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6 results for “Fire safety”
Build-in-Wood Regulation Analysis – Fire Safety
<p>This dataset contains an analysis of selected EU Member State building regulations covering fire safety in residential multi-storey wood buildings. The data has been collected as part of the Build-in-Wood project (<a href="https://www.build-in-wood.eu/)">https://www.build-in-wood.eu/)</a> which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 862820.</p> <p>Disclaimer: The presented data might be outdated, flawed, or otherwise incomplete. Users are responsible for checking the correctness of the presented data.</p>
FireSafetyNet: An Image-Based Dataset with Pretrained Weights for Machine Learning-Driven Fire Safety Inspection
<p>This dataset offers a diverse collection of images curated to support the development of computer vision models for detecting and inspecting Fire Safety Equipment (FSE) and related components. Images were collected from a variety of public buildings in Germany, including university buildings, student dormitories, and shopping malls. The dataset consists of self-captured images using mobile cameras, providing a broad range of real-world scenarios for FSE detection.</p> <p>In the journal paper associated with these image datasets, the open-source dataset FireNet (Boehm et al. 2019) was additionally utilized for training. However, to comply with licensing and distribution regulations, images from <a href="https://www.firenet.xyz/">FireNet</a> have been excluded from this dataset. Interested users can visit the FireNet repository directly to access and download those images if additional data is required. The provided weights (.pt), however, are trained on the provided self-made images and FireNet using YOLOv8.</p> <p>The dataset is organized into six sub-datasets, each corresponding to a specific FSE-related machine learning service:</p> <ol> <li> <p><strong>Service 1: FSE Detection</strong> - This sub-dataset provides the foundation for FSE inspection, focusing on the detection of primary FSE components like fire blankets, fire extinguishers, manual call points, and smoke detectors.</p> </li> <li> <p><strong>Service 2: FSE Marking Detection</strong> - Building on the first service, this sub-dataset includes images and annotations for detecting FSE marking signs.</p> </li> <li> <p><strong>Service 3: Condition Check - Modal</strong> - This sub-dataset addresses the inspection of FSE condition in a modal manner, focusing on instances where fire extinguishers might be blocked or otherwise non-compliant. This dataset includes semantic segmentation annotations of fire extinguishers. For upload reasons, this set is split into <em>3_1_FSE Condition Check_modal_train_data (containing training images and annotations) </em>and <em>3_1_FSE Condition Check_modal_val_data_and_weights (containing validation images, annotations </em>and<em> the best weights).</em></p> </li> <li> <p><strong>Service 4: Condition Check - Amodal</strong> - Extending the modal condition check, this sub-dataset involves amodal detection to identify and infer the state of FSE components even when they are partially obscured. This dataset includes semantic segmentation annotations of fire extinguishers. This dataset includes semantic segmentation annotations of fire extinguishers. For upload reasons, this set is split into <em>4_1_FSE Condition Check_amodal_train_data (containing training images and annotations) </em>and <em>4_1_FSE Condition Check_amodal_val_data_and_weights (containing validation images, annotations </em>and<em> the best weights).</em></p> </li> <li> <p><strong>Service 5: Details Extraction - Inspection Tags</strong> - This sub-dataset provides a detailed examination of the inspection tags on fire extinguishers. It includes annotations for extracting semantic information such as the next maintenance date, contributing to a thorough evaluation of FSE maintenance practices.</p> </li> <li> <p><strong>Service 6: Details Extraction - Fire Classes Symbols</strong> - The final sub-dataset focuses on identifying fire class symbols on fire extinguishers.</p> </li> </ol> <p>This dataset is intended for researchers and practitioners in the field of computer vision, particularly those engaged in building safety and compliance initiatives.</p>
Efficacy and Safety of Fire Needle Therapy Combined With Cortex Phellodendri Compound Fluid Wet Compress for Acute Herpes Zoster: a Randomized Controlled Trial.
ClinicalTrials.gov study NCT06985589. IPD Sharing: NO. Countries: 1. Publications: 6.
Root traits reveal safety and efficiency differences in grasses and shrubs exposed to different fire regimes
<p>Roots are key components of terrestrial ecosystems, yet little is known about how root structure and function vary across a broad range of species, functional groups, and ecological gradients <i>in situ</i>.</p> <p>We assessed how woody and grass root anatomical traits vary among soil depths and different fire frequencies to better understand the water-use strategies exhibited by these two functional groups in tallgrass prairie experiencing woody encroachment. Specifically, we asked: (1) Do root anatomical traits differ with fire frequency or soil depth? (2) Do relationships between anatomical traits that confer hydraulic safety versus efficiency vary by fire frequency or soil depth? (3) Is root anatomy associated with integrative root traits (e.g., root diameter, specific root length (SRL), and root biomass)? (4) When scaled by root biomass, do root water-use traits impact the capacity for water uptake?</p> <p>We collected grass and woody roots from 10, 30, and 50 cm deep soil in areas burned every 1, 4, and 20-years. We then measured xylem conduit diameter, conduit cell wall thickness, conduit number, conduit mechanical safety (t/b), stele area, endoderm thickness, hydraulic diameter, theoretical hydraulic conductivity, and root-system theoretical hydraulic conductance.</p> <p>We observed: (1) Woody roots had high hydraulic conductance in shallow soils and greater mechanical strength in deeper soils, which may provide a competitive advantage in less frequently burned, more diverse plant communities; (2) Shallow grass roots had unique trait combinations at the anatomical and root-system levels (thinner, more numerous conduits and higher root-system hydraulic conductance compared to deeper roots) that likely allow these plants to rapidly use water but tolerate dry soils under multiple fire regimes; and (3) hydraulic safety versus efficiency tradeoffs translate between different hierarchical scales (i.e., from anatomical to integrative root traits).</p> <p>These results provide anatomical evidence to explain water-use dynamics in tallgrass prairie and also provide novel insight regarding functional strategies that may facilitate the conversion from grassland to shrubland in less frequently burned tallgrass prairie. Future work should investigate these dynamics <i>in situ</i>, as they may explain current and future patterns of woody-grass coexistence in tallgrass prairies.</p>
Root traits reveal safety and efficiency differences in grasses and shrubs exposed to different fire regimes
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Aviation Safety Reporting System: Cabin Smoke, Fire, Fumes, or Odor Incidents
A sampling of air carrier reports concerning cabin smoke, fire, fumes or odor related events.
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.
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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.