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52 results for “Process Monitoring”
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.
Development of an IoT based smart potato leaf diseases monitoring and controlling system with image processing
Open the record for dataset details and reuse information.
The DUAL-SYSTEM HYPOTHESIS of ANOSOGNOSIA: the Interplay Between Emotional Processing and Self-Monitoring in Neurodegenerative Patients
ClinicalTrials.gov study NCT06794580. IPD Sharing: NO. Countries: 1. Publications: 0.
Processed EEG for Monitoring of Anesthetic Depth in Intracranial Tumor Surgery
ClinicalTrials.gov study NCT06922500. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Home Monitoring of Complete Blood Count Performed by Patients - a Pilot Study on the Implementation Process in South Baltic Countries.
ClinicalTrials.gov study NCT06809101. IPD Sharing: UNDECIDED. Countries: 3. Publications: 0.
Remote Monitoring of Patient Reported Outcomes to Improve the Efficacy of the Acute Phase Radiotherapy Review Process
ClinicalTrials.gov study NCT06274892. IPD Sharing: NO. Countries: 1. Publications: 0.
AlloSure for the Monitoring of Antibody Mediated Processes After Kidney Transplantation
ClinicalTrials.gov study NCT04057742. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Diel activity, frequency and visit duration of pollinators in focal plants: in situ automatic camera monitoring and data processing
Open the record for dataset details and reuse information.
Processing and Distribution of Hyperspectral Radiometer Data from Ships of Opportunity for Monitoring Phytoplankton in the Ocean
Ocean-colour sensors mounted on satellites can view the entire ocean over a period of a few days. However, to calibrate these sensors and validate the data, satellite observations must be compared with accurate and reliable in situ measurements, collected at the ocean surface. There are many regions of the ocean where these in situ measurements are rarely collected. Ocean-colour sensors can be mounted on research vessels and ships of opportunity. In this project, an ocean-colour sensor (Seabird HyperSAS Solar Tracker) was mounted on a yacht that visits remote regions of the planet where few observations have been collected. This project aims to process this data to a level for use by the scientific community for scientific applications, such as satellite validation.
SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS
SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS VARUN CHANDOLA* AND RANGA RAJU VATSAVAI* Abstract. Biomass monitoring, specifically, detecting changes in the biomass or vegetation of a geographical region, is vital for studying the carbon cycle of the system and has significant implications in the context of understanding climate change and its impacts. Recently, several time series change detection methods have been proposed to identify land cover changes in temporal profiles (time series) of vegetation collected using remote sensing instruments. In this paper, we adapt Gaussian process regression to detect changes in such time series in an online fashion. While Gaussian process (GP) has been widely used as a kernel based learning method for regression and classification, their applicability to massive spatio-temporal data sets, such as remote sensing data, has been limited owing to the high computational costs involved. In our previous work we proposed an efficient Toeplitz matrix based solution for scalable GP parameter estimation. In this paper we apply these solutions to a GP based change detection algorithm. The proposed change detection algorithm requires a memory footprint which is linear in the length of the input time series and runs in time which is quadratic to the length of the input time series. Experimental results show that both serial and parallel implementations of our proposed method achieve significant speedups over the serial implementation. Finally, we demonstrate the effectiveness of the proposed change detection method in identifying changes in Normalized Difference Vegetation Index (NDVI) data.
Monitorization of chromium sputtering (PVD) process on plastic
<p>The file collets the evolution of process parameters during the deposition of a chromium coating by magnetron sputtering (PVD) on a polycarbonate substrate. The process was carried out at Tekniker research center. The monitorized process parameters are: time, gas flow, chamber pressure, substrate temperature, power, current and voltage of the evaporator.</p>
Expansion of Pluripotent Stem Cells in a Closed Bioreactor System Guided by Automated In-Process Monitoring [CytoScanOptima_Array]
GEO Series GSE310775. Homo sapiens. 6 samples. Type: Genome variation profiling by SNP array.
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.