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8,460 results for “MONITORING”
Grond reports for the seismic moment tensor inversions done for "The January 2022 Hunga Volcano explosive eruption from the multi-technological perspective of CTBT monitoring"
<p>This are the Grond reports of the seismic moment tensor inversion done for the manuscript submitted to GJI titled:</p> <p>"The January 2022 Hunga Volcano explosive eruption from the multi-technological perspective of CTBT monitoring"</p> <p>You can view the summary figures of the inversions in the subfolders for each event manually if you wish.</p> <p>However to view the reports interactively you need to have the pyrocko and grond softwares installed. See here for installation instruction for pyrocko: https://pyrocko.org/ and here for grond https://pyrocko.org/grond/docs/current/</p> <p>After correct installation you can view the reports in any browser by executing the command "grond report --so" in the folder which contains the unpacked "report" folder.</p>
TIHM: An open dataset for remote healthcare monitoring in dementia
<p>Dementia is a progressive condition that affects cognitive and functional abilities. There is a need for reliable and continuous health monitoring of People Living with Dementia (PLWD) to improve their quality of life and support their independent living. Healthcare services often focus on addressing and treating already established health conditions that affect PLWD. Managing these conditions continuously can inform better decision-making earlier for higher-quality care management for PLWD. The Technology Integrated Health Management (TIHM) project developed a new digital platform to routinely collect longitudinal observational and measurement data within the home and apply machine learning and analytical models for the detection and prediction of adverse health events affecting the well-being of PLWD. This work describes the TIHM dataset collected during the second phase (i.e., feasibility study) of the TIHM project. The data was collected from homes of 56 PLWD and associated with events and clinical observations (daily activity, physiological monitoring, and labels for health-related conditions). The study recorded an average of 50 days of data per participant, totalling 2803 days.</p> <p> </p> <p>We have provided raw data and guidelines on how to access, visualise, manipulate and predict health-related events within the dataset, available on the <a href="https://github.com/PBarnaghi/TIHM-Dataset">Github</a> repository. The Jupyter Notebooks have been developed using Python 3.9. </p> <p> </p> <p><strong>The dataset is provided for research and patient benefit purposes.<br> Please acknowledge the Surrey and Borders Partnership NHS Foundation Trust in any publication or use of this dataset.</strong></p>
Utility of a Modified ECG Patch for 7-Day Monitoring in the Horse
<p>Figure S1: Example Summary Report from the Cardea SOLO. </p>
Brief Communication: Monitoring active layer dynamic using a lightweight nimble Ground-Penetrating Radar system. A laboratory analog test case
<p>GPR dataset from Leger et al., 2023 (https://tc.copernicus.org/preprints/tc-2022-214/)</p> <p> </p>
Monitoring temporal changes of velocity and attenuation from ultrashort diffuse records
<p>This repository contains the raw field data in the manuscript entitled "Monitoring temporal changes of velocity and attenuation from ultrashort diffuse records" by Ning Gu, Haoran Meng, Bo Yang, Xin Liu, Yehuda Ben-Zion, Junxin Guo, Bin Luo, Zhen Guo, Shuye Huang, Shichuan Yuan, Xiaofei Chen.</p>
Supplementary material 1 from: Shumskaya M, Lorusso N, Patel U, Leigh M, Somervuo P, Schigel D (2023) MycoPins: a metabarcoding-based method to monitor fungal colonization of fine woody debris. MycoKeys 96: 77-95. https://doi.org/10.3897/mycokeys.96.101033
Sequences of the primers used in the experiment and ITS2 fragment amplified from DNA extracted from saw dust of the pins
Dataset for: Contribution to Uncertainty Propagation Associated with On-Site Calibration of Infrasound Monitoring Systems
<p>Dataset containing the response data for the calibration of the MB5 microbarometer (Figure 5), the WNRS (Figure 6) and the sensor (Figure 8).</p>
HortiMED Smart Monitoring
<p>This video shows the Smart Monitoring software module of HortiMED that is in charge of collecting and representing, through graphs and tabular information, the data collected in the demonstrative greenhouses (both data manually collected and data automatically retrieved from IoT systems).</p>
Fig. 3 in Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring
Fig. 3.—Temporal occurrence pattern of New Zealand (dark blue), Antarctic (red), and Australian (yellow) blue whale song detections at each of the five hydrophones. The y-axis represents the number of hours per day that blue whale song was detected, and the x-axis represents the recording period. Grayed out sections represent gaps in recording due to hydrophone refurbishment.
Fig. 2 in Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring
Fig. 2.—Example spectrograms of the three song types recorded in New Zealand waters. (A) New Zealand song on 31 May 2016, (B) Antarctic song on 26 August 2017, and (C) Australian song on 22 January 2017. Spectrograms are configured with a 2048-point fast Fourier transform, Hann window, 50% overlap.
Fig. 4 in Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring
Fig. 4.—Conceptual map illustrating the current understanding of the approximate typical range of each blue whale population. Colors indicate the song type, and patterns represent inferred ecological use of each region. Distribution and occurrence patterns are synthesized from acoustic research published in the literature (e.g., Stafford et al. 2004; Balcazar et al. 2015, 2017; Tripovich et al. 2015; McCauley et al. 2018; Warren et al. 2021), and findings presented in this study.
Fig. 1 in Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring
Fig. 1.—Map of the study area in the South Taranaki Bight region, with hydrophone locations denoted by the stars. Gray lines show bathymetry contours at 50-m depth increments, from 0 to 500 m. Location of the study area within New Zealand is indicated by the inset map.
Wolverine denning behaviour and its implications for monitoring reproductive females
<p>Knowledge about the number of reproductive females is important for monitoring population dynamics, and can be critical for managing human-wildlife conflicts. For wolverines (<em>Gulo</em> <em>gulo</em>), counts of reproductive females is the basis for estimates of population size in Scandinavia, as well as a key measure for compensation payments to Sámi reindeer-herders in Sweden. However, documenting wolverine reproductive events in the field is challenging and requires knowledge of female denning behaviour. Furthermore, females may shift den sites, presenting difficulties in determining whether two den sites belong to the same or two neighbouring females. In this study, we used data from 18 GPS-collared wolverine females, monitored intensively during the denning season (February 15 – May 31) in 2004–2014, to provide baseline information on denning behaviour. We documented reproductive events in 32 of 54 potential denning seasons and identified a total of 245 den sites. Females used 8.8±8.5 (mean±SD) den sites per denning season (median=6, range: 1–28). The number of den site shifts, the distance between subsequent den sites, and the time and distance females spent away from a den site increased during the denning season, while distances between neighbouring females' den sites remained constant. From late April, the distance between consecutive den sites used by the same female overlapped with distances between neighbouring females' den sites, resulting in increased uncertainty regarding whether two den sites belonged to one or two females. <span>Using Bayesian modelling, we calculated probabilities that two den sites belonged to the same female or neighbouring females, conditional on the time of season and the distance between den sites. These findings will allow the monitoring program to adapt its methods for determining if multiple den sites belong to one or two reproductive events, using seasonally-dynamic threshold based on an understanding of wolverine denning behaviour.</span></p>
The NO2 monitoring data for "Satellite-based long-term spatiotemporal trends in ambient NO2 concentrations and attributable health burdens in China from 2005 to 2020"
<p>The current dataset is about NO2 monitoring data used in Geohealth manuscript entitled "Satellite-based long-term spatiotemporal trends in ambient NO2 concentrations and attributable health burdens in China from 2005 to 2020". It was collected from the air quality monitoring stations administered by the China National Environmental Monitoring Center. </p>
Bold Park reptile species capture data for: Decadal abundance patterns in an isolated urban reptile assemblage: Monitoring under a changing climate
<p class="MsoNormal"><span>Fenced pitfall trapping in four sampling sites <span>representing different habitats and fire history</span> over the primary reptile activity period for 35 consecutive years with over 17000 individuals captured during 3300 days of sampling; the trapping regime was modified for the last 28 years.</span></p>
Data from: Decision and response monitoring during working memory are sequentially represented in the human insula
<p>This dataset is associated with the manuscript "Decision and response monitoring during working memory are sequentially represented in the human insula", and provides all preprocessed files necessary to replicate the results.</p> <p>In this study, we recorded intracranial EEG data (specifically, SEEG) from the insula of 12 patients with pharmacoresistant epilepsy while they were performing a verbal working memory task.</p> <p>The present dataset consists of preprocessed neural activity (Time-Frequency Representation, 4-150 Hz), electrode coordinates (in MNI template space), related insula subregion labels, and 3D mesh of bilateral insula. TFR data is sampled at 200 Hz, and epoched into 12-second trials.</p> <p>The code we used to preprocess and analyze the data is hosted <a href="https://github.com/ludovicbellier/InsulaWM">on Github</a>.</p>
Runtime Monitoring of Refinement Progress in CEGAR-based Model Checking: Dataset
<p>Dataset used in the evaluation of Part II of the MSc thesis titled "Extending the Capabilities of the CEGAR Model Checking Algorithm"</p>
Monitoring programme on strict forest reserves in Flanders (Belgium) - site level stand structure, regeneration and vegetation data
<p>This dataset contains comprehensive statistics on stand structure, rejuvenation, and vegetation for each forest reserve included in the monitoring program on strict forest reserves in Flanders (Belgium). The data collection and processing methodology used are described in <a href="https://purews.inbo.be/ws/portalfiles/portal/41050863/Vandekerkhove_etal_2021_MonitoringProgrammeOnStrictForestReservesFlanders.pdf">Vandekerkhove et al., 2021</a>.</p> <p>The dataset encompasses information on 15 distinct strict forest reserves and covers one to three consecutive forest inventories following a 10-year cycle. </p> <p>The "<strong>site_info.xlsx</strong>" file provides details on the 15 sites, including central coordinates, surface area, forest type, and the year when they were set aside. For detailed descriptions of the information provided, please refer to the "<strong>_metadata_site_info.xlsx</strong>" file.</p> <p>The “<strong>statistics_per_reserve.zip</strong>” file contains 12 separate csv-files with the following data:</p> <ul> <li> <p><strong>stat_dendro(_by)(_diam)(_species).csv</strong> : statistics on basic stand structure attributes (volume, basal area, number of trees per hectare, …) for living and dead standing trees (DBH-threshold 5cm): overall values and values per diameter class and/or species </p> </li> <li> <p><strong>stat_carbon.csv </strong>: statistics on biomass and carbon stock related to living trees</p> </li> <li> <p><strong>stat_logs(_by)(_decay)(_species).csv</strong> : statistics on volume per hectare of lying deadwood: values per decay stage and/or species </p> </li> <li> <p><strong>stat_reg(_by)(_height)(_species).csv</strong> : statistics on rejuvenation (numbers per ha of young trees - seedlings up to trees with DBH< 5cm): overall values and values per heightclass and/or species </p> </li> <li> <p><strong>stat_veg.csv</strong> : statistics on vegetation characteristics, including number of species, moss, herb, shrub, tree and waterlayer cover, cumulated canopy cover and soil disturbance by game</p> </li> <li> <p><strong>stat_herbs.csv</strong> : species-specific mean cover, percentage of plots where each species occurs</p> </li> </ul> <p>For descriptions of the common fields used in the above csv-files, please consult the "<strong>_metadata_statistics.xlsx</strong>" file. </p> <p>For detailed and comprehensive information regarding the meaning and characteristics of the calculated variables, please refer to the "<strong>_metadata_variables.xlsx</strong>" file. </p> <p>Plot-level results regarding dendrometry and regeneration are published separately<a href="https://zenodo.org/record/7588680"> here</a>, while information on vegetation can be found<a href="https://zenodo.org/record/7870740"> here</a>.</p> <p><strong>For any inquiries or further information, please contact Kris.vandekerkhove@inbo.be or Anja.leyman@inbo.be</strong>. The provided csv-files will be updated as required to address any issues or include data from additional surveys. . Please check for updated versions periodically.</p> <p><strong>We ask the users of the dataset to notify us of its use.</strong></p>
Datasets used in "Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management"
<p>Data sets used to train, validate, and test machine learning models for the prediction of 10-hour dead fuel moisture content. The data sets are for 375 m and 1 km resolutions. MADIS climatography data is also included. The datasets may be processed using the code supplied at https://github.com/NCAR/fmc_viirs. </p>
Abbildung 6 in Praktische Hinweise und Empfehlungen zur Anwendung von Malaisefallen für Insekten in der Biodiversitätserfassung und im Monitoring
Abbildung 6. Beispiel für ein Luftbild eines Malaisefallenstandorts, aufgenommen mit einer Kamera-Drohne, Latumer Bruch, 02.04.2017 (© M. Sorg/EVK)
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.