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355 results for “Test Developer”
A dataset recorded during development of an affective brain-computer music interface: testing session
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An External Replication on the Effects of Test-driven Development Using a Multi-site Blind Analysis Approach
<p>This dataset contains the <strong>unblinded </strong>version of the data collected and analyzed for the experiment reported in the paper. </p> <p>The semantics of the data can be found in the spreadsheet. For the formulas on how to obtain this data from the raw data, please see the paper. </p>
Developing a deep Learning network to retrieve ocean hydrographic profiles in the North Atlantic from combined satellite and in situ measurements: test datasets.
<p>We provide here the datasets used for the test and assessment of a deep learning algorithm which is presently candidate for the development of a daily 3D ocean product covering the North Atlantic at 1/10° resolution, over the 2010-2018 period, as part of the European Space Agency World Ocean Circulation project (ESA-WOC). The method is based on a stacked Long Short-Term Memory neural network, coupled to a Monte-Carlo dropout approach, and allows to project satellite-derived sea surface temperature, sea surface salinity and absolute dynamic topography data at depth after training with sparse co-located in situ vertical hydrographic profiles (Buongiorno Nardelli, 2020, doi:<a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.3390%2Frs12193151?_sg%5B0%5D=0xE-347r7Hvb80klJcEo811AhUiXq-twG_E6l4yB-BfIKkVtW-lVLGcO02mTFkUczvozYYI0WCPyUBFR3kzWNGGZKg.ftvLheFrzHIJriO4qW2bdxalvR_TWt3MpwUfvto3EemhRgvDRGwJ9Mdy4Xr0IcGCfICivf4j-VqTgKxVvXRogA">10.3390/rs12193151</a>). </p> <p>The test dataset presented here includes different sets of co-located temperature and salinity vertical profiles: </p> <ul> <li>in situ observations extracted from the quality controlled Argo and CTD profiles produced by Copernicus Marine Environment Monitoring Service CORA 5.2 (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) and interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals);</li> <li>climatological profiles extracted from World Ocean Atlas 2013 optimally interpolated monthly fields (Locarnini et al., 2013; Zweng et al., 2013), interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals), upsized to a 1/10° horizontal grid through a cubic spline and linearly interpolated in time between the central day of each month;</li> <li>synthetic profiles obtained through three different techniques: multivariate EOF reconstruction, a 2 layer feed-forward network (with 1000 units in each hidden layer) and a stacked LSTM network (with 2 LSTM layers and 35 hidden units)</li> </ul> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Deep Learning network to retrieve ocean hydrographic profiles from combined satellite and in situ measurements, 2020, <em>submitted</em>.</p> <p>Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E., Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D. R., Hamilton, M. and Seidov, D.: World Ocean Atlas 2013. Vol. 1: Temperature., S. Levitus, Ed.; A. Mishonov, Tech. Ed.; NOAA Atlas NESDIS, 73(September), 40, doi:10.1182/blood-2011-06-357442, 2013.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p> <p>Zweng, M. M., Reagan, J. R., Antonov, J. I., Mishonov, A. V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., Seidov, D. and Bidlle, M. M.: World Ocean Atlas 2013, Volume 2: Salinity, NOAA Atlas NESDIS, 119(1), 227–237, doi:10.1182/blood-2011-06-357442, 2013.</p> <p> </p>
How Developers Review Tests in GitHub?
<p>This dataset contains 330 code reviews with 40 tips, 16 request categories, 8 response categories, and 13 Pull Request and comment features. This dataset's column titles are:</p> <p>Project Owner Project Repo Project URI Project Language Pull Request URI Pull Request ID Pull Request Author Pull Request Merge Commit Hash Review comment URI Review comment ID Review comment Text Solving Commit Hash Solving Commit URI Validation Request Category Tips Response Category Test Case Unspecified Test Method Fix SUT Optional Test Improve Test Refactor Test Test Class Fix Test Test Branch Test Statement Achieve Specific Coverage Goal ML Model Test Prevent Exploit Disagreement Revert Test Removal Unit Test Code Snippet End-to-End Test Edge Case Expected Exception Filepath Set Up Parametric Negative Test Test Double Type Support Based On External Resource Integration Test Positive Test Avoid Wrong API Usage Increase Testability Rename Test Dependency Reproduce Issue More Specific Test Event Test Assert Message Fix Based On Test Boundary Test Regression Test Async Thread Consistency Test Move Test Compilation Check Extract Member Cache Modifier Empty Test File Merge Test Readability Sleep Invalid Test Remove Reflection</p>
Codes and test datasets developed for Mapping paleolacustrine deposits with a UAV-borne multispectral camera: Implications for future drone mapping on Mars.
<p>NASA’s Ingenuity Mars Helicopter has ushered in a new era in planetary exploration by utilizing Unmanned Aerial Vehicles (UAVs) to enhance our understanding of planetary surfaces. This project evaluates the potential of UAVs for mapping Martian environments, using Lake Natron, Tanzania, as an analog for Martian paleolakes.</p> <p>During two field seasons (January and July 2023), we employed a Phantom 4 Pro drone equipped with a MicaSense RedEdge-M multispectral camera and a TerraSpec Halo VNIR-SWIR spectrometer to capture high-resolution imagery and spectral data. Almost all image processing and analysis were performed using Python scripting, except for image mosaic and Digital Elevation Model (DEM) generation.</p> <p>We benchmarked the onboard image processing capabilities using a Raspberry Pi 5 single-board computer. </p> <p>In this repository, we share all the code developed during our study. Processing steps include,<br>1. DN to radiance conversion<br>2. Panel radiance extraction<br>3. Calculate reflectance factors using DLS data<br>4. Calculate reflectance at MicaSense band<br>5. Convert radiance to reflectance using 1 point empirical line method (1p ELM)<br>6. Convert radiance to reflectance using 2 point empirical line method (2p ELM)<br>7. Atmospheric correction using 6SV method<br>8. Convert radiance to reflectance using DLS data<br>9. Calculate Band indices<br>10. Weighted Kmean clustering<br>11. Finding the optimal number of clusters using the elbow method<br>12. Cmean clustering</p> <p>We also included sample image data used in the study. Feel free to contact us for more information/data.</p>
ViF-GTAD: A new Automotive Data Set with Ground Truth for ADAS/AD Development, Testing and Validation
<p>A new dataset for automated driving, which is the subject matter of this paper, identifies and addresses a gap in existing similar perception data sets. While the most state-of-the-art perception data sets primarily focus on provision of various on-board sensor measurements along with the semantic information under various driving conditions, the provided information is often insufficient since the object list and position data provided include unknown and time-varying errors. The current paper and the associated data-set describes the first publicly available perception measurement data that include not only the on-board sensor information from camera, Lidar and radar with semantically classified objects, but also the high precision ground-truth position measurements enabled by the accurate RTK assisted GPS localization systems available on both the ego vehicle and the dynamic target objects. This paper provides insight on the capturing of the data, explicitly explaining the meta data structure and the content, as well as the potential application examples where it has been, and can potentially be, applied and implemented in relation to automated driving and environmental perception systems development, testing and validation.</p>
The development of the Leuven Embedded Figures Test (L-EFT)
<p>The Embedded Figures task has a long history as an important clinical and psychological test. There is however some ambiguity as to what exactly the test measures. This ambiguity is brought into clear focus by the fact that some researchers use the test as a measure of a local or global perceptual bias while others regard the test as a good measure of a much broader cognitive capacity related to intelligence or executive function. Given the importance of this test, particularly in clinical domains such as Autism, we have set out to develop a new version of the embedded figures test that more systematically manipulates the perceptual factors that contribute to the effective embedding of a target in a complex context. The result from two experiments will be presented, in which a range of factors, including continuity, complexity, closure and symmetry are revealed as potentially important. Based on these two experiments, a new set of stimuli will be presented which will form the basis of our new version of the Embedded Figures Test, which we plan to launch as an online test using the format of the Leuven Perceptual Organization Screening Test (L-POST). By more systematically manipulating the perceptual factors that contribute to effective embedding, we hope to offer a much more sensitive test, and a test that is better able to differentiate between genuine perceptual, as appose to executive, contributions to performance on this test.</p>
Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05) in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr
Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05)
Development of standardized definitions for Combined and Composite High Voltage Tests
<p>Driven by the increasing demands on traceable measurement systems and calibration services, being able to fully cover the required needs, a large number of NMIs have put a great effort on the development of standardized wave shapes definitions, low voltage calibrators, measuring instruments and software for combined and composite high voltage tests. The European Metrology Programme for Innovation and Research (EMPIR) supports this work through the normative Joint Research Project 19NRM07 HV-com², which started in 2020 and has received funding from the EMPIR co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation program. </p>
Dataset to: Realistic accelerated stress tests for PEM fuel cells: Test procedure development based on standardized automotive driving cycles
<p>This is the dataset to the published article "Realistic accelerated stress tests for PEM fuel cells: Test procedure development based on standardized automotive driving cycles" (DOI: 10.1016/j.ijhydene.2023.08.292) in which the degradation of two commercial PEM fuel cell stacks was analyzed. <strong>Please cite this publication if you use the dataset in a publication as follows</strong>:</p> <p>P. Thiele, Y. Yang, S. Dirkes, M. Wick, S. Pischinger, Realistic accelerated stress tests for PEM fuel cells: Test procedure development based on standardized automotive driving cycles, Int. J. Hydrogen Energy 52 (Part D) (2024) 1065–1080, https://doi.org/10.1016/j.ijhydene.2023.08.292.</p>
Test data set for macros accompanying the publication Multi-parameter screening method for developing optimized red fluorescent proteins
<p>This a bundle of test data can be used to run the macros accompanying the publication Multi-parameter screening method for developing optimized red fluorescent proteins.</p> <p>These data sets can be used to run the following macros that can be found on GitHub:</p> <ol> <li><a href="https://github.com/molcyto/MC-Ratio-96-wells">https://github.com/molcyto/MC-Ratio-96-wells</a></li> <li><a href="https://github.com/molcyto/MC-Ratio-Petri-dish">https://github.com/molcyto/MC-Ratio-Petri-dish</a></li> <li><a href="https://github.com/molcyto/MC-FLIM-Petri-dish">https://github.com/molcyto/MC-FLIM-Petri-dish</a></li> <li><a href="https://github.com/molcyto/MC-Bleach-96-wells">https://github.com/molcyto/MC-Bleach-96-wells</a></li> <li><a href="https://github.com/molcyto/MC-Scatter5D">https://github.com/molcyto/MC-Scatter5D</a></li> <li><a href="https://github.com/molcyto/MC-FLIM-96-wells">https://github.com/molcyto/MC-FLIM-96-wells</a></li> </ol> <p>Funding:<br> This work was supported by the NWO CW-Echo grant 711.011.018 (M.A.H. and T.W.J.G.), grant 12149 (T.W.J.G.) from the Foundation for Technological Sciences (STW) from the Netherlands</p> <p> </p>
Data Set for the Development and Testing of the MC23 Nonclassical-Energy Functional
<p>This dataset contains files used to train and test the Multi-Configuration 23 (MC23) functional and to compare the results to other methods. It includes files to carry out electronic structure calculations. These include molecular geometries in xyz format, <em>OpenMolcas</em> input files for CASSCF calculations, converged CASSCF natural orbitals, <em>OpenMolcas</em> basis set files, and <em>Gaussian 16</em> formatted checkpoint files for KS-DFT calculations. It also includes data used for data processing such as stoichiometries, absolute energies, and reference energies.</p> <p>Each file in this dataset is a .tar.xz archive. One can extract them by the following command:</p> <pre>tar -xJf name_of_archive.tar.xz</pre> <p>Below is a description of the content of each archive.</p> <p><strong>gaussian_16_fchk.tar.xz</strong> contains <em>Gaussian 16</em> formatted checkpoint files for all KS-DFT calculations used in this work. The files in the archive are named as <em>functional</em>/<em>database</em>/<em>system</em>.fchk</p> <p><strong>openmolcas_basis_set.tar.xz</strong> contains <em>OpenMolcas</em> basis set files used for multireference calculations. To reproduce the results in this work, the basis set files should be placed in the “basis_library” directory in the <em>OpenMolcas</em> installation location.</p> <p><strong>openmolcas_wave_function.tar.xz</strong> contains files needed by <em>OpenMolcas</em> to reproduce the CASSCF wave function used in this work. The files in the archive are named <em>database</em>/<em>system</em>.*.</p> <ul> <li>The file <em>system</em>.xyz contains the Cartesian coordinates. Note that for Data Set 2, the coordinates are in the input files <em>system</em>.inp.</li> <li>The file <em>system</em>.inp contains the <em>OpenMolca</em>s input file to perform CASSCF calculations.</li> <li>The files <em>system</em>.RasOrb, <em>system</em>.rasscf.h5, and <em>system</em>.rasscf.molden contain the converged CASSCF natural orbitals.</li> </ul> <p><strong>gaussian_16_stoichiometry_energy.tar.xz</strong> and <strong>openmolcas_stoichiometry_energy.tar.xz</strong> contain files used for data processing.</p> <ul> <li>Files with names like <em>database</em>.ref contain information used to calculate the final energies and errors. They are tab-delimited files. Each row represents an energy difference (e.g. atomization energy, barrier height, etc.). The first column contains the name of the energy difference (note: spaces may be present in this column). This is followed by the file names of each electronic structure calculation and the stoichiometries used to calculate the energy difference from the absolute energies. Each name or stoichiometry occupies one column. The second from the last column contains the reference value in kcal/mol. The reference values contain spin–orbit coupling. The last column contains the factor by which the final energy should be divided. This factor usually equals 1, but it can be greater than 1 for databases calculating atomization energies per bond or per atom.</li> <li>Files with names like <em>method</em>.elist contain the absolute energies of each electronic structure calculation. They are tab-delimited files. Each row represents an electronic structure calculation, and each row always contains two columns. The first column is the file name of the calculation in the format <em>database</em>/<em>system</em>. The second column is the absolute energy in atomic units extracted from the output file of electronic structure programs.</li> <li>The file named SOC.dat contains the spin–orbit coupling term in kcal/mol to be added to each electronic structure calculation prior to calculating energy differences. It has the same format as files with names like <em>method</em>.elist.</li> </ul> <p>The database names in the directory names use a slightly different convention than the ones in the article describing MC23. A prefix DS2_ or DS3_ is used to indicate the data set to which a database belongs, and the number of data points is removed from the database name. For example, the MR-MGN-BE8 database from Data Set 2 has a file name DS2_MR-MGN-BE.</p>
Developer-Centric Test Amplification: The Interplay Between Automatic Generation and Human Exploration --- Appendix
<p>This online appendix contains the accumulated code occurences during the interviews performed to evaluate our developer-centric test amplification approach and the TestCube plugin. In addition, it documents the inter-rater-reliability analysis we performed.</p>
SPEA TESTBED TEST DATA FOR ML DEVELOPMENT
<p>The dataset files were generated at 3 different temperature with the improved ATE system developed in WP3 of the MET4FOF Project. </p> <p>The data are available for ML purposes and metrological investigation.</p>
Tools and Sample Files to Test the DSS developed in NIVA UC1a
<p>The sample files can be imported by a user to test the DSS, the link to install the tool (<a href="https://gitlab.com/nivaeu/uc1a_docker">https://gitlab.com/nivaeu/uc1a_docker</a> ). Please follow the instructions provided in the overview and instruction file.</p> <p> </p>
Anomaly Engine Development and Testing Datasets
<p>The following datasets have been used for development an testing of Anomaly Engine Webapp. They includes data samples for all the 4 scenarios:<br> 1 - Simple Self Financing<br> 2 - Self Financing by bank account<br> 3 - Indirect SF (by recharge)<br> 4 - Indirect Account Self Financing</p> <p>Each file row represents a graph relationship between a source node (<code>source</code>) and a destination node (<code>target</code>).</p> <p>Files must have the following schema:</p> <ul> <li><code>source_type</code>: Source node label;</li> <li><code>source_attributes</code>: Source node attributes. Can be null;</li> <li><code>target_type</code>: Destination node label;</li> <li><code>target_attributes</code>: Destination node attributes. Can be null;</li> <li><code>relation_type</code>: Relationship label;</li> <li><code>relation_attributes</code>: Relationship attributes. Can be null.</li> </ul> <p>Attributes must be expressed as key-value pairs separated by a semicolon <code>;</code>. For example</p> <pre><code>key1=value1;key2=value2</code></pre> <p> </p> <p>The datasets are also available in <a href="https://gitlab.infinitech-h2020.eu/pilot16/aml-graph-payments-anomaly-detection/-/tree/master/docker/docker_resources/docker_webapp/src/webapp_dash/assets">Infinitech Marketplace</a> </p>
Data to test IDP for agriculture in developing countries
<p>The data has variables capturing net outward foreign direct investment per capita, gross domestic product per capita and trade openness for agriculture in developing countries. The other variables are the official exchange rate, gross secondary school enrolment in per cent and inflation measured as per cent of CPI growth. These are for the total economy. </p> <p>Net outward foreign direct investment per capita (NOFDIPC) was constructed as outward foreign direct investment less inward foreign direct investment for agriculture, forestry and fishing. The sum is divided by the population of both sexes. The foreign direct investment and population data were obtained from FAOSTAT (https://www.fao.org/faostat/en/#data/FDI; https://www.fao.org/faostat/en/#data/OA). The gross domestic product per capita (GDPPC) was computed as agricultural value added divided by the population. Agricultural value added was also obtained from FAOSTAT (https://www.fao.org/faostat/en/#data/MK). Trade openness (AGTO) was computed as agricultural exports plus imports divided by agricultural value added. The exports and imports were obtained from FAOSTAT (https://www.fao.org/faostat/en/#data/TCL). Others; official exchange rate (EXRATE), gross secondary school enrolment in per cent (HC) and inflation measured as per cent of CPI growth (INFLA) were drawn from the world development indicators database of the World Bank (https://databank.worldbank.org/source/world-development-indicators#).</p>
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>
Developing and testing protocols for fouling treatments on oysters
<p>Aim: Develop protocols for effective fouling treatment on oysters (Ostrea edulis and Magallana gigas) in the lab.</p> <p>Fouling treatments were evaluated using lab trials (in tanks) for evaluation of effects of heat treatment of fouling organisms on oysters. A range of different temperatures and exposure times was evaluated. The data consists of parameters associated with the treatments (temperature) and survival of oysters, survival of fouling organisms. Data originates from the Swedish west coast.</p>
Developing and Testing the Enhancing Active Caregiver Training (EnACT) Intervention for Dementia Family Caregivers
ClinicalTrials.gov study NCT04920006. IPD Sharing: YES. Countries: 1. Publications: 5.
ScienceDex guides
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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.