognitive Workload Assessment on Simultaneous Tasks}, year={2025}, volume={16}, number={1}, pages={116-132}, doi={10.1109/TAFFC.2024.3414330}}</pre>
Multimodal 2D and 3D microscopic mapping of growth cartilage by computational imaging techniques – a short review including new research
<p>This dataset contains the phase and amplitude maps obtained with Fourier ptychographic microscopy and the X-ray diffraction tensor tomography dataset described in the journal article "Multimodal 2D and 3D microscopic mapping of growth cartilage by computational imaging techniques".</p>
Multimodal Remote Sensing Dataset for Landslide Change Detection in Haiti
<p>The detection of the collapse of landslides trigerred by intense natural hazards, such as earthquakes and rainfall, allows rapid response to hazards which turned into disasters. The use of remote sensing imagery is mostly considered to cover wide areas and assess even more rapidly the threats. Yet, since optical images are sensitive to cloud coverage, their use is limited in case of emergency response. The proposed dataset is thus multimodal and targets the early detection of landslides following the disastrous earthquake which occurred in Haiti in 2021. It consists of SAR-optical pairs from Sentinel-1 and Sentinel-2 acquired shortly before and after the event. To encourage for machine and deep learning algorithms, the images are cropped into 1713 patches. Since Sentinel-1 SAR images are acquired both in ascending and descending passes, twice more patches are available in this modality. Due to the mountainous nature of the terrain, the images are provided together with a layover-shadow mask computed as described in [Meier <em>et al</em>, 1993]. This mask is destined to limit the impact of geometrical distortions on the algorithm. Similarly, Sentinel-2 optical images are provided with a cloud and cloud shadow mask. Landslide annotations are extracted from the TODO database from NASA, rasterized and croped similarly to the images. The dataset is aimed to be used whether for monomodal or multimodal approaches, and whether for monotemporal or bitemporal approaches.</p> <p>[Meier et al., 1993] Meier, E., Frei, U., and Nüesch, D. (1993). Precise Terrain Corrected Geocoded Images. SAR Geocoding: Data and Systems, pages 173–185. Publisher: Karlsruhe, Germany: Herbert Wichmann Verlag GmbH.</p>
Dataset related to article:MULTIMODAL DIGITAL HEALTH TREATMENTS FOR CHRONIC MIGRAINE ASSOCIATED WITH MEDICATION OVERUSE HEADACHE: A LITERATURE APPRAISAL AND RESULTS OF A SINGLE‑ARM OPEN TRIAL (THE BE‑HOME PROGRAM)
<p><strong><span>Variables related to baseline, months 3, 6 and 12 referred to headache frequency, medication intake, GSE, HIT-6, BDI-II, MSQ and PCS, of patients with CM and MOH who participated to the BE-HOME trial.</span></strong></p>
ISMR 2024 Multimodal Spine Registration
Open the record for dataset details and reuse information.
Iono_Electron Dataset for Multimodal Fusion and Spatiotemporal Predictive Learning
<p>Data supplementary material for <em><strong>Space Weather</strong></em> journal paper with the title: Channel Mixer Layer: Multimodal Fusion Towards Machine Reasoning for Spatiotemporal Predictive Learning of Ionospheric Total Electron Content</p> <p> </p>
Multimodal Sensor Dataset from a Forest Environment in Adões using a Unitree Go1 Platform equipped with a Robosense BPearl LiDAR
<p><strong>Dataset Description:</strong></p> <p>This dataset was collected using a Unitree Go1 robot equipped with multiple sensors:</p> <ul> <li><strong>LiDAR Data:</strong> Acquired from a Robosense Bpearl.</li> <li><strong>IMU Data:</strong> Available through the <code>/high_state</code> topic published by the Unitree Go1.</li> <li><strong>RGB Images:</strong> Captured using a Realsense D435i camera.</li> </ul> <p>To process this dataset, you will need the <code>Unitree_transformer.py</code> Python script, available at the provided <a href="https://github.com/pedrotomas27/python_executables/tree/252ecf0feb9c3e8652daa2e6def7f4c94667207e">URL</a>, along with the necessary <a href="https://github.com/unitreerobotics/unitree_legged_sdk">Unitree package</a>.</p> <p><strong>Note:</strong> The transform for the LiDAR sensor is not included in the rosbag. To include it, manually add the following static transform to your ROS setup:<br><node pkg="tf" type="static_transform_publisher" name="rslidar_broadcaster" args="0.15 0 0.13 0 0.309017 0 base rslidar 0.1" /><br><br><strong>Important:</strong> There was a camera malfunction midway through the dataset collection.</p>
Multimodal Sensor Dataset from a Forest Environment in Adões using a Unitree Go1 Platform equipped with a Velarray M1600 LiDAR
<p><strong>Dataset Description:</strong></p> <p>This dataset was collected using a Unitree Go1 robot equipped with various sensors:</p> <ul> <li><strong>LiDAR Data:</strong> Captured with a Velodyne Velarray M1600.</li> <li><strong>IMU Data:</strong> Available via the <code>/high_state</code> topic published by the Unitree Go1.</li> <li><strong>RGB Images:</strong> Streamed from a Realsense D435i camera.</li> </ul> <p>To process this dataset, you will need the <code>Unitree_transformer.py</code> Python script, which can be found at the provided <a href="https://github.com/pedrotomas27/python_executables/tree/252ecf0feb9c3e8652daa2e6def7f4c94667207e">URL</a>. Ensure that the <a href="https://github.com/unitreerobotics/unitree_legged_sdk">Unitree package</a> is also installed.</p> <p><strong>Note:</strong> The LiDAR transform is not included in the rosbag file. To incorporate this transform, manually add the following static transform to your ROS setup:<br><br><node pkg="tf" type="static_transform_publisher" name="lidar_0_broadcaster" args="0.15 0 0.13 0 0 0 base lidar_0 0.1" /><br><br></p>
Data obtained by placing a multimodal interference filter inside a laser cavity, both with and without graphene
<p>The data shown here were obtained by coating a multimodal interference optical fiber filter with graphene. The data are divided into the following stages:</p> <ul> <li>Spectrum of the multimodal interference filter inside the laser cavity at different pump powers.</li> <li>Spectrum of the multimodal interference filter coated with graphene and PMMA inside the laser cavity at different pump powers.</li> <li>Spectrum of the multimodal interference filter coated with graphene inside the laser cavity at different pump powers.</li> </ul> <p>Additionally, the pulses generated by the laser cavity were obtained in the stages described above.</p>
Multimodal Depression Dataset
<h1>描述</h1> <p>多模态抑郁数据集(Multimodal Depression Dataset)由中国科学院大学和中国科学院心理研究所计算网络心理学实验室收集。</p> <p>该数据集包含来自254名参与者的视频、音频、脑电图(EEG)和步态(Gait)数据。</p> <h2> </h2>
Multimodal Birds Song Dataset: TwoRadioBirds
<p>A multi-modal dataset containing recordings of an adult male and female Zebra Finch bird using a microphone and two accelerometer sensors. The dataset consists of eleven data files of 1 hr. duration each, out of which three are densely annotated, and eight are unlabelled. Each file contains data from a microphone (audio) and two accelerometers (body vibration) channels. The data in all channels are sampled at a rate of 24000 Hz.</p> <p>Link to paper (IEEE Xplore): <a href="https://ieeexplore.ieee.org/abstract/document/10094650">https://ieeexplore.ieee.org/abstract/document/10094650</a></p> <p>Link to paper (arxiV): <a href="https://arxiv.org/pdf/2211.09376.pdf">https://arxiv.org/pdf/2211.09376.pdf</a></p> <p>Link to dataset: <a href="https://doi.org/10.5281/zenodo.7253729">https://doi.org/10.5281/zenodo.7253729</a></p> <p><strong>Cite as:</strong> Kumar, S., Anshuman, B., Rüttimann, L., Hahnloser, R.H. and Arora, V., 2023, June. Balanced Deep CCA for Bird Vocalization Detection. In <em>ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</em> (pp. 1-5). IEEE.</p>
Pancancer survival prediction using a deep learning architecture with multimodal representation and integration
<p>The clinical data, gene expression (mRNA) data, and microRNA expression (miRNA) data were downloaded from the PanCanAtlas TCGA project (https://gdc.cancer.gov/about-data/publications/pancanatlas). The gene copy number variation (CNV) data was downloaded from UCSC Xena (http://xena.ucsc.edu/public/).</p> <p>Files prefixed with 'Pre' represent the preprocessed datasets. The preprocessing details can be found in 'Datasets and data preprocessing' subsection.</p>
MIntRec2. 0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
<p><span>MIntRec2.0, a large-scale benchmark dataset for multimodal intent recognition in multi-party conversations. It contains 1,245 high-quality dialogues with 15,040 samples, each annotated within a new intent taxonomy of 30 fine-grained classes, across text, video, and audio modalities. In addition to more than 9,300 in-scope samples, it also includes over 5,700 out-of-scope samples appearing in multi-turn contexts, which naturally occur in real-world open scenarios, enhancing its practical applicability.</span> <span>This dataset will be released under the CC BY-NC-SA 4.0 license.</span></p>
EgoADL: Multimodal Daily-life Logging in Free-living Environments Using Non-Visual Egocentric Sensors on a Smartphone
<p>EgoADL dataset employs a commodity smartphone as an egocentric sensor hub, which captures the audio, wireless sensing signals (i.e., Wi-Fi), and motion sensor signals continuously. Users can perform arbitrary daily routines with the sensor hub, i.e., an in-pocket smartphone, in free-living environments. EgoADL will recognize ADLs including both human activity and human-object interaction from the sensor data without human intervention.</p>
Multimodal imaging data in PD
<p>In this work we explored structural and metabolic alterations within the nigrostriatal system in a population of <em>de novo</em> PD patients. We assessed the degree and spatial pattern of dopaminergic denervation (FDOPA), microstructural integrity (FW), and iron accumulation (R2*) within several regions of the nigrostriatal system, including spatial divisions of the SNc and striatum.</p>
Differentiation of Progression From Treatment Effects in High-Grade Gliomas: A Clinical Trial With Multimodality MR Imaging
ClinicalTrials.gov study NCT03102203. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Development of a Multimodal Index to Improve the Predictive Value of Success in Weaning From Mechanical Ventilation
ClinicalTrials.gov study NCT04554095. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Multimodal PET Imaging in the Diagnosis and Treatment of Pelvic Tumors
ClinicalTrials.gov study NCT06774209. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Multimodal Neuromonitoring
ClinicalTrials.gov study NCT04737369. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Multimodality- Adapted Dose Modification in Head and Neck Cancer Radiation Therapy 
ClinicalTrials.gov study NCT04933357. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.