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1,389 results for “multimodality”
Multimodala Dataset for multimodal contrastive learning for crop classification
<p>We developed this dataset using an existing dataset name DENETHOR developed by TUM <a href="https://openreview.net/forum?id=uUa4jNMLjrL">https://openreview.net/forum?id=uUa4jNMLjrL</a> to conduct our multi-modal contrastive learning experiments.</p>
Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration
<p>Algorithms for autonomous navigation in environments without Global Navigation Satellite System (GNSS) coverage mainly rely on onboard perception systems. These systems commonly incorporate sensors like cameras and LiDARs, the performance of which may degrade in the presence of aerosol particles. Thus, there is a need of fusing acquired data from these sensors with data from RADARs which can penetrate through such particles. Overall, this will improve the performance of localization and collision avoidance algorithms under such environmental conditions. This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. A detailed description of the onboard sensors and the environment, where the dataset is collected are presented to enable full evaluation of acquired data. Furthermore, the dataset contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format to facilitate the evaluation of navigation, and localization algorithms in such environments. In contrast to the existing datasets, the focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data. Therefore, to validate the dataset, a preliminary comparison of odometry from onboard LiDARs is presented.</p>
SignEEG v1.0 : Multimodal Electroencephalography and Signature Database for Biometric Systems
<p>Noninvasive electroencephalography (EEG) is a method for measuring electrical brain activity from the surface of the scalp. Recent developments in artificial intelligence accelerate the automatic recognition of brain patterns, allowing more reliable and increasingly faster Brain-Computer interfaces, including biometric applications. Biometric research is also focusing on multimodal systems using EEG along with other modalities. This paper presents a new multimodal SignEEG v1.0 dataset based on EEG and hand-drawn signatures from 70 subjects. EEG signals and hand-drawn signatures have been collected with Emotiv Insight and Wacom One sensors, respectively. The multimodal data consists of three paradigms with increasing brain functioning: (i) visualizing a signature image, (ii) doing a signature in mind, and (iii) physically drawing a signature. Extensive experiments have been done in order to provide a solid baseline with machine learning classifiers. We release the raw, pre-processed data and easy-to-follow implementation details.</p>
InterTVA. A multimodal MRI dataset for the study of inter-individual differences in voice perception and identification.
Open the record for dataset details and reuse information.
Data Accompanying "Dynamics of Water Absorption in Callovo-Oxfordian Claystone Revealed With Multimodal X-Ray and Neutron Tomography"
<p>These are the datasets analysed in the publication "Dynamics of Water Absorption in<br> Callovo-Oxfordian Claystone Revealed With Multimodal X-Ray and Neutron Tomography" by Stavropoulou <em>et al.</em> in 2020 in Frontiers in Earth Science, DOI: <a href="https://doi.org/10.3389/feart.2020.00006">https://doi.org/10.3389/feart.2020.00006</a></p> <ol> <li>File 1 contains the 3D reconstructed x-ray and neutron tomography volumes analysed in the paper. State 002 is taken as a reference and the greylevels of all images in the times series for both x-ray and neutrons are rescaled using two characteristic image features (top-cap and air in the case of x-rays) linearly to align with 002. Neutron volumes are rescaled to the same pixel size as 2-bin x-ray volumes, and a mean registration is applied to align neutrons with x-rays.<br> Furthermore, a bilateral filter is applied to the neutron tomographies (domain sigma = 1, range sigma=3000).<br> Full scale images are available at <a href="https://doi.ill.fr/10.5291/ILL-DATA.UGA-42">https://doi.ill.fr/10.5291/ILL-DATA.UGA-42</a><br> </li> <li>File 2 contains the joint histograms for registered pairs of images, as visible in Figure 4 and Figure 5.<br> </li> <li>File 3 contains the results of the digital volume correlation performed with the <a href="https://ttk.gricad-pages.univ-grenoble-alpes.fr/spam/intro.html">spam</a> tookit.<br> The overall "registration" is used to create Figure 6<br> The global correlations available in "gdic" are used to create Figures 7, 8 and 9.</li> </ol>
MarTREC Project Datasets for Effect of Permeability Variation of Expansive Yazoo Clay at the Maritime and Multimodal Transportation Infrastructure in Mississippi
<p>The existence of Yazoo clay soil in Mississippi frequently causes distress to the pavement and cause deformation at the slopes in highways and levees, which are a critical component in Maritime and multimodal transportation infrastructure. Each year, fixing the pavement requires a significant maintenance budget of MDOT. Also, the infiltration of the rainwater in the highway and levee slopes leads to landslides, which require millions of maintenance dollars each year. Due to the shrinkage and swelling behavior of the Yazoo clay, the hydraulic conductivity varies over the different seasons and has higher vertical permeability during the dry season. With high vertical permeability, the rainwater can easily percolate in the pavement subgrade and slopes, which accelerates the failure. The current study investigates the change in unsaturated vertical and horizontal permeability and its effect on the maritime and multimodal infrastructures, especially on the pavement and slopes of highway embankment and levees. The attached datasets include the laboratory test and finite element modeling findings.</p>
Raw data accompanying the manuscript "Multiscale and multimodal optical imaging of the human liver"
<p>These are the raw datasets used to generate the figures for the manuscript entitled "Multiscale and multimodal optical imaging of the human liver". The file CARS_SRS.zip contains folders with all raw CARS and SRS data (TIFF format). The file CLSM.zip contains confocal laser scanning microscopy data using the manufacturers data format (Zeiss). The file LSFM.zip contains light sheet fluorescence microscopy data files using the manufacturers data format (LaVision Biotec). The file OPT.zip contains raw optical projection tomography data at different excitation wavelengths (TIFF format). The file SRSIM.zip contains reconstructed structured illumination microscopy data files (TIFF format).</p>
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)
In complex habitats, environmental variation over small spatial scales can equal or exceed larger-scale gradients. This small-scale variation may allow motile organisms to mitigate stressful conditions by choosing benign microhabitats, whereas sessile organisms may rely on other behaviors to cope with environmental stresses in these variable environments. We developed a monitoring system to track body temperature, valve gaping behavior, and posture of individual mussels (Mytilus californianus) in field conditions in the rocky intertidal zone. Neighboring mussels' body temperatures varied by up to 14°C during low tides. Valve gaping during low tide and postural adjustments, which could theoretically lower body temperature, were not commonly observed. Rather, gaping behavior followed a tidal rhythm at a warm, high intertidal site; this rhythm shifted to a circadian period at a low intertidal site and for mussels continuously submerged in a tidepool. However, individuals within a site varied considerably in time spent gaping when submerged. This behavioral variation could be attributed in part to persistent effects of mussels' developmental environment. Mussels originating from a wave-protected, warm site gaped more widely, and they remained open for longer periods during high tide than mussels from a wave-exposed, cool site. Variation in behavior was modulated further by recent wave heights and body temperatures during the preceding low tide. These large ranges in body temperatures and durations of valve closure events - which coincide with anaerobic metabolism - support the conclusion that individuals experience "homogeneous" aggregations such as mussel beds in dramatically different fashion, ultimately contributing to physiological variation among neighbors.
JDC2015 (ERRATUM 2) - A multimodal dataset from facilitating multi-tabletop lessons in an open-doors day
<p>This dataset is a complement (a correction, actually) to the "JDC2015 - A multimodal dataset from facilitating multi-tabletop lessons in an open-doors day" dataset, also published in Zenodo (see https://zenodo.org/record/198709 for further info on the dataset). This erratum contains a zip file that substitutes the (incomplete) JDC2015-CodingData.zip file in the original dataset.</p>
JDC2015 (ERRATUM) - A multimodal dataset from facilitating multi-tabletop lessons in an open-doors day
<p>This dataset is a complement (a correction, actually) to the "JDC2015 - A multimodal dataset from facilitating multi-tabletop lessons in an open-doors day" dataset, also published in Zenodo (see https://zenodo.org/record/198709 for further info on the dataset). This erratum contains a zip file that substitutes the (corrupt) JDC2015-EyetrackingData.zip file in the original dataset.</p>
ThermoScenes: Multimodal Neural Radiance Fields for Thermal Novel View Synthesis
<p>Thermal+RGB dataset for ThermoNeRF</p>
MagicBathyNet: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-based Classification in Shallow Waters
<p><strong>The dataset</strong></p> <p>MagicBathyNet is a benchmark dataset made up of image patches of Sentinel-2, SPOT-6 and aerial imagery, bathymetry in raster format and seabed classes annotations. MagicBathyNet has been designed to be geographically well distributed. It’s coverage includes two very different coastal areas (in terms of water column characteristics and bottom type): i) Agia Napa area in Cyprus, covering a wide range of typical Mediterranean waters and seabed types, and ii) Puck Lagoon area in Poland, representing in a great degree Baltic Sea waters and bottom.</p> <p>MagicBathyNet contains 3355 RGB co-registered triplets of Sentinel-2 (S2), SPOT-6, and aerial image patches, complemented by 1244 RGB co-registered S2 and SPOT-6 doublets, 3354 DSM (Digital Surface Model) raster patches for the aerial patches and 3396 DSM raster patches for S2 and SPOT-6. Additionally, it contains 533 annotated raster patches for seabed habitat and type, facilitating supervised pixel-based classification. Each patch covers 180x180m, represented by 18x18 pixels in S2 imagery, 30x30 pixels in SPOT-6 imagery and 720x720 pixels in airborne imagery. </p> <p>For the implementation code and pre-trained models visit our project page: <a href="https://www.magicbathy.eu/magicbathynet.html">https://www.magicbathy.eu/magicbathynet.html</a> </p> <p><strong>MagicBathyNet.zip </strong>file contains the original dataset presented in the respective paper.</p> <p><strong>MagicBathyNet_extension_for_Swin-BathyUNet.zip</strong> file is added in the new version to support the experiments and the results presented in "Agrafiotis, P., & Demir, B. (2025). Deep learning-based bathymetry retrieval without in-situ depths using remote sensing imagery and SfM-MVS DSMs with data gaps. <em>ISPRS Journal of Photogrammetry and Remote Sensing</em>, <em>225</em>, 341-361. <a href="https://doi.org/10.1016/j.isprsjprs.2025.04.020">https://doi.org/10.1016/j.isprsjprs.2025.04.020</a> "</p> <p> </p> <p> </p> <p><strong>Citation</strong></p> <p>If you use the code in this repository or the dataset please cite our paper:</p> <p>P. Agrafiotis, L. Janowski, D. Skarlatos, and B. Demir, <a href="https://arxiv.org/abs/2405.15477" target="_blank" rel="noopener noreferrer">"MagicBathyNet: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-based Classification in Shallow Waters"</a>, arXiv:2405.15477, 2024.</p> <p>or </p> <p>P. Agrafiotis, Ł. Janowski, D. Skarlatos and B. Demir, "MAGICBATHYNET: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-Based Classification in Shallow Waters," <em>IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium</em>, Athens, Greece, 2024, pp. 249-253, doi: 10.1109/IGARSS53475.2024.10641355.</p> <p><strong>Folder structure</strong></p> <p>┗ 📂 magicbathynet/<br> ┣ 📂 agia_napa/<br> ┃ ┣ 📂 img/<br> ┃ ┃ ┣ 📂 aerial/<br> ┃ ┃ ┃ ┣ 📜 img_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 s2/<br> ┃ ┃ ┃ ┣ 📜 img_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 spot6/<br> ┃ ┃ ┃ ┣ 📜 img_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📂 depth/<br> ┃ ┃ ┣ 📂 aerial/<br> ┃ ┃ ┃ ┣ 📜 depth_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 s2/<br> ┃ ┃ ┃ ┣ 📜 depth_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 spot6/<br> ┃ ┃ ┃ ┣ 📜 depth_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📂 gts/<br> ┃ ┃ ┣ 📂 aerial/<br> ┃ ┃ ┃ ┣ 📜 gts_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 s2/<br> ┃ ┃ ┃ ┣ 📜 gts_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 spot6/<br> ┃ ┃ ┃ ┣ 📜 gts_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📜 [modality]_split_bathymetry.txt<br> ┃ ┣ 📜 [modality]_split_pixel_class.txt<br> ┃ ┣ 📜 norm_param_[modality]_an.txt<br> ┃<br> ┣ 📂 puck_lagoon/<br> ┃ ┣ 📂 img/<br> ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📂 depth/<br> ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📂 gts/<br> ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📜 [modality]_split_bathymetry.txt<br> ┃ ┣ 📜 [modality]_split_pixel_class.txt<br> ┃ ┣ 📜 norm_param_[modality]_pl.txt</p> <p> </p> <p><strong>Package for benchmarking MagicBathyNet dataset</strong></p> <p>Donwload the package for benchmarking MagicBathyNet dataset in learning-based bathymetry and pixel-based classification here:</p> <p><a href="https://github.com/pagraf/MagicBathyNet">https://github.com/pagraf/MagicBathyNet</a></p> <p> </p> <p><strong>Version history</strong></p> <p>v1.0.0 - First release</p> <p> </p> <p><strong>License</strong></p> <p>Dataset: Creative Commons Attribution Non Commercial 4.0 International</p> <p>Code: Attribution-NonCommercial-ShareAlike 4.0 International License</p> <p>Copyright (c) 2024 The MagicBathyNet Authors</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This work was part of the project MagicBathy which is a research project funded by the European Commission for the period 2023-2025. It is funded under the HORIZON Europe MSCA Postdoctoral Fellowships - European Fellowships (GA 101063294).</p> <p>European Space Agency (ESA) is also acknowledged for providing the SPOT-6 images within its TPM programme in the frame of proposal PP0092443 and Airbus for being the provider of the original SPOT-6 images. The Dep. of Land and Surveys of Cyprus is acknowledged for providing the LiDAR reference data for Cyprus.</p>
GRN_MARVEL_MULTIMODAL_DATASET
<p>The raw audio-video data was collected from Mgarr, a rural town on the western coast from IP cameras. Data has been manually annotated for bicycles, pedestrians and motorcycles. Annotation is in the form of labelling sound events and bounding boxes for entities of interest.</p>
Context-dependent multimodal behaviour in a coral reef fish: Stage 1 & 2 total duration and count data in behaviour trials
<p>Animals are expected to respond flexibly to changing circumstances, with multimodal signalling providing potential plasticity in social interactions. Whilst numerous studies have documented context-dependent behavioural trade-offs in terrestrial species, far less work has considered such decision-making in fish, especially in natural conditions. Coral reef ecosystems host 25% of all known marine species, making them hotbeds of competition and predation. We conducted experiments with wild Ambon damselfish (<em>Pomacentrus amboinensis)</em> to investigate context-dependent responses to a conspecific intruder; specifically, how nest defence is influenced by an elevated predation risk. We found that nest-defending male Ambon damselfish responded aggressively to a conspecific intruder, spending less time sheltering and more time interacting, as well as signalling both visually and acoustically. In the presence of a model predator compared to a model herbivore, males spent less time interacting with the intruder, with a tendency towards reduced investment in visual displays compensated for by an increase in acoustic signalling instead. We therefore provide ecologically valid evidence that the context experienced by an individual can affect its behavioural responses and multimodal displays towards conspecific threats.</p>
Dataset accompanying the paper "Multimodal laminar characterization of visual areas along the cortical hierarchy"
<p>This dataset accompanying the manuscript "<strong>Multimodal laminar characterization of visual areas along the cortical hierarchy</strong>". </p> <p>It contains:</p> <ul> <li>final processed data that are used to compute (and plot) laminar profiles. Laminar profiles are shown as main figures in the manuscript.</li> <li>raw resting-state fMRI data + scanning protocol file</li> </ul>
On the Effectiveness of Text and Image Embeddings in Multimodal Hate Speech Detection
<p>Additional resources for the paper:</p> <h3><strong><a href="https://ieeexplore.ieee.org/abstract/document/10826088">On the Effectiveness of Text and Image Embeddings in Multimodal Hate Speech Detection.</a></strong></h3> <p>Lewis, N., Cavalcante, C. C., Boukouvalas, Z., & Corizzo, R.</p> <p><em>2024 IEEE International Conference on Big Data (BigData)</em> (pp. 3277-3281). IEEE.</p> <pre> </pre> <p> </p> <p>MMHS150K [1] is a manually labeled multimodal dataset that contains $150000$ tweets with two modalities: text, and corresponding image. Tweets are collected from September 2018 until February 2019 and are labeled according to different types of hate speech: no attacks to any community, racist, sexist, homophobic, religion-based attacks, or attacks to other communities. </p> <p>We extract vector embeddings leveraging different text (BERT, OpenAI) and image (ResNet, PVT, ViT) modele backbones and assess their effectiveness in the hate speech detection task.</p> <p> </p> <h2>Citation:</h2> <pre>@inproceedings{lewis2024effectiveness, title={On the Effectiveness of Text and Image Embeddings in Multimodal Hate Speech Detection}, author={Lewis, Nora and Cavalcante, Charles C and Boukouvalas, Zois and Corizzo, Roberto}, booktitle={2024 IEEE International Conference on Big Data (BigData)}, pages={3277--3281}, year={2024}, organization={IEEE} }</pre>
REFLEX Dataset: A Multimodal Dataset of Human Reactions to Robotic Failures and Subsequent Robotic Explanations.
<p>REFLEX Dataset is a comprehensive collection of multimodal Human Behavioral reactions to Robot Failures and Explanations. <br><br>The version 1.0 is a representative sample of this dataset with the reactions from 5 users out of a total 55 users.</p> <p>This version 1.1.0 is the full dataset with the reactions from a total 55 users.<br><br>Please refer to the Readme in the zipped file for further information.</p> <p><br>This data was recorded from a user study and has been processed for anonymization.</p> <h2>About Data</h2> <p>This description gives a detailed process on how the data was collected. It should describe the conditions under which the data was recorded and also the devices used to record the data.</p> <h3>Data Organisation</h3> <p>The data is structured by strategy and participant, as shown below:</p> <pre><code>Strategy Dir/ -Participant Dir/ - analysis - questonnaire - facetorch - openface - gaze - hume - body - voice - time - video_cam1 - video_cam2 </code></pre> <p>We employed five different strategies (C1, C2, C3, D1, D2), collecting data from 11 participants for each strategy. The data for each participant is organized within a corresponding folder.</p> <p>Participants are labeled based on their assigned strategy. For example, data from the first participant under the “Fixed Low” (C1) strategy can be found in the C1-1 subfolder within the C1 directory.</p> <h3>Collected Data</h3> <p>Each participant folder contains various datasets related to different modalities. All visual data are collected using the camera 1 video. The collected data are outlined below:</p> <ul> <li> <p><strong>Anonymized Videos</strong> (<code>video_cam1.mp4</code>, <code>video_cam2.mp4</code>) - Visual Representation:</p> <ul> <li>Video from camera 1 (robot side of view)</li> <li>Video from camera 2 (experiment side of view)</li> </ul> </li> <li> <p><strong>Analysis</strong> (<code>analysis.csv</code>) - Failure Instance Description:</p> <ul> <li>Failure type</li> <li>Explanation strategy</li> <li>Explanation level</li> <li>Phase (Pre, Failure, Explanation, Resolution)</li> <li>Start/End frame and time of failure</li> <li>Task Resolved</li> </ul> </li> <li> <p><strong>Questionnaire</strong> (<code>questionnaire.csv</code>) - Failure Instance Description:</p> <ul> <li>Participant Data (Age, Gender, etc)</li> <li>Answers of explanation-satisfaction rate question for rounds and overall experiment</li> </ul> </li> <li> <p><strong>Facetorch</strong> (<code>facetorch.csv</code>) - <a href="https://github.com/tomas-gajarsky/facetorch" target="_blank" rel="nofollow noopener">Facetorch</a> - Face:</p> <ul> <li>Arousal/Valence levels</li> <li>Presence of Facial Action Units (AUs)</li> <li>Dominant Emotion (Out of six basic emotions and neutral)</li> </ul> </li> <li> <p><strong>OpenFace</strong> (<code>openface.csv</code>) - <a href="https://github.com/TadasBaltrusaitis/OpenFace" target="_blank" rel="nofollow noopener">OpenFace</a> - Face, Gaze, Head:</p> <ul> <li>Eye Gaze (2D and 3D Landmarks)</li> <li>Eye Direction (vector and in radians)</li> <li>Head Pose Estimation (Pose Estimation, Rotation)</li> <li>Face Landmarks (2D and 3D Landmarks)</li> <li>Facial Action Units (0.0-1.0 intensity scores, occurrences)</li> </ul> </li> <li> <p><strong>Gaze</strong> (<code>gaze.csv</code>) - Gaze:</p> <ul> <li>Eye Gaze Classification (e.g., Robot, Task, Miscellaneous)</li> </ul> </li> <li> <p><strong>Hume</strong> (<code>hume.csv</code>) - <a href="https://www.hume.ai/" target="_blank" rel="nofollow noopener">Hume Expression Measurement API</a> - Face:</p> <ul> <li>48 Emotion likelihoods</li> <li>Facial Action Units (0.0-1.0 score)</li> <li>Facial Descriptions (0.0-1.0 score)</li> </ul> </li> <li> <p><strong>Voice</strong> (<code>speech.csv</code>) - <a href="https://www.hume.ai/" target="_blank" rel="nofollow noopener">Hume Expression Measurement API</a> - Speech:</p> <ul> <li>Speech conversation data</li> <li>Emotional likelihoods inferred from prosody</li> </ul> </li> <li> <p><strong>Body</strong> (<code>body.csv</code>) - <a href="https://ai.google.dev/edge/mediapipe/solutions/vision/pose_landmarker" target="_blank" rel="nofollow noopener">MediaPipe Pose Landmark Detection</a> - Body:</p> <ul> <li>Pose classifications (e.g., crossed arms, arms behind back)</li> <li>2D and 3D Pose Landmarks</li> </ul> </li> <li> <p><strong>Time</strong> (<code>time.csv</code>) - <a href="https://ai.google.dev/edge/mediapipe/solutions/vision/pose_landmarker" target="_blank" rel="nofollow noopener">MediaPipe Pose Landmark Detection</a>:</p> <ul> <li>Associated timestamp and time for each frame of camera 1 video.</li> </ul> </li> </ul> <p>Notes</p> <ul> <li>Data was synchronized based on the `video_cam1.mp4`</li> <li>The `hume.csv` and `gaze.csv` files contain data only for frames within failure periods.</li> <li>Failure events were divided into four phases:<br> 1. Pre-failure phase: Period before the failure occurs<br> 2. Failure phase: When the actual failure action takes place<br> 3. Explanation phase: When the robot provides an explanation for the failure<br> 4. Resolution phase: When the robot guides the participant to resolve the issue</li> </ul> <h2>How to Visualize Participant Data</h2> <p>Please visit the github repository: https://github.com/andreasnaoum/reflex-viz</p>
Dataset accompanying manuscript "Correlative imaging of spatio-angular dynamics of biological systems with multimodal instant polarization microscope"
<p>Raw images and microscope calibration metadata for reconstruction of datasets presented in Fig. 1 and Fig. 3 of "Correlative imaging of spatio-angular dynamics of biological systems with multimodal instant polarization microscope". Notebooks demonstrating steps in the label-free and fluorescence anisotropy reconstruction pipelines can be found at https://github.com/mehta-lab/miPolScope.</p>
GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion (presentation recording)
<p>Video recording of the presentation for the publication N. Souli et al., "GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion," 2020 22nd International Conference on Transparent Optical Networks (ICTON), Bari, Italy, 2020, pp. 1-4, doi: 10.1109/ICTON51198.2020.9203087.</p>
End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models
<p>We propose the end-to-end multimodal fact-checking and explanation generation, where the input is a claim and a large collection of web sources, including articles, images, videos, and tweets, and the goal is to assess the truthfulness of the claim by retrieving relevant evidences and predicting a truthfulness label (i.e., support, refute and not enough information), and to generate a rationalization statement to explain the reasoning and ruling process. To support this research, we construct MOCHEG, a large-scale dataset consisting of 21,184 claims where each claim is annotated with a truthfulness label and ruling statement, with 43,148 text evidences and 15,373 image evidences. </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.