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35 results for “Field monitoring”
Fig. 1 in Monitoring field populations of Plutella xylostella (Lepidoptera: Plutellidae) for resistance to eight insecticides in China
Fig. 1. Sampling sites of Plutella xylostella field populations in seven provinces of China.
Data from: Using occupancy modeling to compare environmental DNA to traditional field methods for regional-scale monitoring of an endangered aquatic species
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Data from: Field validation of radar systems for monitoring bird migration
1. Advances in information technology are increasing the use of radar as a tool to investigate and monitor bird migration movements. We set up a field campaign to compare and validate outputs from different radar systems. 2. Here we compare the pattern of nocturnal bird migration movements recorded by four different radar systems at a site in southern Sweden. Within the range of the weather radar (WR) Ängelholm, we operated a "BirdScan" (BS) dedicated bird radar, a standard marine radar (MR), and a tracking radar (TR). 3. The measures of nightly migration intensities, provided by three of the radars (WR, BS, MR), corresponded well with respect to the relative seasonal course of migration, while absolute migration intensity agreed reasonably only between WR and BS. Flight directions derived from WR, BS and TR corresponded very well, despite very different sample sizes. Estimated mean ground speeds differed among all four systems. The correspondence among systems was highest under clear sky conditions and at high altitudes. 4. Synthesis and applications. While different radar systems can provide useful information on nocturnal bird migration, they have distinct strengths and weaknesses, and all require supporting data to allow for species level inference. Weather radars continuously detect avian biomass flows across a wide altitude band, making them a useful tool for monitoring and predictive applications at regional to continental scales that do not rely on resolving individuals. BirdScan and marine radar's strengths are in local and low altitude applications, such as collision risks with man-made structures and airport safety, although marine radars should not be trusted for absolute intensities of movement. In quantifying flight behaviour of individuals, tracking radars are the most informative.
Field Implementation and Monitoring of an Ultra-High Performance Concrete Bridge Deck Overlay
<p>This project focused on field implementation of an ultra-high performance concrete (UHPC) overlay during rehabilitation of an existing concrete bridge (No. 7032) deck in Socorro, New Mexico, USA. Bridge 7032 is a two-lane bridge that is approximately 300 ft. (91.4 m) long and 54 ft. (16.5 m) in width. Rehabilitation of bridge 7032 included removal of deteriorated concrete from the existing deck, installation of a high-performance deck (HPD) leveling course, and installation of a 1 in. (25 mm) UHPC overlay. An UHPC mixture with a 19.5 ksi (134 MPa) compressive strength developed in previous research was selected for this project. This mixture was revised at the beginning of the project, highlighting the need for robust mixtures that can accommodate constituent material substitutions. Prior to placement of the UHPC overlay, the HPD substrate was ceramic bead blasted to remove surface paste and partially expose fine aggregate, thoroughly cleaned, and maintained saturated for 24 hours prior to UHPC placement.</p> <p>The non-proprietary UHPC overlay was successfully placed in four sections between April 10, 2021 and April 27, 2021. During construction of the UHPC overlay, a total of 105 batches were placed. The average direct tensile bond strength of the UHPC overlaid bridge deck was 239 psi (1.65 MPa), which is conservative since several of the tests had fractures that occurred at the epoxy-UHPC interface. Strain gauges and thermocouples were used to monitor the UHPC overlay and existing deck through initial, early-age, and longer-term monitoring programs. Temperature and strain monitoring showed that daily and multi-day strain trends coincided well with daily temperature trends.<br> Major observations during this field implementation project were that four small areas of possible delamination were identified using non-destructive testing, the bond strength was good since the 239 psi (1.65 MPa) average was conservative, preliminary monitoring results were consistent with expectations based on temperature measurements and restraint provided by the concrete superstructure, and relative strains from the monitoring data are being used for detailed analysis of the bridge and overlay behaviors for an ongoing NMDOT project that runs for another two years.</p>
Towards Enhancing Field-Based Vegetation Monitoring: A Deep Learning Approach for Species Identification and Coverage Estimation from Ground-level Imagery
<h1>🌿Species Identification and Coverage Estimation from Ground-level Imagery for Vegetation Monitoring 📷</h1> <p>This repository contains the data and code used in <a href="https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.70024"><strong>Müller, Puliti, and Breidenbach (2025)</strong></a> to train and apply deep learning models for <strong>species coverage estimation</strong> using ground-level imagery.</p> <p>It includes:<br>✅ A <strong>YOLOv8 object detection </strong>model for detecting frames in images and one <strong>species instance segmentation</strong> model for species identification and identifying and segmenting species.</p> <p>✅ Method to parse instance segmentation masks to <strong>species-specific coverage estimates</strong> in images.</p> <p>✅ The <strong>data </strong>used to train and evaluate the models</p> <p> </p> <h2>🚀 Workflow Overview</h2> <p>This demo provides a step-by-step approach for training and applying the models:</p> <p>1️⃣ <strong>Training</strong>:</p> <ul> <li>Train two models using labeled images: <ul> <li><strong>Frame Object Detection</strong> (dataset: <code>Frame_data</code>)</li> <li><strong>Species Instance Segmentation</strong> (dataset: <code>Species_segmentation_data</code>)</li> </ul> </li> </ul> <p>2️⃣ <strong>Confidence Optimization</strong>:</p> <ul> <li>Optimize the confidence threshold based on downstream <strong>cover estimation</strong> performance.</li> </ul> <p>3️⃣ <strong>Inference</strong>:</p> <ul> <li>Predict on test images (<code>Species_cover_data_test</code>).</li> </ul> <p>4️⃣ <strong>Evaluation</strong>:</p> <ul> <li>Compare predictions with <strong>field estimates</strong> (<code>Field_data_NFI</code>).</li> </ul> <p>📌 The code has been tested on <strong>Windows</strong> with <strong>Python 3.10</strong>.</p> <p> </p> <h2>🛠 How to Run the Demo</h2> <p>Follow these steps to set up and run <code>demo.ipynb</code>:</p> <div> <div> <div> </div> </div> <div> <blockquote> <p># Create a new environment<br>conda create -n VegCover python=3.10</p> <p># Activate the environment<br>conda activate VegCover</p> <p># Install dependencies<br>pip install -r requirements.txt</p> <p># Install Jupyter Lab<br>pip install jupyterlab</p> <p># Open the demo notebook<br>jupyter-lab</p> </blockquote> </div> </div> <h2> </h2> <h2>📖 How to Cite</h2> <p>If you use this work, please cite:</p> <p><strong>Müller, P., Puliti, S., & Breidenbach, J. (2025).</strong> Towards Enhancing Field-Based Vegetation Monitoring: A Deep Learning Approach for Species Coverage Estimation from Ground-Level Imagery. <em>Methods in Ecology and Evolution.</em></p> <h2> </h2> <h2>📜 License</h2> <p>This project is licensed under the <strong>GNU Affero General Public License v3.0 or later (AGPL-3.0-or-later)</strong>.</p> <p>🔹 <strong>Key points of this license:</strong></p> <ul> <li>You are free to <strong>use, modify, and distribute</strong> the software.</li> <li>If you modify and deploy this software (even as a web service), you <strong>must share your modifications</strong> under the same AGPL-3.0-or-later license.</li> <li>This ensures that improvements remain open-source and benefit the community.</li> </ul> <p>📖 Full license text: <a href="https://www.gnu.org/licenses/agpl-3.0.en.html">GNU AGPL v3.0</a></p> <div> <pre> </pre> </div>
Deuterium Magnetic Resonance Imaging Using Deuterated Water-Induced 2H-Tissue Labeling Allows Monitoring Cancer Treatment at Clinical Field Strength
<p>Raw 2H-NMR Data of deuterium content in tumor tissues. </p>
Multi-camera field monitoring reveals costs of learning for parasitoid foraging behaviour
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Data from: Field validation of radar systems for monitoring bird migration
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Experimental-data-driven deep learning strategy for Structural Health Monitoring of a plate in acoustic fields
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Evaluation of the Efficacy of Pulmonary Vein Isolation by Pulsed-field Ablation in Persistent Atrial Fibrillation: a Multicenter Study With Follow-up by Implantable Cardiac Monitor
ClinicalTrials.gov study NCT06985901. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Observational Field Study of Acute Esophageal Food Bolus Impaction by Mean Esophageal Manometry and 24h-pH-monitoring
ClinicalTrials.gov study NCT01447823. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Monitoring the Response of Port Wine Stain Birthmarks to Laser Therapy With Wide-field Functional Imaging Technologies
ClinicalTrials.gov study NCT01333553. IPD Sharing: Not stated. Countries: 1. Publications: 0.
XMM-Newton Optical Monitor Chandra Deep Field-South UV Catalog
The XMM-Newton X-ray observatory has performed repeated observations of the Chandra Deep Field-South (CDFS) in 33 epochs (2001 - 2010) through the XMM-CDFS Deep Survey (Comastri et al. 2011, A&A, 526, L9). During the X-ray observations, the XMM-Newton Optical Monitor (XMM-OM) targeted the central 17 x 17 arcmin<sup>2</sup> region of the X-ray field of view, providing simultaneous optical/UV coverage of the CDFS. The resulting set of data can be taken into account to build an XMM-OM catalog of the CDFS, filling the UV spectral coverage between the optical surveys and GALEX observations. This table contains the UV catalog of the XMM-CDFS Deep Survey. Its main purpose is to provide complementary UV average photometric measurements of known optical/UV sources in the CDFS, taking advantage of the unique characteristics of the survey. The data reduction is intended also to improve the standard source detection on individual observations, by cataloguing faint sources through the stacking of their exposure images. The authors reprocessed the XMM-OM data of the survey and stacked the exposures from consecutive observations using the standard SAS tools to process the data obtained during single observations. Average measurements of detections with SAS good quality flags from individual observations and from stacked images have been joined to compile the catalogue. Sources have been validated through the cross-identification within the ESO Imaging Survey (EIS: Arnouts et al. 2001, A&A, 379, 740) and COMBO-17 (Wolf et al. 2004, A&A, 421, 913; 2008, A&A, 492, 933) surveys. Photometric data of 1129 CDFS sources are provided in the main catalog, and optical/UV/X-ray photometric and spectroscopic information from other surveys are also included. The stacking extends the detection limits by ~1 mag in the three UV bands, contributing 30% of the catalogued UV sources. The comparison with the available measurements in similar spectral bands confirms the validity of the XMM-OM calibration. The combined COMBO-17/X-ray classification of the "intermediate" sources (e.g. optically diluted and/or X-ray absorbed AGN) is also discussed in the reference paper. This table was created by the HEASARC in June 2015 based on the union of <a href="https://cdsarc.cds.unistra.fr/ftp/cats/J/ApJS/217/4/">CDS Catalog J/ApJS/217/4/</a> files omcdfst7.dat (the 1,129 sources in the main catalog) and omcdfst8.dat (the 44 sources in the supplementary catalog). This is a service provided by NASA HEASARC .
25 new long-term variables in the GX 339-4 field: two years of MeerKAT monitoring
<p>These are the radio light curves for the 25 new long-term variable sources found in weekly ThunderKAT monitoring of the low-mass X-ray binary GX 339-4 with MeerKAT. These data are part of a publication submitted to Monthly Notices for the Royal Astronomical Society (Driessen et al., submitted) and part of Laura's Driessen PhD thesis (the jodrell Bank Centre for Astrophysics, the University of Manchester, UK).</p> <p>The observations are MeerKAT L-band observations. Each epoch is approximately 10-15 minutes long, these are the first two years of weekly monitoring, from September 2018 to the end of August 2020. The light curves have been extracted from the images using the <a href="https://tkp.readthedocs.io/en/latest/introduction.html">LOFAR Transients Pipeline (TraP)</a>.</p> <p>Each file contains the information for a single source. The files are formatted with columns as follows:</p> <ul> <li>mjd: the modified Julian Date (MJD) of the observation. The MJD is given by MJD=JD-2400000.5 where JD is the Julian Date</li> <li>f_int_median_scaled_Jy: the flux density of the source in Jansky (Jy) determined by the LOFAR TraP, after the systematic correction model has been applied (see Driessen et al. 2021)</li> <li>f_int_median_scaled_err_Jy: the uncertainty on f_int_median_scaled_Jy in Jansky determined by the LOFAR TraP, after the systematic correction model has been applied (see Driessen et al. 2021)</li> <li>freq_eff_Hz: the effect frequency in Hertz (Hz)</li> <li>median_scaled_eta: the chi squared variability parameter for the source in the specified frequency band/subband. This parameter is defined in Driessen et al. 2021, Swinbank et al. 2015 and on the <a href="https://tkp.readthedocs.io/en/latest/introduction.html">LOFAR TraP website</a></li> <li>median_scaled_V: the modulation variability parameter for the source in the specified frequency band/subband. This parameter is defined in Driessen et al. 2021, Swinbank et al. 2015 and on the <a href="https://tkp.readthedocs.io/en/latest/introduction.html">LOFAR TraP website</a></li> <li>name: this is the name of the source as defined in Driessen et al. 2021</li> <li>subband: this file contains the light curve of the source from multi-frequency synthesis (MFS) imaging of the full MeerKAT L-band (from 856 to 1712 MHz) epochs, plus 8 subband images with central frequencies: 909, 1016, 1123, 1230, 1337, 1444, 1551 and 1658 MHz. This column shows which subband or MFS the values in the row are from</li> </ul> <p>The files were made using the Pandas package, so we recommend Python users load them using </p> <pre><code class="language-python">import pandas as pd pd.read_csv(filename, comment='#') </code></pre> <p>If you use the data shared here please ensure that you cite the MNRAS paper (Driessen at al. submitted) and the Zenodo DOI: 10.5281/zenodo.5069119.</p> <p> </p> <p>The MeerKAT telescope is operated by the South African Radio Astronomy Observatory, which is a facility of the National Research Foundation, an agency of the Department of Science and Innovation.<br> LND acknowledges support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 694745).</p>
Winter field hydrological monitoring data for five artificially drained croplands in Eastern Canada.
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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.