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1,956 results for “test data”
Pynbody test data
<p>These sample files are used in the pynbody tests. For more information about pynbody see <a href="https://pynbody.readthedocs.org">https://pynbody.readthedocs.org </a></p> <p> </p> <p>This version adds a file for testing swift planetary simulations</p>
Test data for iwc workflow : Purgedups VGP6
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"An efficient ptychography reconstruction strategy through fine-tuning of large pre-trained deep learning model" train and test data
<ul><li>Model for the article "An efficient ptychography reconstruction strategy through fine-tuning of large pre-trained deep learning model".</li><li>The .pth file is the pre-trained PtyNet-S model and the fine-tuned PtyNet-B model.</li><li>Please contact panxy@ihep.ac.cn if you have any questions.</li></ul>
test data for the 'domutils' Python package
<p>This dataset allow to perform tests and demonstrations with the domutils Python package.</p> <p>Source code is available at:</p> <p>https://github.com/dja001/domutils</p>
mzML mass spectrometry and imzML mass spectrometry imaging test data
<p>The repository contains three mzML and four imzML mass spectrometry datasets, </p><p>The mzML data are compiled in a <strong>single directory 'mzML' and zipped</strong>:</p><ul><li><strong>Col_1.mzML </strong>is a liquid chromatography (LC) ESI MS dataset from an Arabidopsis extraction published in: Sotelo-Silveira, M., Chauvin, A.-L., Marsch-Martínez, N., Winkler, R. & De Folter, S. Metabolic fingerprinting of Arabidopsis thaliana accessions. Frontiers in Plant Science 6, 1–13 (2015). <a href="https://doi.org/10.3389/fpls.2015.00365">https://doi.org/10.3389/fpls.2015.00365</a>.</li><li><strong>Cytochrome_C.mzML</strong> is an electrospray mass spectrometry (ESI MS) dataset of Cytochrome C. The data were discussed in: Winkler, R. ESIprot: a universal tool for charge state determination and molecular weight calculation of proteins from electrospray ionization mass spectrometry data. Rapid Communications in Mass Spectrometry 24, 285- 294 (2010). <a href="https://doi.org/10.1002/rcm.4384">https://doi.org/10.1002/rcm.4384</a>.</li><li><strong>T9_A1.mzML </strong>is a low-temperature plasma (LTP) MS dataset of the interaction between Arabidopsis and Trichoderma, published in 1. Torres-Ortega, R. et al. In Vivo Low-Temperature Plasma Ionization Mass Spectrometry (LTP-MS) Reveals Regulation of 6-Pentyl-2H-Pyran-2-One (6-PP) as a Physiological Variable during Plant-Fungal Interaction. Metabolites 12, 1231 (2022). <a href="https://doi.org/10.3390/metabo12121231">https://doi.org/10.3390/metabo12121231</a>.</li></ul><p>The imzML mass spectrometry imaging data are zipped individually:</p><ul><li><strong>imzML_AP_SMALDI.zip </strong>contains an AP-SMALDI mass spectrometry imaging data set of mouse urinary bladder slides, published by Römpp A, Guenther S, Schober Y, Schulz O, Takats Z, Kummer W, Spengler B., ProteomeXchange dataset PXD001283. 2014., and available from <a href="https://www.ebi.ac.uk/pride/archive/projects/PXD001283">https://www.ebi.ac.uk/pride/archive/projects/PXD001283</a>; Publication: Römpp A, Guenther S, Schober Y, Schulz O, Takats Z, Kummer W, Spengler B; Histology by mass spectrometry: label-free tissue characterization obtained from high-accuracy bioanalytical imaging., Angew Chem Int Ed Engl, 49, 22, 3834-8 (2014). <a href="https://doi.org/10.1002/anie.200905559">https://doi.org/10.1002/anie.200905559</a>, PubMed: 20397170. </li><li><strong>imzML_DESI.zip </strong>is a DESI mass spectrometry imaging data set of human colorectal cancer tissue by Oetjen J, Veselkov K, Watrous J, McKenzie JS, Becker M, Hauberg-Lotte L, Kobarg JH, Strittmatter N, Mróz AK, Hoffmann F, Trede D, Palmer A, Schiffler S, Steinhorst K, Aichler M, Goldin R, Guntinas-Lichius O, von Eggeling F, Thiele H, Maedler K, Walch A, Maass P, Dorrestein PC, Takats Z, Alexandrov T. 2015. Benchmark datasets for 3D MALDI-and DESI-imaging mass spectrometry. GigaScience 4(1):2105 <a href="https://doi.org/10.1186/s13742-015-0059-4">https://doi.org/10.1186/s13742-015-0059-4</a>.</li><li><strong>imzML_LA-ESI.zip</strong> is an LA-ESI mass spectrometry imaging data set of an <i>Arabidopsis thaliana</i> leaf by Zheng, Z., Bartels, B., & Svatoš, A. (2020). Laser Ablation Electrospray Ionization Mass Spectrometry Imaging (LAESI MSI) of Arabidopsis thaliana leaf [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.3678473">https://doi.org/10.5281/zenodo.3678473</a>. </li><li>imzML_LTP.zip was generated by low-temperature plasma ionization ambient mass spectrometry imaging of a chili fruit, published by Maldonado-Torres M, López-Hernández Jé F, Jiménez-Sandoval P, Winkler R. 2014. Plug and play' assembly of a low-temperature plasma ionization mass spectrometry imaging (LTP-MSI) system. Journal of Proteomics 102C:60–65 <a href="https://doi.org/10.1016/j.jprot.2014.03.003">https://doi.org/10.1016/j.jprot.2014.03.003</a>; Mauricio Maldonado-Torres, José Fabricio López-Hernández, Pedro Jiménez-Sandoval, & Robert Winkler. (2017). Low-temperature plasma mass spectrometry imaging (LTP-MSI) of Chili pepper [Data set]. In Journal of proteomics (Vol. 102, pp. 60–65). Zenodo. <a href="https://doi.org/10.5281/zenodo.484496">https://doi.org/10.5281/zenodo.484496</a>.</li></ul><p>All these datasets are publicly available from different repositories; however, If you reuse them, <strong>please attribute the original authors!</strong></p>
Data from: how to generate and test hypotheses about colour: insights from half a century of guppy research
<p><span>Colouration facilitates evolutionary investigations in nature because the interaction between genotype, phenotype and environment is relatively accessible. In a landmark set of studies, Endler addressed this complexity by demonstrating that the evolution of male Trinidadian guppy colouration is shaped by the local balance between selection for mate attractiveness versus crypsis. This became a textbook paradigm for how antagonistic selective pressures may determine evolutionary trajectories in the wild. Recent studies have, however, challenged the generality of this paradigm. Here we respond to these challenges by reviewing five important yet underappreciated factors that contribute to colour pattern evolution: (1) inter-population variation in female preference, (2) differences in how predators versus conspecifics view males, (3) biased assessment of pigmentary versus structural colouration, (4) the importance of accounting for multi-species predator communities, and (5) the importance of considering the multivariate genetic architecture and multivariate context of selection. We elaborate upon these points specifically in relation to two papers that illustrate the challenges raised by the recent body of work on guppies. More broadly, we emphasise the depth of consideration necessary for testing evolutionary hypotheses using complex multi-trait phenotypes such as guppy colour patterns.</span></p>
Hydraulic tomography and thermal tracer test data from a fractured-porous field site in Goettingen (Germany)
<p>These data are supplementary material for:</p> <p>Römhild, L., Ringel, L. M., Liu, Q., Hu, L., Ptak, T., & Bayer, P. (2024). Hybrid discrete fracture network inversion of hydraulic tomography data from a fractured-porous field site. Water Resources Research, 60, e2023WR036035. https://doi.org/10.1029/2023WR036035</p> <p>The data set comprises hydraulic tomography and thermal tracer test field data from a porous-fractured site in Göttingen (Germany). The repository contains a schematic figure of the experimental setup and subfolders for the two different experiments. For more information about the data, we refer to the readme files in the repository, and the soon-to-be-published paper (see above).</p>
SPARCS_test_data_small_membrane
<p>Example images to run the SPARCS computation workflow on.</p>
Data for: Global frequency analyses of canine progressive rod-cone degeneration–progressive retinal atrophy and collie eye anomaly using commercial genetic testing data
<p>Hundreds of genetic variants associated with canine traits and disorders have been identified, with commercial tests offered. However, the geographic distributions and changes in allele and genotype frequencies over prolonged, continuous periods of time are lacking. This study utilized a large set of genotypes from dogs tested for the progressive rod-cone degeneration–progressive retinal atrophy (prcd-PRA) G>A missense PRCD variant (n = 86,667) and the collie eye anomaly (CEA)-associated NHEJ1 deletion (n = 33,834) provided by the commercial genetic testing company (Optigen/Wisdom Panel, Mars Petcare Science & Diagnostics). These data were analyzed using the chi-square goodness-of-fit test, time-trend graphical analysis, and regression modeling in order to evaluate how test results changed over time. The results span fifteen years, representing 82 countries and 67 breeds/breed mixes. Both diseases exhibited significant differences in genotype frequencies (p = 2.7 × 10−152 for prcd-PRA and 0.023 for CEA) with opposing graphical trends. Regression modeling showed time progression to significantly affect the odds of a dog being homozygous or heterozygous for either disease, as do variables including breed and breed popularity. This study shows that genetic testing informed breeding decisions to produce fewer affected dogs. However, the presence of dogs homozygous for the disease variant, especially for prcd-PRA, was still observed fourteen years after test availability, potentially due to crosses of unknown carriers. This suggests that genetic testing of dog populations should continue.</p>
Test data for MetIVA ( An XR-based Interactive Visualization Platform for Real-time Exploring Dynamic Earth Science Data)
<p>Here,we presented the minimum demanded dataset to test the basic fuction of the software of MetIVA, which an XR-base interactive visualization platform for real-time exploring dynamic earth science data. The dynamic realtime data of traffic information (such as traffic volume, traffic spped, jam conditions) is directy obtained from the third-party supplier, such as Mapbox in the test version of MetIVA, the users can change it to other sources. The users have their own accounts on the cloud computing platform to run the numerical models (such as WRF), the outputed results (in NetCDF format) can be sent to cloud storage and the link address need to be provided in the MetIVA.</p>
Data from: Reversal learning and neophobia test results in Chimango Caracaras
<p>In this study, we analyzed the variation in cognitive flexibility in the Chimango Caracara (<em>Milvago chimango</em>), across areas with different levels of urbanization. To assess this, we utilized the reversal learning assay which measures the ability to adapt behavior in response to changes in environmental contingencies. We also investigated the impact of neophobia on this variation. All chimangos studied succeeded in acquiring a color-reward association and reverting this learned association when the contingencies changed. Urban chimangos were faster than their rural and suburban counterparts during the initial discrimination and reversal phases. The reversal phase proved to be the most challenging task. The analysis of the errors made during this phase revealed that acquiring a new association (i.e., regressive errors) was challenging for the individuals studied, in comparison to inhibiting a previously learned one (i.e., perseverative errors). Neophobia was found to be lower in urban individuals compared to suburban and rural raptors. Moreover, neophobia showed a correlation with regressive errors during the reversal phase among rural and suburban chimangos, while no such correlation was observed among city-dwelling chimangos. We suggest that neophobia acted as a regulating factor of cognitive flexibility, mainly for individuals expressing relatively high levels of this personality trait.</p>
Data and Codes of A Deep Learning-Based Consistency Test for Earth System Models on Heterogeneous Many-Core Systems
<p>These are the supporting information to verify the results in the paper, including input data, model outputs, the postprocessing scripts and the source codes.</p>
Shear wave velocities and associated CPT data for S-PCPT testing in the North Sea
<p>This dataset combines shear wave velocity measurements and associated CPT for offshore wind farm sites in The Netherlands and Germany. The data has been collected by RVO in The Netherlands and BSH in Germany and is available in the public domain. The author has processed the data as part of his PhD research at Vrije Universiteit Brussel.</p>
Test Data for iwc Pre-curation PretextMap generation workflow
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Test data for package HELPS
<p>This repository contains the necessary data files for conducting a test run with the HELPS package.</p> <p>The dataset includes two examples of testing data: daily input example and monthly input example.</p> <p>The <strong>daily inputs</strong> <strong>example </strong>are derived from a subset of <a href="https://data.isimip.org/search/tree/ISIMIP2b/InputData/climate/atmosphere/gfdl-esm2m/rcp60/">ISIMIP2b </a>data for the year 2024, featuring daily gridded (0.5-degree resolution) data for mean temperature (tas), relative humidity (hurs), and air pressure (ps). The <strong>monthly inputs example</strong>, generated using <a href="https://jgcri.github.io/basd/">BASD</a> for year 2024, including monthly gridded (0.5-degree resolution) data for tas, hurs, and surface downwelling shortwave radiation (rsds).</p> <p>Users are encouraged to refer to the package vignette and utilize the example data provided to explore the features and functionality of the HELPS package.</p>
MGnify amplicon v5.0 test data for Galaxy iwc
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Test data for mapseq-to-ampvis2 workflow on Galaxy iwc
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Data Set from Quasi-static Cyclic Tests of Scoured 2×3 Pile-Group Foundations for Bridge Structures
<p>This data set is from Quasi-static cyclic tests of scoured 2×3 pile-group foundations for bridge structures, as a data paper submitted to Earthquake Spectra (Submission ID: EQS-24-0190)</p>
Test dataset for "Spatial Integration of Multi-Omics Data from Serial Sections using the novel Multi-Omics Imaging Integration Toolset"
<p>The uploaded tar file contains anonymized and reduced test data for the paper "Spatial Integration of Multi-Omics Data from Serial Sections using the novel Multi-Omics Imaging Integration Toolset". (doi: https://doi.org/10.1101/2024.06.11.598306; https://github.com/mwess/miit)</p> <p>Dataset description:<br>- 9 serial histology sections with the following stains: (HES, HE, HES, HES, HES, MTS, IHC, IHC, HES)<br>- Sections are indexed in the following way (due to some sections not being part of this project): 1,2,3,6,7,8,9,10,11<br>- Each serial section contains: <br> - landmarks with matching labels across all sections.<br> - semi-manually generated tissue masks <br>- Section 2 contain spatial transcriptomics data and one annotation file in geojson format.<br>- Sections 6 and 7 contain imzml data that were generated with MALDI-MSI in positive ion mode (section 6) and negative ion mode (section 7) and additional histology annotations.<br>- MALDI-MSI is reduced. The positive ion data contains only intensities and spectra for spermine. The negative ion mode data contains only intensities and spectra for citrate and zinc.<br>- ST data contains only locations of spots and scalefactors. (I.e. no count data is included.). Barcode ids are randomly generated. <br>- In addition, for each ST spot histopathological annotations and GSEA scores for the Citrate-Spermine Secretion gene signature are provided.</p> <p>Abbreviations:</p> <p>- HES = Hematoxylin-Erythrosine-Saffron<br>- HE = Hematoxylin-Eosin<br>- MTS = Masson's Trichrome Staining<br>- IHC = Immunohistochemistry<br>- ST = Spatial Transcriptomics, here refers to Visium10X arrays.<br>- MALDI-MSI = Matrix-Assisted Laser Desorption Ionization - Mass Spectrometry Imaging.</p> <p> </p>
Reproducibility Test Data of Foundation Model for Cancer Imaging x Mhub
<p>This dataset provides test data for the FMCIB model integrated within the MHub platform, a robust solution for deploying, managing, and testing deep learning models tailored for medical imaging. </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.