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547 results for “Model Tests”
Load, pressure, rubble pile geometry and video data from model-scale tests on shallow water ice-structure interaction
<p>The data is obtained from model-scale experiments on shallow water ice-structure interaction. During the conducted experiments, a ten-meter wide initially intact ice sheet was pushed against a sloping structure of the same width. As the ice failed against the structure, a grounded rubble pile accumulated in front of it. The structure consisted of ten identical one-meter-wide segments and the horizontal load on each of these segments was measured independently with load cells. These measurements are presented as load-time datasets. The horizontal load acting on the false bottom was measured with load cells and are also presented as load-time datasets. Furthermore, the ice pressure on two of the segments was measured with tactile sensors. These pressure measurements are presented as array-based pressure-time datasets. Video footage filmed from two different video angles is included in the data. In addition, the coordinates of the rubble pile geometries at the end of each experiment are published. The data includes the top and side rubble pile geometries. In total, seven experiments were conducted. The data can be used by researchers, engineers and designers who work with ice structure interaction related issues in order to, for instance, optimize the design of offshore structures, improve ice load predictions or develop future experiments and simulations. A full description of the experimental set-up and the published data is submitted to the journal Data in Brief. </p>
Analogue Models for comparing and testing the relationship between inverted normal faults and pure thrusting during the positive tectonic inversion
<p>This dataset contains a series of Analogue Models for comparing and testing positive tectonic inversion mechanisms and their newly formed structures . Furthermore, it includes 2-D seismic reflection profiles that can be compared with the models presented here. Finally, examples of natural cases that show tectonic inversion processes are included. Both, seismic lines and photos are located on a segment of Andean forearc, specifically, in the Domeyko Cordillera and the Preandean Basins, northern Chile.</p>
Data and models in Support of "Joint and Constrained Inversion as Hypothesis Testing Tools"
<p>The model and data files as well as the plotting and run scripts to reproduce the examples in "Joint and Constrained Inversion as Hypothesis Testing Tools".</p>
Supplemental Material to Article "Validation of crack initiation model by means of cyclic full-scale blade test"
<p>This set supplements the figure data to the article "Validation of crack initiation model by means of cyclic full-scale blade test", DOI: <a href="https://doi.org/10.1088/1742-6596/2265/3/032045">https://doi.org/10.1088/1742-6596/2265/3/032045</a>.</p>
Training and test data, plus saved models for the paper "Top-down effects in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to the SVRHM 2022 Workshop @ NeurIPS
<p>Each .pkl file contains a training or test dataset in the form of a Python dictionary (generated with Python 3.8.5) with the following fields:</p><ul><li>'train_images': 640,000 float32 images used for model training. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each.</li><li>'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li><li>'test_images': 64,000 float32 images used for model testing. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each.</li><li>'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li></ul><p>Each .zip file contains a saved model. Details on these are coming soon.</p><p>For more details, see the paper "Top-down effects in an early visual cortex inspired hierarchical Variational Autoencoder" published at the SVRHM 2022 Workshop @ NeurIPS (<a href="https://openreview.net/forum?id=8dfboOQfYt3">link</a>).</p>
Flakify: A Black-Box, Language Model-based Predictor for Flaky Tests – Replication Package
<p>This is the replication package associated with the paper: <em>Flakify: A Black-Box, Language Model-based Predictor for Flaky Tests.</em> We explain how to use it to reproduce the results reported in the paper. A maintainable version of this replication package is available on GitHub (<a href="https://github.com/uOttawa-Nanda-Lab/Flakify">https://github.com/uOttawa-Nanda-Lab/Flakify</a>).</p> <p><strong>Flakify Test Smell Detector</strong></p> <p>This is a step-by-step guideline to detect test smells in the source code of test cases and retain statements that match them.</p> <p><em><strong>Requirements:</strong></em></p> <ul> <li>Eclipse IDE (the version we used was 2021-12)</li> <li>The libraries (the <strong><em>.jar</em></strong> files in the <strong><code>lib\</code></strong> directory)</li> </ul> <p><em><strong>Input Files:</strong></em></p> <p>This is a list of input files that are required to accomplish this step:</p> <ul> <li> <p><em>dataset/FlakeFlagger/FlakeFlagger_filtered_dataset.csv</em></p> </li> <li> <p><em>dataset/FlakeFlagger/FlakeFlagger_class_files/</em></p> </li> <li> <p><em>dataset/IDoFT/IDoFT_filtered_dataset.csv</em></p> </li> <li> <p><em>dataset/IDoFT/IDoFT_class_files/</em></p> </li> </ul> <p>The <strong><code>dataset/FlakeFlagger/FlakeFlagger_filtered_dataset.csv</code></strong> and <strong><code>dataset/IDoFT/IDoFT_filtered_dataset.csv</code></strong> are used to obtain the label (<em>flaky</em>=1 or <em>non-flaky</em>=0) and project name for each test case parsed from <strong><code>dataset/FlakeFlagger/FlakeFlagger_class_files/</code></strong> and <strong><code>dataset/IDoFT/IDoFT_class_files/</code></strong>, respectively.</p> <p><strong><em>Output Files:</em></strong></p> <ul> <li> <p><em>dataset/FlakeFlagger/FlakeFlagger_dataset.csv</em></p> </li> <li> <p><em>dataset/FlakeFlagger/FlakeFlagger_test_cases_full_code/</em></p> </li> <li> <p><em>dataset/FlakeFlagger/FlakeFlagger_test_cases_preprocessed_code/</em></p> </li> <li> <p><em>dataset/IDoFT/IDoFT_dataset.csv</em></p> </li> <li> <p><em>dataset/IDoFT/IDoFT_test_cases_full_code/</em></p> </li> <li> <p><em>dataset/IDoFT/IDoFT_test_cases_preprocessed_code/</em></p> </li> </ul> <p> </p> <p><strong>Replicating the experiment</strong></p> <p>To detect test smells and retain only code statements related to them, the <strong><code>src/FlakifySmellsDetector.java</code></strong> file should be compiled and run using the Eclipse IDE by having all the <em>.jar</em> files in the classpath.</p> <p>The pre-generated executable Jar file <strong><code>src/FlakifySmellsDetector.jar</code></strong> can be executed using the shell script <strong><code>src/FlakifySmellsDetector.sh</code></strong> after changing paths for each dataset as needed, using the following commands:</p> <pre><code class="language-bash">bash FlakifySmellsDetector.sh FlakeFlagger bash FlakifySmellsDetector.sh IDoFT</code></pre> <p>It will generate the dataset required to run Flakify's flaky test prediction model for the datasets given as input. The class file containing each of the test cases is then parsed to produce the corresponding full code and pre-processed code of the test case. The full and pre-processed source code of all test cases are also combined and saved in a CSV file, along with test smells found, project names, and labels.</p> <p> </p> <p><strong>Flakify Replication</strong></p> <p>This is the guideline for replicating the experiments we used to evaluate Flakify for classifying test cases as <em>flaky</em> and <em>non-flaky</em> using both cross-validation and per-project validation.</p> <p><em><strong>Requirements:</strong></em></p> <p>This is a list of all required python packages:</p> <ul> <li><em>python =3.8.5</em></li> <li><em>imbalanced_learn= 0.8.1</em></li> <li><em>numpy= 1.19.5</em></li> <li><em>pandas= 1.3.3</em></li> <li><em>transformer= 4.10.2</em></li> <li><em>torch=1.5.0</em></li> <li><em>scikit_learn= 0.22.1</em></li> </ul> <p><em><strong>Input Files:</strong></em></p> <p>This is a list of input files that are required to accomplish this step:</p> <ul> <li><em>dataset/FlakeFlagger/Flakify_FlakeFlagger_dataset.csv</em></li> <li><em>dataset/IDoFT/Flakify_IDoFT_dataset.csv</em></li> </ul> <p>This file contains the full code and pre-processed code of the test cases in both FlakeFlagger and IDOFT datasets, along with their ground truth labels (<em>flaky</em> and <em>non-flaky</em>).</p> <p><em><strong>Output File:</strong></em></p> <ul> <li> <p><em>results/Flakify_cross_validation_results_on_FlakeFlagger_dataset.csv</em></p> </li> <li> <p><em>results/Flakify_per_project_results_on_FlakeFlagger_dataset.csv</em></p> </li> <li> <p><em>results/Flakify_model_trained_on_FlakeFlagger_dataset.pt</em></p> </li> <li> <p><em>results/Flakify_cross_validation_results_on_IDoFT_dataset.csv</em></p> </li> <li> <p><em>results/Flakify_per_project_results_on_IDoFT_dataset.csv</em></p> </li> <li> <p><em>results/Flakify_model_trained_on_IDoFT_dataset.pt</em></p> </li> </ul> <p> </p> <p><strong>Replicating Flakify experiments</strong></p> <p><strong>Cross-Validation</strong></p> <p>To run the Flakify experiment using cross-validation on the two datasets, navigate to <code>src\</code> folder and run the following commands:</p> <pre><code class="language-bash">bash Flakify_predictor_cross_validation.sh FlakeFlagger bash Flakify_predictor_cross_validation.sh IDoFT</code></pre> <p>This will generate the classification results into <strong><code>results/Flakify_cross_validation_results_on_FlakeFlagger_dataset.csv</code></strong> and <strong><code>results/Flakify_cross_validation_results_on_IDoFT_dataset.csv</code></strong> for the cross-validation experiments on both datasets. It will also save the weights of the two models trained on the FlakeFlagger and IDoFT datasets into <strong><code>results/Flakify_model_trained_on_FlakeFlagger_dataset.pt</code></strong> and <code><strong>results/Flakify_model_trained_on_IDoFT_dataset.pt</strong></code>, respectively.</p> <p> </p> <p><strong>Per-project Validation</strong></p> <p>To run the Flakify experiment using per-project validation on the two datasets, navigate to <code>src\</code> folder and run the following commands:</p> <pre><code class="language-bash">bash Flakify_predictor_per_project.sh FlakeFlagger bash Flakify_predictor_per_project.sh IDoFT</code></pre> <p>This will generate the classification results into <strong><code>results/Flakify_per_project_results_on_FlakeFlagger_dataset.csv</code></strong> and <strong><code>results/Flakify_per_project_results_on_IDoFT_dataset.csv</code></strong> for the whole per-project validation experiments on both datasets.</p> <p> </p> <p><strong>FlakeFlagger Replication</strong></p> <p>This is the guideline for replicating the experiments we used to evaluate the two versions of FlakeFlagger, white-box and black-box, for classifying test cases as <em>flaky</em> and <em>non-flaky</em> using cross-validation on the FlakeFlagger dataset.</p> <p><em><strong>Requirements:</strong></em></p> <p>This is a list of all required python packages:</p> <ul> <li><em>python =3.8.5</em></li> <li><em>imbalanced_learn= 0.8.1</em></li> <li><em>pandas= 1.3.3</em></li> <li><em>scikit_learn= 0.22.1</em></li> </ul> <p><em><strong>Input File:</strong></em></p> <p>This is a list of input files that are required to accomplish this step:</p> <ul> <li><em>dataset/FlakeFlagger/FlakeFlagger_filtered_dataset.csv</em></li> <li><em>dataset/FlakeFlagger/FlakeFlaggerFeaturesTypes.csv</em></li> <li><em>dataset/FlakeFlagger/Information_gain_per_feature.csv</em></li> </ul> <p><em><strong>Output File:</strong></em></p> <ul> <li><em>results/FlakeFlagger_black-box_results.csv</em></li> <li><em>results/FlakeFlagger_white-box_results.csv</em></li> </ul> <p> </p> <p><strong>Replicating FlakeFlagger experiments</strong></p> <p>To run the FlakeFlagger experiments, navigate to <code>src\</code> folder and run the following command:</p> <pre><code class="language-bash">bash FlakeFlagger_predictor.sh white-box bash FlakeFlagger_predictor.sh black-box</code></pre> <p>This will generate the classification results into <strong><code>results/FlakeFlagger_white-box_results.csv</code> </strong>and <strong><code>results/FlakeFlagger_black-box_results.csv</code> </strong>for both white-box and black-box experiments, respectively.</p>
Comparing mixed models and Random Forest association tests using naturalGWAS and a Striped Bass SNP dataset
<p>In this study, we used the phenotype simulation package naturalGWAS to test the performance of Zhao's Random Forest method in comparison to an uncorrected Random Forest test, latent factor mixed models (LFMM), genome-wide efficient mixed models (GEMMA), and confounder adjusted linear regression (CATE). We created 400 sets of phenotypes, corresponding to five effect sizes and 2, 5, 15, or 30 causal loci, simulated from two empirical datasets containing SNPs from Striped Bass representing three and 13 populations. All association methods were evaluated for their ability to detect genotype-phenotype associations based on power, false discovery rates, and number of false positives. Genomic inflation was highest for uncorrected Random Forest and LFMM tests and lowest for Gemma and Zhao's Random Forest. All association tests had similar power to detect causal loci, and Zhao's Random Forest had the lowest false discovery rate in all scenarios. To measure the performance of association tests in small datasets with few loci surrounding a causal gene we also ran analyses again after removing causal loci from each dataset. All association tests were only able to find true positives, defined as loci located within 30k bp of a causal locus, in 3%–18% of simulations. In contrast, at least one false positive was found in 17%–44% of simulations. Zhao's Random Forest again identified the fewest false positives of all association tests studied. The ability to test the power of association tests for individual empirical datasets can be an extremely useful first step when designing a GWAS study.</p>
Modeled QENS dataset - test
<p>Modeled QENS dataset produced by Random-Jump diffusion model (DOI:10.1080/00268978100100521) using different diffusion coefficients for lithium ions in nano-Si from literature</p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
FS1113 test model Room 1
This model was acquired at a remote site in the Red Rock District of the Coconino Forest near Sedona, Arizona. This model is part of a volunteer effort to document pre-historic and proto-historic sites in the region. Additional information about the Red Rock District of the Coconino Forest can be found at the following web sites http://www.sedonaredrocktrails.org/ http://www.fs.usda.gov/recmain/coconino/recreation Information about volunteer opportunities in the Coconino Forest including the creation of the 3D models and other projects can be found at http://www.friendsoftheforestsedona.org/ Source: Objaverse 1.0 / Sketchfab
Hermes test model v2
Model created for Digitization course of the Digitial Humanities MSc programme of AUEB. Source: Objaverse 1.0 / Sketchfab
castle model test concept.
a quick test model i wipped up within a few hours, i know the angle is messed up but i dont have a easy way to fix it, and i dont intend this to reflect my capabilities. Source: Objaverse 1.0 / Sketchfab
Pilot 1 Model-based decision support for testing drought-related adaptation strategies in the Aa of Weerijs river basin, the Netherlands: Hydrological model description, input data sources and model results
<p>This dataset contains: the report with the description of the model structure, the input data sources and the spatial locations within the catchment for which surface and groundwater results data are provided.</p>
Additional raw video and pose estimation data of top view mouse behavior recordings (marble burying test, light-dark box, fear conditioning box) of acute and chronic stress models
<p>This repository contains raw data for 296 different behavioral recordings of mice (marble burying test, light-dark box, fear conditioning box). These include top view raw video .mp4 files (Videos.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from multiple different experiments. The METADATA.csv or METADATA.xlsx files contain all grouping variables and help linking the pose estimation files (located in multiple subfolders of /data) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how this data has be used by us.</p>
Transcriptomic characterization of 2D and 3D human induced pluripotent stem cell-based in vitro models as New Approach Methodologies for developmental neurotoxicity testing
<p><strong>Abstract:</strong> The safety and developmental neurotoxicity (DNT) potential of chemicals remain critically understudied due to limitations of current in vivo testing guidelines, which are low throughput, resource-intensive, and hindered by species differences that limit their relevance to human health. To address these issues, robust new approach methodologies (NAMs) using deeply characterized cell models are essential. This study presents the comprehensive transcriptomic characterization of two advanced human-induced pluripotent stem cell (hiPSC)-derived models: a 2D adherent and a 3D neurosphere model of human neural progenitor cells (hiNPCs) differentiated up to 21 days. Using high-throughput RNA sequencing, we compared gene expression profiles of 2D and 3D models at three developmental stages (3, 14, and 21 days of differentiation). Both models exhibit maturation towards post-mitotic neurons, with the 3D model maturing faster and showing a higher prevalence of GABAergic neurons, while the 2D model is enriched with glutamatergic neurons. Both models demonstrate broad applicability domains, including excitatory and inhibitory neurons, astrocytes, and key endocrine and especially the understudied cholinergic receptors. Comparison with human fetal brain samples confirms their physiological relevance. This study provides novel in-depth applicability insights into the temporal and dimensional aspects of hiPSC-derived neural models for DNT testing. The complementary use of these two models is highlighted: the 2D model excels in synaptogenesis assessment, while the 3D model is particularly suited for neural network formation as observed as well in previous functional studies with these models. This research marks a significant advancement in developing human-relevant, high-throughput DNT assays for regulatory purposes.</p> <p><strong>This data sets contains:</strong></p> <p><strong>Tab. S1</strong> - Significant genes results</p> <p><strong>Tab. S2</strong> - Enriched pathways_GO_Biological Processes</p> <p><strong>Tab. S3</strong> - Enriched pathways_GO_Cellular Components</p> <p><strong>Tab. S4</strong> - Enriched pathways_GO_Molecular Function</p> <p><strong>Tab. S5</strong> - Enriched pathways_KEGG</p> <p><strong>Tab. S6</strong> - EnrichEnriched pathways_Panther</p> <p><strong>Tab. S7</strong> - Enriched pathways_Reactome</p> <p><strong>Tab. S8</strong> - Gene counts</p> <p><strong>Tab. S9</strong> - Gene selection for targeted analysis</p>
Topology testing and demographic modeling illuminate a novel speciation pathway in the Greater Caribbean Sea following the formation of the Isthmus of Panama
<p>Recent genomic analyses have highlighted the prevalence of speciation with gene flow in many taxa and have underscored the importance of accounting for these reticulate evolutionary processes when constructing species trees and generating parameter estimates. This is especially important for deepening our understanding of speciation in the sea where fast-moving ocean currents, expanses of deep water, and periodic episodes of sea level rise and fall act as soft and temporary allopatric barriers that facilitate both divergence and secondary contact. Under these conditions, gene flow is not expected to cease completely while contemporary distributions are expected to differ from historical ones. Here we conduct range-wide sampling for Pederson's cleaner shrimp (<em>Ancylomenes</em> <em>pedersoni</em>), a species complex from the Greater Caribbean that contains three clearly delimited mitochondrial lineages with both allopatric and sympatric distributions. Using mtDNA barcodes and a genomic ddRADseq approach, we combine classic phylogenetic analyses with extensive topology testing and demographic modeling (10 site frequency replicates x 45 evolutionary models x 50 model simulations/replicate = 22,500 simulations) to test species boundaries and reconstruct the evolutionary history of what was expected to be a simple case study. Instead, our results indicate a history of allopatric divergence, secondary contact, introgression, and endemic hybrid speciation that we hypothesize was driven by the final closure of the Isthmus of Panama and the strengthening of the Gulf Stream Current ~3.5 million years ago. The history of this species complex recovered by model-based methods that allow reticulation differs from that recovered by standard phylogenetic analyses and is unexpected given contemporary distributions. The geologically and biologically meaningful insights gained by our model selection analyses illuminate what is likely a novel pathway of species formation not previously documented that resulted from one of the most biogeographically significant events in Earth's history.</p>
Improved estimation of the prevalence of bovine cysticercosis and the diagnostic test characteristics in the absence of a reference standard using Bayesian Latent Class models, the example of Jimma and Ambo Abattoirs, Ethiopia
<p>Bovine cysticercosis is an infection of cattle musculature with the cestode parasite of humans known as Taenia saginata. This bovine cysticercosis data was collected from two Ambattoirs in Ethiopia namely Ambo and Jimma. Dissection of the predilection site, Ag-ELISA, and meat inspection were the diagnostic methods employed. Cysticerci collected during dissection of the predilection site were also confirmed using multiplex PCR. </p>
Modeling the Depletion and Recovery of the Outer Radiation Belt During a Geomagnetic Storm: Combined MHD and Test Particle Simulations
<p>Data associated with JGR: Space Physics paper, "Modeling the Depletion and Recovery of the Outer Radiation Belt During a Geomagnetic Storm: Combined MHD and Test Particle Simulations".</p>
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>
Jingju a cappella singing voice test dataset for "An efficient deep learning model for musical onset detection"
<p>Jingju a cappella singing voice test dataset used in the paper "An efficient deep learning model for musical onset detection".</p> <p>Arxiv paper link: <a href="https://arxiv.org/abs/1806.06773">https://arxiv.org/abs/1806.06773</a></p> <p>Supplementary information and code for the paper: <a href="https://github.com/ronggong/musical-onset-efficient">https://github.com/ronggong/musical-onset-efficient</a></p> <p><strong>Content:</strong></p> <ol> <li>ismir_2018_dataset_for_reviewing.zip: audio, syllable boundary and label annotation</li> <li>jingju dataset train test split filenames.xlsx: train and test split filename list</li> </ol> <p><strong>Citation:</strong></p> <pre>@article{gong2018towards, title={Towards an efficient deep learning model for musical onset detection}, author={Gong, Rong and Serra, Xavier}, journal={arXiv preprint arXiv:1806.06773}, year={2018} } </pre> <p><strong>Contact:</strong></p> <p>Rong Gong: rong.gong<at>upf.edu</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.