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25 results for “network variability”
alexandru-uta/cloud_network_variability_data: Cloud Network Variability Data
<p># Dataset on Cloud Network Variability</p> <p>This is the dataset obtained while benchmarking public and private clouds for the following article:</p> <p>### Alexandru Uta, Alexandru Custura, Dmitry Duplyakin, Ivo Jimenez, Jan Rellermeyer, Carlos Maltzahn, Robert Ricci, Alexandru Iosup. Is Big Data Performance Reproducible in Modern Cloud Networks?. In proceedings of 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2020, February 25-27, Santa Clara, USA.</p> <p>The dataset contains bandwidth variability data for:<br> - Amazon EC2<br> - Google Compute Engine<br> - Microsoft Azure (limited data)<br> - Scaleway (limited data)<br> - SURFsara HPCCloud</p> <p>The dataset contains TCP latency (RTT) data for:<br> - Amazon EC2<br> - Google Compute Engine</p> <p>The dataset contains data regarding token bucket sizes (explained in depth in the aforementioned article) for Amazon EC2.</p> <p>Please note that the full archive is over 3.5 GB of data, made up of hundreds of thousands of small files. This is why we decided to archive the data, which we then split into smaller files, to accommodate for the Github max file size of 100 MB.</p> <p>===== DETAILS ON THE ARCHIVE FORMAT =====</p> <p><strong>1. The Bandwidth Variability data:</strong><br> - archived in the bandwidth_variability_data.tar.bz2.parta, .partb, .partc<br> - after unpacking the archive, the data is split in directories per machine type, and experiment type, for example:<br> --- perfvar-aws-m5xlarge-fullspeed: contains iperf3 output files for continuous communication between 2 m5.xlarge VMs in Amazon EC2.<br> --- perfvar-google-4cpu-bursty-5s30s: contains iperf3 output files for bursty communication (5 seconds communication, 30 seconds break; repeat)</p> <p><strong>2. The Latency Variability data:</strong><br> - archived in the file latency_study.tar.bz2<br> - after unpacking the archive, the directories contain TCP dump RTT data and iperf3 outputs</p> <p><strong>3. The Token Bucket AWS study:</strong><br> - archived in the file token_bucket_study.tar.bz2<br> - contains files of form INSTANCE_TYPE-REGION-TIMESTAMP.{bw, raw, tb}<br> - files with extension .raw contain raw iperf3 client output<br> - files with extension .bw contain bandwidth samples taken at 1 second intervals from the iperf3 utility<br> - files with extension .tb contain a triple of form <time_to_token_bucket_depletion, high_token_bucket_bandwidth, low_token_bucket_bandwidth><br> - example of a file name: c5.2xlarge-us-west-1-1567733720.raw</p> <p> </p>
Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations
<p>This repository provides supplementary material to our paper: <a href="https://doi.org/10.1088/1361-651X/ab7f8c">https://doi.org/10.1088/1361-651X/ab7f8c</a></p> <p><strong>DAMASKPhenoPowerLaw75x75x75TestCase.zip</strong><br> An exemplary DAMASK simulation and corresponding output, generated from DAMASK v2.0.3. We used this to debug more productively the implementation of the post-processing tools. Furthermore we employed this simulation in the paper to identify why the graph clustering grain reconstruction method in many cases fuses neighboring grains in similar orientation.</p> <p><strong>DAMASKPhenoPowerLaw256x256x256ProductionRun.zip</strong><br> All input to run the DAMASK simulation that we discussed in the paper.</p> <p><strong>DAMASKPDTSettings256x256x256ProductionRun.zip</strong><br> All damaskpdt settings files to execute the individual post-processing studies of the paper.</p> <p><strong>DAMASKPDTSlurmSubmissionScripts256x256x256ProductionRun.zip</strong><br> All SLURM scripts we used to execute the compilation of damaskpdt and post-processing on TALOS.</p> <p><strong>DAMASKPDTSlurmLogs256x256x256ProductionRun.zip</strong><br> All logs from the SLURM job management system from the individual post-processing runs.</p> <p><strong>DAMASKPDTSourceCode_USedForAnalyticalDistanceToVoronoiCellFacets.zip</strong><br> The source code to the tool we developed during the revision process of our paper to verify the methods<br> via computing analytically exact distances to the facets of the Poisson-Voronoi tessellation from the<br> DAMASK microstructure instantiation.<br> <br> <strong>DAMASKPDTSourceCode_Production.zip</strong><br> The source code we used to post-process all results from the DAMASK simulations.</p> <p><strong>GitHub repository:</strong><br> https://github.com/mkuehbach/damaskpdt</p>
Data for publication "The ZiCOS-M CO2 sensor network: measurement performance and CO2 variability across Zürich"
<p>Please see README.md for a description of this package. </p> <p>This work was funded by the European Union's Horizon 2020 research and innovation programme, grant agreement number 101037319, named Pilot Applications in Urban Landscapes - towards integrated city observatories for greenhouse gases (PAUL) and is known as ICOS Cities.</p> <p> </p>
Radiometer network dataset of 10 Hz spectral irradiance and derived variables (LIAISE campaign)
<p><strong>Description</strong></p> <p>Measurements from the spatial network of 15 radiometers deployed at the LIAISE field campaign in 2021.</p> <p>14 July 2021 until 28 July 2021.</p> <p><strong>Dataset Contents</strong></p> <ul> <li><em>rsds: </em>Total shortwave downwelling solar irradiance @ 10 Hz, 1 sec, and 1 minute resolution (Level 2, derived dataset)</li> <li><em>prw: </em>Total column integrated water vapour @ 1 sec resolution (Level 2, derived dataset)</li> <li><em>spectrum: </em>Pre-calibrated solar spectral irradiance measurements @ 10 Hz resolution (Level 1, source dataset)</li> <li><em>raw data:</em> straight from the sensors (Level 0)</li> </ul> <p><strong>Dataset Quality</strong></p> <p>All data is quality controlled and completed with metadata and quality flags. Level 2 data is calibrated against high quality references, and derived from Level 1 data.</p> <p>Methodology, performance, and usage all described in detail in an upcoming pre-print.</p> <p><strong>References and more info</strong></p> <ul> <li><a href="https://egusphere.copernicus.org/preprints/2022/egusphere-2022-726/">Radiometer reference paper (FROST)</a></li> <li>LIAISE <a href="https://liaise.aeris-data.fr/">campaign website</a>, <a href="https://liaise.aeris-data.fr/page-catalogue/?uuid=594dbc5d-986e-4679-b0ec-7d1e29b5cab9">LIAISE database</a></li> <li>Dataset description paper: <a href="https://arxiv.org/abs/2307.06980">pre-print on Arxiv</a></li> <li>Code to produce these data <a href="https://doi.org/10.5281/zenodo.10159129">on Zenodo</a></li> </ul> <p><strong>Version History</strong></p> <p>v1.2: fixed bug of incorrect wavelength labeling, all bands but 900 and 940 nm were affected. This only concerns the 10 Hz spectrum data, rest unchanged.</p> <p>v1.1: added raw (level 0) data, fixed typo in file name for the 10 Hz spectrum zip (L2 -> L1).</p>
Data from: Convolutional neural networks trained on internal variability predict forced response of TOA radiation by learning the pattern effect
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Social network position experiences more variable selection than weaponry in wild subpopulations of forked fungus beetles
<p>1. The phenotypic expression and fitness consequences of behaviors that are exhibited during social interactions are especially sensitive to their local social context. This context-dependence is expected to generate more variation in the sign and magnitude of selection on social behavior than that experienced by static characters like morphology. Relatively few studies, however, have examined selection on behavioral traits in multiple populations. 2. We estimated sexual selection in the wild to determine if the strength and form of selection on social phenotypes is more variable than that on morphology. 3. We compared selection gradients on social network position, body size, and weaponry of male forked fungus beetles (Bolitotherus cornutus) as they influenced mating success across nine natural subpopulations. 4. Male horn length consistently experienced positive sexual selection. However, the sign and magnitude of selection on individual measures of network centrality (strength and betweenness) differed significantly among subpopulations. Moreover, selection on social behaviors occurred at local scale ("soft selection"), whereas selection on horn length occurred at the metapopulation scale ("hard selection"). 5. These results indicate that an individual with a given social phenotype could experience different fitness consequences depending on the network it occupies. While individuals seem to be unable to escape the fitness effects of their morphology, they may have the potential to mediate the pressures of selection on behavioral phenotypes by moving among subpopulations or altering social connections within a network.</p>
Pre-generated network files for "Intersecting near-optimal spaces: European power systems with more resilience to weather variability"
<p>These are network files that can be used to investigate the impacts of weather variability on the European power system using PyPSA-Eur as in <a href="https://github.com/aleks-g/intersecting-near-opt-spaces/tree/v1.0">https://github.com/aleks-g/intersecting-near-opt-spaces/tree/v1.0</a>. Find more information about the approach in the README of that repository.</p> <p>These network files are a shortcut to reproduce the results and use a fixed configuration ("v1.0"). For other configurations, it may be necessary to download ERA5 reanalysis cutouts (more on this in the git repository).</p> <p>Instructions can be found in the git repository.</p>
Dataset for Embedded Temporal Convolutional Networks for Essential Climate Variables Forecasting
<p>The dataset contains time series of surface soil moisture at three locations, namely Idaho, Indiana, and Oklahoma.</p> <p>For each location, the folder contains multiple gif images, one for each year, each containing 12 frames corresponding to monthly averages.</p> <p>More information at </p> <p>https://github.com/gtsagkatakis/ETCN</p>
A neural network-based estimate of the seasonal variability of total alkalinity in the East China Sea shelf
<p>In order to estimate the seasonal variability of total alkalinity in the ECS shelf, an artificial neural network (ANN) model was developed using 5 cruise datasets from 2008 to 2018. The model used temperature, salinity, and dissolved oxygen to estimate A<sub>T</sub> with a root-mean-square error of ~7 umol kg<sup>-1</sup>, and was applied to fill missing alkalinity data for 8 cruises during 2013-2016. In addition, monthly water column A<sub>T</sub> for the period 2000-2016 was also obtained passing temperature, salinity, and dissolved oxygen from the Changjiang Biology Finite-Volume Coastal Ocean Model (FVCOM) Data. Spatial distributions, seasonal cycles and correlations of surface A<sub>T</sub> indicated that the seasonal fluctuation of the Changjiang River discharge is the major factor affecting seasonal variation of surface total alkalinity in the ECS shelf. The largest seasonal fluctuation of surface total alkalinity was found on the inner shelf near the Changjiang Estuary.</p>
Social network position experiences more variable selection than weaponry in wild subpopulations of forked fungus beetles
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Data from biotic variability and synchrony across hierarchical levels and freshwater networks
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Data-driven brain network models differentiate variability across language tasks
<p>Data and script associated with the manuscript titled "Data-driven brain network models differentiate variability<br> across language tasks". </p>
Figure 5. Minimum spanning haplotype network derived from a 658 base-pair cytochrome c oxidase subunit I in Six degrees of separation in barnacles? Assessing genetic variability in the sea-turtle epibiont Stomatolepas elegans (Costa) among turtles, beaches and oceans
Figure 5. Minimum spanning haplotype network derived from a 658 base-pair cytochrome c oxidase subunit I (COI) fragment from 57 Stomatolepas elegans collected from nine different Lepidochelys olivacea nesting on Playa Teopa, Jalisco, Mexico, six S. elegans from Caretta caretta from the western Atlantic, and six S. praegustator from C. caretta from the western Atlantic. Circle sizes are proportional to the frequency of each haplotype, with haplotype 1 being most common. Coloured pie slices are also proportional, and represent the number of S. elegans from each turtle characterized by the respective haplotype. Colours represent the nine Mexican turtles randomly sampled for S. elegans populations. Open circles with numbers indicate Atlantic haplotypes. Solid black circles designate hypothetical missing haplotypes. The network includes S. elegans haplotypes 1–21, and S. praegustator haplotypes 19, 26–30. Haplotypes 1–17, shown in colour, represent Jalisco, Mexico specimens collected from nine different turtles in the Pacific, and haplotypes 18–21 and 26–30, shown as unshaded circles, represent southeastern United States Atlantic specimens collected from six different C. caretta (see Table 1).
Variability of Atmospheric CO2 Over the Arctic Ocean: Insights From the O-Buoy Chemical Observing Network
<p>This dataset contains relevant files for the GEOS-Chem chemical transport model simulations used in the following manuscript: Graham, K. A., Friedrich, G., Rauschenberg, C. D., Williams, C. R., Bottenheim, J. W., Chavez, F. P., Halfacre, J. W., Holmes, C. D., Perovich, D. K., Shepson, P. B., Simpson, W. R., Tans, P. P., & Matrai, P. A. (2022). Variability of Atmospheric CO<sub>2</sub> Over the Arctic Ocean. <em>Journal of Geophysical Research: Atmospheres</em>. In Review.</p>
Variability of Ventricular Mass, Volume, & Ejection Fraction in Pediatric Cardiomyopathy Patients-Pediatric Heart Network
ClinicalTrials.gov study NCT00123071. IPD Sharing: Not stated. Countries: 2. Publications: 4.
Data from: Sensitivity of marine protected area network connectivity to atmospheric variability
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Data from: High temporal variability in the occurrence of consumer–resource interactions in ecological networks
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Data from: Environmentally induced changes in correlated responses to selection reveal variable pleiotropy across a complex genetic network
Selection in novel environments can lead to a coordinated evolutionary response across a suite of characters. Environmental conditions can also potentially induce changes in the genetic architecture of complex traits, which in turn could alter the pattern of the multivariate response to selection. We describe a factorial selection experiment using the nematode Caenorhabditis remanei in which two different stress-related phenotypes (heat and oxidative stress resistance) were selected under three different environmental conditions. The pattern of covariation in the evolutionary response between phenotypes or across environments differed depending on the environment in which selection occurred, including asymmetrical responses to selection in some cases. These results indicate that variation in pleiotropy across the stress response network is highly sensitive to the external environment. Our findings highlight the complexity of the interaction between genes and environment that influences the ability of organisms to acclimate to novel environments. They also make clear the need to identify the underlying genetic basis of genetic correlations in order understand how patterns of pleiotropy are distributed across complex genetic networks.
Supplementary material 2 from: Lumbierres M, Kissling WD (2023) Important first steps towards designing the freshwater, marine and terrestrial Essential Biodiversity Variable (EBV) workflows for the European Biodiversity Observation Network. Research Ideas and Outcomes 9: e109120. https://doi.org/10.3897/rio.9.e109120
Agenda of EuropaBON virtual workshop on EBV workflows
Supplementary material 1 from: Lumbierres M, Kissling WD (2023) Important first steps towards designing the freshwater, marine and terrestrial Essential Biodiversity Variable (EBV) workflows for the European Biodiversity Observation Network. Research Ideas and Outcomes 9: e109120. https://doi.org/10.3897/rio.9.e109120
EuropaBON list of Essential Biodiversity Variables
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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.