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135 results for “Multimodal dataset”
Multimodal poster presentation feedback dataset
<p>This is a dataset used for a multimodal poster presentation experiment and data analysis performed at the Division of Speech, Music and Hearing (TMH) at KTH Royal Institute of Technology in Stockholm Sweden in 2019 and 2020. The compressed elan_files.zip file contains raw ELAN annotated data, and the various .csv files contain formatted data for training for statistical learning models. Used in an upcoming publication.</p>
MuMu: Multimodal Music Dataset
<p>MuMu is a Multimodal Music dataset with multi-label genre annotations that combines information from the Amazon Reviews dataset and the Million Song Dataset (MSD). The former contains millions of album customer reviews and album metadata gathered from Amazon.com. The latter is a collection of metadata and precomputed audio features for a million songs. </p> <p>To map the information from both datasets we use MusicBrainz. This process yields the final set of 147,295 songs, which belong to 31,471 albums. For the mapped set of albums, there are 447,583 customer reviews from the Amazon Dataset. The dataset have been used for multi-label music genre classification experiments in the related publication. In addition to genre annotations, this dataset provides further information about each album, such as genre annotations, average rating, selling rank, similar products, and cover image url. For every text review it also provides helpfulness score of the reviews, average rating, and summary of the review. </p> <p>The mapping between the three datasets (Amazon, MusicBrainz and MSD), genre annotations, metadata, data splits, text reviews and links to images are available here. Images and audio files can not be released due to copyright issues.</p> <ul> <li>MuMu dataset (mapping, metadata, annotations and text reviews)</li> <li>Data splits and multimodal feature embeddings for ISMIR multi-label classification experiments </li> </ul> <p>These data can be used together with the Tartarus deep learning library https://github.com/sergiooramas/tartarus.</p> <p>NOTE: This version provides simplified files with metadata and splits.</p> <p><strong>Scientific References</strong></p> <p>Please cite the following papers if using MuMu dataset or Tartarus library.</p> <p>Oramas, S., Barbieri, F., Nieto, O., and Serra, X (2018). Multimodal Deep Learning for Music Genre Classification, Transactions of the International Society for Music Information Retrieval, V(1).</p> <p>Oramas S., Nieto O., Barbieri F., & Serra X. (2017). Multi-label Music Genre Classification from audio, text and images using Deep Features. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017). https://arxiv.org/abs/1707.04916</p> <p> </p>
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>
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.
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>
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>
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>
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>
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>
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>
RU-AI: A Large Multimodal Dataset for Machine Generated Content Detection
<p>This repository contains all the collected and aligned data for RU-AI dataset. It is constructed based on three large publicly available datasets: Flickr8K, COCO, and Places205, by adding their corresponding machine-generated pairs from five different generative models in each modality. </p>
MSD-I: Million Song Dataset with Images for Multimodal Genre Classification
<p>The Million Song Dataset (https://labrosa.ee.columbia.edu/millionsong/) is a collection of metadata and precomputed audio features for 1 million songs. Along with this dataset, a dataset with annotations of 15 top-level genres with a single label per song was released. In our work, we combine the CD2c version of this genre datase (http://www.tagtraum.com/msd_genre_datasets.html) with a collection of album cover images. </p> <p><br> The final dataset contains 30,713 tracks from the MSD and their related album cover images, each annotated with a unique genre label among 15 classes. Based on an initial analysis on the images, we identified that this set of tracks is associated to 16,753 albums, yielding an average of 1.8 songs per album.</p> <p>We randomly divide the dataset into three parts: 70% for training, 15% for validation, and 15% for test, with no artist and album overlap across these sets. This is crucial to avoid possible overfitting, as the classifier may learn to predict the artist instead of the genre. </p> <p> </p> <p>Content:</p> <p>MSD-I dataset (mapping, metadata, annotations and links to images)<br> Data splits and feature vectors for TISMIR single-label classification experiments </p> <p>These data can be used together with the Tartarus deep learning python module https://github.com/sergiooramas/tartarus.</p> <p> </p> <p>Scientific References:</p> <p>Please cite the following paper if using MSD-I dataset or Tartarus software.</p> <p>Oramas, S., Barbieri, F., Nieto, O., and Serra, X (2018). Multimodal Deep Learning for Music Genre Classification, Transactions of the International Society for Music Information Retrieval, V(1).</p>
Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms
<p><strong>Synthetic Lunar Terrain (SLT) </strong>is a dataset based on a reconstruction of a typical <strong>cratered lunar surface landscape </strong>at the <a href="https://set.adelaide.edu.au/atcsr/space-research/exterres-laboratory" target="_blank" rel="noopener">EXTERRES Laboratory</a> at University of Adelaide, Roseworthy Campus. On a surface area of <strong>3.6m x 4.8m</strong>, multiple synthetic craters with different sizes and geometries were sculpted into<strong> lunar regolith simulant</strong>. A <strong>9kW metal-halide lamp</strong> illuminated the scene, providing high contrast drop-shadows from the rims of craters and similar surface features that are characteristic for the Earth's moon.</p> <p>The purpose of this dataset is to provide multimodal recordings of visual information to develop and test algorithms on a hardware analogue of the moon rather than relying on computer simulations. In particular, comparisons between <strong>neuromorphic vision sensors</strong> like <strong>event-based cameras</strong> and imaging with <strong>conventional monocular cameras</strong> are at the core of this work. For this purpose, an event-based camera (Gen4 Prophesee with Prophesee-Sony IMX636 sensor) was mounted downward-pointing next to a optical camera (Basler a2A1920-160ucPRO with Sony IMX392 sensor) on an extendable rod which was moved above the surface in a slow and continuous sweep. In total, SLT consists of camera recordings from 21 different positions/settings, with clockwise and anti-clockwise motions under varying, extreme lighting conditions.</p> <p>The event-stream and grayscale image data can be further referenced via a detailed <strong>3D point cloud</strong> obtained by a FARO Focus S70 3D Scanner. This 3D Scan was post-processed, realigned and resampled into a 3D point cloud of <strong>~6.25M points, </strong>with a surface density of <strong>1.862 p/mm²</strong>, providing a ground-truth for the crater geometries.</p> <p>In detail, SLT contains the following:</p> <ul> <li>eventbased.zip: <ul> <li><strong>42 camera orbits</strong> in the binary <strong>EVT 3.0</strong> format (<a href="https://docs.prophesee.ai/stable/data/encoding_formats/evt3.html" target="_blank" rel="noopener">Prophesee docs</a>) </li> <li>corresponding <strong>.mp4 </strong>event-frame video rendering for visualization purposes (33.333ms accumulation time at 30FPS)</li> <li>corresponding<strong> .bias</strong> file containing settings used during recording</li> </ul> </li> <li>code.zip: <ul> <li>Standalone C++ code of the <strong>metavision EVT3-to-RAW file decoder</strong>, allowing to convert the binary EVT 3.0 format into a plaintext <strong>.csv </strong>that includes <ul> <li>the coordinates of the event-pixel,</li> <li>the polarity change,</li> <li>and the time-stamp of the event.</li> </ul> </li> <li>This code is an unmodified redistribution from the <a href="https://www.prophesee.ai/metavision-intelligence/" target="_blank" rel="noopener">Metavision SDK</a>, version 4.6.0, released by Prophesee under Apache License 2.0.</li> </ul> </li> <li> optical.zip: <ul> <li><strong>42 image sequences</strong> in <strong>.tif</strong> format (LZW, 1920x1200px, 8bit, grayscale) <ul> <li>Length of image sequences varies between about 300 to 700 images per sequence</li> </ul> </li> </ul> </li> <li>3d_scan.zip: <ul> <li><strong>SLT3d_scan.ply:</strong> 3D point cloud of the scene Stanford Polygon File Format</li> <li><strong>SLT3d_scan.xyz:</strong> 3D point cloud with plaintext x y z coordinates, white-space separated</li> </ul> </li> <li>cratermap.png: <ul> <li>Annotations of <strong>130 different surface features</strong> that have been manually identified as crater-like with approximate x,y-coordinates.</li> </ul> </li> <li>positionmap.png: <ul> <li>Illustration of the different positions from which the rod was moved over the scene (not to scale).</li> </ul> </li> <li>sample.zip: <ul> <li>A sample containing 1 event-camera orbit with the corresponding image sequence (for convenience only, to test the dataset without the need to download it's entirety)</li> </ul> </li> </ul> <p>The global coordinate frame of this dataset puts the origin at the centre of the scene. The shorter side of the terrain is roughly aligned with the x-axis, the longer side with the y-axis. The z-axis represents height/depth (compare with <strong>cratermap.png</strong>). The different conditions (compare with <strong>positionmap.png</strong>) from which the data was taken are encoded as follows:</p> <ul> <li><strong>A1, ..., A9</strong> refer to the left side of the scene (negative x)</li> <li><strong>B1, ..., B9 </strong>refer to the right side of the scene (positive x)</li> <li><strong>S1, S2, S3</strong> and <strong>S4 </strong>describe special lighting conditions and/or parameter settings</li> <li><strong>CW </strong>refers to a "clockwise" sweeping of the camera-rod, relative to the position</li> <li><strong>ACW</strong> refers to an "anti-clockwise" sweeping of the camera-rod, relative to the position</li> </ul> <p>The light from the metal-halide lamp was directed through a small opening, shining along the positive y-axis. In some of the setups, an obstacle was placed between the surface and the opening, blocking out part of the light to create a light-dark separator on the surface, emulating the <strong>terminator</strong> on the Moon between it's day and night side, resulting in highly contrastive images.</p> <p>We encourage you to consult and cite our related publication, should you find SLT useful.</p> <ul> <li>Märtens, M., Farries, K., Culton, J. and Chin, TJ. "<strong>Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms</strong>", Proceedings of "<em>International Symposium on Artificial Intelligence, Robotics and Automation in Space (I-SAIRAS), 2024</em>", pp. 609-614</li> </ul> <p> </p>
Dataset related to article "Distribution of pamiparib, a novel inhibitor of poly(ADP-ribose)-polymerase (PARP), in tumor tissue analyzed by multimodal imaging"
<p>This record contains data related to article "Distribution of pamiparib, a novel inhibitor of poly(ADP-ribose)-polymerase (PARP), in tumor tissue analyzed by multimodal imaging"</p> <p><span>Pamiparib is a potent and selective oral PARP1/2 inhibitor (PARPi). Pamiparib has good bioavailability and showed greater cytotoxic potency and similar DNA-trapping capacity compared to olaparib. It is not affected by ATP-binding cassette transporters. Consequently, pamiparib may be useful in overcoming drug resistance caused by poor drug distribution in tumor due to overexpression of these efflux pump [1]. Mass spectrometry imaging (MSI) is a powerful technology that allows to study drugs distribution in tissues while maintaining spatial information [2]. Here, MSI was applied to visualize pamiparib in tumor in combination with spatial metabolomics and lipidomics, LC-MS/MS analysis, immunofluorescence analysis, and histological staining to gain a comprehensive understanding of how pamiparib is distributed. The results show that pamiparib was evenly distributed in ovarian tumor models, including those that overexpress P-glycoprotein (P-gp). In contrast, olaparib was not detected by MSI in any of the analyzed tumors, despite the comparable sensitivity of the analytical method. This difference in tumor distribution was confirmed by LC-MS/MS analysis. </span></p>
MultiFranceFences: A novel deep learning dataset for automated fence detection from multimodal aerial imagery
<p>The <strong>MultiFranceFences</strong> dataset is a large-scale, multimodal remote sensing benchmark for the semantic segmentation of fences across various landscapes in France. This dataset integrates high-resolution orthophotographs (RGB through BDOrtho) and Digital Surface Models (DSM) derived from LiDARHD data. </p> <p>MultiFranceFences is suitable for deep learning models in semantic segmentation, including state-of-the-art models like UNet, D-LinkNet, and the newly proposed H-IncepUNet, which integrates handcrafted features and multi-scale feature extraction modules for enhanced fence detection.</p> <p><strong>Dataset features:</strong></p> <ul> <li><strong>Multimodal imagery</strong>: Combines orthophotographs and DSM data from LiDARHD for fences semantic segmentation (folders <em>ortho</em> and <em>lidar</em>).</li> <li><strong>Buffer options</strong>: 2-meter and 3-meter buffer fence annotations to fit varying detection requirements (folders <em>fences_2m</em> and <em>fences_3m</em>).</li> <li><strong>Diverse landscapes</strong>: Covers rural, and natural environments across France.</li> <li><strong>Validated dataset</strong>: Manually cleaned and validated to remove erroneous fence labels under tree canopies or areas with limited visibility.</li> </ul> <p>Each patch is named according to the nomenclature of the original BDOrtho tile, followed by the specific x and y coordinates of the patch within that tile.</p>
Multimodal Identity Preserved Tracking (MIPT) Dataset
<p>Human behavioral analysis applications in the fields of ambient assisted living (AAL) and human security monitoring require continuous video analysis of individuals. Although intelligent systems deployed in these areas are intended to have a positive impact on the persons involved, subsequent continuous monitoring naturally raises ethical concerns and questions about privacy implications. To address these issues, we present a foundation for identity-preserving 3D human behavior analysis. The dataset is large, at a total of ~85k annotated frames. To reduce privacy intrusion, it consists entirely of spatio-temporally aligned depth and thermal sequences. Annotation is provided as 3D bounding boxes, along with pose labels and consistent person IDs for use in tracking. The dataset is designed to be flexible. Data representation in either image view or point clouds and the option for projected 2D bounding boxes, allows use in a variety of 2D or 3D tasks. Target applications of our work are privacy-sensitive domains that currently require continuous monitoring using RGB-based systems, including ambient assisted living tasks (e.g., motion rehabilitation, fall detection, vital sign detection) and human security monitoring applications, such as construction safety, critical care and correctional facility monitoring.</p> <p>This database may be used for non-commercial research purpose only. If you publish material based on this database, we request that you include a reference to our paper [1].</p> <p>[1] T. Heitzinger and M. Kampel “<em>A Foundation for 3D Human Behavior Detection in Privacy-Sensitive Domains</em>”,<br> in <em>32</em><em>nd</em><em> British Machine Vision Conference (BMVC)</em>, 2021</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.