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8,460 results for “MONITORING”
Fig 12 from: Tanaka S (2017) Locusta migratoria (Orthoptera: Acrididae) embryos monitor neighboring eggs for hatching synchrony. Journal of Orthoptera Research 26: 103-115. https://doi.org/10.3897/jor.26.20935
Fig 12 - The frequency distributions of hatching intervals for Locusta migratoria egg pairs derived from different (middle panel) and from the same pods (top and bottom panels). For explanation of the experimental design, see Fig. 11. Different lower-case letters indicate significant differences in mean values at the 5% level with Steel-Dwass test. The diagrams on the right of the figure show combinations of eggs from two pods expressed as white and black eggs, respectively.
Fig 13 from: Tanaka S (2017) Locusta migratoria (Orthoptera: Acrididae) embryos monitor neighboring eggs for hatching synchrony. Journal of Orthoptera Research 26: 103-115. https://doi.org/10.3897/jor.26.20935
Fig 13 - Hatching patterns of Locusta migratoria eggs derived from different pods that were kept singly on day 10 and paired in contact with each other on day 12. Some eggs from the respective pods were singly kept as controls. Two pods laid on the same day (A) and different days (B) were used. In the mixed pairs (middle panel), the first and second hatchings are shown in black and light-colored bars. Different lower-case letters indicate significant differences in mean values at the 5% level with Tukey's multiple test. The diagrams on the right of the figure show combinations of eggs from two pods expressed as white and black eggs, respectively. The frequency distributions of hatching intervals for the mixed pairs are shown (C and D).
Fig 1 from: Tanaka S (2017) Locusta migratoria (Orthoptera: Acrididae) embryos monitor neighboring eggs for hatching synchrony. Journal of Orthoptera Research 26: 103-115. https://doi.org/10.3897/jor.26.20935
Fig 1 - Hatching patterns of L. migratoria eggs kept in the pods at 30°C under continuous illumination. Cumulative percentages of hatched eggs from 22 egg pods plotted against the time after the start of hatching for each pod (A). The frequency distribution of ranges from the beginning to end of hatching period from the respective pods (B).
Supplementary Materials for article entitled 'Sociolinguistic Monitoring and L2 Speakers of English', published in Linguistics
<p>see the publication for details</p>
Рис. 1. Зонирование Амурской обΛасти на территории мониторинга попуΛяции ΑаΛьневосточного аиста. Синяя и зеΛеная зоны — учетные Αанные 2018 г., жеΛтая зона — учетные Αанные 2019 г., серая зона — опросные Αанные 2011–2017 гг. Fig. 1. Oriental stork monitoring zones in the Amur region. The blue and green zones are the 2018 survey, the yellow zone is the 2019 survey, the gray zone is the 2011–2017 survey data in Oriental stork (Ciconia boyciana Swinhoe) breeding population survey in the Amur region in 2018-2019
Рис. 1. Зонирование Амурской обΛасти на территории мониторинга попуΛяции ΑаΛьневосточного аиста. Синяя и зеΛеная зоны — учетные Αанные 2018 г., жеΛтая зона — учетные Αанные 2019 г., серая зона — опросные Αанные 2011–2017 гг. Fig. 1. Oriental stork monitoring zones in the Amur region. The blue and green zones are the 2018 survey, the yellow zone is the 2019 survey, the gray zone is the 2011–2017 survey data
Monitoring data of Zhujiatang landslide
<p>Data is for scientific research use only.</p>
Activity Monitor Tube
SciDraw upload
Support videos for the article "Damage categorization in full-scale, full-composite ship hull under high-energy impacts by unsupervised-learning-enabled acoustic emission monitoring and laser shearography inspection"
<p>Video 1: Video showing one of the impact from a general perspective</p> <p>Video 2: Slow-motion video of the second impact</p>
Figure 8 in Monitoring the feeding and parental care behavior of a pair of free-living owls (Tyto furcata) in the nest during the reproductive period in Rio de Janeiro, Brazil
Figure 8. Activities schedule of a Tyto furcata family registered within a urban nest box in Campos dos Goytacazes. Each bar shows the total number of days of the activity.
Figure 7 in Monitoring the feeding and parental care behavior of a pair of free-living owls (Tyto furcata) in the nest during the reproductive period in Rio de Janeiro, Brazil
Figure 7. The average time per day (minutes) dedicated per Tyto furcata adults to teaching the feeding behavior for the brood, from the birth until left the nest. Campos dos Goytacazes, RJ.
Supporting files for 'The role of water in APCI-MS online monitoring of gaseous n-alkanes'
Open the record for dataset details and reuse information.
Reproducibility archive for HCC detection and post-surgery monitoring using cfDNA methylomes
Open the record for dataset details and reuse information.
Photon number trajectories for "Monitoring the energy of a cavity by observing the emission of a repeatedly excited qubit"
<p>Includes 1000 photon number trajectories, obtained the same way as in Fig. 3c of the article.</p>
INVESTITSIYA LOYIHALARINI NAZORAT VA MONITORING QILISH TIZIMINI YARATISH VA TAKOMILLASHTIRISH
Open the record for dataset details and reuse information.
Data from: Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning
<p><strong>Paper Abstract:</strong></p> <p>Trees across the urban-rural continuum are recognized for their ecological importance and ecosystem services. Municipalities often utilize spatial canopy cover data for monitoring this resource. Monitoring frameworks typically rely on fine-scale maps derived from very high spatial resolution sensors, which are high quality but expensive and unwieldy for consistent wide-area monitoring. In this paper, we explore how free Landsat imagery, supported by very high-resolution imagery interpretation and/or digital hemispherical photographs, can be used to effectively map canopy cover at a scale appropriate for municipal monitoring. We compare linear models and random forest machine learning for predicting canopy cover across a landscape (general) and within specific land covers (specialized). We create 2018 canopy cover maps and track progress towards forestry objectives in a region of southern Ontario, Canada. Random forest models using all reference data perform best for general use (R<sup>2</sup>: 0.90, RMSE: 10.1%), separating non-canopy vegetation (e.g., agricultural fields) from tree canopy. Specialized models are useful in forest land cover patches, where hemispherical photographs relate with Landsat at a moderate strength (R<sup>2</sup>: 0.67, RMSE: 2.73%), and in residential areas, capturing the totality of canopy cover variation (R<sup>2</sup>: 0.85, RMSE: 5.66%). Accuracy was assessed with standard cross-validation, which is useful given limited resources. However, following best practice, an independent reference sample was also leveraged to assess the best general model (R<sup>2</sup>: 0.86, RMSE: 11.4%), indicating that cross-validation was slightly overoptimistic. Results show that Caledon, a rural-dominant municipality within the study area, is the greenest (34% canopy cover). The two cities (Brampton and Mississauga) have 15.9% and 17.5% canopy cover. Residential canopy criteria indicate “Good” performance in Caledon, “Moderate” in Mississauga, and “Low” in Brampton based on our 2018 assessment. The methods described here can provide municipalities with a low-cost approach for tree canopy monitoring across complex landscapes.</p> <p> </p> <p><strong>Data details:</strong></p> <p>See paper. </p>
Audio Visual in-situ Monitoring Dataset for Laser Directed Energy Deposition (LDED) of Maraging Steel C300
<p>This dataset presents a set of acoustic signals and coaxial CCD images captured during a single-bead wall experiment in robotic Laser Directed Energy Deposition (LDED) using Maraging Steel C300. The acoustic data was recorded using a high-fidelity Prepolarized microphone sensor (Xiris WeldMIC), capturing the intricate sound profiles associated with the LDED process at a sampling rate of 44,100 Hz. The coaxial CCD melt pool images are captured at 30 Hz.</p> <p><strong>Laser Directed Energy Deposition:</strong></p> <p>This dataset was generated with a robotic LDED process that consists of a six-axis industrial robot (KUKA KR90) coupled with a two-axis positioner, a laser head, and a coaxial powder-feeding nozzle.</p> <p> </p> <p><strong>File Naming Convention:</strong></p> <ul> <li>Audio files within the <strong>audio_files</strong> folder are named following the pattern <strong>sample_ExperimentID_SampleID.wav</strong>. Given that there's only one experiment and one sample provided in this demo dataset, the naming will be consistent, for example, <strong>sample_1_1.wav</strong> for the first file.</li> <li>Coaxial melt pool image files within the <strong>images </strong>folder are named following the pattern <strong>sample_ExperimentID_SampleID.jpg</strong>. </li> </ul> <p><strong>Annotation Details:</strong></p> <ul> <li>The <strong>annotations_1.csv</strong> file contains detailed labels for each audio file and image file, correlating to the conditions observed during the experiment, aiding in quick identification and analysis.</li> </ul> <p><strong>Handcrafted features for ML modelling:</strong></p> <ul> <li>The <strong>audio_features.h5</strong> file contains various physics-informed acousitc feature extracted through Python, which can be used for baseline ML modelling purpose.</li> </ul> <p><strong>Experimental Parameters:</strong> The dataset reflects a controlled experiment setup with the following specifications:</p> <ul> <li>Geometry: Single bead wall structure</li> <li>Dimensions: 90 mm * 42.5 mm</li> <li>Number of layers: 50</li> <li>Laser beam diameter: 2 mm</li> <li>Layer thickness: 0.85 mm</li> <li>Stand-off distance: 12 mm</li> <li>Laser profile: Gaussian</li> <li>Laser wavelength: 1064 nm</li> </ul> <p><strong>Process Parameters:</strong></p> <ul> <li>Laser power: 2.3 kW</li> <li>Speed: 25 mm/s</li> <li>Dwell time: 0 s</li> <li>Powder flow rate: 12 g/min</li> </ul> <p>This dataset aims to facilitate the development and testing of acoustic-based, or multi-sensor fusion-based defect detection models for real-time quality monitoring in LDED processes. It can also serve as a reference point for further research on sensor fusion, machine learning, and real-time monitoring of manufacturing processes.</p>
Fig. 1 in Heracleum Sosnowskyi Manden. Monitoring In Protected Areas - A Case Study In Rēzekne Municipality, Latvia
Fig. 1. Studied sites in Rēzekne municipality. LKS-92 coordinate system. Due to the large differences among the studied sites, small sites are not visible in the map (Author: E. Tripāne).
FIGURE 8 in Novel analysis of locality data can inform better inventory and monitoring practices for paleontological resources at John Day Fossil Beds National Monument Oregon, USA
FIGURE 8. Boxplot of last yield over area index by hiatus length. Note the similar mean between all hiatus lengths.
FIGURE 4 in Novel analysis of locality data can inform better inventory and monitoring practices for paleontological resources at John Day Fossil Beds National Monument Oregon, USA
FIGURE 4. Aerial photograph with example measurements of T2.0" and L7.0". Fieldworkers recorded locality data by locating their place on the air photograph, measuring inches from the top and inches from the left, and writing the air photograph number and measurements in field notes.
FIGURE 5 in Novel analysis of locality data can inform better inventory and monitoring practices for paleontological resources at John Day Fossil Beds National Monument Oregon, USA
FIGURE 5. Map of field collection data for Test Area from 1999-2000. Each point represents one field number. Precise locality information is available to qualified researchers upon request from JODA's museum program.
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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.