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136 results for “Analytical data”
Multi-Source Distributed System Data for AI-powered Analytics
<p><strong>Abstract:</strong></p> <p>In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems.<br> The major contributions have been materialized in the form of novel algorithms.<br> Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms.<br> Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance. <br> Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research.<br> Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation.</p> <p><strong>General Information:</strong></p> <p>This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset.</p> <p>You may find details of this dataset from the original paper:</p> <p><em>Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics". </em></p> <p><strong>If you use the data, implementation, or any details of the paper, please cite!</strong></p> <p> </p> <p>BIBTEX:</p> <p>_________________________________________</p> <pre>@inproceedings{nedelkoski2020multi, title={Multi-source Distributed System Data for AI-Powered Analytics}, author={Nedelkoski, Sasho and Bogatinovski, Jasmin and Mandapati, Ajay Kumar and Becker, Soeren and Cardoso, Jorge and Kao, Odej}, booktitle={European Conference on Service-Oriented and Cloud Computing}, pages={161--176}, year={2020}, organization={Springer} } </pre> <p>___________________________</p> <p>The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth. We provide two datasets, which differ on how the workload is executed. The <em><strong>sequential_data</strong> </em>is generated via executing workload of sequential user requests. The <strong><em>concurrent_data </em></strong>is generated via executing workload of concurrent user requests.</p> <p>The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). <strong>In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format.</strong></p> <p><strong><strong>Important:</strong> The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods. Please read the IMPORTANT_experiment_start_end.txt file before working with the data.</strong></p> <p>Our GitHub repository with the code for the workloads and scripts for basic analysis can be found at: <a href="https://github.com/SashoNedelkoski/multi-source-observability-dataset/">https://github.com/SashoNedelkoski/multi-source-observability-dataset/</a></p>
Repository Analytics and Metrics Portal (RAMP) 2021 data
<p>The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (<a href="http://ramp.montana.edu/">http://rampanalytics.org</a>), consisting of data from all participating repositories for the calendar year 2021. For a description of the data collection, processing, and output methods, please see the "methods" section below.</p> <p>The record will be revised periodically to make new data available through the remainder of 2021.</p>
Data for: Analytical transfer function for volcano deformation with T-dependent viscoelasticity
<p>Rocks can modulate the triggering, duration, and style of volcanic eruptions. When heated, the host rocks surrounding a magmatic reservoir is typically considered as a viscoelastic material. The viscoelastic rheology (viscosity especially) is temperature dependent; however, the dynamics and consequence on surface deformation resulting from heterogeneous crustal temperature and viscosity around magmatic reservoirs have not been explored systematically. </p> <p>This dataset incorporates the parameters and numerical codes used for generating results in the manuscript 'History-dependent volcanic ground deformation from broad-spectrum viscoelastic rheology around magma reservoirs' submitted to GRL and authord by Yang Liao, Leif Karlstrom, and Brittany Erickson. The dataset consists of a README file detailing the data structure, a matlab .mat file that contains the parameters assumed in the magma chamber model, and several matlab program .m files that can be applied to the .mat file to generate results presented in the manuscript. </p>
Input data - Wasteaware Cities Benchmark Indicators - WABI 2023 - Global data analytics
<p>This is the input dataset for the research publication "<em>Socio-economic development drives solid waste management performance in cities: A global analysis using machine learning</em>". It features </p> <ul> <li>Metadata info used by R codes</li> <li>Full data set for the WABI, used by the R codes</li> <li>Data required for plotting the map in Figure 1</li> </ul> <p>The independent variables data set refers to specific indicators of the WABI methodology (<a href="https://www.sciencedirect.com/science/article/pii/S0956053X14004905">https://www.sciencedirect.com/science/article/pii/S0956053X14004905</a>) which generates solid waste management and resource recovery profiles for cities. It is applied here for 40 cities around the world. The data set contains also values for a series of explanatory variables, which are measures of the level of socioeconomic development at country level.</p> <p> </p> <p> </p> <p> </p>
Supplementary Datasets for: 'A processing and analytics system for microscopy data workflows: the Pycroscopy ecosystem of packages'
<p>The repository contains four independent datasets that are a part of the publication (<a href="https://arxiv.org/abs/2302.14629">arXiv:2302.14629</a>), which delineates the capabilities of the Pycroscopy ecosystem of packages. The details of the individual datasets can be found below. </p> <p>1) bfo_iv_final.hf5: Dataset of I-V curves captured by conductive atomic force microscopy on a BiFeO3 sample. The data has been transformed so that we plot not the log of the current density (J) as a function of the square root of the electric field. The dataset was originally presented in the paper 10.1038/s41467-017-01334-5 </p> <p>2) bto_atomic.dm3: Atomically resolved data BaTiO3 thin film acquired with scanning transmission electron microscopy. These were originally captured in the dm3 file format. This dataset was a part of the publication: doi.org/10.1002/adma.202106426</p> <p>3) EELS_STO.dm3: Scanning transmission electron microscope (STEM)-Electron energy loss spectroscopy (EELS) dataset of SrTiO3.</p> <p>4) STO-stack.h5: High-angle annular dark-field imaging (HAADF) scanning transmission electron microscope (STEM) image stack of SrTiO3. This image stack contains 25 images.</p> <p> </p>
An analytical study of capillary rise dynamics: Critical conditions and hidden oscillations - Research Data
<p>This dataset provides the research data and software for the publication:</p> <p>M. Fricke, E. Ouro-Koura, S. Raju, R. von Klitzing, J. De Coninck, D. Bothe: An analytical study of capillary rise dynamics:<br> Critical conditions and hidden oscillations</p> <p>which was published in the Journal Physica D: Nonlinear Phenomena. The experimental data for the capillary rise of silicon oil, ethanol and ether has been extracted from the publication</p> <p>D. Quéré: Inertial capillarity, EPL 39 533, DOI:10.1209/epl/i1997-00389-2 (1997)</p> <p>using image analysis methods. It is stored in the csv files Silicon_Oil_Quere1997, Ethanol_Quere1997.csv and Ether_Quere1997. Please use the Python scripts silicon-oil.py, ethanol.py and ether.py to solve the ordinary differential equation model and to generate the plots presented in our publication. The Python library matplotlib was used for plotting. A simple explicit Euler scheme proved sufficient to solve the ordinary differential equation in its dimensionless form. It is implemented in the file capRiseOdeSolver.py.</p>
H2020 ENODISE Analytical Data Set configuration C ECL
<p>Preliminary wake-interaction noise and steady-loading noise estimates for a contrarotating propeller system, using analytical modeling.</p>
Data and analytical codes for: Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure
<p>Data and analytical codes for: Ishizawa et al. (2023) Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure, bioRxiv, 2023.07.04.546222</p> <p> https://www.biorxiv.org/content/10.1101/2023.07.04.546222v1</p> <p> </p>
Mobile Technology and Data Analytics to Identify Real-time Predictors of Caregiver Well-Being
ClinicalTrials.gov study NCT04556591. IPD Sharing: YES. Countries: 1. Publications: 4.
Repository Analytics and Metrics Portal (RAMP) 2021 data
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Data for: Analytical transfer function for volcano deformation with T-dependent viscoelasticity
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Data, analytical, and plotting codes needed to reproduce meta-analyses on within-season divorce in birds
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Data from: Meta-analytical evidence for frequency-dependent selection across the tree of life
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Repository Analytics and Metrics Portal (RAMP) 2020 data
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A 2.5-year campus-level smart meter database with equipment data for energy analytics
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Analytical and supplemental data from: Ammonites as paleothermometers: Isotopically reconstructed temperatures of the Western Interior Seaway track global records
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Meta-analytical data on soil organic, particulate organic, and mineral-associated organic carbon under nitrogen fertilization, elevated atmospheric carbon dioxide, atmospheric warming, increased precipitation, drought, and their combined effects
Data were harvested from journal articles found on the Web of Science Core Collection and the ProQuest Agricultural and Environmental Database that studied soil organic matter fraction carbon responses to global changes (nitrogen fertilization, elevated atmospheric carbon dioxide, atmospheric warming, increased and decreased precipitation, and combined effects). Soil organic carbon fractions were designated as particulate organic carbon or mineral-associated organic carbon based on size and density cutoffs. Relevant metadata, including article information (authors, publication year), environmental information (soil type, climate, and land use), and experiment information (rates, methods) were also added to the dataset.
Data for Mahgoub et al. analytic pipeline
<p>Data for Mahgoub et al. analytic pipeline</p>
BigFuzz: Efficient Fuzz Testing for Data Analytics using Framework Abstraction
<p>BigFuzz supplementary material.</p>
The observed data used in paper titled "An analytic method for calculating parameters of the van Genuchten model for soil water retention curve"
<p>In the file, the 46 soil samples from UNSODA were used to test the proposed method of estimating the parameters of VG model, including the physical and hydraulic properties data, PSD, SWRC, saturated hydraulic conductivity, porosity and saturated water content. Also, this data file includes some process data and results data, <em>D</em><sub><em>i</em>, psd</sub>, predicted <em>K<sub>s</sub></em> , the data relate to <em>a </em>and <em>n </em>and sensitivity analysis.</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.