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177 results for “Early warnings”
Codes and data for the article 'Early warning signal for river-borne diseases with almost no data'
<p>Codes and data for the paper "Early warning signal for river-borne diseases with almost no data".</p> <p>This collection includes data on the prevalence of river-borne diseases and related environmental variables.<br>The codes are for extracting the tree structure of the river from the map and using the extended HMM model to predict the presence of contaminated samples in each part of the river.</p>
Early warning signal reliability varies with COVID-19 waves
<p><strong>Abstract</strong></p> <p>Early warning signals (EWSs) aim to predict changes in complex systems from phenomenological signals in time series data. These signals have recently been shown to precede the emergence of disease outbreaks, offering hope that policy makers can make predictive rather than reactive management decisions. Here, using a novel, sequential analysis in combination with daily COVID-19 case data across 24 countries, we suggest that composite EWSs consisting of variance, autocorrelation, and skewness can predict non-linear case increases, but that the predictive ability of these tools varies between waves based upon the degree of critical slowing down present. Our work suggests that in highly monitored disease time series such as COVID-19, EWSs offer the opportunity for policy makers to improve the accuracy of urgent intervention decisions but best characterise hypothesised critical transitions.</p> <p><strong>Dataset</strong></p> <p>The deposited dataset contains scripts used in the early warning signal and generalised additive model analysis, the generation of figures, and the custom R functions underpinning the work. Raw COVID-19 case data is also provided if users prefer to access files directly rather than sourcing from the host repositories (all credit is provided to the original publishers).</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>
The Metadata of "Are the Lists of Questionable Journals Reasonable: A Case Study of Early Warning Journal List"
<p>The metadata of the article (<em>Are the Lists of Questionable Journals Reasonable: A Case Study of Early Warning Journal List</em>).</p>
The Prevention of Failure to Rescue Using Early Warning Scoring
ClinicalTrials.gov study NCT01197326. IPD Sharing: NO. Countries: 1. Publications: 1.
Oxygen Reserve Index: Utility as Early Warning for Desaturation in Morbidly Obese Patients
ClinicalTrials.gov study NCT03021551. IPD Sharing: NO. Countries: 1. Publications: 6.
Early Warning System for Clinical Deterioration on General Hospital Wards
ClinicalTrials.gov study NCT01280942. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Impact of Early Sepsis Care Guided by the National Early Warning Score 2 in the Emergency Department
ClinicalTrials.gov study NCT05731349. IPD Sharing: YES. Countries: 1. Publications: 1.
Trauma Study: Early Warning of Progression Toward Hemodynamic Deterioration After Trauma
ClinicalTrials.gov study NCT04912232. IPD Sharing: NO. Countries: 1. Publications: 8.
Data from: Temperature as an early warning signal of honeybee colony failure
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Data from: Early warning signals of malaria resurgence in Kericho, Kenya
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Long-term empirical evidence, early warning signals, and multiple drivers of regime shifts in a lake ecosystem
<p>1. Catastrophic regime shifts in various ecosystems are increasing with the intensification of anthropogenic pressures. Understanding and predicting critical transitions are thus a key challenge in ecology. Previous studies have mainly focused on single environmental drivers (e.g., eutrophication) and early warning signals (EWSs) prior to population collapse. However, how multiple environmental stressors interact to shape ecological behaviour and whether EWSs were detectable prior to the recovery process in lake ecosystems are largely unknown.</p> <p>2. We present long-term empirical evidence of the critical transition and hysteresis with the combined pressures of climate warming, eutrophication and trophic cascade effects by fish stocking in a subtropical Chinese lake in the Yangtze floodplain. The catastrophic regime shifts are cross-validated by 64-year multi-trophic level monitoring data and paleo-diatom records.</p> <p>3. We show that EWSs are detectable in both the collapse and recovery trajectories and that including body size information in composite EWSs requires shorter time series data and can improve the predictive ability of regime shifts. Although full recovery has not yet been observed, EWSs prior to recovery provide us with the opportunity to take measures for a clear-water regime.</p> <p>4. Climate warming and top-down cascade effects have a negative influence on water clarity by altering lower trophic level abundance and body size, which in turn have a negative effect on macrophyte abundance. Furthermore, we identify a shift in the dominant driving forces from bottom-up to top-down after regime shifts, decoupling the relationships between nutrients and biological components and thus decreasing the efficiency of nutrient reduction.</p> <p>5. Synthesis This study provides new insights into ecological hysteresis under multiple external stressors and improves our understanding of trait-based EWSs in both the collapse and recovery processes in natural freshwater ecosystems. For management practice, our work suggests that slowing down climate warming and weakening the fish predation pressure on food webs are necessary to increase the effectiveness of nutrient reduction in the restoration of lakes.</p>
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"
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SI-B for "Application of biological early warning systems in wastewater treatment plants: Introducing a promising approach to monitor changing wastewater composition"
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GJI:Learning source, path, and site effects: CNN-based Onsite Intensity Prediction for Earthquake Early Warning
<p>Dataset used for the study. Submitting to GJI. Wish me luck.</p>
Codes: An early warning indicator trained on stochastic disease-spreading models with different noises
<p>This dataset contains the training data (Version V1) and all the codes (Version V2) of the paper entitled "An early warning indicator trained on stochastic disease-spreading models with different noises." </p> <p>Time series and corresponding residuals from white noise (equation 2.5), environmental noise (equation 2.8), and demographic noise (equation 2.9) are stored in the training_data_WhiteN, training_data_EnvN, and training_data_DemN folders, respectively. All residuals of the time series are contained in the training_resids folder, which also includes labels and groups of the training data. For details on the data generation process, please refer to section 3.1 in the paper. </p>
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>
Deep learning for early warning signals of tipping points : supplementary data
<p>This data accompanies the publication by Bury et al. “Deep learning for early warning signals of tipping points” published in PNAS and the Github repository <a href="https://github.com/ThomasMBury/deep-early-warnings-pnas">https://github.com/ThomasMBury/deep-early-warnings-pnas</a>. It contains the model time series data that are used to train the deep learning algorithm. The directory ts_500 contains 500k time series used to train the 500-classifier. The directory ts_1500 contains 200k time series used to train the 1500-classifier. Both directories contain files <em>labels.csv </em>and <em>groups.csv </em>which provide numbers corresponding to the labels (Fold, Hopf, Branch, Null) and groups (Training, Validation, Test) for each time series respectively.</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.