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104 results for “Network Measurement”
Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Tule 222 Well Meteorological Station (TUWL), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.
The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Tule 222 Well Meteorological Station (TUWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmettuwl/.
Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the West Mesa Meteorological Station (WSMS), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.
The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Tule 222 Well Meteorological Station (WSMS). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetwsms/.
Measurement of Fuel load (organic biomass) Quantities of Organic Soils, Understory Plants and Trees at Sites within the Bonanza Creek LTER Regional Site Network in Interior Alaska, 2019
This dataset contains fuel load measurements for a subset of the BNZ LTER's RSN sites (n = 28). The data for each site separted by 16 fuel types (or plant functional types) including organic soil layers (fibric, mesic), vascular understory (evergreen shrub, short deciduous shrub, graminoid, forb, tall deciduous shrub, tree seedling/sapling, dead-downed wood), nonvascular understory (feather moss, Sphagnum moss, colonizer moss, lichen) and trees (evergreen tree, deciduous tree).
Data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks"
<p>This upload contains the data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks", (D.O.I: <a href="https://doi.org/10.3390/jsan9010012">10.3390/jsan9010012</a>) published in the the special issue on "Localization in Wireless Sensor Networks" of the <a href="https://www.mdpi.com/journal/jsan"><em>Journal of Sensor and Actuator Networks</em></a> (ISSN 2224-2708).</p> <p>The data and code included allows to replicate the results of the article.</p>
Received Signal Srength (RSS) urban measurements from GSM and UMTS networks for cellular-based positioning
<p>GSM (2G) and UMTS (3G) urban measurement data for cellular-based positioning. The data has been collected in Tampere city, Finland with a mobile phone with proprietary software. The data is given in Matlab *mat format and it contains a cell variable called BS_grid* which shows the GPS coordinates (x,y,z) (converted in local coordinates, in meter values) and the collected RSS value (in dB) per transmitter (i.e., BS or Node B). Each cell contains a N x 4 matrix, whose rows are [x y z RSS]. N is the number of measurements points in which the corresponding Base Station were heard. The size of the BS_grid* cells is equal to the number of heard Base Stations in the measured area. </p> <p>Example of research results based on these measurements can be found for example in:</p> <ul> <li>H. Nurminen, J. Talvitie, S. Ali-Loytty, P. Muller, E.S. Lohan, R. Piche, M. Renfors, "Statistical path loss parameter estimation and positioning using RSS measurements", Journal of Global Positioning Systems, vol. 12(1), 2013, ISSN 1446-3156.</li> </ul>
Measurements of Carbon-14 of Carbon monoxide (14CO) in a global network
<p>This is a data set containing measurements of [14CO] in a new global network led by the University of Rochester. Measurements are from samples collected approximately biweekly during 2021 at Barrow, Mace Head, Mauna Loa, Barbados, American Samoa, Reunion Island and Baring Head atmospheric observatories.</p>
Speedtest-like Measurements in 3G/4G Networks: the MONROE Experience
<p>The goal of this research is to collect a wide range speedtest-like traces for the mobile broadband (MBB) networks, as seen from actual users. In our experiment, the MONROE node contains the core components, with containers that run active experiments. Traffic generated by the applications passes through the selected MiFi modem where a NAT is in place, then goes through the ISP network, and the Internet, toward the selected server. Each node runs also Tstat, a specialized passive sniffer. Tstat captures traffic on each MBB interface and extracts statistics by passively observing packets exchanged with the network. Another instance of Tstat runs on the server side, thus capturing and processing traffic at the other end of the path.<br> <br> Each MONROE node regularly runs a basic set of experiments. Among these, the HTTP download experiment uses single thread curl to download a 40 MB file for a maximum of 10 seconds from dedicated and not-congested servers in Sweden. we collected measurements during September and October 2016 in four countries and different sites. We consider only stationary nodes. The experiment ran every 3 hours in synchronized fashion. The active application and passive flow-level traces on the client and server sides cannot give us information about the technology and signal strength at the MBB channel during the experiment. Therefore, we use the metadata collected by the MONROE platform to augment the information about the access link status. The MONROE metadata are event-based data collected by passively monitoring the statistics exposed directly from the MiFi modems through their management interface. This data is transmitted and stored in the project database for analysis, and can be easily correlated to each node and interface.</p>
Figure 1. CNN architecture (adopted from Krizhevsky et al. '12)-Measuring Customer Behavior with Deep Convolutional Neural Networks
<p>The architecture of a CNN can be described as following. A small pixel region goes to input neurons and then connects to a first convolution hidden layer (Figure1). There we can see a set of learnable filters, which are activated during the presentation some particular type of feature in pixel region in the input. On this phase, CNN does shift invariance, which is carried by feature map. Subsampling layer goes next. There we have two processes: local averaging and sampling. As a result, we get declining resolution of feature map. To correspond this task CNN needs supervised learning. Before starting the experiment, we gave a set of labeled videos with different emotional experience. The system analyses images and finds similar features. Then the system creates a map, where it arranges videos in accordance with similar features. Thereby, images with similar emotions form certain class. To test the system, we add other videos and correct the system when it refers them improperly. The proposed model consists of four convolutional layers, followed by max-pooling layers, and three fully-connected layers with a final classificatory presented with MLP (with six basic outputs, corresponding to basic emotions for emotion classification and two outputs for motion classification for typical and non-typical behavior). The input data was presented as infrared camera output.</p>
Proxies' response times measured by clients in an emulated community network
<p>13 virtual nodes were deployed in Planetlab testbed (https://www.planet-lab.org) to emulate a small community network with 8 clients and 5 proxies. Each client probed all proxies every 10 seconds during two days. The same file (http://ovh.net/files/1Mb.dat) was requested in all probes. A probe was considered successful if the file was completely downloaded by the client. In this case, the response time was registered by the client, considering the time elapsed from the moment the client sent the request until the last byte of the response was received.</p> <p>This dataset contains the proxies' response times that were measured by clients in sucessful probes.</p>
Network analysis highlights increased generalisation and evenness of plant-pollinator interactions after conservation measures
<p><strong>DATASET used in the article entitled</strong> “Network analysis highlights increased generalisation and evenness of plant-pollinator interactions after conservation measures”.</p> <p>We supply weighted and binary matrices used for plant-pollinator network analyses, before and after the implementation of conservation measures.</p> <p>We also supply the list of plant and pollinator species recorded in this study.</p>
Dataset of Cellular Network Measurements
<p>This dataset contains 326,157 measurements in cellular networks, collected from August 2021 until Juni 2023 in Germany. The measurement focus on a high density in rural areas and were performed by car, bicycle and by foot. The measurements cover primary the Telefonica/o2 network.</p>
Dataset for Agile 5G Network Measurements: Operator Benefits of Employing Aerial Mobility
<p>Dataset of paper "Agile 5G Network Measurements: Operator Benefits of Employing Aerial Mobility".</p> <p>The measurement data contains two different scenarios: Scenario 1 and Scenario 2, according to the paper. These two datasets include two different kinds of files: those related to measurements taken with a phone and those taken with a drone (UAV).</p>
Multichannel Displacement measurement via self mixing interferometry and neural network : training and test datasets
<p>Self mixing interferometry is a simple and robust sensing method which can be used (among other things) to measure the displacement of a target along the light propagation axis. While conceptually simple, the actual use of this method is less straightforward than originally envisioned because reconstructing the target displacement from the interferometric signal is often tricky. A small neural network can do this task very well after proper training, as described in [10.1364/OE.419844], with dataset [10.5281/zenodo.7303745]. </p> <p>Here, the dataset is composed by a training set and a test set, in a specific configuration in which 3 self-mixing sensors measure simultaneously the same target displacement. Both datasets contain the displacement itself and the 3 interferometric signals (1 per sensing channel)</p> <p><strong>The training set </strong>relies on two python/numpy data files corresponding to <strong>harmonic displacements</strong> for different frequencies ranging from 53 and 93 Hz and amplitudes from 3.5 to 7.5 µm : </p> <ul> <li>Training_set_2_lostchannel_displacement.npy : 93744-elements long numpy array containing the target's displacement in units of µm/ms with a 1.024 ms time step. </li> <li>Training_set_2_lostchannel_signal.npy : numpy array of shape (3, 93744, 256, 1) containing the interferometric signals. The first dimension refers to the channel (1, 2 or 3), the second dimension is the number of segments of 256 points. Each segment of 256 points correspond to a 1.024 ms window of signal, matching one element of the displacement. For instance, the displacement value in `displacement[416]` corresponds to the interferometric signal segment `signal[0,416,:,0]` for channel 1, `signal[1,416,:,0]` for channel 2 and `signal[2,416,:,0]` for channel 3. </li> </ul> <p><strong>The test </strong>set follows the same architecture and format as the training set, but contains only <strong>random displacements </strong>generated by a delta-correlated signal, which we Fourier filter with a fifth order Butterworth filter between 10 and 100 Hz :</p> <ul> <li>"Displacement_test.npy" : with shape (246078, 1)</li> <li>"Signal_test.npy" : with shape (3, 246078, 256, 1)</li> <li>Only the displacement type has changed from harmonic to random, from the training to the test datasets.</li> </ul> <p>These datasets have been used to train and test a 3 channel neural network (after data augmentation) in order to emphasize the high availability potential of multichannel schemes, against backscattered power fluctuations. </p>
A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements
<p>Mobile networks have become highly complex systems. In order to better understand how network features affect performance and suggest additional improvements, it is crucial to examine them from an empirical perspective. In the following, we present a large-scale dataset of measurements collected over fourth generation (4G) and fifth generation (5G) operational networks, providing Long Term Evolution (LTE), Narrowband Internet of Things (NB-IoT) and 5G New Radio (NR) connectivity. We collected our dataset during a period of seven weeks in Rome, Italy, by performing several tests on the infrastructures of two major mobile network operators (MNOs). The open-sourced dataset has enabled multi-faceted analyses of network deployment, coverage, and end-user performance, and can be further used for designing and testing artificial intelligence (AI) and machine learning (ML) solutions for network optimization tasks.</p> <p><br>If you use our dataset in your research, we kindly request that you cite the following paper:</p> <p>K. Kousias <em>et al</em>., "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," in <em>IEEE Communications Magazine</em>, vol. 62, no. 5, pp. 44-49, May 2024, doi: 10.1109/MCOM.011.2200707.</p>
A mathematical model to predict network growth in physarum polycephalum as a function of extracellular matrix viscosity, measured by a novel viscometer
Open the record for dataset details and reuse information.
Measurement of Plant Traits on Hylocomium splendens Samples Collected at Sites within the Bonanza Creek LTER Regional Site Network in Interior Alaska, 2019
This dataset contains measurements of plant traits on Hylocomium splendens species collected at a subset of sites from the Regional Site Network (n = 26). The two species, Hylocomium splendens and Vaccinium uliginosum, are the two most ubiquitous nonvascular and vascular species at these sites. The traits measured on these species relate to the fire ecology of each species. Traits measured for Hylocomium splendens include: length, width, aspect ratio (width/length), specific leaf area (SLA) and moisture content % at maximum water retention capacity. There is a corresponding dataset with trait data for Vaccinium uliginosum.
Measurement of Plant Traits on Vaccinium uliginosum Samples Collected at Sites within the Bonanza Creek LTER Regional Site Network in Interior Alaska, 2019
This dataset contains measurements of plant traits on Vaccinium uliginosum species collected at a subset of sites from the Regional Site Network (n = 26). The two species, Hylocomium splendens and Vaccinium uliginosum, are the two most ubiquitous nonvascular and vascular species at these sites. The traits measured on these species relate to the fire ecology of each species. Traits measured for Vaccinium uliginosum include: rhizome depth, number of rhizomes, plant height, moisture content % of aboveground tissues, aboveground tissue ratios and dry mass of leaves. There is a corresponding dataset with trait data for Hylocomium splendens.
Net ecosystem exchange measurements throughout the 2020 growing season across an N fertilization gradient:Nutrient Network. A cross-site investigation of bottom-up control over herbaceous plant community dynamics and ecosystem function.
This experiment is one implementation of a globally distributed experiment, known as the Nutrient Network. At Cedar Creek, as in over 70 other sites in grasslands around the world, the experiment aims to describe impacts of increased nutrients (nitrogen, phosphorus, potassium, sulfur and other metals) and decreased herbivory (removal of mammals by fencing). Two overarching questions are being explored with these manipulations: 1. To what extent are plant production and diversity co-limited by multiple nutrients in herbaceous-dominated communities? 2. Under what conditions do grazers or fertilization control plant biomass, diversity, and composition? By utilizing identical protocols at diverse grassland sites around the world, NutNet aims to uncover both the generalities in ecosystem functioning, and the contingencies or differences which can obscure those common mechanisms. In addition to the standard NutNet protocol, e247 includes an additional low Nitrogen gradient (1 gram Nitrogen per meter squared per year and 5 grams Nitrogen per meter squared per year in addition to the standard 10 grams Nitrogen per meter squared per year).
Measures of Freight Network Resiliency During the Covid-19 Pandemic
<p>Recent headlines depict significant shifts in operations within the freight community in particular, e.g., HOS laws suspended at a national level for the first time in 82 years1; national carriers shifting operations completely to grocery supply chains2; fleet operators laying off employees in response to manufacturing closures3. As a result of the current COVID-19 pandemic, there is a great need to capture freight movement data (not otherwise collected) to measure the effects of the COVID-19 response and recovery practices on freight network resiliency. In this project, we consider an expanded definition of the freight network, beyond roads and warehouses, to include truck drivers and driver support systems. </p> <p>Driver support systems include physical infrastructure like public and private rest stops as well as operational protections like Hours of Service (HOS). COVID-19 responses by public agencies and private citizens have affected drivers and driver support systems by three mechanisms. First, increased demand for medical supplies, food and packaged goods creates a need for more trucks and drivers, and the increased need for quick shipments promotes an environment in which speeding and unsafe driving practices may prevail. Second, with HOS restrictions lifted by the National Highway Transportation Safety Administration (NHTSA) driver fatigue may occur at greater frequency leading to unsafe driving conditions and higher likelihood of accidents. Third, the effects of social distancing mandates can lead to closures of critical, but oft forgotten, freight infrastructure like rest areas and truck stops, leaving drivers without necessary rest opportunities. While any single mechanism has detrimental effects on driver health and safety, the economy, and national recovery efforts, when combined, the system can be pushed to failure. Pandemic responses have only exacerbated critical industry issues like driver shortages, lack of available parking, and HOS compliance issues stemming from electronic logbooks. The purpose of this work was to develop and implement a driver health and safety survey during the pandemic. </p>
Measured scattering parameters for the coupling of stochastic electromagnetic fields to transmission line networks of single-wire lines above a ground plane in a reverberation chamber
<p>This data set contains the measuremed scattering parameters between two antennas and a transmission line network under test in a reverberation chamber. The purpose of this measurement was an experimental validation of a numerical simulation model for the stochastic field coupling to a transmission line network. For the experiment, an exemplary network consisting of three single-wire lines above a ground plane was created. Different configurations of the network were tested and the average squared magnitude of the coupled voltage at the terminals of the network was analyzed and discussed.</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.