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648 results for “uncertainties”
Data Set "Systematic QM Region Construction in QM/MM Calculations Based on Uncertainty Quantification"
<p>Data set accompanying the publication "Systematic QM Region Construction in QM/MM Calculations Based on Uncertainty Quantification"</p> <p>This dataset contains:</p> <p>- PDB files of the reactant and product starting structure</p> <p>- modified AMBER95 force field file</p> <p>- AMS fragment files for the ligands and ions</p> <p>- AMS input files for all geometry optimizations and single point calculations</p>
Modeling the economic and environmental impacts of land scarcity under deep uncertainty
<p>Code and input files for the Dolan et al. 2021 paper "Modeling the economic and environmental impacts of land scarcity under deep uncertainty"</p>
A critical perspective on uncertainty appraisal and sensitivity analysis in life cycle assessment - supporting material
<p>Publication dataset - A critical perspective on uncertainty appraisal and sensitivity analysis in life cycle assessment (<em>accepted for publication in the Journal of Industrial Ecology</em>).</p>
A multi-uncertainty-set-based robust transmission expansion planning model using an efficient linear AC network
<p>The file uploaded provides input data for the paper "A multi-uncertainty-set-based robust transmission expansion planning model using an efficient linear AC network".</p>
Tutorial "Uncertainty assessment for thermal diffusivity measurement by laser flash method"
<p>E-learning module about the assessment of uncertainty associated with thermal diffusivity measurement performed at high temperature by the laser flash method. Part of a tutorial series prepared in the framework of the Hi-TRACE project. </p>
Architecture-based Uncertainty Impact Analysis for Confidentiality (Reproduction Set)
<p>Reproduction set for master thesis of Niko Benkler.</p> <p>Zip file contains code, data set and installation guides. </p> <p>Please open README.md to get information about content structure.</p>
Bootstrap methods for quantifying the uncertainty of binding constants in the hard modeling of spectrophotometric titration data
<p>Supporting simulation code and data for the manuscript "Bootstrap methods for quantifying the uncertainty of binding constants in the hard modeling of spectrophotometric titration data"</p>
Multi-Level Monte Carlo Models for Flood Inundation Uncertainty Quantification - Dataset
<p>Dataset used for the analysis of Multi-level Monte Carlo methods for flood inundation uncertainty quantification. This includes:</p> <ol> <li>Flood model simulations for Dyce, Glasgow and Inverurie across three resolutions (5m/10m/20m).</li> <li>Data for violin plot figures with code.</li> </ol>
Supporting data for article comparison and Uncertainty Analysis of Species Distribution Models
<p>Downloaded from Web of Science for the supporting data of article comparison and Uncertainty Analysis of Species Distribution Models.</p>
Supplementary Data: Real-time optimal flood control decision making under uncertainty
<p>The files in this record contain data for real-time optimal flood control decision making and risk propagation under multiple uncertainties considered for publication in Water Resources Research.</p> <p>The files consist of:</p> <ul> <li>Pubugou Reservoir data;</li> <li>Source code and results of the Martingale Model of Forecast Evolution (MMFE);</li> <li>Source code and results of the SMAA-2 model;</li> <li>Source code and results of SMAA-TOPSIS model;</li> <li>Source code and results of the stochastic programming with recourse model.</li> </ul>
Global sensitivity and uncertainty analysis of an atmospheric chemistry transport model: the FRAME model (version 9.15.0) as a case study
<p>Atmospheric chemistry transport models (ACTMs) are widely used to underpin policy decisions associated with the impact of potential changes in emissions on future pollutant concentrations and deposition. It is therefore essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input pollutant emissions. ACTMs incorporate complex and non-linear descriptions of chemical and physical processes which means that interactions and non-linearities in input–output relationships may not be revealed through the local one-at-a-time sensitivity analysis typically used. The aim of this work is to demonstrate a global sensitivity and uncertainty analysis approach for an ACTM, using as an example the FRAME model, which is extensively employed in the UK to generate source-receptor matrices for the UK Integrated Assessment Model and to estimate critical load exceedances. An optimised Latin hypercube sampling design was used to construct model runs within ± 40 % variation range for the UK emissions of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub>, from which regression coefficients for each input-output combination and each model grid (>10,000 across the UK) were calculated. Surface concentrations of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub> (and of deposition of S and N) were found to be predominantly sensitive to the emissions of the respective pollutant, while sensitivities of secondary species such as HNO<sub>3</sub> and particulate SO<sub>4</sub><sup>2-</sup>, NO<sub>3</sub><sup>-</sup> and NH<sub>4</sub><sup>+</sup> to pollutant emissions were more complex and geographically variable. The uncertainties in model output variables were propagated from the uncertainty ranges reported by the UK National Atmospheric Emissions Inventory for the emissions of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub> (± 4 %, ± 10 % and ± 20 % respectively). The uncertainties in the surface concentrations of NH<sub>3</sub> and NO<sub>x</sub> and the depositions of NH<sub>x</sub> and NO<sub>y</sub> were dominated by the uncertainties in emissions of NH<sub>3</sub>, and NO<sub>x</sub> respectively, whilst concentrations of SO<sub>2</sub> and deposition of SO<sub>y</sub> were affected by the uncertainties in both SO<sub>2</sub> and NH<sub>3</sub> emissions. Likewise, the relative uncertainties in the modelled surface concentrations of each of the secondary pollutant variables (NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>-</sup>, SO<sub>4</sub><sup>2-</sup> and HNO<sub>3</sub>) were due to uncertainties in at least two input variables. In all cases the spatial distribution of relative uncertainty was found to be geographically heterogeneous. The global methods used here can be applied to conduct sensitivity and uncertainty analyses of other ACTMs.</p> <p>The dataset contains model outputs used for the sensitivity and uncertainty analyses.</p>
Signal, Uncertainty, and Conflict in Phylogenomic Data for a Diverse Lineage of Microbial Eukaryotes (Diatoms, Bacillariophyta)
<p>This data depository contains analysis results from Parks, Wickett, and Alverson 2017 (Signal, Uncertainty, and Conflict in Phylogenomic Data for a Diverse Lineage of Microbial Eukaryotes (Diatoms, Bacillariophyta) (Mol. Biol. Evol. doi:10.1093/molbev/msx268)), and is made freely available to the research community.</p> <p>The file and subfolders here are as follows:</p> <p>gene_alignments<br> - contains compressed (tarred and gzipped) folders with all gene alignments at 0.2, 0.5 and 0.8 alignment column occupancy cutoffs. In each folder, there are also text files listing which gene alignments fall under which taxon occupancy subsetting strategy (i.e., 10-20% taxon occupancy, 40-60% taxon occupancy, 80-100% taxon occupancy, etc).</p> <p>gene_trees<br> - contains all (compressed) gene trees (bootstrapped versions, 100 bootstrap pseudo-replicates)) used in Astral analyses for each alignment column occupancy cutoff (0.2, 0.5, 0.8); nodes with less than 33% bootstrap support are collapsed.</p> <p>hmms.mafft_aligned<br> - contains (compressed) hmm specifications for each major diatom morphotype (radial and polar centrics, araphid and raphid pennates) from the 0.2 alignment column occupancy subset of the data. A summary of the sampling scheme and the hmm results/counts are also available in HMM_sampling.docx.</p> <p>mmetsp_nuclear_transcriptome_assemblies<br> - these are the compressed nuclear transcriptome assemblies that were done in-house (i.e., mostly MMETSP samples). Assemblies do not include organellar or rDNA loci.</p> <p>species_trees<br> - contains (compressed) species trees for all phylogenetic strategies and alignment column occupancy cutoff/data subset strategies.</p> <p>Suppl_1.MMETSP_basic_summaries.xlsx<br> - this an identical file to Parks, Wickett and Alverson 2017 supplementary file 1. This file contains taxon, strain and SRA information for all assembled taxa, and a variety of assembly metric information.</p> <p> </p>
Dataset from: "Uncertainty-Aware Interpretable Prognosis for Wave Energy Converters with Recurrent Expansion"
<p>This dataset comprises run-to-failure sensor data derived from a mathematical model simulating wave energy converter behavior, particularly for Oscillating Water Column Turbines (OWCTs). The dataset includes vibration, temperature, pressure, acceleration, strain, flow, torque, rotation, and remaining useful life (RUL) readings for one life cycle. Through normalization, the data is scaled uniformly for robust analysis and interpretation. Researchers can leverage this dataset to develop and validate their prognostic models for OWCTs.</p> <p>To cite this dataset, please refer to:</p> <p>Berghout Tarek and Benbouzid Mohamed. (2024). Uncertainty-Aware Interpretable Prognosis for Wave Energy Converters with Recurrent Expansion. SSRN, 1–22. <span><a href="https://dx.doi.org/10.2139/ssrn.4825408" target="_blank" rel="noopener"><span>http://dx.doi.org/10.2139/ssrn.4825408</span></a> </span></p>
Code and Data for "Probabilistic Eddy Identification with Uncertainty Quantification"
<p>The code and data used in the paper "Probabilistic Eddy Identification with Uncertainty Quantification".</p>
Uncertainty sensitivity estimates for EXIOBASE 3.8.2 footprints
<p>This data set provides uncertainty sensitivity estimates for EXIOBASE 3.8.2 footprints. Sensitivity levels of footprints of all EXIOBASE regions/extensions were calculated using Linear Error propagation (assuming a 0.1 relative standard deviation) such as that each of the 49 EXIOBASE regions has 124 footprint sensitivity estimates and a total of 6076 sensitivity estimates. This dataset was calculated as part of the paper "<em>Uncertainty propagation in EE-MRIO footprint estimates</em>" Badr & Stadler (2024).</p> <p>We reccoment interpreting footprint sensititivity levels as: 0-0.02: low sensitivity, 0.02-0.03: medium sensitivity, 0.03-0.04: Highly sensitive, 0.04 or more: Extremely sensitive. </p> <p>The paper repository can be found on: gitlab.com/hitea/variance-in-uncertainties-in-mrios</p>
Navigating uncertainty in environmental DNA detection of a nuisance marine macroalga
<p>Early detection of nuisance species is crucial for the conservation and management of threatened ecosystems, reducing the risk of widespread establishment. Environmental DNA (eDNA) data can increase the sensitivity of biomonitoring programs, oftentimes with minimal cost and effort. However, eDNA analyses have inherent errors that can complicate the integration of molecular survey methods into existing management frameworks. Therefore, it is crucial for eDNA studies to consider imperfect detections and estimate error rates accordingly. Detecting nuisance species in low abundance with minimal uncertainty is vital to increase the chance of containment and eradication. We developed a novel eDNA assay to detect a nuisance marine macroalga across its colonization front using surface seawater samples from Papahānaumokuākea Marine National Monument (PMNM), one of the world's largest marine reserves. <em>Chondria tumulosa</em>, a cryptogenic red alga with invasive characteristics, has been documented forming dense mats that overgrow coral reefs and smother native flora and fauna in PMNM. We verified the eDNA assay using site-occupancy detection modeling from quantification polymerase chain reaction (qPCR) data, calibrated with visual estimates of benthic cover of <em>C. tumulosa </em>that ranged from < 1% to 95%. Results were subsequently validated with high-throughput sequencing of amplified eDNA and negative control samples. Overall, the probability of detecting <em>C. tumulosa </em>at occupied sites was at least 92% when multiple qPCR replicates were positive. Modeled false-positive inferences were 3% or less and false-negative errors were 11% or less. The developed assay is suitable for routine monitoring at shallow sites (less than 10 m), even when <em>C. tumulosa </em>abundance was less than 1%. Successful implementation of eDNA tools in conservation decision-making relies on balancing uncertainties in both visual and molecular detection methods. Our results and modeling demonstrated the assay's sensitivity to <em>C. tumulosa</em>, and we outline the necessary steps to infer ecological presence-absence from molecular detection data. By providing a reliable, cost-effective tool for detecting low-abundance species, eDNA analyses have the potential to enhance the surveillance of nuisance species and inform timely management interventions.</p>
Inferring Surface NO2 over Western Europe: A Machine Learning Approach with Uncertainty Quantification
<p>The data that serves to substantiate the analysis presented in the article.</p>
Data for "Uncertainties too large to predict tipping times of major Earth system components from historical data"
<p>All data needed to reproduce the figures from the Science Advances manuscript "Uncertainties too large to predict tipping times of major Earth system components from historical data" by Ben-Yami et al.</p> <p>Figs12SamplePathData.zip and Fig3SamplePathData.zip includes the generated synthetic timeseries of the conceptual models in Figs 1-3.</p> <p>The .txt files are the different AMOC observational time series, with the file names structured as dataset_fingerprint.txt. C18 and C18_2GMT are the subpolar gyre SSTs minus one times and twice the global mean SSTs, respectively. The dipole fingerprint is as defined in the text. The first column is the date, and the second column the fingeprint values. The values are monthly means and thus the value for the day of the month in the date is not significant.</p>
Data for "Uncertainty quantification in geochemical mapping: a review and recommendations"
<p>Data for "Uncertainty quantification in geochemical mapping: a review and recommendations".</p>
Uncertainties in greenhouse gas emission factors: A comprehensive analysis of switchgrass-based biofuel production
<p>This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass-based biofuel production in Michigan. Using three life cycle assessment (LCA) databases— US lifecycle inventory database (USLCI), GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real-world applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production.</p>
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.