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23 results for “uncertainty evaluation”
Raw Data for Evaluation of Measurement Uncertainty in Structural Health Monitoring Systems Under Temperature Influence
<p>The documentation on these laboraty tests is titled "Documentation.pdf"</p> <p> </p> <p>Raw data from distance measurements using laser triangulation sensors acquired under different temperatures are provided. Six sensors were tested per experiment (CSV file), and in each experiment the boundary conditions are varied as follows:<br><br>00RawData_LTS_1m: The entire measurement system is subject to temperature change, with initial distances chosen as LTS1/LTS2=17 mm, LTS3/LTS4=21 mm nd LTS5/LTS6=25 mm.<br><br>01RawData_LTS_1m_SwitchedDistances: The entire measurement system is subject to temperature change, with the selected initial distances of LTS1/LTS2=25 mm, LTS3/LTS4=17 mm nd LTS5/LTS6=21 mm.<br><br>02RawData_LTS_1m_SwitchedDistances2: The entire measurement system is subject to temperature change, with initial distances selected as LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>03RawData_LTS_1m_OnlySensor: Only the sensors of the measuring system are subject to temperature change, where the selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>04RawData_LTS_1m_OnlyMeasuringAmplifier: Only the measuring amplifiers of the measuring system are subject to temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>05RawData_LTS_1m_OnlyCable: Only the cables of the measurement system are subject to the temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>Tested temperature range: -10°C to 50°C<br>Measuring frequency: 1 Hz<br>Measuring amplifier: Q.bloxx.XL A107 Gantner Instruments<br>Cable: 4-pole, 1.00 m length<br>Sensor: OM20-P0026.HH.YIN laser triangulation sensor from Baumer</p>
Regional scale shear wave velocity profiles for ground response analyses and uncertainties evaluations – the Piedmont Region (NW Italy) Database
<p>The databases provide detailed information for the Piedmont region in Northwest Italy, offering a view of its geological and geophysical characteristics:</p> <ul> <li><strong>Geological-Geomorphological Database</strong>: Includes 13 distinct Geological-Geomorphological Domains (GGD) in shapefile format. It supports spatial analysis and visualization, based on data from the Geological Map of the Piedmont Region at a 1:250,000 scale.</li> <li><strong>Geotechnical Database</strong>: Contains geotechnical data on bedrock depth and texture attributes derived from available logs in CSV format. Georeferenced using UTM coordinates (WGS84 UTM32N), it includes depth values and texture codes (C for clay, G for gravel, S for sand, R for rock, X for not available).</li> <li><strong>Geophysical Database</strong>: Provides data on shear wave velocity (Vs) profiles in CSV format. Georeferenced with UTM coordinates (WGS84 UTM32N), it includes layer interface depth and shear wave velocity above each layer.</li> </ul>
Simulation data for eddy current rail testing - simulation accuracy and evaluation uncertainty quantification
<p>This dataset serve to quantify the simulation error and the evaluation uncertainties in the context of eddy current rail testing. It was obtained during the AIFRI project (Artificial Intelligence for Rail Inspection) with the Faraday software by INTEGRATED Engineering Software, using its BEM Solver. For an analysis see the article below.</p>
Data for SI-Hg D2 validation report for the calibration of elemental mercury gas generators including information on repeatability, reproducibility and uncertainty evaluation at emission and ambient levels extended to the sub ng/m3 level
<p>In deliverable 2 of the SI-Hg project the first validation results of the SI-Hg calibration protocol are reported. Within the SI-Hg project a protocol for the metrological calibration of elemental mercury gas generators used in the field was developed. For the validation the output of two different mercury gas generators was calibrated according to the protocol. As metrological reference standard the primary mercury gas standard from the Van Swinden Laboratory (VSL) was used. The measurements described in the protocol could be performed during the validation and the data was processed using a script to determine the output of the candidate generator and the uncertainty of the mercury concentration. Based on the validation measurements and data processing several improvements for the calibration protocol were identified and were used to improve the calibration protocol. </p><p>In this repository data obtained during the validation is published. The files of the following comparisons between reference generator and candidate generator can be found in this repository:</p><ul><li>VSL vs VSL<ul><li>m1<ul><li>09022022 calibration mercury gas generator VSL vs VSL m1</li><li>VSL_vs_VSL_m1</li></ul></li><li>m2 <ul><li>05072022 calibration mercury gas generator VSL vs VSL m2</li><li>VSL_vs_VSL_m2</li></ul></li><li>m3<ul><li>07072022 calibration mercury gas generator VSL vs VSL m3</li><li>VSL_vs_VSL_m3</li></ul></li></ul></li><li>VSL vs PSA before modification<ul><li>m1<ul><li>15032022 calibration mercury gas generator VSL vs PSA fixed m1</li><li>single_point_VSL_vs_PSA_fixed_m1_4</li><li>single_point_VSL_vs_PSA_fixed_m1_6</li><li>single_point_VSL_vs_PSA_fixed_m1_8</li><li>single_point_VSL_vs_PSA_fixed_m1_12</li></ul></li><li>m2<ul><li>28032022 calibration mercury gas generator VSL vs PSA fixed m2</li><li>single_point_VSL_vs_PSA_fixed_m2_4</li><li>single_point_VSL_vs_PSA_fixed_m2_6</li><li>single_point_VSL_vs_PSA_fixed_m2_8</li><li>single_point_VSL_vs_PSA_fixed_m2_12</li></ul></li><li>m3 <ul><li>06042022 calibration mercury gas generator VSL vs PSA fixed m3</li><li>single_point_VSL_vs_PSA_fixed_m3_4</li><li>single_point_VSL_vs_PSA_fixed_m3_6</li><li>single_point_VSL_vs_PSA_fixed_m3_8</li><li>single_point_VSL_vs_PSA_fixed_m3_12</li></ul></li><li>m4 <ul><li>12042022 calibration mercury gas generator VSL vs PSA fixed m4</li><li>single_point_VSL_vs_PSA_fixed_m4_4</li><li>single_point_VSL_vs_PSA_fixed_m4_6</li><li>single_point_VSL_vs_PSA_fixed_m4_8</li><li>single_point_VSL_vs_PSA_fixed_m4_12</li></ul></li><li>less tubing <ul><li>14042022 calibration mercury gas generator VSL vs PSA fixed less tubing</li><li>single_point_VSL_vs_PSA_fixed_less_tubing</li></ul></li><li>less tubing and air as complementary gas <ul><li>19042022 calibration mercury gas generator VSL vs PSA fixed less tubing in air</li><li>single_point_VSL_vs_PSA_fixed_less_tubing_air</li></ul></li></ul></li><li>VSL vs PSA after modification<ul><li>m1 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m1 20230324</li><li>PSA_fixed_air_m1_9</li><li>PSA_fixed_air_m1_11</li><li>PSA_fixed_air_m1_14</li></ul></li><li>m2 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m2 20230327</li><li>PSA_fixed_air_m2_9</li><li>PSA_fixed_air_m2_11</li><li>PSA_fixed_air_m2_14</li></ul></li><li>m3 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m3 20230329</li><li>PSA_fixed_air_m3_9</li><li>PSA_fixed_air_m3_11</li><li>PSA_fixed_air_m3_14</li></ul></li><li>m4 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m4 20230907</li><li>PSA_fixed_air_m4_9</li><li>PSA_fixed_air_m4_11</li><li>PSA_fixed_air_m4_14</li></ul></li><li>m5 air as complemantary gas <ul><li>Calibration PSA fixed mercury gas generator air m5 20230911</li><li>PSA_fixed_air_m5_9</li><li>PSA_fixed_air_m5_11</li><li>PSA_fixed_air_m5_14</li></ul></li><li>m1 nitrogen (N2) as complementary gas<ul><li>Calibration PSA fixed mercury gas generator nitrogen m1 20230330</li><li>PSA_fixed_N2_m1_9</li><li>PSA_fixed_N2_m1_11</li><li>PSA_fixed_N2_m1_14</li></ul></li><li>m2 N2 as complementary gas <ul><li>Calibration PSA fixed mercury gas generator nitrogen m2 20230331</li><li>PSA_fixed_N2_m2_9</li><li>PSA_fixed_N2_m2_11</li><li>PSA_fixed_N2_m2_14</li></ul></li><li>m3 N2 as complementary gas <ul><li>Calibration PSA fixed mercury gas generator nitrogen m3 20230405</li><li>PSA_fixed_N2_m3_9</li><li>PSA_fixed_N2_m3_11</li><li>PSA_fixed_N2_m3_14</li></ul></li><li>measurement at TUV<ul><li>PSA_Fixed_at_TUV</li></ul></li></ul></li></ul>
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>
Data from: Evaluating UCE data adequacy and integrating uncertainty in a comprehensive phylogeny of ants
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Data from: Comparing alternative harvest strategies to address robustness to recruitment variability and uncertainty: Implications for Alaska Sablefish tested with management strategy evaluation
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Design for Measurement Uncertainty software and 1D instrument combination evaluations
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Evaluating UCE data adequacy and integrating uncertainty in a comprehensive phylogeny of ants
<p>Data from manuscript "Evaluating UCE data adequacy and integrating uncertainty in a comprehensive phylogeny of ants". Includes assembled contigs, unaligned and aligned ultraconserved element loci (UCE) and concatenated matrices, input and output of phylogenetic analyses, custom scripts used.</p>
Design Space Evaluation for Confidentiality under Architectural Uncertainty - Dataset
<p>This dataset contains additional files used, adapted and/or created for the bachelor's thesis of Oliver Liu. It includes the code for the architecture candidate filter, as well as models and configuration files used for the evaluation.</p>
Brewer #150 UV measurements and ancillary data needed for its uncertainty evaluation
<p>All files uploaded are needed to evaluate the uncertainty of the UV measurements recorded by the Brewer MKIII double monochromator (No. 150). The uncertainty analysis was carried out using the Monte Carlo technique, which was implemented in a separate R file (doi: 10.5281/zenodo.8046562). Files contain all the necessary information to characterize Brewer #150 (cosine correction, raw counts, lamp irradiance, responsivity instabilities, noise and wavelength shifts).</p>
Evaluation and uncertainty analysis of the land surface hydrology in LS3MIP models over China
<p>The attached is the dataset assoicated with the paper titled "Evaluation and uncertainty analysis of the land surface hydrology in LS3MIP models over China" which was submitted to Journal of Earth and Space Science.</p><p>The Land Surface, Snow and Soil moisture Model Intercomparison Project (LS3MIP) offers valuable land surface hydrology products from the land modules of current Earth System Models (ESMs). In this paper, historical LS3MIP hydrological variables including precipitation (PR), evapotranspiration (ET), soil moisture (SM), total runoff (Ro), and snow cover fraction (SCF) were extensively evaluated with various high-quality reference datasets over Chinese mainland. The six ESMs in LS3MIP were driven by four meteorological forcing datasets. The results indicated that the LS3MIP multi-model means (MMEs) of most variables are underestimated overall, while they show high spatial consistency in term of linear trends, with the percentage area ranging 56% ~ 85% between simulations and reference datasets. After computing and ranking multi statistical metrics (bias, correlation coefficient, normalized standard deviation, and unbiased root-mean-square biases), it is found that the CESM2 model produces the best performance of land surface hydrological variables, while as the meteorological forcing dataset GSWP3 exhibits the highest quality. Furthermore, the analysis of variance method (ANOVA) was then used to trace sources of the uncertainty of the LS3MIP hydrological variables for 1900–2012 (1948–2012 for Ro). In ANOVA, the simulation uncertainties may be decomposed into three sources: model, atmospheric forcing datasets and their interactions. In LS3MIP historical hydrological variables over China, model uncertainty is the dominant factor overall although it shows regional differences, and the dependence of uncertainty on the model differs among hydrological regimes. This highlights the urgent requirements to improve the land surface model representation in future research.</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>
Arctique - ARtificial Colon Tissue Images for Qualitative Uncertainty Evaluation
<p>This dataset was introduced and published in the NeurIPS 2024 paper, <em>"Arctique: An Artificial Histopathological Dataset Unifying Realism and Controllability for Uncertainty Quantification"</em>. It includes various versions, in particular a version containing 50,000 training images and 1,000 test images, each paired with corresponding instance and semantic masks, as well as 400 additional variations across 50 selected test images to support research on uncertainty quantification.</p> <p><strong>Versions:</strong></p> <ul> <li><strong>Version v3</strong>: The version used for experimental results presented in the associated research paper. This dataset features improved realism and includes the core images and labels as in <strong>v2</strong>, supplemented with noise-augmented variations specifically used to evaluate algorithmic performance under challenging conditions. It comprises 1,500 synthetic images<br>without noise, 1,500 with added noise, and 1,500 depth mask images, along with a range of noisy variations.</li> <li><strong>Version v2</strong>: The full dataset, consisting of 50,000 training images and 1,000 test images, along with their associated instance and semantic masks. Additionally, this version includes 400 augmented variations for 50 selected test images.</li> <li><strong>Version v1</strong>: A small example subset of the dataset provided for review purposes, containing a limited number of images and annotations to allow preliminary exploration.</li> </ul> <p>Each version of the Arctique dataset is split into training and test sets and variations, each containing the following directories:</p> <p>The <strong>images </strong>directory contains all synthetically generated images stored as PNG files. Each image has a resolution of 512x512 pixels with RGB channels and is named "img_<ID>", where <ID> is a unique integer identifier for each image.</p> <p>The <strong>masks </strong>directory includes subdirectories containing various masks related to the images:</p> <ul> <li><em>cytoplasm</em>: Contains 2D semantic masks for the cell cytoplasm. Each mask corresponds to an image named "<ID>.tif", where "<ID>" is the identifier for that image. The mask file is named using the same identifier.</li> <li><em>instance_3d</em>: Contains a directory for each image, named "<ID>. Inside each directory, there is a 3D stack numpy file representing the instance IDs in a 3D volumetric array. Additionally, it includes a sequence of 2D instance segmentation masks, named "slice_<ID>_<slice_count>.png", each representing equidistant slices through the 3D volume along the depth axis.</li> <li><em>instance</em>: Contains 2D instance masks for the cell nuclei. Each mask corresponds to an image named "<ID>.tif", and the mask file is named with the same identifier.</li> <li><em>semantic</em>: Contains 2D semantic masks for the cell nuclei. Similar to the instance masks, each mask corresponds to an image named "<ID>.tif", with the mask file named using the same identifier.</li> </ul> <p>Note that all semantic masks appear as black images when viewed with a standard image viewer. This is because the cell type IDs, ranging from 1 to 5, are used as greyscale values, which appear dark in the images. The modelled cell types are:</p> <p>Cell Types</p> <table> <tbody> <tr> <td>1</td> <td>Epithelial Cells</td> <td>'EPI'</td> </tr> <tr> <td>2</td> <td>Plasma Cells</td> <td>'PLA'</td> </tr> <tr> <td>3</td> <td>Lymphocytes</td> <td>'LYM'</td> </tr> <tr> <td>4</td> <td>Eosinophils</td> <td>'EOS'</td> </tr> <tr> <td>5</td> <td>Fibroblasts</td> <td>'FIB'</td> </tr> </tbody> </table> <p> </p> <p>The <strong>metadata </strong>directory contains JSON metadata files named "metadata_<ID>" for each image. Each JSON file includes a list of Python dictionaries, one for each cell object visible in the image. Consider submission appendix F for a detailed explanation of each dictionary.</p> <p>The <strong>parameters </strong>directory contains JSON files named "parameters_<ID>", which detail the parameters used to generate each image. Each JSON file is a Python dictionary with all the parameter values necessary to reproduce the scene. Consider submission appendix F for a detailed explanation of each parameter.</p>
Data from the thesis diagnosis of waste, application possibilities and future demand for green coconut shells, with evaluation of information uncertainty
<p>Information on the quantities of green coconut shells produced in coastal regions is not well known, which can make it difficult to manage these frequently wasted materials, as well as the understanding that they can be included in a production process as a raw material, with various possibilities of use and income generation. Given this situation, the objective of this research is to develop a diagnosis of the potential use of green coconut shells, after water consumption, in public places in municipalities in the southern and southern coast of Bahia. It is also intended to identify the future demand of the potential possibility for the recovery of the bark, and provide subsidies that can be used to organize the use of this material. In this context, the method applied in this research consists of five steps: (i) literature review; (ii) diagnosis of bark situation in the study region; (iii) evaluation of the uncertainty of the estimated amount of shells; (iv) possibilities for applying green coconut shells and preparing the analysis of strengths, weaknesses, opportunities and threats (SWOT); and (v) obtaining future demand for the potential output for the region through quantitative techniques for predicting time series with uncertainty assessment. The main scientific contributions of this work consist, therefore, (a) in the diagnosis of the waste of green coconut shells in the region; (b) the possibilities of application of coconut shells; (c) in the demand forecasting method elaborated for time series without trends and with high variability; and (d) in assessing the uncertainty of the quantitative information obtained. Thus, the study developed can be used as a subsidy, either for replicating the research process in other types of waste allocated in any region, or for the adoption of public policies to manage these materials, or even for the encouragement and creation of an appropriate productive scenario for the management of green coconut shells in the study region, which contributes to the understanding of the potential for generating environmental, economic and social value from the use and processing of these wasted materials.</p>
Data from: How much is new information worth? Evaluating the financial benefit of resolving management uncertainty.
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Implications of life history uncertainty when evaluating status in the Northwest Atlantic population of white shark (Carcharodon carcharias)
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Dataset for publication "Uncertainty Evaluation on the Absolute Phase Error of Digitizers"
<p>This is the dataset related to the paper</p> <p>Delle Femine, Antonio, Gallo, Daniele, Landi, Carmine, & Luiso, Mario. (2019). Uncertainty Evaluation on the Absolute Phase Error of Digitizers. Transactions of the Institute of Measurement and Control. http://doi.org/10.5281/zenodo.3600435</p> <p>that is identical to</p> <p>A. Delle Femine, D. Gallo, C. Landi, M. Luiso, “Uncertainty evaluation on the absolute phase error of digitizers”, Transactions of the Institute of Measurement and Control, Volume: 42 issue: 4, page(s): 749-758, 01 February 2020, doi: 10.1177/0142331219879836</p> <p> </p> <p> </p> <p>Excel file contains the data for Figures 6, 7, 8, 9.</p>
Data from: Calibration uncertainty in molecular dating analyses: there is no substitute for the prior evaluation of time priors
Calibration is the rate-determining step in every molecular clock analysis and, hence, considerable effort has been expended in the development of approaches to distinguish good from bad calibrations. These can be categorized into a priori evaluation of the intrinsic fossil evidence, and a posteriori evaluation of congruence through cross-validation. We contrasted these competing approaches and explored the impact of different interpretations of the fossil evidence upon Bayesian divergence time estimation. The results demonstrate that a posteriori approaches can lead to the selection of erroneous calibrations. Bayesian posterior estimates are also shown to be extremely sensitive to the probabilistic interpretation of temporal constraints. Furthermore, the effective time priors implemented within an analysis differ for individual calibrations when employed alone and in differing combination with others. This compromises the implicit assumption of all calibration consistency methods, that the impact of an individual calibration is the same when used alone or in unison with others. Thus, the most effective means of establishing the quality of fossil-based calibrations is through a priori evaluation of the intrinsic palaeontological, stratigraphic, geochronological and phylogenetic data. However, effort expended in establishing calibrations will not be rewarded unless they are implemented faithfully in divergence time analyses.
Data from: Calibration uncertainty in molecular dating analyses: there is no substitute for the prior evaluation of time priors
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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.