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122
datasets available to search
ShareScore release 0.7.1
Dataset results
122 results for “tracker”
Use of Wearable Activity Tracker in Elderly Undergoing Abdominal Surgery
ClinicalTrials.gov study NCT03175783. IPD Sharing: NO. Countries: 1. Publications: 7.
Counterfactual Strategies, Physical Activity, and Wearable Trackers
ClinicalTrials.gov study NCT05192226. IPD Sharing: YES. Countries: 1. Publications: 12.
mACTonHEALTH: Psychological Flexibility and Activity Tracker - Protocol
ClinicalTrials.gov study NCT03351712. IPD Sharing: Not stated. Countries: 1. Publications: 34.
Effectiveness of Activity Trackers to Reduce Sedentary Behaviour in Sedentary Adults
ClinicalTrials.gov study NCT03853018. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Virtual Prospective Study Exploring Activity Trackers and COVID-19 Infections
ClinicalTrials.gov study NCT04623138. IPD Sharing: NO. Countries: 1. Publications: 15.
Fit for Two: Incorporating Wearable Trackers Into Clinical Care for Pregnant Women With Diabetes (FFT)
ClinicalTrials.gov study NCT03302377. IPD Sharing: NO. Countries: 1. Publications: 1.
Clinical Application of Pulse Rate-monitoring Activity Trackers in Thyrotoxicosis
ClinicalTrials.gov study NCT03009357. IPD Sharing: NO. Countries: 1. Publications: 2.
Multicamera calibration for multiobject tracker for mice
<p>Noninvasive tracking of all individuals in unmarked mouse group using multicamera fusion and deep learning</p>
Dataset for: Trust Trackers for Computation Offloading in Edge-Based IoT Networks
<p>The dataset used to generate graphs for: Matthew Bradbury, Arshad Jhumka and Tim Watson.<br> Trust Trackers for Computation Offloading in Edge-Based IoT Networks. In IEEE INFOCOM, 1–10. IEEE, 10–13 May 2021.</p> <p>Also includes instructions for experiment setup.</p> <p>Scripts from https://github.com/MBradbury/iot-trust-task-alloc are required to analyse and graph these results.</p>
Avaliação da abordagem Developer Tracker
<p>Dados da avaliação que seguiu um roteiro de entrevistas semiestruturado.</p>
Data from: Beyond novelty effect: a mixed-methods exploration into the motivation for long-term activity tracker use
Objectives: Activity trackers hold the promise to support people in managing their health through quantified measurements about their daily physical activities. Monitoring personal health with quantified activity tracker-generated data provides patients with an opportunity to self-manage their health. Many activity tracker user studies have been conducted within short time frames, however, which makes it difficult to discover the impact of the activity tracker's novelty effect or the reasons for the device's long-term use. This study explores the impact of novelty effect on activity tracker adoption and the motivation for sustained use beyond the novelty period. Materials and Methods: This study uses a mixed-methods approach that combines both quantitative activity tracker log analysis and qualitative one-on-one interviews to develop a deeper behavioral understanding of 23 Fitbit device users who have used their trackers for at least two months (range of use = 69 - 1073 days). Results: Log data from users' Fitbit devices revealed two stages in their activity tracker use: the novelty period and the long-term use period. The novelty period for Fitbit users in this study was approximately three months, during which they might have discontinued using their devices. Discussion: The qualitative interview data identified various factors that motivate users to continuously use Fitbit devices in different stages. The discussion of these results provides design implications to guide future development of activity tracking technology. Conclusion: This study reveals important dynamics emerging over long-term activity tracker use, contributes new knowledge to consumer health informatics and human-computer interaction, and offers design implications to guide future development of similar health-monitoring technologies that better account for long-term use in support of patient care and health self-management.
Asparagusic Golgi Trackers: Original Data
<p>The original data underlying the publication "Asparagusic Golgi Trackers" are arranged according to the supporting information: synthetic procedures and product characterization data, automatically analyzed high-throughput data, laser scanning confocal and fluorescence lifetime imaging microscopy data.</p>
Reliability of Consumer Sleep Trackers in Patients Suffering From Obstructive Sleep Apnea Syndrome
ClinicalTrials.gov study NCT02967367. IPD Sharing: NO. Countries: 1. Publications: 0.
Activity Trackers for Monitoring During Rehabilitation After Total Knee Arthroplasty
ClinicalTrials.gov study NCT03368287. IPD Sharing: NO. Countries: 0. Publications: 3.
Evaluation of Wheelchair In-Seat Activity Tracker
ClinicalTrials.gov study NCT04168450. IPD Sharing: YES. Countries: 1. Publications: 0.
Evaluating Motivational Interviewing and Habit Formation to Enhance the Effect of Activity Trackers on Physical Activity
ClinicalTrials.gov study NCT03837366. IPD Sharing: NO. Countries: 0. Publications: 1.
The MSK-Tracker Study
ClinicalTrials.gov study NCT03559205. IPD Sharing: NO. Countries: 1. Publications: 0.
Throwing Device Tracker for Youth Injury Prevention
ClinicalTrials.gov study NCT04098107. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Beyond novelty effect: a mixed-methods exploration into the motivation for long-term activity tracker use
Open the record for dataset details and reuse information.
Beyond the Code: Mining Self-Admitted Technical Debt in Issue Tracker Systems
<p>Self-admitted technical debt (SATD) is a particular case of Technical Debt (TD) where developers explicitly acknowledge their sub-optimal implementation decisions. Previous studies mine SATD by searching for specific TD-related terms in source code comments.By contrast, in this paper we argue that developers can admit technical debt by other means, e.g., by creating issues in tracking systems and labelling them as referring to TD. We refer to this type of SATD as issue-based SATD or just SATD-I. We study a sample of 286 SATD-I instances collected from five open source projects, including Microsoft Visual Studio and GitLab Community Edition. We show that only 29% of the studied SATD-I instances can be tracked to source code comments. We also show that SATD-I issues take more time to be closed, compared to other issues, although they are not more complex in terms of code churn. Besides, in 45% of the studied issues TD was introduced to ship earlier, and in almost 60%it refers to Design flaws. Finally, we report that most developers pay SATD-I to reduce its costs or interests (66%). Our findings suggest that there is space for designing novel tools to support technical debt management, particularly tools that encourage developers to create and label issues containing TD concerns.</p>
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.