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FIGURE 2 in Paragoniosomatinae, a new subfamily of Gonyleptidae (Arachnida: Opiliones) based on a new species from the Chapada Diamantina relict cloud forests, Brazil
FIGURE 2. Paragoniosoma cachaceiro gen. nov., sp. nov. A–D. Habitus. A-B. Dorsal view: A. Male (holotype UFPB 733), B. Female (paratype UFPB 732). C-D. Lateral view: C. Male, D. Female. E. Eye-mound, anterior view. F–G. Pedipalp, ventral view: F. male, G. female. H. Coxa IV of male, lateral view, showing curvature of prolateral apical apophysis. Scale bars = 1 mm.
FIGURE 11 in A new species of Rhinophis Hemprich, 1820 (Reptilia: Uropeltidae), from cloud forest of the Knuckles massif of Sri Lanka
FIGURE 11. Holotype of Rhinophis philipsi, BMNH 1946.1.17.2 (formerly 29.2.5.1). Scale bar increments 1 mm. Photos by H. Taylor (BMNH).
FIGURE 5 in A new species of Rhinophis Hemprich, 1820 (Reptilia: Uropeltidae), from cloud forest of the Knuckles massif of Sri Lanka
FIGURE 5. Preserved paratypes Rhinophis gunasekarai sp. nov., (A) dorsal, (B) ventral aspects of NMSL-NH 2020.05.02 (SVL 143 mm), and (C) dorsal, (D) ventral aspects of DWC2020.05.01 (SVL 154 mm).
9-10-1288 Does the viscosity of the sub-phase effect the oxidation kinetics of surface active films on "glassy" cloud droplets
<p>Binned Neutron reflectivity data for the titled experiment.</p>
Resource Management - Cloud Continuum
<p>Dataset and classification of articles and proceedings for Resource Management in the Edge-Fog and Cloud Continuum. Resource management aspects include data aggregation, offloading, P2P data sharing, scheduling (auto-scaling) and load-balancing.</p>
Data and scripts for 'Potential and limitations of machine learning for modeling warm-rain cloud microphysical processes'
<p>Data and scripts for "Potential and limitations of machine learning for modeling warm-rain cloud microphysical processes" by Axel Seifert and Stephan Rasp, J. Adv. Modeling Earth Systems, 12, 2020, https://doi.org/10.1029/2020MS002301</p>
Literature on Cloud Capacity Planning
<p>This release captures the state-of-the-art (as of 2020) in cloud capacity planning literature and provides a set of complementary scripts to analyze this literature. The dataset which is central to this release (<code>publications.yaml</code>) maps 57 cloud capacity planning approaches as published in literature to the taxonomy on cloud capacity planning which the authors of this release have proposed. The approaches were gathered with a systematic literature survey process, aggregating multiple common sources and executing a set of automated and manual filtering steps.</p> <p><strong>Taxonomy</strong></p> <p>The taxonomy and the process used to derive it is described in detail in the MSc Thesis of Georgios Andreadis at Delft University of Technology (to be published end of August 2020), on cloud capacity planning. We describe the taxonomy here to provide context to the raw data.</p> <p>The taxonomy divides the process underlying capacity planning systems into the following categories:</p> <ol> <li>System Model <ul> <li>Workloads</li> <li>Resources</li> <li>Model Inputs</li> </ul> </li> <li>Forecast Model <ul> <li>Modeling Strategy</li> <li>Model Structure</li> </ul> </li> <li>Decision Support <ul> <li>Role</li> <li>Type of Advice</li> <li>Advice Method</li> </ul> </li> </ol> <p>For each of these categories, the taxonomy prescribes a set of possible <em>classes</em> (possible instantiations of the category). We list these for each category, below, preceded by its abbrevation as appearing in the dataset:</p> <ol> <li>System Model <ul> <li>Workloads <ul> <li><code>VM</code>: Virtual Machines</li> <li><code>DB</code>: Databases</li> <li><code>S</code>: Streaming Workloads</li> <li><code>BD</code>: Big Data Frameworks</li> <li><code>WS</code>: Web Service</li> <li><code>B</code>: Batch Jobs</li> </ul> </li> <li>Resources <ul> <li><code>C</code>: Compute Hardware</li> <li><code>S</code>: Storage Hardware</li> <li><code>N</code>: Network Hardware</li> <li><code>E</code>: Energy Hardware (Storage and Supply)</li> <li><code>H</code>: Heat Control Hardware</li> <li><code>V</code>: Virtualized Resources (VM, containers, etc.)</li> </ul> </li> <li>Model Inputs <ul> <li><code>H</code>: Historical Data</li> <li><code>RS</code>: Resource Specifications</li> <li><code>B</code>: (Micro)Benchmarks or Systematic Performance Tests</li> <li><code>S</code>: SLAs</li> <li><code>P</code>: Pricing Data</li> <li><code>LC</code>: Lease Contracts</li> <li><code>HP</code>: Human Personnel-related Factors</li> </ul> </li> </ul> </li> <li>Forecast Model <ul> <li>Modeling Strategy <ul> <li><code>A</code>: Analytical</li> <li><code>S</code>: Simulation</li> <li><code>E</code>: Real-world Experimentation</li> </ul> </li> <li>Model Structure <ul> <li><code>U</code>: Unconditional Extrapolation</li> <li><code>W</code>: What-if Scenarios</li> </ul> </li> </ul> </li> <li>Decision Support <ul> <li>Role <ul> <li><code>F</code>: Forecast</li> <li><code>A</code>: Adaptation Advice</li> </ul> </li> <li>Type of Advice <ul> <li><code>N</code>: Number of Resources</li> <li><code>T</code>: Type of Resources</li> <li><code>L</code>: Locality of Resources</li> </ul> </li> <li>Advice Method <ul> <li><code>H</code>: Heuristic</li> <li><code>R</code>: Regression</li> <li><code>L</code>: Local Search</li> <li><code>SS</code>: Stochastic Search</li> <li><code>SP</code>: Stochastic Programming</li> <li><code>NN</code>: Neural Network</li> <li><code>GT</code>: Game Theory</li> <li><code>GA</code>: Genetic Algorithm</li> <li><code>NLP</code>: (Non)Linear Programming</li> </ul> </li> </ul> </li> </ol> <p><strong>File Structure</strong></p> <p>This release is structured as follows:</p> <ul> <li><code>publications.yaml</code>: This is the dataset of mappings of publications to the taxonomy. Each item in the array represents a publication, with a set of true-false classifications per category for each class. <ul> <li>The <code>id</code> field of each publication identifies the publication (first-author and publication year).</li> <li>The <code>summary</code> field of each publication summarizes the publication in a short sentence.</li> <li>The <code>classification</code> field contains a set of true-false classifications per category for each class.</li> <li>The <code>notes</code> field is an optional field containing any additional notes kept by the author of this dataset on their classification, in the case where doubts arose during the classification process.</li> </ul> </li> <li><code>taxonomy.py</code>: Script which parses the YAML dataset into different CSV views per category, to facilitate meta-analysis. Also prints out a full (long-table) representation of the mappings.</li> <li><code>taxonomy_analysis.py</code>: Jupyter notebook which contains several meta-analysis processing steps, including trend, cluster, and correlation analysis.</li> <li><code>README.md</code>: A file containing this description.</li> </ul>
Data from RACE: Resource Aware Cost-Efficient Scheduler for Cloud Fog Environment
<p>The quality of human life increases with increased usage of the Internet of Things (<i>IoT</i>). However, this massive use of IoT produces a large amount of data which creates a problem for data storage and analysis. Cloud computing is used to solve such issues like storage and processing requirements for data generated by IoT. However, several applications like traffic control, health observation, and games, etc., are time-sensitive and need a quick response. The delay created by sending data to the Cloud and then returning the Server response to the user of such programs has an unintended impact. To overwhelmed these limitations, the Fog computing concept was launched in 2012.</p> <p>Fog computing is an elongation of Cloud computing in which user services are extended to networking devices. In recent times the Fog computing is considered as a favorable approach to cope with the delay-sensitive applications. Moreover, Fog computing in combination with the Cloud computing model can also provide promising solutions to deal with compute-intensive applications. The scheduling challenges faced by the Fog service provider are to fulfill user requests with the maximum resource utilization of Fog devices while reducing the monetary cost of Cloud resource usage. In this research, a resource-aware scheduler has been proposed to distribute the incoming application modules to Fog devices that maximize resource utilization at the Fog layer, reduces the monetary cost of using Cloud resources with minimum execution time of applications, and minimum bandwidth usage.</p> <p>The proposed scheduler Resource Aware Cost-Efficient Scheduler (RACE) consists of two algorithms: ModuleScheduler: which categorizes the incoming application modules according to their computation and bandwidth requirements. The ModuleScheduler also creates a prioritized list considering the categories of application modules. Compare Module: Finds the suitable Fog device for modules execution in the prioritized list. The proposed scheduler (RACE) schedule the modules at the Fog layer based on their computation requirement and bandwidth requirement until the devices at the Fog layer have enough CPU capacity to accommodate the modules.</p>
Supplementary material 1 from: Acosta-Galvis AR, Saldarriaga-Gómez AM, Ramírez B, Vargas-Ramírez M (2020) A new Terrarana frog of genus Pristimantis from an unexplored cloud forest from the eastern Andes, Colombia. ZooKeys 961: 129-156. https://doi.org/10.3897/zookeys.961.51971
Additional specimens examined. IAvH-Am: Alexander von Humboldt Biological Resources Research Institute, Villa de Leyva, Colombia; MPUJ: Lorenzo Uribe Museum of Natural History, Pontificia Universidad Javeriana, Bogotá D.C., Colombia
Natural-Color-Cloud-and-Contrail-Image-Dataset
<p>The natural color cloud and contrail image (NCCI) dataset is a dataset for satellite cloud image super resolution. The NCCI dataset is generated based on Himawari-8 satellite data. The NCCI dataset contains 1100 satellite images, including 1000 natural color images and 100 contrail images. It is worth noting that contrails have consideration in our dataset.</p>
Data from: Linking coordinated hydraulic traits to drought and recovery responses in a tropical montane cloud forest
Understanding plant hydraulic functioning and water balance during drought has become key in predicting species survival and recovery. However, the insightful studies that couple physiological and morphological attributes do not exist in many ecosystems, such as the vulnerable Tropical Montane Cloud Forests (TMCF). In this study, we evaluate drought resistance and recovery for saplings for five tree species spanning deciduous to evergreen habits from a Mexican TMCF. Methods Drought treatments withheld water until plants reached species-specific P50 or P88 values (pressures required to induce a 50 or 88 percent loss in hydraulic conductivity), at which point they were rewatered. Drought resistance were considered within the isohydric-anisohydric framework and compared to leaf gas exchange, water status, pressure-volume curves, specific leaf area, and stomatal density. Results TMCF species closed stomata well before significant losses in hydraulic conductivity (isohydric). Yet, despite the coordination of these traits, they did not predict how long it took species to reach critical hydraulic thresholds. Instead, maximum photosynthesis rates explained these times reinforcing the linkage between hydraulic and carbon dynamics. Despite varying hydraulic conductivities, stomatal responses, and times to hydraulic thresholds, all study plants except for two individuals (out of 60) recovered following rewatering. The recovery of photosynthesis and stomatal conductance was explained by the P50 values and isohydry. Conclusions This study raises new questions surrounding drought management strategies, recovery processes, and how lethal thresholds are defined. Further studies need to consider the role of water and carbon balance in allowing for both survival and recovery to drought.
Climate model data from "Changes in local and global climate feedbacks in the absence of interactive clouds: Southern Ocean-climate interactions in two intermediate-complexity models"
<p>This Dataset contains the model output described in the study<br> "Changes in local and global climate feedbacks in the absence of interactive clouds: Southern Ocean-climate interactions in two intermediate-complexity models"<br> by Pfister and Stocker 2020, published in Journal of Climate.</p> <p>The two zip files contain the model output of the two models Bern3D-LPX and LOVECLIM, in folder structures explained below.</p> <p>Bern3D-LPX:</p> <p>The 3 folders contain model simulations tuned to different ECS values (2, 3 and 6 Kelvin).<br> Each folder contains three subfolders corresponding to three simulations: Control, 2xCO2 and 4xCO2.<br> For each simulation, two netcdf model output files are given: a timeseries file for quick overview of various spatially averaged variables (e.g., global mean temperature), and a full output file for local analyses as done in the study.</p> <p>For the main simulations with an ECS of 3 Kelvin, annual mean output is provided for the first 500 years of each simulation. Thereafter, the full output is available only for selected years, which can be read out from the netcdf time dimension or, e.g., the netcdf variable "baseyear".</p> <p>Simulations with an ECS of 2 and 6 Kelvin are only used for Figure 8 and its discussion, therefore their full output file was written with less yearly outputs than the main simulation with ECS=3 Kelvin to reduce data load.</p> <p><br> LOVECLIM:</p> <p>The 2 folders contain the 2xCO2 and 4xCO2 simulations.<br> No separate Control simulations were made, but the first 1000 years of each simulation are unperturbed and used as a control reference (details in Pfister and Stocker 2020, J.Clim.).</p> <p>The two netcdf files for each simulation correspond to atmospheric variables (atmmmyl_cat.nc) and ocean variables (CLIO3m_cat_CO2_2_regridded.nc). Note that the spatial resolution of the atmosphere and ocean component of LOVECLIM are different. Monthly output is provided for the given variables of the full 2000-year-simulations.</p> <p> </p> <p>For a detailed description how these model outputs were analyzed, please refer to Pfister and Stocker 2020, J. Clim.</p> <p> </p> <p> </p>
Long-term cloud forest response to climate warming revealed by insect speciation history
<p>Montane cloud forests are areas of high endemism, and are one of the more vulnerable terrestrial ecosystems to climate change. Thus, understanding how they both contribute to the generation of biodiversity, and will respond to ongoing climate change, are important and related challenges. The widely accepted model for montane cloud forest dynamics involves upslope forcing of their range limits with global climate warming. However, limited climate data provides some support for an alternative model, where range limits are forced downslope with climate warming. Testing between these two models is challenging, due to the inherent limitations of climate and pollen records. We overcome this with an alternative source of historical information, testing between competing model predictions using genomic data and demographic analyses for a species of beetle tightly associated to an oceanic island cloud forest. Results unequivocally support the alternative model: populations that were isolated at higher elevation peaks during the Last Glacial Maximum are now in contact and hybridising at lower elevations. Our results suggest that genomic data is a rich source of information to further understand how montane cloud forest biodiversity originates, and how it is likely to be impacted by ongoing climate change.</p>
Functional and genetic diversity changes through time in a cloud forest ant assemblage
<p>Climate change in the Neotropics is causing upslope range shifts. We used arrays of ant species collected in a cloud forest at 1,500 m in Área de Conservación Guanacaste (ACG), northwestern Costa Rica, collected in two time periods (1998-2000 and 2008-2011) to measure changes in species richness and diversity over a decade. Using metrics of community structure, we found that the species assemblage in the collections from the 1990s were significantly phylogenetically clustered and functionally less diverse as compared to collections from the early 2000's. At both time points, the assemblages were significantly phylogenetically clustered and while the difference in functional diversity between the time points was not significant, the ant assemblage has become lighter in color (on average) over time. When individual species are considered the overall pattern of replacement is consistent with the cloud forest ant assemblage being colonized by arrivals from lower-elevation forests. The invertebrate communities on cloud forested mountain tops are especially vulnerable to a changing climate as there are two factors working together; no higher terrain to which they can move and the invasion of more and more taxa from lower downslope. This vulnerability is already measurable.</p>
Memory tracing, profiling and autotuning for containerised cloud functions
<p>This dataset contains CSV files with the raw measurements of 4x10 traces as well as scripts and reference data for distinguishing two usage profiles based on raw measurements of 2x20 traces. It accompanies the publication «Resource Management for Cloud Functions with Memory Tracing, Profiling and Autotuning» presented at WoSC6 @ Middleware 2020.</p>
Application Deployment using Containers with Auto-scaling for Microservices in Cloud Environment
<p>This dataset release supports the results presented in the paper "Application Deployment using Containers with Auto-scaling for Microservices in Cloud Environment" by S. N. Srirama, M. Adhikari, and S. Paul, accepted in Journal of Network and Computer Applications, ISSN: 1084-8045, vol. 160, pp. 1-20, 2020. Elsevier.</p> <p>When referring to the dataset please cite the paper above.</p>
Cloud-native Deploy-ability: An Analysis of Required Features of Deployment Technologies to Deploy Arbitrary Cloud-native Applications
<p>Deployment modeling example used in the paper.</p>
Simulation data for the model of the Sagittarius stream in the presence of the Large Magellanic Cloud
<p>This archive contains simulations of the disrupting Sagittarius galaxy in the combined potential of the Milky Way and the Large Magellanic Cloud.</p> <p><strong>Sgr_snapshot</strong><br> contains the final (present-day) snapshot from the fiducial simulation with a triaxial Milky Way halo and M_LMC=1.5e11 Msun (see the readme file in that folder for details).</p> <p><strong>Sgr_snapshot_noLMC</strong><br> contains the same data but for a model without the LMC (which does not reproduce some aspects of the observations, but is nevertheless useful for a comparison with the other one).</p> <p><strong>potentials_triax</strong><br> contains the initial and subsequently evolving potentials of both the Milky Way and LMC, represented by multipole expansions, as well as the trajectory of the LMC and the reflex motion-induced acceleration of the Milky Way -- everything that is needed to study the dynamics of the Sgr stream and other objects in a time-dependent potential of the interacting Milky Way and LMC. This model corresponds to the stream simulation in the previous folder.</p> <p><strong>potentials_axisym</strong><br> contains the same data, but for another Milky Way halo model, which is axisymmetric rather than triaxial (note that in either case, its axis ratios vary with radius). It may be more convenient in certain applications, and produces a stream that fits the observations almost as well as the triaxial model.</p> <p><strong>scripts</strong><br> contains the Python scripts illustrating how to integrate orbits in a time-dependent potential, and how to construct initial conditions for these simulations (the parameter files for various choices of Milky Way halo potentials are also included). These scripts use the Agama framework, available at http://agama.software</p>
FIGURE 10 in A new species of planthopper in the genus Myxia (Hemiptera: Auchenorrhyncha Cixiidae) from the Reserva Privada el Silencio de Los Angeles Cloud Forest in Costa Rica
FIGURE 10. Aedeagus and periandrium of Myxia baynardi sp. n.: (A) right lateral view, (B), left lateral view, (C) dorsal view, and (D) ventral view.
FIGURE 5 in A new species of planthopper in the genus Myxia (Hemiptera: Auchenorrhyncha Cixiidae) from the Reserva Privada el Silencio de Los Angeles Cloud Forest in Costa Rica
FIGURE 5. Adult Myxia baynardi sp. n.; (A) male head, pronotum, and mesonotum dorsal view, (B) male head and pronotum frontal view, and (C) male head, pronotum and mesonotum lateral view, scale = 1 mm.
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.