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111 results for “Integration Testing”
Figure 2 in On the Integrity of Online Testing for Introductory Statistics Courses: A Latent Variable Approach
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A Trial to Assess the Effect of an Intervention Integrating Contingency Management (Financial Incentives) to Enhance Hepatitis C Treatment Uptake Following Dried Blood Spot Hepatitis C RNA Testing Amo
ClinicalTrials.gov study NCT04428346. IPD Sharing: NO. Countries: 0. Publications: 0.
GENErating Behavior Change, An Integrative Health Coaching and Genetic Risk Testing Pilot
ClinicalTrials.gov study NCT01766271. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Toxicity Testing in the 21st Century: An Integrated Approach to Genetic Safety Testing using the TGx-DDI Genomic Biomarker and High-Throughput CometChip®
GEO Series GSE196373. Homo sapiens. 192 samples. Type: Expression profiling by array; Expression profiling by high throughput sequencing.
Integrating whole blood transcriptomic collection procedures into the current anti-doping testing system, including long-term storage and re-testing of anti-doping samples
GEO Series GSE183133. Homo sapiens. 30 samples. Type: Expression profiling by high throughput sequencing.
Human Transcriptomic Integrated Approach for Testing and Assessment Oral Reference Dose for Steatosis
GEO Series GSE135117. Homo sapiens. 24 samples. Type: Expression profiling by array.
Synthetic dataset for the testing of an MT-Mag geophysical integration workflow
<p>This datasets is related to the manuscript "<strong>Utilisation of probabilistic MT inversions to constrain magnetic data inversion: proof-of-concept and field application</strong>", by Jérémie Giraud, Hoël Seillé, Gerhard Visser, Mark D. Lindsay, Vitaliy Ogarko, and Mark W. Jessell, intended for publication in Solid Earth. </p> <p>It is organised as follows. <br> <br> MT FOLDER<br> model subfolder: contains the synthetic resistivity model, 2 formats available:<br> - ModEM format (.mod)<br> - WinGLink format (.out)<br> <br> responses subfolder: contains the synthetic model responses, 2 formats available:<br> - ModEM format (.dat): This data has not been perturbed by synthetic noise. <br> - EDI format (.edi): This data has been perturbed by 5% Gaussian noise, this is the data used in the synthetic part of the study.<br> <br> coordinates of the synthetic MT sites with respect to the model: coordinates.txt <br> - it assumes the origin (0,0) in the center of the synthetic 3D model.<br> <br> Mag FOLDER<br> model subfolder: contains the magnetic susceptibility model at the Tomofast format<br> - mag_voxet_true_model.txt <br> <br> responses subfolder: contains the synthetic model responses, for both the noisy and clean data. <br> - fwd_mag_data_clean.txt<br> - fwd_mag_data_with_noise.txt<br> <br> Rock units FOLDER: contains the file with indices of the rock unit model, in 3D, of the modified Mansfield model. <br> It is of dimensions 128 * 128 * 36. The indices are stored as a column vector. </p>
Revisiting Machine Learning based Test Case Prioritization for Continuous Integration
<p>Aborted version</p>
The Impact of Continuous Integration into Test Code Evolution: An Empirical Study
<p>The set of datasets used during the study</p>
Synthetic datasets used for numerical testing of geology-geophyiscs integration
<p>This datasets is a companion dataset to the manuscript "<strong>Integration of automatic implicit geological modelling in geophysical inversion with posterior topological analysis</strong>", by Jérémie Giraud, Guillaume Caumon, Lachlan Grose, Vitaliy Ogarko, and Paul Cupillard, for publication in Solid Earth. <br> </p> <p>It contains models and data shown in the paper that are not available elsewhere.<br> <br> The folder organisation is as follows, where <strong>bold</strong> refers to folders and subfolders, and text in <em>italic</em> corresponds to a succinct description of the contents.</p> <p> </p> <p>|-- <strong>synthetic 1 </strong>> <em>synthetic dataset and results for the first synthetic example</em><br> | |-- <strong>geol_data_layered_model.pckl </strong>> <em>geological data and model</em><br> | |-- <strong>grav_data_synthetic1.pckl </strong>> <em>gravity data produced by the true model</em><br> | |-- <strong>inverted_model_no_correction.txt </strong>> <em> inversion results</em><br> | |-- <strong>inverted_model_with_correction.txt </strong>> <em> inversion results</em><br> | |--<strong> note.txt </strong>> <em> metadata</em><br> | |--<strong> starting_model.txt </strong>> <em> starting model for inversion</em><br> | |--<strong> true_model.txt </strong>> <em> true model</em><br> <br> |-- <strong>synthetic 2 </strong>> <em>synthetic dataset and results for the second synthetic example</em><br> | |-- <strong>case<num>.pckl </strong>> <em>inversion results for case with number 1..5 as in the manuscript</em><br> | |-- <strong>grav_data_synthetic2.pckl </strong>> <em>gravity data produced by the true model</em><br> | |-- <strong>inverted_model_no_correction.txt </strong>> <em> inversion results</em><br> | |-- <strong>inverted_model_with_correction.txt </strong>> <em> inversion results</em><br> | |--<strong> note.txt </strong>> <em> metadata</em><br> | |--<strong> starting_model_case5.txt </strong>> <em> starting_model_case5</em><br> | |--<strong> model_start_unconformity.pckl </strong>> <em> starting model for inversion, cases 1..5.</em><br> | |--<strong> true_mod_geol_data.pckl </strong>> <em> true model and geological data</em><br> | |--<strong> start_model_and_geol_data.pckl </strong>> <em> starting geological model and corresponding geological data</em></p> <p> </p>
Revisiting Machine Learning based Test Case Prioritization for Continuous Integration
<p>This repository contains a replication package for a research paper submitted to the 45th International Conference on Software Engineering (https://conf.researchr.org/home/icse-2023). We provide our code, data, and result for the ease of replicating our experiments.</p> <p><strong>Code</strong></p> <ul> <li>In the <strong>collect_data</strong> subdirectory, scripts for constructing TCP datasets are provided. We do dependency analysis using Understand (https://www.scitools.com/), so please download the related tools in advance.</li> <li>In the<strong> rl</strong> subdirectory, we provide the python implementation for algorithms RL, COLEMAN, PPO2-PO, ACER-PA, PPO1-LI.</li> <li>In the <strong>supervised_learning</strong> subdirectory, we provide implementations for MART, RankNet, RankBoost, CA, L-MART, which mainly rely on Ranklib (https://sourceforge.net/p/lemur/wiki/RankLib/.). We also provide implementation for DeepOrder.</li> </ul> <p><strong>Data</strong></p> <ul> <li>The <strong>origin</strong> subdirectory contains the original datasets collected from github using our scripts, including 11 projects.</li> <li>The <strong>smote</strong> subdirectory contains the datasets pre-processed by SMOTE.</li> </ul> <p><strong>Result</strong></p> <ul> <li>Results for <strong>RQ1</strong>, <strong>RQ2</strong>, <strong>RQ3</strong>, and <strong>threats to validity</strong> are provided in the corresponding subdirectories. Scripts for plotting figures are also provided.</li> </ul> <p><strong>Reference</strong></p> <p>We adopt code from previous work</p> <p>Learning-to-Rank vs Ranking-to-Learn: Strategies for Regression Testing in Continuous Integration (https://dl.acm.org/doi/abs/10.1145/3377811.3380369) Github repository: https://github.com/icse20/RT-CI</p> <p>Reinforcement Learning for Test Case Prioritization (https://ieeexplore.ieee.org/abstract/document/9394799) Github repository: https://github.com/moji1/tp_rl</p> <p>DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing (https://ieeexplore.ieee.org/abstract/document/9609187) Github repository: https://github.com/AizazSharif/DeepOrder-ICSME21</p> <p>A Multi-Armed Bandit Approach for Test Case Prioritization in Continuous Integration Environments (https://ieeexplore.ieee.org/abstract/document/9086053) Github repository: https://github.com/jacksonpradolima/coleman4hcs</p>
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.