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362 results for “risk analysis”
Data and Software for "Probabilistic Trade-offs Analysis for Sustainable and Equitable Management of Climate-Induced Water Risks"
<p><span>Research data supporting the study "Probabilistic trade-offs analysis for sustainable and equitable management of climate-induced water risks"</span></p> <p><span>This repository provides data of the Stochastic Dual Dynamic Programming (SDDP) model, and the output results of the simulations of the various policies and climate scenarios considered in this study, as well as the code used for postprocessing and visualizing the results.</span></p> <p><strong><span>Contents</span></strong></p> <ol> <li><strong><span>Data: Model Inputs</span></strong><span><br>This folder contains the physical river network, reservoir and water demand, and economic data derived from the observed database.<br>The key files are:</span></li> <ul> <li><span>Input_HydrologicalData</span></li> <li><span>Input_SystemData</span></li> </ul> <li><strong><span>Results: Model Output Analysis</span></strong><span><br>This folder includes outputs from the Stochastic Dual Dynamic Programming (SDDP) model under various policies and climate scenarios. The results showcase optimized sectoral water use, including irrigated areas, hydropower generation, and allocations for agriculture, energy, and urban demands across spatial locations (upstream and downstream).<br>Key files include:</span></li> <ul> <li><strong><span>SDDP Model Outputs</span></strong><span> (MATLAB format): </span></li> <ul> <li><span>EnergyPriority_Baseline.mat</span></li> <li><span>EnergyPriority_2070.mat</span></li> <li><span>EnergyPriority_2100.mat</span></li> <li><span>AgriculturePriority_Baseline.mat</span></li> <li><span>AgriculturePriority_2070.mat</span></li> <li><span>AgriculturePriority_2100.mat</span></li> </ul> <li><strong><span>Extracted Model Results</span></strong><span> (Excel format): </span></li> <ul> <li><span>Organized for each policy and climate scenario to facilitate analysis.</span></li> </ul> </ul> <li><strong><span>Software: Data Analysis and Visualization</span></strong><span><br>Python scripts designed for outputs data analysis and visualization are included to reproduce the primary figures from the study.<br>Scripts provided:</span></li> <ul> <li><span>CDF_outflow.py</span><span>: Analyzes cumulative distribution functions for river discharge.</span></li> <li><span>CDF_sectors.py</span><span>: Examines sectoral water use distributions.</span></li> <li><span>PCP_SI.py</span><span>: Generates Parallel Coordinate Plots for trade-offs analysis.</span></li> </ul> <li><strong><span>Instructions: README File</span></strong><span><br>A comprehensive README file explains:</span></li> <ul> <li><span>Details of model input data.</span></li> <li><span>Instructions to interpret the SDDP model outputs.</span></li> </ul> </ol> <p><strong><span>Instructions:</span></strong><span><br></span><span>The Python scripts process Excel files from the model output results folder to generate and visualize the figures for the paper. Each step is clearly documented within the scripts.</span></p>
IoT Sensor Deployment in the Wildland Urban Interface: Leveraging Fire Risk Analysis
<p>Included here are individual burn maps used for evaluating algorithm results in the paper: IoT Sensor Deployment in the Wildland Urban Interface: Leveraging Fire Risk Analysis. This paper will be presented at the IEEE World Forum on Internet of Things in November 2024 and available on IEEE Xplore after that.</p> <p>Also included are maps of fuel load and elevation (geotifs) and the daily weather (in .csv format) for the region of interest used in the burn probability simulator Burn-P3+ to generate the individual burn maps.</p> <p>This paper investigates various algorithms for distributing Internet of Things sensors within the Wildland-Urban Interface to enhance early wildland fire detection. Utilizing geospatial data analysis and a validated wildland fire growth model burn maps were generated to guide sensor placement strategies across a defined region of interest. The algorithms evaluated include an even grid distribution, random distributions, and genetic algorithm-based methods. Each algorithm was tested against 50,000 selected burn maps to assess detection rates, with sensor counts ranging from 50 to 800 across 500 experimental runs. Results indicate that while the even grid distribution yielded the highest detection rates, the practicality of such a method in real-world applications is limited. Genetic algorithms showed promise, but require further exploration to more accurately simulate random distribution used in field deployment. Surprisingly, weighting sensor placement based on wildland fire growth risk did not significantly impact detection effectiveness, suggesting the need for additional research into the representativeness of selected burn maps.</p> <p>Partial code for the sensor deployment algorithms discussed in the above mentioned paper is <a href="https://github.com/richardjpurcell/sensor-deployment-algorithms">available on GitHub</a>.</p>
Supplementary Data for risk analysis
<p>The files in this record contain data for risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic bayesian network.</p> <p>The files consist of:</p> <ul> <li>Reservoir data and river flood routing parameters</li> <li>Code and results of the Monte Carlo simulations</li> <li>Code and results of the Bayesian network</li> </ul>
Haemoparasite Infection Risk in Multi-Host Avian System: An Integrated Analysis
<p><strong>Data used in the study: Haemoparasite Infection Risk in Multi-Host Avian System: An Integrated Analysis</strong></p> <ul> <li><strong>Podmokła et al. 2024_data.csv</strong> - This file contains the processed data used in the analysis.</li> <li><strong>Podmokła et al. 2024_raw data_landscape and population variables.csv</strong> - This file includes raw data on landscape and population variables. <ul> <li><strong>Population Metrics</strong>: Density of the same species and all species combined in the study area.</li> <li><strong>Landscape Variables</strong>: High-resolution satellite remote-sensing data reflecting vegetation cover (NDVI), moisture levels (NDMI), and distances to key landscape features such as forest edges, coastlines, pastures, and fields.<br><br></li> </ul> </li> <li><strong>Podmokła et al. 2024_raw data_parasites.csv</strong> - This file contains raw data on <em>Haemoproteus</em>, <em>Plasmodium</em>, and <em>Trypanosoma</em> infection status</li> </ul>
Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: PEG-asparaginase treatment for acute lymphoblastic leukaemia in children: a network meta-analysis
<p>Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: PEG-asparaginase treatment for acute lymphoblastic leukaemia in children: a network meta-analysis</p>
Compound flood risk analysis in CONUS
<p>This repository includes the developed E3SM source code for running MOSART simulation with a downstream boundary and a suite of codes for performing the statistical analysis of compound flood risk assessments. For the access to the full MOSART simulation output, please contact the author. </p>
Data from: Sensitivity analysis of collision risk at wind turbines based on flight altitude of migratory waterbirds
<p>This dataset contains information on the distribution of geese and swans and the three-dimensional flight trajectories. The former was obtained through vehicle field surveys, interviews, and a literature review. The latter was obtained using ornithodolites.</p>
Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India
<p>This Zenodo resource contains the data used to perform analysis in the article "Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India".</p> <p>Data</p> <p>The data is organized in the form of tables.</p> <p>hypothesis-test-data</p> <p>This table contains data used to perform the two tailed hypothesis test on gender mortality in different regions.</p> <pre><code>* Region * Male_Deaths - Number of male COVID-19 deaths in region. * Female_Deaths - Number of female COVID-19 deaths in region. * Male_cases - Number of male COVID-19 positive in region. * Female_cases - Number of female COVID-19 positive in region. </code></pre> <p>lasso-covid19India</p> <p>This table contains data used for analysis on cases throughout India.</p> <p>Columns from COVID-19 India data</p> <pre><code>* State_Code * State * District * Confirmed * Active * Recovered * Deceased </code></pre> <p>Columns taken from NFHS data</p> <pre><code>* Sex_ratio_of_the_total_population_females_per_1000_males * Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm214_ * Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm2_ * Women_who_are_overweight_or_obese_BMI__250_kgm214_ * Men_who_are_overweight_or_obese_BMI__250_kgm2_ * All_women_age_1549_years_who_are_anaemic_ * Men_age_1549_years_who_are_anaemic_130_gdl_ * Women_Blood_sugar_level__high_140_mgdl_ * Men_Blood_sugar_level__high_140_mgdl_ * Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ * Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ </code></pre> <p>lasso-KA+TN-bulletin</p> <p>This table contains data used for analysis on the sub-cohort of Karnataka and Tamil Nadu.</p> <p>Data from Media Bulletin</p> <pre><code>* District * Total_Positives * total_deaths * male_deaths * female_deaths * Male_cases_in_data * Female_cases_in_data </code></pre> <p>Calculated Data</p> <pre><code>* Estimated_Male_cases - Estimated male cases using total positives column and existing case data * Estimated_Female_Cases - Estimated female cases using total positives column and existing case data * Male_Mortality - Estimated Male Cases / male_deaths * Female_Mortality - Estimated Female Cases / female_deaths </code></pre> <p>Columns taken from NFHS data</p> <pre><code>* Sex_Ratio_females_every_1000_males * State Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm214_ * Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm2_ * Women_who_are_overweight_or_obese_BMI__250_kgm214_ * Men_who_are_overweight_or_obese_BMI__250_kgm2_ * All_women_age_1549_years_who_are_anaemic_ * Men_age_1549_years_who_are_anaemic_130_gdl_ * Women_Blood_sugar_level__high_140_mgdl_ * Men_Blood_sugar_level__high_140_mgdl_ * Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ * Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ </code></pre> <p>Code</p> <p>The code is available at this <a href="https://github.com/harishpb26/Sex-disaggregated-Analysis-of-Risk-Factors-of-COVID-19-Mortality-Rates-in-India">Github Repository</a>.</p>
Code. Midlife proteome-wide analysis identifies plasma biomarkers for 25-year dementia risk linked to diverse pathophysiology
<p>The code used for the analyses for the paper entitled "Midlife proteome-wide analysis identifies plasma biomarkers for 25-year dementia risk linked to diverse pathophysiology". </p>
Integrative multi-ancestry genetic analysis of gene regulation in coronary arteries prioritizes disease risk loci
<p>All full-sample files contain results generated in coronary artery tissue from 138 American adults. Subset analyses utilized 80 individuals selected from the original 138. Scripts accompanying some of these data in downstream analyses can be viewed on our Github, which also contains a link to the current version of our accompanying manuscript: https://github.com/MillerLab-CPHG/CAD_QTL</p> <p>Full summary statistics for eQTL associations using mixQTL (https://github.com/hakyimlab/mixqtl/wiki) by chromosome are located in UVA_coronary_mixQTL_sumstats_by_chromosome.zip</p> <p>Full summary statistics for eQTL associations using mixQTL in the subset of 100% European-ancestry study sample members by chromosome are located in Hodonsky_mixQTL_Euro_sumstats.zip</p> <p>Full summary statistics for eQTL associations using mixQTL in the genetically diverse downsampled subset by chromosome are located in Hodonsky_mixQTL_downsample_sumstats.zip</p> <p>Full summary statistics for nominal pass for all genes identified as significant in the permutation pass using QTLtools (https://qtltools.github.io/qtltools/) adjusting for local ancestry by gene by chromosome are located in Local_ancestry_UVA_coronary_QTLtools_nominal_sumstats.zip</p> <p>Full summary statistics for sQTL associations with splice junctions using QTLtools by gene are located in sQTL_results_UVA_coronary_full_sumstats.zip</p>
Olaparib in Men With High-Risk Biochemically-Recurrent Prostate Cancer Following Radical Prostatectomy, With Integrated Biomarker Analysis
ClinicalTrials.gov study NCT03047135. IPD Sharing: NO. Countries: 1. Publications: 1.
Aspirin Resistance and Stroke Risk: Platelet Function Analysis in Patients With Ischemic Events
ClinicalTrials.gov study NCT01586975. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Types of Intracellular Bacteria in Atherosclerotic Plaques and Analysis of Risk Factors
ClinicalTrials.gov study NCT06935279. IPD Sharing: Not stated. Countries: 1. Publications: 0.
OZONE_EXO: Comparative Analysis of Protocols for Dental Exactions in Patients at Risk of MRONJ: Case-control Study
ClinicalTrials.gov study NCT06072404. IPD Sharing: NO. Countries: 1. Publications: 4.
Meta-analysis of the Portfolio Dietary Pattern and Cardiometabolic Risk
ClinicalTrials.gov study NCT03534414. IPD Sharing: NO. Countries: 1. Publications: 5.
Lifecourse genome-wide association study meta-analysis refines the critical life stages for adiposity’s influence on breast cancer risk
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Cross-species analysis of genetic architecture and polygenic risk scores for non-contact ACL rupture in dogs and humans
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Occupational exposure to silica and risk of heart disease: a systematic review with meta-analysis
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Data from: Sensitivity analysis of collision risk at wind turbines based on flight altitude of migratory waterbirds
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Location specific risk factors for intracerebral hemorrhage: Systematic review and meta-analysis
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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)
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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.