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2,170 results for “clouds”
FIGURE 5 in A new terrestrial frog (Anura: Craugastoridae) from the montane cloud forests of the southeastern Ecuadorian Andes
FIGURE 5. Color variation in specimens of Pristimantis nimbus sp. nov. A) MZUA.AN.1465 (SVL 25.0 mm), B) MZUA.AN.1472 (SVL 18.5 mm), C) MZUA.AN.0705 (SVL 17.7 mm).
FIGURE 4 in Discovery of another new species of Charadrahyla (Anura, Hylidae) from the cloud forest of northern Oaxaca, México
FIGURE 4. Type locality of Charadrahyla esperancensis (A), and Community cartel indicating the protection of the northern region where the new species lives (B)
FIGURE 2 in Discovery of another new species of Charadrahyla (Anura, Hylidae) from the cloud forest of northern Oaxaca, México
FIGURE 2. Dorsal (A) and ventral view (B) of the preserved holotype of Charadrahyla esperancensis (MZFC 28699); dorsal and lateral view of the head of C. esperancensis (C); male (D) and female (E) of C. taeniopus (MZFC 5150, 7714 respectively).
FIGURE 1 in Discovery of another new species of Charadrahyla (Anura, Hylidae) from the cloud forest of northern Oaxaca, México
FIGURE 1. Specimens in life of Charadrahyla esperancensis: A,B,C: male holotype (MZFC 28699), D,E,F (male specimens, not collected) (F, same specimen that E, showing the rounded yellow spots on posterior parts of thighs, and C. taeniopus: G (male from Tlatlauquitepec, Puebla), H (female from Hidalgo).
FIGURE 3 in Discovery of another new species of Charadrahyla (Anura, Hylidae) from the cloud forest of northern Oaxaca, México
FIGURE 3. Ventral aspects of hand and foot of the holotype of Charadrahyla esperancensis (MZFC 28699). Bar= 10 mm.
FIGURE 2 in Rediscovery of the Endangered frog Phrynopus montium (Shreve, 1938) (Amphibia, Anura, Craugastoridae) in the cloud forest of central Peru
FIGURE 2. Lateral and ventral views of live individuals of Phrynopus montium, found in September 2014 near the type locality. A–B (MUSM 33259, SVL 26.3 mm), C–D (MUSM 33260, SVL 20.9 mm). Photos by R. von May.
FIGURE 3 in Rediscovery of the Endangered frog Phrynopus montium (Shreve, 1938) (Amphibia, Anura, Craugastoridae) in the cloud forest of central Peru
FIGURE 3. Lateral view of head and ventral view of left hand of preserved individuals of Phrynopus montium. A–B (MUSM 33259), C–D (MUSM 33260). Photos by R. von May.
FIGURE 1 in Rediscovery of the Endangered frog Phrynopus montium (Shreve, 1938) (Amphibia, Anura, Craugastoridae) in the cloud forest of central Peru
FIGURE 1. Site near road between Cachiyacu and Hacienda Cascas, Región Junín, Perú, where two individuals of Phrynopus montium were found in September 2014. Photo by R. von May.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy. in Deep learning brings speed, accuracy to the life sciences.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy.
Dataset for the paper "Ensemble optimization retrieval algorithm of hydrometeor profiles for the Ice Cloud Imager submillimeter-wave radiometer'
<p>1. "Retrieval_Database" file contains the pre-calculated retrieval database.</p> <p>2. "Algorithm_Input" file contains the input of the ensemble optimization retrieval algorithm.</p> <p>3. "TrueProfiles" file contains the true profiles corresponding to the input brightness temperatures. </p> <p>4. "Algorithm_Output" file contains the output of the ensemble optimization retrieval algorithm.</p>
Final point cloud results of SPC-MVSNet on DTU dataset
<p>SPC-MVSNet在DTU数据集上的最终点云结果</p>
Final point cloud results of SPC-MVSNet on Tanks and Temples benchmark
<p>SPC-MVSNet在坦克和寺庙基准上的最终点云结果。</p>
FIGURE 5 in Bedazzled: a new, striking species of Corades from the outskirts of Quito questions our knowledge of Andean cloud forest butterflies (Lepidoptera, Nymphalidae, Satyrinae)
FIGURE 5. Habitat of Corades yanacocha sp. n. at 3500 m s.s.l. in the Yanacocha Biological Reserve (phot. P. Boyer).
FIGURE 1 in Bedazzled: a new, striking species of Corades from the outskirts of Quito questions our knowledge of Andean cloud forest butterflies (Lepidoptera, Nymphalidae, Satyrinae)
FIGURE 1. ML COI based tree of Corades occurring in the northern Andes indicating the position of C. yanacocha sp. n. with bootstrap values on the nodes. Values less than 70 are not shown.
FIGURE 4 in Bedazzled: a new, striking species of Corades from the outskirts of Quito questions our knowledge of Andean cloud forest butterflies (Lepidoptera, Nymphalidae, Satyrinae)
FIGURE 4. Corades yanacocha sp. n. male genitalia, A. in lateral view, B. aedeagus in lateral view, C. aedeagus in dorsal view.
Data for "Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression"
<p>The dataset for the manuscript titled</p> <p>"Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression".</p> <blockquote> <p>Oh, G. and Austin, P. H.: Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-352, 2024.</p> </blockquote> <p> </p> <p>The dataset contains input parameters used for the SAM LES model run (Khairoutdinov and Randall, 2003) based on the CGILS S2 parameters (CGILS.zip) and the main raw output used for the manuscript (clusters.zip). The latter contains a list of Parquet files for the 12-hour model run, sampled every minute. The raw output consists of the coordinates of all cloudy cells in the model domain, which is used to construct the cloud size distribution as described in the manuscript.</p>
Supplementary Data from manuscript entitled: Cloud processing dominates the vertical aerosol profiles in marine air masses over the Great Barrier Reef
<p>The data used in the manuscript "Cloud processing dominates the vertical aerosol profiles in marine air masses over the Great Barrier Reef" is available. It contains information from different instruments on board the research aircraft. The manuscript describes the instrumentation details. There is a read_me file in each folder with further information.</p>
Evaluating Cloud Properties at Scott Base: Comparing Ceilometer Observations with ERA5, JRA55, and MERRA2 Reanalyses Using an Instrument Simulator (Pre-review)
<p>Due to its remote location and extreme weather conditions, atmospheric measurements are rare in Antarctica. Partially due to this lack of observational constraints, large biases in the representation of clouds have been identified over Southern hemisphere high latitudes in various grenerations of the Coupled Model Intercomparison Project (CMIP) models, numerical weather prediction models and reanalyses. It has been shown in previous studies that ground-based remote sensing measurements of cloud across the region are critical for complementing satellite data sets due to the importance of boundary layer and low-level cloud processes. These processes are poorly sampled by satellite-based measurements which are typically obscured by overlying cloud cover or are prone to ground clutter. Here we provide a dataset which includes CL51 ceilometer observations of low clouds made during the period 14th February 2022 and 31st December 2023 at Scott Base, Antarctica (77.8 S, 166.7 E). Complemeted by ERA5, JRA55 and MERRA2 reanalyses output that has been processed using an instrument simulator. These datasets allow direct comparison between the reanalyses datasets and the ceilometer observations.</p> <p> </p>
Tracking climate vulnerability across spatial distribution and functional traits in Magnolia gentryi from the Peruvian tropical montane cloud forest
<p>Understanding the responses of tree species' functional traits to climate variability is essential for predicting the future of Tropical Montane Cloud Forest (TMCF) tree species through acclimation, especially in Andean montane environments where fog pockets act as moisture traps. We studied the distribution of <em>Magnolia gentryi</em> to measure its spatial arrangement and identify local hotspots, while also evaluating the extent to which climate-related factors are associated with its distribution. Finally, we analyzed variations in 13 functional traits of <em>M. gentryi</em> and the climate links to infer the shaping plant acclimate capacity. Our results show that Andean TMCF climatic factors constrain <em>M. gentryi</em> spatial distribution with significant patches or gaps, associated with high precipitation rates and mean minimum temperature. The functional traits of <em>M. gentryi</em> are constrained by Andean TMCF climatic factors, resulting in reduced within-species acclimation in functional traits associated with a hydric deficit. The association between functional traits and climate oscillation is crucial for understanding the growth conditions of relict-endemic species and is essential for conservation efforts. Changes in forest trait diversity and species composition occur because of fluctuations in hydraulic safety–efficiency gradients.</p>
An improved multiphase chemistry mechanism for methylamines: Significant dimethylamine cloud production
<p>Processing data for Figures in publication.</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)
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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.