Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
49
datasets available to search
ShareScore release 0.9.0
Dataset results
49 results for “Bayesian networks”
Data from: Predicting forest insect flight activity: a Bayesian network approach
Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the H. ater and A. ferus Bayesian network models had the best fit for low Type I and overall errors, and H. ligniperda had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on H. ligniperda flight activity predictions, whereas time of day and year had the greatest influence on H. ater and A. ferus activity. Type II model errors for the prediction of no flight activity is improved by increasing the model's predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways.
Bayesian Network analysis for Single Cell Multiomics
<p>The data from stratified random samples of scRNA expression, surface marker and SNF cluster membership were integrated with high-resolution CT (HRCT) Scores of COVID-19 patients. The healthy and recovered individuals were assigned an HRCT score of zero, indicating absence of active pneumonia. The integrative modeling analysis was carried out using the wiseR package for end-to-end Bayesian network learning, inference and dashboard deployment. All continuous variables in the integrated data were discretized using the k-means algorithm with k=3 for biological interpretability as low, medium and high. A discrete Bayesian Network was learned from the data using hill climbing optimization for finding the directed acyclic graph encoding the structural dependencies between variables. Eleven Bayesian network structures were ensembled averaged to derive the consensus structure. The consensus structure was then parametrized with marginal and conditional probability distributions using Monte Carlo Markov Chain (MCMC) approximate inference method.</p>
Supplementary Data:Risk Analysis for Real-time Flood Control Operation of a Multi-reservoir System Using a Dynamic Bayesian Network
<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 considered for publication in Water Resources Research.</p> <p>The files consist of:</p> <ul> <li>Reservoir data and river flood routing parameters</li> <li>Flood data</li> <li>Code and results of the Monte Carlo simulations</li> <li>Code and results of the Bayesian network</li> </ul>
MATLAB Implementation for Wind Turbine Prognosis Using Uncertainty Bayesian-Optimized Lightweight Neural Network
<p>These MATLAB codes accompany the paper titled "---," currently submitted to the 11th International Electronic Conference on Sensors and Applications (ECSA-11). The paper presents a novel approach to wind turbine prognosis for maintenance purposes using the Uncertainty Bayesian-Optimized Extreme Learning Machine (UBO-ELM) algorithm.</p> <p>The codes provided here implement the methodology described in the paper, including data preprocessing, model training and evaluation, uncertainty quantification, and visualization of results. These codes are intended for researchers and practitioners in the field of wind energy systems and predictive maintenance.</p> <p>Please note that the paper is currently under review at ECSA-11. Once the paper is approved and the embargo is lifted, these codes will be accessible openly. Users are kindly requested to cite our paper when utilizing these codes for their research.</p>
Dataset for: Trait-mediated speciation and human-driven extinctions in proboscideans revealed by unsupervised Bayesian neural networks
<p>This repository contains the output of all birth-death neural network simulations and the empirical analyses of Proboscidean diversification. For details see the Readme of the individual directories.</p>
Efficacy and safety of different monoclonal antibodies for osteoarthritis: a Bayesian network meta-analysis
Open the record for dataset details and reuse information.
Bayesian Networks in Pediatric Cardiac Surgery
ClinicalTrials.gov study NCT05537168. IPD Sharing: NO. Countries: 1. Publications: 6.
Data from: Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network
Open the record for dataset details and reuse information.
Data from: Predicting forest insect flight activity: a Bayesian network approach
Open the record for dataset details and reuse information.
Public Dataset for the Paper "Using Hybrid Bayesian Networks to Detect Audience Behaviour Changes in Youtube"
<p>This is the dataset used in the production of the paper "Using Hybrid Bayesian Networks to Detect Audience Behaviour Changes in Youtube" to be published in the EMSS 2020. Official site: http://www.msc-les.org/conf/emss2020/</p>
Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing - Model Weights, Chains, BNN Samples, and Simulated Datasets
<p>The model weights, chains, simulated datasets, and BNN samples used to produce the results shown in LSST DESC Collaboration paper "Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing." All files presented here are meant for use in tandem with the python package "ovejero" (<a href="https://github.com/swagnercarena/ovejero">https://github.com/swagnercarena/ovejero</a>).</p>
Data from: Bayesian inference of reticulate phylogenies under the multispecies network coalescent
The multispecies coalescent (MSC) is a statistical framework that models how gene genealogies grow within the branches of a species tree. The field of computational phylogenetics has witnessed an explosion in the development of methods for species tree inference under MSC, owing mainly to the accumulating evidence of incomplete lineage sorting in phylogenomic analyses. However, the evolutionary history of a set of genomes, or species, could be reticulate due to the occurrence of evolutionary processes such as hybridization or horizontal gene transfer. We report on a novel method for Bayesian inference of genome and species phylogenies under the multispecies network coalescent (MSNC). This framework models gene evolution within the branches of a phylogenetic network, thus incorporating reticulate evolutionary processes, such as hybridization, in addition to incomplete lineage sorting. As phylogenetic networks with different numbers of reticulation events correspond to points of different dimensions in the space of models, we devise a reversible-jump Markov chain Monte Carlo (RJMCMC) technique for sampling the posterior distribution of phylogenetic networks under MSNC. We implemented the methods in the publicly available, open-source software package PhyloNet and studied their performance on simulated and biological data. The work extends the reach of Bayesian inference to phylogenetic networks and enables new evolutionary analyses that account for reticulation.
Data from: Managing seagrass resilience under cumulative dredging affecting light: predicting risk using dynamic Bayesian networks
Coastal development is contributing to ongoing declines of ecosystems globally. Consequently, understanding the risks posed to these systems, and how they respond to successive disturbances, is paramount for their improved management. We study the cumulative impacts of maintenance dredging on seagrass ecosystems as a canonical example. Maintenance dredging causes disturbances lasting weeks to months, often repeated at yearly intervals. We present a risk-based modelling framework for time varying complex systems centred around a dynamic Bayesian network (DBN). Our approach estimates the impact of a hazard on a system's response in terms of resistance, recovery and persistence, commonly used to characterise the resilience of a system. We consider whole-of-system interactions including light reduction due to dredging (the hazard), the duration, frequency and start time of dredging, and ecosystem characteristics such as the life-history traits expressed by genera and local environmental conditions. The impact on resilience of dredging disturbances is evaluated using a validated seagrass ecosystem DBN for meadows of the genera Amphibolis (Jurien Bay, WA, Australia), Halophila (Hay Point, Qld, Australia) and Zostera (Gladstone, Qld, Australia). Although impacts varied by combinations of dredging parameters and the seagrass meadows being studied, in general, 3 months of duration or more, or repeat dredging every 3 or more years, were key thresholds beyond which resilience can be compromised. Additionally, managing light reduction to less than 50% can significantly decrease one or more of loss, recovery time and risk of local extinction, especially in the presence of cumulative stressors. Synthesis and applications. Our risk-based approach enables managers to develop thresholds by predicting the impact of different configurations of anthropogenic disturbances being managed. Many real-world maintenance dredging requirements fall within these parameters, and our results show that such dredging can be successfully managed to maintain healthy seagrass meadows in the absence of other disturbances. We evaluated opportunities for risk mitigation using time windows; periods during which the impact of dredging stress did not impair resilience.
Wildfire Risk Assessment for Strategic Forest Management in the Southern United States: a Bayesian Network Modeling Approach
Open the record for dataset details and reuse information.
Supplementary material 1 from: Sprondel N, Donner J, Mahlkow N, Köppel J (2016) Urban climate and heat stress: how likely is the implementation of adaptation measures in mid-latitude cities? The case of façade greening analyzed with Bayesian networks. One Ecosystem 1: e9280. https://doi.org/10.3897/oneeco.1.e9280
Questionnaire for Bayesian network analysis
Analyzing Bayesian Network Data, Beta Distribution, and IT2F-Z
Open the record for dataset details and reuse information.
Exploring the Relationships Between Health Behaviours and Related Outcomes in UK University Students: a Bayesian Network Approach
Open the record for dataset details and reuse information.
Supplementary material 1 from: Barton DN (2023) Value 'generalisation' in ecosystem accounting - using Bayesian networks to infer the asset value of regulating services for urban trees in Oslo. One Ecosystem 8: e85021. https://doi.org/10.3897/oneeco.8.e85021
Bayesian Belief Network
Data Repository for "Integrating Water Quality Data with a Bayesian Network Model to Improve Spatial and Temporal Phosphorus Attribution: Application to the Maumee River Basin"
<p>Data for "Integrating Water Quality Data with a Bayesian Network Model to Improve Spatial and Temporal Phosphorus Attribution: Application to the Maumee River Basin". This repository contains all the processed data used in the simulation (in "processed" folder), part of the raw data (in "raw" folder), and the SWAT simulation results (in "SWAT" folder). The code for processing the raw data, which are either provided here or publicly available online, is provided in the <a href="https://doi.org/10.5281/zenodo.8132662">code repository</a>. The links to the publicly available raw data are also provided in the code repository.</p>
Data from: Managing seagrass resilience under cumulative dredging affecting light: predicting risk using dynamic Bayesian networks
Open the record for dataset details and reuse information.
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.