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648 results for “uncertainties”
In-situ observations of nitrate loss factor for "Estimation method is the primary source of uncertainty in cropland nitrate leaching estimate in China"
<p>This database includes In-situ observations of nitrate loss factor. Details can be found in paper named "Estimation method is the primary source of uncertainty in cropland nitrate leaching estimate in China".</p>
Stellar models for "Realistic Uncertainties for Fundamental Properties of Asteroseismic Red Giants and the Interplay Between Mixing Length, Metallicity and Numax" by Li, Yaguang et al. (2024)
<p>This dataset includes a grid of stellar models from Li, Yaguang et al. (2024), titled "Realistic Uncertainties for Fundamental Properties of Asteroseismic Red Giants and the Interplay Between Mixing Length, Metallicity and Numax".</p> <p>The stellar models are first-ascent red giant branch (RGB) models, sampled using a Sobol sequence within the following six-dimensional initial parameter space: mass (0.7, 2.3) solar mass, Y_init (0.22, 0.37), [M/H] (-0.1.0, 0.60) dex, α_MLT (1.3, 2.7), fov_core (0, 0.02), fov_shell (0, 0.008). The evolutionary tracks are computed up to the point on the RGB where the p-mode large separation (Δν) equals 2 μHz. Approximately 32,768 unique evolutionary tracks are presented, divided into 33 individual chunks.</p> <p>To load the models into a Python Pandas dataframe, use the following command: "import pandas as pd; pd.read_parquet(filepath)". Each model (dataframe row) is provided with global parameters (Teff, radius, luminosity, mass, age, ...) and pure p modes with l=0-2 (frequency, mode inertia, angular degree, ...).</p> <p>For detailed descriptions of the stellar models, please refer to the original paper and the github repository.</p>
Towards Robust Hemolysis Modeling with Uncertainty Quantification: A Universal Approach to Address Experimental Variance
<p>This repository contains the implementation of <strong>Robust Hemolysis Modeling with Uncertainty Quantification: A Universal Approach to Address Experimental Variance</strong>.</p> <p>The provided Python script demonstrates the construction of the MCMC (Markov Chain Monte Carlo) method and illustrates how to use MCMC for generating hemolysis distributions. Please note that the actual hemolysis calculations should be performed using your preferred CFD (Computational Fluid Dynamics) software.</p> <p>If there are any questions, please contact:</p> <p>blum@ame.rwth-aachen.de </p>
MATLAB Codes for: Fault Diagnosis in Drones via Multiverse Augmented Extreme Recurrent Expansion of Acoustic Emissions with Uncertainty Bayesian Optimisation
<p>The following MATLAB codes belong to the paper following paper which has been publication in MDPI Machines. This repository includes all the necessary MATLAB scripts and functions used in the research for diagnosing faults in drones using advanced acoustic emission analysis and optimization techniques. The dataset used in this paper is referenced in the article. Please check the publication for the dataset reference. Download the dataset, decompress it, and place it in the same repository as these codes to ensure proper functionality. For any queries or further information, please refer to this paper.</p> <p>Berghout, Tarek, and Mohamed Benbouzid. 2024. "Fault Diagnosis in Drones via Multiverse Augmented Extreme Recurrent Expansion of Acoustic Emissions with Uncertainty Bayesian Optimisation" <em>Machines</em> 12, no. 8: 504. https://doi.org/10.3390/machines12080504 </p> <p> </p>
Synthetic dataset for the testing of local conditioning of regularization function using geological uncertainty.
<p>This companion datasets relates to the model shown supplementary information to the manuscript "<strong>Integration of geological uncertainty into geophysical inversion by means of local gradient regularization</strong>", by J. Giraud, M. Lindsay, V. Ogarko, M. Jessell, R. Martin and E. Pakyuz-Charrier, submitted to Solid Earth. The archive contains the input and output geophysical data, starting and inverted models, probabilistic geological model and conditioning volume derived from the calculation of Shannon's entropy. </p> <p>This description will be updated with the accurate reference to the journal publication and acknowledgement prior to publication. </p>
Prior and posterior uncertainties of sea ice volume and snow volume
Prior and posterior uncertainties of sea ice volume (SIV, columns 4-6) and snow volume (SNV, columns 7-9) respectively for three regions in km3. Column 1 indicates observation, column 2 indicates uncertainty range ("product" refers to uncertainty specification provided with product), column 3 indicates uncertainty range of additional hypothetical snow product ("–" means no snow product is used). In each of columns 4-9 the lowest uncertainty range is highlighted in bold face font. The two bottom rows give estimates for the uncertainty due to model error, i.e. the residual uncertainty with optimal control vector.
The Impact of Nuclear Reaction Rate Uncertainties on the Evolution of Core-collapse Supernova Progenitors
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018ApJS..234...19F/abstract">Fields et al. (2018)</a>. MESA version 7624.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/1538-4365/aaa29b">10.3847/1538-4365/aaa29b</a></p>
InCLosure Code for Guaranteed Enclosures Under Interval Dependency: Supplementary Material for Article "A Logical Formalization of the Notion of Interval Dependency: Towards Reliable Intervalizations of Quantifiable Uncertainties"
<p>InCLosure Input and Output Files for Guaranteed Enclosures Under Interval Dependency: Supplementary Material for Article "A Logical Formalization of the Notion of Interval Dependency: Towards Reliable Intervalizations of Quantifiable Uncertainties", Online Mathematics Journal, July 2019. Download latest release of InCLosure via <a href="https://doi.org/10.5281/zenodo.2702404">https://doi.org/10.5281/zenodo.2702404</a></p>
Computational model results for "Uncertainties of Glacial Isostatic Adjustment model predictions in North America associated with 3D structure"
<p>The mean GIA signals of RSL, u-dot and g-dot with 1σ, 2σ and 3σ uncertainties in North America. </p>
Moment tensor inversion and uncertainty analysis for 40 Uttarakhand Earthquakes (2010-2022)
<p>This repository provides detailed descriptions of the files that were used for the Moment tensor and uncertainty analysis study of earthquakes in the Uttarakhand Himalayas. These files contain the Moment Tensor (MT) estimation results and uncertainty quantification of 40 earthquakes using different networks.</p> <p> </p> <p><strong>Contents:</strong></p> <p>1. waveform_fits.docx - Waveform fits for MT estimation</p> <p>2. confidence_plots.docx - The confidence parameters associated with each MT</p> <p>3. depth_vs_misfit_plot.docx - The confidence in the MT solution for each depth against the misfit values</p> <p>4. weight_files.zip - Weight files for 40 events read by the MTUQ package</p> <p>5. CMT_solution_files.zip - Centroid Moment Tensor (CMT) solutions for 40 events</p>
Neural network prediction of strong lensing systems with domain adaptation and uncertainty quantification
<p>This project combines the emerging field of Domain Adaptation with Uncertainty Quantification, working towards applying machine learning to real scientific datasets with limited labelled data. For this project, simulated images of strong gravitational lenses are used as source and target dataset, and the Einstein radius θ E and its uncertainty are determined through regression.</p> <p>Applying machine learning in science domains such as astronomy is difficult. With models trained on simulated data being applied to real data, models frequently underperform - simulations cannot perfectlty capture the true complexity of real data. Enter domain adaptation (DA). The DA techniques used in this work use Maximum Mean Discrepancy (MMD) Loss to train a network to being embeddings of labelled "source" data gravitational lenses in line with unlabeled "target" gravitational lenses. With source and target datasets made similar, training on source datasets can be used with greater fidelity on target datasets.</p> <p>Scientific analysis requires an estimate of uncertainty on measurements. We adopt an approach known as mean-variance estimation, which seeks to estimate the variance and control regression by minimizing the beta negative log-likelihood loss. To our knowledge, this is the first time that domain adaptation and uncertainty quantification are being combined, especially for regression on an astrophysical dataset.</p>
The effect of uncertainty in humidity and model parameters on the prediction of contrail energy forcing
<p>Previous work has shown that while the net effect of aircraft condensation trails (contrails) on the<br>climate is warming, the exact magnitude of the energy forcing per meter of contrail remains uncertain.<br>In this paper, we explore the skill of a Lagrangian contrail model (CoCiP) in identifying flight<br>segments with high contrail energy forcing. We find that skill is greater than climatological<br>predictions alone, even accounting for uncertainty in weather fields and model parameters.</p> <p>We estimate the uncertainty in weather by using the ensemble ERA5 weather reanalysis from the European<br>Centre for Medium-Range Weather Forecasts (ECMWF) as Monte Carlo inputs to CoCiP. We unbias and correct<br>under-dispersion on the ERA5 humidity data by forcing a match to the distribution of in situ humidity<br>measurements taken at cruising altitude. We set aside CoCiP energy forcing estimates calculated using<br>one of the ensemble members as a proxy for ground truth, and report the skill of CoCiP in identifying<br>segments with large positive proxy energy forcing. We further estimate the uncertainty in the model<br>parameters in CoCiP by performing Monte Carlo simulations with CoCiP model parameters drawn from<br>uncertainty distributions consistent with the literature.</p> <p>When CoCiP outputs are averaged over seasons to form climatological predictions, the skill in<br>predicting the proxy is 44%, while the skill of per-flight CoCiP outputs is 84%. If these results carry<br>over to the true (unknown) contrail EF, they indicate that per-flight energy forcing predictions can<br>reduce the number of potential contrail avoidance route adjustments by 2x, hence reducing both the cost<br>and fuel impact of contrail avoidance.</p>
Observed data, predictions and uncertainty associated with the updated distribution of clay minerals in the World Ocean
<p>Sparase observed data for various clay mineral species are .csv file format.</p> <p>Predictions and uncertainty for four seafloor clay mineral species (relative percentages, Kaolinite, Illite, Smectite, Chlorite) are generated via geospatial machine learning (GML). Methodology is outlined in "The updated distribution of clay mineral in the World Ocean"<span>. These files are in net-CDF file format. Files are cell-centered. Further, latitudes and longitudes of grid cells are denoted in the variables of the .nc files.</span></p>
A Deep Learning Approach for TEM Data Denoising, Inversion and Uncertainty Analysis with Monte Carlo Dropout
<p>This dataset includes the code and data for training the inversion network used in the study. The provided files cover data loading, preprocessing, and network training for transient electromagnetic (TEM) data inversion. For details on the included files and instructions on usage, please refer to the README.txt file.</p>
Code and Data for Sturm and Silva (2024) A nudge to the truth: atom conservation as a hard constraint in models of atmospheric composition using an uncertainty-weighted correction
<p>This record contains the Julia photochemical model (https://doi.org/10.5281/zenodo.13385541) output in csv format used for training XGBoost in ProjectionConservationRF.py to emulate ozone photochemical formation. Nonphysical predictions that violate conservation of atoms are corrected using a closed-form, constrained least-squares approach that factors in uncertainty and scale using species-level weights. The file ozoneNOx_visualization.py contains an example and visualization for a smaller system, the primary photolytic cycle from which the Leighton relationship can be derived.</p> <p>The corresponding preprint is available here: <a href="https://doi.org/10.48550/arXiv.2408.16109">https://doi.org/10.48550/arXiv.2408.16109</a></p>
Maternity uncertainty in cobreeding beetles: females lay more and larger eggs and provide less care
<p>Cobreeding, which occurs when multiple females breed together, is likely to be associated with uncertainty over maternity of offspring in the joint brood, preventing females from directing resources towards their own offspring. Cobreeding females may respond to such uncertainty by shifting their investment towards the stages of offspring development when they are certain of maternity and away from those stages where uncertainty is greater. Here we examined how uncertainty of maternity influences investment decisions of cobreeding females by comparing cobreeding and single breeding females in the burying beetle, Nicrophorus vespilloides; a species in which females can breed together on a single carcass but cannot recognise their own offspring. We found that cobreeding females shifted investment towards the egg stage of offspring development by laying more and larger eggs than females breeding alone. Furthermore, cobreeding females reduced their investment to post-hatching care of larvae by spending less time providing care than females breeding alone. We show that females can respond to the presence of a competitor by shifting allocation towards egg laying and away from post-hatching care, thereby directing resources to their own offspring. Our results demonstrate that responses to parentage uncertainty are not restricted to males, but that, unlike males, females respond by shifting their investment to different components of reproduction within a single breeding attempt. Such flexibility may allow individuals to cope with a variety of negative social or physical environments</p>
Time-Dependent Probabilistic Tsunami Inundation Assessment Using Mode Decomposition to Assess Uncertainty for an Earthquake Scenario
<p>This is the dataset of the manuscript submitted to JGR Ocean (Feb 2021).</p>
Strategic basin and delta planning increases the resilience of the Mekong Delta under future uncertainty
<p># Geospatial data and analysis results for:</p> <p>Schmitt R. J. P., Giuliani, M., Bizzi, S., Kondolf, G. M., Daily, G. C., Castelletti, A. (2021). Strategic basin and delta planning increases the resilience of the Mekong Delta under future uncertainty (accepted for publication in the Proceedings of the National Academy of Sciences). </p> <p><br> # Prepared by R. Schmitt (rschmitt@stanford.edu), July 2021. </p> <p># Abstract: </p> <p>The climate resilience of river deltas is threatened by rising sea levels, accelerated land subsidence, and reduced sediment supply from contributing river basins. Yet, these uncertain and rapidly changing threats are rarely considered in conjunction. Here we provide an integrated assessment, on basin- and delta-scales, to identify key planning levers for increasing the climate resilience of the Mekong Delta. We find, first, that 23 % to 90 % of this unusually productive delta might fall below the sea level by 2100, with the large uncertainty driven mainly by future management of groundwater pumping and associated land subsidence. Second, maintaining sediment supply from the basin is crucial, under all scenarios, to maintaining delta land and enhancing the climate resilience of the system. We then use a bottom-up approach to identify basin development scenarios that are compatible with maintaining sediment supply at current levels. This analysis highlights, third, that strategic placement of hydropower dams will be more important for maintaining sediment supply than either projected increases in sediment yields or sediment management at individual dams. Our results demonstrate (1) the needs for integrated planning across basin and delta scales, (2) the role of river sediment management as a nature-based solution to increase delta resilience, and (3) global benefits from strategic basin management to maintain resilient deltas, especially under uncertain and changing conditions.</p> <p> </p> <p># Contents:<br> Mekong_Basin_geomorphic provinces.gpkg: location of geomorphic provinces and the associated sediment load. Digtized and modified from Kondolf et al., 2014<br> Schmitt_et_al_PNAS_2020-26127P.m: Script demonstrating the robust analysis and the derivation of decision surfaces (e.g., Fig. 3, f, g and Figure 4)<br> Data PNAS_2020-26127P.mat: Resimulation data (i.e., sediment yield and dam sediment trapping multipliers and the response in terms of sediment delivery to the delta) </p>
Code and figure data from "Projections of northern hemisphere extratropical climate underestimate internal variability and hence uncertainty"
<p>Code and figure data from "Projections of northern hemisphere extratropical climate underestimate internal variability and hence uncertainty".</p>
Exploring uncertainties in the evolution of massive stars with METISSE
<p>In the era of advanced electromagnetic and gravitational wave detectors, it has become increasingly important to effectively combine and study the impact of stellar evolution on binaries and star clusters. Systematic studies dedicated to exploring uncertain parameters in stellar evolution are required to account for the recent observations of the stellar populations. While fitting formulae to stellar tracks in the form of Single Star Evolution (SSE) code remain a popular choice for modelling stellar evolution in population synthesis codes, they are less adaptable to changes in the stellar tracks. Hence, we have developed a Method of Interpolation for Single Star Evolution (METISSE) as an alternative to SSE. It makes use of interpolation between sets of pre-computed stellar tracks to approximate evolution parameters for a population of stars. METISSE is comparable to SSE in performance and can reproduce tracks from different stellar evolution codes quite accurately. In this work, we apply METISSE with detailed stellar tracks computed by the Modules for Experiments in Stellar Astrophysics (MESA), Bonn Evolutionary Code (BEC), to study the impact of uncertainties in stellar evolution on a population of massive stars. We find that different physical ingredients used in the evolution of stars, such as the treatment of radiation dominated envelopes, can impact their evolutionary outcome, including remnant masses and maximal radial expansion. The differences in the predictions of different stellar models can help us account for the present day observations of stellar populations.</p>
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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.