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324 results for “test cases”
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
Data from: Exploring patterns of beta-diversity to test the consistency of biogeographical boundaries: a case study across forest plant communities of Italy
Aim. To date, despite their great potential biogeographical regionalization models have been mostly developed on descriptive and empirical bases. This paper aims at applying the beta-diversity framework on a statistically representative data set to analytically test the consistency of the biogeographical regionalization of Italian forests. Location. Italy Taxon. Vascular plants Methods. Forest plant communities were surveyed in 804 plots made in a statistically representative sample of forest communities, made by 201 sites of Italian forests across the three biogeographical regions of the country: Alpine, Continental, and Mediterranean. We conducted an ordination analysis and an analysis of beta diversity, decomposing it into its turnover and nestedness components. Results. Our results provide only partial support to the consistency of the biogeographical regionalization of Italy. While the differences in forest plant communities support the distinction between the Alpine and the other two regions, differences between Continental and Mediterranean regions had lower statistical support. Pairwise beta-diversity and its turnover component are higher between- than within biogeographical regions. This suggests that different regional species pools contribute to assembly of local communities and that spatial distance between-regions has a stronger effect than that within-regions. Main conclusions. Our findings confirm a biogeographical structure of the species pools that is captured by the biogeographical regionalization. However, non-significant differences between the Mediterranean and Continental biogeographical regions suggest that this biogeographical regionalization is not consistent for forest plant communities. Our results demonstrate that an analytical evaluation of species composition differences among regions using beta-diversity analysis is a promising approach for testing the consistency of biogeographical regionalization models. This approach is recommended to provide support to the biogeographic regionalization used in some environmental conservation polices adopted by EU.
Plume spreading test case data
<p>The plume test case output data from the models Thetis, FESOM-C and GETM are presented. Additionally, the key scripts to perform the numerical mixing analysis are provided.</p>
Test cases (input), test scripts (output), and source code
<p>Test cases (input), test scripts (output), and source code used in the paper "NLP-assisted Test Generation Toward Script-free Web Testing" submitted to ICSME2021 NIER track</p>
Data from: Testing the role of mating preference in a case of incomplete ecological speciation with gene flow
Mating preference can evolve as a side effect of ecological adaptation and simultaneously contribute to speciation in certain scenarios. However, theoretical predictions have been difficult to test experimentally because there has not been any simple way to relate empirical and theoretical parameters. Recently, it has been shown that the r coefficient and other statistics can be used to estimate mating preference in wild-captured mating pairs. In the present work we use these estimators to test whether mating preference has significantly increased in a context favorable for ecological divergence. We perform the study using two groups of samples from populations of the marine gastropod Littorina saxatilis: 1) a group of bimodal populations affected by ecological divergence, and 2) a group of unimodal populations. The results show that mating preference (unlike the trait affected by the preference) does not increase when two ecotypes meet and mate in sympatry, which suggests that mating preference must be controlled by strong stabilizing natural selection, and/or limited by mating cost. This would suggest that mating preference would rarely contribute to speciation under the face of gene flow in these ecotypes.
Data from: Testing the potential of DNA barcoding in vertebrate radiations: the case of the littoral cichlids (Pisces, Perciformes, Cichlidae) from Lake Tanganyika
We obtained 398 cytochrome c oxidase subunit I barcodes of 96 morphospecies of Lake Tanganyika (LT) cichlids from the littoral zone. The potential of DNA barcoding in these fishes was tested using both species identification and species delineation methods. The best match (BM) and best close match (BCM) methods were used to evaluate the overall identification success. For this, three libraries were analysed in which the specimens were categorized into Operational Taxonomic Units (OTU) in three alternative ways: (A) morphologically distinct, including undescribed, species, (B) valid species and (C) complexes of morphologically similar or closely related species. For libraries A, B and C, 73, 73 and 96% (BM) and 72, 70 and 94% (BCM) of the specimens were correctly identified. Additionally, the potential of two species delineation methods was tested. The General Mixed Yule Coalescent (GMYC) analysis suggested 70 hypothetical species, while the Automatic Barcode Gap Discovery (ABGD) method revealed 115 putative species. Although the ABGD method had a tendency to oversplit, it outperformed the GMYC analysis in retrieving the species. In most cases where ABGD suggested oversplitting, this was due to intraspecific geographical variation. The failure of the GMYC method to retrieve many species could be attributed to discrepancies between mitochondrial gene trees and the evolutionary histories of LT cichlid species. Littoral LT cichlids have complex evolutionary histories that include instances of hybridization, introgression and rapid speciation. Nevertheless, although the utility of DNA barcoding in identification is restricted to the level of complexes, it has potential for species discovery in cichlid radiations.
Enhanced IEEE RTS-96 test case w/ synthesized dynamic parameters
<p>Enhanced IEEE RTS-96 test case w/ synthesized dynamic parameters<br /> Compatible with Siemens PTI PSS/E v33, adapted from Grigg et al., 1999</p> <p>Salient generator models<br /> IEEE T1 exciter models<br /> IEEE G2 governor models</p>
Enhanced Poland test case w/synthesized dynamic parameters and protective devices
<p>Enhanced Poland test case w/ synthesized dynamic parameters and protective devices<br> Compatible with Siemens PTI PSS/E v33, adapted from MATPOWER 5.0 case2383wp.m (http://www.pserc.cornell.edu/matpower/)<br> <br> Used as a test case for the paper: `A Learning Scheme for Microgrid Islanding and Reconnection.' IEEE Transactions on Power Systems, in press. ( http://ieeexplore.ieee.org/document/7935511/ , https://arxiv.org/abs/1611.05317 )</p> <p>Dynamic parameters:<br> Machine models:<br> -GENSAL: Salient generator.</p> <p> Machine governors:<br> -IEEEG2: IEEE Type 2 speed-governing system.</p> <p> Machine excitation system:<br> -IEEET1: IEEE Type 1 excitation system.</p> <p>Protective relays:<br> -TIOCR1: Time inverse overcurrent relay (used on all lines in network).<br> -UVUFBLU1: Under voltage/frequency load shedding relay (used on all buses in network).<br> -FRQTPAT: Under frequency generator trip relay (used on all machines, one per set point).</p>
A Preliminary Investigation on the Usage of Quantum Approximate Optimization Algorithms for Test Case Selection - Online Appendix
<p>QAOA-TCS - Quantum Regression Test Case Selection <br>This repository contains all the necessary resources to reproduce the results of the QAOA-TCS method. </p> <p>Dataset Files <br>The "datasets" folder contains: <br>- "sir_programs" </p> <p>SIR Programs <br>The "datasets/sir_programs" folder contains, for each SIR program considered by this project, all the files needed to gather statement coverage, execution costs, and past fault coverage information. </p> <p>For example, in the "flex" program folder: <br>- The file "fault-matrix.txt" contains rows representing flex's test cases. Each row has columns representing different versions of the program. Each cell (i,j) contains a binary value (0 or 1) indicating whether the i-th test case detects a fault in the j-th version. This configuration is called the fault matrix and provides historical fault coverage information. <br>- The folder "json_flex" contains a folder for each test case, with files like "flexi.gcov.json" to recover statement coverage and execution costs. These files detail which basic blocks were executed and how many times, enabling the calculation of total statement coverage and execution costs for each test case. </p> <p>Source Code Files </p> <p>DIVGA.m <br>The "MATLAB/DIVGA.m" file contains the pipeline for the DIVGA algorithm. For simplicity, the statement coverage, execution costs, and fault coverage data already gathered by "Notebook.ipynb" are written into text files, which DIVGA.m reads to bypass the actual datasets. </p> <p>DIVGA.m must be reconfigured for each target program. Update parameters like M, N, and gamultiobj routine settings. Ensure H_size in line 104 is less than max{N, M} + 1. Update the denominator in line 53 based on the total number of code lines in the target program. Adjust the result reporting target files as well. </p> <p>Notebook.ipynb <br>This file includes pipelines for dataset analysis, algorithm execution, and empirical comparisons. </p> <p>It has two main sections: </p> <p>1. QAOA-TCS vs SelectQA and Classical Algorithms <br> - Pipelines analyze SIR programs and compare QAOA-TCS, SelectQA, and classical algorithms. Manual configuration is needed when changing the target program, including updating file paths for Pareto fronts and configuring frontiers to build the reference. <br> - Statistical analysis requires populating the variables "algorithm_nondom_sirprogram" with the number of non-dominated solutions found during each of the 10 runs. </p> <p>Results Files <br>The "results" folder contains the outcomes of QAOA-TCS, DIV-GA, Additional Greedy, and SelectQA after experiment execution. These files enable empirical evaluations and comparisons between the methods. </p>
Fault test cases for series compensated transmission line with the capacitor at the middle of the line
<p><strong>Description</strong></p> <p>This data set contains time series data of different simulated fault cases in high voltage series compensated transmission lines. The simulation is done in PSCAD using the Python automation library. The capacitor is located in the middle of the line. The model of the line is given in the attached pdf file. The CT and VT are located at bus S. The distance are counted from bus S. The modelled line is 500 kV and 200 km long. The simulation is run for 1 second with 10 kHz sampling rate. </p> <p>Each file contains 10 data in the following order, </p> <p>Time<br> Voltage phase A<br> Voltage phase B<br> Voltage phase C<br> Current phase A<br> Current phase B<br> Current phase C<br> Voltage across capacitor phase A<br> Voltage across capacitor phase B<br> Voltage across capacitor phase C</p> <p>The variables used to study different test cases are shown below,</p> <p>Fault inception angle (referred to phase A voltage): 0 and 90 degree<br> Source impedance ratio: 0.20 and 0.35<br> Capacitor compensation level: 94 µF(50%), 78 µF(60%), 67 µF(70%)<br> Fault resistance: 0.10, 10, 20 ohm<br> Fault location: 10 km, 30 km, 50, 70 km, 90 km, 95 km, 105 km, 110 km, 130 km, 150 km, 160 km, 170 km, 180 km<br> Fault type: No-fault, AG, BG, CG, ABG, CAG, BCG, ABCG, AB, CA, BC, ABC</p> <p>All these factors lead to 5616 test cases in total. The test cases are organized in four (4) zipped folders each containing 1404 test cases. The PSCAD model file and an example python script that can be used to automate the simulations are also included herewith for anyone interested to replicate the results. </p> <p> </p> <p>The name of the folders contains the information regarding fault inception angle and source impedance ratio for all test cases inside that folder, for example, the folder name <strong>Outputs_0_0.20</strong> can be decoded as,</p> <p>0: Fault inception angle (phase A)<br> 0.20: Source impedance ration</p> <p>The files names inside the folders include information about the other variables used in generating the test case. For example, the name<strong> test_f_10_0_67_0.01</strong> can be interpreted as,</p> <p>test_f: f for forward fault<br> 10: fault location, 10 km<br> 0: Fault type<br> 67: Compensation level<br> 0.01: Fault resistance</p> <p>Fault types are interpreted as in PSCAD.</p> <p>0 = No-Fault,<br> 1 = Phase A to Ground<br> 2 = Phase B to Ground<br> 3 = Phase C to Ground<br> 4 = Phase AB to Ground<br> 5 = Phase AC to Ground<br> 6 = Phase BC to Ground<br> 7 = Phase ABC to Ground<br> 8 = Phase AB<br> 9 = Phase AC<br> 10 = Phase BC<br> 11 = Phase ABC</p> <p>The different line parameters used in the PSCAD model is as below,</p> <p><strong>Conductor</strong></p> <p>Type: Chukar<br> Geometric mean radius: 20.345 mm<br> DC resistance: 0.0318Ω/km<br> Strands: 84<br> Strand radius: 1.8491 mm<br> Relative permeability: 1.0<br> Sag: 12 m<br> Height: 22 m<br> Sub-conductor: 2<br> Sub-conductor spacing: 0.4572 m<br> Ground wire number: 2<br> Radius: 5.5245 mm<br> DC resistance: 2.8645Ω/km<br> Relative permeability: 1.0<br> Sag: 10 m<br> Height: 10 m</p> <p><strong>Impedance</strong></p> <p>Positive sequence 0.018 +j0.339 Ω/km<br> Zero sequence 0.266 +j1.017 Ω/km</p> <p> </p> <p><strong>These simulations were run for assessing time-domain protections. Therefore, the parallel CB protection across the series capacitor is disabled, as one can safely assume even with the highest current levels, the CB will take at least 20 ms to operate. This is higher than the time limits for time-domain protection. </strong></p>
Data extraction form - A Systematic Literature Review on Prioritizing Software Test Cases using Markov Chains
<p>A data extraction form was created to gather all relevant data from the identified studies and manage the selection process in this systematic literature review. Some of the main information presented in this form was followed by a protocol, identifier (id) for each returned study, bibliographic reference, and answers to research questions. This catalog helps us in the data extraction and synthesis procedures and may be used by potentially interested, for example, for updating or replication. </p>
Case studies used in the test of the MCDA-MSS for energy systems analysis, described according to its 156 features
<p>Case studies used in the test of the MCDA-MSS, described according to its 156 features.</p>
Test cases for the study of ADC model for OSBL
<p>This archive contains the data sets that are used in the manuscript "A new hybrid mass-flux/ high-order turbulence closure for ocean vertical mixing" by the same authors.</p>
Luma Test Cases
<p>Luma - Photosensitive epilepsy seizure detection tool test case videos </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 study on the Application of Markov Chains to Prioritize Test Cases
<p>The entire results and the inputs to run the experiment are available in this dataset. The data is composed of five folders one for each case study and one for the running example presented in Section 3. Within each folder, it is possible do see the input files, as well as the results, both for H1 and H2. The input formats can be consulted to understanding and potentially replication of the study. This dataset also contains the algorithm, in Python. The algorithm is available on GitHub, we did not put the link because of the blind review.</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.