Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
37
datasets available to search
ShareScore release 0.9.0
Dataset results
37 results for “measurement uncertainty”
Dataset and program scripts for the reproducibility of the hierarchical data structure file. Related to the manuscript entitled: Hierarchical Representation of Measurement Data, Metrological Uncertainty and Metadata for Calibrated Battery Tests
<p>We present an interoperable hierarchical data representation for battery tests, leading to improved scalability of data transmission and enhanced data accessibility and comprehensibility for both human interpretation and machine processing. The hierarchical data format includes the raw trace electrical measurement data, the metrological calibration and uncertainty data, the metadata such as experimental settings, instruments and software versions, as well as post-processed data such as electrochemical model fit parameters. This data representation allows repetition of the battery test under the exact same conditions such that identical results are achieved within defined error bounds. This is in line with the general F.A.I.R. data approach and provides repeatability and traceability in the battery value chain. As an application of the hierarchical data representation, we show the classification of cells as pass/fail being performed with quantitative confidence levels. We demonstrate the complete workflow of establishing the hierarchical data structure for electrochemical impedance spectroscopy (EIS), starting from metrological traceability of the calibration and uncertainty analysis towards the storage of the structured data as a single integrated file that preserves the hierarchical data format.</p>
Data used to create figures in the ACP Letters manuscipt "The value of remote marine aerosol measurements for constraining radiative forcing uncertainty" by Regayre et al. (2020)
<p>This dataset was created from perturbed parameter ensembles (PPEs) using the HadGEM-UKCA atmospheric composition climate model. All data needed to reproduce figures in the Regayre et al. (2020) ACP Letters article "The value of remote marine aerosol measurements for constraining radiative forcing uncertainty" are included. Other output from the PPEs can be obtained by contacting the lead author.</p> <p>The following data are included here:</p> <ul> <li>CCN measurement data degraded to match the model-measurement comparison resolution.</li> <li>Unconstrained and constrained CCN<sub>0.2</sub> output from the PPE used to make Figure 1. These compressed files contain 48 .dat files. Each .dat file contains the PPE mean, variance and 95% creidble interval data. Files are named consecutively, containing data from 90<sup>o</sup>S to 90<sup>o</sup>N at 0<sup>o</sup>E, then continuing Eastward. When combined, these files provide data for each latitude/longitude pair at the N48 spatial resolution.</li> <li>A zip file of an netcdf file containing 26-dimensional data for parameter values, used to create the sample of 1 million model variants from our statistical emulators of model output.</li> <li>A zip file containing a folder of files made of one million ones and zeros that indicate the retention/rejection criteria from applying our constraint methodology for various constraint combination scenarios, for each model variant. A value of 1 indicates the model variant was retained. Data in these files is in the same order as the unconstrained sample file of parameter values.</li> <li>Compressed files containing global, annual mean RF<sub>aci</sub> and ERF<sub>aci</sub> values for the unconstrained set of one million model variants. The compressed netcdf files contain RF (ERF), RF<sub>aci</sub> (ERF<sub>aci</sub>) and RF<sub>ari</sub> (ERF<sub>ari</sub>) values.</li> </ul>
Effects of intolerance of uncertainty on subjective and psychophysiological measures during threat acquisition and delayed threat extinction
<p>This dataset includes measurements of intolerance of uncertainty (Intolerance of Uncertainty Scale [Freeston et al., 1994]), trait anxiety (State-Trait Anxiety Inventory [Spielberger et al., 1983]), skin conductance response (SCR), fear potentiated startle (FPS) and fear ratings (RAT) acquired in a differential fear conditioning paradigm with habituation and threat acquisition training on one day and extinction training, mood induction (by presenting negative vs. neutral slides), re-extinction training, reinstatement and reinstatement-test 24h later. Overall, 66 participants (female = 44, aged between 18 and 40 years, M = 25.76, SD = 5.82) took part in the study. Several participants had to be excluded due to technical issues (n = 3), non-responding (SCR: n = 2; auditory startle blink: n = 1) and no SCRs to the CSs (n = 1). Visual CSs were two shapes resembling snowflakes. The US consisted of a train of three 2 ms electrotactile square-waves (inter stimulus interval, ISI: 50 ms) and was delivered 7.9 s after each CS+ onset (100% reinforcement rate) during threat acquisition training and three times during reinstatement. The duration of the ITIs ranged from 10 to 13 s (M = 11.5). For SCR measurements, a 1 Hz lowpass filter and a gain of 5 or 10 μΩ were applied. SCR data were scored by using the semi-automatic scoring system Autonomate (Green et al., 2014), down sampled to 10 Hz and scored as the first response within 0.9 to 4 s after CS onset as SCR from trough to peak with a maximum rise time of 5 s. SCRs were square root transformed to reduce skew and z-scored within individuals across trials for day 1 and day 2 separately. To elicit the auditory startle blink, a 95 dB white noise burst was presented simultaneously on both ears. Startle probes were administered 6 or 8 s after the ITI-onset and 6 or 7 s after CS-onset. A gain of 5000 at 1000 Hz and a band-pass filter (28–500 Hz) were applied. Data were rectified and integrated online (averaged over 20 samples) and scored semi-automatically by using a custom-made computer program (EDA View, developed by Prof. Dr. Matthias Gamer, University of Würzburg) as trough to peak 20–120 ms after startle probe onset. For analyses, FPS data was z-scored within individuals across trials for day 1 and day 2 separately. To acquire fear ratings, participants rated throughout the experiment, how much stress, fear, and tension they experienced, when they last saw the CSs. Answers had to be logged in via button press within 7 s on a visual analog scale (VAS) ranging from zero (answer = none) to 100 (answer = maximum). Unlogged ratings were considered as missing values.</p>
Data to publication: Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures
<p>This dataset contains the results of an experimental campaign, presented in the publication "Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures". The publication covers fibre optic measurements inside large-scale specimens subjected to external load. The specimens comprised reinforced concrete slabs, strengthened with reinforcement bars made from iron-based shape memory alloy.</p>
Dataset of "An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification"
<p>Dataset of the article "An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification" (https://doi.org/10.1016/j.expthermflusci.2022.110756). Local similarity between non-time-resolved snapshots is enforced by KNN to extract high-resolution velocity fields and estimate the uncertainty of the measurements.</p> <p>The codes processing data here are on https://github.com/erc-nextflow/KNN-PTV.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085, NEXTFLOW).</p>
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>
Examples dataset in conformity assessment with measurement uncertainty
<p>Thie dataset summarizes examples that can be used to validate the software developed as part of 17SIP05 CASoft project, which aims at popularizing the use of the methodology described by the reference document JCGM106:2012 for decision-making in conformity assessment problems. </p>
[Dataset] One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors, located in Graz, Austria
<p><strong>Highlights:</strong></p> <ul> <li>High-precision measurement data acquired within a scientific research project, using high-quality measurement equipment and implementing extensive data quality assurance measures.</li> <li>The dataset includes data from one full operational year in a 1-minute sampling rate, covering all seasons.</li> <li>Measured data channels include global, beam and diffuse irradiances in horizontal and collector plane. Heat transfer fluid properties were determined in a dedicated laboratory test.</li> <li>In addition to the measured data channels, calculated data channels, such as thermal power output, mass flow, fluid properties, solar incidence angle and shadowing masks are provided to facilitate further analysis.</li> <li>Uncertainties of data channels are provided based on data sheet specifications and GUM error propagation.</li> <li>The dataset refers to a real-scale application which is representative of typical large-scale solar thermal plant designs (flat plate collectors, common hydraulic layout).</li> <li>Additional information is provided in a "Data in Brief" journal article: <a href="https://doi.org/10.1016/j.dib.2023.109224">https://doi.org/10.1016/j.dib.2023.109224</a></li> </ul> <p> </p> <p><strong>Collector array description: </strong>The data is from a flat plate collector array with a total gross collector area of 516 m<sup>2</sup> (361 kW nominal thermal power). The array consists of four parallel collector rows with a common inlet and outlet manifold. Large-area flat-plate collectors from Arcon-Sunmark A/S are used in the plant. Collectors are all oriented towards the south (180°), have a tilt angle of 30° and a row spacing of 3.1 m. The collector array is part of a large-scale solar thermal plant located at Fernheizwerk Graz, Austria (latitude: 47.047294 N, longitude: 15.436366 E). The plant feeds into the local district heating network and is one of the largest Solar District Heating installations in Central Europe.</p> <p> </p> <p><strong>Data files:</strong></p> <ul> <li><strong>FHW_ArcS__main__2017.csv</strong> – This is the main dataset. It is advised to use this file for further analysis. The file contains the full time series of all measured and all calculated data channels and their (propagated) measurement uncertainty (53 data channels in total). Calculated data channels are derived from measured channels (see script make_data.py below) and have the suffix __calc in their channel names. Uncertainty information is given in terms of standard deviation of a normal distribution (suffix __std); some data channels are assumed to have no uncertainty (e.g., sun azimuth or shadowing).</li> <li><strong>FHW_ArcS__main__2017.parquet</strong> – Same as FHW_ArcS__main__2017.csv, but in parquet file format for smaller file size and improved performance when loading the dataset in software.</li> <li><strong>FHW_ArcS__parameters.json</strong> – Contains various metadata about the dataset, in both human and machine-readable format. Includes plant parameters, data channel descriptions, physical units, etc.</li> <li><strong>FHW_ArcS__raw__2017.csv </strong>– Dataset with time series of all measured data channels and their measurement uncertainty. The main dataset FHW_ArcS__main__2017.csv, which includes all calculated data channels, is a superset of this file.</li> </ul> <p> </p> <p><strong>Scripts: </strong></p> <ul> <li><strong>make_data.py</strong> – This Python script exposes the calculation process of the calculated data channels (suffix __calc), including error propagation. The main calculations are defined as functions in the module utils_data.py.</li> <li><strong>make_plots.py</strong> – This Python script, together with utils_plots.py, generates several figures based on the main dataset.</li> </ul> <p> </p> <p><strong>Data collection and preparation</strong>: AEE — Institute for Sustainable Technologies (AEE INTEC), Feldgasse 19, 8200 Gleisdorf, Austria; and SOLID Solar Energy Systems GmbH (SOLID), Am Pfangberg 117, 8045 Graz, Austria</p> <p> </p> <p><strong>Data owner</strong>: solar.nahwaerme.at Energiecontracting GmbH, Puchstrasse 85, 8020 Graz, Austria</p> <p> </p> <p><strong>Additional information</strong> is provided in a journal article in "Data in Brief", titled <a href="https://doi.org/10.1016/j.dib.2023.109224">"One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors in Graz, Austria"</a>.</p> <p> </p> <p><strong>Note: </strong>A Gitlab repository is associated with this dataset, intended as a companion to facilitate maintenance of the Python code that is provided along with the data. If you want to use or contribute to the code, please do so using the Gitlab project: <a href="https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017">https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017</a></p> <p> </p>
Measurement report: Characterization of uncertainties of fluxes and fuel sulfur content from ship emissions at the Baltic Sea
<p>This data submission is connected to a scientific paper submitted to<br> Atmospheric Chemistry and Physics ("Measurement report: Characterization of uncertainties of fluxes and fuel sulfur content from ship emissions at the Baltic Sea" by Walden et al.). It consists of measurement results conducted beside the ship routs at the Baltic Sea near Helsinki, Finland. The gaseous and particle concentrations were measured along with the meteorological parameters, and the fluxes were calculated by the micrometeorological methods. The content of sulfur in the marine fuel, FSC, used by the passing ships was also calculated. We paid attention to calculate the uncertainties of the measurement results, both for the fluxes and for the FSC.</p> <p>The released data of:<br> 1. Gases, particles and met data (SO<sub>2</sub>, NO, NO<sub>2</sub>, O<sub>3</sub>, CO<sub>2</sub>, and N<sub>tot</sub> (number concentration of nanoparticles) as minute values. </p> <p>Data_ACP_Fig4_acbd.xlsx.</p> <p> <br> 2. Size distribution of nanoparticles (number concentration of nanoparticles at size class). Data_ACP_Fig6.xlsx</p> <p> <br> 3. Profiles of 30 min averages of gases, nanoparticles and meteorological parameters (SO<sub>2</sub>, NO, NO<sub>2</sub>, O<sub>3</sub>, CO<sub>2</sub>, and N<sub>tot</sub> (number concentration of nanoparticles), wind direction and wind speed, friction velocity, stability parameter and Monin-Obukhov length. Calculated values of atmospheric turbulence parameters and calculated fluxes of CO2 and nanoparticles by gradient and/or eddy covariance method.</p> <p>Data_ACP_Fig8_abcd_Fig9_abcd.xlsx<br> <br> 4. CO2 fluxes by Eddy covariance method from land based and sea based measurements. Concentration of CO2 in seawater and in air.</p> <p>Data_ACP_Fig10_ab.xlsxEngl</p>
Data from: Transformation of measurement uncertainties into low-dimensional feature vector space
<p>Advances in technology allow the acquisition of data with high spatial and temporal resolution. These datasets are usually accompanied by estimates of the measurement uncertainty, which may be spatially or temporally varying and should be taken into consideration when making decisions based on the data. At the same time, various transformations are commonly implemented to reduce the dimensionality of the datasets for post-processing, or to extract significant features. However, the corresponding uncertainty is not usually represented in the low-dimensional or feature vector space. A method is proposed that maps the measurement uncertainty into the equivalent low-dimensional space with the aid of approximate Bayesian computation, resulting in a distribution that can be used to make statistical inferences. The method involves no assumptions about the probability distribution of the measurement error and is independent of the feature extraction process as demonstrated in three examples. In the first two examples Chebyshev polynomials were used to analyse structural displacements and soil moisture measurements; while in the third, principal component analysis was used to decompose global ocean temperature data. The uses of the method range from supporting decision making in model validation or confirmation, model updating or calibration and tracking changes in condition, such as the characterisation of the El Niño Southern Oscillation. </p>
Dataset for the uncertainty assessment of confocal measurements of industrial samples
<p>These original measurement data relate to the publication: J. Paredes, G. Kortaberria: Towards task-specific uncertainty assessment for imaging confocal microscopes, <a href="https://www.euspen.eu/knowledge-base/ICE23191.pdf">ICE23191.pdf (euspen.eu)</a>. Please refer to this open access publication for a detailed description of the measurement setup and procedure.</p><p>All data are in ASCII-format. Each file contains 2 columns, where they are the measured <i>x/y</i> and <i>z</i>-coordinates of the 2D profile extracted from the surface. All the coordinates are recorded in micrometers. The 0/90 at the end of the file names represent the orientation of the extracted profile being<i> x</i> and <i>y</i> direction respectively.</p><p>The topoghraphy files contain 3 columns, they are the measured<i> x</i>, <i>y</i>, and <i>z</i>-coordinates of the surface.</p><p><strong>Acknowledgement</strong></p><p>This project 20IND07 TracOptic has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme. Funder name: European Metrology Programme for Innovation and Research (EMPIR).</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>
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>
Metadata with submitted Emission Control Science and Technology Journal manuscript Traceable uncertainty of exhaust flow meters embedded in portable emission measurement systems
<p>Metadata with submitted Emission Control Science and Technology Journal <em>Traceable uncertainty of exhaust flow meters embedded in portable emission measurement systems</em></p> <p>Link to article: https://link.springer.com/article/10.1007/s40825-025-00260-z</p>
Data from: Transformation of measurement uncertainties into low-dimensional feature vector space
Open the record for dataset details and reuse information.
Experimental measurements and uncertainty analysis for validation of the Building Electrical Efficiency Analysis Model (BEEAM)
Open the record for dataset details and reuse information.
Seafloor Density Measurements, Prediction, and Associated Uncertainty for "Predicting global marine sediment density using the random forest regressor machine learning algorithm"
<p>Global seafloor density prediction results using the random forest regressor machine learning algorithm. </p> <p>Dataset S1. Seafloor density measurements. Columns are labeled with a header and include associated drilling project and measurement type for each sample. File format: CSV text file</p> <p>Dataset S2. Seafloor density prediction results from the random forest regressor machine learning algorithm at 5×5-arc minute resolution. Units are g/cm^3. File format: netCDF (.nc)</p> <p>Dataset S3. Seafloor density prediction standard deviation from the random forest regressor machine learning algorithm at 5×5-arc minute resolution. Units are g/cm^3. File format: netCDF (.nc)</p>
Quantification of uncertainties introduced by data-processing procedures of sap flow measurements using the cut-tree method on a large mature tree
<p>Motivation: Sap flow sensors are crucial instruments to understand whole-tree water use. The lack of direct calibration of the available methods on large trees and the application of several data-processing procedures may jeopardize our understanding of water uptake dynamics by increasing the uncertainties around sensor-based estimates. We directly compared the heat ratio method (HRM) sap flow measurements to water uptake measured gravimetrically using the cut-tree method on a large mature aspen tree to quantify those uncertainties for ten consecutive days.</p> <p>Dataset: In this dataset, we provide sap flux density (ten-minutes intervals; g.cm-2.hr-1; corrected for wounding and sapwood thermal diffusivity) obtained from four HRM sap flow sensors installed at 2.5 m high on the focus tree (20 m tall, 60 years old trembling aspen in the boreal mixedwood region of Alberta) between July 18th and August 22nd 2017. We present the code and data (weather data from neighboring weather station) used to calculate whole-tree sap flux (L.hr-1) from each of the individual sensors using different methods of radial integration of sap flux density across the sapwood area estimated via different calculations, as well as different zero-flow corrections used. The cut-tree procedure was applied to the focus tree, and gravimetric measurements of water uptake (ten-minutes intervals) were made using a recording scale. We directly compared the different estimates of hourly, daily and cumulative sap flows obtained with gravimetric measurement of water uptake. We present the code providing the statistical analysis and results reported in the associated publication (Merlin, M., Solarik, K.A., Landhäusser, S.M. Quantification of uncertainties introduced by data-processing procedures of sap flow measurements using the cut-tree method on a large mature tree. 2020. Agricultural and Forest Meteorology, http://dx.doi.org/10.1016/j.agrformet.2020.107926)</p>
Design for Measurement Uncertainty software and 1D instrument combination evaluations
Open the record for dataset details and reuse information.
Uncertainty Analysis of MSL's CCT-K7.2021 Key Comparison Measurements Using Uncertain Numbers
<p>This dataset is associated with a publication of the same name (currently submitted to Metrologia). It contains Python modules and text files and can be used to repeat the analysis described in the article. The GUM Tree Calculator (GTC) Python software package is required (version 1.4.0, or above: https://github.com/MSLNZ/GTC).</p>
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.