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.
2,170
datasets available to search
ShareScore release 0.9.0
Dataset results
2,170 results for “clouds”
Data for cloud retrieval algorithm
Open the record for dataset details and reuse information.
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>
Original point cloud from laser scanning of the Kladno railway station
Open the record for dataset details and reuse information.
Thin cloud removal dataset for Sentinel-2 images
<p>This is a thin cloud removal dataset for Sentinel-2A images in CR-GAN-PM[1] article. This dataset contains 20 paired thin cloud and clear Sentinel-2 images.</p> <p>If you use this dataset for your research, please cite us accordingly:</p> <p>#Reference: </p> <p>[1] J. Li, Z. W, Z. Hu, J. Z, M. Li, L. Mo and M. Molinier, “Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,” ISPRS J. Photogramm. Remote Sens., vol. 166, pp. 373–389, Aug. 2020. <a href="http://doi.org/10.1016/j.isprsjprs.2020.06.021">http://doi.org/10.1016/j.isprsjprs.2020.06.021</a>.</p> <p><br> [2] J. Li, Z. Wu, Z. Hu, Z. Li, Y. Wang, and M. Molinier, “Deep learning based thin cloud removal fusing vegetation red edge and short wave infrared spectral information for Sentinel-2A imagery,” Remote Sens., vol. 13, no. 1, p. 157, Jan. 2021. <a href="http://doi.org/10.3390/rs13010157">http://doi.org/10.3390/rs13010157</a>.</p>
Data presented in the paper "Impact of Giant Sea Salt Aerosol Particles on Precipitation in Marine Cumuli and Stratocumuli: Lagrangian Cloud Model Simulations"
<p>View Readme.</p>
Photometric data of the Magellanic Cloud clusters NGC330, NGC1818, NGC1755 and NGC2164
<p>NGC330fig2Milone18.RDMAG, NGC1755.XYM, NGC1818.XYM and NGC2164.XYMAGBe are the photometric data for the Magellanic Cloud clusters NGC330, NGC1755, NGC1818 and NGC2164, respectively. The owner of the data is Antonino Milone. </p> <p>n330_decont.csv, n1818_decont.csv, n1755_decont.csv and n2164_decont.csv are the main-sequence stars employed in the paper "Stellar mergers as the origin of the blue main-sequence band in young star clusters" after decontamination. </p>
Subgrid-scale effects in cloud-like atmospheric flows: Colliding thermals - volume 1
<p>Datasets to perform surrogate modeling of subgrid-scale effects in cloud-like atmospheric flows. Please see README.md file for detailed description and metadata.</p>
Fatiando a Terra Data: British Columbia, Canada - Lidar point cloud
<p>This is a point cloud sliced to the small <a href="https://apps.gov.bc.ca/pub/bcgnws/names/21973.html">Trail Islands</a> to the North of Vancouver to reduce the data size. The islands have some nice looking topography and their isolated nature creates problems for some interpolation methods, like simple nearest neighbours.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Cut out a section of the original data to focus only on these small islands; Change UTM coordinates to geodetic latitude and longitude (WGS84); Keep only the ground reflection picks; Export to a compressed CSV for easier loading with Pandas.</p> <p><strong>Source: </strong><a href="https://www2.gov.bc.ca/gov/content/data/geographic-data-services/lidarbc">LidarBC</a></p> <p><strong>Source license: </strong><a href="https://www2.gov.bc.ca/gov/content/data/open-data/open-government-licence-bc">Open Government Licence - British Columbia</a></p> <p><strong>Repository:</strong> <a href="https://github.com/fatiando-data/british-columbia-lidar">https://github.com/fatiando-data/british-columbia-lidar</a></p>
FIGURE 8 in A new species of planthopper in the genus Myconus (Hemiptera: Auchenorrhyncha: Fulgoroidea: Achilidae) from the Los Angeles Cloud Forest, Costa Rica
FIGURE 8. Holotype of Messoides uniformis Metcalf, 1938 (at USNM), A) dorsal view and B) labels.
FIGURE 1 in A new species of planthopper in the genus Myconus (Hemiptera: Auchenorrhyncha: Fulgoroidea: Achilidae) from the Los Angeles Cloud Forest, Costa Rica
FIGURE 1. Habitat where specimens of Myconus jacquelinae sp. n. were collected.
FIGURE 5 in A new species of planthopper in the genus Myconus (Hemiptera: Auchenorrhyncha: Fulgoroidea: Achilidae) from the Los Angeles Cloud Forest, Costa Rica
FIGURE 5. Hind tibia and tarsus of Myconus jacquelinae sp. n. showing spinulation.
Terrestrial and aerial photos, GCPs and derived point clouds of a sinkhole in Northern Thuringia
<p>Aiming at comparing the results of using either terrestrial or aerial photos for structure from motion photogrammetry of a sinkhole in Northern Thuringia we took these photos in 2017, 2018 and 2019 and surveyed ground control points. Therefore, the data allows both, the comparison of the results of using terrestrial or aerial photos for the 3D reconstruction, and the mulit-year monitoring of geomorphic changes within the sinkhole.</p>
Rate file for diffuse and translucent cloud chemistry
<p>Rate file for astrochemical models shown in Section 5.7.2 of the book "Astrochemistry: Chemistry in Interstellar and Circumstellar Space" by DA Williams and C Cecchi-Pestellini</p>
Cloud Properties and Changes in RCEMIP
<p>Derived data and scripts used in Stauffer and Wing (2022)</p>
3D-MSNet: A point cloud based deep learning model for untargeted feature detection and quantification in profile LC-HRMS data
<p>Supplementary data of 3D-MSNet</p>
Distribution. Chelemha Cloud Forest Reserve (C Guatemala). in Soricidae
Distribution. Chelemha Cloud Forest Reserve (C Guatemala).
Data available for ''Vertical Gradient of Size-Resolved Aerosol Composition over the Arctic Reveals Cloud Processed Aerosol in-cloud and above Cloud"
<p>Folder containing the data of CCSEM and STXM measurements.</p>
"Halo Region around Shallow Cumulus Clouds in Large Eddy Simulations"
<p>This dataset is used to examine the halo regions around shallow cumulus clouds. We performed large eddy simulations of shallow cumulus clouds based on the Barbados Oceanographic and Meteorological Experiment (BOMEX) using Met Office-NERC (National Environment Research Concil) Cloud model (MONC). A set of simulations with different model resolutions is performed. The available horizontal grid spacings are 10 m, 25 m, 50 m and 100 m and the corresponding vertical resolutions are 10 m, 25 m, 25 m, and 40 m, respectively. All simulations have the same model top, that is, 3 km. To save the computational cost, we use the same horizontal grid boxes (600 X 600). To test the robustness, we perform sensitivity simulations with modified mixing length scales in the sub-grid scale turbulence scheme. We also perform large eddy simulations using another numerical model, Cloud Model 1 (CM1) to check if the results are sensitive to the numerical details (advection schemes) and domain size. The simulations with CM1 have the same model resolution configurations as in MONC, but they have the same domain size (7.2 X 7.2 X 3)km^3. For more details of the simulation set up, please refer to our submitted paper. As the whole simulation has a huge dataset, we only choose part of the results that can reproduce the figures in our submitted paper for repository. </p>
FIGURE 4 in Ternstroemia guerrerensis (Pentaphylacaceae), a new endangered cloud forest tree from Guerrero, Mexico
FIGURE 4. Distribution of T. guerrerensis in Guerrero, Mexico.
Figure 4 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939
Figure 4 - Time table for the eEcoLiDAR project (assuming a start in March 2017). The work plan covers tasks for the NLeSC engineers, the proposed PhD student, and two associated Postdoc projects.
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.