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1,956 results for “test data”
Cyclic test data of six unreinforced masonry walls with different boundary conditions
<p>Previous test data on unreinforced masonry walls focused on the global response of the wall. A new dataset on six wall tests, which is publically available, allows linking global to local deformations of masonry walls, which can be useful for advancing performance-based design and assessment methods for unreinforced masonry buildings. This data paper presents the<br> results of a test series on six identical unreinforced masonry walls that were constructed using hollow clay brick units and standard cement-based mortar.<br> The test units were subjected to quasi-static cycles of increasing drift demands and the tests differed with regard to the applied axial load and the moment restraint applied at the top of the walls. The walls were tested up to failure. Throughout the loading the deformations of the walls were recorded using a digital photogrammetric measurement system tracking the movement of 312 points per test unit.</p>
Dynamic testing of a four-storey building with reinforced concrete and unreinforced masonry wall: Data set
<p>This paper presents a publically available data set recorded during the shake-table test of a structure with reinforced concrete (RC) and unreinforced masonry (URM) walls. The shake-table test, performed at the TREES laboratory of EUCENTRE (Pavia, Italy), was part of a larger research initiative at EPFL (Lausanne, Switzerland) that addresses the seismic behaviour of mixed RC-URM structures. The half-scale test unit was subjected to several shakings of different intensity levels. The paper presents the geometry of the test unit, the properties of the construction materials, the instrumentation, and outlines the organization of the recorded data. Two sets of data are available: the unprocessed data and a second set of processed data where conventional and optical measurements are synchronised. This second set contains also some derived data, which allows to quickly plot key quantities such as base shear and top displacement. The aim of the paper is to provide all information required by the reader for analysing the test data and using it for validation purposes of numerical and mechanical models. The performance of the test unit is described in a companion paper.</p>
Genotype reproducibility testing in next-generation sequencing data
<p>Code, log and results summary for testing the reproducibility of genotypes with three pairs of hemiclones in the Sussex LH<sub>M </sub><em>D.melanogaster </em>population sample. Discovery and genotyping of genomic sequence variants was done using GATK HaplotypeCaller, and Genomestrip. Numerical comparison of genotype calls within each pairs of hemiclone individuals was performed using GATK GenotypeConcordance.</p> <p> </p> <p>The pre-print manuscript for this data is available on biorxiv: "Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample" http://biorxiv.org/content/early/2016/10/17/081554 doi: http://dx.doi.org/10.1101/081554</p>
Test Data Generation from Business Rules
<p><strong>Overview of Data</strong></p> <p>The site includes data only for the two subjects: Ceu-pacific and JBilling. For both the subjects, the “<em>.model” shows the model created from the business rules obtained from respective websites, and “</em>_HighLevelTests.csv” shows the tests generated. Among csv files, we show tests generated by both BUSTER and Exhaust as well.</p> <p><strong>Paper Abstract</strong></p> <p>Test cases that drive an application under test via its graphical user interface (GUI) consist of sequences of steps that perform actions on, or verify the state of, the application user interface. Such tests can be hard to maintain, especially if they are not properly modularized—that is, common steps occur in many test cases, which can make test maintenance cumbersome and expensive. Performing modularization manually can take up considerable human effort. To address this, we present an automated approach for modularizing GUI test cases. Our approach consists of multiple phases. In the first phase, it analyzes individual test cases to partition test steps into candidate subroutines, based on how user-interface elements are accessed in the steps. This phase can analyze the test cases only or also leverage execution traces of the tests, which involves a cost-accuracy tradeoff. In the second phase, the technique compares candidate subroutines across test cases, and refines them to compute the final set of subroutines. In the last phase, it creates callable subroutines, with parameterized data and control flow, and refactors the original tests to call the subroutines with context-specific data and control parameters. Our empirical results, collected using open-source applications, illustrate the effectiveness of the approach.</p>
Data + Analyses: "Gaze-dependent Coding of Somatosensory Reach Targets after Effector Movement: Testing the Impact of Online Information, Movement Timing, and Target Distance"
<p>This upload contains the experiment scripts (written in Presentation), data, and analyses (performed with MATLAB and SPSS) underlying the publication<strong> </strong>by Mueller & Fiehler (2017). <em>PloS one</em>. doi:<strong>10.1371/journal.pone.0180782</strong></p>
DDF_test_data
Open the record for dataset details and reuse information.
Compound Flood Risk Guidelines test data
<p>Time series data of flood drivers (discharge, rainfall, coastal water levels) for various case studies in support of compound fllood risk guidelines.</p> <p>version 1: data for Charleston, NC, USA & BrisBane, AUS.</p> <p>version 2; added data for Toamasina, MDG</p> <p>version 3: consistent file structure</p> <p>version 4: reduce coastal time series to 30min temporal resolution</p>
Twin test 1: Effect of vegetation on urban flows. PIV data from NTUA WT experiment and LDV from KIT WT experiment
<p>The first Twin Test (TW1) of the TWEET-IE project (<a href="http://www.tweet-ie.eu">www.tweet-ie.eu</a>) involved measurements of the flow past a surface mounted cube with openings, representing a building exposed to an atmospheric boundary layer. Tests were performed for smooth building walls but also with modelled vegetation covering the windward façade and the roof of the building. The measurements were performed both at Karlsruhe Institute of Technology (KIT) and the National Technical University of Athens (NTUA), in wind tunnels, at common locations around the building. Laser Doppler Anemometry (LDA) was used at KIT and Particle Image Velocimetry (2C-2D and 3C-2D PIV) at NTUA. In the present data set shows the effect of vegetation on the flow velocities and a comparison of the twin wind tunnel measurements. </p>
Training and test data, plus saved models for the upcoming paper `Top-down perceptual inference shaping the activity of early visual cortex'
<p>Each .pkl file contains a training or test dataset in the form of a Python dictionary (generated with Python 3.8.5) with the following fields:</p><ul><li>'train_images': 640,000 float32 images used for model training. These are 40px images that contain 1600 pixel intensities each.</li><li>'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li><li>'test_images': 64,000 float32 images used for model testing. These are 40px images that contain 1600 pixel intensities each.</li><li>'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li></ul><p>The .zip file contains a saved model snapshot and various intermediate evaluative data. Details on these are coming soon.</p>
Data from: Testing the heat treatment dose for Agrilus planipennis prepupae using the Humble water bath
<p>The lethal heat treatment dose (time and temperature) for the pre-pupal life stage of <em>Agrilus planipennis</em> Fairmaire (Coleoptera: Buprestidae), emerald ash borer, was determined through an in vitro application using a carefully calibrated heat treatment apparatus. The lethal and sublethal effects of heat on A. planipennis prepupae were assessed through a ramped heat delivery application, simulating industrial kilns and conventional heat chamber operations, for treatments combining target temperatures of 54 °C, 55 °C, and 56 °C, and exposure durations of 0 min (i.e., kiln temperature ramp only), 15 min, or 30 min. Prepupal emerald ash borer larvae did not survive exposure to 56 °C for 15 min or longer, or to 55 °C for 30 min. Sublethal effects were observed for all other treatments. Sublethal effects included delayed development and failure to complete the pupal and adult life stages. The datasets and associated R statistical computing langauge code are deposited here.</p>
Processing, Spectroscopic and Laboratory Testing Data from a Medical Grade Hot-Melt Extrusion Process
<p>This dataset contains a collection of raw processing data, spectroscopic data, and laboratory test results of medical-grade polymer extrusion experiments. The data was collected in several experiments conducted in a hot-melt extrusion process. The process involved extruding PLA through a slit die and drawing the extruded strands onto spools to obtain the desired dimensional and mechanical properties. The strands were later knitted to form the final medical implant. Throughout the experiments, the extrusion process and equipment were upgraded and refined. Various operational scenarios were simulated under different nozzle configurations. The experiments start using a single-screw extruder and later progress to a double-screw extruder. Medical Grade PURASORB PLA (PLDLA 96/4) material was used when the hardware upgrades were complete. This dataset contains many variations in experimental conditions. However, enough overlap exists to derive working datasets from this compiled raw data.</p> <p> </p> <p>Two working datasets have been derived from this compiled raw data. Using a double-screw extruder, both working Datasets investigate polymer degradation in the hot-melt extrusion process. Both derived datasets are included in this collection.</p> <p> </p> <p>Two Jupyter notebooks are included in this data collection. The first notebook gives an example of how an initial dataset can be derived from the raw data using data science techniques. The second notebook gives an example of how a final dataset can be created from the initial dataset.</p>
Regression-Test History Data for Flaky Test-Research, Dataset
<p>The dataset comprises developer test results of Maven projects with flaky tests across a range of consecutive commits from the projects' git commit histories. The Maven projects are a subset of those investigated in an <a href="https://doi.org/10.1145/3428270">OOPSLA 2020 paper</a>. The commit range for this dataset has been chosen as the flakiness-introducing commit (FIC) and iDFlakies-commit (see the OOPSLA paper for details). The commit hashes have been obtained from the <a href="https://github.com/TestingResearchIllinois/idoft/blob/main/tic-fic-data.csv">IDoFT dataset</a>.</p> <p>The dataset will be presented at the <a href="https://conf.researchr.org/home/icse-2024/ftw-2024">1st International Flaky Tests Workshop 2024 (FTW 2024)</a>. Please refer to <a href="https://doi.org/10.1145/3643656.3643901">our extended abstract</a> for more details about the motivation for and context of this dataset.</p> <p>The following table provides a summary of the data.</p> <table> <tbody> <tr> <td><strong>Slug (Module)</strong></td> <td><strong>FIC Hash</strong></td> <td><strong>Tests</strong></td> <td><strong>Commits</strong></td> <td><strong>Av. Commits/Test</strong></td> <td><strong>Flaky Tests</strong></td> <td><strong>Tests w/ Consistent Failures</strong></td> <td><strong>Total Distinct Histories</strong></td> </tr> <tr> <td>TooTallNate/Java-WebSocket</td> <td> 822d40</td> <td>146</td> <td> 75</td> <td> 75</td> <td>24</td> <td> 1</td> <td>2.6x10^9</td> </tr> <tr> <td>apereo/java-cas-client (cas-client-core)</td> <td> 5e3655</td> <td>157</td> <td> 65</td> <td>61.7</td> <td> 3</td> <td> 2</td> <td>1.0x10^7</td> </tr> <tr> <td>eclipse-ee4j/tyrus (tests/e2e/standard-config)</td> <td> ce3b8c</td> <td>185</td> <td> 16</td> <td> 16</td> <td>12</td> <td> 0</td> <td> 261</td> </tr> <tr> <td>feroult/yawp (yawp-testing/yawp-testing-appengine)</td> <td> abae17</td> <td> 1</td> <td>191</td> <td>191</td> <td> 1</td> <td> 1</td> <td> 8</td> </tr> <tr> <td>fluent/fluent-logger-java</td> <td> 5fd463</td> <td> 19</td> <td>131</td> <td>105.6</td> <td>11</td> <td> 2</td> <td>8.0x10^32</td> </tr> <tr> <td>fluent/fluent-logger-java</td> <td> 87e957</td> <td> 19</td> <td>160</td> <td>122.4</td> <td>11</td> <td> 3</td> <td>2.1x10^31</td> </tr> <tr> <td>javadelight/delight-nashorn-sandbox</td> <td> d0d651</td> <td> 81</td> <td>113</td> <td>100.6</td> <td> 2</td> <td> 5</td> <td>4.2x10^10</td> </tr> <tr> <td>javadelight/delight-nashorn-sandbox</td> <td> d19eee</td> <td> 81</td> <td> 93</td> <td>83.5</td> <td> 1</td> <td> 5</td> <td>2.6x10^9</td> </tr> <tr> <td>sonatype-nexus-community/nexus-repository-helm</td> <td> 5517c8</td> <td> 18</td> <td> 32</td> <td> 32</td> <td> 0</td> <td> 0</td> <td> 18</td> </tr> <tr> <td>spotify/helios (helios-services)</td> <td> 23260</td> <td>190</td> <td>448</td> <td>448</td> <td> 0</td> <td> 37</td> <td> 190</td> </tr> <tr> <td>spotify/helios (helios-testing)</td> <td> 78a864</td> <td> 43</td> <td>474</td> <td>474</td> <td> 0</td> <td> 7</td> <td> 43</td> </tr> </tbody> </table> <p> </p> <p>The columns are composed of the following variables:</p> <ul> <li><strong>Slug (Module):</strong> The project's GitHub slug (i.e., the project's URL is https://github.com/{Slug}) and, if specified, the module for which tests have been executed.</li> <li><strong>FIC Hash:</strong> The flakiness-introducing commit hash for a known flaky test as described in this OOPSLA 2020 paper. As different flaky tests have different FIC hashes, there may be multiple rows for the same slug/module with different FIC hashes. </li> <li><strong>Tests:</strong> The number of distinct test class and method combinations over the entire considered commit range.</li> <li><strong>Commits:</strong> The number of commits in the considered commit range</li> <li><strong>Av. Commits/Test:</strong> The average number of commits per test class and method combination in the considered commit range. The number of commits may vary for each test class, as some tests may be added or removed within the considered commit range.</li> <li><strong>Flaky Tests:</strong> The number of distinct test class and method combinations that have more than one test result (passed/skipped/error/failure + exception type, if any + assertion message, if any) across 30 repeated test suite executions on at least one commit in the considered commit range.</li> <li><strong>Tests w/ Consistent Failures:</strong> The number of distinct test class and method combinations that have the same error or failure result (error/failure + exception type, if any + assertion message, if any) across all 30 repeated test suite executions on at least one commit in the considered commit range.</li> <li><strong>Total Distinct Histories:</strong> The number of distinct test results (passed/skipped/error/failure + exception type, if any + assertion message, if any) for all test class and method combinations along all commits for that test in the considered commit range.</li> </ul>
Testing strengths, limitations and biases of current Pulsar Timing Arrays detection analyses on realistic data
<p>In this project, we carried out an extensive investigation of the performance of current Pulsar Timing Arrays (PTA) analyses on simulated PTA datasets where we modeled the gravitational waves (GW) signal as the incoherent superposition of sinusoidal signals from a cosmic population of super-massive black hole binaries (SMBHBs). Here we publish the dataset referred to in the paper as the <em><strong>SMBHB_set</strong></em>: 100 realisations of PTA datasets with 25 pulsars and a GWB signal of nominal amplitude 2.4e-15. For each realisation, listed from 1001 to 1100, this repository contains the pulsars .par and .tim files, the mcmc chain (sampling over 66 parameters: 60 for pulsars intrinsic noise parameters and log amplitude and slope of a common red noise process), and the SMBHB details for that specific realisation of the GWB. The columns of the <em>MBHB_list.dat</em> files contain (from left to right): log10 MBH chirp mass (in solar masses in the source frame), mass ratio, source redshift, log10 of GW fundamental mode (n=2) in the observer frame, phi and theta angles in the sky, source inclination angle, source polarisation angle, source initial orbital phase, source initial direction of periastron and source initial eccentricity.</p> <p>If you make use of any of this data, please cite:<br><br></p> <div>@article{ refId0,</div> <div> author = {{Valtolina, Serena} and {Shaifullah, Golam} and {Samajdar, Anuradha} and {Sesana, Alberto}},</div> <div> title = {Testing strengths, limitations, and biases of current pulsar timing arrays’ detection analyses on realistic data},</div> <div> DOI= "10.1051/0004-6361/202348084",</div> <div> url= "https://doi.org/10.1051/0004-6361/202348084",</div> <div> journal = {A&A},</div> <div> year = 2024,</div> <div> volume = 683,</div> <div> pages = "A201",</div> <div>}</div>
Tuning-less Object Naming with a Foundation Model - Data recorded during testing
<p>We implement a real-time object naming system that enables learning a set of named entities never seen. Our approach employs an existing foundation model that we consider ready to see anything before starting. It turns seen images into relatively small feature vectors that we associate with index to a gradually built vocabulary without any training of fine-tuning of the model. Our contribution is using the association mechanism known from transformers as attention. It has features that support generalization from irrelevant information for distinguishing the entities and potentially enable associating with much more than indices to vocabulary. As a result, the system can work in a one-shot manner and correctly name objects named in different contents. We also outline implementation details of the system modules integrated by a blackboard architecture. Finally, we investigate the<br>system's quality, mainly how many objects it can handle in this way.</p>
Data for: D3.6 - Assessment of organoleptic and nutritional quality of fish products from the demonstration tests
<p>Data for: D3.6 - Assessment of organoleptic and nutritional quality of fish products from the demonstration tests </p> <p>https://ifishienci.eu/wp-content/uploads/2024/01/iFishIENCi_D3.6.pdf</p> <p>Corresponding Author</p> <p>Name: Anneli Rost<br>ttz Bremerhaven, Germany<br>Address: Knurrhahnstraß 22-24 /Packhalle X 27572 Bremerhaven<br>Email: arost@ttz-bremerhaven.de</p>
Data from: Testing metabolic cold adaptation and the climatic variability hypotheses across the latitudinal range of a widespread, supratidal water beetle
<p>Temperature significantly impacts ectotherm physiology, with thermal and metabolic traits varying with latitude but the drivers of this variation remain unclear, despite obvious consequences in the face of ongoing global change. This study explores metabolic cold adaptation (MCA) and the climatic variability hypothesis (CVH) to evaluate local adaptation and phenotypic plasticity of metabolic rates and thermal limits in two populations of the supratidal rockpool beetle <em>Ochthebius lejolisii</em> from localities experiencing contrasting thermal variability. Reciprocal acclimation was conducted under spring temperature regimes of both localities, incorporating local diurnal variation. Metabolic rates were measured by closed respirometry, and thermal tolerance limits estimated through thermography. In line with MCA, the northern population (colder climate) showed higher metabolic rates and Q10s at lower temperatures than the southern population. As predicted by the CVH, the southern population (more variable climate) showed higher upper thermal tolerance but only the northern population was able to acclimate upper thermal limits. This pattern suggests the existence of trade-offs in thermal adaptation in this species, likely increasing the vulnerability of populations on Mediterranean coasts to the projected increases in extreme temperatures under ongoing climate change.</p>
Simulated Herbarium data for testing the accuracy with which specimen data can predict the timing and duration of population-level flowering displays
<p>This dataset provides code and example data for simulating specimen collections of flowering plants across North America, and for developing phenological predictions of population-level flowering onset and termination for these data. It further presents code for assessing the accuracy of these predictions relaticve to known (simulated) population-level flowering dates at the location of each collection.</p>
Similarity data set used to test Synchronous Growth Changes (SGC) on dendrochronological data using tree-ring series from the ITRDB
<p>Dataset used to test the SGC, SSGC and AGC in:</p> <div> <div>Visser, RM. 2021 On the similarity of tree-ring patterns: Assessing the influence of semi-synchronous growth changes on the Gleichläufigkeitskoeffizient for big tree-ring data sets. <em>Archaeometry</em> 63(1): 204–215. DOI: <a href="https://doi.org/10.1111/arcm.12600">https://doi.org/10.1111/arcm.12600</a>.</div> </div> <p>The dataset contains the database used in this study</p> <ul> <li><em>itrdb_structure.sql</em> described the structure of the database (PostgreSQL/PostGIS)</li> <li>Tables <ul> <li><em>GC_??_tbl</em> are tables with ?? denoting the continent (see below) containg the comparisons between tree-ring series and the growth changes <ul> <li>The following columns are present: <ul> <li>ID1 and ID2: These are the ID's of the series compared.</li> <li>SGC: Synchronous Growth Changes</li> <li>SSGC: Semi Synchronous Growth Changes</li> <li>Overlap: the number of tree-rings compared</li> </ul> </li> <li>Data files with values in each table. The continents are as defined in the ITRDB (https://www.ncei.noaa.gov/access/paleo-search/?dataTypeId=18) <ul> <li>GC_af_tbl_202005 (Africa)</li> <li>GC_as_tbl_202005 (Asia)</li> <li>GC_au_tbl_202005 (Australia)</li> <li>GC_ca_tbl_202005 (Canada)</li> <li>GC_eu_tbl_202005 (Europe)</li> <li>GC_mx_tbl_202005 (Mexico)</li> <li>GC_sa_tbl_202005 (South America)</li> <li>GC_us_tbl_202005 (North America)</li> </ul> </li> </ul> </li> <li><em>headers</em>: <ul> <li>The following columns are present: <ul> <li>continent: two letter code of the continent (ITRDB)</li> <li>filename: orginal filename as deposited in the ITRDB</li> <li>line_nr: line number of the header</li> <li>header_text: text of the header related to the line number</li> </ul> </li> <li>Datafile: headers_201905222007.csv</li> </ul> </li> <li><em>names</em>: <ul> <li>The following columns are present: <ul> <li>filename: orginal filename as deposited in the ITRDB</li> <li>name_orig: orginal name of the tree-ring series as deposited in the ITRDB</li> <li>name_new: the IDs of the tree-ring series were replaced with a two‐letter code for the continent (AF, AS, AU, CA, EU, SA, US) and a sequence code to prevent duplicate IDs. These are used as ID1 and ID2 in the tables <em>GC_??_tbl</em></li> </ul> </li> <li>Datafile: names_201905240643.csv</li> </ul> </li> </ul> </li> <li>file: <em>geo_location_201906250635.csv</em> <ul> <li>Contains the locations related to each site in the database</li> <li>The following columns: <ul> <li>filename: orginal filename as deposited in the ITRDB</li> <li>continent: two letter code of the continent (ITRDB)</li> <li>lat: latitude</li> <li>long: longitude</li> <li>geom_point: WGS84 coordinates expressed as well-known text (WKT)</li> </ul> </li> </ul> </li> </ul> <p>For the related code, see also: </p> <p>Ronald Visser. (2022). Code and data related to semi-synchronous growth changes and the similarity of tree-ring patterns (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7157738</p> <p>Or: https://github.com/RonaldVisser/SGC</p>
Research Data for Comparative Evaluation of RT-PCR and Antigen-based Rapid Diagnostic Tests (Ag-RDTs) for SARS-CoV-2 Detection: Performance, Variant Specificity, and Clinical Implications
<p>This dataset represents laboratory findings for the comparative evaluation of the diagnostic performance of Ag-RDTs (Flourescence Immunoassay and Lateral Flow Immunoassay) with RT-PCR</p>
Soil frost heave test data
<p>Soil freezing test data of silty clay, silt and sand were collected. The test data in the document included the frost heaving rate of soil under different plasticity index, dry density, temperature and water content (from left to right).</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.