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1,956 results for “test data”
Testing Zenodo Data Archive
<p>Test</p>
Test data for running snakePipes : WGBS workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run the WGBS workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for the mouse (<strong>GRCm38</strong>) genome.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example <strong>command.sh</strong></li> </ul>
Test data for running snakePipes : RNA-seq workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run the RNA-seq workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for mouse (<strong>GRCm38</strong>) genome.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example <strong>command.sh</strong></li> </ul>
Test data for running snakePipes : HiC workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run the HiC workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for the mouse (mm9) genome.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example command in <strong>README</strong>.</li> </ul>
Test data for running snakePipes : ATAC-seq workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run the ATAC-seq workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for fruit fly (<strong>dm6</strong>) genome.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example <strong>command.sh</strong></li> </ul>
Test data for running snakePipes : ChIP-seq workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run the ChIP-seq workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for human (<strong>hg38</strong>) genome.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example <strong>command.sh</strong></li> </ul>
Supplementary Data: Testing the uniqueness of deep terrestrial life
<p>.R script and data for analyses.</p>
Data for mixed mode fracture test 1 (CARPIUC Benchmark)
<p>The proposed benchmark consists in <strong>simulating crack propagation tests</strong> performed on mortar, with any type of adequate material model or numerical method. Two crack propagation tests are proposed, inspired to some extent by the well-known Nooru-Mohamed [1] tests. They present <strong>initiation, propagation, reorientation, link-up and branching</strong>. The goal is to compare your simulation results with the measured <strong>crack paths</strong> and <strong>force-displacement curves</strong>.</p> <p>The input data consists in specimen geometry, experimentally determined material properties (Young modulus, tensile strength, compressive strength and fracture energy) and the <strong>measured boundary conditions</strong>.</p>
GeodMod test data - small
<p>Test data for GeodMod</p> <p> </p>
cimr test data
<p>data for cimr build-deploy tests</p>
cimr test data: 02
<p>cimr test data</p>
Supporting data for "A global test of the subsidized island biogeography hypothesis".
<p>Dataset for: Menegotto A., Rangel T.F., Schrader J., Weigelt P., Kreft H. 2019. A global test of the subsidized island biogeography hypothesis. Global Ecology and Biogeography, 29, 320-330.</p>
wal-yan/target-methylseq-qc test-data
<p>This is the test-data for wal-yan/target-methylseq-qc test-data</p>
An Exploratory Study on Code Quality, Testing, Data Accuracy, and Practical Use Cases of IoT Wearables
<p>Data used in IoT Wearable Study</p>
Galaxy Data Manager foor GTDB-Tk database - test database
<div>This tar.gz is just a dummy file that mimics the structure of the GTDB-tk releases as shown below and can be used as test case for <div>the data manager !</div> </div>
Data for Effective Unit Test Generation for Java Null Pointer Exceptions
<p>This is to provide all contents of our work, NPETest, including all raw experimental data used in our paper which will be published at ASE`24.</p> <p> </p> <p>NPETest is an unit test generation tool for Java projects, which utilizes both static and dynamic analysis techniques for effective NPE detection. This tool is implemented on the top of EvoSuite, a publicly available unit test generation tool for Java.</p> <p>For more technical details, please read our paper which will be published at ASE`24.</p> <p> </p> <p>The descriptions for the uploaded files are as follows:</p> <p>npetest_result.zip: results of NPETest for all benchmarks, containing the generated test-cases</p> <p>evosuite_opt_result.zip: results of EvoSuite with fine-tuned options for all benchmarks, containing the generated test-cases</p> <p>evosuite_def_result.zip: results of EvoSuite with default options for all benchmarks, containing the generated test-cases</p> <p>randoop_NPEX.tar.gz: results of Randoop for NPEX benchmarks, containing only the log-files. </p> <p>randoop_other.tar.gz: results of Randoop for Bears, BugSwarm, Defects4J, Genesis benchmarks, containing only the log-files.</p> <p>subject_gits.tar.gz: information of the buggy version for each benchmark. </p> <p>NPETestArtifact-main.zip: all contents of NPETest from the public Github respository <a href="https://github.com/kupl/NPETestArtifact" target="_blank" rel="noopener">NPETestArtifact</a>.</p> <p> </p> <p> </p> <p>The detailed description for NPETest (e.g., Install, Usage) of the tool is available on the public repository: <a href="https://github.com/kupl/NPETestArtifact" target="_blank" rel="noopener">NPETestArtifact</a>.<br>You can also download VM from the following link: <a href="https://doi.org/10.5281/zenodo.13371823" target="_blank" rel="noopener">Zenodo</a></p>
DIC Images and Data of In-Plane Cyclic Testing of a 3D-Woven Layer-to-Layer Angle Interlock Composite
<p>Data behind the publications:</p> <ul> <li>C. Oddy, M. Song, C. Stewart, B. El Said, M. Ekh, S. Hallett and Martin Fagerström: On and Off-Axis Cyclic Behaviour of 3D-Woven Composites: Experimental Testing and Macroscale Modelling. Submitted for international publication.</li> </ul> <p>For each test sample orientation and excel file is provided. This includes the machinedata giving the time, machine displacement and force. On another sheet, informationfrom the DIC analysis is given, including the time and force signal organised accordingto the image frame number. The DIC images are also provided.</p> <p>The testing was carried out using a serial camera DIC system. However, as part of this publication, the images have only been provided from one of the camera systems. They have also been exported at a lower image quality than the original analysis. The authors' can recommend the use of open source DIC software, for example DICe (https://github.com/dicengine/dice), to carry out any type of further image processing. Processing the stereo image as a 2D analysis at a lower image quality may lead to some deviations in the extracted strain or displacement fields. The authors however have compared the results and have not seen any notable inconsistencies. We hope that these images can be useful to you in your own research endeavours! </p> <p> </p>
Collected Data on Bending, Vibration, and Push-out Tests of Shallow Steel-Timber Composite Beams – Nordic System
Open the record for dataset details and reuse information.
Data for the training and testing of ccAFv2
<p><span>Single-cell transcriptomics has unveiled a vast landscape of cellular heterogeneity in which the cell cycle is a significant component. We trained a high-resolution cell cycle classifier (ccAFv2) using single cell RNA-seq (scRNA-seq) characterized human neural stem cells. The features of this classifier are that it classifies six cell cycle states (G1, Late G1, S, S/G2, G2/M, and M/Early G1) and a quiescent-like G0 state, and it incorporates a tunable parameter to filter out less certain classifications. The ccAFv2 classifier performed better than or equivalent to other state-of-the-art methods even while classifying more cell cycle states, including G0. We showcased the versatility of ccAFv2 by successfully applying it to classify cells, nuclei, and spatial transcriptomics data in humans and mice, using various normalization methods and gene identifiers. We provide methods to regress the cell cycle expression patterns out of single cell or nuclei data to uncover underlying biological signals. The classifier can be used either as an R package integrated with Seurat (</span><span><a href="https://github.com/plaisier-lab/ccafv2_R"><span>https://github.com/plaisier-lab/ccafv2_R</span></a></span><span>) or a PyPI package integrated with scanpy (</span><span><a href="https://pypi.org/project/ccAF/"><span>https://pypi.org/project/ccAF/</span></a></span><span>). We proved that ccAFv2 has enhanced accuracy, flexibility, and adaptability across various experimental conditions, establishing ccAFv2 as a powerful tool for dissecting complex biological systems, unraveling cellular heterogeneity, and deciphering the molecular mechanisms by which proliferation and quiescence affect cellular processes.</span></p>
Reproducibility Test Data of 26 MHub Models
<p>This dataset provides a comprehensive collection of test data for 26 models integrated within the MHub platform, a robust solution for deploying, managing, and testing deep learning models tailored for medical imaging. Each model is accompanied by a zip file containing data specific to its "default" workflow.</p> <h3>Dataset Composition:</h3> <ul> <li><strong>Sample Folder:</strong> Contains the input data utilized for testing the model’s functionality.</li> <li><strong>Reference Folder:</strong> Contains the corresponding output provided by the original model contributor.</li> <li><strong>Test.yml File:</strong> This file includes the original contributor’s test setup, which has been accepted by the MHub team.</li> </ul> <h3>Sample Data Source:</h3> <p>The sample images used in this dataset are sourced from public datasets available through the <strong><a href="https://datacommons.cancer.gov/repository/imaging-data-commons" target="_blank" rel="noopener">Imaging Data Commons (IDC)</a></strong>, a repository that provides access to a wide range of medical imaging data. This ensures that the test cases reflect real-world clinical scenarios, facilitating robust validation of model performance.</p> <h3>Purpose and Utility:</h3> <p>The primary objective of this dataset is to enable the rigorous testing and validation of model performance within MHub workflows. To assess the performance of a model, users can process the sample data and compare the resulting output to the reference data. Additionally, users may inspect the sample and reference data independently to better understand the input-output structure that defines each model’s workflow.</p> <p>This dataset streamlines the process of model validation. By providing a standardized testing framework, the dataset facilitates reproducible results and accelerates the development of reliable AI models for medical imaging.</p> <h3>About MHub:</h3> <p>MHub (<a href="https://mhub.ai" target="_new" rel="noopener">mhub.ai</a>) is an innovative platform designed to simplify the deployment, management, and testing of deep learning models for medical imaging. It enables researchers and clinicians to integrate AI-based solutions into clinical workflows while ensuring reproducibility and scalability. The platform provides a modular framework where users can execute complex workflows, such as image segmentation, classification, and registration, leveraging state-of-the-art AI models. MHub's goal is to accelerate the development and clinical adoption of medical imaging models by providing a streamlined, user-friendly environment for testing and validating new algorithms.</p> <p>For more information on the platform and its capabilities, visit <a href="https://mhub.ai" target="_blank" rel="noopener">mhub.ai</a>.</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.