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8,460 results for “MONITORING”
Fig. 4 in The Use of Testate Amoebae in Monitoring Peatland Restoration Management: Case Studies from North West England and Ireland
Fig. 4. Lead content (ppm) of the top metre of peat at Holcroft Moss.
Figure 6 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey
Figure 6. Weekly numbers of monitored chukars released into the study area.
Figure 3 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey
Figure 3. Monitoring station in the research area.
Figure 5 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey
Figure 5. Responses of birds to project team members during the first month after release.
Figure 4 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey
Figure 4. Natural predators of the chukars that visited the aviary on the first day of the study.
Figure 1 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey
Figure 1. Geographical location of the study area.
Figure 2 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey
Figure 2. Attaching transmitters to monitor chukar individuals.
Figure 1 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 1. Location of Kharkiv on the map of Europe, and a general view of the city area.
Figure 5 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 5. Sex ratio in adult individuals of N. noctula during 2013 in Kharkiv.
Figure 6 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 6. Sex ratio in first-year individuals of N. noctula during 2013 in Kharkiv.
Figure 7 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 7. Sex ratio of E. serotinus for all age groups together during 2013 in Kharkiv.
Figure 4 in The monitoring of feather mites (Acari, Astigmata) of the Warbler (Aves: Sylviidae) species in the Kızılırmak delta, Samsun, Turkey
Figure 4. Monthly and annual comparison of the number of feather mite individuals.
GNSS Ground Monitoring Data of the Hongyanzi Landslide Area in China from November 11, 2021 to June 4, 2022
<p>This dataset comprises Global Navigation Satellite System (GNSS) ground monitoring data collected from five distinct locations within the Hongyanzi landslide area in China. The data spans a critical time period from November 11, 2021, to June 4, 2022, providing valuable insights into the temporal deformation behavior of the landslide.</p> <p>To enhance the applicability and comparability of these GNSS measurements, we have processed the raw three-dimensional (3D) displacement vectors and projected them onto the Line-Of-Sight (LOS) direction of Interferometric Synthetic Aperture Radar (InSAR) observations. This transformation facilitates direct comparisons between the GNSS-derived deformation rates and those derived from InSAR data, enabling a more comprehensive understanding of the landslide dynamics.</p> <p>The dataset includes:</p> <ul> <li><strong>Time series of 1D displacement measurements</strong> along the InSAR LOS direction for each of the five GNSS stations.</li> </ul> <p>The high-resolution temporal coverage of this dataset, coupled with the transformation to the InSAR LOS direction, makes it an invaluable resource for researchers investigating the geomechanics of the Hongyanzi landslide, as well as for validation and calibration of remote sensing-based deformation monitoring techniques.</p> <p><strong>Potential Uses</strong>:</p> <ul> <li>Validation of InSAR-derived deformation maps and velocity fields.</li> <li>Study of landslide kinematics and triggering mechanisms.</li> <li>Integration with other geophysical and geological datasets for comprehensive hazard assessments.</li> <li>Development and testing of advanced deformation monitoring and early warning systems.</li> </ul> <p><strong>Access and Citation</strong>:<br>This dataset is freely available for research purposes under the <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener noreferrer">Creative Commons Attribution 4.0 International License</a>. Please cite this dataset using the following reference (to be filled in upon upload completion):</p> <p>Aoqing Guo, 2024. GNSS Ground Monitoring Data for the Hongyanzi Landslide Area in China: Transformed to InSAR LOS Direction (November 11, 2021 - June 4, 2022). Zenodo. 10.5281/zenodo.13254357.</p>
Aerosol products presented in "ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications"
<p>ALICENET output products on aerosol optical and physical properties and vertical layering presented in “Bellini, A., Diémoz, H., Di Liberto, L., Gobbi, G. P., Bracci, A., Pasqualini, F., and Barnaba, F.: Alicenet – An Italian network of Automated Lidar-Ceilometers for 4D aerosol monitoring: infrastructure, data processing, and applications, AMT, https://doi.org/10.5194/egusphere-2024-730, 2024”.</p> <p>The aod*.txt files include the following information:</p> <p>- date: date in UTC<br>- AOD_ALICENET: AOD as retrieved by ALICENET at 1064 nm<br>- AOD_AERONET/SKYNET: AOD measured by a co-located photometer from AERONET/SKYNET (level 2) at 1020 nm<br>- AE: Angstrom Exponent from AERONET/SKYNET (level 2)</p> <p>The contiunous.aerosol.layer.rome.txt file includes the following information:</p> <p>- date: date in CET<br>- continuous_aerosol_layer: Continous Aerosol Layer heights as retrieved by ALICENET</p> <p>The mixed.aerosol.layer.rome.txt file includes the following information:</p> <p>- date: date in CET<br>- mixed_aerosol_layer: Mixed Aerosol Layer heights as retrieved by ALICENET</p> <p>This work received partial financial support from the EC H2020 Project RI-URBANS (GA No 101036245), and benefited from work done within the Action PROBE (CA18235), supported by COST (European Cooperation in Science and Technology).</p>
Data for "Entanglement Dynamics in Monitored Kitaev Circuits: Loop Models, Symmetry Classification, and Quantum Lifshitz Scaling"
<p>We provide the data and scripts used to produce the figures shown in our publication "Entanglement Dynamics in Monitored Kitaev Circuits:<br>Loop Models, Symmetry Classification, and Quantum Lifshitz Scaling".</p>
Activation and connectivity maps - A chronometric relationship between circuits underlying learning and error monitoring in the basal ganglia and salience network
<p>Activation and connectivity maps of the study "A chronometric relationship between circuits underlying learning and error monitoring in the basal ganglia and salience network".</p> <ul> <li>Error-correct.nii corresponds to the statistical map of group-level differences in BOLD signal between correct and erroneous responses shown in figure 4;</li> <li>Late-initial.nii corresponds to the statistical map of group-level differences in BOLD signal between the initial and late learning periods shown in figure 5;</li> <li>Conn_error-correct_dACC.nii corresponds to the results from the seed-to-voxel gPPI analysis, using the dACC as seed region, showing areas of higher functional connectivity in erroneous compared to correct responses, shown in figure 8.</li> </ul>
Script and data from: The best of two worlds: toward large-scale monitoring of biodiversity combining metabarcoding and optimised parataxonomic validation.
<h2>Description</h2> <div> <p>Zenodo linked to : Penel, B., Meynard, C.N., Benoit, L., Bourdonné, A., Clamens, A., Soldati, L., Migeon, A., Chapuis, M.-P., Piry, S., Kergoat, G. and Haran, J. (2025), The best of two worlds: toward large-scale monitoring of biodiversity combining COI metabarcoding and optimized parataxonomic validation. Ecography, 2025: e07699. <a href="https://doi.org/10.1111/ecog.07699">https://doi.org/10.1111/ecog.07699</a></p> <div> <div> <div> <div> <p><strong>Publication abstract </strong></p> </div> </div> </div> <p>In a context of unprecedented insect decline, it is critical to have reliable monitoring tools to measure species diversity and their dynamic at large-scales. High-throughput DNA-based identification methods, and particularly metabarcoding, were proposed as an effective way to reach this aim. However, these identification methods are subject to multiple technical limitations, resulting in unavoidable false-positive and false-negative species detection. Moreover, metabarcoding does not allow a reliable estimation of species abundance in a given sample, which is key to document and detect population declines or range shifts at large scales. To overcome these obstacles, we propose here a Human-Assisted Molecular Identification (HAMI) approach, a framework based on a combination of metabarcoding and image-based parataxonomic validation of outputs and recording of abundance. We assessed the advantages of using HAMI over the exclusive use of a metabarcoding approach by examining 492 mixed beetle samples from a biodiversity monitoring initiative conducted throughout France. On average, 23% of the species are missed when relying exclusively on metabarcoding, this percent being consistently higher in species-rich samples. Importantly, on average, 20% of the species identified by molecular-only approaches correspond to false positives linked to cross-sample contaminations or mis-identified barcode sequences in databases. The combination of molecular methodologies and parataxonomic validation in HAMI significantly reduces the intrinsic biases of metabarcoding and recovers reliable abundance data. This approach also enables users to engage in a virtuous circle of database improvement through the identification of specimens associated with missing or incorrectly assigned barcodes. As such, HAMI fills an important gap in the toolbox available for fast and reliable biodiversity monitoring at large scales.</p> <div> <h4><strong>File description: </strong></h4> <h4>MiSeq raw sequences of the COI barcode from 492 Coleoptera field samples :</h4> <div>The Raw_sequencage_data ZIP directory contains the FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each Coleoptera field samples in duplicate using the MiSeq platform GenSeq (ISEM - University of Montpellier)</div> <div> </div> <div>The HAMI_data_script_results_R zip directory contains Rmarkdown script files (.Rmd and .html) and associated data used to analyse the systemic errors of the metabarcoding approach (N= 492 Coleoptera field samples).</div> <div> </div> <div>The HAMI_pipeline zip directory contains all the codes associated with the HAMI pipeline, as well as a ReadMe file and a test data set.</div> <div> </div> <div>The Residual_chimera.zip directory contains lists of MOTUs associated to residual chimeric sequences that were not filtered using FROGS pipeline but secondarily detected with the <em>de novo</em> approach implemented in HAMI pipeline with ‘isBimeraDenovo’ R function from DADA2 v1.28.0. It contains two distinct files according to the two sequencing runs.</div> <div> </div> <div>The NUMTS_filtered.zip directory contains lists of MOTUs that were excluded of the final dataset according to the NUMTS filtering. File xxx_pseudogene_f1_deteled.csv corresponds to MOTUs that were excluded according to the first filtrering step based on DNA sequencing. File xxx_pseudogene_f2_deteled.csv corresponds to merged MOTUs that were excluded according to the second filter based on occurrence and percentage of identity. This folder contains files for the two sequencing runs.</div> </div> </div> </div>
ACT-AP Mobile Monitoring
<h1>1 About This Document</h1> <p>This directory contains air pollution concentration data from the Adult Changes in Thought – Traffic-Related Air Pollution (ACT-TRAP) mobile monitoring study (Blanco et al., 2022).</p> <p> </p> <h1>2 Data Source</h1> <p>The ACT-TRAP mobile monitoring study was an air pollution monitoring campaign that was conducted between March 2019 and March 2020 in the greater Seattle area. Briefly, the campaign collected 2-minute pollutant concentrations at 309 stop locations throughout the greater Seattle area. Pollutants were simultaneously measured with high temporal resolution (measurements every 1-60 sec). These included particle number concentration (PNC, an indicator of ultrafine particulates or UFP) from four different instruments, black carbon (BC), nitrogen dioxide (NO<sub>2</sub>), carbon dioxide (CO<sub>2</sub>), and fine particulate matter (PM<sub>2.5</sub>). Each stop location was visited approximately 29 times during all seasons and days of the week between the hours of approximately 5 AM and 11 PM.</p> <p> </p> <h1>3 The Data</h1> <p>We summarized high-resolution instrument data into median (and mean) stop concentrations, and these were used to calculate annual average concentrations for each of the 309 mobile monitoring sites. Using universal kriging-partial least squares (UK-PLS) regression models with hundreds of geographic covariate predictors, we generated out-of-sample TRAP predictions for monitoring locations, cohort locations (for epidemiologic purposes), census block centroids, and a grid (for visual purposes).</p> <p> </p> <p>The relevant data files are:</p> <p> </p> <p>1. Stop-level data (stop_data.csv)</p> <p>2. Annual average estimates and predictions for the mobile monitoring sites (N=309) (monitoring_location_estimates_and_predictions.csv)</p> <p>3. Annual average model predictions for:</p> <p>a. a grid in the region (grid_predictions.csv)</p> <p>b. 2010 Census block centroids for Washington state (census_block_predictions.csv)</p> <p>4. The geographic covariates available for modeling for the: </p> <p>a. monitoring sites (dr0311_mobile_covars.csv)</p> <p>b. grid (dr0311_grid_covars.csv)</p> <p>c. 2010 census block covariates (block10_intpts_wa.csv)</p> <p>5. UK-PLS model performances for annual average TRAP from the mobile monitoring campaign (model_performances.csv). </p> <p>6. Spatial files for</p> <p>a. A polygon encompassing all of the monitoring sites. There are three versions:</p> <p> i. The first includes all land and water areas (monitoring_area.rda) and is the simplest.</p> <p> ii. Same as above but excludes major water areas (monitoring_land.rda). This is useful for mapping, for example.</p> <p> iii. Same as above but excludes all water areas (monitoring_land_zero_water.rda). This is the file used to make predictions to ensure that no predictions are made on bodies of water.</p> <p> </p> <p>On-road data were also collected while the vehicle was in motion. Please inquire if you are interested in these data.</p> <p> </p> <h1>4 Data Dictionaries</h1> <p>Data dictionaries for all the campaign data can be found in data_dictionaries.docx.</p> <p> </p> <p>Geographic covariates come from the MESA Air geodatabase. Details on these covariates can be found in MESAAirDOOP_20190501.pdf and here: <a href="https://deohs.washington.edu/sites/default/files/MESAAirDOOP_Rev12.pdf">https://deohs.washington.edu/sites/default/files/MESAAirDOOP_Rev12.pdf</a>.</p> <p> </p> <h1>5 Reference</h1> <p>Blanco MN, Gassett A, Gould T, Doubleday A, Slager DL, Austin E, Seto E, Larson TV, Marshall JD, Sheppard L. Characterization of Annual Average Traffic-Related Air Pollution Concentrations in the Greater Seattle Area from a Year-Long Mobile Monitoring Campaign. Environ Sci Technol. 2022 Aug 16;56(16):11460-11472. doi: 10.1021/acs.est.2c01077. Epub 2022 Aug 2. PMID: 35917479; PMCID: PMC9396693.</p> <p> </p>
Mussel Raft Monitoring Dataset in Ría de Arousa
<p>Mussel Raft Monitoring Dataset of Oceanographic data (temperature, salinity, pressure, turbidity), Weather data (temperature, humidity, wind, rain) and Load cell data with platform movements (load cells, acceleration, gyroscope, magnetometer, sound, gps, etc.) in Two Mussel Raft in Ria de Arousa (Galicia-Spain)</p>
Table 1 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U. S. A.
<p><i>Table 1.</i> Details of digital acoustic monitoring (DMON) deployment sites and recording times over the duration of the study.</p><table><tbody><tr><th>Site</th><th>Coordinates</th><th>Deployment date</th><th>Recovery date</th><th>Deployment duration (time)</th><th>Recorded minutes</th></tr></tbody><tbody><tr><th>Reef Offshore</th><td>44 Ǫ 35.140 N, 124 Ǫ 07.120 W 44 Ǫ 35.121 N, 124 Ǫ 07.000 W 44 Ǫ 34.985 N, 124 Ǫ 07.117 W 44 Ǫ 35.247 N, 124 Ǫ 06.815 W 44 Ǫ 35.221 N, 124 Ǫ 06.859 W 44 Ǫ 34.650 N, 124 Ǫ 13.218 W 44 Ǫ 34.857 N, 124 Ǫ 13.419 W 44 Ǫ 34.929 N, 124 Ǫ 13.215 W 44 Ǫ 34.929 N, 124 Ǫ 13.215 W 44 Ǫ 34.929 N, 124 Ǫ 13.215 W</td><td>16 May 2014 12 Jun 2014 26 Jun 2014 29 Jul 2014 16 Sep 2014 12 Jun 2014 26 Jun 2014 29 Jul 2014 16 Sep 2014 20 Sep 2014</td><td>23 May 2014 20 Jun 2014 7 Jul 2014 8 Aug 2014 18 Sep 2014 20 Jun 2014 7 Jul 2014 8 Aug 2014 29 Sep 2014 13 Oct 2014</td><td>6 d 20 h 00 min 7 d 21 h 30 min 10 d 14 h 40 min 9 d 21 h 40 min 2 d 13 h 20 min 7 d 21 h 40 min 10 d 23 h 10 min 10 d 0 h 30 min 12 d 17 h 40 min 12 d 12 h 00 min</td><td>985 1,138 1,529 1,427 441 1,138 1,580 1,444 1,835 1,801</td></tr></tbody></table>
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.