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858 results for “monitoring data”
Data from: Climate-driven prediction of land water storage anomalies: An outlook for water resources monitoring across the conterminous United States
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Data from: When to monitor and when to act: value of information theory for multiple management units and limited budgets
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Data from: Time to monitor livestock carcasses for biodiversity conservation and public health
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Data from: Towards large scale automated cage monitoring – Diurnal rhythm and impact of interventions on in-cage activity of C57BL/6J mice recorded 24/7 with a non-disrupting capacitive-based technique
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Data from: Unidirectional pulmonary airflow patterns in the savannah monitor lizard
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Data from: Understanding genetic variation in in vivo tolerance to artesunate: implications for treatment efficacy and resistance monitoring
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Data from: Genotyping-in-Thousands by Sequencing panel development and application for high-resolution monitoring of introgressive hybridization within sockeye salmon
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Ancillary total PM data from the MAIA Surface Monitor Network
The MAIA Surface Monitor Stage 0 files are an ancillary dataset containing processed particulate matter (PM) measurements collected from a global in-situ surface monitoring network. The files are generated by the MAIA surface monitoring subsystem software at NASA’s Atmospheric Science Data Center (ASDC).
Ancillary speciated PM data from the MAIA Surface Monitor Network
The MAIA Surface Monitor Stage 0 files are an ancillary dataset containing processed particulate matter (PM) measurements collected from a global in-situ surface monitoring network. The files are generated by the MAIA surface monitoring subsystem software at NASA’s Atmospheric Science Data Center (ASDC).
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>
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>
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.
Microscopy and flow cytometry raw data for: A genetically encoded biosensor to monitor dynamic changes of c-di-GMP with high temporal resolution
<p><br>This repository contains the microscopy and flow cytometry raw data for the following publication:</p> <p><strong>A genetically encoded biosensor to monitor dynamic changes of c-di-GMP with high temporal resolution</strong></p> <p>Andreas Kaczmarczyk, Simon van Vliet, Roman Peter Jakob, Raphael Dias Teixeira, Inga Scheidat, Alberto Reinders, Alexander Klotz, Timm Maier, Urs Jenal</p> <p>Biozentrum, University of Basel, 4056 Basel, Switzerland</p> <p>Correspondence to: urs.jenal[at]unibas.ch, andreas.kaczmarczyk[at]unibas.ch</p>
Laboratory water quality monitoring data from full scale CS#3 DWDN for the DBP prediction model
<p>Lab measurements of water quality based on manual sampling including ordinary monitoring and special monitoring performed in SafeCREW project.</p> <p>Laboratory LIMS database.</p> <p>Provide water quality measurements based on the planned monitoring schedule. Schedule tailored to SafeCREW project needs plus usual legal water sampling.</p>
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.