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104 results for “Network Measurement”
CSRM Level 2 dataset: Seismometer orientation measurements of broadband seismic stations in the China Digital Seismograph Network
<p>This dataset contains detailed information on the azimuths of more than one thousand stations in the China Digital Seismic Network (CDSN) since 2014. Deng <em>et al</em>. (2024) utilized 5,456,816 three-component waveform data recorded by 1,056 broadband seismic stations of the CDSN from 2014 to 2022 to evaluate and correct the azimuths of the network. The primary research method employed was far-field <em>P</em>-wave polarization analysis, including principal component analysis and minimum tangential energy methods. By integrating the advantages of these two methods, they conducted a detailed analysis of the azimuths across the CDSN and carried out in-depth examinations and cause analyses for stations with significant azimuth deviations (>5°). Through a comprehensive analysis of the calculation results, network operation logs, and on-site inspections, they obtained detailed information on the azimuths of more than one thousand stations in the CDSN since 2014. <strong>Appendix I</strong> lists azimuth deviations for 956 stations, while <strong>Appendix II</strong> documents temporal variations in the azimuths for 104 stations.</p> <p>本数据库包含自 2014 年以来中国数字地震台网超过千个台站方位角的详细信息。Deng等 (2024) 依托中国数字地震台网 2014 至 2022 年间 1056 个宽频带地震台站所记录的 5,456,816 个三分量波形数据, 开展了台网方位角的评估与校正工作。研究方法主要采用远场 <em>P</em> 波偏振分析, 包括主成分分析和最小切向能量法。结合这两种方法的优点, 他们对中国数字地震台网的方位角进行了详细分析, 并对方位角偏差较大的台站 (>5°) 进行了深入检查与原因分析。通过对计算结果、台网运维日志及现场检查的综合分析, 他们获得了自 2014 年以来中国数字地震台网超过千个台站方位角的详细信息 (<strong>附件一</strong>列出了 956 个台站的方位角偏差, <strong>附件二</strong>记录了 104 个随时间变化的方位角信息) 。</p> <p><strong>Reference</strong>: Deng, W., Han, G., Li, J., & Sun, L. Seismometer Orientation Measurements of Broadband Seismic Stations in the China Digital Seismograph Network. <strong><a href="https://doi.org/10.1785/0120240075">Paper link</a></strong></p> <p>If you face any problem or issue in the usage of this dataset, please feel free to communicate with the corresponding author Juan Li (<strong>juanli@mail.iggcas.ac.cn</strong>).</p>
Video Supplement for Himes et al. (2024): "Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements"
<p>This archive contains the video supplement for</p> <p>Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements</p> <p>by Himes et al. (2024), submitted to Atmospheric Measurement Techniques. The file contains an animation of the V2.1 and NRT average retrieved extinction coefficient between 19.5--21.5 km at 997 nm for the 2024 Ruang eruptions.</p>
Application of inverse theory for high spatial resolution reconstructions of thermospheric vector wind fields from Doppler shifts measured by a ground-based network of all-sky Fabry-Perot interferometers
<p>Several types of all-sky viewing Fabry-Perot Interferometers (FPI) have been developed since the 1990s for ground-based remote sensing of thermospheric winds. The Scanning Doppler Imager (SDI) is one such instrument, which provides temporally simultaneous line-of-sight observations from hundreds of independent look directions per instrument exposure. A geographically distributed network of such instruments increases spatial coverage and, at many locations, also provides overlapping observations along multiple independent lines-of-sight. Together, these characteristics significantly increase the density and fidelity that is possible for reconstructed thermospheric vector wind fields, compared to a traditional narrow-field FPI, but at the cost of complexity and difficulty.<br><br></p> <p>Presently, we describe an application of inverse theory to reconstruct three-component vector thermospheric neutral wind fields using data from multiple SDI instruments. The salient features of the method used here are the ability to reconstruct three-component winds on a dense grid that is sampled regularly in latitude, longitude, and time, without assuming any a-priori underlying structure of the winds. This requires solving an inverse problem that does not in general yield a unique solution unless additional constraints are enforced. We describe this step, also known also as regularization, along with the strategy used to maximize the spatial resolution of the derived wind fields by automatically determining the minimum level of regularization that can produce stable inversions. We present example results obtained from applying this technique to one night of data from a network of SDIs in Alaska, and discuss the implications of these results for current understanding of thermospheric dynamics.</p>
Development of a Multi-Level Dynamic Model to Measure the Resilience Level of Transportation Infrastructure Networks
<p>The recent increase in disasters is making the largest critical infrastructure system namely the transportation infrastructure system susceptible to unexpected damage. Discontinuation of services provided by transportation infrastructures will create significant societal, economic, and collateral damages. Therefore, this study aims to identify dimensions to measure the resilience of the transportation infrastructures. This study also aims to develop a model to measure the resilience of the transportation infrastructures resilience. To fulfill the aims of this study, a questionnaire was developed which was supported by a comprehensive literature review. 92 valid responses were received and analyzed qualitatively and quantitatively. Statistically significant variables were used to develop a resilience measurement tool. The developed tool will provide relative resilience measures for multiple projects which will help in identifying the most vulnerable segment of the transportation infrastructure network. Exploratory factor analysis (EFA) was performed to identify the constructs and structural equation modeling (SEM) was used to develop the model. Without previous experience in reconstruction works, handling integrated assets becomes very critical. Also, such inexperience makes it difficult to handle emergency resources properly. However, such issues regarding integrated assets can be resolved by investing in locating integrated assets away from the roadways, so if a break in a railroad crossing or utility line occurs or emergency repairs are needed, the impact on the roadway operations can be minimized. To avoid issues related to access to previous disaster data for the roadway this study suggess investing in preparing an interactive online platform for recording and reviewing data related to disasters as well as previous resilience enhancing activities for the roadway with easy access credentials. The findings of this study will support practitioners and decision-makers in investing in the appropriate resilience enhancement activity project for funding and investment.</p>
Virtual Axle Detector based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network
<p>We recorded the measurement data used in the present study on a single-span steel trough railway bridge located on a long-distance traffic line in Germany. The bridge is 18.4 m long in total with a free span of 16.4 m. A total of 10 seismic uniaxial accelerometers of the type PCB-39B04 (PCB Synotech) with a sensitivity of 1000 mV/g (±10\%), a broadband resolution of 0.000003 gRMS, a measurement range of ±5 gpk and a frequency range of 0.06 to 450 Hz (±5\%) were installed. The measurements are triggered via the rising slope of the wheel load measuring point G1, the measurements from the ring buffer are stored from ten seconds before the trigger together with the 50 seconds long measurement after the triggering. The recorded signals thus all have a length of 60 seconds. All sensor signals were recorded with a sampling frequency of fs = 600 Hz using the catmanAP software and the CX22 data recorder connected to an MX1601B universal amplifier and an MX1616B strain gauge amplifier (all products are from HBK). </p>
LENS: A LEO Satellite Network Measurement Dataset - 202404 - Part 2
<p>LENS dataset 2024-04 Part 2</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset (CSV) - 202405
<p>LENS dataset 2024-05 (CSV format)</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202405 - Part 3
<p>LENS dataset 2024-05 Part 3</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset (CSV) - 202404
<p>LENS dataset 2024-04 (CSV format)</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202405 - Part 2
<p>LENS dataset 2024-05 Part 2</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202404 - Part 1
<p>LENS dataset 2024-04 Part 1</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202405 - Part 1
<p>LENS dataset 2024-05 Part 1</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202404 - Part 3
<p>LENS dataset 2024-04 Part 3</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202404 - Part 4
<p>LENS dataset 2024-04 Part 4</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
Research Compendium for Himes et al. (2024): "Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements"
<p>This archive is the Reproducible Research Compendium for</p> <p>Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements</p> <p>by Himes et al. (2024), submitted to Atmospheric Measurement Techniques.</p> <p>This compendium includes all files related to MARGE associated with the manuscript.</p> <p>NN model files are split into smaller files for convenience, given their sizes. To recombine the files, do, e.g., <br> cat cnn_weights_NH-LW.h5* > cnn_weights_NH-LW.h5</p> <p>User interested in running MARGE will need to clone the GitHub repo (https://github.com/exosports/MARGE), apply the patch file to checksum fc95b3c, organize the relevant files into directories as listed in the configuration files (Zenodo does not support organizing files into directory structures) and calculate the number of training, validation, and test cases to be stored in the relevant input file specified in the configuration file. MARGE is under the Reproducible Research Software License (https://planets.ucf.edu/resources/reproducible-research/software-license/). For more details on MARGE, see the User Manual on GitHub.</p>
Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends
<p>In this section, it was given that Annex Figures and Annex Tables related to the article "Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends" published in "Environmental and Climate Technologies" journal. </p>
Multi-year high time resolution measurements of fine PM at 13 sites of the French Operational Network (CARA program)
<p>These datasets correspond to long-term measurements of atmospheric aerosol components from Aerosol Chemical Speciation Monitor (ACSM) and multi-wavelength Aethalometer (AE33) instruments collected between 2015 and 2021 at 13 (sub)urban sites as part of the French CARA program. </p> <p>The datasets contain the mass concentrations of major chemical species within PM1, namely organic aerosols (OA), nitrate (NO3-), ammonium (NH4+), sulfate (SO42-), non-sea-salt chloride (Cl-), and equivalent black carbon (eBC). </p> <p>Rigorous quality control, technical validation, and environmental evaluation processes were applied, adhering to both the guidance from the French reference laboratory for air quality monitoring and the Aerosol, Clouds, and Trace gases Research Infrastructure (ACTRIS) standard operating procedures.</p> <p>These data are discussed in an article in submission, please cite it when using the data.</p>
Dataset of "3D generative adversarial networks for turbulent flow estimation from wall measurements"
<p>Dataset of the article 'Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements' (https://doi.org/10.1017/jfm.2024.432). The codes processing data here are on https://github.com/erc-nextflow/3D-GAN.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 949085, NEXTFLOW). Views and opinions expressed are, however, those of the authors only, and do not necessarily reflect those of the European Union or the ERC. Neither the European Union nor the granting authority can be held responsible for them. A.C.M. acknowledges financial support from the Spanish Ministry of Universities under the Formación de Profesorado Universitario (FPU) programme 2020. R.V. acknowledges financial support from ERC (grant agreement no. 2021-CoG-101043998, DEEPCONTROL).</p>
LENS: A LEO Satellite Network Measurement Dataset - 202406 - Part 3
<p>LENS dataset 2024-06 Part 3</p> <p>Please see https://github.com/clarkzjw/LENS for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202406 - Part 2
<p>LENS dataset 2024-06 Part 2</p> <p>Please see https://github.com/clarkzjw/LENS for the complete description of the dataset.</p>
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.