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2,170 results for “clouds”
Figure 6 in The Trichoptera of Panama. VI. Seven new species of microcaddisflies (Insecta: Trichoptera: Hydroptilidae) from Mount Totumas Cloud Forest and Biological Reserve
Figure 6. Neotrichia atopa sp. n., male genitalia. A) Segments VIII–X, lateral (base of phallus crosshatched). B) Segments IX–X, dorsal. C) Segment IX, ventral. D) Phallus, lateral. E) Phallus, dorsal.
Figure 2 in The Trichoptera of Panama. VI. Seven new species of microcaddisflies (Insecta: Trichoptera: Hydroptilidae) from Mount Totumas Cloud Forest and Biological Reserve
Figure 2. Costatrichia devestiva sp. n., male genitalia. A) Segments VIII–X, and segment VII mesoventral process, lateral (base of phallus crosshatched). B) Segments IX–X, dorsal. C) Segments VIII–IX and segment VII mesoventral process, ventral. D) Phallus, lateral phallus, dorsal.
3D Point Cloud of a railway slope - MOMIT (Multi-scale observation and monitoring of railway infrastructure threats) EU project - H2020-EU.3.4.8.3. - Grant agreement ID: 777630
<p>3D point cloud of a railway trench in Lavancia-Épercy (France). The 3D point cloud has been generated from pictures obtained by means of a UAV (DJI Matrice 600 Pro) and processed using Agisoft Metashape. The 3D point cloud is composed of 110,356,682<strong> </strong> million points containing XYZ and RGB information.</p> <p>The original file is in .bin format and is compressed in zip format.</p> <p> </p>
Rt-Cloud Sample Project 'AmygActivation' DICOM Data
<p>This upload contains the same data as published in <a href="https://doi.org/10.5281/zenodo.3677090">our previous zenodo dataset upload</a>. Unlike our previous upload, this version contains data after transferring the DICOMs directly from the Siemens Skyra 3T to our Linux machine (as done in real-time experiments). The purpose of this separate upload is to serve as sample data for our <a href="https://github.com/brainiak/rt-cloud">real-time cloud software</a>, for a <a href="https://github.com/amennen/amygActivation">specific sample project</a>. The brain data are contributed by author S.A.N. and are authorized for non-anonymized distribution.</p>
Dataset for "Atmospheric oxygen isotopic fractionation in clouds: a bin–resolved microphysics model approach"
<p>This dataset contains the raw model outputs for the paper titled "Atmospheric oxygen isotopic fractionation in clouds: a bin–resolved microphysics model approach" by Thibault Hiron and Andrea Flossmann.</p> <p>The structure of the data and the explaination for the filenames are to be found in the ReadMe.txt file.</p>
3D cloud metrics for OCO-2 observations
<p>The dataset contain contains supplementary data and the 3D cloud metrics described and analyzed in "Analysis of 3D Cloud Effects in OCO-2 XCO2 Retrievals”, Massie, S. T., Cronk, H., Merrelli, A., Schmidt, K. S., Chen, H., and Baker, D., Atmospheric Measurement Techniques, 14, 1475–1499, 2021.</p> <p>https://doi.org/10.5194/amt-14-1475-2021</p> <p>The supplementary data files include the average OCO-2 - TCCON XCO2 differences from several key figures in the manuscript (6, 7, 12). The OCO-2 data was extracted from Version 10 XCO2 retrievals from a subset of the data record used for testing and development by the OCO-2 algorithm team.</p> <p><br> figure6_ocean_distkm.dat<br> figure6_land_distkm.dat<br> These text files contain the average OCO-2 - TCCON XCO2 differences for several variations of the OCO2 XCO2 (raw and bias corrected, quality filter 0 or 1) as a function of the cloud distance metric. The first file includes only OCO-2 ocean glint data, and corresponds to Figure 6 from the manuscript. The second file contains the equivalent data for OCO-2 land (not plotted in the manuscript).</p> <p> </p> <p>figure7_ocean_csnoiseratio.dat<br> figure7_land_csnoiseratio.dat<br> These text files contains the average OCO-2 - TCCON XCO2 differences for several variations of the OCO-2 XCO2 (raw and bias corrected, quality filter 0 or 1) as a function of the color slice noise ratio. The first file includes only OCO-2 ocean glint data, and corresponds to Figure 7 from the manuscript. The second file contains the equivalent data for OCO-2 land (not plotted in the manuscript).</p> <p> </p> <p>figure12_ocean.dat<br> figure12_land.dat<br> These text files contain the average OCO-2 - TCCON XCO2 differences for several variations of the OCO-2 XCO2, same as above, but as a function of both the cloud distance metric and the color slice noise ratio. The x-variable is the cloud distance metric, and the y-variable is the colorslice noise ratio.<br> The first file includes only OCO-2 glint data, and corresponds to Figure 12 from the manuscript. The second file contains the equivalent data for OCO-2 land (not plotted in the manuscript).</p> <p> </p> <p><br> 3D cloud metric data:<br> 3d_cloud_metrics_oco2_v9_2014.zip<br> 3d_cloud_metrics_oco2_v9_2015.zip<br> 3d_cloud_metrics_oco2_v9_2016.zip<br> 3d_cloud_metrics_oco2_v9_2017.zip<br> 3d_cloud_metrics_oco2_v9_2018.zip<br> 3d_cloud_metrics_oco2_v9_2019.zip</p> <p>These files are created with observation arrays that match those contained within the version 9 OCO-2 Lite XCO2 product available at NASA Earthdata:</p> <p>OCO-2 Science Team/Michael Gunson, Annmarie Eldering (2018), OCO-2 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, Retrospective processing V9r, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), 10.5067/W8QGIYNKS3JC</p> <p>Each file is a daily aggregrate, with matched observation lists to the parent version 9 Lite XCO2 files. Each file contains a copy of the sounding_id from the original Lite product files. These files are grouped into yearly zip files to facilitate easier file transfer. Each daily file is in netCDF4 format with appropriate metadata to describe the fields and their units. The filenames are derived from the parent version 9 Lite XCO2 file with "3Dmetrics" added as a suffix. For example, the first file from 2014 is named "oco2_LtCO2_140906_B9003r_180927215925s_3Dmetrics.nc4" which is derived from the Version 9 Lite product file "oco2_LtCO2_140906_B9003r_180927215925s.nc4".<br> </p>
Automated vegetation cover estimation from close-range photogrammetric point clouds in mountain terrain for comparison of vegetation location properties - Dataset
<p>Vegetation cover data of the used plots, showing values for manually digitized, in-situ, and photogrammetric methods.</p>
Data for the publication "How frequent is natural cloud seeding from ice cloud layers (<-35°C) over Switzerland?"
<p>This repository contains the data for the paper:</p> <p>Authors: Ulrike Proske, Verena Bessenbacher, Zane Dedekind, Ulrike Lohmann, and David Neubauer<br> Titel: How frequent is natural cloud seeding?<br> Date: 2020</p> <p>Note that the scripts arezeno to be found in the accompanying package (https://doi.org/10.5281/zenodo.3987754)</p>
Accounting for New Types of Resource Consumption in a Federated Cloud
<p>As infrastructures and cloud services evolve, resource consumption is more flexible and users are often allowed to reserve resources without actual consumption. Relevant standardization bodies have developed new types of accounting record specifications, and new or updated tools are required to keep track of resource usage. The reaction to this is the development of a new accounting tool – GOAT.</p> <p>GOAT – GO Accounting Tool – is a service running in the background and waiting for a connection from a compatible client. The client connects to a cloud management framework, extracts computing data about projects, servers, networks, and storages, filters them accordingly, and sends them to a server for further processing. Multiple clients can use the server at once. When the server receives accounting data, it is transformed into the configured format and writes them to the destination file. The consumer collects data into a central accounting database where it is processed to generate statistical summaries.<br> For now, the GOAT project supports two cloud computing platforms on the client side – OpenNebula and Openstack. Thanks to its use of standard accounting record formats it can work with different consumers. The ones used in the real world are APEL and Prometheus.</p> <p>This Demonstration shows how the GOAT client extracts accounting data from a cloud management platform, sends them to the GOAT server where they are transformed, and how Prometheus and Grafana process them and present various views of resource usage.</p>
The Complex Geometry and Dynamical Role of Stellar Wind Bubbles in Turbulent Molecular Clouds
<p>Research Data Management Package for paper in Monthly Notices of the Royal Astronomical Society with same title and author list</p>
A Survey on the Adoption of Patterns for Engineering Software for the Cloud - Response Dataset
<p>This work takes as a starting point a collection of patterns for engineering software for the cloud and tries to find how they are regarded and adopted by professionals. We investigate (1) their relevance for professional software developers, (2) the extent to which product and company characteristics influence their adoption, and (3) how adopting some patterns might correlate with the likelihood of adopting others. For this purpose, we surveyed 102 practitioners using an online questionnaire. </p> <p>Amongst other findings, we conclude that most companies are using these patterns, with the overwhelming majority (97%) using at least one. We observe that the mean pattern adoption tends to increase as companies mature, namely when varying the product operation complexity, active monthly users, and company size. Finally, we establish clear correlations in the adoption of specific pairs of patterns, with conditional probabilities as high as 94%, which hints on how some practices are dependent or influence the adoption of others.</p>
Fig. 1 in A new spider genus (Araneae: Linyphiidae: Erigoninae) from a tropical montane cloud forest of Mexico
Fig. 1. Xim trenzado gen. et sp. nov. A–C, G. ♁ (ECOTAAR-004950). D–F, H. ♀ (ECOTAAR-005063). A–F. Habitus. A, D. Dorsal view. B, E. Lateral view. C, F. Ventral view. G, H. Carapace in frontal view. Scale bars: A–F = 0.25 mm.
Fig. 4 in A new spider genus (Araneae: Linyphiidae: Erigoninae) from a tropical montane cloud forest of Mexico
Fig. 4. Xim trenzado gen. et sp. nov. A–B. ECOTAAR-004950. C–F, H–I. ECOTAAR-004962. G. ECOTAAR-004952. A–G. Male left femur I. D–G. Details of macrosetae. A, D. Dorsal view. B, E. Prolateral view. C, F. Ventral view. G. Apical view. H. Tarsal organ of leg I. I. Trichobothrium of retrolateral palpal tibia. Scale bars: A–C = 70 μm; D–F = 30 μm; H = 5 μm; I = 10 μm.
Fig. 2 in A new spider genus (Araneae: Linyphiidae: Erigoninae) from a tropical montane cloud forest of Mexico
Fig. 2. Xim trenzado gen. et sp. nov., male left palpus. A–C. ECOTAAR-004974. D–E, G–I. ECOTAAR-004950. F. ECOTAAR-004952. A, D. Mesal view. B, E. Ventral view. C, F. Ectal view. G. Dorsal view. H. Apical view. I. Detail of E showing radix and embolus. Scale bars: A–E = 50 μm; F = 20 μm.
Fig. 7 in A new spider genus (Araneae: Linyphiidae: Erigoninae) from a tropical montane cloud forest of Mexico
Fig. 7. Strict consensus tree of the eight most parsimonious trees, showing only the distal clades where Xim trenzado gen. et sp. nov. is situated. Tree with unambiguous character optimization; character and character-state numbers given above and below marks, respectively; color of marks denotes homoplasious (white) or non-homoplasious (black) character-state changes.
Fig. 5 in A new spider genus (Araneae: Linyphiidae: Erigoninae) from a tropical montane cloud forest of Mexico
Fig. 5. Xim trenzado gen. et sp. nov., ♀, epigynum. A–B. ECOTAAR-005063. C–F. ECOTAAR-005183. A, G. Ventral view. B. Posterior view. C–D, H. Dorsal view. D. Detail of C showing left copulatory duct and spermatheca. E. Antero-dorsal view. F. Detail of E showing left copulatory duct and spermatheca. Scale bars: A–C, E, G–H = 40 μm; D, F = 20 μm.
Fig. 6 in A new spider genus (Araneae: Linyphiidae: Erigoninae) from a tropical montane cloud forest of Mexico
Fig. 6. Strict consensus tree of the eight most parsimonious trees, showing only the distal clades where Xim trenzado gen. et sp. nov. is situated. Tree with Bremer support values noted beside nodes.
Data for: Scalable and Live Trace Processing with Kieker Utilizing Cloud Computing
<p>Knowledge of the internal behavior of applications often gets lost over the years. This circumstance can arise, for example, from missing documentation. Application-level monitoring, e.g., provided by Kieker, can help with the comprehension of such internal behavior. However, it can have large impact on the performance of the monitored system. High-throughput processing of traces is required by projects where millions of events per second must be processed live. In the cloud, such processing requires scaling by the number of instances.</p> <p>In this paper, we present our performance tunings conducted on the basis of the Kieker monitoring framework to support high-throughput and live analysis of application-level traces. Furthermore, we illustrate how our tuned version of Kieker can be used to provide scalable trace processing in the cloud.</p> <p>This is the dataset containing the results of our conducted benchmarks.</p>
Autonomic Provisioning and Application Mapping on Spot Cloud Resources
<p>1. Attached files: </p> <p>500_100_fmincon_0.95_1.mat <br /> Experiment with 500 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>500_100_heuristic_0.95_1.mat<br /> Experiment with 500 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2000_70_fmincon_0.95_1.mat<br /> Experiment with 2000 users, 70ms max response time, 95% availability, exact algorithm.</p> <p>2000_70_heuristic_0.95_1.mat<br /> Experiment with 2000 users, 70ms max response time, 95% availability, our algorithm.</p> <p>2000_100_fmincon_0.9_1.mat<br /> Experiment with 2000 users, 100ms max response time, 90% availability, exact algorithm.</p> <p>2000_100_heuristic_0.9_1.mat <br /> Experiment with 2000 users, 100ms max response time, 90% availability, our algorithm.</p> <p>2000_100_fmincon_0.95_1.mat <br /> Experiment with 2000 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>2000_100_heuristic_0.95_1.mat <br /> Experiment with 2000 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2000_100_fmincon_0.999_1.mat <br /> Experiment with 2000 users, 100ms max response time, 99.9% availability, exact algorithm.</p> <p>2000_100_heuristic_0.999_1.mat <br /> Experiment with 2000 users, 100ms max response time, 99.9% availability, our algorithm.</p> <p>2000_300_fmincon_0.95_1.mat <br /> Experiment with 2000 users, 300ms max response time, 95% availability, exact algorithm.</p> <p>2000_300_heuristic_0.95_1.mat <br /> Experiment with 2000 users, 300ms max response time, 95% availability, our algorithm.</p> <p>10000_100_fmincon_0.95_1.mat <br /> Experiment with 10000 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>10000_100_heuristic_0.95_1.mat <br /> Experiment with 10000 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2. Data format:</p> <p>MATLAB data format, can be load from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</p>
Model-driven Application Refactoring to Minimize Deployment Costs in Preemptible Cloud Resources
<p>1. Attached files: </p> <p>This archive contains 1440 MATLAB files, each one containing the results of a single experiment.<br /> The name of each file follows the following format:</p> <p> A_B_C_null_D_0.95_E_F.mat</p> <p>Where the fields A, B, C, D, E, and F are described as follows.</p> <p>A: number of users.<br /> Considered values are: 2500, 5000, 10000, 20000, 40000.</p> <p>B: variation in the profile of the requests.<br /> Considered values are: ref, var1, var2, var3, var4, var5.<br /> ref -> reference experiment, no users are halved or doubled<br /> var1 -> users in class 1 halved, other users doubled <br /> var2 -> users in class 2 halved, other users doubled <br /> var3 -> users in class 3 halved, other users doubled <br /> var4 -> users in class 4 halved, other users doubled <br /> var5 -> users in class 5 halved, other users doubled </p> <p>C: variation of the replaceability set of the application server.<br /> Considered values are: ref, k1, k2, k3, k4, k5.<br /> ref -> reference experiment, no rates are halved or doubled<br /> k1 -> only possible substitution has rate k1 halved and other rates doubled<br /> k2 -> only possible substitution has rate k2 halved and other rates doubled <br /> k3 -> only possible substitution has rate k3 halved and other rates doubled <br /> k4 -> only possible substitution has rate k4 halved and other rates doubled <br /> k5 -> only possible substitution has rate k5 halved and other rates doubled </p> <p>D: variation of the design constraints.<br /> Considered values are: none, 4, 34, 234.<br /> none -> no components can be replicated<br /> 4 -> only the application server can be replicated<br /> 34 -> only the application server and the database server can be replicated<br /> 234 -> all the components can be replicated</p> <p>E: Optimization algorithm.<br /> Considered values are: norefactoring, replacement, reassignment, full<br /> norefactoring -> experiment with no refactorings<br /> replacement -> experiment with only replacement refactoring<br /> reassignment -> experiment with only reassignment refactoring<br /> full -> experiment with replacement and reassignment refactorings</p> <p>F: Experiment seed.<br /> Considered values are from 1 to 20</p> <p> </p> <p>2. Data format:</p> <p>MATLAB data format, can be loaded from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</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.