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Dataset results
321 results for “test performance”
Online Appendix for "An Exploratory Study of the State of Practice of Performance Testing in Java-Based Open Source Projects"
<p>Here we collect some raw data and additional material to the paper <em>"An Exploratory Study of the State of Practice of Performance Testing in Java-Based Open Source Projects"</em>, by Philipp Leitner and Cor-Paul Bezemer.</p>
Performance regression testing initiatives: A systematic mapping - Replication Package
<p>This repository contains the supporting material for the manuscript titled "Performance regression testing initiatives: A systematic mapping", currently under revision to the Journal of Information and Software Technology. </p> <p>Repository Content:</p> <ul> <li> <p><strong>Protocol Performance Regression Testing.xlsx</strong>: it includes the *protocol* specification, i.e., the data extraction process used for the selection of the studies and gathering all relevant information. The outcome of the systematic mapping consists of 68 *selected studies* and information supporting the answers to four research questions: *RQ1*, *RQ2*, *RQ3*, and *RQ4*;</p> </li> <li> <p><strong>scripts.zip</strong>: it includes all the scripts used in the forward snowballing. Two additional files support the replication of the scripts: (1) README.md explains how to run the scripts; (2) requirements.txt file reports the installation prerequisites and their dependencies;</p> </li> <li> <p><strong>SM-PRT-overview.xlsx</strong>: this spreadsheet reports the information about tools and replication packages, benchmark suites and subject systems adopted per study;</p> </li> <li> <p><strong>README-SM-PRT-overview.md.docx</strong>: this file describes the columns of the spreadsheet above to facilitate the usage of the collected information.</p> </li> </ul>
Model-Based Testing for Performance Requirements Dataset
<p>Ths is a dataset for the paper Model-Based Testing for Performance Requirements.</p>
Data for injection and recovery test performed in the paper "Improved Sensitivity for Space Domain Awareness Observations with the Murchison Widefield Array"
<p>This repo contains the script and the pool of noise images used to perform the injection and recovery test in the paper titled "Improved Sensitivity for Space Domain Awareness Observations with the Murchison Widefield Array"</p>
Fig. 5 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 5. Visualization of a feature with high importance and a feature with low importance from a configuration B. The importance of these features for the identification accuracy was determined using permutation tests (see the methods). For each taxon or group (rows) several randomly selected specimens (columns) are shown. For the two selected Global Average Pooling layer features, the corresponding features of the preceding (Max Pooling) layer are visualized as those show specific image parts that had higher activations.Yellow represents the maximal activation strength; dark blue represents the minimal activation strength.
Fig. 2 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 2. Schematic representation of the networks used. On top are the convolutional layers of VGG16, grouped into five blocks. Output of each Max Pooling layer is fed into Global Average Pooling layer. Numbers near each block name indicate number of features in the Global Average Pooling layer. Height of layers roughly corresponds to resolution (except for Global Average Pooling layer), while width roughly corresponds to the number of feature maps or features produced.Then, in approach A, outputs of five blocks are concatenated and passed to the linear classifier. In approach B, output of only one block (block 3 in the final configuration) is passed to the linear classifier. In approach C CNN outputs are as in approach A, but instead connected to a DNN with two layers of 320 fully connected (FC) neurons followed by a prediction layer (PL), with number of neurons equal to number of species classified. Finally, approach D features CNN as in approach B which is connected to DNN as in approach C.
Fig. 1 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 1. Dorsal habitus photos of males and females of Tuxedo spp., Pygovepres vaccinicola, and Phallospinophylus setosus generated for and used in this study.
Fig. 4 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 4. Validation accuracy and accuracy on test data for SVM linear classifier (A, B) and DNN approaches (C, D) for the three datasets.
Fig. 3 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 3. Identification accuracy for the male (top) and female (bottom) Tuxedo dataset for the five selected resolutions and the individual blocks 1–5 and the concatenated block.
A Quantitative Approach for the Assessment of Microservice Architecture Deployment Alternatives by Automated Performance Testing
<p>Reprodicibility package, raw data and results for the paper "A Quantitative Approach for the Assessment of Microservice Architecture Deployment Alternatives by Automated Performance Testing". </p>
Dataset for USVSEG performance test
<p>Dataset for USVSEG performance test</p> <p> </p> <p> </p> <p> </p>
Energy consumption performance of a VTOL UAV in and out of ground effect by flight test
<p>Video 1 shows the flight status of the tri tilt-rotor UAV Egretta30 in ground effect, mainly in the span-dominated ground effect region. Video 2 demonstrates a flow visualization experiment to observe the variations in wingtip vortices at different heights above the ground.</p>
Physical Performance Testing and Frailty in Prediction of Early Postoperative Course After Cardiac Surgery
ClinicalTrials.gov study NCT05166863. IPD Sharing: UNDECIDED. Countries: 1. Publications: 8.
Predictive Value of Cognitive Tests Performed for the Diagnosis of Alzheimer's Disease and Related Disorders
ClinicalTrials.gov study NCT01316562. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Performance of Tests for Schistosoma Haematobium Diagnosis
ClinicalTrials.gov study NCT06808750. IPD Sharing: YES. Countries: 1. Publications: 0.
Eye-ECG Approach to Emergencies : Diagnostic Performance of the HINTS Test
ClinicalTrials.gov study NCT04118361. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Impact of Different Face Masks on Maximal and Submaximal Performance Testing During COVID-19
ClinicalTrials.gov study NCT05150613. IPD Sharing: NO. Countries: 1. Publications: 6.
Diagnostic Performance of Screening Tests for Cushing s Syndrome
ClinicalTrials.gov study NCT00361777. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Study Tests Whether a Standardized LVR Performed With the Blue Egg Device Improves Cardiopulmonary Exercise Capacity
ClinicalTrials.gov study NCT00326690. IPD Sharing: Not stated. Countries: 2. Publications: 13.
Factors Contributing to Single and Dual Task Performance of Four Square Step Tests
ClinicalTrials.gov study NCT03785639. IPD Sharing: NO. Countries: 1. Publications: 1.
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.