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8,460 results for “MONITORING”
A Transcriptome Fingerprinting Assay for Clinical Immune Monitoring (MS Datasets)
GEO Series GSE100162. Homo sapiens. 56 samples. Type: Expression profiling by array.
Figure 3 from: Moldovan OT, Øvrevik Skoglund R, Banciu HL, Dinu Cucoș A, Levei EA, Perșoiu A, Lauritzen S-E (2019) Monitoring and risk assessment for groundwater sources in rural communities of Romania (GROUNDWATERISK). Research Ideas and Outcomes 5: e48898. https://doi.org/10.3897/rio.5.e48898
Figure 3 The flow of the WPs and their relationship.
Figure 1 from: Moldovan OT, Øvrevik Skoglund R, Banciu HL, Dinu Cucoș A, Levei EA, Perșoiu A, Lauritzen S-E (2019) Monitoring and risk assessment for groundwater sources in rural communities of Romania (GROUNDWATERISK). Research Ideas and Outcomes 5: e48898. https://doi.org/10.3897/rio.5.e48898
Figure 1 The project logo.
Jira Monitoring Data September 2017
<pre>This is the second of four datasets that our research "Comparing Static and Dynamic Weighted Software Coupling Metrics" published in <a href="https://doi.org/10.1007/978-3-030-30275-7_22">https://doi.org/10.1007/978-3-030-30275-7_22</a> is based on. This dataset contains the monitoring data from our installation of Atlassian Jira, from September 2017. </pre>
Jira Monitoring Data February 2017
<p>This is the first of four datasets that our research "Comparing Static and Dynamic Weighted Software Coupling Metrics" published in https://doi.org/10.1007/978-3-030-30275-7_22 is based on. This dataset contains the monitoring data from our installation of Atlassian Jira, from February 2017.</p>
Jira Monitoring Data February 2018
<pre>This is the third of four datasets that our research "Comparing Static and Dynamic Weighted Software Coupling Metrics" published in <a href="https://doi.org/10.1007/978-3-030-30275-7_22">https://doi.org/10.1007/978-3-030-30275-7_22</a> is based on. This dataset contains the monitoring data from our installation of Atlassian Jira, from February 2018.</pre>
Neuromuscular block monitoring by smartphone application (i-TOF© system): an observational pilot study
<p>Dataset of the observational pilot study on Neuromuscular block monitoring by smartphone application (i-TOF© system) compared to TOF-WATCH device.</p> <p>The present study was approved by Ethical Committee (Prot. 18.18 TS ComEt) of the University Hospital Campus Biomedico of Rome.</p>
Data and results for manuscript "A monitoring system for spatiotemporal electrical self-potential measurements in cryospheric environments"
<p>This package contains data and python scripts required to reproduce the figures of the accepted manuscript</p> <p>Weigand, M., Wagner, F. M., Limbrock, J. K., Hilbich, C., Hauck, C., and Kemna, A.: A monitoring system for spatiotemporal electrical self-potential measurements in cryospheric environments, Geosci. Instrum. Method. Data Syst. Discuss., https://doi.org/10.5194/gi-2020-5, 2020.</p> <p>Final Paper DOI: https://doi.org/10.5194/gi-9-1-2020</p> <p>https://gi.copernicus.org/preprints/gi-2020-5/</p> <p>Please refer to the Readme.txt file for further instructions.</p>
Improved chloride quantification in quadrupole aerosol chemical speciation monitors (Q-ACSMs)
<p>Particulate chloride is an important component of fine particulate matter in marine air masses. Recent field studies also report elevated concentrations of gas-phase reactive chlorine species and particulate chloride related to anthropogenic activities. This work focuses on particulate chloride detection and quantification issues observed for some quadrupole aerosol chemical speciation monitors (Q-ACSM), which are designed for long-term measurement of ambient aerosol composition. The ACSM reports particle concentrations based on the difference between measurements of ambient air (sample mode) and particle-free ambient air (filter mode). For our long-term campaign in Krakow, Poland, the Q-ACSM reports apparent negative total chloride concentration for most of the campaign when analyzed with the default fragmentation table. This is the result of the difference signal from <em>m/z</em> 35 (<sup>35</sup>Cl<sup>+</sup>) being negative which dominates over the positive difference signal from <em>m/z</em> 36 (H<sup>35</sup>Cl<sup>+</sup>). Highly time-resolved experiments with NH<sub>4</sub>Cl, NaCl and KCl particles show that the signal response of <em>m/z</em> 35 is non-ideal, where the signal builds up and decreases slowly for all three salts, leading to a negative difference measurement. In contrast, the <em>m/z</em> 36 signal exhibits a near step-change response for NH<sub>4</sub>Cl during sampling and filter period, resulting in a positive difference signal. The response of <em>m/z</em> 36 for NaCl and KCl is not as prompt as for NH<sub>4</sub>Cl but still fast enough to have a positive difference signal. Furthermore, it is shown that this behavior is mostly temperature-independent. Based on these observations, this work presents an approach to correct the chloride concentration time series by adapting the standard fragmentation table coupled with a calibration of NH<sub>4</sub>Cl to obtain a relative ionization efficiency (RIE) based on the signal at <em>m/z</em> 36 (H<sup>35</sup>Cl<sup>+</sup>). This correction can be applied for measurements in environments where chloride is dominated by NH<sub>4</sub>Cl. Caution should be exercised when other chloride salts dominate the ambient aerosol.</p> <p> </p> <p>This repository contains the data corresponding to each figure presented in the published main text.</p>
Real-Time Early Detection and Monitoring of Flooding Using Low-Cost Highly Sensitive Ultrasound Sensing of Water Level
<p>Flooding poses safety hazards to motorists, emergency and maintenance crews and may cause costly damage to transportation infrastructure and its operation. Flash flooding, in particular, causes the most flood-related deaths According to NOAA, in 2017 alone, flash flooding also caused $60.7 billion worth of economic damage. Low-water crossings are among the first places where deaths and significant damages to vehicles occur during flooding. With flash flooding, when a critical corridor is blocked by a high level of water, it affects the safety of the general public. To keep the critical corridors open as long as possible, and to minimize losses from flooding, accurate early detection of the rising water level is essential. The flood level detection system has to have flood stage maps in the geographic information system for the street-, roadway-, and critical-freight corridors. This area encompasses public roads in urbanized areas that provide access and connection to the primary roads for ports, public transportation or other transportation facilities. The main goal of this project is to develop cost-effective and high efficient solar-powered water level detection units and implement real-time water level monitoring for water both pavement and river stream. The project was performed with three objectives: to develop the low cost reliable real-time data of the ultrasound water level detection system, increasing its data reliability and resolution; 2) to improve an energy-saving processing system; and to deploy the ultrasound water level detection system and real-time monitoring system for water both pavement and river stream for safety measures. The obtained results and findings imply the developed monitoring system can present reliable water level data with efficient power and data transmission system.</p>
Monitoring Distributed Systems under Partial Synchrony (video)
Full video presentation of the paper: Monitoring Distributed Systems under Partial Synchrony.<br><br>Appears in Session 6 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
Data from: Diel activity, frequency and visit duration of pollinators in focal plants: in situ automatic camera monitoring and data processing
Data collection on interactions between organisms and their environment has traditionally been conducted by on-site human observations, a time-consuming enterprise that could explain the shortage of around-the-clock observations of free-ranging wild animals. In this paper, I outline a time-efficient procedure to collect data on flower-visiting animals. The objectives were, first, to model diel activity rhythms by using cosine-based mixed-effects regression models (cosinor method) on data from an established automatic video monitoring system and, secondly, to test the use of a cheap off-the-shelf digital camera modified for automated monitoring of flower visitors. Two different model systems were studied: foraging bumblebees visiting focal white clovers, monitored around-the-clock (193 h) to model diel activity; and honeybees visiting thistles, monitored over a shorter period (5 h) to test the applicability and reliability of a new method for monitoring pollinators. The data were automatically entered and processed using R-scripts after manual filtering of the images, obviating the need for manual data entry prior to analysis. For diel activity in bumblebees, the model that gave the best fit included the 24-h fundamental period and one harmonic, a 12-h period to modulate the signal, together with temperature. The bumblebees were exclusive diurnal, with activity starting about 5 h after sunrise, peaking sharply in the afternoon and ending about 1 h before sunset. In addition to time of day, activity also increased with temperature. The off-the-shelf digital camera, Canon PowerShot®, with motion detection script, was triggered by every flower-visiting honeybee. In addition to recorded visitor frequency and visitor duration, it enabled high-resolution images, which could be important for species identification. Automatic camera recording is advantageous for close-up monitoring, compared with continuous video recording, because the latter demands more time and effort in reviewing the material. It could be used to study a range of different species such as pollinators, on-plant behaviour of herbivorous animals, cavity dwellers or cavity breeders. Moreover, the procedures for automatic data entry, data processing and statistical analysis for modelling diel activity rhythms could have great relevance for researchers using other types of camera monitoring systems operating 24 h per day.
Data from: A machine learning method to monitor China's AIDS epidemics with data from Baidu Trends
Background: AIDS victims' unwillingness to report their disease, due to social discrimination against them, makes it hard for disease control departments to accurately monitor the disease's dynamics through traditional surveillance tools, such as over-the-counter drug sales and hospital or self-reported data. With the diffusion and adoption of the Internet, the 'big data' aggregated from Internet search engines, which contain users' information on the concern or reality of their health status, provide a new opportunity for AIDS surveillance. This paper uses search engine data to monitor and forecast AIDS in China. Methods: A machine learning method, artificial neural networks (ANNs), is used to forecast AIDS occurrences and deaths. Search trend data related to AIDS from the largest Chinese search engine, Baidu.com, are collected and selected as the input variables of ANNs, and officially reported actual AIDS occurrences and deaths are used for the output variable. Three criteria, the mean absolute percentage error, the root mean squared percentage error, and the index of agreement, are used to test the forecasting performance of the ANN method. Results: Based on the monthly time-series data from January 2011 to June 2017, this article finds that, under three criteria, the ANN method can lead to satisfactory forecasting of AIDS occurrences and deaths, regardless of the change of the number of search queries. Conclusions: Internet-based data should be adopted as a real-time, cost-effective complement to a traditional AIDS surveillance system.
Data from: Quantification of the sit-to-stand movement for monitoring age-related motor deterioration using the Nintendo Wii Balance Board
Simple methods for quantitative evaluations of individual motor performance are crucial for the early detection of motor deterioration. Sit-to-stand movement from a chair is a mechanically demanding component of activities of daily living. Here, we developed a novel method using the ground reaction force and center of pressure measured from the Nintendo Wii Balance Board to quantify sit-to-stand movement (sit-to-stand score) and investigated the age-related change in the sit-to-stand score as a method to evaluate reduction in motor performance. The study enrolled 503 participants (mean age ± standard deviation, 51.0 ± 19.7 years; range, 20-88 years; male/female ratio, 226/277) without any known musculoskeletal conditions that limit sit-to-stand movement, which were divided into seven 10-year age groups. The participants were instructed to stand up as quickly as possible, and the sit-to-stand score was calculated as the combination of the speed and balance indices, which have a tradeoff relationship. We also performed the timed up and go test, a well-known clinical test used to evaluate an individual's mobility. There were significant differences in the sit-to-stand score and timed up and go time among age groups. The mean sit-to-stand score for 60s, 70s, and 80s were 77%, 68%, and 53% of that for the 20s, respectively. The timed up and go test confirmed the age-related decrease in mobility of the participants. In addition, the sit-to-stand score measured using the Wii balance board was compared with that from a laboratory-graded force plate using the Bland-Altman plot (bias = −3.1 [ms]-1, 95% limit of agreement: −11.0 to 3.9 [ms]-1). The sit-to-stand score has good inter-device reliability (intraclass correlation coefficient = 0.87). Furthermore, the test-retest reliability is substantial (intraclass correlation coefficient = 0.64). Thus, the proposed STS score will be useful to detect the early deterioration of motor performance.
Data from: Harnessing olfactory bulb oscillations to perform fully brain-based sleep-scoring and real-time monitoring of anesthesia depth
Real-time tracking of vigilance states related to both sleep or anaesthesia has been a goal for over a century. However, sleep scoring cannot currently be performed with brain signals alone, despite the deep neuromodulatory transformations that accompany sleep state changes. Therefore, at heart, the operational distinction between sleep and wake is that of immobility and movement, despite numerous situations in which this one-to-one mapping fails. Here we demonstrate, using local field potential (LFP) recordings in freely moving mice, that gamma (50–70 Hz) power in the olfactory bulb (OB) allows for clear classification of sleep and wake, thus providing a brain-based criterion to distinguish these two vigilance states without relying on motor activity. Coupled with hippocampal theta activity, it allows the elaboration of a sleep scoring algorithm that relies on brain activity alone. This method reaches over 90% homology with classical methods based on muscular activity (electromyography [EMG]) and video tracking. Moreover, contrary to EMG, OB gamma power allows correct discrimination between sleep and immobility in ambiguous situations such as fear-related freezing. We use the instantaneous power of hippocampal theta oscillation and OB gamma oscillation to construct a 2D phase space that is highly robust throughout time, across individual mice and mouse strains, and under classical drug treatment. Dynamic analysis of trajectories within this space yields a novel characterisation of sleep/wake transitions: whereas waking up is a fast and direct transition that can be modelled by a ballistic trajectory, falling asleep is best described as a stochastic and gradual state change. Finally, we demonstrate that OB oscillations also allow us to track other vigilance states. Non-REM (NREM) and rapid eye movement (REM) sleep can be distinguished with high accuracy based on beta (10–15 Hz) power. More importantly, we show that depth of anaesthesia can be tracked in real time using OB gamma power. Indeed, the gamma power predicts and anticipates the motor response to stimulation both in the steady state under constant anaesthetic and dynamically during the recovery period. Altogether, this methodology opens the avenue for multi-timescale characterisation of brain states and provides an unprecedented window onto levels of vigilance.
Data from: Measuring β‐diversity by remote sensing: a challenge for biodiversity monitoring
Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer bio- diversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, especially when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this context, airborne or satellite remote sensing allow information to be gathered over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (β-diversity) might add crucial information related to relative abundance of dif- ferent species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript we propose novel techniques to measure β-diversity from airborne or satellite remote sensing, mainly based on: i) multivariate statistical analysis, ii) the spectral species concept, iii) self-organizing feature maps, iv) multi- dimensional distance matrices, and the v) Rao's Q diversity. Each of these measures addresses one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating β-diversity from remotely sensed imagery and potentially relating them to species diversity in the field.
Primary documentation on the scientific study of indicators of continuous monitoring and flash monitoring of glycemia in children and adolescents with type 1 diabetes mellitus
<p>The main purpose of creating an electronic database was to evaluate the performance of continuous glucose monitoring (CGM) and flash monitoring (FMS) in children and adolescents diagnosed with type 1 diabetes mellitus. The database is intended for entering, systematizing, storing and displaying patient data (date of birth, age, date of diagnosis of type 1 diabetes mellitus, length of illness, date of first visit to an endocrinologist, with the installation of a CGM or FMS), glycated hemoglobin indicators initially , during the study and ultimately, as well as CGM, FMS data (average glucose level, glycemic variability, percentage of cases above the target range, percentage of cases within the target range, percentage of cases below the target range, number of hypoglycemic episodes and their average duration, frequency of daily scans and frequency of sensor readings).</p><p>The database is the basis for comparative statistical analysis of dynamic monitoring indicators in groups of patients with the presence or absence of diabetic complications (neuropathy, retinopathy and nephropathy). The database presents the results of a prospective, open, controlled, clinical study obtained over a year and a half. The database includes information on 307 patients (adolescent children) aged 3 to 17 years inclusive. During the study, the observed patients were divided into two groups: Group 1 - patients diagnosed with type 1 diabetes mellitus and with diabetic complications, 152 people, Group 2 – patients diagnosed with type 1 diabetes mellitus and with no diabetic complications, 155 people. All registrants of the database were assigned individual codes, which made it possible to exclude personal data (full name) from the database.</p><p>The database is executed in the Microsoft Office Excel program and has the character of a depersonalized summary table, which consists of two blocks-sheets: patients of groups 1 and 2 and is structured according to the following sections: "Patient number"; "Patient code"; "Date of birth"; "Age of the patient"; section "Date of diagnosis of DM1" indicates the date of the official diagnosis of type 1 diabetes mellitus at the first hospitalization of the patient, this information is borrowed from medical information systems; section "Length of service DM1" reflects information about the duration of the patient's illness; the section "Date of the first visit" contains information about the date of the registrant's visit to the endocrinologist with the installation of FMS / CGM devices; the section "Frequency of self-monitoring with a glucometer" contains information about the frequency of measuring blood glucose levels by the patient at home using a glucometer until the establishment of FMS / CGM.</p><p>Sections "HbA1c initially (GMI)", "HbA1c (GMI)", "HbA1c final (GMI)", display the indicators of the level of glycated hemoglobin from the total for the period of the beginning of the study, at the intermediate stages of the study and at the end of observation.</p><p>The database structure has a number of sections accumulating information obtained with CGM/FMS, in particular: the section "Average glucose level"; the section "% above the target range", reflecting the percentage of the patient's stay with glycemia above the target indicators during the day; the section "% within the target range", reflecting the percentage of the patient's stay within the target glycemia indicators per day; the section "% below the target range", reflecting the percentage of the patient's stay with glycemia below the target indicators during the day; the section "Hypoglycemic phenomena", reflecting the number of cases of hypoglycemia in patients within 2 weeks; the section "Average duration", reflecting the average duration of hypoglycemic phenomena registered in the patient; the section "Sensor data received", indicating the percentage of time the patient was with an active device sensor; the section "Daily scans" show the frequency of scans of the patient's glycemic level (once a day); the section "%CV" displays the variability of the patient's glycemia recorded by the device. The listed sections are repeated in the database in accordance with the number of follow-up visit.</p><p>Also in the database there is a section "Mid. values", which contains indicators of the average values of patient data for all of the above sections, both in the first and in the second group of patients.</p><p>When working with the database, the use of filters (in the "Data" tab) containing the names of indicators allows you to enter information about new registrants in a convenient form or correct existing data, as well as sort and search for one or more specified indicators.</p><p>The electronic database allows you to systematize a large volume of results, distribute data into categories, search for any field or set of fields in the input format, systematize the selected array, makes it possible to directly use this data for statistical analysis, as well as to view and print information on specified conditions with the location of fields in a convenient sequence.</p>
Tutorials of the procedures intended for a remote monitoring of OA
<p><span>Video tutorials of different procedures and technologies designed for a remote assessment of knee osteoarthritis.</span></p>
Monitoring Structure and Coordination Chemistry of Co4O4-based Oxygen Evolution Catalysts by Nitrogen-14/-15 and Cobalt-59 NMR Spectroscopy
<p>This the raw data set for the publication "Monitoring Structure and Coordination Chemistry of Co4O4-based Oxygen Evolution Catalysts by Nitrogen-14/-15 and Cobalt-59 NMR Spectroscopy"</p>
LTM_AC_crop: dataset of Long term monitoring of crop yield in young arable alley cropping systems in Belgium
<p>Dataset including results from crop yield and crop quality measurements obtained on a set of six alley cropping fields in Belgium during the first decade after their establishment. Sampled crops include maize, wheat, barley and potato. Measurements started in 2015 and are at present still continued. A transect approach is used in wich crop production is monitored at fixed distances from the trees (2m, 5m and 12m). Ton dry matter is assessed as well as crop specific quality parameters (a.o. for winter cereals: protein content & thousend kernel weight, silage maize: protein content & digestability, potato: under water weight and diameter fractions). For a detailed description of methodologies used see also: Pardon et al. (2018) (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.agsy.2018.08.008" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.agsy.2018.08.008</span></span></a>).</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.