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56 results for “internet of things”
Screening and Early Warning of Chronic Obstructive Pulmonary Disease Combined With Sleep Respiratory Disease Based on Medical Internet of Things
ClinicalTrials.gov study NCT04833725. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Supporting Lifestyle Change in Obese Pregnant Mothers Through Wearable Internet-of-Things (SLIM)
ClinicalTrials.gov study NCT04826861. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Sleep and Activity Patterns in Pre-menopausal Breast Cancer Patients on Tamoxifen Using a Wrist-worn Internet of Things Device
ClinicalTrials.gov study NCT04116827. IPD Sharing: NO. Countries: 1. Publications: 7.
Data for: Monitoring the effects of ovariectomy on seasonal movement behavior in suburban female white-tailed deer using Internet of Things-enabled devices
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GECCO Industrial Challenge 2018 Dataset: A water quality dataset for the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition at the Genetic and Evolutionary Computation Conference 2018, Kyoto, Japan.
<p>Dataset of the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2018, Kyoto, Japan</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, M. Rebolledo, S. Moritz, S. Chandrasekaran, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p>GECCO Industrial Challenge: 'Internet of Things: Online Anomaly Detection for Drinking Water Quality'</p> <p>Description:</p> <p>For the 7th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2017 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.<br> Additionally to the competition, for the first time in GECCO history we are now able to provide the opportunity for all participants to submit 2-page algorithm descriptions for the GECCO Companion. Thus, it is now possible to create publications in a similar procedure to the Late Breaking Abstracts (LBAs) directly through competition participation!</p> <p> </p> <p>Accepted Competition Entry Abstracts<br> - Online Anomaly Detection for Drinking Water Quality Using a Multi-objective Machine Learning Approach (Victor Henrique Alves Ribeiro and Gilberto Reynoso Meza from the Pontifical Catholic University of Parana)<br> - Anomaly Detection for Drinking Water Quality via Deep BiLSTM Ensemble (Xingguo Chen, Fan Feng, Jikai Wu, and Wenyu Liu from the Nanjing University of Posts and Telecommunications and Nanjing University)<br> - Automatic vs. Manual Feature Engineering for Anomaly Detection of Drinking-Water Quality (Valerie Aenne Nicola Fehst from idatase GmbH)</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/">http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/</a></p>
Coverage and Deployment Analysis of Narrowband Internet of Things in the Wild - Dataset
<p>Coverage and Deployment Narrowband Internet of Things (NBIoT) measurements in Oslo and Rome.</p>
Data from: Modeling the internet of things, self-organizing and other complex adaptive communication networks: a cognitive agent-based computing approach
Background: Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. Purpose: It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. Method: We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. Results: The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach.
Raw data from Mobile scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the raw data from the Mobile scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p> <p>This dataset also has a second part that contains the raw audio data recorded in this scenario. That part of the dataset is access controlled, see <a href="http://dx.doi.org/10.5281/zenodo.2537984">this deposit</a> for more details.</p>
Processed data from Office scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the processed data from the office scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Code for the lecture notes: "Writing Internet of Things Applications with Task Oriented Programming"
<p>This dataset contains the supplementary files for the lecture and practical "Writing Internet of Things Applications with Task-Oriented Programming" that was given at the Composability, Comprehensibility and Correctness of Working Software, 8th Summer School, Budapest, Hungary, June 17–21, 2019.</p> <p>This work acknowledges the support of the ERASMUS+ project “Focusing Education on<br> Composability, Comprehensibility and Correctness of Working Software”, no. 2017–1–SK01–<br> KA203–035402.</p> <p>https://people.inf.elte.hu/cefp/</p> <p>It was originally uploaded at: https://ftp.cs.ru.nl/Clean/mTask/CEFP19/</p> <p>The dataset contains the following files:</p> <ul> <li>mtask-slides1.pdf: the slides for the first part of the lecture.</li> <li>mtask-slides2.pdf: the slides for the second part of the lecture.</li> <li>mtask-reader.pdf: the reader containing the assignments and instructions for using mTask.</li> <li>mtask-linux-x64.tar.gz: the prepared assignments and Clean/mTask distribution for 64-bit linux (see below).</li> <li>mtask-windows-x64.zip: the prepared assignments and Clean/mTask distribution for 64-bit windows (see below).</li> <li>mtask-macos-x64.tar.gz: the prepared assignments and Clean/mTask distribution for Mac OS X (see below).</li> </ul> <p>Each assignments archive contains of the following folders:</p> <ul> <li>clean: contains a Clean distribution.</li> <li>client: is an mTask desktop client in case the participant does not have a microcontroller at hand.</li> <li>mTask/programs: contains a Hello World! program to test the setup.</li> <li>mTask/library: contains the mTask library.</li> <li>mTask/cefp19: contains the assignments that used in the practical.</li> </ul> <p> </p>
Internet of Things Factory Simulation based on Siemens Tecnomatix Plant Simulation
<p>Internet of Things (IoT) Factory simulation based on Tecnomatix Plant SImulation. The simulation shows the manufacture line of IoT devices. It starts from manufacturing the IoT Case with 3D Printer. Then, a mobile robot transports the case to the robot station. Another parts of IoT device are also transported by the mobile robot. In the robot station, a manipulator robot places the part on the transporter on the conveyor. After everything is assembled, the IoT device is transported to the storage by the mobile robot. </p>
Attitudes and preferences toward internet of things technologies in hospitality
<p>The dataset contains information about the attitudes of 1000 Italian citizens toward the use of customer-oriented internet of things technologies in hotel settings. The data were collected through a computer-assisted web questionnaire in October 2021 and include:<br> -the respondents' demographic data<br> -the respondents' vacation habits<br> - the respondents' attitudes toward the use of technology in vacation and everyday life<br> -the respondents' preferences for technological/traditional services<br> -and the respondents' willingness to pay for highly technological hotels.<br> The original survey was in Italian. Therefore the dataset contains three excel sheets, the first including the raw data (data), the second including the original version of the questionnaire (ITAquestionnaire), and the third including the English version (ENGquestionnaire).</p>
Data from: Modeling the internet of things, self-organizing and other complex adaptive communication networks: a cognitive agent-based computing approach
Open the record for dataset details and reuse information.
iPOTs: Internet of Things-based pot system controlling optional treatment of soil water condition for plant phenotyping under drought stress
GEO Series GSE171578. Oryza sativa. 36 samples. Type: Expression profiling by high throughput sequencing.
IR4.0 and internet of things: future directions towards enhanced connectivity, automation, and sustainable innovation
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Internet Of Things,K.J.Somaiya Institute of Engineering & Information Technology, Mumbai,
<p>Internet Of Things,K.J.Somaiya Institute of Engineering & Information Technology, Mumbai,</p>
Index of supplementary files from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This record serves an an index to the other dataset releases that are part of the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1.</p> <p>We have chosen to split the dataset into several parts to meet Zenodo size requirements and make it easier to find specific pieces of data. In total, the following datasets exist:</p> <ol> <li><strong>Raw data</strong><br> These datasets contain raw data, as collected directly from the devices doing the recording. It includes readings from several different sensors, as well as observed WiFi and BLE signals with their signal strength, and in one case, audio recordings. This raw data can be used to repeat our own experiments, or to apply different schemes to it to have a baseline for comparisons. Four datasets exist, mapped to the three scenarios discussed in the paper: <ol> <li><a href="http://dx.doi.org/10.5281/zenodo.2537699">Car Scenario</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537701">Office Scenario</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537703">Mobile Scenario</a> + <a href="http://dx.doi.org/10.5281/zenodo.2537984">audio data in separate deposit</a> (with access control)</li> </ol> </li> <li><strong>Processed Data</strong><br> The processed data is generated from the raw data using the processing code (which can be found in <a href="https://dx.doi.org/10.5281/zenodo.2543721">the code repository</a>). The resulting data contains computed features from the five papers under investigation plus derived machine learning datasets, and can be used to see in detail how the schemes behave in specific situations. These datasets tend to be fairly large. Three datasets exist: <ol> <li><a href="http://dx.doi.org/10.5281/zenodo.2537705">Car Scenario</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537707">Office Scenario</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537709">Mobile Scenario</a></li> </ol> </li> <li><strong>Result Data</strong><br> Finally, the result datasets contain the results of the evaluation (i.e., the computed error rates and generated plots, plus associated caches). The code used to derive these results can once again be found in the <a href="http://dx.doi.org/10.5281/zenodo.2543721">source code repository</a>. Here, five datasets exist, one for each investigated paper: <ol> <li><a href="http://dx.doi.org/10.5281/zenodo.2537711">Karapanos et al.</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537713">Schürmann and Sigg</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537715">Miettinen et al.</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537717">Truong et al.</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537719">Shrestha et al.</a></li> </ol> </li> </ol>
Raw data from Car scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the raw data from the Car scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Raw data from Office scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the raw data from the Office scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Processed data from Car scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the processed data from the Car scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
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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.