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9 results for “User Dynamics”
Performance of users with Cerebral Palsy playing GABLE Games together with their results to the Left/Right Dynamic balance tool
<p>This dataset contains data generated by users of GABLE platform. The data shows the performance of some users with Cerebral Palsy playing GABLE Games together with their results to the Left/Right Dynamic balance tool. More information about GABLE project can be found at: www.projectgable.eu</p>
Extending the application of connectivity metrics within the framework of the characterization of the dynamic behaviour of a WDS subjected to users' activity
<p>Water distribution networks (WDNs) are complex combinations of nodes and links, and the current tendency is to modify their topological structure through the closure of isolation valves for monitoring and water quality reasons. For their analysis, several approaches based on graph theory have recently been proposed, mainly considering steady-state flow conditions. However, in their real functioning, WDNs are continuously subjected to pressure transients generated by manoeuvres on regulation devices or by users’ activity. This study investigates the application of some metrics from graph theory, already used in the context of steady-state analysis, for assessing the effects of changes in the topological structure of a network ‒ due for example to sectorization or branching operations ‒ on its transient response when subjected to manoeuvres on devices such as hydrants, pumps, etc. or users’ activity. The analysis shows that some connectivity metrics can effectively reflect the dynamic pressure behaviour of the network and, thus, provide useful indications for design and management operations taking into account unsteady flow features.</p>
Dynamics of Instagram Users
<p>These two data sets are gathered from Instagram users who were chosen randomly.</p> <p>The Main data set encompasses data for 1K users including 500 men and 500 women. The Test data set encompasses data for 100 users including 50 men and 50 women.</p> <p>Data gathered for each user includes :</p> <p>1- number of posts</p> <p>2- number of followers</p> <p>3- number of followings</p> <p>4- number of likes for the tenth previous post</p> <p>5- number of likes for the eleventh previous post</p> <p>6- number of likes for the twelfth previous post</p> <p>7- number of self-presenting posts from nine previous posts</p> <p>8- gender</p>
Unveiling Competition Dynamics in Mobile App Markets through User Reviews
<p>This replication package contains the datasets and evaluation results for the research titled <i>"<strong>Unveiling Competition Dynamics in Mobile App Markets through User Reviews"</strong>, </i>by Quim Motger, Xavier Franch, Vincenzo Gervasi and Jordi Marco.</p><p>Latest version of the full code is available at: <a href="https://github.com/quim-motger/app-market-analysis">https://github.com/quim-motger/app-market-analysis</a></p>
Time and Dynamics of Instagram Users
<p>These four datasets are gathered from Instagram users who were chosen randomly.</p> <p>The MainDataset encompasses data for 818 users. The TestDataset encompasses data for 78 users.</p> <p>Data gathered for each user includes :</p> <p>1- number of posts</p> <p>2- number of followers</p> <p>3- number of followings</p> <p>4- number of likes for the tenth previous post</p> <p>5- number of likes for the eleventh previous post</p> <p>6- number of likes for the twelfth previous post</p> <p>7- number of self-presenting posts from nine previous posts</p> <p>8- gender</p> <p><br> The MainDataset_after_150_days and TestDataset_after_150_days encompass data of the users of the Main data set and the Test data set, respectively, for after 150 days. For example, User_1 in the MainDataset has 486 posts and in the MainDataset_after_150_days has 562 posts, which means over the course of 150 days he had published 76 posts.</p>
Handling Dynamic Environment Changes for Behavior-Based User Authentication
<p><strong>Description:</strong></p> <p>This environment-independent user authentication dataset is from our MASS 2020 paper<strong>: <em>Towards Environment-independent Behavior-based User Authentication Using WiFi</em></strong>. This dataset contains the physiological characteristics captured by WiFi from 10 participants for 10 different activities. Each participant performs 20 rounds for each activity. The experiments are conducted in two different environments, the campus office, and the home apartment. The system performance is tested on the cross-environment scenarios (training in one environment and testing in another environment).</p> <p>Note: The MASS 2020 paper is based on our MobiHoc 2017 paper, <strong><em>Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT</em></strong>. The MobiHoc 2017 work focused on user authentication using CSI extracted from human activity while the MASS 2020 work focused on the domain adaptation of user authentication using activity CSI.</p> <p>The dataset of our MobiHoc 2017 work is also published: <a href="https://zenodo.org/record/7750976#.ZBfTZ3bMKUk">https://zenodo.org/record/7750976#.ZBfTZ3bMKUk</a></p> <p> </p> <p><strong>Format: </strong>.dat format</p> <p><strong>Section 1: Device Configuration</strong></p> <ul> <li>Two commercial laptops, Dell E6430, as transmitter and receiver. Run with a Linux 14.04 operating system with 4.2.0 kernel. Equipped with 3 MINI PCI-E internal antennas. </li> <li>Intel 5300 network interface card (NIC) for CSI collection. The detail information regarding the CSI tool can be found at <a href="https://dhalperi.github.io/linux-80211n-csitool/faq.html">https://dhalperi.github.io/linux-80211n-csitool/faq.html</a>.</li> <li>WiFi packet transmission is set to 1000 pkts/s</li> </ul> <p><strong>Section 2: Data Format</strong></p> <p>We provide raw data received by the CSI tool. The data files are saved in the dat format. The details are shown in the following:</p> <ol> <li>10 participants are included in two different experiments.</li> <li>Each participant performed 20 rounds for each activity.</li> <li>The dataset file name is presented as "User_Day_Action_Location". The detailed information as: <ul> <li>User: The participants that CSI was collected from.</li> <li>Day: The date this data was collected. </li> <li>Action: The specific activity performed.</li> <li>Location: The specific location the experiment was conducted.</li> </ul> </li> </ol> <p><strong>Section 3: Experimental Setups</strong></p> <p>There are two experiment setups for our data collection. An image of the experimental setup and the illustration of activities from two different environments is included in the dataset. Each activity was performed in a designated location. In each activity location, the specific activity was conducted in 4 different proximate locations at least one foot away from each other. </p> <ol> <li>Residential Apartment <ul> <li>Environment: The experiments are conducted in a residential apartment with a size 33ft × 17ft.</li> <li>Participant: 10 users are students from Rutgers University (aged from 20 to 30).</li> <li>Activity: 7 activities were performed. <table> <caption>Detailed Activities Performed in Apartment</caption> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Activity</strong></td> </tr> <tr> <td> A→B</td> <td>Walking (trajectory 1)</td> </tr> <tr> <td> B→C</td> <td>Walking (trajectory 2)</td> </tr> <tr> <td> B</td> <td>Picking up a remote control</td> </tr> <tr> <td> C</td> <td>Sitting in a chair </td> </tr> <tr> <td> D</td> <td>Exercising</td> </tr> <tr> <td> E</td> <td>Operating on the oven</td> </tr> <tr> <td> F</td> <td>Using the stove</td> </tr> </tbody> </table> <p> </p> </li> </ul> </li> <li>Office <ul> <li>Environment: The experiments are conducted in an office with a size 21ft × 12ft.</li> <li>Participant: 5 users are students from Rutgers University (aged from 20 to 30).</li> <li>Activity: 3 activities were performed. <table> <caption>Detailed Activities Performed in Office</caption> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Activity</strong></td> </tr> <tr> <td> G</td> <td>Sitting in a seat</td> </tr> <tr> <td> H</td> <td>Stretching the body</td> </tr> <tr> <td> I</td> <td>Typing on a keyboard</td> </tr> </tbody> </table> </li> </ul> </li> </ol> <p> </p> <p><strong>Section 4: Data Description</strong></p> <p>We separate our raw data into different folders based on different environment types. In each environment type, data are further distributed in terms of date. Each file includes all data from three internal antennas. All data files are in .dat format. We also provide Matlab scripts for CSI analysis and visualization. The following variables can be revealed from the codes:</p> <ol> <li>CSI: This is the Channel State Information (CSI) received from one receiver antenna. It describes the signal propagation from the transmitter to the receiver, and it is very sensitive to the impact of environmental changes. Each data reveals CSI from 30 subcarriers. </li> <li>Relative Phase: Relative Phase is a measurement to describe the degree of synchronization between data received from different antennas. It can be used to determine the phase offset for further signal preprocessing.</li> <li>Time: This is the time interval in which the data file contains. It measures time by the number of seconds. It can be used to determine how long the signal has been received.</li> </ol> <p><strong>Section 5: Codes</strong></p> <ul> <li>analysis_spectrogram.m: load a .dat file and extract all data by Data description(I.e, CSI, and Relative Phase).</li> </ul> <p><strong>Section 6: Citations</strong></p> <p>If your paper is related to our works, please cite our papers as follows.</p> <p><a href="https://ieeexplore.ieee.org/document/9356038">https://ieeexplore.ieee.org/document/9356038</a></p> <p>C. Shi, J. Liu, N. Borodinov, B. Leao and Y. Chen, "Towards Environment-independent Behavior-based User Authentication Using WiFi," <em>2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)</em>, Delhi, India, 2020, pp. 666-674, doi: 10.1109/MASS50613.2020.00086</p> <p><strong>Bibtex:</strong></p> <p>@INPROCEEDINGS{9356038,<br> author={Shi, Cong and Liu, Jian and Borodinov, Nick and Leao, Bruno and Chen, Yingying},<br> booktitle={2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)}, <br> title={Towards Environment-independent Behavior-based User Authentication Using WiFi}, <br> year={2020},<br> volume={},<br> number={},<br> pages={666-674},<br> doi={10.1109/MASS50613.2020.00086}}<br> </p> <p>The current version of the dataset is shrunk due to its size. If you wish to acquire the full version or you have any questions regarding the dataset, contact us by email: cl1361@scarletmail.rutgers.edu. </p>
End-user involvement to improve predictions and management of populations with complex dynamics and multiple drivers
Open the record for dataset details and reuse information.
Graston Technique vs Dynamic Oscillation Stretching Technique For High Heel Users And Its Impact On Body Posture
ClinicalTrials.gov study NCT07138924. IPD Sharing: NO. Countries: 1. Publications: 1.
Users of Remote Conferencing and Compression of Sound Dynamics : Auditory Effects
ClinicalTrials.gov study NCT06264245. IPD Sharing: NO. Countries: 2. Publications: 0.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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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.