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2,170 results for “clouds”
Figures 1-9 from: Jordal BH, Kirkendall LR (2019) Rainforest and cloud forest Scolytodes (Curculionidae, Scolytinae, Hexacolini) from the Arthropods of La Selva inventory in Costa Rica: new species, new synonymy, new records. ZooKeys 863: 1-34. https://doi.org/10.3897/zookeys.863.33183
Figures 1-9 Dorsal, lateral and frontal view of the female holotype of 1, 4, 7S.angulus, 2, 5, 8S.niger and 3, 6, 9S.simplex.
Simulated function placements in predefined mixed cloud-edge clusters
<p>Experiments in a simulation environment to evaluate the placement quality of the Skippy Scheduler, an optimized container scheduler for serverless edge computing in Kubernetes.</p>
MOVIE S5 Normal clearance of murine respiratory tract is mediated by discontinuous Muc5b and Muc5ac mucus clouds
<p><strong>Movie S5. Mucus transport of the Muc5ac on the trachea of </strong><strong><em>Muc5b<sup>–/– </sup></em></strong><strong>mice. </strong>The mouse trachea was mounted and submerged in a buffer containing Alcian blue, tilted and the mucus transport in the distal part of the tissue recorded by video microscopy.</p>
MOVIE S2. Normal clearance of murine respiratory tract is mediated by discontinuous Muc5b and Muc5ac mucus clouds
<p>Movie S2. Mucus-trapped beads moves slower than free beads. Mouse trachea was mounted and submerged in a buffer containing Alcian blue and 1 µm fluorescent beads, the tissue tilted, and the movement of mucus and beads recorded by video microscopy.</p>
MOVIE S3. Normal clearance of murine respiratory tract is mediated by discontinuous Muc5b and Muc5ac mucus clouds
<p><strong>Movie S3. Charcoal particles are collected and transported by mucus bundles. </strong>The mouse trachea was mounted and submerged in a buffer containing Alcian blue and charcoal, the tissue tilted and the movement of charcoal on the proximal part of the trachea recorded by video microscopy.</p>
FIGURE 6 in A new genus and species of cixiid planthopper (Hemiptera: Auchenorrhyncha: Fulgoroidea) from the Reserva Privada el Silencio de Los Angeles Cloud Forest in Costa Rica
FIGURE 6. Forewing venation of Myxia gen. n. belinda sp. n.
Aircraft observations of warm clouds in Jiangxi,China
<p>20141107.xlsx: Number concentration measured by CAS and CIP, with other data measured by AIMMS-20 in 2014.11.07</p> <p>20141110.xlsx: Number concentration measured by CAS and CIP, with other data measured by AIMMS-20 in 2014.11.10</p> <p>20141111.xlsx: Number concentration measured by CAS and CIP, with other data measured by AIMMS-20 in 2014.11.11</p> <p>20141203.xlsx: Number concentration measured by CAS and CIP, with other data measured by AIMMS-20 in 2014.12.03</p> <p> </p> <p>particle size.xlsx: Minimum and maximum values for each bin of particle size.</p> <p>*For example, the first bin of CAS ranges from 0.54 - 0.61um, the second one ranges from 0.61 - 0.68um …… There are totally 30 bins in CAS and 62 bins in CIP.</p> <p>The particle size data is the same for all four processes, it can be used with all four files above.</p>
HULIS and WSOC optical property data in cloud water collected at Taishan in 2021
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NIBIO_MLS: a forest point cloud panoptic segmentation dataset from mobile laser scanning (Geoslam Horizon)
<h1>General description</h1> <p>This dataset consists of a ML-ready labelled mobile laser scanning (MLS) point cloud dataset including 16 manually labelled forest plots (approx. 250 m2) for forest panoptic segmentation and thus including both semantic and instance labels. The data was collected using a Geoslam Horizon RT and processed using Geoslam Hub.</p> <h1>Labels</h1> <p>The data were then labelled into the following semantic classes (<em>label</em>):</p> <ul> <li>1= ground</li> <li>2= vegetation: these include both branches, leaves, and low vegetation</li> <li>3= lying deadwood</li> <li>4= stems</li> </ul> <p>In addition for each tree, a unique tree identifier (<em>treeID</em>) was also assigned to each point.</p> <h1>Data split</h1> <p>Each plot was split into train (50%), validation (25%), and test (25%) sets by dividing the circular plot into four slices, out of which the first two were used for training, the third for validation, and the fourth for test. </p> <p>Thus the users might play around with merging the train and validation dataset as they prefer. These two sets can be used during model training, hyperparameter tuning, and model selection. However, the test set should be kept as an independent set to be used for benchmarking against the values reported in the two studies indicated below. </p> <h1>Citation</h1> <p>To cite this datasets and for a more detailed description use:</p> <p>Wielgosz, M., Puliti, S., Xiang, B., Schindler, K. and Astrup, R., 2024. SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data. <em>Remote Sensing of Environment; </em></p> <h2>Other studies using these data</h2> <p>Wielgosz, M., Puliti, S., Wilkes, P. and Astrup, R., 2023. Point2Tree (P2T)—Framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest. <em>Remote Sensing</em>, <em>15</em>(15), p.3737; available <a href="https://www.mdpi.com/2072-4292/15/15/3737" target="_blank" rel="noopener">here</a></p> <h1>Funding</h1> <p>This work is part of the Center for Research-based Innovation SmartForest: Bringing Industry 4.0 to<br>the Norwegian forest sector (NFR SFI project no. 309671, smartforest.no).</p> <h1>⚖️ Licensing</h1> <p>📄 Please refer to the specific licenses below for details on how the data can be used.</p> <h4>🔑 Key Licensing Principles:</h4> <ul> <li>✅ You may access, use, and share the dataset and models freely.</li> <li>🔄 Any derivative works (e.g., trained models, code for training, or prediction tools) must also be made publicly available under the same licensing terms.</li> <li>🌍 These licenses promote <strong>collaboration</strong> and <strong>transparency</strong>, ensuring that research using this dataset benefits the broader scientific and open-source community 🙌</li> </ul>
Green AI in the Cloud: Energy-Efficient Architectural Tactics for ML-Enabled Systems on Public Platforms
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Emulator of PR-DNS: Part II, dataset for training the emulator of thermodynamics and cloud droplet fields in Particle-Resolved Direct Numerical Simulation
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Measurement report: The influence of particle number size distribution and hygroscopicity on the microphysical properties of cloud droplets based on a mountain site field campaign
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Fit summary of "X-Shooting ULLYSES: Massive Stars at low metallicity IX: Empirical constraints on mass-loss rates and clumping parameters for OB supergiants in the Large Magellanic Cloud"
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Linked collectors and determiners for: Rainforest and cloud forest Scolytodes (Curculionidae, Scolytinae, Hexacolini) from the Arthropods of La Selva inventory in Costa Rica: new species, new synonymy, new records.
Natural history specimen data linked to collectors and determiners held within, "Rainforest and cloud forest Scolytodes (Curculionidae, Scolytinae, Hexacolini) from the Arthropods of La Selva inventory in Costa Rica: new species, new synonymy, new records". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/292005e8-3ace-44fa-886d-ff382cec56ba">https://bionomia.net/dataset/292005e8-3ace-44fa-886d-ff382cec56ba</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/292005e8-3ace-44fa-886d-ff382cec56ba">https://gbif.org/dataset/292005e8-3ace-44fa-886d-ff382cec56ba</a>. Formatted as a Frictionless Data package.
MuSHRoom iPhone dataset colmap pose & point cloud
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Sampling in Cloud Benchmarking: A Critical Review and Methodological Guidelines
<p>This replication package contains the data and code to replicate our critical review on sampling in cloud benchmarking.</p> <p> </p> <p><strong>Paper</strong></p> <p>Akbari, Saman, and Manfred Hauswirth. "Sampling in Cloud Benchmarking: A Critical Review and Methodological Guidelines." <em>2024 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)</em>. IEEE, 2024. DOI: <a href="https://doi.org/10.1109/CloudCom62794.2024.00034" target="_blank" rel="noopener">10.1109/CloudCom62794.2024.00034</a>.</p> <pre>@inproceedings{akbari2024sampling, title={Sampling in Cloud Benchmarking: A Critical Review and Methodological Guidelines}, author={Akbari, Saman and Hauswirth, Manfred}, booktitle={2024 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)}, pages={160--167}, year={2024}, organization={IEEE} }</pre> <p> </p> <p><strong>Abstract</strong></p> <blockquote> <p>Cloud benchmarks suffer from performance fluctuations caused by resource contention, network latency, hardware heterogeneity, and other factors along with decisions taken in the benchmark design. In particular, the sampling strategy of benchmark designers can significantly influence benchmark results. Despite this well-known fact, no systematic approach has been devised so far to make sampling results comparable and guide benchmark designers in choosing their sampling strategy for use within benchmarks. To identify systematic problems, we critically review sampling in recent cloud computing research. Our analysis identifies concerning trends: (i) a high prevalence of non-probability sampling, (ii) over-reliance on a single benchmark, and (iii) restricted access to samples. To address these issues and increase transparency in sampling, we propose methodological guidelines for researchers and reviewers. We hope that our work contributes to improving the generalizability, reproducibility, and reliability of research results.</p> </blockquote>
Data for publication "Chasing ice crystals: interlinking cloud microphysics and dynamics in cloud seeding plumes with Lagrangian trajectories"
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Data from: Low modularity and specialization in a commensalistic epiphyte–phorophyte network in a tropical cloud forest
Species interactions can shape the structure of natural communities. Such sets of interactions have been described as complex ecological networks, an example of which is the commensal network formed by epiphyte–phorophyte interactions. Vascular epiphytes germinate and grow on phorophytes (support trees), assuming a horizontal distribution (among the phorophyte species) and a vertical distribution (from the base of the tree trunk to the crown of phorophytes, i.e., through ecological zones). Here we investigated the organization of these structural dimensions of the epiphyte–phorophyte network in a Brazilian tropical montane cloud forest. The analyzed network, comprising 66 epiphyte species and 22 phorophyte species, exhibited a nested structure with a low degree of specialization, a typical pattern for epiphyte–phorophyte networks in forests. The network was slightly modular, with 65% of the species common to three modules, and had vertical structure corresponding to the vertical organization of the phorophytes. The size (diameter at breast height) of phorophyte individuals influenced the network structure, possibly due to the increase in habitat area, the time available for colonization by epiphytes, and a greater number of microenvironments. We found that the distribution of the epiphyte species differed between the phorophyte ecological zones, with greater richness in the lower portions and greater abundance in the upper portions of the phorophytes. The results provide relevant guidance for future research on the characteristics and the vertical and horizontal organization of vascular epiphyte and phorophyte networks.
Sentinel-2 parallax-based cloud detection - sample dataset
<p>This repository archives the Sentinel-2 sample data used in:</p> <p>D. Frantz, E. Haß, A. Uhl, J. Stoffels, and J. Hill (2018): Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects. Remote Sensing of Environment 215, 471-481. <a href="https://doi.org/10.1016/j.rse.2018.04.046">https://doi.org/10.1016/j.rse.2018.04.046</a></p>
Data and Codes for Evaluating and Improving Scale-Awareness of a Convective Parameterization Closure Using Cloud-Resolving Model Simulations of Convection
<p>Provide necessary fields averaged over different subdomain sizes from 64 km to 4 km (see 64 to 4 .7z files) processed from the output of CRM simulation of MC3E case (for TWP-ICE case, please get the processed data and associated codes from http://doi.org/10.5281/zenodo.4542461). Also, associated codes for calculation of important fields (like dCAPEls, dCAPEe, Msa and so on) are also provided in code.7z. Please see all "note.txt" files in code.7z to know how to use these codes.</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.