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648 results for “uncertainties”
FIGURE 3 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 3 | A. Cambeva balios from Mampituba River basin, UFRGS 16295, 63.8 mm SL. B. Cambeva davisi from Ribeira de Iguape River basin, MZUEL 17202, 72.8 mm SL, fixed in alcohol. C. Cambeva iheringi from Piagui River, coastal drainage of São Paulo State, MNRJ 24008, 75.6 mm SL.
FIGURE 1 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 1 | Geographical distribution of Cambeva species in the coastal drainages of Southern and Southeastern Brazil (crossed circles indicate their past known distribution). Some dots represent more than one locality.
FIGURE 2 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 2 | Schematic drawing of morphometric measurements applied to Cambeva species: (1) standard length, (2) head length, (3) head width, (4) predorsal length, (5) prepelvic length, (6) pre-anal length, (7) scapular girdle width, (8) trunk length, (9) pectoral-fin length, (10) pelvic-fin length, (11) distance between pelvic-fin base and anus, (12) caudal peduncle length, (13) caudal peduncle depth, (14) body depth, (15) length of dorsal-fin base, (16) length of anal-fin base, (17) snout length, (18) interorbital distance, and (19) eye diameter. Illustration made by Alexandre Ribeiro.
Winter Precipitation-Type Models for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
<p>This contains trained model weights, scalers, and evaluation metrics for the winter precipitation-type models trained as part of the paper "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications". </p>
Supporting Information for Accelerating Combustion Mechanism Discovery with Automated Uncertainty, Sensitivity, Thermodynamics, and Kinetics Calculations
<p>Supplementary material to accompany the manuscript "Accelerating Combustion Mechanism Discovery with Automated Uncertainty, Sensitivity, Thermodynamics, and Kinetics Calculations" by Sevy Harris and Richard H West.</p> <ul> <li>The software (mostly Python scripts) is in autoscience_workflow.zip. </li> <li>DFT results (Gaussian log files, Arkane input files, Arkane output files) for all species and reactions are in dft.zip</li> <li>RMG-built detailed kinetic models are in mechanisms.zip </li> <li>Additional plots and results (as described in the manuscript) are in supporting_information.pdf</li> </ul>
Citation count error data for "Data inaccuracy quantification and uncertainty propagation for bibliometric indicators"
<p>This is the original collected data on citation count errors resulting from citation matching errors in Web of Science data for the publication "Data inaccuracy quantification and uncertainty propagation for<br>bibliometric indicators". The first column, <code>CITCOUNT_ALL</code>, gives the total (corrected) citation count for a publication, which is the citation count according to WoS plus the additionally manually identified citations (missed by WoS's algorithm). The second column, <code>CITCOUNT_WOS</code>, is the WoS citation count. The numeric difference between the two column values in one row is the number of additionally manually identified citations.</p>
Data and code for 'Influence of cross-correlation on the modelled uncertainty in stress–strain behavior of soft clays'
<p>This dataset contains data and code used in the research work for the manuscript "Influence of cross-correlation on the modelled uncertainty in stress–strain behavior of soft clays". The study considered two case studies, Haarajoki clay and Suurpelto clay. Two settlement calculation methods were used: compression index method and Janbu (tangential stiffness) method. In addition, clay database FI-CLAY/14/856 was extended and used to study cross-correlations between compressibility paramaters at different clay sites. Version 2 of FI-CLAY/14/856 is provided, including some other updates and corrections also.</p> <p>The Monte Carlo simulation with Gaussian copula was implemented with Python in Jupyter Notebook environment. In addition to data and code, supplementary figures are also provided. The contents of the dataset-folder are briefly described below:</p> <ul> <li>1_Data_Oedometer_test <ul> <li>Oedometer test data for Haarajoki clay and Suurpelto clay: <ul> <li>Data tables that include the clay specimen identifications, index properties, and oedometer test results (.xlsx)</li> <li>Oedometer raw data files that include all the available stress-strain measurements of both constant-rate-of-strain and incrementally loaded odometer tests (.xlsx)</li> </ul> </li> <li>Extended clay database FI-CLAY/14/856 (version 2) (.xlsx)</li> </ul> </li> <li>2_Code_Jupyter_Notebooks <ul> <li>Python code used to run the Monte Carlo simulations and to create the results figures (.ipynb)</li> <li>Readme-file (.txt)</li> </ul> </li> <li>3_Figures_Online_Supplement <ul> <li>Scatterplots with histograms that show the simulated compressibility parameters in each case (.pdf)</li> </ul> </li> </ul> <p> </p> <p>More information on database FI-CLAY/14/856 can be found from the original article (https://www.tandfonline.com/doi/full/10.1080/17499518.2020.1864410) and 304dB datbase compilation by TC304 (http://140.112.12.21/issmge/tc304.htm).</p> <p> </p>
Data for Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO2 Hydrogenation on Ni(111)
<p>Data, scripts, and all generated mechanisms for the preprint and article "Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO<sub>2</sub> Hydrogenation on Ni(111)"</p>
Compendium of examples: good practice in evaluating measurement uncertainty
<p>This document illustrates good practice in the evaluation of measurement uncertainty. It contains examples from a variety of areas in calibration and testing, and illustrates the use of the methods from the “Guide to the expression of Uncertainty in Measurement” and its supplements, as well as Bayesian approaches. </p> <p>Compendium_M36.pdf: document</p>
Dataset used in "Uncertainty-Aware Learning for Improvements in Image Quality of the Canada-France-Hawaii Telescope" (https://arxiv.org/abs/2107.00048)
<p>'x_train.p', 'y_train.p': pickle files for training split containing 50,757 samples</p> <p>'x_val.p', 'y_val.p': pickle file for validation split containing 5,640 samples</p> <p>'x_test.p', 'y_test.p': pickle file for test split containing 6,267 samples</p> <p>'feature_names.p': pickle file containing names of all 119 features</p>
A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 using Gaussian Markov random fields -- Datasets and results
<p>Data archives for test experiments (Section 3) and Pine Island Glacier application (Section 4) from the manuscript "Kevin Bulthuis and Eric Larour, A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-Level System Model v4.19 using Gaussian Markov random fields"</p> <p>Source code is available at https://doi.org/10.5281/zenodo.5532775.</p>
Parameter uncertainty quantification of wake models to analyze effects of wake superposition: data and code
<p>Codebase for wake deficit, wake superposition, and wake-added turbulence modeling within Markov-chain Monte Carlo framework. Data for results and figures in associated paper is also included.</p>
A meta-proteogenomic approach to peptide identification incorporating assembly uncertainty and genomic variation
<p>Supplementary data to "A meta-proteogenomic approach to peptide identification incorporating assembly uncertainty and genomic variation"</p>
Uncertainty and debate in statements describing Wikidata Works of art
<p>This dataset comprises a selection of statements from <a href="https://doi.org/10.5281/zenodo.7307852">all artworks in Wikidata</a>. </p> <p>In particular, </p> <ul> <li><strong>natures.json</strong> stores statements with a “Nature of statements” qualifier. Statements, independently of rank, can be decorated with an additional triple using predicate P5102. 54 terms among 283 available may mark the statement as uncertain or debated (e.g. debated, hypothesis, possibly). For example, the painting “Abstract Speed + Sound” (Q19882431) by Giacomo Balla is deemed to be possibly part of a triptych. </li> <li><strong>non-asserted.json </strong>contains those statements which are not asserted. Competing statements are represented via a ranking mechanism (e.g., Preferred, Normal and Deprecated). Individual statements are not actually asserted, but an extra triple is added those that are deemed true. For example, the painting “Madonna with the Blue Diadem” (Q738038) has been attributed to Raphael (non asserted statement, ranked as normal) and Gianfrancesco Penni (asserted statement, ranked as preferred and additionally asserted). </li> <li><strong>null-valued.json</strong> contains all statements with a null-valued objects. A statement can be associated with a blank node. This is meant to imply that the statement is associated with an unknown value, rather than a missing statement. For example, “Missal for the use of the ecclesiastics of Clermont' (Q113302686), an illuminated manuscript from the 14th century, has been recorded with both an unknown creator and author.</li> <li><strong>sourcing-circumstances.json </strong>stores statements with a “Sourcing circumstance” qualifier. As for Natures of statements only statements with an uncertain qualifier have been selected.</li> </ul>
Dataset - Dual-mode room temperature self-calibrating photodiodes approaching cryogenic radiometer uncertainty
<p>This page contains selected data from the publication "Dual-mode room temperature self-calibrating photodiodes approaching cryogenic radiometer uncertainty", Marit S Ulset <em>et al</em> 2022 <em>Metrologia</em> <strong>59</strong> 035008, <strong>DOI</strong> 10.1088/1681-7575/ac6a94.</p> <p> </p> <p>Description of files:</p> <p>Fig5.txt: Data for Fig 5a. Plotted values of non-equivalence as a function of beam position on the photodiode.</p> <p>Fig6.txt: Non-equivalence (gamma) in parts per million (ppm) and responsivity in mK/mW as a function of power level P in mW. </p> <p>Fig7.txt: Spectral directional emissivity determined under 10° with respect to the sample surface normal for Wafer P7 at 20°C. Uncertainty is given as standard uncertainty (k=1).</p> <p>Fig14.txt: Calculated time constants for the four different steps in a thermal heating cycle (electrical low, optical, electrical high, optical). The average value in the published paper contains an error, as one dataset was used twice. The file also shows correct the average value, when all datasets are used only once. A corrigendum was submitted to Metrologia, but it was considered not necessary, and hence not published. </p> <p>Fig15.txt: Plotted values for apparent IQD in parts per million (ppm) for three different calculation algorithms. Uncertainty is given as propagated type A standard uncertainty.</p> <p>Fig16.txt: Plotted values for measured IQD in parts per million (ppm) as a function of absorbed optical power in µW, for two different measurement methods - OC and FB method.</p>
Improving Robustness of Deep Neural Networks for Aerial Navigation by Incorporating Input Uncertainty
<p>CEA covered the scenario of UAV navigation through a set of gates with unknown locations using a DNN-based navigation model. The implemented navigation model uses two DL components (perception and control), and uses (Bayesian) uncertainty estimation methods to capture the uncertainty (confidence) associated with the predictions of each component. The safety requirements in the UAV mission are related to the confidence (uncertainty) associated with the predictions from these components. CEA observed and analysed the uncertainty from each DNN under specific situations that can pose a risk to the UAV mission. Then, the observations were used to define STL rules to track the confidence of the DNN-based navigation system. Finally, mitigation behaviours (e.g., hover, land, DNN-based autonomous flight) are triggered depending on the satisfaction (or violation) of the STL rules. Moreover, the proposed ROS2-based architecture for safe navigation contributed to the definition and improvement of the COMP4DRONES reference architecture, showing in practice how the proposed safety monitoring architecture relates and integrates with the components from other system functions.</p>
Animations of Uncertainty in Scalar Volume Data
<p>A collection of videos presenting the animations created for the master's thesis with the title "<strong>Visualization of Uncertainty in 3D Scalar Data Using Extended 2D Transfer Functions and Animations" </strong>of Tobias Neeb, master's student at Hochschule Worms (DE). </p> <p>The videos present animations of the application examples given in the thesis. Goal of the thesis here is to investigate possible new insights regarding uncertainty visualization by extending the functionality of classification widgets of the 2D transfer function-editor in OpenWalnut, by introducting color maps, widget animations and a variety of new widget shapes. </p> <p>The animations present a volume rendering of different scalar datasets from which the mean and standard deviation has been derived. The histogram of the 2DTF-editor here presents the 2D-distribution of mean and standard deviation(stdDev) as value pairs, the x-axis presenting the mean-field, while the y-axis presents the stdDev-field.</p>
Architecture-based Uncertainty Impact Analysis to ensure Confidentiality - Data Set
<p>Data set of the Paper "Architecture-based Uncertainty Impact Analysis to ensure Confidentiality". For more information, please see the README.md. For more information please visit https://abunai.dev</p>
Daset from the paper "Compact object mergers: exploring uncertainties from stellar and binary evolution with SEVN"
<p>This repository contains the dataset produced by the population-synthesis code SEVN for the paper:<br> "Compact object mergers: exploring uncertainties from stellar and binary evolution with SEVN"</p> <p>In this paper, we exploit the SEVN code (publicly available at <a href="https://gitlab.com/sevncodes/sevn">https://gitlab.com/sevncodes/sevn</a>) to analyse the formation and properties of binary compact objects.</p> <p>The repository also includes the initial condistions used as input and the SEVN version used to run the simulations.</p> <p>#Content</p> <p>The repository contains the following folders:</p> <p>- data_from_simulations: the folder contains all the data produced by the simulations and used in the Iorio+22 paper<br> - InitialConditions: the folder contains all the intial conditions (and the code to generate them) that have been used for the Iorio+22 paper<br> - SEVN_iorio22: this folder contains the version of the SEVN code that has been used to run the simulations in the Iorio+22 paper</p> <p>Each folder contains a specific README with additional information</p> <p> </p> <p>#Contatcts</p> <p>giuliano.iorio.astro@gmail.com</p> <p> </p> <p> </p>
Experimental measurements and uncertainty analysis for validation of the Building Electrical Efficiency Analysis Model (BEEAM)
<div> <div> <div> <div> <div>This dataset includes experimental measurements taken on a laboratory testbed at Colorado State University that was used for model validation of a software toolkit, the Building Electrical Efficiency Analysis Model (BEEAM). This toolkit was developed for comparing electrical efficiency of AC versus DC distribution systems in buildings. The testbed emulated loads found in a small office building and included laptop computer chargers, LED lighting systems, and miscellaneous DC and AC loads. Measurements were taken under AC and DC configurations in electrically balanced and unbalanced loading conditions. Also included in the dataset is an uncertainty analysis. A complete description of the testbed, hardware, measurements and uncertainty analysis is contained in the paper cited below.</div> </div> </div> </div> </div> <div> </div> <div>Avpreet Othee, James Cale, Arthur Santos, Stephen Frank, Daniel Zimmerle, Omkar Ghatpande, Gerald Duggan and Daniel Gerber, <em>"A Modeling Toolkit for Comparing AC and DC Electrical Distribution Efficiency in Buildings," Energies, 2023 (accepted, publication in progress).</em> </div>
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.