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179 results for “self monitoring”
Lightweight Self-adaptive Cloud-IoT Monitoring across Fed4FIRE+ Testbeds (LiSCIo)
<p>Monitoring will be crucial to properly orchestrate next-gen services. Indeed, monitoring’s output can be exploited to choose where to deploy application services for the first time and to decide when and where to migrate them in case their QoS and contextual requirements cannot be satisfied by the current deployment and infrastructure state. However, only a few works have focused so far on the design and prototyping of monitoring tools for next-gen Cloud-IoT computing platforms.</p> <p>In this context, <a href="https://github.com/di-unipi-socc/FogMon">FogMon</a>, described in (Brogi et al., 2019) and (Forti et al., 2021), is an open-source C++ distributed monitoring service targeting heterogeneous infrastructures along the Cloud-IoT continuum, e.g. Fog computing. FogMon monitors hardware and virtualised resources at different Cloud-IoT computing nodes, end-to-end network QoS between such nodes, as well as available IoT devices. Besides, it features a self-organising peer-to-peer overlay topology with self-restructuring mechanisms and differential monitoring updates, which feature scalability, fault-tolerance, and low communication overhead.</p> <p>The LiSCIo project aimed at assessing FogMon over increasing infrastructures from 20 to 40 Cloud and Edge nodes, spanning two testbeds within the Fed4Fire+ federated infrastructure. Particularly, LiSCIo implemented a new version of the service, i.e. <a href="https://github.com/di-unipi-socc/FogMon-LiSCIo/tree/2.0">FogMon 2.0</a>, which was thoroughly fixed and tuned over a large number of experiments carried on Fed4Fire+ facilities. Throughout the project, data have been collected on all the measurements performed by FogMon 1.x and by FogMon 2.0 (viz. node hardware, IoT, latency, bandwidth) to assess their footprint on hardware resources and bandwidth in all settings, and the relative error on its estimates of latency and bandwidth against ground-truth configurations, enforced via GRE tunnels.</p>
GERDAT014 Self-management and self-monitoring literature search
<p>Dataset used for the literature on suitable self-monitoring or self-management tools or applications that could be used for older patients with cancer and/or multimorbidity</p>
Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick)
<p>This video is the fourth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick)</p> <p>Bio: <strong><a href="https://warwick.ac.uk/fac/sci/wmg/people/profile/?wmgid=1147">Dr Mark Elliott</a> </strong>Mark is an Associate Professor at the Institute of Digital Healthcare, WMG, University of Warwick (UoW). Mark’s core research focuses on human movement and physiology analytics. His research uses signal processing and data science approaches to monitor, measure and model human movement and physiology to infer health status. He is the PI of the WMG Motion Capture Laboratory. His work further extends into the broader area of using wearable and on-the- body sensing devices to make objective measures of human behaviour and behaviour change. Much of Dr Elliott’s research is highly applied and involves collaborating with commercial and NHS partners. He has received funding from EPSRC, Innovate UK and SBRI Healthcare, as well as direct industrial funding. He is currently Data Analytics Theme Lead for the EPSRC funded OATech+ Network and on the steering committee for the EPSRC funded VSimulators facilities at Bath and Exeter.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/ChdbggScUgo</p>
Interference in the shared-stroop task: a comparison of self- and other-monitoring
<p>Co-acting participants represent and integrate each other's actions, even when they are not required to monitor one another. However, monitoring the actions of a partner is an important component of successful interactions, and particularly of linguistic interactions. Moreover, monitoring others may rely on similar mechanisms to those that are involved in self-monitoring. In order to investigate the effect of monitoring on shared linguistic representations, we combined a monitoring task with the shared Stroop task. In the shared Stroop task, one participant named the colour of words in one colour (e.g., red) while ignoring stimuli in the other colour (e.g., green); the other participant either named the colour of words in the other colour or did not respond. Crucially, participants either had to provide feedback about the correctness of their partner's response (Experiment 3) or did not (Experiment 2). The results showed that interference was greater when both participants responded than when they did not, but only when partners provided feedback. We argue that feedback increased joint task interference because in order to monitor their partner, participants had to represent their target utterance, and this representation interfered with self-monitoring of their own utterance.</p>
Log2Lose: Incenting Weight Loss and Dietary Self-monitoring in Real-time to Improve Weight Management Among Adults With Obesity
ClinicalTrials.gov study NCT04770909. IPD Sharing: YES. Countries: 1. Publications: 2.
Interference in the shared-stroop task: a comparison of self- and other-monitoring
Open the record for dataset details and reuse information.
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>Abstract:<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>
[Data] Real-time monitoring and quality assurance for laser-based directed energy deposition: integrating co-axial imaging and self-supervised deep learning framework
<p>The experimental setup utilized a co-axial color Charged Couple Device (CCD) camera, integrated into the laser deposition head. This camera operates at a frame rate of 30 frames per second and captures the morphology of the process area. The captured images consist of three RGB channels with a 640 × 480 pixels resolution. To enable the camera to capture the radiation from the process zone, a beam splitter is installed on Precitec's laser applicator head. An optical notch filter within the 650–675 nm range also blocks the laser wavelengths.</p> <p>The dataset consists of four categories that covers the process map of DED process [.rar file].<br>The dataset consist of around 48,000 images that are labelled into 4 categories [P1-P2-P3-P4]. The images correspond to DED process zone captured co-axially<br>The categories are function of linear laser energy deposited. The folder is already split into Train and Test.</p>
Quantum Material-Based Self-Propelled Microrobots for the Optical "On-the-Fly" Monitoring of DNA
<p>Quantum dot-based materials have been found to be excellent platforms for biosensing and bioimaging applications. Herein, self-propelled microrobots made of graphene quantum dots (GQD–MRs) have been synthesized and explored as unconventional dynamic biocarriers toward the optical “on-the-fly” monitoring of DNA. As a first demonstration of applicability, GQD–MRs have been first biofunctionalized with a DNA biomarker (i.e., fluorescein amidite-labeled, FAM-L) via hydrophobic π-stacking interactions and subsequently exposed toward different concentrations of a DNA target. The biomarker–target hybridization process leads to a biomarker release from the GQD–MR surface, resulting in a linear alteration in the fluorescence intensity of the dynamic biocarrier at the nM range (1–100 nM, <em>R</em><sup>2</sup> = 0.99), also demonstrating excellent selectivity and sensitivity, with a detection limit as low as 0.05 nM. Consequently, the developed dynamic biocarriers, which combine the appealing features of GQDs (e.g., water solubility, fluorescent activity, and supramolecular π-stacking interactions) with the autonomous mobility of MRs, present themselves as potential autonomous micromachines to be exploited as highly efficient and sensitive “on-the-fly” biosensing systems. This method is general and can be simply customized by tailoring the biomarker anchored to the GQD–MR’s surface.</p>
Dataset for the paper "Ocean wave energy harvesting with high energy density and self-powered monitoring system"
<p>Dataset for the paper "Ocean wave energy harvesting with high energy density and self-powered monitoring system“.</p>
Development of a Self-Powered Structural Health Monitoring System for Transportation Infrastructure
<p>Corresponding data set for Tran-SET Project No. 17PTAM03. Abstract of the final report is stated below for reference:</p> <p>"Roadways and bridges play an important role in the economic and social health of society by connecting commerce and people. Economic growth and population expansion pose considerable burden on the aging infrastructure (i.e., pavements and bridges). There is a pressing need to develop structural health monitoring (SHM) technologies capable of collecting infrastructure utilization data. Doing so inexpensively with self-powered systems will revolutionize infrastructure monitoring technology, and will improve decision making enabling roadway and bridge preservation. In this study, a self-powered battery-less structural health monitoring (SHM) system was developed. It is powered by a thermal energy harvester equipped with thermoelectric generators (TEGs) driven by temperature differentials between the top of asphalt pavements and their lower layers. An innovative 2-tier TEG harvester was designed to limit the downtime of the SHM system when the temperature differentials are insufficient to power a single unit. The 2-tier system requires a minimum of 2.1⁰C in temperature differential to generate the minimum of 40 mV needed to power the SHM system. The SHM system consists of a DC-DC booster to increase the voltage generated by the harvester, a buck controller to bring this voltage down to the 3.3 Volts required for powering the microcontroller, a microcontroller and a wireless transceiver for transmitting the data. Another transceiver carried on-board a pilot vehicle is needed to retrieve the data. Software was developed in this project to allow data communication between the two transceivers. The SHM system developed accepts analogue voltage input from any sensor that generates analogue voltage, (e.g., piezoelectric axle load sensors, strain gauges, temperature gauges and so on). A prototype of this SHM system was constructed and tested in the lab and it is ready for field implementation."</p>
Dataset associated with article: Self-sufficient seismic boxes for monitoring glacier seismology in Greenland
<p>Dataset associated with article: Self-sufficient seismic boxes for monitoring glacier3 seismology in Greenland</p> <p>Contains:<br> - Seismic data of both SG-boxes and regular geophones from Gornergletscher fieldtest, 2021( Seismic_Data_Gorner_Fieldtest.zip) <br> --> SG-box data naming: GO"station_number"SG <br> --> Geophone data naming: GO"station_number"GP<br> <br> - Weather data Gornergletscher fieldtest from Monte Rosa, Meteo Swiss Weather station ( Weather_data_Gorner_Fieldtest_2021.zip) <br> --> 1hr wind averages <br> --> 1hr temperature averages<br> <br> - MSR logger data from SG-boxes from Gornergletscher fieldtest, 2021. Every 5 min these log battery power, tilt (along three axes, temperature and humidity inside the box and light strength on two sides of the SG-box. ( MSR_logger_data_SGboxes_Gorner_Fieldtest.zip )<br> <br> - Seismic data of SG box (Sensor code BSM) next to weather station first acquisition Greenland 2021 ( Seismic_Data_SG_Box_first_acquisition_Greenland_2021.zip) <br> --> .pri0 is East component, .pri1 is North component, .pri2 is Vertical component.<br> <br> - Weather data from weather station next to SG-box (sensor code BSM) during first acquisition Greenland 2021 ( Weather_station_data_Greenland_2021.zip) <br> --> The weather station logs a value every two hours.</p> <p> </p>
Dataset for "Characterization of rainwater infiltration within a controlled experiment by self-potential monitoring and modeling"
<p>This dataset provides the raw experimental data for the manuscript "Characterization of rainwater infiltration within a controlled experiment by self-potential monitoring and modeling". By imposing rainfall, we monitored the self-potential, volumetric water content, and temperature at different depths of a soil-column model within a water infiltration process. </p> <p> </p>
Effectiveness of self-management of medication and self-monitoring of blood pressure, diet, and physical exercise on blood pressure in patients with poorly controlled hypertension (MEDICHY study): randomized and controlled trial
<p>Dataset study medichy ISRCTN144433778</p>
Self-monitoring Activity: a Randomized Trial of Game-oriented Applications
ClinicalTrials.gov study NCT02341235. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Gout Self-Monitoring Aiming to Reach Target
ClinicalTrials.gov study NCT03274063. IPD Sharing: YES. Countries: 1. Publications: 1.
Enhancing Child Dietary Self-monitoring
ClinicalTrials.gov study NCT06193967. IPD Sharing: NO. Countries: 1. Publications: 12.
Comparison of Real-tiMe ContInuous gLucosE moNitoriNg With Self-monitorIng of Blood Glucose in Young AduLts With Type 1 diabeteS
ClinicalTrials.gov study NCT03445377. IPD Sharing: YES. Countries: 1. Publications: 1.
Brain Emotion Circuitry-Targeted Self-Monitoring and Regulation Therapy (BE-SMART)
ClinicalTrials.gov study NCT03183388. IPD Sharing: NO. Countries: 1. Publications: 1.
Screening for Atrial Fibrillation With Self Pulse Monitoring
ClinicalTrials.gov study NCT05818592. IPD Sharing: YES. Countries: 1. Publications: 7.
ScienceDex guides
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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.