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Dataset results
53 results for “Early warning system”
Expansion of the early warning system for avian influenza in the EU to evaluate the risk of spillover from wild birds to poultry
<p>GIF 1: Animated map of the spatiotemporal variations of the estimated number of infectious wild birds between February 2023 and March 2024</p> <p>GIF 2: Animated map of the spatiotemporal variations of transmission rate from wild birds to indoor chicken between February 2023 and March 2024</p> <p>GIF 3: Animated map of the spatiotemporal variations of transmission rate from wild birds to outdoor chicken between February 2023 and March 2024</p> <p>GIF 4: Animated map of the spatiotemporal variations of transmission rate from wild birds to indoor duck between February 2023 and March 2024</p> <p>GIF 5: Animated map of the spatiotemporal variations of transmission rate from wild birds to outdoor duck between February 2023 and March 2024</p> <p>GIF 6: Animated map of the spatiotemporal variations of the probability of HPAI introduction into indoor chicken farms between February 2023 and March 2024</p> <p>GIF 7: Animated map of the spatiotemporal variations of the probability of HPAI introduction into outdoor chicken farms between February 2023 and March 2024</p> <p>GIF 8: Animated map of the spatiotemporal variations of the probability of HPAI introduction into indoor duck farms between February 2023 and March 2024</p> <p>GIF 9: Animated map of the spatiotemporal variations of the probability of HPAI introduction into outdoor duck farms between February 2023 and March 2024</p> <p>GIF 10: Animated map of the spatiotemporal variations of the probability of HPAI introduction into poultry farms between February 2023 and March 2024 (scenario A)</p> <p>GIF 11: Animated map of the spatiotemporal variations of the probability of HPAI introduction into poultry farms between February 2023 and March 2024 (scenario B)</p>
Extracted and Anonymised Qualitative Data on Students' Acceptance of an Early Warning System
<p>The data published in this record was adopted in the following study: </p> <p><em><strong>Exploring Higher Education students' experience with AI-powered educational tools: The case of an Early Warning System </strong></em></p> <p>The study analyses the students' experience of an early warning system developed at a fully online university. The study is based on 21 semi-structured interviews that yielded a corpus of 21,761 words, for which a mixed inductive and deductive codification approach was applied after thematic analysis. We focused on 11 themes, 52 subthemes, and 396 coded segments to perform content analysis. Our findings revealed that the students, primarily senior workers with a high-level academic self-efficacy, had little experience with this type of system and low expectations about it. However, a usage experience triggered interest and meaningful reflections on the mentioned tool. Nevertheless, a comparative analysis between disciplines related to Computer Science and Economics showed higher confidence and expectation about the system and artificial intelligence overall by the first group. These results highlight the relevance of supporting students' further experiences and understanding of artificial intelligence systems in education to accept them and mainly to participate in iterative development processes of such tools to achieve quality, relevance, and fairness.</p> <p>The three records attached as part of the dataset include:</p> <p>1- The General CodeTree with exemplar coding excerpts in Spanish<br> 2- Extract of transcriptions in English<br> 3- Full Report in Spanish as extracted from NVIVO, including the extracted codes for the synthesis (1,2) in blue, and the comments made by the two researchers engaged in the interrater agreement.<br> 4- General Content Analysis (Spreadsheet ODS)</p> <p> </p>
Integrated Machine Learning model in Early Urban Flooding Warning System - Data
<p>AI_DATA.npy - Inundation data (mm) generated from MIKE+ model that has been converted to numpy array</p> <p>INDEX.npy - The index where inundation is > 0 </p> <p>source.tif - Source tif image for creating map from ML models</p>
ProbFire: a probabilistic fire early warning system for Indonesia
<p>This repository holds ProbFire model input datasets and pre-trained model weights and feature scaling parameters. Model code and description is available at https://github.com/ToFEWSI/ProbFire.</p>
Dataset: An early warning system for EM follow-up of GW events
<p>Visit</p> <p>https://gstlal.docs.ligo.org/ewgw-data-release/index.html</p> <p>for a web version.</p>
Infrasonic Early Warning System for Explosive Eruptions
<p>The two datasets 2018JB015561-ds01 and 2018JB015561-ds02 provide data used to create most of the figures and to reach most of the findings of the manuscript <em>[Ripepe, M.; Marchetti, E.; Delle Donne, D.; Genco, R.; Innocenti, L.; Lacanna, G.; Valade, S. Infrasonic Early Warning System for Explosive Eruption. J. Geophys. Res. Solid Earth <strong>2018</strong>, 123, 9570–9585].</em></p> <p>2018JB015561-ds01 is a matlab (,mat) file containing a sample of infrasound detections (December, 2nd, 2013) between 09:00 and 24:00 UTC. This dataset is used to create part of Figure 3 of the manuscript. Infrasound detections are obtained from raw infrasound data as discussed in Section 4. Infrasound detections (evaluated every 5 seconds) are used to create the IP, which is evaluated every minute as discussed in detail in Section 5.</p> <p><strong>Data Set S1. </strong>2018JB015561-ds01: The file consists three variables: <em>t</em>, that corresponds to matlab time; <em>pr</em>, that corresponds to the acoustic pressure of the detection as recorded at the array; <em>az</em>, that corresponds to the infrasound back-azimuth and is used to limit the analysis to infrasound produced by the volcano. The data has a time stamp of 5 seconds.</p> <p>2018JB015561-ds02 is a matlab (.mat) file containing the infrasound parameter, between January, 1st, 2008 and December, 31st, 2016. The infrasound parameter is obtained from infrasound detections following the procedure described in Section 5, and used eventually to provide the automatic notification as described in section 6. This dataset is used to create Figure 5 of the manuscript.</p> <p><strong>Data Set S2. </strong>2018JB015561-ds02: It contains two variables: <em>t</em>, that corresponds to matlab time; <em>ip</em>, that corresponds to the infrasound parameter. The data has a time stamp of 1 minute.</p> <p>The movie shows an example of how the IP calculated with the ETN infrasound array is efficiently tracking the changes of eruptive activity at Etna and can be used to deliver an alert of ongoing volcanic activity.</p> <p><strong>Movie S1. </strong>2018JB015561-ms01: The movie shows the eruptive activity at Etna volcano recorded between 23:00 UTC of December, 3rd, 2015, and 12:00 UTC of December, 4th, 2015. It shows movies from a thermal camera synchronized with the corresponding values of seismic tremor and Infrasound Parameter. The Early Warning level is over imposed on the IP.</p> <p> </p>
Digital solutions and early warning system for decision support and risk management in water reuse for irrigation
<p>Video presentation for IWA World Water Congress & Exhibition, 11-15 September 2022, Copenhagen, Denmark.</p>
Could we have seen it coming? Towards an early warning system for asylum applications in the EU
<p>Data and files for Barker E.R. and Bijak J (2022) Could we have seen it coming? Towards an early warning system for asylum applications in the EU [V1.1]. QuantMig Project Deliverable D9.3. University of Southampton.</p> <p>Included is a full data description file.</p>
Early Warning System for Clinical Deterioration on General Hospital Wards
ClinicalTrials.gov study NCT01280942. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Aeroecology meets aviation safety: early warning systems in Europe and the Middle East prevent collisions between birds and aircraft
The aerosphere is utilized by billions of birds, moving for different reasons and from short to great distances spanning tens of thousands of kilometres. The aerosphere, however, is also utilized by aviation which leads to increasing conflicts in and around airfields as well as en-route. Collisions between birds and aircraft cost billions of euros annually and, in some cases, result in the loss of human lives. Simultaneously, aviation has diverse negative impacts on wildlife. During avian migration, due to the sheer numbers of birds in the air, the risk of bird strikes becomes particularly acute for low-flying aircraft, especially during military training flights. Over the last few decades, air forces across Europe and the Middle East have been developing solutions that integrate ecological research and aviation policy to reduce mutual negative interactions between birds and aircraft. In this paper we (1) provide a brief overview of the systems currently used in military aviation to monitor bird migration movements in the aerosphere, (2) provide a brief overview of the impact of bird strikes on military low-level operations, and (3) estimate the effectiveness of migration monitoring systems in bird strike avoidance. We compare systems from the Netherlands, Belgium, Germany, Poland and Israel, which are all areas that Palearctic migrants cross twice a year in huge numbers. We show that the en-route bird strikes have decreased considerably in countries where avoidance systems have been implemented, and that consequently bird strikes are on average 45% less frequent in countries with implemented avoidance systems in place. We conclude by showing the roles of operational weather radar networks, forecast models and international and interdisciplinary collaboration to create safer skies for aviation and birds.
Raw data for "Application of biological early warning systems in wastewater treatment plants: Introducing a promising approach to monitor changing wastewater composition"
Open the record for dataset details and reuse information.
SI-B for "Application of biological early warning systems in wastewater treatment plants: Introducing a promising approach to monitor changing wastewater composition"
Open the record for dataset details and reuse information.
Development of an IoT-Based Early Warning System in Irrigation Channels to Supports Sustainable Environmental Management in Yogyakarta
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Dataset of Usability and Acceptance of Alboom: A Crowd-Based Early Warning System for Harmful Algal Blooms
<p>Alboom is a crowd-based smartphone application and database system to record, store, analyze, share, and provide early warning information regarding harmful algal blooms (HABs). Similar to other participatory-based applications, the willingness of community members to be actively involved is paramount to the success of the application implementation. The dataset is the investigation results of usability and acceptance of Alboom. The questionnaires were built using an augmented technology acceptance model (TAM) to identify factors that affect the continual use intention of the application. In addition to the native TAM variables, such as perceived ease of use and usefulness as well as attitude, other factors, including HAB awareness, social influence, and reward, were also studied. Furthermore, the usability factor was also examined, specifically using the System Usability Scale (SUS) approach.</p>
Preventing Hemolysis and Reducing Reverse Osmosis Waste With Instantaneous Measurement of Water Quality and Early Warning System in Hemodialysis: A New Method, "Pure Water Eye"
ClinicalTrials.gov study NCT07247539. IPD Sharing: YES. Countries: 1. Publications: 3.
The Early Warning System for the Diabetic Encephalopathy
ClinicalTrials.gov study NCT02420470. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Wearable Suicidal Early Warning System for Adolescents
ClinicalTrials.gov study NCT03030924. IPD Sharing: NO. Countries: 1. Publications: 2.
Development of an Automatically Generated and Wearable-based Early Warning System
ClinicalTrials.gov study NCT05699967. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Research on Key Technologies and System Optimization of Early Warning and Resuscitation of Cardiac Arrest
ClinicalTrials.gov study NCT04955288. IPD Sharing: NO. Countries: 1. Publications: 29.
Validation of an Early Warning Score Based Triage System in the Emergency Department
ClinicalTrials.gov study NCT01243021. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ScienceDex guides
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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.