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Dataset results
138 results for “dynamic mapping”
Dynamics of Immune Map and Outcomes After On-pump Cardiac Surgery: Protocol for a Prospective Cohort Study
ClinicalTrials.gov study NCT05400356. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Novel fine-scale aerial mapping approach quantifies grassland weed cover dynamics and response to management
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Data from: Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data
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Data from: Dynamically prognosticating patients with hepatocellular carcinoma through survival paths mapping based on time-series data
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High resolution dynamic mapping of the C. elegans intestinal brush border
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Landscape mosaic map archive for "Forest cover dynamics in the shifting landscape mosaic of the continental United States from 2001 to 2016"
<p>This data archive contains two zipfiles, each containing a raster map of the continental United States showing the landscape mosaic classification at 30-meter resolution as described in the citing publication.</p>
Figure S4.17. Dynamic weekly suitability map for fin whales around São Miguel island, Azores.
<p>Dynamic weekly suitability map for fin whales around São Miguel island, Azores.</p>
Figure S4.13. Dynamic weekly suitability map for bottlenose dolphins around São Miguel island, Azores.
<p>Dynamic weekly suitability map for bottlenose dolphins around São Miguel island, Azores.</p>
Figure S4.5. Dynamic weekly suitability map for Risso's dolphins around São Miguel island, Azores.
<p> Dynamic weekly suitability map for Risso's dolphin around São Miguel island, Azores.</p>
Figure S4.1. Dynamic weekly suitability map for sperm whales around São Miguel island, Azores.
<p>Dynamic weekly suitability map for sperm whales around São Miguel island, Azores.</p>
Figure S4.9. Dynamic weekly suitability map for Atlantic spotted dolphins around São Miguel island, Azores.
<p>Dynamic weekly suitability map for Atlantic spotted dolphins around São Miguel island, Azores.</p>
Figure S4.15. Dynamic weekly suitability map for blue whales around São Miguel island, Azores.
<p>Dynamic weekly suitability map for blue whales around São Miguel island, Azores.</p>
Figure S4.19. Dynamic weekly suitability map for sei whales around São Miguel island, Azores.
<p> Dynamic weekly suitability map for sei whales around São Miguel island, Azores.</p>
Figure S4.7. Dynamic weekly suitability map for stripped dolphins around São Miguel island, Azores.
<p>Dynamic weekly suitability map for stripped dolphins around São Miguel island, Azores.</p>
Figure S4.11. Dynamic weekly suitability map for short-beaked common dolphins around São Miguel island, Azores.
<p>Dynamic weekly suitability map for short-beaked common dolphins around São Miguel island, Azores.</p>
Figure S.4.3. Dynamic weekly suitability map for short-finned pilot whales around São Miguel island, Azores.
<p>Dynamic weekly suitability map for short-finned pilot whales around São Miguel island, Azores.</p>
High-resolution (10-meter) Dynamic Water body map of the Hindu Kush Himalaya region (DWH10) for the year 2022
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A Dynamic Points Removal Benchmark in Point Cloud Maps
<p>Uniformat Dataset LiDAR Point Cloud Data [PCD format: with pose and points (x,y,z,/i)] <br>We recommend you read this wiki page first to know about data: <a href="https://kth-rpl.github.io/DynamicMap_Benchmark/data/">https://kth-rpl.github.io/DynamicMap_Benchmark/data/</a><br>For methods detail: <a href="https://github.com/KTH-RPL/DynamicMap_Benchmark">DynamicMap_Benchmark repo</a>, <a href="https://github.com/KTH-RPL/dufomap">DUFOMap</a>, <a href="https://github.com/MKJia/BeautyMap">BeautyMap</a>.</p> <ul> <li>00: <a href="https://www.cvlibs.net/datasets/kitti/index.php">KITTI sequence</a> 00 [VLP-64] from frame 4390 to 4530</li> <li>05: <a href="https://www.cvlibs.net/datasets/kitti/index.php">KITTI sequence</a> 05 [VLP-64] from frame 2350 to 2670</li> <li>av2: <a href="https://www.argoverse.org/av2.html">Argoverse 2.0</a> one sequence on <em>07YOTznatmYypvQYpzviEcU3yGPsyaGg__Spring_2020. </em>[2 x VLP-32]</li> <li>KTH campus: <a href="https://mcdviral.github.io/">CVPR'24 MCD</a>(Leica RTC360) For convenience teaser running, we include only 18 frames in data. Please check <a href="https://mcdviral.github.io/">the MCD project page</a> for more.</li> <li>semindoor: semi-indoor dataset collected by [VLP-16], collected by ourselves. </li> <li>twofloor: complex structure with two floors[Livox mid-360], collected by ourselves.</li> </ul> <p> </p> <table> <tbody> <tr> <td>Dataset</td> <td>Description</td> <td>Sensor Type</td> <td>Total Frame Number</td> </tr> <tr> <td>KITTI sequence 00</td> <td>in a small town with few dynamics (including one pedestrian around)</td> <td>VLP-64</td> <td>141</td> </tr> <tr> <td>KITTI sequence 05</td> <td>in a small town straight way, one higher car, the benchmarking paper cover image from this sequence.</td> <td>VLP-64</td> <td>321</td> </tr> <tr> <td>Argoverse2</td> <td>in a big city, crowded and tall buildings (including cyclists, vehicles, people walking near the building etc.</td> <td>2 x VLP-32</td> <td>575</td> </tr> <tr> <td>KTH campus (no gt)</td> <td>Collected by us (Thien-Minh) on the KTH campus. Lots of people move around on the campus. The DUFOMap paper cover image is from this one.</td> <td>Leica RTC360</td> <td>18</td> </tr> <tr> <td>Semi-indoor</td> <td>Collected by us (Qingwen & Mingkai), running on a small 1x2 vehicle with two people walking around the platform.</td> <td>VLP-16</td> <td>960</td> </tr> <tr> <td>Twofloor (no gt)</td> <td>Collected by us (Bowen Yang) in a quadruped robot. A two-floor structure environment with one pedestrian around.</td> <td>Livox-mid 360</td> <td>3305</td> </tr> </tbody> </table> <p> </p> <p>Cite as:</p> <blockquote> <p>@inproceedings{zhang2023benchmark,<br> author={Zhang, Qingwen and Duberg, Daniel and Geng, Ruoyu and Jia, Mingkai and Wang, Lujia and Jensfelt, Patric},<br> booktitle={IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, <br> title={A Dynamic Points Removal Benchmark in Point Cloud Maps}, <br> year={2023},<br> pages={608-614},<br> doi={10.1109/ITSC57777.2023.10422094}<br>}<br>@article{jia2024beautymap,<br> author={Jia, Mingkai and Zhang, Qingwen and Yang, Bowen and Wu, Jin and Liu, Ming and Jensfelt, Patric},<br> journal={IEEE Robotics and Automation Letters}, <br> title={{BeautyMap}: Binary-Encoded Adaptable Ground Matrix for Dynamic Points Removal in Global Maps}, <br> year={2024},<br> volume={9},<br> number={7},<br> pages={6256-6263},<br> doi={10.1109/LRA.2024.3402625}<br>}<br>@article{daniel2024dufomap,<br> author={Duberg, Daniel and Zhang, Qingwen and Jia, Mingkai and Jensfelt, Patric},<br> journal={IEEE Robotics and Automation Letters}, <br> title={{DUFOMap}: Efficient Dynamic Awareness Mapping}, <br> year={2024},<br> volume={9},<br> number={6},<br> pages={5038-5045},<br> doi={10.1109/LRA.2024.3387658}<br>}</p> </blockquote> <p> </p> <p>If you use this data, feel free to add your project to <a href="https://kth-rpl.github.io/DynamicMap_Benchmark/papers/">https://kth-rpl.github.io/DynamicMap_Benchmark/papers/</a></p>
Dynamics Information Mapped In The Evaluation Framework
<p>Dynamics Information Mapped In The Evaluation Framework.</p>
Dataset (IX) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>Well-tempered metadynamics simulation data of compounds <strong>1 </strong>and<strong> 2</strong> of the related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
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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.