Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2
datasets available to search
ShareScore release 0.9.0
Dataset results
2 results for “nuScenes”
Lanelet2 for nuScenes: Map Dataset
<p><a href="https://github.com/fzi-forschungszentrum-informatik/Lanelet2">Lanelet2</a> maps for the <a href="http://www.nuscenes.org/nuscenes">nuScenes</a> dataset, which enable the usage of diverse map-based anchor paths and spatial semantic information. For details see our <a href="https://openaccess.thecvf.com/content/CVPR2023W/E2EAD/html/Naumann_Lanelet2_for_nuScenes_Enabling_Spatial_Semantic_Relationships_and_Diverse_Map-Based_CVPRW_2023_paper.html">paper</a> and <a href="https://felixhertlein.github.io/lanelet4nuscenes/">project page</a>. We also provide a <a href="https://pypi.org/project/lanelet2anchors/">pip package</a> to facilitate the usage.</p> <p>The maps were generated automatically and subsequently manually refined.</p> <p> </p> <p>If you use this resource for scientific research, please consider citing</p> <pre><code>@InProceedings{naumannHertleinLanelet2NuScenes2023, author = {Naumann, Alexander and Hertlein, Felix and Grimm, Daniel and Zipfl, Maximilian and Thoma, Steffen and Rettinger, Achim and Halilaj, Lavdim and Luettin, Juergen and Schmid, Stefan and Caesar, Holger}, title = {Lanelet2 for nuScenes: Enabling Spatial Semantic Relationships and Diverse Map-Based Anchor Paths}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {3247-3256} }</code></pre>
nuScenes Knowledge Graph
<p>Ontologies and Knowledge Graphs of the ICCV 2023 workshop paper "nuScenes Knowledge Graph - A comprehensive semantic representation of traffic scenes for trajectory prediction".</p><h2>Content</h2><ul><li><strong>nSKG</strong> (nuScenes Knowledge Graph): knowledge graph for the <a href="https://www.nuscenes.org/nuscenes">nuScenes dataset</a>, that models all scene participants and road elements, as well as their semantic and spatial relationships</li><li><strong>nSTP</strong> (nuScenes Trajectory Prediction Graph): heterogeneous graph of the nuScenes dataset for trajectory prediction in <a href="https://pytorch-geometric.readthedocs.io/en/latest/">PyTorch Geometric (PyG)</a> format. It extends nSKG for example by transformation into agents' local coordinate systems, relevant agent extraction, semantic relationships between agents.</li><li><strong>nuScenes_agent_onto</strong>: ontology for the traffic participants (agents)</li><li><strong>nuScenes_map_onto</strong>: ontology for the extended map</li><li><strong>stardog_rules</strong>: <a href="https://www.w3.org/TR/rdf-sparql-query/">SPARQL</a> rules for mapping nuScenes concepts to the agent ontology</li></ul><h2>How to use</h2><p><strong>nSKG</strong> represents the KG created on the basis of the nuScenes ontologies nuScenes_agent_onto.ttl and nuScenes_map_onto.ttl and materializing the <a href="https://www.nuscenes.org/nuscenes#data-annotation">nuScenes annotation dataset</a>. It can be used for applications where relational information between entities are important. The ontologies are in <a href="https://www.w3.org/TR/turtle/">Turtle</a> format and can be viewed by ontology editors such as <a href="https://protege.standord.edu/">Protege</a>.</p><p><strong>nSTP</strong> represents the extended version of nSKG, where agents are represented in local coordinate systems to enforce shift- and rotation-invariance. It also includes semantic relationships between agents, e.g. whether agents are on neighboring lanes, the same lane or might intersect. This is done based on the semantic scene graph describe in <a href="https://arxiv.org/abs/2111.10196">(Towards Traffic Scene Description: The Semantic Scene Graph)</a>. The data is provided in <a href="https://pyg.org/">PyTorch Geometric</a> format and directly be used to train graph neural network for trajectory prediction.</p><h2>Acknowledgements</h2><p>Special thanks to Motional for the permission to distribute this modified version of the nuScenes dataset.</p><h2>Citation</h2><p>If you use this work please cite</p><blockquote><p>@inproceedings{ <br>title={nuScenes Knowledge Graph - A comprehensive semantic representation of traffic scenes for trajectory prediction}, <br>author={Leon Mlodzian and Zhigang Sun and Hendrik Berkemeyer and Sebastian Monka and Zixu Wang and Stefan Dietze and Lavdim Halilaj and Juergen Luettin},<br>booktitle={International Conference on Computer Vision (ICCV), Workhsop on Scene Graphs and Graph Representation Learning (SG2RL)}, <br>year={2023} <br>}</p></blockquote>
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.