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FIGURE 4 in Morphometric analysis to differentiate taxonomically seven species of Eleutherodactylus (Amphibia: Anura: Leptodactylidae) from an Andean cloud forest of Colombia
FIGURE 4. Side of Eleutherodactylus head showing how tympanumeye distance and tympanum diameter were measured.
FIGURE 2. A in Morphometric analysis to differentiate taxonomically seven species of Eleutherodactylus (Amphibia: Anura: Leptodactylidae) from an Andean cloud forest of Colombia
FIGURE 2. A. Cluster analysis CA2. Data set included 58 cases (only adult individuals) and 40 qualitative variables. B. Cluster analysis CA3. Data set included 58 cases (only adult individuals) and the diagnostic variables for the Eleutherodactylus species. In parenthesis, number of individuals of each species.
FIGURE 3. Discriminant analysis. Data set included 140 in Morphometric analysis to differentiate taxonomically seven species of Eleutherodactylus (Amphibia: Anura: Leptodactylidae) from an Andean cloud forest of Colombia
FIGURE 3. Discriminant analysis. Data set included 140 of four species (Eleutherodactylus douglasi, E. merostictus, E. miyatai and E. prolixodiscus) and 11 quantitative variables. The model utilized stepwise discrimination in which all variables were included in the model and then, at each step, the variable that contributed least to the prediction of group memberships was eliminated.
FIGURE 1 in Morphometric analysis to differentiate taxonomically seven species of Eleutherodactylus (Amphibia: Anura: Leptodactylidae) from an Andean cloud forest of Colombia
FIGURE 1. Cluster analysis CA1. Data set included 159 cases (all individuals) and 40 qualitative variables. It was analyzed with Manhattan distances and the UPGMA algorithm; missing data were not substituted. In parenthesis, number of individuals of each species.
Representation of the Autoconversion from Cloud to Rain
<p>Modified WRF model codes</p> <p>Initial and boundary conditions for the simulations (wrfinput and wrfbdy, and OBS_DOMAIN files for FDDA)</p>
Point cloud from terrestrial laser scanning of a coppiced beech tree (Fagus sylvatica) in Wytham Woods, UK
<p>Point cloud of an old beech coppice stool located in Wytham Woods, UK. </p> <p>A 3D model of the tree can be found <a href="https://skfb.ly/6WLqp">here</a> on SketchFab.</p> <p>Data was captured on 20/4/2021 with a RIEGL VZ-400 terrestrial laser scanner from 6 scan locations around the tree. The weather was good, with little to no noticeable wind. Data is a binary PLY format with reflectance, deviation and PCV fields as well as RGB. Note this data set still includes some ground returns.</p> <p>Please acknowledge the data set authors if using this data.</p>
The role of collision speed, cloud density, and turbulence in the formation of young massive clusters via cloud-cloud collisions
<p>This repository contains the essential unprocessed data to recreate the results in this paper (10.1093/mnras/staa2857; <a href="https://ui.adsabs.harvard.edu/abs/2020MNRAS.499.1099L/abstract">Liow and Dobbs 2020</a>), which contains the following directories:</p> <p><strong>initial_conditions </strong></p> <ul> <li>This directory contains the initial condition dumpfiles. There are 24 main models in this paper. They are named XY_abc, where 'X' is either 'B', 'C', or 'D', representing low, standard, and high initial cloud density respectively; 'Y' is either '1' or '2', representing low and high turbulence; 'abc' is a three-digit number representing the collision Mach number.</li> <li>Additional models in appendices have prefixes attached to the filenames. The prefixes are either 'R' (resolution test models; Appendix A), 'T' (random turbulent seeds models; Appendix B), or 'M' (massive clouds models; Appendix D). The value after 'R' shows the number of SPH particles used, a.k.a. resolution.</li> <li>The dumpfiles were produced using PHANTOM v1.3.0 (Price et al. 2018; https://phantomsph.bitbucket.io/) in binary format and can be visualised using SPLASH v2.8.0 (Price 2007; https://splash-viz.readthedocs.io/en/latest/intro.html) with code units 100,000 MSun and 10 pc. </li> </ul> <p><strong>ten_percents</strong> </p> <ul> <li>This directory contains the dumpfiles at t<sub>10%</sub>, the time when 10% of the gas mass is converted to sink mass. Most analysis in the paper was done at this snapshot. They can be visualised using SPLASH similar to the initial condition dumpfiles.</li> </ul> <p><strong>ev_files</strong></p> <ul> <li>This directory contains ASCII files that track the global properties (energies, total linear/angular momentum, etc) throughout the simulation. The suffixes '_0?' right before '.ev', not relevant to the analysis, show the number of times the simulations were continued. </li> </ul> <p><strong>all_turbulent_fields</strong></p> <ul> <li>This directory contains turbulent fields used in most models ('turb001' and 'turb002') and in the random turbulent seed models (otherwise), written in binary format. See 'README' for the random seeds used, and 'velfield.f' for the code to generate the turbulent fields.</li> </ul> <p>Not included in this repository are the intermediate dumpfiles and the sink particle data, but the data shown in this repository is sufficient to reproduce the time and snapshots in the paper. Post-processed data (e.g. global star formation rates, sink particle information at t<sub>10% </sub>and other specific snapshots) and results are not included here to preserve data authenticity.</p> <p>Readers are welcome to contact the authors for the post-processed data and more information.</p>
Figure 3 in Space use by two arboreal rodent species in a Neotropical cloud forest
Figure 3. Home range of the nine individuals of Habromys schmidlyi and the seven individuals of Reithrodontomys microdon at Taxco, Guerrero. Home ranges of females are drawn with gray lines, while home ranges of males are drawn with black lines. The gray shadows in the background represent the home range of R. microdon in the image of H. schmidlyi and vice versa.
Figure 5 in Space use by two arboreal rodent species in a Neotropical cloud forest
Figure 5. Trees belonging to cloud forest or oak forest in which individuals of Habromys schmidlyi and Reithrodontomys microdon were trapped or radio-tracked.
Figure 2 in Space use by two arboreal rodent species in a Neotropical cloud forest
Figure 2. Number of individuals of each small mammal species trapped in each stratum in 'Cerro del Huixteco' State Park, Taxco, Guerrero, Mexico.
Figure 4 in Space use by two arboreal rodent species in a Neotropical cloud forest
Figure 4. Home range of the seven individuals of Habromys schmidlyi and the five individuals of Reithrodontomys microdon at Zacualpan, Estado de México. Home ranges of females are drawn with gray lines, while home ranges of males are drawn with black lines.
Figure 1 in Space use by two arboreal rodent species in a Neotropical cloud forest
Figure 1. Location of Taxco in Guerrero state and Zacualpan in Estado de México, in addition to their locations in North America.
Figure 5 in New species of Nasutopedia from cloud forests of the Neotropical region (Hymenoptera: Apidae: Tapinotaspidini)
Figure 5. Nasutopedia laeta sp. nov.: holotype male: (a) head, frontal; (b) lateral view; (c) male paratype, metasoma. Scale bars: 0.5 mm.
Figure 4 in New species of Nasutopedia from cloud forests of the Neotropical region (Hymenoptera: Apidae: Tapinotaspidini)
Figure 4. Nasutopedia puncticutis sp. nov.: holotype male: (a) head, frontal; (b) lateral view; (c) female paratype (BBSL517258), metasoma. Scale bars: 0.5 mm.
Figure 3 in New species of Nasutopedia from cloud forests of the Neotropical region (Hymenoptera: Apidae: Tapinotaspidini)
Figure 3. Nasutopedia morena sp. nov., holotype male: (a) head, frontal; (b) metasoma; (c) body lateral view. Scale bars: 0.5 mm.
Figure 2 in New species of Nasutopedia from cloud forests of the Neotropical region (Hymenoptera: Apidae: Tapinotaspidini)
Figure 2. Nasutopedia borealis sp. nov., holotype male: (a) head, frontal; (b) metasoma; (c) body lateral view. Scale bars: 0.5 mm.
Figure 1 in New species of Nasutopedia from cloud forests of the Neotropical region (Hymenoptera: Apidae: Tapinotaspidini)
Figure 1. Distribution map of the species of the new described species of Nasutopedia: Nasutopedia puncticutis sp. nov., blue square; Nasutopedia laeta sp. nov., orange triangle; Nasutopedia borealis sp. nov., red circle; Nasutopedia morena sp. nov., blue circle; grey area represents the elevation terrain elevation above 900 m above sea level.
Himawari-8 Cloud-top phase products
<p>This product is derived from a decision tree algorithm for advanced Himawari imager (AHI) observations, and it provides cloud-top phase (temperature) on each AHI pixel. The cloud-top is distinguished as liquid water, ice, and mixed phases, and further classed into clear (or probably clear, probably cloudy), water, supercooled, mixed, thick ice, cirrus, overlap and overshooting in conjunction with the cloud-top height and optical thickness. The original product is a full-disk data with highest resolution of 2 km at the sub-satellite point (140.7 °E), then it is projected to equal-longitude-latitude grids (110-150°E, 10-50°N) and with resolution of 0.1°×0.1°, while the temporal resolution is 30 minutes. </p> <p>In the dataset, CldHeight means Cloud-top height from ACHA algorithm; CldTemperature means Cloud-top temperature from ACHA algorithm; CldType means Cloud types (clear=0, probably_clear=1, probably_cloudy=2, water=3, supercooled=4, mixed=5, Thick Ice=6, cirrus=7, overlap=8, overshooting=9)</p> <p>Data is stored as a binary matrix (401*401) corresponding to the domain of (110-150°E, 10-50°N),with a Header(128 byte).</p>
A combination of morphological and photosynthetic functional traits maintains the vertical distribution of bryophytes in a subtropical cloud forest
<p><span>The distribution and performance of bryophyte species are known to vary with vertical gradients, due to changes in environmental factors, especially light conditions. However, the morphological and physiological drivers of bryophyte distribution along forest vertical gradients are poorly understood. </span></p> <p><span><span><b>Methods</b><b>:</b> We conducted a comparative analysis of 28 morphological and photosynthetic functional traits in 18 species of mosses and liverworts distributed among three vertical microhabitats (ground, tree trunk, and branch) to analyze trait variance among the microhabitats and bryophyte life–forms in a subtropical cloud forest in Ailao Mountain, Yunnan, southwest China. Principle component analysis (PCA) was used to summarize trait differences among bryophyte species.</span></span></p> <p><span><b>Key Results: </b></span><span>In contrast to trunk and ground dwellers, branch dwellers tend to reduce light interception (smaller leaf and cell sizes, lower chlorophyll content); protect against damage from intense irradiation (higher ratios of carotenoids to chlorophyll); raise light energy utilization (higher photosynthetic capacity) and cope with lower environmental moisture conditions (pendant life-forms, thicker cell wall). Principal component analysis showed that ecological strategies of bryophytes in response to levels of irradiation were specialized in branch–dwellers, although those in ground and trunk–dwelling species were less distinct.</span></p> <p><b>Conclusions:</b> Environmental filtering shaped the functional traits combination and spatial distribution of bryophytes along the vertical gradients. Bryophyte species from upper canopy of cloud forests show narrow variation in functional traits under intense light , whereas species in the lower vertical strata associated with low levels of light intensity exhibited contrasting, but more diverse ecological strategies.</p>
Satellite-retrieved cloud top radiative cooling data in 2014 over global ocean
<p>Satellite-retrieved cloud-top radiative cooling data in 2014 over global ocean, used in a manuscript submitted to GRL (Zheng et al., 2021, GRL).</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.