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Dataset results
858 results for “monitoring data”
Data sets for "On the detection of a solar radio burst event that occurred on 28 August 2022 and its effect on GNSS signals as observed by ionospheric scintillation monitors distributed over the American sector" by Wright et al.
<p>Copy of data described in 'On the detection of a solar radio burst event that occurred on 28 August 2022 and its effect on GNSS signals as observed by ionospheric scintillation monitors distributed over the American sector' by Wright et. al and to appear in the Journal of Space Weather and Space Climate (JSWSC), 2023.</p><p> </p>
Population links between an insectivorous bird and moths disentangled through national scale monitoring data
<p>Code in support of "Population links between an insectivorous bird and moths disentangled through national scale monitoring data"</p> <p>Luke Christopher Evans, Malcolm D. Burgess, Simon G. Potts, William E. Kunin, Tom H. Oliver</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>
Data for monitoring stakeholder engagement
<p>This data is collected during the project’s stakeholder engagement activities with a view to effectively measuring and reporting the progress of these activities. With that in mind, a dedicated methodological tool has been designed and is being employed throughout the duration of INVITE, namely the Stakeholder Matrix. In this respect, this Stakeholder matrix includes anonymised and aggregated data about key stakeholders (organization type, gender and region / country).</p>
Feasibility of Monitoring Health Data in Pediatric Patients Undergoing Chemotherapy
ClinicalTrials.gov study NCT04134429. IPD Sharing: YES. Countries: 1. Publications: 1.
Record and Analyze the Data of Patient Undergoing Anesthesia for Laparoscopic Cholecystectomy From Patient Monitoring Instruments
ClinicalTrials.gov study NCT03691103. IPD Sharing: YES. Countries: 1. Publications: 3.
PROStep: A Feasibility Trial Using PROs and Step Data to Monitor Patients With Lung and GI Cancers
ClinicalTrials.gov study NCT04616768. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Dynamic Data-Driven Management of Atrial Fibrillation Using Implantable Cardiac Monitors: The MONITOR-AF Study
ClinicalTrials.gov study NCT06352060. IPD Sharing: NO. Countries: 1. Publications: 4.
Data from "Quantification of major particulate matter species from a single filter type using infrared spectroscopy – Application to a large-scale monitoring network"
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Data from: Multi-tool marine metabarcoding bioassessment for baselining and monitoring species and communities in kelp habitats
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Data from: High-resolution monitoring of Salish Sea estuarine communities through participatory science
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Data from high throughput SNP-chip as cost effective new monitoring tool for assessing invasion dynamics in the comb jelly Mnemiopsis leidyi
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Data from: Efficient wildlife monitoring: Deep learning-based detection and counting of green turtles in coastal areas
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Data from: Trails-as-transects: phenology monitoring across heterogeneous microclimates in Acadia National Park, Maine
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Data from: The role of exotic ladybeetles in the decline of native ladybeetle populations: evidence from long-term monitoring
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Monitoring data for oyster reefs and nearby controls, including high frequency environmental parameters, carbonate chemistry, oyster growth, and other data
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Data from: Genetic monitoring of brown trout released into a novel environment: Establishment and genetic impact on natural populations
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Data from: Monitoring microvascular changes over time with a repositionable 3D ultrasonic capacitive micromachined row-column sensor
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Data from: Long-term monitoring of seed dispersal by Asian elephants in a Sundaland rainforest
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Data from: Time is of the essence: using archived samples to develop a GT-seq panel to preserve continuity of ongoing genetic monitoring
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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.