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Dataset results
39 results for “video monitoring”
US National Native Bee Monitoring RCN Data Management Workshop: Public Domain Videos
<p>The US National Native Bee Monitoring Research Coordination Network (RCN) held a two-day workshop on data management best practices for native bee inventory, survey, and monitoring data on March 28 and 30, 2023. Videos in this data set were played at the workshop. These videos are released into the public domain. This data set includes the following videos:</p> <ul> <li>Ecological Metadata Standards to Enable Data Reuse by Julien Brun</li> <li>Useful Photo Management for Bee Species by Sam Droege</li> <li>Trait Data Models and Vocabulary by Jen Hammock</li> <li>Symbiota: open-source community portals for insect data management by Andrew Johnston</li> <li>Moving data from the field to the world by Jonathan Koch</li> <li>Exploring data using Discover Life by Clare Maffei</li> <li>USDA Data Sharing Policies and Opportunities by Cynthia Sims Parr</li> <li>Why Share Species Interaction Data? by Jorrit H. Poelen</li> <li>Big-Bee: Sharing Bee Interactions & Traits by Katja C. Seltmann</li> <li>Let’s talk about data by Katja C. Seltmann</li> <li>Responsible use of museum specimens & their data by Erika M. Tucker</li> </ul>
US National Native Bee Monitoring RCN Data Management Workshop: CC BY Videos
<p>The US National Native Bee Monitoring Research Coordination Network (RCN) held a two-day workshop on data management best practices for native bee inventory, survey, and monitoring data on March 28 and 30, 2023. Videos in this data set were played at the workshop. These videos are released with a CC BY license. Please cite the presenter(s) of the video(s) you use. This data set includes the following videos:</p> <ul> <li>The ABeeCs of Data Attribution: Please use your magic words by David Bloom</li> <li>Darwin Core Geography: How to make your locality data complete and accurate by David Bloom</li> <li>Best Practices for Managing Native Bee Molecular Data by Michael G. Branstetter</li> <li>A Trait Database for Bees by Elizabeth A. Crisfield</li> <li>Biotic interaction data and invasive species assessment by Quentin Groom</li> <li>OpenTraits Network (OTN) & TRY Plant Trait Database by Jens Kattge</li> <li>Semantics modeling of phenotypic trait data with ontologies by Diego S. Porto</li> <li>WorldFAIR: towards making plant-pollinator data FAIR by Maarten Trekels</li> </ul>
US National Native Bee Monitoring RCN Data Management Workshop: CC BY NC Videos
<p>The US National Native Bee Monitoring Research Coordination Network (RCN) held a two-day workshop on data management best practices for native bee inventory, survey, and monitoring data on March 28 and 30, 2023. Videos in this data set were played at the workshop. These videos are released with a CC BY-NC license. These videos are for non-commercial use only. Please cite the presenter(s) of the video(s) you use. This data set includes the following videos: </p><ul><li>APHIS National Honey Bee Pests and Diseases Survey by Anne LeBrun</li><li>Importance of FAIR in Biodiversity Science and Research by Elizabeth R. Ellwood</li><li>Bugflow: A Community Driven Repository for Entomology Digitization Resources by Crystal Maier</li><li>A Primer on iNaturalist Bee Data by Keng-Lou James Hung, Paige Chesshire, Michael Orr, Alice Hughes, Jess Mullins, Katherine Parys, Patricia Simpson, Lindsie McCabe, Neil Cobb, and John Ascher</li></ul>
Transit Bus Left Side Camera Video Traffic Monitoring Dataset
<p>This dataset contains the raw data used in the research and development study reported in “Automated Traffic Surveillance Using Existing Cameras on Transit Buses”.</p> <p>This dataset consists of 11 video clips, each approximately 20 min. in duration, taken from the driver (left) side read camera of an in-service Ohio State University (OSU) Campus Area Bus Service (CABS) 40-foot transit bus running the West Campus Loop route. One set, consisting of 7 videos, was collected on a sunny day in October 2019. A second set, consisting of 3 videos, was collected during and after periods of rain and heavy rain in March 2022. Each video is accompanied by manually extracted ground truth of vehicles, and some other objects, that are in the roadway and observed by the camera.</p> <p> </p>
Video from: Integrated wildlife monitoring, a pilot trial in Spain
<p>This study aims to report the implementation of the first nationwide pilot trial of balanced IWM in Europe, using a network of 11 pilot monitoring sites in Spain. The insights obtained in this pilot IWM trial will help to further develop a comprehensive IWM and serve as a reference for implementing IWM systems in other regions. For this purpose, a grid of 20 camera traps (Browning Strike Force HD ProX, Browning Arms Company®, Morgan, Utah, USA) was deployed for two months at each study site (n=11). REM was applied to wild boar (<em>Sus scrofa</em>), red deer (<em>Cervus elaphus</em>), and red fox (<em>Vulpes vulpes</em>) as the most frequently detected species. Relative abundance indexes, namely trapping rate and relative occupancy index, were calculated for the remaining, less frequently detected species. We also constructed static social networks, one for each study site (shown in the video).</p>
Data from: Towards automated ethogramming: Cognitively-inspired event segmentation for streaming wildlife video monitoring
<p><span>Our dataset, Nest Monitoring of the Kagu, consists of around ten days (253 hours) of continuous monitoring sampled at 25 frames per second. Our proposed dataset aims to facilitate computer vision research that relates to event detection and localization. We fully annotated the entire dataset (23M frames) with spatial localization labels in the form of a tight bounding box. Additionally, we provide temporal event segmentation labels of five unique bird activities: Feeding, Pushing leaves, Throwing leaves, Walk-In, and Walk-Out. The feeding event represents the period of time when the birds feed the chick. The nest-building events (pushing/throwing leaves) occur when the birds work on the nest during incubation. Pushing leaves is a nest-building behavior during which the birds form a crater by pushing leaves with their legs toward the edges of the nest while sitting on the nest. Throwing leaves is another nest-building behavior during which the birds throw leaves with the bill towards the nest while being, most of the time, outside the nest. Walk-in and walkout events represent the transitioning events from an empty nest to incubation or brooding, and vice versa. We also provide five additional labels that are based on time-of-day and lighting conditions: Day, Night, Sunrise, Sunset, and Shadows. In our manuscript, we provide a baseline approach that detects events and spatially localizes the bird in each frame using an attention mechanism. Our approach does not require any labels and uses a predictive deep learning architecture that is inspired by cognitive psychology studies, specifically, Event Segmentation Theory (EST). We split the dataset such that the first two days are used for validation, and performance evaluation is done on the last eight days.</span></p>
Supplementary Files: "Improved baited remote underwater video (BRUV) for 24h re-al-time monitoring of surface and deep-sea marine species"
<p>In the supplementary material you will find: Figure S1: Photographs of the different parts of the innovative BRUV design; Video S1: Video footage of a bluntnose sixgill shark; Video S2: Types of markings in blue sharks bodies to identify individuals; Video S3: Other marine pelagic species.</p>
Monitoring Disease Activity Using Video Capsule Endoscopy (VCE) in Crohn's Disease (CD) Subjects
ClinicalTrials.gov study NCT01942720. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Video Monitor Size on Adenoma Detection Rate
ClinicalTrials.gov study NCT01952418. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Towards automated ethogramming: Cognitively-inspired event segmentation for streaming wildlife video monitoring
Open the record for dataset details and reuse information.
Data from: A hands-on guide to use network video recorders, internet protocol cameras, and deep learning models for dynamic monitoring of trout and salmon in small streams
Open the record for dataset details and reuse information.
"Towards skin-acetone monitors with selective sensitivity: dynamics of PANI-CA films" Raw Videos
<p>These are the original, unedited videos from which all data in the paper "Towards skin-acetone monitors with selective sensitivity: dynamics of PANI-CA films" was extracted by image analysis. This paper was published in <em>PLOS One</em>.</p>
Supplementary Videos for "Monitoring electrochemical dynamics through single-molecule imaging of hBN surface emitters in organic solvents"
<p><strong>Supplementary Video 1: Out-of-plane emitter control. </strong>The video included is a wide-field view of an hBN flake immersed in acetonitrile while an electrochemical potential of the ITO working electrode is cycled in the three-electrode out-of-plane configuration between +1.5 V and 0 V vs Ag/AgCl. The change in potential induces a change in density of emitters. This data corresponds to a flake used in spectral analysis (data presented in <strong>Fig. 3c–e</strong>). Continuous ~1.6 kW cm<sup>–2</sup> illumination with a 561 nm laser is used. The original images were acquired at a rate of 50.091 ms per frame but here we combined frames to present a lighter video with a lower sampling rate (1 s). The total experiment took 750 seconds, but we present the image stack at a rate of 10 frames per second to make the video only 75 seconds. The scale bar is 5 µm.</p> <p><strong><span>Supplementary Video 2: In-plane emitter control.</span></strong> Here we present a wide-field video of the hBN flake shown in <strong>Figure 4b-d</strong> in between two titanium electrodes. Continuous ~1 kW cm<sup>–2</sup> illumination is used with a 561 nm laser while the polarization of the electrodes is cycled in the two-electrode in-plane configuration, inducing a change in density of emitters correlated with potential. The high-density region follows the negatively charged electrode. The original images were acquired with a 50.091 ms rate, but here we combined frames to present a lighter video with a lower sampling rate (2.5 s). The total experiment took 540 seconds, but we display 5 of the stacked images per second, making make the video around 40 seconds. The scale bar is 5 µm.</p>
Video Game Hearing Tests for Remote Monitoring of Ototoxicity
ClinicalTrials.gov study NCT05847556. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Smartphone-based Remote Symptom Monitoring to Improve Postoperative Rehabilitation Exercise Adherence After Video-assisted Thoracic Surgery (VATS) for Lung Cancer
ClinicalTrials.gov study NCT05990946. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: MotionMeerkat: integrating motion video detection and ecological monitoring
Open the record for dataset details and reuse information.
[Videos] Design of resilient smart highway systems with data-driven monitoring from networked cameras
<p>Traditional high-way transportation systems are monitored based on traffic counters. Such sensors provide much less information compared to traffic cameras and make the system less secure/resilient to attacks/disasters. Thanks to the success of deep learning for object detection/segmentation on images and the publicly available large-scale image datasets with object labels, fusing the information from both traffic counters and traffic cameras has the potential to improve the security and resilience of existing high- way transportation systems. The purpose of the project is to investigate such a potential by developing a deep-learning-based highway video monitoring method that can reliably estimate the fine-grained (car/truck/motorcycle) traffic flow of a high-way network. First, we need to collect a large- scale traffic video dataset with traffic flow estimations from corresponding traffic counters. Then, we need to find efficient deep learning methods for extracting fine-grained local traffic information from individual traffic videos. At last, we need to correlate this information with traffic counters for sensor fusion and detection of defective counters.</p>
Drone Flight Videos for Vegetation Monitoring, Fire Sensor Detection, and Power Line Inspection
<p>This video set showcases drone flights conducted in three different scenarios: vegetation monitoring in natural areas, recognition and localization of a fire detection sensor, and visual inspection of a power line. The videos serve as practical examples of how drones can be used in environmental monitoring and critical infrastructure maintenance applications. The visualization of the results and analysis of the collected data is performed through the GridWatch platform, enabling efficient and detailed monitoring. </p>
Athena Gepvision Monitoring Tool video presentation
<p>Gender equality plan monitoring tool GepVision Athena, grant agreement n° 101006416</p>
Support videos for the article "Damage categorization in full-scale, full-composite ship hull under high-energy impacts by unsupervised-learning-enabled acoustic emission monitoring and laser shearography inspection"
<p>Video 1: Video showing one of the impact from a general perspective</p> <p>Video 2: Slow-motion video of the second impact</p>
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.