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733 results for “passive”
Tracking marine tetrapod carcasses using a low-cost mixed methodology with GPS trackers, passive drifters, and citizen science
<div> <ol> <li> <span><span>Drift experiments are essential to understand stranding patterns and to estimate the mortality of beached animals.</span><span> Most studies do not use telemetry technology due to the high costs of this methodology. </span></span><span><span>The objective of this paper is to describe the possibilities of tracking marine tetrapod carcasses with a low-cost and replicable methodology.</span> <span>The study was conducted </span></span><span><span>in the Southern Subtropical Shelf (~ 28º–34º S), </span><span>a highly productive and key ecological </span><span>region of the southwestern Atlantic Ocean (SWA).</span></span><span> </span> </li> <li> <p><span><span>W</span></span><span><span>e designed and tested a low-cost mixed methodology including </span><span>Global Positioning System </span><span>trackers, passive drifters (reused glass bottles), and Citizen Science (through instant message platform and email) for tracking carcasses of marine </span><span>tetrapods</span><span>. We performed four drift experiments, </span><span>during the</span><span> four seasons </span><span>of</span><span> 2019</span><span>.</span> </span><span><span>We released 787 drifters (600 non-biological and 187 carcasses</span><span> of seabirds, </span><span>sea turtles</span><span>, and cetaceans</span><span>)</span></span><span><span> at sea, at five</span><span> equally</span> <span>separated </span><span>distances (5</span><span>–</span><span>25 km) from the coast.</span> <span>Beach surveys and Citizen Science were implemented to </span><span>recover </span><span>the beached drifters.</span></span></p> </li> <li> <p><span><span>We</span><span> re</span><span>covered </span><span>71.83% of non-biological </span><span>and 27.27% of carcasses </span><span>released</span><span>. We tracked the movements of 38 carcasses </span><span>(25 sea turtles and 13 </span><span>cetaceans</span><span>) with 17 GPS devices. The drift</span><span>ing</span><span> time</span><span>,</span><span> until reaching the beach</span><span>,</span><span> ranged from 12h to 17 days</span><span> for carcasses and 12h to 406 days for bottles</span><span>. </span></span><span><span>Citizen Science was the most important source of rec</span><span>overies </span><span>of non-biological drifters, representing 66.67% of the total bottles rec</span><span>ove</span><span>r</span><span>ed</span><span>.</span> <span>For carcasses, active search was the most important recovery source, representing 64.7% of the total carcasses</span><span> recovered</span><span>.</span></span></p> </li> <li> <p><span>Our study contributes with new findings about drift patterns of marine tetrapods in the SWA and describes an accessible low-cost mixed methodology for small and medium-budget projects that can be </span><span>replicated in other coastal regions of the world for tracking a wide range of marine tetrapod species.</span></p> </li> </ol> </div>
Passive acoustic monitoring indicates Barred Owls are established in northern coastal California and management intervention is warranted
<p>Barred Owls (<em>Strix varia</em>) have recently expanded westward from eastern North America, contributing to substantial declines in Northern Spotted Owls (<em>Strix occidentalis caurina</em>). Passive acoustic monitoring (PAM) represents a potentially powerful approach for tracking range expansions like the Barred Owl's, but further methods development is needed to ensure that PAM-informed occupancy models meaningfully reflect population processes. Focusing on the leading edge of the Barred Owl range expansion in coastal California, we used a combination of PAM data, GPS-tagging, and active surveys to (1) estimate breeding home range size, (2) identify patterns of vocal activity that reflect resident occupancy, and (3) estimate resident occupancy rates. Mean breeding season home range size (452 ha) was reasonably consistent with the size of cells (400 ha) sampled with autonomous recording units (ARUs). Nevertheless, false-positive acoustic detections of Barred Owls frequently occurred within cells not containing an activity center such that site occupancy estimates derived using all detected vocalizations (0.61) were unlikely to be representative of resident occupancy. However, the proportion of survey nights with confirmed vocalizations (VN) and the number of ARUs within a sampling cell with confirmed vocalizations (VU) were indicative of Barred Owl residency. Moreover, the false positive error rate could be reduced for occupancy analyses by establishing thresholds of VN and VU to define detections, although doing so increased false negative error rates in some cases. Using different thresholds of VN and VU, we estimated resident occupancy to be 0.29–0.44, which indicates that Barred Owls have become established in the region but also that timely lethal removals could still help prevent the extirpation of Northern Spotted Owls. Our findings provide a scalable framework for monitoring Barred Owl populations throughout their expanded range and, more broadly, a basis for converting site occupancy to resident occupancy in PAM programs. </p>
Preferential information extraction from space-based passive microwave measurements enables accurate characterization of snow depth variability at continental scales
<p>This is a repository contains </p> <p>1)training data (x_data, y_data, snow_max) </p> <p>2) Developed Deep Learning model (snow_model.py)</p> <p>3) Training weights (*.hdf files)</p> <p>for publication " Preferential information extraction from space-based passive microwave measurements enables accurate characterization of snow depth variability at continental scales"</p> <p> </p>
Open‐source workflow approaches to passive acoustic monitoring of bats
<ol> <li>The affordability, storage, and power capacity of compact modern recording hardware has evolved passive acoustic monitoring (PAM) of animals and soundscapes into a non-invasive, cost-effective tool for research and ecological management and is particularly effective for bats and toothed whales that consistently echolocate. The use of PAM at large scales hinges on effective automated detectors and species classifiers which, combined with distance sampling approaches, have enabled species abundance estimation of toothed whales. But standardized, user-friendly, and open-access automated detection and classification workflows are in demand for this key conservation metric to be realized for bats.</li> <li>We used the PAMGuard toolbox including its new deep learning classification module to test the performance of four open-source workflows for automated analyses of acoustic datasets from bats. Each workflow used a different initial detection algorithm followed by the same deep learning classification algorithm and was evaluated against the performance of an expert manual analyst.</li> <li>Workflow performance depended strongly on the signal-to-noise ratio and detection algorithm used: the full deep learning workflow had the best classification accuracy (≤67%) but was computationally too slow for practical large-scale bat PAM. Workflows using PAMGuard's detection module or triggers onboard an SM4BAT or AudioMoth accurately classified up to 47%, 59% and 34%, respectively, of calls to species. Not all workflows included noise sampling critical to estimating changes in detection probability over time, a vital parameter for abundance estimation. The workflow using PAMGuard's detection module was 40 times faster than the full deep learning workflow and missed as few calls (recall for both ~0.6), thus balancing computational speed and performance. </li> <li>We show that complete acoustic detection and classification workflows for bat PAM data can be efficiently automated using open-source software such as PAMGuard and exemplify how detection choices, whether pre- or post-deployment, hardware or software-driven, affect the performance of deep learning classification and <span>the downstream ecological information that can be extracted from acoustic recordings. In particular, understanding, and quantifying detection/classification accuracy and the probability of detection are key to avoid introducing biases that may ultimately affect the quality of data for ecological management. </span> </li> </ol>
LES data presented in "The role of passive cloud volumes in the transition from shallow to deep atmospheric convection"
<p>This repository contains the LES data presented in the manuscript entitled "The role of passive cloud volumes in the transition from shallow to deep atmospheric convection" by C.V. Vraciu, I.L. Kruse, J.O. Haerter, submitted to the Geophysical Research Letters.</p>
Passive Open-Path Fouriertransform Infrared spectroscopy (OP-FTIR) measurements in the Arctic environment of Ny Ålesund / Svalbard 2023
<p>In the Polar MOSES campaign 2023, a ground-based remote sensing method – OP-FTIR spectroscopy- which is proven to be a flexible long-path technique for the characterization of larger areas was used. This method is able to simultaneously detect various volatile atmospheric compounds relevant for environmental assessment with a single rapid measurement. Many greenhouse gas molecules (e.g., CO<sub>2</sub>, H<sub>2</sub>O, CH<sub>4</sub>) have unique signatures (absorption or emission bands) in the measured spectral IR range. IR spectroscopy allows spatial and non-invasive characterization of emissions and is a useful method to capture emissions along the coastline, both seaward and landward.</p> <p>The passive technique was applied in a mobile mode to obtain IR spectra at different relevant locations. In order to investigate the near surface atmospheric composition, the mobile passive OP-FTIR spectroscopic scanning measurements was executed with Bruker RAPID devices to cover marine and terrestrial areas. The RAPID passive detection systems was deployed for 360° remote sensing of threats at distances up to 5000 m. Initially, the survey's primary objective was to qualitatively interpret spectra, enabling the identification of emission sources and the characterization of the heterogeneous emission pattern. This was achieved through the measurements in different directions to allow the direct comparisons of the various compartments under investigation and their potential interactions.</p>
Dataset for "Benchmarking for the automated detection of southern yellow-cheeked crested gibbon calls from passive acoustic monitoring data"
<p>"<span>Benchmarking automated detection and classification approaches for long-term acoustic monitoring of endangered species: a case study on gibbons from Cambodia</span>"</p> <div> <p><span>Recent advances in deep learning and transfer learning have revolutionized our ability for the automated detection of acoustic signals from long-term soundscape recordings. Here, we provide a benchmark for the automated detection of southern yellow-cheeked crested gibbon (<em>Nomascus gabriellae</em>) calls recorded in Jahoo, Cambodia. For the benchmarking, we compared the performance of support vector machines (SVMs), a quasi-DenseNet architecture (Koogu), transfer learning with ResNet50 models trained on the ‘ImageNet’ dataset (ResNet), and transfer learning with embeddings from a global birdsong model (BirdNET). We also investigated the impact of varying the number of training samples on the performance of these models. Transfer learning models based on <span>BirdNET embeddings had superior performance with a smaller number of training samples, whereas Koogu and ResNet models only had acceptable performance with a larger number of training samples (>200 gibbon samples). We deployed the BirdNET-based model over </span>> 130,000 hours<span> of continuous soundscape data, which, after manual review, resulted in >12,000 verified true positive detections. We found that gibbon calling events occurred mostly in the early morning hours between 05:00 to 0:600 local time. We had fewer gibbon detections during the monsoon period and found substantial variation in spatial patterns of calling events across months and years. </span>We show that automated detection can be used to investigate long-term spatial and temporal patterns of gibbon calling events. Reliable automated detection approaches are a critical first step for using passive acoustic monitoring to assess endangered gibbon populations at ecologically relevant temporal- and spatial-scales. </span></p> <p> Detailed instructions regarding use are provided on GitHub.</p> </div> <p>Link to GitHub: https://github.com/DenaJGibbon/benchmark-gibbon-calls.</p> <p>Please cite both if you use these data: </p> <p>Clink, D., Cross-Jaya, H., Kim, J., Ahmad, A. H., Hong, M., Sala, R., Birot, H., Agger, C., Vu, T. T., Thi, H. N., Chi, T. N., & Klinck, H. (2024). Dataset for "Benchmarking for the automated detection of southern yellow-cheeked crested gibbon calls from passive acoustic monitoring data" [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12706803" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12706803</a></p> <p>Clink DJ, Cross-Jaya H, Kim J, Ahmad AH, Hong M, Sala R, Birot H, Agger C, Vu TT, Thi HN, Chi TN. Benchmarking for the automated detection and classification of southern yellow-cheeked crested gibbon calls from passive acoustic monitoring data. bioRxiv. 2024:2024-08.</p>
Comparison of Active vs Passive Neural Mobilizations Effects in Improving Burning Pain, Muscular Strength, and Range of Motion in Patients With Diabetic Neuropathy
ClinicalTrials.gov study NCT07141992. IPD Sharing: NO. Countries: 1. Publications: 1.
Passive Fit of Implant-Supported Complete-Arch Prostheses Using Digital Workflows
ClinicalTrials.gov study NCT07315620. IPD Sharing: NO. Countries: 1. Publications: 30.
Passive Immunity Trial for Our Nation to Treat COVID-19 in Hospitalized Adults
ClinicalTrials.gov study NCT04362176. IPD Sharing: NO. Countries: 1. Publications: 50.
Effects of Post-Match Foam Rolling, Static Stretching, and Passive Rest on Acute Cardiac-Autonomic, Hemodynamic, and Neuromuscular Recovery in National Wrestlers
ClinicalTrials.gov study NCT07305506. IPD Sharing: NO. Countries: 1. Publications: 7.
Post-authorisation Passive Enhanced Safety Surveillance of Seasonal Influenza Vaccines: Pilot Study in England
ClinicalTrials.gov study NCT03278067. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Wearable Assisted Viral Evidence (WAVE) Study A Decentralized, Prospective Study Exploring the Relationship Between Passively-collected Data From Wearable Activity Devices and Respiratory Viral Infect
ClinicalTrials.gov study NCT06207929. IPD Sharing: UNDECIDED. Countries: 1. Publications: 9.
Passive Tactile Stimulation for Stroke Rehabilitation
ClinicalTrials.gov study NCT03814889. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Active and Passive Music Therapy Interventions
ClinicalTrials.gov study NCT03362944. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Colonoscope Passive Bending Function
ClinicalTrials.gov study NCT00518349. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Descriptive, Post-marketing, Passive Surveillance Safety Study of ADACEL™ Vaccine
ClinicalTrials.gov study NCT00258882. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Work Physiological-Biomechanical Analysis of a Passive Exoskeleton to Support Occupational Lifting and Flexing Processes
ClinicalTrials.gov study NCT03725982. IPD Sharing: NO. Countries: 1. Publications: 2.
Heart Catheterization Using Magnetic Resonance Imaging (MRI) Fluoroscopy and Passive Guidewires
ClinicalTrials.gov study NCT03152773. IPD Sharing: NO. Countries: 1. Publications: 1.
Assessing the Efficacy of Passive Exoskeletons for Construction Work: Lab-Based Study
ClinicalTrials.gov study NCT04567797. IPD Sharing: NO. Countries: 1. Publications: 16.
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.