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31
datasets available to search
ShareScore release 0.9.0
Dataset results
31 results for “Autonomous driving”
Path Planning for Autonomous Bus Driving in Highly Constrained Environments - Simulation Results
<pre>This video contains simulation results for the scientific article "Path Planning for Autonomous Bus Driving in Highly Constrained Environments".</pre>
Driving Performance of People With Parkinson's Using Autonomous In-Vehicle Technologies
ClinicalTrials.gov study NCT04660500. IPD Sharing: NO. Countries: 1. Publications: 1.
data from Enhancing Autonomous Driving through Intelligent Navigation
Open the record for dataset details and reuse information.
Autonomous rhythmic activity in glioma networks drives brain tumor growth
GEO Series GSE215365. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Loss of cell-autonomously secreted laminin-α2 drives muscle stem cell dysfunction in LAMA2-related muscular dystrophy
GEO Series GSE307443. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Nuclear YAP overexpression drives the human alveolar type 1 cell (AT1) cprogram in a cell autonomous manner in lentiviral transduced iPSC-derived alveolar type 2 cells (iAT2s)
GEO Series GSE221344. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Cell-autonomous Inflammation of BRCA1-deficient Ovarian Cancers Drives Intrinsic Immunoreactivity and Immune Resistance through STING and VEGF
GEO Series GSE162935. Mus musculus. 20 samples. Type: Expression profiling by high throughput sequencing.
Testing the Fault-Tolerance of Multi-Sensor Fusion Perception in Autonomous Driving Systems
<p>Here we provide some example videos of safety violations of Apollo caused by sensor faults</p>
Annotated Vehicle Dataset for BBSL Specification-based Testing in Autonomous Driving Systems (Modified from KITTI)
<p>This dataset consists of four ZIP files containing annotated images used for experiments in the research of formal specification and specification-based testing for image recognition in autonomous driving systems. The dataset has been derived and modified from the KITTI dataset.</p> <ul> <li> <p><strong>image1.zip</strong>: Contains 349 images. These images are part of the first subset used in the experiments.</p> </li> <li> <p><strong>label1.zip</strong>: Contains the 2D bounding box annotations for vehicles corresponding to the images in <strong>image1.zip</strong>. There are 349 annotation files, and in total, 2,736 vehicles are annotated.</p> </li> <li> <p><strong>image2.zip</strong>: Contains 1,300 images. These images are part of the second subset used in the experiments.</p> </li> <li> <p><strong>label2.zip</strong>: Contains the 2D bounding box annotations for vehicles corresponding to the images in <strong>image2.zip</strong>. There are 1,300 annotation files, and in total, 5,644 vehicles are annotated.</p> </li> </ul> <p>The dataset was utilized in the research project focused on Bounding Box Specification Language (BBSL), a formal specification language designed for image recognition in autonomous driving systems. This research explores specification-based testing methodologies for object detection systems.</p> <p>The BBSL project and related testing tools can be accessed on GitHub: <a href="https://github.com/IOKENTOI/BBSL-test" target="_new" rel="noopener">https://github.com/IOKENTOI/BBSL-test</a>.</p> <p>The original KITTI dataset used for modification can be found at <a href="https://www.cvlibs.net/datasets/kitti/index.php">[KITTI dataset source link]</a>.<br>If you use this dataset, please cite the original KITTI dataset:<br>@inproceedings{Geiger2012CVPR,<br> author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},<br> title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},<br> booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},<br> year = {2012}<br>}</p>
AI4MARS: A Dataset for Terrain-Aware Autonomous Driving on Mars
This dataset was built for training and validating terrain classification models for Mars, which may be useful in future autonomous rover efforts. It consists of ~326K semantic segmentation full image labels on 35K images from Curiosity, Opportunity, and Spirit rovers, collected through crowdsourcing. Each image was labeled by 10 people to ensure greater quality and agreement of the crowdsourced labels. It also includes ~1.5K validation labels annotated by the rover planners and scientists from NASA’s MSL (Mars Science Laboratory) mission, which operates the Curiosity rover, and MER (Mars Exploration Rovers) mission, which operated the Spirit and Opportunity rovers.
Datasets for "Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Reinforcement Learning"
<p>Go to https://zenodo.org/doi/10.5281/zenodo.8388482 for the updated version!!</p> <p> </p> <p>These datasets are the official data of the submission "Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Reinforcement Learning", anonymous for the double-blind submission system.<br>A README.md file will contain all the information related to the datasets and a metadata.json file will contain their metadata according to the Schema.org format.</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.