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547 results for “Model Tests”
OpenFOAM test result data accompanying 'Numerical representation of mountains in atmospheric models'
<p>OpenFOAM test result data accompanying the PhD thesis, 'Numerical representation of mountains in atmospheric models'</p>
Test Model for GTT Computational Fluid Dynamics Simulation by TransAT
<p>This archive contains the model and associated code need to run the GTT benchmark test for TransAT.</p>
Turbulent and laminar flow in karst conduits under unsteady flow conditions – interpretation of pumping tests by discrete conduit-continuum modeling
<p>Due to the duality in terms of (1) the groundwater flow field and (2) the discharge conditions, flow patterns of karst aquifer systems are complex. Estimated aquifer parameters may differ by several orders of magnitude from local (borehole) to regional (catchment) scale because of the large aquifer heterogeneity and anisotropy. One approach to deal with this scale problem in karst aquifers is the application of large-scale experiments such as long-term high-abstraction conduit pumping tests, stimulating measurable groundwater drawdown in both, the karst conduit system as well as the fissured and fractured aquifer matrix. The numerical discrete conduit-continuum modeling approach MODFLOW-2005 Conduit Flow Process Mode1 (CFPM1) is employed to simulate laminar and non-laminar conduit flow, induced by large-scale experiments, in combination with Darcian matrix flow. Effects of large-scale experiments were simulated for idealized settings. Subsequently, diagnostic plots and analyses of different fluxes are applied to interpret differences in the simulated conduit drawdown and general flow patterns.</p> <p>The main focus is set on the question to which extent different conduit flow regimes will affect the conduit as well as the matrix drawdown depending on the hydraulic properties of the conduit system, i.e. conduit diameter and relative roughness In this context, CFPM1 is applied to investigate the significance of turbulent flow consideration for the simulation of karst conduit flow. This work quantifies the relative error that results from assuming laminar conduit flow for the interpretation of a synthetic large-scale pumping test in karst.</p> <p>https://doi.org/10.1002/2017WR020658</p> <p> </p>
Supplemental Dataset for paper: Methane ebullition from subtropical peat: testing an ebullition model reveals the importance of pore structure
<p>A dataset requested for publication of Journal Article, "<strong>Methane ebullition from subtropical peat: testing an ebullition model reveals the importance of pore structure"</strong></p>
Replication Package: Microservice-tailored Generation of Session-based Workload Models for Representative Load Testing
<p>This is the replication package for the publication <em>Microservice-tailored Generation of Session-based Workload Models for Representative Load Testing</em>, MASCOTS 2019. It holds the experiment setup, results, and detailed analyses of the results.</p> <p>The README.md (or README.pdf) contains further descriptions and instructions.</p>
Numerical Tests of a Superfluid Effective Field Theory in the 3d O(2)-model.
<p>Dataset and Analysis tools for the project Numerical Tests of a Superfluid Effective Field Theory in the 3d O(2)-model. This dataset resulted from FCT advanced computing grants, 2022.15885.CPCA.A2.</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>
Testing a Theoretical Model of Parent-Child Relationship on Sleep Problem Mediated by Problematic Smartphone Usage and Nomophobia
<p>The dataset is derived from 672 senior high schools’ students from six schools in Yogyakarta city, Indonesia. The dataset provides important information about data of <em>parent-child relationships, problematic smartphone usage, nomophobia, and sleep problems</em>.</p>
Simulation output of the reference setup in "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses"
<p>This supplementary material for "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses" contains the simulation output of the 20 reference runs. Additional data is available upon request from the corresponding author.</p>
GEOLAB-PEBSTER-TU Darmstadt: Large-scale model test on Piled Embankments with Basal Steel Mesh Reinforcement - Measurements.
<h1><strong>Large-scale model test on basal steel-mesh reinforced piled embankments (PEBSTER, a transnational access project of GEOLAB)</strong></h1> <p>Welcome to the GEOLAB PEBSTER TU Darmstadt project on Zenodo! This repository contains the measurement data of the large-scale model test on Piled Embankments with a Basal Steel Reinforcement (PEBSTER). Pile-supported embankments with basal geosynthetic reinforcement are commonly built in soft soil areas. Steel mesh reinforcement is particularly appealing for high embankments (Topolnicki et al., 2019). Its high axial stiffness, compared to geosynthetics, reduces horizontal deformation at the embankment base, minimizes bending moments in the piles, and ultimately enhances embankment stability.</p> <h2>PEBSTER</h2> <p>This dataset includes measurements from a large-scale test on steel-reinforced piled embankments, conducted at the Institute of Geotechnics of the Technical University of Darmstadt (Schneider et al., 2024a,b,c). This test is part of a broader research initiative called Piled Embankments with Basal Steel Reinforcement (PEBSTER), which also includes four small-scale test (van Eekelen et al., 2024), conducted at Deltares, Delft, Netherlands.</p> <h2>GEOLAB</h2> <p>GEOLAB is a project of the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 101006512, addressing Europe's Critical Infrastructure (CI) challenges in the water, energy, urban, and transport sectors.</p> <p>The GEOLAB Research Infrastructure (RI) consists of 11 unique installations across Europe to study subsurface behavior and its interaction with structural CI elements and the environment. During the GEOLAB Transnational Access (TA), users outside the consortium gained access to the GEOLAB installations to perform research and innovation.</p> <h2>PEBSTER Research group</h2> <p>The project was conducted by a research group that includes the Institute of Geotechnics of the Technical University of Darmstadt, Germany, Deltares, Netherlands, Keller (Germany, France, Poland), and SHM System, Poland.</p>
Reproducibility Test Data of 4 MHub Models
<p>This dataset provides test data for 4 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> </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>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>
Datasets for reproducing the results in "True random number generators with flicker noise: stochastic model, min-entropy calculation and online test"
<p>Datasets for reproducing the results in "True random number generators with flicker noise: stochastic model, min-entropy calculation and online test"</p> <p>Includes scripts for generating raw results, postprocessing scripts, as well as scripts/notebooks for generating plots for the manuscript</p>
Data from: Population genomic tests of models of adaptive radiation in Lake Victoria region cichlid fish.
Adaptive radiation is usually thought to be associated with speciation, but the evolution of intraspecific polymorphisms without speciation is also possible. The radiation of cichlid fish in Lake Victoria is perhaps the most impressive example of a recent rapid adaptive radiation, with 600+ very young species. Key questions about its origin remain poorly characterized, such as the importance of speciation versus polymorphism, whether species persist on evolutionary time scales, and if speciation happens more commonly in small isolated or in large connected populations. We used 320 individuals from 105 putative species from Lakes Victoria, Edward, Kivu, Albert, Nabugabo and Saka, in a radiation-wide AFLP genome scan to address some of these questions. We demonstrate pervasive signatures of speciation supporting the classical model of adaptive radiation associated with speciation. A positive relationship between the age of lakes and the average genomic differentiation of their species, and a significant fraction of molecular variance explained by above-species level taxonomy suggest the persistence of species on evolutionary time scales, with radiation through sequential speciation rather than a single starburst. Finally the large gene diversity retained from colonization to individual species in every radiation suggests large effective population sizes and makes speciation in small geographical isolates unlikely.
Data from: A field test of a model for the stability of androdioecy in the freshwater shrimp, Eulimnadia texana
The evolution of hermaphroditism from dioecy is a poorly studied transition. Androdioecy (the coexistence of males and hermaphrodites) has been suggested as an intermediate step in this evolutionary transition or could be a stable reproductive mode. Freshwater crustaceans in the genus Eulimnadia have reproduced via androdioecy for 24+ million years and thus are excellent organisms to test models of the stability of androdioecy. Two related models that allow for the stable maintenance of males and hermaphrodites rely on the counterbalancing of three life history parameters. We tested these models in the field over three field seasons and compared the results to previous laboratory estimates of these three parameters. Male and hermaphroditic ratios within years were not well predicted using either the simpler original model or a version of this model updated to account for differences between hermaphroditic types ('monogenic' and 'amphigenic' hermaphrodites). Using parameter estimates of the previous year to predict the next year's sex ratios revealed a much better fit to the original relative to the updated version of the model. Therefore, counter to expectations, accounting for differences between the two hermaphroditic types did not improve the fit of these models. At the moment, we lack strong evidence that the long-term maintenance of androdioecy in these crustaceans is the result of a balancing of life history parameters; other factors, such as metapopulation dynamics or evolutionary constraints, may better explain the 24+ million year maintenance of androdioecy in clam shrimp.
Optimal Spectral Sampling Forward Model Test Data
<p>Binary and ASCII files for tests of the OSS forward model application.</p>
Test dataset with ground truth for segmentation models testing
<p>Test dataset with ground truth for segmentation models testing</p>
Figure 2 in Model-based approach to test hard polytomies in the Eulaemus clade of the most diverse South American lizard genus Liolaemus (Liolaemini, Squamata)
Figure 2. Description of hypothesis-testing procedures. Models were constructed in MESQUITE v. 2.74 and simulations of 1000 gene trees were generated for each of the 13 loci for each of the eight models (step 1). We then compared the simulated trees for each locus against the corresponding model in order to obtain an approximation of a probability distribution of expected deep coalescence events (step 2). Finally, we counted the number of deep coalescences of the real gene tree within the model and obtained the probability of observing that value given the model (step 3). If the empirical data fell outside the 95% confidence interval, then the hypothesized number of deep coalescence events for that gene tree (real data) is not expected under the specified model, thus rejecting that hypothesis.
Figure 4 in Model-based approach to test hard polytomies in the Eulaemus clade of the most diverse South American lizard genus Liolaemus (Liolaemini, Squamata)
Figure 4. Simulation results. Total number of loci supporting each model after comparisons of the data against 1000 simulated data sets for each locus for each model, and compared with data. We compared the 1000 simulated trees against the model and obtained an approximation of a probability distribution of the deep coalescences expected, and then compared the number of deep coalescences on the observed tree (real data) against the probability distribution. Here, we the show number of loci that fell within the 95% confidence intervals of those probability distributions.
Figure 3 in Model-based approach to test hard polytomies in the Eulaemus clade of the most diverse South American lizard genus Liolaemus (Liolaemini, Squamata)
Figure 3. Empirical Eulaemus phylogenetic hypotheses. Phylogenetic results using the full matrix of 188 taxa (A, B) and a reduced set of 40 taxa chosen to subsample the largest clades (C to F). A, topology recovered from the concatenated matrix; B, species tree recovered by the minimizing deep coalescence (MDC) approach; C, *BEAST results of partial matrix with representatives of largest clades; values on branches are estimated divergences times (upper) and posterior probability values estimated (lower) or separated by a slash; bold branches represent nodal support> 0.90; D, BEST result; E, MDC result of partial matrix; and F, partial concatenated matrix. In (A), (D), and (F) nodes with support <0.90 were collapsed. The calibration point for estimation of divergence times is shown with a white circle in the node corresponding to the divergence of the Eulaemus and Liolaemus clades.
Reproduction package for publication 'Testing afterglow models of FRB 200428 with early post-burst observations of SGR 1935+2154'
<p>The scripts in this package allows for the reproduction of all figures and data within the publication. Observing data can be found in the LOFAR long-term archive. For further information about reproducing figures pertaining to observing data please contact the authors. </p> <p>X-ray data pertaining to Figure 4 is available from the public reproduction packages of the relevant cited publications. </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.