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Dataset results
63 results for “water detection”
From Microorganisms to Biosignatures: Subcritical Water Extraction as a Sample Preparation Technique for Future Life Detection Missions
<p>Data for all figures included in "From Microorganisms to Biosignatures: Subcritical Water Extraction as a Sample Preparation Technique for Future Life Detection Missions". Amino acid concentrations, enantiomeric excesses, and relative distributions for E. coli cells and B. subtilis spores after subcritical water extraction for 30 min at 200 °C using water and dilute acid.</p>
Minimizing Search Areas for Leak Detection in Water Distribution Networks - Code
<p>This database includes the code used to analyze and produce the results for the following research article:</p> <p>Minimizing Search Areas for Leak Detection in Water Distribution Networks by B. Snider, G. Lewis, A.S. Chen, L. Vamvakeridou-Lyroudia, S. Djordjevic, D.A. Savic. Journal of Hydroinformatics. (Accepted - awaiting publication).</p> <p> </p> <p> </p>
Image Dataset for 'AI-enabled biosensing for rapid pathogen detection: from liquid food to agricultural water'
<p>This dataset is presented in the following publication. Please cite this publication if you use the dataset.</p> <p><em>Jiyoon Yi, Nicharee Wisuthiphaet, Pranav Raja, Nitin Nitin, J. Mason Earles. (2023). AI-enabled biosensing for rapid pathogen detection: from liquid food to agricultural water. Water Research, 120258. doi: <a href="https://doi.org/10.1016/j.watres.2023.120258">10.1016/j.watres.2023.120258</a></em></p>
Role of Lung Ultrasonography in Detecting Extravascular Lung Water in Major Oncosurgeries
ClinicalTrials.gov study NCT04659681. IPD Sharing: NO. Countries: 1. Publications: 2.
Echocardiographic Detection of Extravascular Lung Water Increase During Weaning From Mechanical Ventilation
ClinicalTrials.gov study NCT02412202. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Comparison of Polyp Detection and False Alarm Rates in Water Exchange and Air Insufflation Colonoscopy
ClinicalTrials.gov study NCT04727814. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.
Comparison of Adenoma Detection Rate Among Water, Carbon Dioxide and Air Methods of Minimal Sedation Colonoscopy
ClinicalTrials.gov study NCT01782014. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Detection of Schistosomiasis CAA in Travellers After High-risk Water Contact
ClinicalTrials.gov study NCT02194712. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Water Exchange With Narrow Band Imaging on Adenoma Detection
ClinicalTrials.gov study NCT03781648. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Measurement of Extravascular Lung Water to Detect and Predict Primary Graft Dysfunction Following Lung Transplant
ClinicalTrials.gov study NCT01605214. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Comparing the Adenoma Detection Rate With Air Insufflation, Water Immersion and Water Exchange
ClinicalTrials.gov study NCT01894191. IPD Sharing: Not stated. Countries: 2. Publications: 6.
Water Exchange Colonoscopy With Artificial Intelligence-assisted Detection
ClinicalTrials.gov study NCT06173258. IPD Sharing: NO. Countries: 2. Publications: 16.
Computer-Aided Water Exchange Colonoscopy With and Without Linked-Color Imaging for Detection of Clinically Significant Serrated Lesions
ClinicalTrials.gov study NCT07127471. IPD Sharing: NO. Countries: 4. Publications: 2.
Effect of Water Exchange Plus Acetic Acid Staining on the Detection of Flat Polyps During Colonoscopy
ClinicalTrials.gov study NCT04464135. IPD Sharing: NO. Countries: 1. Publications: 4.
Data from: Detection of invasive mosquito vectors using environmental DNA (eDNA) from water samples
Open the record for dataset details and reuse information.
USFS Durham Lab water analysis method detection limit (MDL) limit of quantification (LOQ), 2010 - present
This dataset documents limits in the precision of water analyses produced by the USFS, NRS lab in Durham, NH. Generally precision of water analyses is limited at low concentrations due to limits of detection or limits of precise quantification. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
GECCO Industrial Challenge 2018 Dataset: A water quality dataset for the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition at the Genetic and Evolutionary Computation Conference 2018, Kyoto, Japan.
<p>Dataset of the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2018, Kyoto, Japan</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, M. Rebolledo, S. Moritz, S. Chandrasekaran, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p>GECCO Industrial Challenge: 'Internet of Things: Online Anomaly Detection for Drinking Water Quality'</p> <p>Description:</p> <p>For the 7th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2017 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.<br> Additionally to the competition, for the first time in GECCO history we are now able to provide the opportunity for all participants to submit 2-page algorithm descriptions for the GECCO Companion. Thus, it is now possible to create publications in a similar procedure to the Late Breaking Abstracts (LBAs) directly through competition participation!</p> <p> </p> <p>Accepted Competition Entry Abstracts<br> - Online Anomaly Detection for Drinking Water Quality Using a Multi-objective Machine Learning Approach (Victor Henrique Alves Ribeiro and Gilberto Reynoso Meza from the Pontifical Catholic University of Parana)<br> - Anomaly Detection for Drinking Water Quality via Deep BiLSTM Ensemble (Xingguo Chen, Fan Feng, Jikai Wu, and Wenyu Liu from the Nanjing University of Posts and Telecommunications and Nanjing University)<br> - Automatic vs. Manual Feature Engineering for Anomaly Detection of Drinking-Water Quality (Valerie Aenne Nicola Fehst from idatase GmbH)</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/">http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/</a></p>
Validation of an eDNA-based method for the detection of wildlife pathogens in water
<p>Monitoring the occurrence and density of parasites and pathogens can identify high infection-risk areas and facilitates disease control and eradication measures. Environmental DNA (eDNA) techniques are increasingly used for pathogen detection due to their relative ease of application. Since many factors affect the reliability and efficacy of eDNA-based detection, rigorous validation and assessment of method limitations is a crucial first step. We evaluated an eDNA detection method using in-situ filtration of large volume water samples, developed to detect and quantify aquatic wildlife parasites by qPCR. We assessed method reliability using Batrachochytrium dendrobatidis, a pathogenic fungus of amphibians and the myxozoan Tetracapsuloides bryosalmonae, causative agent of salmonid proliferative kidney disease, in a controlled experimental setup. Different amounts of parasite spores were added to tanks containing either clean tap water or water from a semi-natural mesocosm community. Overall detection rates were higher than 80 %, but detection was not consistent among replicate samples. Within tank variation in detection emphasises the need for increased site-level replication when dealing with parasites and pathogens. Estimated parasite DNA concentrations in water samples were highly variable and significant increase with higher spore concentrations was observed only for B. dendrobatidis. Despite evidence for PCR inhibition in DNA extractions from mesocosm water samples, the type of water did not affect detection rates significantly. Direct spiking controls revealed that the filtration step reduced detection sensitivity. Our study identifies sensitive quantification and sufficient replication as major remaining challenges of eDNA-based methods for detection of parasites in water.</p>
Training dataset for land type detection on Sentinel-2 images (annotations: build-up, rural, forest, water)
Open the record for dataset details and reuse information.
Portable Roadway High Water Detection System for Driver Safety and Infrastructure Assessment
<p>Corresponding data set for Tran-SET Project No. 18SATSA05. Abstract of the final report is stated below for reference:</p> <p>"The state of Texas, local governments, and community groups are adopting several initiatives to address this problem. The main goal of this project is to develop an approach to enhance prediction and detection of roadway flooding and help efforts to prevent driving into flood roadways. The motivation of this work was initiated by the alarming number of vehicle related flood fatalities in Texas, mostly at low-water crossings. Texas leads the nation in the number of flood fatalities and about 77% of flood fatalities in Texas are motor vehicle-related. This percentage is much higher than the national percentage reported in several previous studies. Motor vehicle-related flood fatalities in Texas are generally correlated with monthly climate, topography, and population density. For example, about half of the fatalities caused by Hurricane Harvey were transportation-related. The presence of numerous low water crossings throughout Texas contribute to the higher recurrence rates of floods that pose a danger to vehicles. The research team has been challenged to identify effective measures to improve safety at low-water crossings. Therefore, the research team developed an approach that combines data analysis, modeling, and high water detection and communication to improve safety at low-water crossings. Researchers conducted the following tasks: literature review, vehicle-related flood fatality analysis, review of transportation fatalities cause by Hurricane Harvey, and proposed the design of a warning systems at low-water crossings that can detect and predict water depth and velocity at these locations. It was suggested that flood detection and warning can be significantly augment by using sensing technologies that can detect not only the water depth but also the water velocity in combination with a physically-based hydrologic modeling system that can provide forecasts of water depth and velocity at locations of interest. The results of the model and information of the sensors can be used to provide timely and adequate warning to approaching motorists that flooded roadway conditions exist further up the road."</p>
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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.