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Dataset results
179 results for “self monitoring”
Study of Self-monitoring of Weight & Blood Pressure (Via patientMpower Platform) in Hemodialysis
ClinicalTrials.gov study NCT03403491. IPD Sharing: NO. Countries: 1. Publications: 1.
Self-monitoring and Reflection's Impact on Psychotherapy Outcomes: A Trial Protocol.
ClinicalTrials.gov study NCT06038747. IPD Sharing: YES. Countries: 1. Publications: 2.
Comparing Self Monitored Blood Glucose (SMBG) to Continuous Glucose Monitoring (CGM) in Type 2 Diabetes
ClinicalTrials.gov study NCT01237301. IPD Sharing: Not stated. Countries: 1. Publications: 1.
[Data] Self-Supervised Bayesian Representation Learning of Acoustic Emissions from Laser Powder Bed Fusion Process for In-situ Monitoring
<div> <div> <div> <p>Different Laser Powder Bed Fusion (LPBF) process spaces were deliberately introduced by employing two distinct 316L stainless steel powder distributions (with particle sizes >45 μm and < 45 μm) and processing them with two sets of laser parameters, resulting in the creation of four datasets [D1, D2, D3, and D4]. These datasets encompass LoF pores, conduction mode, and keyhole formations, each associated with three LPBF regimes denoted as D1, D2, D3, and D4. The experiments utilized a Sisma MYSINT 100 commercial LPBF printer and an airborne AE sensor system with a flat frequency response ranging from 0 to 150 kHz. Validation of the ground truths for the three laser regimes across the four datasets, representing distinct process spaces, was accomplished through the confirmation of cross-sectional images. In the course of fabricating a cube using a powder bed and laser, data acquisition from an AE sensor was triggered when the optical intensity reached a threshold of 0.5 V for each scan length. The photodiode trigger gain was adjusted to saturate at 5 V, and the ensuing continuous-time window, where the optical signal remained at 5 V for 12.5 ms, was calculated and segmented to generate the dataset. Irrespective of the specific regime (Lack of Fusion, Conduction, and Keyhole) or the cube being fabricated (with two powder distributions), the signals obtained during this process were then segmented into a 12.5 ms window comprising 5000 data points. To eliminate any noise, an offline application of a low-pass Butterworth filter with a 150 kHz cut-off frequency was employed, aligned with the frequency response specification of the AE sensor. Each dataset has two files against it [raw/groundtruth label].</p> </div> </div> </div>
Self-Supervised Learning for Avian Diversity Monitoring SC22
<p><strong>Clusters </strong>The clusterization generated from the output of the pre-trained backbone</p> <p><strong>Morton Spectrograms</strong> The original spectrograms with which we trained the model to check the clusterization </p> <p><strong>Pretrained Model</strong> The pre-trained model</p> <p><strong>Single Image Attentional Maps</strong> The attentional maps and masks generated for a single image</p> <p><strong>features attentional maps and names</strong> Features, attentional maps and names of all the Spectrogram Images</p>
Supplementary Material for the Paper "Design Recommendations for Self-Monitoring in the Workplace: Studies in Software Development"
<p>Contains the supplementary material for the paper "Design Recommendations for Self-Monitoring in the Workplace: Studies in Software Development" submitted to CSCW'18. All contents are explained in the file README.txt.</p> <p><strong>Abstract:</strong><br> One way to improve the productivity of knowledge workers is to increase their self-awareness about productivity at work through self-monitoring. Yet, little is known about expectations of, the experience with and the impact of self-monitoring in the workplace. To address this gap, we studied software developers, as one community of knowledge workers. We used an iterative, feedback-driven development approach (N=20) and a survey (N=413) to infer design elements for workplace self-monitoring, which we then implemented as a technology probe called WorkAnalytics. We field-tested these design elements during a three-week study with software development professionals (N=43). Based on the results of the field study, we present design recommendations for self-monitoring in the workplace, such as using experience sampling to increase the awareness about work and to create richer insights, the need for a large variety of different metrics to retrospect about work, and that actionable insights, enriched with benchmarking data from co-workers, are likely needed to foster productive behavior change at work.</p> <p><strong>Source Code:</strong></p> <p>The source code of WorkAnalytics can be found on <strong><a href="https://github.com/sealuzh/PersonalAnalytics">GitHub</a></strong> (under the original name PersonalAnalytics). WorkAnalytics was built with Microsoft's Dot.Net framework in C# and can be used on the Windows 7, 8 and 10 operating system.</p>
Technology-augmented Self-monitoring Model Among Patients With Type 2 Diabetes and Hypertension
ClinicalTrials.gov study NCT02799953. IPD Sharing: NO. Countries: 1. Publications: 5.
Effect of Peer Mentoring and Blood Pressure Self-monitoring on Hypertension Control.
ClinicalTrials.gov study NCT03297229. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Self-monitoring Physical Activity With a SMARTphone Application in Cancer Patients: a Feasibility Study (SMART)
ClinicalTrials.gov study NCT02391454. IPD Sharing: Not stated. Countries: 1. Publications: 1.
CARNet : Self-Monitoring and Co-driving in Rheumatology With Internet : Rheumatoid Arthritis Cohort (Usual Care Study)
ClinicalTrials.gov study NCT02200068. IPD Sharing: Not stated. Countries: 1. Publications: 8.
GoalTracker: Comparing Self-Monitoring Strategies for Weight Loss
ClinicalTrials.gov study NCT03254953. IPD Sharing: NO. Countries: 1. Publications: 1.
Effectiveness Evaluation of a Dengue Self-monitoring System
ClinicalTrials.gov study NCT05688748. IPD Sharing: NO. Countries: 1. Publications: 2.
A Nurse-Led Weight Monitoring Intervention For Heart Failure Quality of LIfe and Self-Care
ClinicalTrials.gov study NCT07184541. IPD Sharing: NO. Countries: 1. Publications: 24.
Home-based Rehabilitation Monitoring System With Wearable Devices and Self-Report Application
ClinicalTrials.gov study NCT06410755. IPD Sharing: NO. Countries: 1. Publications: 43.
Motivational Interviewing and WhatsApp-Based Monitoring for Metabolic Control and Self-Efficacy in Adolescents With T1DM
ClinicalTrials.gov study NCT06635460. IPD Sharing: YES. Countries: 1. Publications: 5.
Evaluating System Accuracy of Blood Glucose Monitoring Systems for Self-testing in Managing Diabetes Mellitus
ClinicalTrials.gov study NCT01729546. IPD Sharing: Not stated. Countries: 1. Publications: 1.
COmputerized CTG Self-MOnitoring Versus Standard Doppler Assessment in Late-onset FGR: COSMOS Study
ClinicalTrials.gov study NCT05034861. IPD Sharing: YES. Countries: 1. Publications: 10.
Reducing Older Adults' Sedentary Behavior by Self-monitoring
ClinicalTrials.gov study NCT04003324. IPD Sharing: NO. Countries: 1. Publications: 2.
Rate Control Self-adjustment in Patients With Permanent or Persistent Atrial Fibrillation Using Device Home Monitoring
ClinicalTrials.gov study NCT05066971. IPD Sharing: NO. Countries: 1. Publications: 4.
A Mobile Phone Self-Monitoring Tool to Increase Emotional Self-Awareness and Reduce Depression in Young People
ClinicalTrials.gov study NCT00794222. IPD Sharing: Not stated. Countries: 1. Publications: 3.
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
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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.