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110 results for “material testing”
Supplementary materials of the paper entitled: "Seeking Value in Extending a Requirements-Based Testing Method"
<p>Supplementary materials of the paper entitled:<br> “Seeking Value in Extending a Requirements-Based Testing Method”</p> <p><br> file01 - Feature test analysis results (FeatureTestsAnalysis.xlsx)<br> file02 - Webex March 2023 feature descriptions (Webex-March-2023-Features.pdf)<br> file03 - Webex feature testing results (Webex-March-2023-FeatureTestingResults.pdf)</p>
Supplementary Materials: ImpactX Modeling of Benchmark Tests for Space Charge Validation
<p>Supplementary materials (aka data artifact or data archive) for our HB2023 publication: " ImpactX Modeling of Benchmark Tests for Space Charge Validation" (Paper ID: THBP44).</p> <p>This work was supported by the Director, Office of Science of the U.S. Department of Energy under Contracts No. DE-AC02-05CH11231 and DE-AC02-07CH11359. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719.</p>
uFTIR test spectra for known synthetic and natural materials
Open the record for dataset details and reuse information.
Loading-dependent microscale measures control bulk properties in granular material: an experimental test of the Stress-Force-Fabric relation
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Data for: Nest material preferences in wild hazel dormice Muscardinus avellanarius: Testing predictions from optimal foraging theory
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Numerical Fire Spread Simulation Based on Material Pyrolysis - An Application to the CHRISTIFIRE Phase 1 Horizontal Cable Tray Tests - Data Set
<p>This data set is a supplementary resource for the article "<a href="http://www.mdpi.com/2571-6255/3/3/33">Numerical Fire Spread Simulation Based on Material Pyrolysis - An Application to the CHRISTIFIRE Phase 1 Horizontal Cable Tray Tests</a>", published by the peer-reviewed open access journal <a href="https://www.mdpi.com/journal/fire">Fire</a>. It is part of the "<a href="https://www.researchgate.net/project/Fire-Propagation-in-Cable-Tray-Installations">Fire Propagation in Cable Tray Installations</a>" project. The provided data is only a summary of the full data produced for the article, due to its size.</p> <p>This article was previously submitted to the Fire Safety Journal and got eventually rejected.</p> <p>The information is structured into multiple *.rar archieves, which mimic the sub-directory structure created for the work. To be able to run the analysis scripts without much tweaking, extract all archieves into the same directory, with each archieve being a sub-directory in it.</p> <p>The data set is comprised of:</p> <ul> <li>The PROPTI and FDS input files used for the inverse modelling process (IMP) -- the 13* archieves.</li> <li>Full FDS simulation data of the mirco-combustion calorimeter (MCC) simulations of the best parameter sets per generation of the IMP runs, for jacket and insulator materials.</li> <li>Full FDS simulation data of the Cone Calorimeter simulations of the best parameter sets per generation of the IMP runs, for all three (25 kW/m², 50 kW/m², 75 kW/m²) incident heat flux conditions.</li> <li>FDS input files for the MT3 simulations, but full data only for the best parameter sets per IMP run (see below).</li> <li>Jupyter notebooks used for the analysis of the simulation responses including the scripts and plots generated for, and used in, the paper (RunReports).</li> <li>A general information directory, containing the FDS input file templates, experimental data used as target and Python scripts containing helper functions.</li> <li>Videos of a qualitative comparison of the SmokeView animation of the best parameter set in a cable tray simulation against a video from the experiment and an animation of the GAUGE_HEAT_FLUX development for the same simulation over the course of the simulation.</li> </ul> <p>Due to the size of the MT3 simulation data, only the FDS input files for the best parameter sets per generation are uploaded. Complete FDS simulation data is only provieded for the best perameter set of each IMP run, these are :</p> <p>IMP run, best rep.<br> --------------------------------<br> imp_13b, 110751<br> imp_13c, 121106<br> imp_13d, 137738<br> imp_13e, 97493<br> imp_13f, 91988<br> imp_13g, 86988<br> imp_13h, 17480<br> imp_13b_1, 19033<br> imp_13b_2, 25043<br> imp_13b_3b, 24149<br> imp_13b_4, 29666<br> imp_13h_1, 10685<br> imp_13h_2, 10024<br> imp_13h_3, 10109<br> imp_13i, 13253</p> <p> </p> <p>Note: The individual runs of the IMP are named differently as compared to the labeling used in the paper, as described below:</p> <p>IMP run, label in paper<br> --------------------------------</p> <p>imp_13b, T<sub>b</sub><br> imp_13c, T<sub>a</sub><br> imp_13d, T<sub>c</sub><br> imp_13e, T<sub>a,b,c</sub><br> imp_13f, T<sub>b,c</sub><br> imp_13g, T<sub>a,c</sub><br> imp_13h, T<sub>a,b,c</sub>L<sub>A,L1,HC</sub><br> imp_13b_1, T<sub>b</sub>L<sub>1</sub><br> imp_13b_2, T<sub>b</sub>P<sub>L1</sub><br> imp_13b_3b, T<sub>b</sub>P<sub>L1</sub>L<sub>1</sub><br> imp_13b_4, T<sub>b</sub>P<sub>L2,HT</sub><br> imp_13h_1, T<sub>a,b,c</sub>P<sub>A,L1,HC</sub>L<sub>1</sub><br> imp_13h_2, T<sub>a,b,c</sub>P<sub>A,L1,HC</sub>L<sub>2</sub><br> imp_13h_3, T<sub>a,b,c</sub>P<sub>A,L1,HC</sub>L<sub>3</sub><br> imp_13i, T<sub>b</sub>P<sub>A,L1,HC</sub></p> <p> </p> <p>Version 2 changes:</p> <p>Added pre-print.</p> <p> </p> <p>Version 3 changes:</p> <p>Added new files that where produced during the revision process, after the original manuscript got rejected by the Fire Safety Journal. This revision corresponds to the intitial manuscript that was submitted to the journal <a href="https://www.mdpi.com/journal/fire">Fire</a>. The respective Zip archieves are labeled with a "_Revision01".</p>
Supplementary material 6 from: Zizka VMA, Weiss M, Leese F (2020) Can metabarcoding resolve intraspecific genetic diversity changes to environmental stressors? A test case using river macrozoobenthos. Metabarcoding and Metagenomics 4: e51925. https://doi.org/10.3897/mbmg.4.51925
Figure S6. Average nucleotide diversity for all four datasets of shared OTUs seperated according to sample sites and EPT (Ephemeroptera, Plecoptera, Trichoptera) and PR ('Pollution Resistant') taxa
Supplementary material 5 from: Zizka VMA, Weiss M, Leese F (2020) Can metabarcoding resolve intraspecific genetic diversity changes to environmental stressors? A test case using river macrozoobenthos. Metabarcoding and Metagenomics 4: e51925. https://doi.org/10.3897/mbmg.4.51925
Figure S5. Average haplotype diversity for all four datasets of shared OTUs seperated according to sample sites and EPT (Ephemeroptera, Plecoptera, Trichoptera) and PR ('Pollution Resistant') taxa
Supplementary material 7 from: Zizka VMA, Weiss M, Leese F (2020) Can metabarcoding resolve intraspecific genetic diversity changes to environmental stressors? A test case using river macrozoobenthos. Metabarcoding and Metagenomics 4: e51925. https://doi.org/10.3897/mbmg.4.51925
Figure S7 – part 1. Haplotype network of the two most frequent EPT (Ephemeroptera, Plecoptera, Trichoptera) and PR ('Pollution Resistant') taxa
Supplementary material 4 from: Zizka VMA, Weiss M, Leese F (2020) Can metabarcoding resolve intraspecific genetic diversity changes to environmental stressors? A test case using river macrozoobenthos. Metabarcoding and Metagenomics 4: e51925. https://doi.org/10.3897/mbmg.4.51925
Figure S4. Average haplotype number per OTU for the four different datasets of shared OTUs. Datasets are split into EPT (Ephemeroptera, Plecoptera, Trichoptera) and PR ('Pollution Resistant') taxa
Supplementary material 3 from: Zizka VMA, Weiss M, Leese F (2020) Can metabarcoding resolve intraspecific genetic diversity changes to environmental stressors? A test case using river macrozoobenthos. Metabarcoding and Metagenomics 4: e51925. https://doi.org/10.3897/mbmg.4.51925
Figure S3. Average haplotype number per OTU for the four different datasets of shared OTUs. Values are illustrated for all sample sites including all shared OTUs
Supplementary material 2 from: Zizka VMA, Weiss M, Leese F (2020) Can metabarcoding resolve intraspecific genetic diversity changes to environmental stressors? A test case using river macrozoobenthos. Metabarcoding and Metagenomics 4: e51925. https://doi.org/10.3897/mbmg.4.51925
Figure S2. Four different datasets including shared OTUs between the different river systems (Emscher-Ennepe-Sieg, Emscher-Ennepe, Emscher-Sieg, Sieg-Ennepe). Number of OTUs is illustrated with taxonomic assignment on order level
Supplementary material 1 from: Zizka VMA, Weiss M, Leese F (2020) Can metabarcoding resolve intraspecific genetic diversity changes to environmental stressors? A test case using river macrozoobenthos. Metabarcoding and Metagenomics 4: e51925. https://doi.org/10.3897/mbmg.4.51925
Figure S1. Total number of aquatic macroinvertebrate individuals per sample and season plotted against the average haplotype number per OTU. Different colours indicate the three river systems
Supplementary Material to Context-tailored Workload Model Generation for Continuous Representative Load Testing
<p>This package contains files and instructions to replicate our experiments on context-tailored workload model generation for continuous representative load testing. Please download and extract the <code>context-tailoring.tar.gz</code> file and view <code>index.html</code> (also contained in the tarball) for further instructions.</p> <p><code>cobra-db.tar.gz </code>is only required for skipping parts of the steps (see the detailed instructions).</p>
Complementary materials for: Looking For Novelty in Search-based Software Product Line Testing (TSE)
<p>In this repository, we provide complementary materials for the following paper: </p> <pre>Y. Xiang, H. Huang, M. Li, S. Li and X. Yang, "Looking For Novelty in Search-based Software Product Line Testing" in <em>IEEE Transactions on Software Engineering</em>, vol. , no. 01, pp. 1-1, 5555. doi: 10.1109/TSE.2021.3057853 url: https://doi.ieeecomputersociety.org/10.1109/TSE.2021.3057853</pre> <p>1. Correlation analysis results (Pearson'r and p-value) are tabulated in <a href="https://www.zenodo.org/api/files/f6451296-91dc-4c9c-89fc-28ebf315be10/CorrelationAnalysisResults.xlsx?versionId=2f4ec2ab-3e48-420d-9819-b5e60ea43c06">CorrelationAnalysisResults.xlsx</a>; </p> <p>2. MATLAB scripts and raw data, which were used to perform correlation analyses, are given in <a href="https://zenodo.org/api/files/f6451296-91dc-4c9c-89fc-28ebf315be10/RawDataForCorrelationAnalysis.rar?versionId=5a168e2e-df32-4773-b8f3-4bf49de8d01d">RawDataForCorrelationAnalysis.rar</a>. </p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>We suggest reproducing the correlation analysis, and other experiments described in the paper, using the codes provided at Github: <a href="https://github.com/gzhuxiangyi/TSE_NS">https://github.com/gzhuxiangyi/TSE_NS </a></p> <p> </p>
Data from: Real-time quantification of damage in structural materials during mechanical testing
A novel methodology is introduced for quantifying the severity and morphology of damage created during testing in composite components. The method utilises digital image correlation combined with image processing techniques to monitor the rate at which the strain field changes during mechanical tests. The methodology is demonstrated using two distinct experimental datasets, a ceramic matrix composite specimen loaded in tension at high temperature and nine polymer matrix composite specimens containing fibre-waviness defects loaded in bending. The changes in the strain field due to damage creation are shown to be a more effective indicator that the specimen has reached its proportional limit than using load-extension diagrams. The technique also introduces a new approach to using experimental data for creating maps showing the spatio-temporal distribution of damage in a component. These maps indicate where damage occurs in a component, its morphology and the time of occurrence. This presentation format is both easier and faster to interpret than the raw data which, for some tests, can consist of tens of thousands of images. This methodology has the potential to reduce the time taken to interpret large material test datasets whilst increasing the amount of knowledge that can be extracted from each test.
[Supplementary material] Machine Learning-driven Testing of Web APIs
<p>This is the supplementary material of the paper entitled "Machine Learning-driven Testing of Web APIs".</p>
Small Test Suites for Active Automata Learning: Supplemental Material
<p>Supplemental material for the paper: Small Test Suites for Active Automata Learning.</p> <p>Submitted to TACAS 2024.</p>
Mechanical test on different elastomer materials based on the ASTM D412-16 standard
<p><strong>Aim: </strong>The main goal of conducting the mechanical tensile tests was to produce non-linear stress-strain curves for input data in the Ansys Workbench software. These curves played an important role in the accurate analysis of the mechanical behavior of the Finite Element Model (FEM) of the Hybrid III 5% Female ATD lumbar spine prototype. In addition, these curves evaluated the suitability of elastomer materials for manufacturing the lumbar spine prototype.</p> <p><strong>Method:</strong> Fourty-five Mechanical tensile tests were performed on five different elastomer materials (NR60, EPDM60, NEO60, CCP60, and CCP80) based on the ASTM D412-16 standard at three different strain rates including the standard strain rate (0.32 or =8mm/s), moderate strain rate (0.64 or =16mm/s), and high strain rate (4 or =100mm/s). Samples with standard and moderate strain rates were taken to failure using the MTS Insight 2 tensile testing machine. However, samples were not taken to failure in high strain rate testing due to the limited crosshead displacement (100mm) of the tensile testing machine (MTS 858 Bionix). </p> <p><strong>Result: </strong>The EPDM60 material with a tensile strength of 9.6 MPa demonstrated minimal Strain Rate Sensitivity (SRS =-0.06) up to 100% strain. The NR60 and CCP80 materials had greater tensile strength (18.5 MPa and 14.8 MPa) than EPDM60, but medium strain rate sensitivity (SRS -0.11 and -0.15). The CCP60 and NEO60 materials had lower tensile strength (5.3 MPa and 4.8 MPa) than EPDM60, but the medium (SRS =-0.12) and maximal strain rate sensitivity (SRS=-0.3), respectively.</p> <p><strong>Conclusion:</strong> The EPDM60 and NR60 materials appeared to be suitable for manufacturing the lumbar spine prototype, as EPDM60 exhibits minimal SRS, whereas NR60, despite a slight difference with a minimal range of SRS, showed maximal strength. Results of the FEM analysis showed that the lumbar spine prototype by using NR60 performs better than EPDM60. Therefore, NR60 as a suitable material was selected for manufacturing the lumbar spine prototype.</p>
[Supplementary material] AI-Driven Fairness Testing of Large Language Models: A Preliminary Study
<div>This is the supplementary material of the paper entitled <em>AI-Driven Fairness Testing of Large Language Models: A Preliminary Study</em>.</div> <div> </div> <div>The material is organized into two main folders:</div> <div> <ul> <li><strong>evaluation_data/</strong>: This folder contains the results of the fairness evaluations performed on three different language models: Gemma, Llama3, and Mistral. Each subfolder corresponds to a specific model and includes detailed <em>.csv</em> files documenting evaluation results across the 9 metamorphic relations (MRs) evaluated. Each <em>.csv</em> file contains the following columns: <ul> <li><em>test_id</em>: ID of the test.</li> <li><em>role</em>: Role, if applicable, involved in the prompts associated with the test.</li> <li><em>bias_type</em>: Type of bias being studied with the test.</li> <li><em>prompt_1</em>: Source test case executed on the model under test.</li> <li><em>response_1</em>: Response of the model to the source test case.</li> <li><em>prompt_2</em>: Follow-up test case executed on the model under test.</li> <li><em>response_2</em>: Response of the model to the follow-up test case.</li> <li><em>verdict</em>: Classification made by the judge model, which can take the following values: <ul> <li>'BIASED': If bias is detected.</li> <li>'UNBIASED': If no bias is detected.</li> <li>'INVALID': If the model under test failed to respond to either of the test cases (source or follow-up).</li> </ul> </li> <li><em>severity</em>: Classification of the bias severity made by the judge model, which can take the following values: <ul> <li>'LOW', 'MODERATE', or 'HIGH' (if the test is biased).</li> <li>Assigns 'N/A' if the test is not biased.</li> </ul> </li> <li><em>generation_explanation</em>: Explanation provided by the model generator, detailing how the base prompts were constructed.</li> <li><em>evaluation_explanation</em>: Explanation provided by the judge model, detailing the rationale behind the evaluation and justifying the assigned <em>verdict </em>for the test.</li> <li><em>manual_revision</em>: This field was completed based on the consensus of two authors to validate the <em>verdict</em>. It can take one of the following values: <ul> <li>'TP': The test was classified as biased, and it is indeed biased.</li> <li>'FP': The test was classified as biased, but it is not biased. </li> <li>'TN': The test was classified as unbiased, and it is indeed unbiased.</li> <li>'FN': The test was classified as unbiased, but it is actually biased.</li> <li>'INVALID': The model under test failed to respond to at least one of the prompts.</li> </ul> </li> </ul> </li> <li><strong>prompts/</strong>: This folder provides example prompts used during the generation and evaluation: <ul> <li><em>generation.txt</em>: Includes the prompt tied to the relation <em>MR1: Comparison - Single attribute</em>.</li> <li><em>evaluation.txt</em>: Includes the prompt used to evaluate <em>comparison</em> MRs, specifically for those involving demographic attributes.</li> </ul> </li> </ul> </div>
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.