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1,956 results for “test data”
Example Survey and Collection Survey Data for "A Uniqueness-based Approach to Provide Descriptive JUnit Test Names"
<p>Example Survey and Collection Survey Data for "A Uniqueness-based Approach to Provide Descriptive JUnit Test Names"</p>
Test data for GToTree
<p>Test data for GToTree (https://github.com/AstrobioMike/GToTree/wiki)</p>
Test Data set
<p>Test data set for flow field</p>
Fracture caging test data acrylic block tests
<p>Fracture caging test data - acrylic block tests</p>
IBF test data
<p>placeholder</p>
Refactoring Assertion Roulette and Duplicate Assert test smells: a controlled experiment [DATA]
<p>Data of the experiment with the RAIDE tool.</p>
Data from: Individual contribution to niche expansion in amphibians: A test of the niche variation hypothesis
<p>The niche variation hypothesis (NVH) suggests that populations released from competition are able to expand their realized ecological niche (Van valen 1965; Bolnick et al. 2007). This increase in total niche width (TNW) can arise by i) increasing heterogeneity among individuals' niche through individual specialization, i.e. inter-individual variation in resource use, (IS – Bolnick et al., 2002; 2003) occurring when different individuals of a population use a subset of the population's resource pool, or ii) by expanding the niche of all individuals within the population (Bolnick et al. 2007). Although several morphological and phenotypical studies have confuted the NVH (e.g. Meiri et al. 2005), there is generally strong support for this hypothesis when behavioural or ecological traits are considered, and in particular when the trophic niche is measured at the individual level (Bolnick et al. 2007; Maldonado et al., 2017). In this study our primary aim is to corroborate the NVH, providing robust evidence for a significant and positive relationship between population TNW and IS in amphibian populations, at a global scale.</p>
Data for: The biomechanics of tooth strength: testing the utility of simple models for predicting fracture in geometrically complex teeth
<p>Teeth must fracture foods while avoiding being fractured themselves. This study evaluated dome biomechanical models used to describe tooth strength. Finite element analysis (FEA) tested whether the predictions of the dome models applied to the complex geometry of an actual tooth. A finite element model (FEM) was built from microCT scans of a human M3. The FEA included three loading regimes simulating contact between 1) a hard object and a single cusp tip, 2) a hard object and all major cusp tips, and 3) a soft object and the entire occlusal basin. Our results corroborate the dome models with respect to the distribution and orientation of tensile stresses, but document heterogeneity of stress orientation across the lateral enamel. This implies that high stresses might not cause fractures to fully propagate between cusp tip and cervix under certain loading conditions. The crown is most at risk of failing during hard object biting on a single cusp. Geometrically simple biomechanical models are valuable tools for understanding tooth function but do not fully capture aspects of biomechanical performance in actual teeth whose complex geometries may reflect adaptations for strength.</p>
Annotated data for testing the High throughput Oligogenic Prioritizer (HOP)
<p>This repository contains the annotated OLIDA and 1KGP patients data that is necessary to reproduce the results of the HOP predictor presented in the article "Prioritization of oligogenic variant combinations in whole exomes". <br> <br> The archive contains 102 files divided in two folders: </p> <ul> <li>olida_annotated_data contains 2 json files with the training set combinations and the testing set combinations respectively, annotated with the required features for predictions.</li> <li>patients_annotated_data contains 100 files, one for each 1KGP individual that was used to test the predictor, each annotated with the required features for predictions. </li> </ul> <p>These datasets are meant to be used with the corresponding scripts provided on github: https://github.com/oligogenic/HOP</p>
test data zendo
<p>test data</p>
Supplementary Materials and Raw Data for "Automated Test Suite Generation for Software Product Lines based on Quality-Diversity Optimisation"
<p>Supplementary Materials and Raw Data for "Automated Test Suite Generation for Software Product Lines based on Quality-Diversity Optimisation"</p> <p>1. OnlineSupplement.pdf------Online supplementary data for the paper</p> <p>2. RQ1-4.rar----Raw data for the paper</p> <p>3. Runtime to generate, optimise and execution test suites.xlsx -----Data used in Section 7 PRACTICAL IMPLICATIONS</p> <p>Source code of the algorithms used to produce these data can be found at GitHub https://github.com/gzhuxiangyi/SPLTestingMAP</p>
Data for training and testing the PIO-Net.
<p>Data for training and testing the PIO-Net proposed in physics-informed deep operator learning based on reduced-order modelling for retrieving the ocean interior density from the surface</p>
Test Dataset for MapSeq Data Manager Galaxy Wrapper
<p>Test Dataset used to test the Data Manager for the MapSeq Galaxy Wrapper</p>
Empirical Study Data for Test Program-Based Generative Fuzzing for Differential Testing of the Kotlin Compiler
<p>Empirical Study Dataset for MSc. Thesis titled "Test Program-Based Generative Fuzzing for Differential Testing of the Kotlin Compiler". The data contains automatically generated Kotlin files, the results of differentially testing the generated files, and aggregated data containing file information, and compilation features.</p>
HNSCC PyDicer Test Data
<p>TCIA HNSCC data which has been converted using PyDicer for use in testing.</p> <p>Citations & Data Usage Policy </p> <p>Users of this data must abide by the <a href="https://wiki.cancerimagingarchive.net/x/c4hF">TCIA Data Usage Policy</a> and the <a href="https://creativecommons.org/licenses/by/3.0/">Creative Commons Attribution 3.0 Unported License</a> under which it has been published. Attribution should include references to the following citations:</p> <p>Data Citation</p> <p>Grossberg A, Elhalawani H, Mohamed A, Mulder S, Williams B, White AL, Zafereo J, Wong AJ, Berends JE, AboHashem S, Aymard JM, Kanwar A, Perni S, Rock CD, Chamchod S, Kantor M, Browne T, Hutcheson K, Gunn GB, Frank SJ, Rosenthal DI, Garden AS, Fuller CD, M.D. Anderson Cancer Center Head and Neck Quantitative Imaging Working Group. (2020) <strong>HNSCC [ Dataset ]</strong>. The Cancer Imaging Archive. DOI: <a href="https://doi.org/10.7937/k9/tcia.2020.a8sh-7363">https://doi.org/10.7937/k9/tcia.2020.a8sh-7363</a></p> <p><strong>In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research:</strong></p> <p>Publication Citation</p> <p>Grossberg A, Mohamed A, Elhalawani H, Bennett W, Smith K, Nolan T, Williams B, Chamchod S, Heukelom J, Kantor M, Browne T, Hutcheson K, Gunn G, Garden A, Morrison W, Frank S, R osenthal D, Freymann J, Fuller C. (2018) <strong>Imaging and Clinical Data Archive for Head and Neck Squamous Cell Carcinoma Patients Treated with Radiotherapy</strong>. <em>Scientific Data</em> 5 :180173 (2018) DOI: <a href="https://doi.org/10.1038/sdata.2018.173">10.1038/sdata.2018.173</a></p> <p>Publication Citation</p> <p>Elhalawani, H., Mohamed, A., White, A. <em>et al.</em> <strong>Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges</strong>. <em>Sci Data</em> <strong>4, </strong>170077 (2017). DOI: <a href="https://doi.org/10.1038/sdata.2017.77">10.1038/sdata.2017.77</a></p> <p>TCIA Citation</p> <p>Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. <strong>The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository</strong>, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: <a href="https://doi.org/10.1007/s10278-013-9622-7">10.1007/s10278-013-9622-7</a></p>
The Data and Codes for Training, Testing, and Prognostic Validation of A ResNet Ensemble for Moist Physics (ResCu-en)
<p>Note: the monthly averaged NCAM, SPCAM, and CAM5 results are uploaded as *_h0.tar.gz!</p> <p>The data and codes for Training, Testing, and Prognostic Validation of A ResNet Ensemble for Moist Physics (ResCu-en) are stored in this repositary.</p> <p>This project is built on python3.7 and tensorflow-gpu2.3.0, and the scripts for analysis and plots are on jupyter-notebook.</p> <p>Please make sure to install all python packages used in an environment.</p> <p>Please read the ReadME-2.txt.</p> <p>For the entire training and testing datasets in both the baseline and +4K SST climates. Please download them from Dryad (<a href="https://doi.org/10.6075/J0CZ35PP">https://doi.org/10.6075/J0CZ35PP</a> and https://doi.org/10.6075/J03J3BGF), Onedrive (https://1drv.ms/u/s!ArKTPPs6U_9DjxPJeSReKlbsLzyh?e=PDlWYJ), and Dropbox (https://www.dropbox.com/s/yc4fx35laqwt0fu/SPCAM_ML_4K.tar.gz?dl=0 and https://www.dropbox.com/s/4pxahzwt9v55u2m/SPCAM_ML_RAD.tar.gz?dl=0).</p>
MOOC Post test Data
<p>Co authors from across institutes in Pakistan</p>
Test data for Cactus on Galaxy
<p>Test datasets needed for the Galaxy wrapper for Cactus (https://github.com/ComparativeGenomicsToolkit/cactus/)</p>
Raw Starlink/OneWeb monitoring data from Altrincham UK for March 2023 - Test for IAU 385
<p>This zip file contains data for one calendar month of low level scan data (March 2023) from a home-based monitoring station in Altrincham, UK. This is a test dataset to be presented at the IAU Symposium 385: "Astronomy and Satellite Constellations: Pathways Forward", to see if it is useful.</p> <p>2GHz band downlink band data from a HackRF and beacon monitoring at 11.325 GHz via a Kerberos SDR</p> <p>Daily TLE files and 10 minute scan plots</p> <p>Readme file on data formats and script used to make the 10 minute plots</p>
Cyclic test data
<p>Experimental data cyclic tests Colombian electrowelded wire mesh</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.