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.
110
datasets available to search
ShareScore release 0.9.0
Dataset results
110 results for “material testing”
Data and supplementary material for the paper "Development of an IntelliCage based Cognitive Bias Test for Mice."
<p>All raw data, R scripts for analysis and the supplementary material related to the paper "Development of an IntelliCage based Cognitive Bias Test for Mice." are available to the scientific public here. A preprint version of the paper will be published on bioRxiv. </p> <p>Version 2 contains R scripts, version 1 does not.</p> <p>Version 3: For better clarity, the data were saved as .ods files. Each .ods file contains the data for one developmental step. The R scripts are still available as .txt files. In addition, an ARRIVE checklist was added.</p> <p>Version 4: xlsx instead of ods fiels</p> <p>Version 5: Extended supplemet PDF file<br> Added GroupTwo_Entries and GroupThree_Entries txt files</p> <p>A pre-print version can be found at bioRxiv: https://www.biorxiv.org/content/10.1101/2022.10.19.512853v1 </p>
Supplementary materials of "Calibrated weighted permutation test detects ancient language connections in the Circumpolar area (Chukotian-Nivkh and Yukaghir-Samoyedic)"
<p>Supplementary materials of the paper: Alexei S. Kassian, George Starostin, Mikhail Zhivlov, Sergey A. Spirin. Calibrated weighted permutation test detects ancient language connections in the Circumpolar area (Chukotian-Nivkh and Yukaghir-Samoyedic). <em>Journal of Historical Linguistics</em>, 2024, <a href="https://doi.org/10.1075/jhl.00014.kas">https://doi.org/10.1075/jhl.00014.kas</a>. </p> <p>The Supplement includes the datasets, descriptions, comments and all technical files which makes our linguistic experiment fully reproducible:</p> <ul> <li>Supplement text (pdf): 1. Consonant classes; 2. Weighted permutation test; 3. Negative control and calibration of permutation test outcomes; 4. Pairwise comparison between Yeniseian, Burushaski and all the Na-Dene wordlists; 5. CC-matches in statistically significant pairs (Chukotian-Itelmen, Nivkh, Samoyed, Yukaghir, Yeniseian, Na-Dene, Burushaski); 6. Basic information on the language families and groups; 7. Linguistic comments on individual Swadesh forms; 8. Transcription system.</li> <li>Supplement tables (xls): 1. Stability of the Swadesh concepts; 2. 110-item wordlists for the languages of the Circumpolar area; 3. Weighted permutation test for the negative control group; 4. Consonant classes.</li> <li>Technical files of the permutation test: 1. Circumpolar languages; 2. Negative control group (incl. dataset); 3. Positive control group; 4. Yeniseian-Dene-Burushaski hypothesis</li> </ul> <p>The software package is available at <a href="https://github.com/dmkrylov/starling-permutation-test">https://github.com/dmkrylov/starling-permutation-test</a></p>
Supplementary material and supplementary data files for: Handling logical character dependency in phylogenetic inference: Extensive performance testing of assumptions and solutions using simulated and empirical data
Open the record for dataset details and reuse information.
Test materials for the Adaptive Melodic Discrimination Test
<pre>This repository contains stimuli and psychometric data for the adaptive melodic discrimination test constructed in Harrison et al. (2017). These materials can be used to construct a computerised adaptive melodic discrimination test. An implementation of the test is available from the authors upon request. This version (v5) corresponds to the final version of the test constructed in Harrison et al. (2017); it includes the easier items added to the item bank in Study 3. It also contains a corrected melodic_discrimination_psych_data.csv file; the previous version was incomplete. These test materials may be freely used for research purposes. Please cite Harrison et al. (2017) in any publications using these materials (see below for full citation info). ==Reference== Harrison, P. M. C., Collins, T., & Müllensiefen, D. (2017). Applying modern psychometric techniques to melodic discrimination testing: Item response theory, computerised adaptive testing, and automatic item generation. Scientific Reports, 7, 1–18. https://doi.org/10.1038/s41598-017-03586-z</pre>
Raw data and supplementary material of the fifth inter-laboratory testing program (RRT5, self-healing concrete with macrocapsules) of the EU COST action SARCOS
<p>This data is the result of a collaboration of scientists working on the development of self-healing concrete within the framework of the European Cooperation in Science and Technology (COST) Action “Self-healing as preventive repair of concrete structures” SARCOS CA15202.</p> <p>In the framework of SARCOS 6 inter-laboratory testing programs are being executed to investigate possible standard test methods for self-healing concrete, each of the testing programs focusing on a different self-healing technique:<br> (1) Concrete with mineral additions,<br> (2) Concrete with the addition of magnesium oxide,<br> (3) Concrete enhanced with crystalline admixtures,<br> (4) High performance fibre reinforced concrete enhanced with crystalline admixtures,<br> (5) Concrete with preplaced macrocapsules containing polymeric healing agent, and<br> (6) Concrete with encapsulated bacteria.</p> <p>The data which can be found here have been obtained in the inter-laboratory testing program 5 "Concrete with preplaced macrocapsules containing polymeric healing agent". In total 6 labs participated in this testing program: Ghent University, Politecnico di Torino, Riga Technical University, Cracow University of Technology, Cambridge University, and KU Leuven (Ghent Technology Campus). All specimens were cast at Ghent University and were then distributed to the different labs, where they were tested.</p> <p>The testing program consisted of tests on both concrete and mortar specimens. The reinforced concrete specimens were cracked in a displacement-controlled three-point bending setup. Subsequently, they were subjected to two capillary water absorption tests, each with a different waterproofing technique. The mortar specimens were not reinforced, instead they were provided with a Carbon Fibre Reinforced Polymer (CFRP) laminate at the top. They were cracked in a force-controlled three-point bending setup, and immediately an active crack width control technique was applied to restrain the crack width of the specimens to a desired crack width range. After measuring of the crack width, the water permeability of the mortar specimens was accessed in a water flow test. In the end, the specimens were cracked open to assess the spread of the polyurethane healing agent.</p>
Supplementary materials for article focused on traffic information enrichment: Animation and complete overview of all road segments on which basis summary test results were calculated
<p><strong>Supplementary materials </strong>(Appendix A and B) for the article:</p> <p>Traffic Information Enrichment: Creating Long-Term Traffic Speed Prediction Ensemble Model for Better Navigation through Waypoints</p> <p><em>Abstract: </em>Traffic speed prediction for a selected road segment from a short-term and long-term perspective is among the fundamental issues of intelligent transportation systems (ITS). During the course of the past two decades, many artefacts (e.g., models) have been designed dealing with traffic speed prediction. However, no satisfactory solution has been found for the issue of a long-term prediction for days and weeks using the vast spatial and temporal data. This article aims to introduce a long-term traffic speed prediction ensemble model using country-scale historic traffic data from 37,002 km of roads, which constitutes 66% of all roads in the Czech Republic. The designed model comprises three submodels and combines parametric and nonparametric approaches in order to acquire a good-quality prediction that can enrich available real-time traffic information. Furthermore, the model is set into a conceptual design which expects its usage for the improvement of navigation through waypoints (e.g., delivery service, goods distribution, police patrol) and the estimated arrival time. The model validation is carried out using the same network of roads, and the model predicts traffic speed in the period of 1 week. According to the performed validation of average speed prediction at a given hour, it can be stated that the designed model achieves good results, with mean absolute error of 4.67 km/h. The achieved results indicate that the designed solution can effectively predict the long-term speed information using large-scale spatial and temporal data, and that this solution is suitable for use in ITS.</p> <p>Simunek, M., & Smutny, Z. (2021). Traffic Information Enrichment: Creating Long-Term Traffic Speed Prediction Ensemble Model for Better Navigation through Waypoints. <em>Applied Sciences</em>, 11(1), 315. <a href="https://doi.org/10.3390/app11010315">https://doi.org/10.3390/app11010315</a></p> <p> </p> <p><strong>Appendix A</strong><br> Examples of the deviation between the average speed and the FreeFlowSpeed for selected hours.</p> <p> </p> <p><strong>Appendix B</strong><br> The text file provides a complete overview of all road segments on which basis summary test results were calculated in Section 6 of the article.</p>
Supplementary material 3: List of tested and analyzed data sharing tools (non-exhaustive) from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
List of tested and analyzed data sharing tools (non-exhaustive)
Supplementary materials for "Testing the goodness of the EVS gender role attitudes scale"
<p>The files include supplementary results for background analyses performed for the manuscript "Testing the goodness of the EVS gender role attitudes scale" submitted to the journal <em>Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique</em>. One file reports the result from exploratory factor analyses run on the EVS 2008 scale with 7 and 8 items; the other one shows intermediate results from the stepwise regression models performed on the same scale from EVS 2008 and EVS 1999.</p> <p>Lomazzi, V. (2017). Testing the Goodness of the EVS Gender Role Attitudes Scale. <em>Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique</em>, <em>Forthcoming</em>. https://doi.org/10.1177/0759106317710859</p>
[Supplementary material] Test Oracle Generation for REST APIs
<p>This is the supplementary material of the paper entitled <em>Test Oracle Generation for REST APIs</em></p> <p>To use the most up-to-date version of AGORA+, please refer to the official GitHub repository: https://github.com/isa-group/Beet</p> <p>The Ubuntu Virtual Machine containing all the projects configured can be found here: https://figshare.com/articles/dataset/AGORA_Virtual_Machine/28266926</p> <p> </p> <h1>How to Open the Replication Package (Multi-part ZIP Archive)</h1> <p><span lang="EN-US">The replication package is distributed as a multi-part ZIP archive. This is a standard way to split a large ZIP file into smaller chunks for easier uploading and downloading. The archive consists of the following files:</span></p> <ul> <li><span lang="EN-US">ReplicationPackage.z01 </span></li> <li><span lang="EN-US">ReplicationPackage.z02</span></li> <li><span lang="EN-US">ReplicationPackage.z03 </span></li> <li><span lang="EN-US">ReplicationPackage.z04 </span></li> <li><span lang="EN-US">ReplicationPackage.z05</span></li> <li><span lang="EN-US">ReplicationPackage.zip </span></li> </ul> <p><span lang="EN-US">These files <strong>must be in the same folder</strong> before unzipping.</span></p> <p><span lang="EN-US">Below are step-by-step instructions for <strong>Windows</strong> and <strong>Linux</strong> users.</span></p> <h1><span lang="EN-US">For Windows Users</span></h1> <h2><span lang="EN-US">Option 1: Using 7-Zip (Recommended)</span></h2> <ol> <li><span lang="EN-US">Download and install 7-Zip: <a href="https://www.7-zip.org/">https://www.7-zip.org/</a></span></li> <li><span lang="EN-US">Place all the files (.z01, .z02, ..., .z05, and .zip) in the <strong>same folder</strong>.</span></li> <li><span lang="EN-US">Right-click on ReplicationPackage.zip.</span></li> <li><span lang="EN-US">Choose <strong>7-Zip -></strong></span><strong><span lang="EN-US"> Extract Here</span></strong><span lang="EN-US"> or <strong>Extract to ReplicationPackage\</strong>.</span></li> </ol> <p><span lang="EN-US">7-Zip will automatically combine the parts and extract the full content.</span></p> <h2>Option 2: Using WinRAR</h2> <ol> <li><span lang="EN-US">Download and install <strong>WinRAR</strong>: <a href="https://www.rarlab.com/">https://www.rarlab.com/</a></span></li> <li><span lang="EN-US">Ensure all parts are in the same directory.</span></li> <li><span lang="EN-US">Right-click on ReplicationPackage.zip.</span></li> <li><span lang="EN-US">Select <strong>Extract Here</strong> or <strong>Extract to ReplicationPackage\</strong>.</span></li> </ol> <h1><span lang="EN-US">For Linux Users</span></h1> <ol> <li><span lang="EN-US">Make sure all the parts are in the same directory.</span></li> <li><span lang="EN-US">Install p7zip if not already installed:</span></li> </ol> <p><span lang="EN-US"> sudo apt install p7zip-full</span></p> <p><span lang="EN-US"> 3. Navigate to the folder containing the files:</span></p> <p><span lang="EN-US"> cd /path/to/your/downloaded/files</span></p> <p> 4. Run the following command:</p> <p> 7z x ReplicationPackage.zip</p> <p><span lang="EN-US">This will extract the full archive by automatically reading the .z01, .z02, etc.</span></p> <h1><span lang="EN-US">NOTES</span></h1> <ul> <li><strong><span lang="EN-US">Do not</span></strong><span lang="EN-US"> try to unzip the .z01, .z02, etc., individually. Only unzip the main .zip file (the one with the .zip extension).</span></li> <li><span lang="EN-US">If you see an error like <em>"not a valid zip file"</em>, it usually means:</span></li> <ul> <li><span lang="EN-US">Not all parts were downloaded correctly.</span></li> <li><span lang="EN-US">The files are not in the same folder.</span></li> <li><span lang="EN-US">The file names were modified (e.g., by the browser).</span></li> </ul> </ul>
NorSand4AI: A Comprehensive Triaxial Test Simulation Database for NorSand Constitutive Model Materials
<p>Dataset of the homonymous paper: a first-of-its-kind database that addresses the size and complexity issues of creating synthetic datasets for nonlinear constitutive modeling of soils by simulating both drained and undrained triaxial tests. Two datasets are provided: the first one considers a nested Latin Hypercube Sampling of input parameters encompassing 2000 soil types, each subjected to 40 initial test configurations, resulting in a total of 160000 triaxial test results (NorSandTXL_H5.zip). The second one considers nested quasi-Monte Carlos sampling techniques (Sobol and Halton) of input parameters encompassing 2048 soil types, each subjected to 42 initial test configurations, resulting in a total of 172032 triaxial test results (NorSand_2048_42_Drained.zip and NorSand_2048_42_Undrained.zip). Each simulation dataset comprises a 4000 by 10 matrix that can be used for general multivariate forecasting benchmarks, in addition to direct geotechnical and soil science applications.</p>
Test results from quasi-static impact tests over SMC materials reinforced with glass fibre (ramdom and textile)
<p>Test results from quasi-static impact tests over SMC materials with structural properties conceived for automotive applications, reinforced with glass fibre (samples with ramdom distribution of fibres and samples reinforced with GF textile)</p>
Synthetic Göttingen Sentence Test material created with a text-to-speech system
<p>The speech material of the German Göttingen Sentence Test [1] was synthesized using a commercial text-to-speech system (Acapela Cloud Service). More details will be found in [2].</p> <p>Files:</p> <p>goesa_synth_female.zip<br> contains all 200 sentences with a synthetic female voice and the corresponding speech adjusted noise, which was generated by superimposing the speech material 30 times according to [3].</p> <p>goesa_synth_male.zip<br> contains all 200 sentences with a synthetic male voice and the corresponding speech adjusted noise, which was generated by superimposing the speech material 30 times according to [3].</p> <p> </p>
Supplemental Material to Article "Validation of crack initiation model by means of cyclic full-scale blade test"
<p>This set supplements the figure data to the article "Validation of crack initiation model by means of cyclic full-scale blade test", DOI: <a href="https://doi.org/10.1088/1742-6596/2265/3/032045">https://doi.org/10.1088/1742-6596/2265/3/032045</a>.</p>
Supplementary materials of the paper entitled: "Metamorphic Testing Meets Regression Testing: A Case Study of Scientific Software Maintenance"
<p>Supplementary materials of the paper entitled:</p> <p>“Metamorphic Testing Meets Regression Testing: A Case Study of Scientific Software Maintenance”</p> <p>file01 - source code for detecting and comparing output relations of a given metamorphic relation<br> file02 - interview transcripts and qualitative coding results<br> file03 - system script for compiling SWMM, creating sSWMM simulation outputs, and logging execution time<br> file04 - source code for SWMM 5.1.014<br> file05 - source code for SWMM 5.1.015<br> file06 - a suite of 40 regression tests (40 .inp files)<br> file07 - result table for 760 cases (19 MRs × 40 .inp files)<br> file08 - 491 automatically generated follow-up .inp files via reuse<br> file09 - result table for mutation analysis<br> file10 - output files for mutation analysis with metamorphic testing:<br> a. Original source output<br> b. Original follow-up output<br> c. Mutant source output<br> d. Mutant follow-up output</p>
Supporting material for the paper " Test Smells Learning by a Gamification Approach"
<p>Table reporting the frequencies of answers to the 31 questions (1 = Strongly Disagree, 5 = Strongly Agree) to the survey about the use of the TSGame tool.</p> <p>Figures reporting:</p> <ul> <li>the architecture of the TSGame tool</li> <li>Detection Game Web page</li> <li>Refactoring Game Web page</li> <li>Solution Sharing Web page</li> </ul>
Supporting material for the paper "Testing Robot Challenge: a Serious Game for Testing Learning"
<p><strong>Experimental data</strong></p> <p>Table reporting the frequencies of student answers to the 29 questions (1 = Strongly Disagree, 5 = Strongly Agree) to the survey about the use of the Testing Robot Challenge Web application.</p> <p> </p> <p><strong>Figures</strong></p> <ul> <li>Game Main Page</li> <li>End Game Page</li> <li>Game mechanics of the Testing Robot Challenge</li> <li>Game Selection Page</li> <li>Home Page</li> <li>Software Architecture</li> </ul>
Supporting material for "Pharmacological validation of individual animal locomotion, temperature and behavioural analysis in group-housed rats using a novel automated home cage analysis system: a comparison with the modified Irwin test"
<p>The data were uploaded to support the manuscript "Pharmacological validation of individual animal locomotion, temperature and behavioural analysis in group-housed rats using a novel automated home cage analysis system: a comparison with the modified Irwin test" for the submission to Journal of Pharmacological and Toxicological Methods.</p>
Supplementary material for the paper "PyNose: A Test Smell Detector for Python"
<p>This archive contains:</p> <ol> <li>Lists of projects used in our study.</li> <li>A full list of examples of the newly introduced Suboptimal Assert test smell.</li> <li>An example of a change graph used to discover Python-specific test smells.</li> <li>The results of the small-scale mapping study: a full list of papers, a full list of test smells, and the correlation between them.</li> </ol> <p>You can find all the details in README.txt</p>
Data for: Nest material preferences in wild hazel dormice Muscardinus avellanarius: Testing predictions from optimal foraging theory
<p class="MsoNormal">Obtaining nesting material presents an optimal foraging problem, collection of materials incurs a cost in terms of risk of predation and energy spent, and individuals must balance these costs with the benefits of using that material in the nest. The hazel dormouse, <em>Muscardinus avellanarius</em> is an endangered British mammal in which both sexes build nests. However, whether material used in their construction follows the predictions of optimal foraging theory is unknown. Here, we analyse the use of nesting materials in forty two breeding nests from six locations in Southwest England. Nests were characterised in terms of which plants were used, the relative amount of each plant, and how far away the nearest source was. We find that dormice exhibit a preference for plants closer to the nest, but that the distance they are prepared to travel depends on the plant species. Dormice travelled further to collect honeysuckle <em>Lonicera periclymenum</em>, oak <em>Quercus robur</em>, and beech <em>Fagus sylvatica</em> than any other plants. Distance did not affect the relative amount used, although the proportion of honeysuckle in nests was highest, and more effort was expended collecting honeysuckle, beech, bramble <em>Rubus fruticosus</em> and oak compared to other plants. Our results suggest that not all aspects of optimal foraging theory apply to nest material collection. However, optimal foraging theory is a useful model to examine nest material collection, providing testable predictions. As found previously honeysuckle is important as a nesting material, and should be taken account when assessing suitability of sites for dormice.</p>
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>file00 - Readme.txt<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>
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.