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648 results for “uncertainties”
Figures 2–7 in A new species of Solaropsisfrom Amapá, Brazil (gastropoda: Solaropsidae) triggering uncertainty about the genus and redefinition of some species
Figures 2–7. Holotype of Solaropsis caperata MZSP 144000 (W = 50.2 mm): 2, apical view; 3, inferior view; 4, left view; 5, apertural view; 6, detail of region half a whorl preceding the peristome, showing characteristic depression, dorsal-slightly inferior view; 7, detail of apical region, apical view, scale = 1 mm.
Figures 10–16 in A new species of Solaropsisfrom Amapá, Brazil (gastropoda: Solaropsidae) triggering uncertainty about the genus and redefinition of some species
Figures 10–16. Paratypes of Solaropsis caperata. 10, MZSP 154136 (W 45.1 mm), frontal view; 11, same apical view; 12, same, inferior view; 13, MZSP 154137 (W 39.0 mm), frontal view; 14, same, frontalslightly inferior view to show depression; 15, same, apical view; 16, inferior view.
Figure 1 in A new species of Solaropsisfrom Amapá, Brazil (gastropoda: Solaropsidae) triggering uncertainty about the genus and redefinition of some species
Figure 1. Map of collection area, in Amapá (green), Rio Cajarí Extractive Reserve (purple), municipality of Laranjal do Jari.
Data and Codes of Characterizing Uncertainties of Earth System Modeling with Heterogeneous Many-core Architecture Computing
<p>These are the supporting information to verify the results in the paper, including input data, model outputs, the postprocessing scripts and the source codes.</p>
GBIF data for Marcer A., Chapman A., Wieczorek J.R., Picó F.X., Uribe F., Waller J. and Ariño A. (2022) "Uncertainty matters: ascertaining where specimens in natural history collections come from and its implications for predicting species distributions." Ecography.
<p>This is the dataset used in the following publication:</p> <p>Marcer A., Chapman A., Wieczorek J.R., Picó F.X., Uribe F., Waller J. and Ariño A. (2022) "Uncertainty matters: ascertaining where specimens in natural history collections come from and its implications for predicting species distributions." Ecography.</p> <p>Code can be found at: https://github.com/arnaldmarcer/NHC-GeoUncertainty</p>
A Classification of Architectural Uncertainty regarding Confidentiality - Dataset
<p>Dataset for the paper "A Classification of Architectural Uncertainty regarding Confidentiality". For more information, please see the README.md.</p>
A Classification of Software-Architectural Uncertainty regarding Confidentiality
<p>Dataset for the paper "A Classification of Software-Architectural Uncertainty regarding Confidentiality". For more information, please see the README.md.</p>
Dataset: Architectural Optimization for Confidentiality under Structural Uncertainty
<p>Dataset for the publication Architectural Optimization for Confidentiality under Structural Uncertainty. The Zip file contains eclipse products containing our used models for the evaluation.</p> <p> </p> <p> </p>
Uncertainty quantification in cerebral circulation simulations focusing on the collateral flow: Surrogate model approach with machine learning
<p>Data and code underlying the findings reported in the paper titled "Uncertainty quantification in cerebral circulation simulations focusing on the collateral flow: Surrogate model approach with machine learning."</p>
Data from: Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials
Landscape genetics lacks explicit methods for dealing with the uncertainty in landscape resistance estimation, which is particularly problematic when sample sizes of individuals are small. Unless uncertainty can be quantified, valuable but small datasets may be rendered unusable for conservation purposes. We offer a method to quantify uncertainty in landscape resistance estimates using multi-model inference as an improvement over single-model based inference. We illustrate the approach empirically using co-occurring, woodland-preferring Australian marsupials within a common study area: two arboreal gliders (Petaurus breviceps, and Petaurus norfolcensis) and one ground-dwelling Antechinus (Antechinus flavipes). First, we use maximum-likelihood and a bootstrap procedure to identify the best-supported isolation by resistance (IBR) model out of 56 models defined by linear and non-linear resistance functions. We then quantify uncertainty in resistance estimates by examining parameter selection probabilities from the bootstrapped data. The selection probabilities provide estimates of uncertainty in the parameters that drive the relationships between landscape features and resistance. We then validate our method for quantifying uncertainty using simulated genetic and landscape data showing that for most parameter combinations it provides sensible estimates of uncertainty. We conclude that small datasets can be informative in landscape genetic analyses provided uncertainty can be explicitly quantified. Being explicit about uncertainty in landscape genetic models will make results more interpretable and useful for conservation decision-making, where dealing with uncertainty is critical.
Cell Anomaly Localisation using Structured Uncertainty Prediction Networks
<p>Fluorescent and brightfield datasets for "Cell Anomaly Localisation using Structured Uncertainty Prediction Networks", part of MIDL 2022.</p>
A Classification of Software-Architectural Uncertainty regarding Confidentiality
<p>Dataset for the paper "A Classification of Software-Architectural Uncertainty regarding Confidentiality". For more information, please see the README.md.</p>
Evaluating Uncertainty in Aerosol Forcing of Tropical Precipitation Shifts
<p>This dataset contains simplified data and code to reproduce the main figures in "Evaluating Uncertainty in Aerosol Forcing of Tropical Precipitation Shifts" accepted for publication in Earth System Dynamics, 2022, same authors. </p> <p>A Jupyter notebook and csv data files are provided in each folder to reproduce the respective figure.</p>
Dataset & Code related to article 'Bilateral Adaptive Graph Convolutional Network on CT based COVID-19 Diagnosis with Uncertainty-Aware Consensus-Assisted Multiple Instance Learning'
<p>This record contains the 7768 lung masks <strong>manual annotations, implementation code, and pre-trained models</strong> related to the article 'Bilateral Adaptive Graph Convolutional Network on CT based COVID-19 Diagnosis with Uncertainty-Aware Consensus-Assisted Multiple Instance Learning'</p> <p>Also we include the visualised, selected top D reliable CT slices for all COVID-19 patients in the test dataset for better understanding. </p> <p>For the detailed usage of the data and code, please refer to https://github.com/smallmax00/BAGCN-Covid19</p> <p> </p>
Validation of an Uncertainty Propagation Method for Moving-Boat ADCP Discharge Measurements
<p>ADCP intercomparisons data from Génissiat (2010) and Chauvan (2016). Data used in the article <strong>Validation of an Uncertainty Propagation Method for Moving-Boat ADCP Discharge Measurements</strong>, <em>Water Resources Research</em>, Despax et al..</p>
Prototype Implementation: Uncertainty-aware Confidentiality Analysis Using Architectural Variations
<p>Dataset for the bachelor thesis "Uncertainty-aware Confidentiality Analysis Using Architectural Variations".</p> <p>The ZIP file contains the Eclipse project of the prototype with installation instructions and the models used, modeled in the Palladio Component Model.</p>
Data set for the manuscript "Uncertainty quantification and physics-informed forecasting for improved urban flood modeling"
<p>This is a data set for the manuscript "Uncertainty quantification and physics-informed forecasting for improved urban flood modeling."</p>
Dataset for publication 'Reconnecting Stochastic Methods with Hydrogeological Applications: Uncertainty Analysis and Risk Assessment for the Design of Optimal Monitoring Networks'
<p>This dataset includes all data and information on how to reproduce the results and the figures of the paper 'Reconnecting Stochastic Methods with Hydrogeological Applications: Uncertainty Analysis and Risk Assessment for the Design of Optimal Monitoring Networks'.</p>
Supplementary data to research paper "Linking local climate scenarios to global warming levels: Applicability, prospects and uncertainties"
<p>This file contains supplementary data for the research paper "<span>Linking local climate scenarios to global warming levels: Applicability, prospects and uncertainties"", submitted to IOP Publishing Environmental Research: Climate. The DOI and link to the paper will be added once it is published.</span></p> <p><span>Each file lists the annual mean temperature anomalies relative to the period 1991-2020 for a model of the Austrian climate scenarios OEKS15 (Leuprecht, 2018). The full dataset is available here: https://data.hub.geosphere.at/dataset/oks15_bias_corrected<br></span></p> <p> </p> <div> <div>Leuprecht, A. (2018). <em>ÖKS15 Bias Corrected EURO-CORDEX Model Precipitaion, Radiation, Temperature</em> [dataset]. <a href="https://doi.org/10.60669/B37Q-JD39">https://doi.org/10.60669/B37Q-JD39</a></div> </div>
Predictive Modeling of Bearing Degradation: LSTM Neural Networks for Uncertainty Quantification
<p>These MATLAB codes are part of a research project focused on predicting bearing degradation through vibration measurements. The codes implement LSTM (Long Short-Term Memory) neural network models trained under different objectives, including uncertainty quantification and RMSE (Root Mean Square Error) minimization. The objective of the research is to compare the performance of these models in predicting bearing health and assessing the associated uncertainty.</p> <p><strong>Note:</strong> The current codes are under embargo access as the corresponding paper has been submitted to the ESCA 11 conference. The codes will be made openly accessible upon acceptance of the paper and during the presentation dates. Please cite our paper when using these codes.</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.