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227 results for “tool validation”
Data associated with the following publication: Developing the Playground Play Value and Usability Audit Tool (PVUA): An Evaluation of Content Validity via an Expert Panel
<p>This data set contains the supporting data associated with the following publication:</p> <p>Morgenthaler, T., Loebach, J., Lynch, H., Pentland, D., Kottorp, A., & Schulze, C. (in press). Developing the Playground Play Value and Usability Audit Tool (PVUA): An Evaluation of Content Validity via an Expert Panel. Children, Youth and Environments. [DOI was not yet available when the data set was published]</p> <p>The data set includes the following files:</p> <ul> <li>read me file [contains all relevant information to understand and reuse this data set] </li> <li>13 additional files [for description, see read me file]</li> </ul> <p>For more information, please contact the lead researcher, Thomas Morgenthaler (tom.morgenthaler@gmail.com or 121101888@umail.ucc.ie)</p> <p> </p>
Defect Prediction Tool Validation Dataset 2
<p><strong>This dataset is used to address the Research Questions in the study at Transactions on Software Engineering</strong>: <strong>Within-Project</strong> <strong>Defect Prediction of Infrastructure-as-Code using Product and Process Metrics. </strong></p> <p><strong>See also: https://github.com/stefanodallapalma/TSE-2020-05-0217.</strong></p> <p>It provides</p> <p>* <strong>repositories.json</strong> - a list of repositories selected from open-source GitHub repositories based on the Ansible language.</p> <p>* <strong>fixing-commits.json</strong> - a list of defect-fixing commits extracted from those repositories.</p> <p>* <strong>fixed-files.json</strong> - a list of Ansible files fixed in those defect-fixing commits and respective bug-inducing commits.</p> <p>* <strong>failure-prone-files.json</strong> - a list of failure-prone files through the repository's commit history.</p> <p>* <strong>metrics.zip </strong>- csv files consisting of releases (set of files) and their IaC-oriented, delta and process metrics extracted from each analyzed repository</p> <p>* <strong>projects.zip </strong>- for each analyzed project, it contains the data (models, performance, and results of Recursive Feature Elimination) used to answer the Research Questions.</p> <p><strong>Context</strong></p> <p><em>Infrastructure-as-code (IaC)</em> is the DevOps strategy that allows management and provisioning of infrastructure through the definition of machine-readable files and automation around them, rather than physical hardware configuration or interactive configuration tools.</p> <p>On the one hand, although IaC represents an ever-increasing widely adopted practice nowadays, still little is known concerning how to best maintain, speedily evolve, and continuously improve the code behind the IaC strategy in a measurable fashion. <br> On the other hand, source code measurements are often computed and analyzed to evaluate the different quality aspects of the software developed.<br> In particular, Infrastructure-as-Code is simply "code", as such it is prone to defects as any other programming languages.</p> <p>This dataset targets the YAML-based Ansible language to devise <strong>within-project defects prediction</strong> approaches for IaC based on Machine-learning.</p> <p><strong>Content</strong></p> <p>The dataset contains metrics extracted from 85 open-source GitHub repositories based on the Ansible language that satisfied the following criteria:</p> <p>* The repository has at least one push event to its master branch in the last six months;<br> * The repository has at least 2 releases;<br> * At least 10% of the files in the repository are IaC scripts;<br> * The repository has at least 2 core contributors;<br> * The repository has evidence of continuous integration practice, such as the presence of a .travis.yaml file;<br> * The repository has a comments ratio of at least 0.1%;<br> * The repository has commit frequency of at least 2 per month on average;<br> * The repository has an issue frequency of at least 0.01 events per month on average;<br> * The repository has evidence of a license, such as the presence of a LICENSE.md file<br> * The repository has at least 100 source lines of code.</p> <p>Metrics are grouped into three categories:</p> <p>* <strong>IaC-Oriented:</strong> metrics of structural properties derived from the source code of infrastructure scripts. Click [here](https://www.sciencedirect.com/science/article/pii/S0164121220301618) for more info.</p> <p>* <strong>Delta</strong>: metrics that capture the amount of change in a file between two successive releases, collected for each IaC-oriented metric.</p> <p>* <strong>Process</strong>: metrics that capture aspects of the development process rather than aspects about the code itself. Description of the process metrics in this dataset can be found [here](https://pydriller.readthedocs.io/en/latest/processmetrics.html).</p> <p>In addition to the metrics, the dataset contains the pre-trained models (*.joblib) in the folders rq1 and rq2 of projects.zip.</p> <p>You can load the model in Python as follows:</p> <p>```<br> from joblib import load<br> model = load('projects/owner/repository/rq1/random_forest.joblib'), mmap_mode='r')</p> <p>best_estimator = model['estimator'] # The estimator that maximized the AUC-PR</p> <p>cv_results = model['cv_results'] # The results of each step of the validation procedure</p> <p>best_index = mode['best_index'] # The index to access the best cv_results<br> ```</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p> </p> <p>This work is supported by the European Commission grants no. 825040 (RADON H2020).</p> <p><br> <strong>Inspiration</strong></p> <p>What source code properties and properties about the development process are good predictors of defects in Infrastructure-as-Code scripts?</p>
Database on Certified Reference Materials measured with PAT tools for validation and verification purposes
<p>The H2020 PAT4Nano project aims to develop and demonstrate Process Analytical Technologies (PAT) tools for nanosuspension characterization which have sufficiently high resolution, accuracy, and speed, for real-time industrial process monitoring and control. Real time monitoring is desired for example to obtain: small, high precision, specialty batch of materials, processing monitoring of nucleation/growth/milling of materials at different scales (lab, pilot, production), and for producing feedback loops (adapt T, pH, etc.,) needed for process control.<br> Laser diffraction (LD), Spatially Resolved Dynamic Light Scattering (SR-DLS), Cross-Correlation Dynamic Light Scattering (CC-DLS), Ultrasound Nanoparticle Sizer (UNPS), Raman, and Transmission Electron Microscopy (TEM) are the main PAT tools used in this project. For validation and verification purposes of these measurement techniques, polystyrene and silica samples (200 and 1000 nm particle size) were selected as (Certified) Reference Materials ((C))RMs) by the consortium partners. The results described in this database are particle size measurements using PAT methods in an offline mode. The particle size and particle size distribution data are presented as the D10, D50 and D90 and PDI/span measured with each PAT tool.<br> Raman spectra of the CRMs are presented as well. Here, particle size data was extracted by using chemometric software. Lastly, TEM images of the CRMs are included in the database to cross-correlate and cross-validate the results of the spectroscopic and scattering PAT tools.</p>
Decomposition Tool Validation Dataset 1
<p>This dataset provides artifacts, benchmarks and models used to evaluate the accuracy of the performance model as well as the corresponding results:</p> <ul> <li>Deployment package of the thumbnail generation function</li> <li>50 images collected to benchmark the actual deployment</li> <li>TOSCA model with the specified open workload and performance requirement</li> <li>Predictions given by the performance model and measurements taken from the cloud platform</li> </ul>
Catalytic Rules and Validation Results for "EzMechanism: An Automated Tool to Propose Catalytic Mechanisms of Enzyme Reactions"
<p>Dataset containing the "Rules of Enzyme Catalysis" as created during the development of EzMechanism and the validation results of the software. For more information see https://www.biorxiv.org/content/10.1101/2022.09.05.506575v1, and the M-CSA website in https://www.ebi.ac.uk/thornton-srv/m-csa/</p>
Data set associated to the publication "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology"
<p>Data set of the scientific publication entitled "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology":</p> <p>Seismological sensors</p> <p>Microphones</p> <p>Barometers</p> <p>Accelerometers</p> <p>Detailed test report.</p>
Datasets used in the Lab Validation of the RADON Verification Tool
<p>This repository contains the datasets that have been used to perform the lab validation of the RADON verification tool. In order to replicate the experiments, please unzip the file "validation-datasets.zip" and run the following command:</p> <pre><code class="language-bash">./run_all.rb {path_to_VT} list.json</code></pre> <p> </p>
Validation of Spectral Light Simulation Tools: Dataset of Simulated and Measured Indoor Light Exposure
<p>Since the discovery of a new photoreceptor in our eye, and with the growing awareness about the related ipRGC-influenced light (IIL) responses, design applications related to these responses are flourishing. Optimizing our ocular light exposure in buildings can have beneficial effects on our health, well-being, and performance through the action of this photoreceptor. To compare different design options and optimize the lighting conditions for building occupants, lighting simulations are typically used. However, as our IIL responses depend on various aspects of the light exposure including its spectral characteristics, spectral simulations are required. The dataset shared here was originally collected to validate two spectral simulation tools, <em>ALFA</em> and <em>Lark</em>, for the study of building design in relation to occupants’ IIL responses. The validation was done by comparing the simulation outputs against actual measurements, and assessing how reliable these tools were in predicting spectral irradiance under different indoor light conditions. Data were collected in two different experimental setups, one under daylight conditions only and the other one under electric light conditions only. The experimental protocol and README files contain detailed information on how the data was collected and what data was collected.</p>
Running the validation assistant tool on pesticide dossiers in IUCLID
<p>Before submitting a dossier, <strong>applicants</strong> are recommended to use the Validation Assistant tool to check the dossier is technically complete. It is important to resolve all validation assistant quality warnings as this will support the Admissibility Check of the RMS/EMS. The <strong>RMS/EMS</strong> should also run the validation assistant report before declaring the admissibility of a dossier to make sure the dossier is complete.</p> <p>If the report shows a business rule failure (anything starting with BR, e.g. BR_PPP_033) this will prevent the applicant from successfully submitting the dossier and therefore must be resolved.</p> <p>This demonstration is supporting RMS/EMS and applicants on how to run the validation assistant tool and extract the report. </p>
Decomposition Tool Validation Dataset 2
<p>This dataset provides artifacts, benchmarks and models used to evaluate the utility of the optimization approach as well as the corresponding results:</p> <ul> <li>Base TOSCA models for generating the considered model set</li> <li>TOSCA models obtained through the optimization approach and the naive one</li> <li>Analytics of the resulting models in terms of memory, concurrency and costs</li> </ul>
Dataset supporting publication: "Geofit: Experimental Investigations and Numerical Validation of Shallow Spiral Collectors as a Basis for Development of a Design Tool for Geothermal Retrofitting of Existing Buildings"
<p>Dataset supporting publication: “Geofit: Experimental Investigations and Numerical Validation of Shallow Spiral Collectors as a Basis for Development of a Design Tool for Geothermal Retrofitting of Existing Buildings” (publication available in <a href="https://zenodo.org/record/7273966#.Y2JgBnbMJPY">GEOFIT Zenodo</a>)</p> <p>The H2020 GEOFIT (grant no. 792210) project will implement and demonstrate easy-to-install and economical geothermal systems in combination with heat pumps for energy-efficient building retrofits at five pilot sites across Europe - a historic building (ITA), a school (ESP), an indoor swimming pool (IRL), an office building (FRA) and a single-family house (IRL) (GEOFIT,2018). Heat pump tests and experimental laboratory tests with shallow geothermal heat collector types are carried out in climate chambers at the AIT. Material data of different soil types are determined in the thermophysics laboratory. Furthermore, CFD simulations of the conducted experiments are calculated with ANSYS Fluent. All this provides data and know-how for the development of a design tool for ground collector configurations such as helices and slinky loops, which are particularly relevant for building retrofits in GEOFIT. Experimental work focused on near-surface spiral geothermal heat exchanger configurations that can be installed at a maximum depth of five metres. Real-scale experiments were carried out for vertically oriented spiral collectors (helix) in real soil. One objective was to develop a measurement concept in the laboratory environment to create the framework for a reliable database. This database is used as a basis for the further development or new development of engineering design tools. Distributed resistance temperature sensors and a fibre-optic temperature measurement system (DTS) were used. The moisture content of the soil was recorded using soil moisture sensors. A heat flow was conditioned by means of a helix shaped electric heating cable in a 1m³ cuboid soil container. The measurements were carried out in a climate chamber at a defined constant temperature of 10 °C. The evaluation of the transient response behaviour is spatially resolved. This results in coordinate-related temperature points, which describe temperature gradients in all axes of the container over time. Three different types of soil were investigated. The temperature behaviour of humus soil, sand and a mixture of these was investigated experimentally in smaller experiments and the material data such as heat capacity, thermal conductivity and density were determined thermophysically in the laboratory. Based on this data, a CFD model was developed which can be used to modify the geometry parameters of the helix.</p>
Observational data to validate an observational tool using inter-rater reliability
<p>The dataset includes a set of observations made from videos in The Observer XT software by 17 observers (16 novice observers and 1 expert observer) to validate an observation grid. The data made available are the observations, without the videos, and do not include any personal information.</p>
Supporting data and tool, for the paper "A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe"
<p>This 'Zenodo' contains supporting data and tools, for the paper 'A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe'.</p> <p>The repository includes the source code (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/DELTA_7.2.zip">DELTA_7.2.zip</a>) and the dataset (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/GMD_Vitali_et_all_data_20230516.zip">GMD_Vitali_et_all_data_20230516.zip</a>).</p>
Validation of the STarT Back Screening Tool in the Military
ClinicalTrials.gov study NCT03127826. IPD Sharing: YES. Countries: 1. Publications: 6.
R scripts for the practical exercises with QGIS in the book "Land Use Cover Datasets and Validation Tools"
<p>This dataset includes a series of R scripts required to carry out some of the practical exercises in the book “Land Use Cover Datasets and Validation Tools”, available in open access.</p> <p>The scripts have been designed within the context of the R Processing Provider, a plugin that integrates the R processing environment into QGIS. For all the information about how to use these scripts in QGIS, please refer to Chapter 1 of the book referred to above.</p> <p>The dataset includes 15 different scripts, which can implement the calculation of different metrics in QGIS:</p> <ul> <li>Change statistics such as absolute change, relative change and annual rate of change (Change_Statistics.rsx)</li> <li>Areal and spatial agreement metrics, either overall (Overall Areal Inconsistency.rsx, Overall Spatial Agreement.rsx, Overall Spatial Inconsistency.rsx) or per category (Individual Areal Inconsistency.rsx, Individual Spatial Agreement.rsx)</li> <li>The four components of change (gross gains, gross losses, net change and swap) proposed by Pontius Jr. (2004) (LUCCBudget.rsx)</li> <li>The intensity analysis proposed by Aldwaik and Pontius (2012) (Intensity_analysis.rsx)</li> <li>The Flow matrix proposed by Runfola and Pontius (2013) (Stable_change_flow_matrix.rsx, Flow_matrix_graf.rsx)</li> <li>Pearson and Spearman correlations (Correlation.rsx)</li> <li>The Receiver Operating Characteristic (ROC) (ROCAnalysis.rsx)</li> <li>The Goodness of Fit (GOF) calculated using the MapCurves method proposed by Hargrove et al. (2006) (MapCurves_raster.rsx, MapCurves_vector.rsx)</li> <li>The spatial distribution of overall, user and producer’s accuracies, obtained through Geographical Weighted Regression methods (Local accuracy assessment statistics.rsx).</li> </ul> <p>Descriptions of all these methods can be found in different chapters of the aforementioned book.</p> <p>The dataset also includes a readme file listing all the scripts provided, detailing their authors and the references on which their methods are based.</p>
Data for the practical exercises in the book "Land Use Cover Datasets and Validation Tools"
<p>This dataset contains all the data that is required to carry out the practical exercises in the book “Land Use Cover Datasets and Validation Tools”, available in open access.</p> <p>The dataset includes data for three different case studies: The Asturias Central Area (Spain), the Ariège Valley (France) and Marqués de Comillas (Mexico). For the Asturias Central Area and the Ariège Valley, the dataset includes Land Use Cover (LUC) maps for several years of reference as well as data (simulation outputs, model drivers) for different modelling exercises. For Marqués de Comillas, the dataset includes a LUC map and a set of reference points used to validate it.</p> <p>The dataset includes a readme file listing all the files it contains and auxiliary files describing the data. For further information on the study area and the files used in the practical exercises, users are referred to Chapter 1 of the book.</p>
Data from: Validation of Quality-of-Life assessment tool for Ethiopian old age people
<p><strong>Background</strong>: Reliable quality of life assessment is critical for identifying health issues, evaluating health interventions, and establishing the best health policies and care packages. The World Health Organization Quality of Life-Old Module is a tool for assessing the subjective quality of life in old age people. It's validated and available in more than 20 languages, except Amharic. Hence, this study was intended to translate it into Amharic language and validate it among old age people in Ethiopia.</p> <p><strong>Methods</strong>: A cross-sectional study was conducted among 180 community-dwelling old age people in Ethiopia, from January 16 to March 13, 2021. Psychometric validation was achieved through Cronbach's alpha of the internal consistency reliability test, and construct validity from confirmatory factor analysis.</p> <p><strong>Results</strong>: The study participants aged from 60 to 90 years old with a mean age of 69.44. Females made up 61.7% of the population, and 40% of them could not read and write. The results showed a relatively low level of quality of life, with the total transformed score of 58.58 ± 23.15. The Amharic version of the World Health Organization Quality of Life-Old Module showed a Cronbach's Alpha value of 0.96 and corrected item-total correlations of more than 0.74. Confirmatory factor analysis confirmed the six-factor model with a chi-square (X2) of 341.98 with a p-value less than 0.001. The comparative fit index (CFI) was 0.98, Tucker-Lewis's index (TCL) was 0.97, and the root mean square error of approximation (RMSEA) was 0.046.</p> <p><strong>Conclusion</strong>: The Amharic version of the World Health Organization Quality of Life-Old Module indicated good internal consistency reliability and construct validity. The tool can be utilized to provide care to Ethiopian community-dwelling old age people.</p>
BD_Delphi-validation_NbS-CoBAs-tool
<p>2 BBDD of the Delphi validation of tool for NbS psychosocial benefits assessment in urban regeneration processes (<em>NbS-CoBAs tool</em>): A panel of 10 experts in different fields (environmental and social psychology, urbanism, urban regeneration, and Nature-based Solutions) were involved in the validation process with Delphi. The two data bases are from the first and the second steps of the Delphi method.</p>
Dataset for the Indonesian Newly Validated Collaborative Practice Assessment Tool
<p>The dataset is used for the analysis of an article titled: A psychometric evaluation of the Indonesian version of the Collaborative Practice Assessment Tool (CPAT) for assessing interprofessional collaborative practice in health practitioners and students.</p>
Mini Nutritional Assessment tool validation project
<p><strong>Background</strong>: The health status of older people is usually overlooked in many low-income countries like Ethiopia. Appropriate nutritional assessment improves the health of old age people. The Mini Nutritional Assessment tool is a noninvasive and cheap practical evaluation tool that provides a simple and quick method of evaluating the nutritional status of old age people. The tool has multiple versions of confirmed validity in diverse languages, except in Amharic. Furthermore, the tool has still not been properly validated for Ethiopian old age people.</p> <p><strong>Objective</strong>: The purpose of this study was to translate the Mini Nutritional Assessment tool into the Amharic language and validate it among old age people in Bahir Dar City.</p> <p><strong>Methods</strong>: This tool translation and validation study followed normal COSMIN Study design and reporting guidelines. The study was conducted in three stepwise phases from January 16 to March 13, 2021. The first phase was reviewing and selecting nutritional assessment tools for old age people. In the second phase, the selected Mini Nutritional Assessment tool was translated and reviewed by experts. Using the heterogenous purposive sample, ten healthcare specialists with professional experience in the care of old age people were chosen for this step. The experts examined the face and content validity of the Amharic version of the instrument in two rounds of the Delphi technique. Finally, after incorporating the experts' suggestions and comments, a cross-sectional study was conducted among old age people for psychometric validation. The participant-to-variables ratio of the 10:1 rule of thumb was followed for the minimum sample size. Since the Mini Nutritional Assessment tool has 18 items, 180 community-dwelling old-age people were selected in multistage cluster sampling from Belay Zeleke, one of the sub-cities of Bahir Dar City. Principal component analysis was used to measure construct validity while Cronbach's alpha was employed to assess internal consistency reliability.</p> <p><strong>Results</strong>: As experts reviewed, all items in the translated tool are socially acceptable and have no taboo or sensitive words. The translated tool's content validity ratio was 0.93, and its scale validities (S-CVI/Ave and S-CVI/AU) were 0.97 and 0.83, respectively. Moreover, 180 community-dwelling old age people aged 60 to 90 years old participated in a psychometric evaluation study. Construct validity of the tool was confirmed with factor loadings ranging from 0.47 to 0.89 with a Cronbach's alpha of 0.65. The tool had a sensitivity and specificity of 97% and 83%, respectively.</p> <p><strong>Conclusion</strong>: The Amharic version of the Mini Nutritional Assessment tool showed good cross-cultural adaption, internal consistency reliability, and construct validity in Bahir Dar community-dwelling old age people. The tool can be used in regular care activities for aged people.</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.