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67 results for “testbeds”
FlockLab Testbed Profile
<p>Profile results of the FlockLab testbed. Three situations are investigated:</p> <p>1. Where the sensor nodes perform no actions<br> 2. Where the sensor nodes continuously read and report background noise levels<br> 3. Where a single node broadcasts and all other nodes listen for these packets. This is repeated for all nodes in the network.</p> <p>Also logged is the current consumption during these activities.</p>
Float+SOCAT sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed
<p>Here we provide sampling masks used in the study "The importance of adding unbiased Argo observations to the ocean carbon observing system" (Heimdal & McKinley, 2024, Scientific Reports). In this paper, we reconstruct surface ocean pCO2 using the Large Ensemble Testbed (Gloege et al., 2021, https://doi.org/10.1029/2020GB006788) and the pCO2-Residual method (Bennington et al., 2022, https://doi.org/10.1029/2021MS002960). We provide 2 different sampling masks used in the experiments presented in Heimdal & McKinley (2024). These masks represent two different float sampling schemes (+SOCAT) including 500 floats, corresponding to historical Argo float observations (https://fleetmonitoring.euro-argo.eu/dashboardpatterns) and potential optimized float sampling (following Chamberlain et al., 2023, <a href="https://doi.org/10.1175/JTECH-D-22-0093.1" target="_blank" rel="noopener">https://doi.org/10.1175/JTECH-D-22-0093.1</a>). </p>
NANCY SNS JU Project - VR Video Streaming & iPerf3 on O-RAN 5G Testbed Dataset
<p>This dataset was developed in the context of the NANCY project and it is the output of the experiments involving streaming a virtual reality (VR) video in a 5G coverage expansion scenario. Additionally, iPerf3 experiments in both TCP and UDP modes were carried out. The coverage expansion scenario involves a main operator and a micro-operator which extends the main operator’s coverage and can also provide additional services.</p> <p>The dataset includes network traffic, which was captured and stored in a .pcap files, as well as various performance metrics that were collected by an xApp running in the near-real-time Radio Access Network Intelligent Controller.</p> <p>The NANCY project has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union's Horizon Europe research and innovation programme under Grant Agreement No 101096456.</p>
Temperature sensor data for locations in Smart-Santander testbed of Fed4FIRE+
<p>This is a database dump with temperature sensor-values (called "phenomenons" in testbed API), collected during the Fed4FIRE+ project SECTOR (Algorithm to determine a cost-effective, optimal SpatiaL-dEployment for smart-City environmenTal sensOr netwoRks).</p> <p> </p>
Synthetic EUNOMIA Mastodon Testbed Dataset
<p>EMTD is a specifically crafted dataset of posts on an isolated Mastodon testbed, using dummy Mastodon user account for emulating user posts and information cascades. The dataset consists of 616 posts, with over 100 information cascades, consisting of 3-4 posts from individual users.</p>
Packet reception traces from the WSN-Testbed at the I4, Friedrich-Alexander University Erlangen-Nuremberg
<p>This is a 24h packet reception trace from an office-based WSN-Testbed at the I4, Friedrich-Alexander University Erlangen-Nuremberg. 9 Tmote Sky node were set to continuous receive packets. </p> <p>The data was collected using statprinter.c, which is part of the code provided with https://doi.org/10.5281/zenodo.582277. The attached files represent the raw data (.log), as well as a processed version (.log.csv) </p> <p>The trace was was stated on 2014-04-09.</p>
Ecosystem simulations at Fernow Experimental Forest using the soil biogeochemical testbed
Results from single point simulations using the soil biogeochemical testbed, which consists of the CASA-CNP vegetation model and the MIMICS-CN and CASA-CN soil models. Single point simulations were run at the point encompassing the Fernow Experimental Forest in West Virginia, USA, and results were compared to long-term observations from a whole-watershed nitrogen addition experiment.
Dataset for "Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime" by Pahlavan et al. (2023)
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NANCY SNS-JU project "Italtel Italian in-lab testbed dataset 2"
<p><span>In the context of the NANCY project (https://nancy-project.eu/), </span><span>this Dataset provides input data for the development of the B-RAN and attacks models for the NANCY framework, to model training and model inference functions. The data collected in the Italian in-lab testbed, namely “dataset 2”, focus on the Edge segment of the network and consider the presence of some of the NANCY components, to assess their impact with respect to the baseline defined through the first collected dataset (D6.6 “Italian in-lab testbed dataset 1”), assessment that will be carried out and completed, together with the technology evaluation, in subsequent project activities.<span> </span> <br>More specifically, the data collected in the Italtel Italian in-lab testbed, where a MEC assisted 5G network scenario with a video streaming application for generating traffic is provided, consider an Edge server based on ARMv8 CPU architecture, and some components specifically developed by the Partners for the NANCY project; these components are integrated in the Italtel environment and are part of the testbed topology set-up: </span></p> <p><span><span>·<span> </span></span></span><span>the “<strong>Anomaly detection application module</strong>”, provided by CRAT partner (Consortium for Research in Automation and Telecommunications) and focusing on detecting anomalous utilization of computing and network resources</span></p> <p><span><span>·<span> </span></span></span><span>the” <strong>VOSySmonitor and vManager</strong>”, provided by VOS (Virtual Open Systems), a novel virtualization technology, designed by NANCY, to host offloaded VNFs, which can be deployed in a bare-metal fashion ensuring “application isolation”</span></p> <p><span><span>·<span> </span></span></span><span>“<strong>Malicious traffic generation application</strong>” and “<strong>PAPI extension for ARM performance counter interaction</strong>”, provided by SSS (Sant'Anna Higher School of Pisa) for the technology validation in the context of the Italian testbed set-up</span></p> <p><span><span>·<span> </span></span></span><span>“<strong>Italtel VTU application</strong>”, provided by ITL, which can convert audio and video streams from one format to another, at multiple encodings schemes, changing resolution, bitrate, and video parameters.</span></p> <p><span>The dataset contains time series, collected by transmitting video content through the Italtel VTU application. The collected dataset is representative resource-intensive video traffic that has the greatest impact on 5G/B5G network planning and provisioning. The video streaming dataset includes data directly measured while watching the video on the mobile devices and data directly measured while generating downstream video stream traversing the gNB (i.e., downstream scenario), and vice versa (i.e., upstream scenario). In each experiment, we fixed the location of the UE and the gNB.</span></p>
DOE1 and DOE2 - Sensor data set radial forging at AFRC testbed
<p><strong>Sensor data set, radial forging at AFRC testbed</strong></p> <p><strong>General information on the data set</strong></p> <p>Radial forging is widely used in industry to manufacture components for a broad range of sectors including automotive, medical, aerospace, rail and industrial. The Advanced Forming Research Centre (AFRC) at the University of Strathclyde, Glasgow, houses a GFM SKK10/R radial forge that has been used as a testbed for this project. Using two pairs of hammers operating at 1200 strokes/min, and providing a maximum forging force per hammer of 150 tons, the radial forge is capable of processing a range of metals, including steel, titanium and inconel. Both hollow and solid material can be formed with the added benefit of creating internal features on hollow parts using a mandrel. Parts can be formed at a range of temperatures from ambient temperature to 1200 °C.</p> <p>For the provided data set, a total of 95 parts were forged over two days of operation. Each part was forged with machine parameters outlined in the file <code>Met4FoF STRATH test plan v4.xlsx</code>. Deviations from the test plan occurred during the second batch and are outline in the sheet <code>Batch 2</code>. Each forged part was then measured using a CMM to provide dimensional output relative to a target specification and tolerances. T<strong>he CMM records 16 dimensional measurements</strong>.</p> <p>The aim of the measurement setup is to predict the quality (in terms of dimensional properties) of the forged part from the sensor measurements during the forging process.</p> <p><strong>Structure of the data</strong></p> <ul> <li>The sensor readings for the forging of the parts are provided in csv files in the folders <code>ScopeTraces_DOE1</code> and <code>ScopeTraces_DOE2</code> for DOE1 and DOE2 respectively. For DOE1 the scope traces are named <code>Scope0001.csv</code> to <code>Scope0050.csv</code>. For DOE2 there were deviations and the Scope Traces to billet mapping is outlined in <code>Met4FoF STRATH test plan v4.xlsx</code>. Each file contains the readings (columns) against time (rows). The first column displays the clock times (in milliseconds).</li> <li>A commentary on the sensors is provided in the file <code>ForgedPartDataStructureSummaryv3.xlsx </code>(NOTE: Some columns do not have sensor descriptions as this information is not available).</li> <li>The CMM data is provided in the file <code>CMM_DOE1.xlsx</code> and <code>CMM_DOE2.xlsx</code> for DOE1 and DOE2 respectively.</li> </ul>
Underwater leg gait simulation and testbed
<p>Video 1: Underwater gait simulation (Robot Gait.mp4): multibody simulation for the underwater gait in a quadrupedal robot</p> <p>Video 2: Testbed (Testbed.mp4): Tested gait of a one leg in an underwater environment.</p>
Testbed Results for PhD Thesis
<p>These testbed results are to be analysed with the code available from: https://bitbucket.org/MBradbury/slp-algorithms-tinyos</p>
Extended evaluation datasets for IIoT TestBed (Release version 1 from November 28th, 2023)
<p>This contribution serves as an extension of the existing evaluation data set and thus also for the evaluation of planning strategies for the IIoT test bed environment, which was presented here: https://zenodo.org/records/10212298.</p> <p>This evaluation data set presented here comprises a total of four different files, each containing 1000 entries and thus describing 1000 different initial situations. </p> <p> </p> <p>The objective of this dataset is to assess and compare the policies achieved with different algorithms.</p> <p>The first data set (#1) is identical to the data set already published in https://zenodo.org/records/10212319. The same environmental conditions apply here as in the training, however the situations for the agents are unseen. </p> <p>In the second evaluation data set (#2), the number of carriers used at the same time was increased by 25%, i.e. from 16 to 20.</p> <p>In evaluation data set three (#3), the number of products to be completed varies between 50 and 500. </p> <p>In evaluation data set four (#4), the lot size was limited to 1, which means that each order only includes one product. This increases the diservity (product types and families) that are manufactured at the same time, which can lead to higher set-up efforts. <br><br>In evaluation data set five (#5), the number of carriers used at the same time was increased by 50%, i.e. from 16 to 24.</p> <p><br>In evaluation data set six (#6), the previously used work plans are no longer used, instead 3 new, previously unknown product variants are presented:<br>variant number 7: A -> C -> E -> G -> I<br>variant number 8: A -> B -> D -> F -> H -> I<br>variant number 8: A -> B -> C -> G -> H -> I</p> <p>In evaluation data set seven (#7), The probability of occurrence of the errors as well as the error duration were doubled.<br><br></p> <p>In evaluation data set eight (#8), various environmental changes are combined together. <br>- The probability of occurrence of errors and the error duration have been increased by a factor of 1.2<br>- The number of products to be produced has been set to 128 instead of 64 per episode<br>- The batch size can vary between 1 and 10 instead of 1 and 3<br>- The number of carriers has been set to 20 instead of 16<br>- The previous 6 routings have been extended by 3 new, previously unknown work plans (analogous to dataset #6), so that an order can now be composed of 9 different product variants.</p> <p> </p>
Datafiles for: Benchmarking State-of-the-art DIRECT-type Methods on the BBOB Noiseless Testbed
<p>Datafiles for the paper "Benchmarking State-of-the-art DIRECT-type Methods on the BBOB Noiseless Testbed" submitted for the GECCO 2023 BBOB workshop.</p>
Evaluation Data of the Paper: The Trilemma of Large-Data Availability in Web-based Testbeds
<p>This dataset includes the evaluation data for the Paper "The Trilemma of Large-Data Availability in Web-based Testbeds".<br> Unfortunately, Zenedo does not allow dataset being larger than 50 GB. Thus, all measurements are included, but not all scenarios are published within this dataset.</p> <p>File Names follow the naming of:</p> <ul> <li><number of applications>x<number of messages>_scenario.py: The randomly created scenario files used at the evaluation.</li> <li><number of applications>x<number of messages>_measurment_prior_<number of measurement>.py: The measurement files of aTLAS prior to our implementation.</li> <li><number of applications>x<number of messages>_measurment_with_<number of measurement>.py: The measurement files of aTLAS with our implementation.</li> </ul>
Sensor data set, electromechanical cylinder at ZeMA testbed (2kHz)
<p><strong>General information on the data set</strong></p> <p>The data set was generated at the ZeMA testbed. A working cycle lasts 2.8s and consists of a forward stroke, a waiting time and a return stroke. The data set does not consist of the entire working cycles. Only one second of the return stroke of each working cycle is used.</p> <p> </p> <p><strong>Structure of the data</strong></p> <ul> <li>data saved in HDF5 file as a 3D-matrix</li> <li>one row represents one second of the return stroke of one working cycle (6292 rows: 6292 cycles)</li> <li>one column represents one datapoint of the cycle, that is resampled to 2 kHz (2000 columns)</li> <li>one page represent one sensor (11 pages: 11 sensors)</li> </ul> <p> </p> <p><strong>Allocation of the pages to the sensors</strong></p> <p>page 1: microphone<br> page 2: acceleration plain bearing<br> page 3: acceleration piston rod<br> page 4: acceleration ball bearing<br> page 5: axial force<br> page 6: pressure<br> page 7: velocity<br> page 8: active current<br> page 9: motor current phase 1<br> page 10: motor current phase 2<br> page 11: motor current phase 3</p> <p> </p> <p><strong>Remark</strong></p> <p>The datasets are not in SI units. For conversion, you can use the PDF documentation.</p> <p> </p> <p><strong>Further information</strong></p> <p>For an introduction and tutorial to this data, a set of Jupyter notebooks is available <a href="https://github.com/harislulic/ZeMA-machine-learning-tutorials">here</a>. These notebooks contain Python code and a documentation of example machine learning tasks and analysis of this data set. In the near future, these will be extended to also include uncertainties in the input data.</p>
Sensor data set of 3 electromechanical cylinder at ZeMA testbed (2kHz)
<p><strong>General information on the data set</strong></p> <p>The data set was generated at the ZeMA testbed. A working cycle lasts 2.8s and consists of a forward stroke, a waiting time and a return stroke. The data set does not consist of the entire working cycles. Only one second of the return stroke of each working cycle is used.</p> <p> </p> <p><strong>Structure of the data</strong></p> <ul> <li>data saved in three HDF5 file as a 3D-matrix, one file is for one axis</li> <li>one row represents one second of the return stroke of one working cycle<br> axis 3: 6292 cycles<br> axis 5: 6083 cycles<br> axis 7: 5732 cycles</li> <li>one column represents one datapoint of the cycle, that is resampled to 2 kHz (2000 columns)</li> <li>one page represent one sensor (11 pages: 11 sensors)</li> </ul> <p> </p> <p><strong>Allocation of the pages to the sensors</strong></p> <p>page 1: microphone<br> page 2: acceleration plain bearing<br> page 3: acceleration piston rod<br> page 4: acceleration ball bearing<br> page 5: axial force<br> page 6: pressure<br> page 7: velocity<br> page 8: active current<br> page 9: motor current phase 1<br> page 10: motor current phase 2<br> page 11: motor current phase 3</p> <p> </p> <p><strong>Remark</strong></p> <p>The datasets are not in SI units. For conversion, you can use the PDF documentation.</p> <p> </p> <p><strong>Further information</strong></p> <p>For an introduction and tutorial to this data, a set of Jupyter notebooks is available <a href="https://github.com/harislulic/ZeMA-machine-learning-tutorials">here</a>. These notebooks contain Python code and a documentation of example machine learning tasks and analysis of this data set. In the near future, these will be extended to also include uncertainties in the input data.</p>
ACloud LMS Testbed
<p>The testbed that LMS set up under the framework of EIT Manufacturing project ACloud.</p>
Self-adaptive Search Equation-Based Artificial Bee Colony Algorithm with CMA-ES on the Noiseless BBOB Testbed
<p>This file contains the data for results of SSEABC algorithm on BBOB functions testbed.</p>
5G-IANA: Nokia Testbed – Drivetest results for UL & DL Throughput and RTT with a OnePlus9 UE
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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.