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355 results for “Test Developer”
An Evaluation of a Multi-target Stool DNA (Mt-sDNA) Test, Cologuard, for CRC Screening in Individuals Aged 45-49 and at Average Risk for Development of Colorectal Cancer: Act Now
ClinicalTrials.gov study NCT03728348. IPD Sharing: YES. Countries: 1. Publications: 1.
Novel developments in clamped geometries for fracture toughness testing
<p>Stable fracture toughness test geometries are useful in not only determining the monotonic fracture toughness, but also in capturing the R-curve behaviour and damage tolerance of materials under cyclic loading and extreme environments. The doubly clamped boundary condition offers such crack stability even in the most brittle materials. This talk will cover all aspects of clamped beams and wires in tension and bending as fracture toughness test geometries including their geometric factor solutions for linear elastic and elastic-plastic cases. Finite element simulations are used to explain the mechanics of crack stability for various beam and wire aspect ratios and crack configurations. Their varied applications in bulk materials, architectured systems, thin film multilayers and graded coatings will be shown.</p> <p><strong>Related publications</strong></p> <ol> <li>A. K. Mishra, A. Lambai and V. Jayaram, B. Nagamani Jaya, “The edge-notched clamped beam bend specimen as a fracture toughness test geometry”, Theoretical and Applied Fracture Mechanics, 105, 2020, 102409 (DOI: 10.1016/j.tafmec.2019.102409)</li> <li>B. Nagamani Jaya, Sanjit Bhowmick, S. A. Syed Asif, Oden L. Warren and Vikram Jayaram, “Optimization of clamped beam geometry for fracture toughness testing of micron-scale samples” Phil Mag Special Issue on Nanomech IV, Vol 95, 2015, 1945-1966 (DOI: 10.1080/14786435.2015.1010623)</li> <li>B. Nagamani Jaya and Vikram Jayaram, “Crack stability in edge notched clamped beam specimen under bending: modeling and experiments”, International Journal of Fracture, Vol 188, Issue 2, 2014, 213-228 (DOI: 10.1007/s10704-014-9956-2)</li> <li>B. Nagamani Jaya, Vikram Jayaram and Sanjay K. Biswas, “A new method for fracture toughness determination of graded (Pt,Ni)Al bond coats by microbeam bend tests”, Philosophical Magazine Special Issue on Nanomechanical Testing in Materials Research and Development III, Vol 92, Issue 25-27, 2012, 3326-3345. (DOI: 10.1080/14786435.2012.669068)</li> </ol>
Refactoring Test Smells: A Perspective from Open-Source Developers
<p>Presentation video for the <strong>5th Brazilian Symposium on Systematic and Automated Software Testing (SAST)</strong>, during the <strong>11th Brazilian Conference on Software: Practice and Theory (CBSoft 2020)</strong></p>
Sibling rivalry: Males with more brothers develop larger testes
<p>When females mate with multiple partners in a reproductive cycle, the relative number of competing sperm from rival males is often the most critical factor in determining paternity. Gamete production is directly related to testis size in most species, and is associated with both mating behavior and perceived risk of competition. Deer mice, <i>Peromyscus maniculatus</i>, are naturally promiscuous and males invest significantly more in sperm production than males of <i>P. polionotus</i>, their monogamous sister‐species. Here, we show that the larger testes in <i>P. maniculatus</i> are retained after decades of enforced monogamy in captivity. While these results suggest that differences in sperm production between species with divergent evolutionary histories can be maintained in captivity, we also show that the early rearing environment of males can strongly influence their testis size as adults. Using a second‐generation hybrid population to increase variation within the population, we show that males reared in litters with more brothers develop larger testes as adults. Importantly, this difference in testis size is also associated with increased fertility. Together, our findings suggest that sperm production may be both broadly shaped by natural selection over evolutionary timescales and also finely tuned during early development.</p>
Perturbed Synthetic SWOT Datasets for Testing and Development of a Kalman Filter Approach to Estimate Daily Discharge
<p><strong>1. Introduction</strong></p> <p>Datasets are used to evaluate the performance of a Kalman filter approach to estimate daily discharge. This is a perturbed version of synthetic SWOT datasets consisting of 15 river sections, which are commonly agreed datasets for evaluating the performance of SWOT discharge algorithms (Frasson et al., 2020, 2021). The benchmarking manuscript entitled “A Kalman Filter Approach for Estimating Daily Discharge Using Space-based Discharge Estimates” is currently under review at Water Resources Research. Once the manuscript is accepted, its DOI will be included here.</p> <p> </p> <p><strong>2. </strong><strong>File description</strong></p> <p>The datasets are generally divided into two categories: river information (River_Info) and time series data (Timeseries_Data). River information provides fundamental and general river characteristics, whereas time series data offers daily reach-averaged data for each reach. In time series data, the data mainly contains three components: true data, perturbed measurements, and true and perturbed flow law parameters (A0, an, and b). For each reach, there are 10000 realizations of perturbed measurements per time step and there are 100 realizations of time-invariant perturbed flow law parameters through a Monte Carlo simulation (Frasson et al., 2023). Moreover, to support our proposed Kalman filter approach to estimate daily discharge, the datasets provide the median of the perturbed discharge, river width, water surface slope, and change in the cross-sectional area, as well as the uncertainty of the perturbed discharge and change in the cross-sectional area based on the interquartile range (Fox, 2015).</p> <p>To support reproducibility and facilitate example usage, we now include a MATLAB code package (<code>KalmanFilter_Code.zip</code>) that demonstrates how to run the Kalman filter approach using the Missouri Downstream case as an example. </p> <p>Datasets are contained in a .mat file per river. The detailed groups and variables are in the following:</p> <p><strong>River_Info</strong></p> <p>Name: River name, data type: char</p> <p>QWBM: Mean annual discharge from the water balance model WBMsed (Cohen et al., 2014)</p> <p>rch_bnd: Reach boundaries measured in meters from the upstream end of the model</p> <p>gdrch: Good reaches in the study. They were used to exclude small reaches defined around low-head dams and other obstacles where Manning’s equation should not be applied.</p> <p><strong>Timeseries_Data</strong></p> <p>t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1, time step.</p> <p>A: Reach-averaged cross-sectional area of flow in m<sup>2</sup>. Dimension: Reach, time step.</p> <p>Q_true: True reach-averaged discharge (m<sup>3</sup>/s). Dimension: Reach, time step.</p> <p>Q_ptb: Perturbed discharge (m<sup>3</sup>/s), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>med_Q_ptb: Median perturbed discharge (m<sup>3</sup>/s) across the 10000 realizations. Dimension: Good reach, time step.</p> <p>sigma_Q_ptb: Uncertainty of the perturbed discharge (m<sup>3</sup>/s), calculated based on the interquartile range. Dimension: Good reach, time step.</p> <p>W_true: True reach-averaged river width (m). Dimension: Reach, time step.</p> <p>W_ptb: Perturbed river width (m), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>med_W_ptb: Median perturbed river width (m) across the 10000 realizations. Dimension: Good reach, time step.</p> <p>H_true: True reach-averaged water surface elevation (m). Dimension: Reach, time step.</p> <p>H_ptb: Perturbed water surface elevation (m), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>S_true: True reach-averaged water surface slope (m/m). Dimension: Reach, time step.</p> <p>S_ptb: Perturbed water surface slope (m/m), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>med_S_ptb: Median perturbed water surface slope (m/m) across the 10000 realizations. Dimension: Good reach, time step.</p> <p>dA_true: True reach-averaged change in the cross-sectional area (m<sup>2</sup>). Dimension: Good reach, time step.</p> <p>dA_ptb: Perturbed change in the cross-sectional area (m<sup>2</sup>), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>med_dA_ptb: Median perturbed change in the cross-sectional area (m<sup>2</sup>) across the 10000 realizations. Dimension: Good reach, time step.</p> <p>sigma_dA_ptb: Uncertainty of the perturbed change in the cross-sectional area (m<sup>2</sup>), calculated based on the interquartile range. Dimension: Good reach, time step.</p> <p>A0_true: True baseline cross-sectional area (m<sup>2</sup>). Dimension: Good reach, 1.</p> <p>A0: Perturbed baseline cross-sectional area (m<sup>2</sup>), including 100 realizations for each parameter. Dimension: Good reach, 100.</p> <p>na_true: True friction coefficient. Dimension: Good reach, 1.</p> <p>na: Perturbed friction coefficient, including 100 realizations for each parameter. Dimension: Good reach, 100.</p> <p>b_true: True exponent coefficient. Dimension: Good reach, 1.</p> <p>b: Perturbed exponent coefficient, including 100 realizations for each parameter. Dimension: Good reach, 100.</p>
A multi-omics systems vaccinology resource to develop and test computational models of immunity: 1st challenge dataset and submissions
<p>The goal of the CMI-PB prediction contest is to foster a collaborative research community that can collectively tackle challenges and accelerates scientific progress beyond the capabilities of individual researchers or groups. The CMI-PB consortium has curated multi-source data from multiple individuals, encompassing Ab titers (around four antibodies/features), cell frequency (approximately 20 cell types/features), gene expression (roughly 50,000 RNA transcripts/features), and plasma proteomics (around 50 proteins/features). The challenge requires integrating these diverse data sources to predict different immune responses or tasks. Specifically, you will utilize multi-source data from several individuals on day 0 (baseline) to predict specific immune responses at later time points (1, 3, 7, and 14 days post-booster vaccination).</p> <p>The first CMI-PB challenge, which is an internal challenge, was conducted using datasets from 2020 (train) and 2021 (test). In the following sections, we provide detailed information on the datasets, challenge tasks, submission format, descriptions, and access to the necessary data files for participants to develop their models and make predictions.</p> <p><br><strong>A) Multiomics CMI-PB dataset:</strong></p> <p>We propose a study design that enables a systems-level understanding of the immune responses through computational modeling. Our cohort comprises aP vs. wP infancy-primed subjects boosted with Tdap. We recruit individuals born before 1995 (wP) and after 1996 (aP), collect baseline plasma and blood samples, and then at 1, 3, 7 and 14 days post booster vaccination.</p> <p>With the obtained samples processed, we generated omics data by:</p> <ul> <li> <p>Bulk PBMCs transcriptomics,</p> </li> <li> <p>Plasma proteomics using Olink, which provides a quantitative readout of cytokines, chemokines, and other immune factors,</p> </li> <li> <p>Cell frequency in PBMCs using flow cytometry,</p> </li> <li> <p>Tdap-specific antibodies levels</p> </li> </ul> <p><strong>B) List of tasks can be accessed using the “List of tasks for challenge 1.docx” file, and submissions need to submit in provided format here: “submission template challenge 1.tsv”</strong></p> <p><strong>C) Datasets for model building and making predictions:</strong></p> <p> Data files are divided into two categories: 1) raw dataset and 2) computable matrices.</p> <ol> <li> <p><strong>Raw dataset: </strong>This raw-most dataset is divided into training and test datasets. </p> </li> <li> <p><strong>Computable matrices: </strong>There are three different types of computable matrices. a) Full: These files are generated by dividing raw files into sub-files specific to planned days specific to vaccination. b) harmonized: These are generated by preserving only overlapping features between train and test datasets. b) imputed: MICE imputation is performed to impute missing values in the dataset.</p> </li> </ol> <p><strong>D) Submission evaluation</strong></p> <p>This folder contains all submitted models with ranking files and code for evaluating these models.</p> <p><strong>To learn more about the CMI-PB prediction challenge, visit our website at www.cmi-pb.org.</strong></p>
Files used to develop and test SLAG, Seeded Local Assembly of Genes
<p>This dataset consists of configuration files and results files involved in developing and testing SLAG, an iterative Perl pipeline to construct local assemblies seeded on query sequences. SLAG is intended for situations where a full genome assembly is economically or technically infeasible, and thus it emphasizes methods that can deal with shallow read depth. SLAG and its user-runnable test suite have been deposited at https://github.com/cfcrane/SLAG. This Dryad repository contains files that were used in preparing a manuscript, "SLAG: A Program for Seeded Local Assembly of Genes in Complex Genomes", which has been submitted to Molecular Ecology Resources. The repository is divided into sections for scripts, simulated reads, real reads, and benchmarking against two functionally similar programs, aTRAM2 and SRAssembler.</p>
Dataset for the research paper "How and Why Developers Migrate Python Tests from unittest to pytest"
<p>This is the dataset for the proposed paper "How and Why Developers Migrate Python Tests from unittest to pytest". <br> <br> <br> The `10_systems` zip file contains the aggregated and intermediate files for the systems used for precision and recall analysis.</p> <p>The `top_100_systems` zip file contains the aggregated and intermediate files for the top 100 python systems analyzes.</p> <p>The `__rq_reason` contains data to assess the advantages and disadvantages found in 100 issues or pull requests. The second column indicates whether issues/PRs were selected to be analyzed and the following columns indicate if the advantages (A) or disadvantages (D) are present or not.</p>
How and Why Developers Migrate Python Tests [Master dissertation]
<p>Dataset for the master dissertation "How and Why Python Developers Migrate to Pytest".</p> <p>The `10_systems` zip file contains the aggregated and intermediate files for the systems used for precision and recall analysis.</p> <p>The `top_100_systems` zip file contains the aggregated and intermediate files for the top 100 python systems analyzes.</p> <p>The `rq5_*` files contain data to assess the advantages and disadvantages found in 100 issues or pull requests and in the Grey Literature Review. The columns indicate if the advantages (A) or disadvantages (D) are present or not.</p> <p>Lastly, the `rq6_*` files present a similar structure, with themes defined while performing a thematic analysis for qualitative research questions.</p>
Meiotic drive adaptive testes enlargement during early development in the stalk-eyed fly.
<p><span>The sex ratio 'SR' X-linked meiotic drive system in stalk-eyed flies destroys Y-</span><span>bearing sperm. Unlike other SR systems, drive males do not suffer fertility loss. They have greatly enlarged testes which compensate for gamete killing. We predicted that enlarged testes arise from extended development with resources re-allocated from the accessory glands, as these tend to be smaller in drive males. To test this, we tracked the growth of the testes and accessory glands of wild-type and drive males over 5–6 weeks post-eclosion before males attained sexual maturity. Neither of the original predictions is supported by these data. Instead, we found that the drive male testes were enlarged at eclosion, reflecting a greater allocation of resources to the testes during pupation. Testes grow at a higher rate during early adult development, but there was no evidence that this retards the growth of the accessory glands. Further experiments are proposed to investigate whether smaller accessory glands only arise in drive males post-copulation or when flies are subjected to nutritional stress. Our experimental findings support the idea that enlarged testes in drive males arise as an adaptive allocation of resources to traits that enhance male reproductive success.</span></p>
An empirical study on the influence of developers' experience on software test code quality
<p>Software developers' engagement in open-source software projects lies in different levels of participation, e.g., core or peripheral developers. Recent studies have investigated the role of developers' contributions and their influence on software quality. However, few studies investigate the relationship between the developers' experience and test code quality in software projects. In this study, we aim to investigate the relationship between the developers' experience and the test code quality from the perspective of test smells. We performed an empirical study to analyze the insertion and removal of test smells in four open-source Java projects. We collected 18 test smells and calculated their authorship through the projects' Tags. The four software projects contain 386 test classes and 5,178 test smells. We found out that the insertion of 67.28\% of test smells occurs during the test class creation, and the removal of 20.88\% of test smells occurs during the evolution of projects. In addition, core developers are responsible for inserting 88.91\% and removing 89.82\% test smells. Core developers insert and remove more test smells than the peripheral developers. Most test smell removal is due to test code deletion, which may indicate that both core and peripheral developers are unaware of test smells in test code.</p>
Rotterdam EyePACS AIROGS Lite development and test set
<p>Development and test set for the AIROGS Lite challenge (<a href="https://airogs-lite.grand-challenge.org/">https://airogs-lite.grand-challenge.org/</a>).</p>
Comparing developer-provided to user-provided tests for fault localization and automated program repair: Artifacts
<p>Artifacts for the paper <em>Comparing developer-provided to user-provided tests for fault localization and automated program repair.</em></p> <p>Note that the artifacts are maintained in the following repositories:</p> <ul> <li>https://github.com/rjust/defects4j</li> <li>https://bitbucket.org/rjust/tests-tested-data</li> <li>https://bitbucket.org/rjust/fault-localization-data</li> </ul>
2SPT Test Time Series: Quality Control for Community Based Sea Ice Model Development
<p>2SPT Test Time Series: Quality Control for Community Based Sea Ice Model Development</p> <p>------------------------------------------------------------------------------------</p> <p>This dataset comes from Regional Arctic System Model (RASM) simulations by Andrew F. Roberts at Naval Postgraduate School, described and used in Figure 3 in the manuscript:</p> <p>Roberts, Hunke, Allard, Bailey, Craig, Lemieux and Turner (2018): Quality Control for Community Based Sea Ice Model Development.</p> <p>Files for the series in Figure 3 as they appear in that paper to demonstrate the Two-Stage Paired Thickness Test:</p> <p>Figure 3a:</p> <p>R1009RBRcevp01a.cice.h.hi.1996_2000.North_Pole.nc</p> <p>R1009RBRcevp01a_2.cice.h.hi.1996_2000.North_Pole.nc</p> <p> </p> <p>Figure 3b:</p> <p>R1009RBRcevp01a.cice.h.hi.1996_2000.North_Pole.nc</p> <p>R1009RBRceap01a.cice.h.hi.1996_2000.North_Pole.nc</p> <p> </p> <p>Figure 3c:</p> <p>R1009RBRcevp01a.cice.h.hi.1996_2000.North_Bathurst_Island.nc</p> <p>R1009RBRceap01a.cice.h.hi.1996_2000.North_Bathurst_Island.nc</p> <p> </p> <p>Where the name "evp" or "eap" in the file name corresponds to the EVP and EAP simulations as described in the manuscript. These are selected timeseries from 1996 to 2000 from the RASM simasimulations described in section 2 of the above manuscript. CICE namelist settings are identical to the description for the dataset:</p> <p>Roberts, A. (2018). RASM simulations: Quality Control for Community Based Sea Ice Model Development [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1308236</p> <p> </p>
Tomographic data for testing, demonstrating, and developing methods of removing ring artifacts
<p>These tomographic data were used for demonstrating our methods of eliminating ring artifacts published in Optics Express, <em>Nghia T. Vo, Robert C. Atwood, and Michael Drakopoulos, "Superior techniques for eliminating ring artifacts in X-ray micro-tomography," <strong>26</strong>, 28396-28412 (2018)</em><em>. </em>In sinogram, the artifacts appear as straight lines or stripe artifacts. The data have many types of stripe artifacts: full stripes, partial stripes, unresponsive stripes, fluctuating stripes, and blurry stripes. They are very useful for testing and developing methods of removing ring artifacts.</p> <p>Documentation: <a href="https://sarepy.readthedocs.io/">https://sarepy.readthedocs.io/</a></p> <p>Python implementations of these methods:</p> <p><a href="https://github.com/nghia-vo/sarepy">https://github.com/nghia-vo/sarepy</a></p> <p>In Tomopy:</p> <p><a href="https://tomopy.readthedocs.io/en/latest/api/tomopy.prep.stripe.html">https://tomopy.readthedocs.io/en/latest/api/tomopy.prep.stripe.html</a></p> <p>In Savu:</p> <p><a href="http://github.com/DiamondLightSource/Savu/tree/master/savu/plugins/ring_removal">https://github.com/DiamondLightSource/Savu/tree/master/savu/plugins/ring_removal</a></p> <p>In Algotom:</p> <p><a href="https://github.com/algotom/algotom/blob/master/algotom/prep/removal.py">https://github.com/algotom/algotom/blob/master/algotom/prep/removal.py</a> </p>
ANN development + final testing datasets
<p><strong>File name definitions:</strong></p> <p>'...v_50_175_250_300...' - dataset for velocity ranges [50, 175] + [250, 300] m/s</p> <p>'...v_175_250...' - dataset for velocity range [175, 250] m/s</p> <p>'ANNdevelop...' - used to perform 9 parametric sub-analyses where, in each one, many ANNs are developed (trained, validated and tested) and the one yielding the best results is selected</p> <p>'ANNtest...' - used to test the best ANN from each aforementioned parametric sub-analysis, aiming to find the best ANN model; this dataset includes the 'ANNdevelop...' counterpart</p> <p> </p> <p><strong>Where to find the input (independent) and target (dependent) variable values for each dataset/excel ?</strong></p> <p>input values in 'IN' sheet </p> <p>target values in 'TARGET' sheet</p> <p> </p> <p><strong>Where to find the results from the best ANN model (for each target/output variable and each velocity range)?</strong></p> <p>open the corresponding excel file and the expected (target) vs ANN (output) results are written in 'TARGET vs OUTPUT' sheet</p> <p> </p> <p> </p> <p><strong>Check reference below</strong> (to be added when the paper is published)</p> <p><a href="https://www.researchgate.net/publication/328849817_11_Neural_Networks_-_Max_Disp_-_Railway_Beams">https://www.researchgate.net/publication/328849817_11_Neural_Networks_-_Max_Disp_-_Railway_Beams</a></p>
Development of a Standard Test Method for Characterization of Asphalt Modifiers and Aging-Related Degradation Using an Extensional Rheometer
<p>Corresponding data set for Tran-SET Project No. 17BLSU01. Abstract of the final report is stated below for reference:</p> <p>"An extensional deformation test method using a Sentmanat Extensional Rheometer (SER) fixture inside a Dynamic Shear Rheometer (DSR) is developed in this study to investigate the degradation of the polymer due to aging and to investigate the effect of modifier type. A relationship between different percentages of modifier and ductility of the modified binder is also investigated. The sample geometrics used in this study are 1 mm 0.72 mm and 3 mm 0.72 mm. A total of one hundred and sixty-two samples were tested. Three modifiers Styrene-Butadiene-Styrene (SBS), Polyphosphoric Acid (PPA) and latex were used. One PG 76-22, one PG 64-22 and one polymer-modified asphalt emulsion (PAME) were used. First peak elongation force, (F1) is the binders’ stiffness and Second peak elongation force, (F2) is the polymer characteristics. F2 is more visible comparatively at the higher temperature. In most cases, F2 reduces after Rolling Thin Film Oven (RTFO) and Pressure Aging Vessel (PAV) aging. To normalize the stiffness effect of F1 on F2, in this study F2/F1 was used to analyze aging susceptibility of modifiers. All the testing temperatures used in this study exhibited a reduction in F2/F1 due to RTFO aging and further reduction due to PAV aging. Therefore, through this study, it is recommended that this parameter can be used to determine aging susceptibility of polymer in a polymer-modified asphalt binder. F2 is only obtained from the SBS and latex modified binders and emulsions. Addition of PPA did not show any F2, making SBS the most effective modifier among SBS, PPA and latex. F2 has a linear correlation with the percent of the polymer in the PMAE, SBS modified PG 64-22, SBS and PPA modified PG 64-22 and latex modified PG 64-22 with R2 values equal to 0.9934, 0.9323, 0.9893 and 0.9535 respectively, indicating extensional deformation test with SER very promising. Ductility analyses using final angular strain values indicate that modifiers increase ductility significantly while aging reduces ductility. Additional research is required for testing ultra-violet (UV) aged sample, and a DSR-based SER test specification will be developed subsequently."</p>
Dataset for "Provide Developer-Approved Descriptive Names for Unit Tests"
<p>Dataset for "Provide Developer-Approved Descriptive Names for Unit Tests"</p> <p>Author: Jianwei Wu, James Clause</p> <p>Please contact at wjwcis@udel.edu for any questions.</p>
Simulated and real datacubes for developing and testing changepoint algorithms for spatially correlated, short and noisy time-series
<p>A set of datacubes where z-dimension is time, thus each (x,y,.) is a timeseries. The idea is to detect sudden changes in each series, assuming 1) the series can be quite short and noisy 2) the change occurs in spatial patches. The set has synthetic examples with known change-events, and a real-world dataset with unknown change-events. The data files are related to the pape</p> <p>T Rajala, P Packalen, M Myllymäki, A Kangas (2023): Improving detection of changepoints in short and noisy time-series with local correlations: Connecting the events in pixel neighbourhoods, "Journal of Agricultural, Biological and Environmental Statistics", https://doi.org/10.1007/s13253-023-00546-1</p> <p>More of the NFI data is available from Natural Resources Institute Finland, https://kartta.luke.fi/index-en.html</p> <p>See `data/00data_readme.txt` for further details.</p> <p> </p>
Supporting Data Set for Paper "Can Videos as a By-Product of GUI Testing Help Developers Understand GUI Tests?"
<p>This data set is a supporting material for an accepted paper "Can Videos as a By-Product of GUI Testing Help Developers Understand GUI Tests?" on 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW 2023).</p> <p>This dataset consists of</p> <ul> <li>a consent form of the study in English;</li> <li>a tutorial video for TakeNote App (see <em>TakeNote Tutorial-v02</em>);</li> <li>a questionnaire in HTML format;</li> <li>used videos (in <em>HTML Video Player with Videos and VTT files</em>) and screenshots;</li> <li>the source code of the HTML Video Player (in <em>HTML Video Player with Videos and VTT files</em>);</li> <li>obtained and coded results from the questionnaire (see <em>Study-Data-4EmpiRE-v22</em>);</li> <li>calculation steps of the Mann-Whitney U Test;</li> <li>the source code of the TakeNote App.</li> </ul>
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.