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547 results for “Model Tests”
Test of a mountain pine beetle winter mortality model
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Data from: Exploring rainforest diversification using demographic model testing in the African foam-nest treefrog (Chiromantis rufescens)
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Data for: Predicting age and mass at maturity from feeding behavior and diet in M. sexta: An empirical test of a life history model
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An Experimental Test of Lanchester’s Models of Combat in the Neotropical Termite Nasutitermes corniger (Blattodea: Termitidae)
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TESTAR State Model extracted while executing MyThaiStar as web system under test
<p>TESTAR extracted State Model datasets with TESTAR tool using MyThaiStar web application as System Under Test (SUT). This State Models has been generated to be used as an example to be automatically generated and introduced locally in DECODER PKM, from H2020 DECODER Project.</p> <p>TESTAR tool is an open source tool (www.testar.org) for automated testing through graphical user interface (GUI) currently being developed by the Universitat Politecnica de Valencia and the Open University of the Netherlands.</p> <p>MyThaiStar (<a href="https://github.com/devonfw/my-thai-star">github.com/devonfw/my-thai-star</a>) is the reference application that Capgemini uses internally to promote best programming practices and the correct use of last technologies. It’s is developed with Devon Framework, the standard tool for development at the company.</p> <p>PKM is the Persistent Knowledge Monitor developed as main infrastructure from H2020 DECODER Project (www.decoder-project.eu) under grant agreement number 824231.</p> <p>As TESTAR explores automatically the SUT, it will use the Document Object Model (DOM) information extracted from MyThaiStar SUT, to generate and save a TESTAR State Model in the OrientDB graph database. This model contains information about the Widgets, States and Actions, that were found in the SUT.<br> <br> - MyThaiStar.json.gz: JSON file exported from OrientDB that contains a database with the TESTAR State Model. It can be imported into OrientDB using the TESTAR tool, to analyze and interact with the State Model.<br> <br> - ArtefactStateModel_MyThaiStar_2020.1_zpnffj5c3407972370_2020-06-15_12h14m24s: for DECODER project purposes, the knowledge extracted with TESTAR in the generation of the State Model has been summarized and referenced in an artifact JSON file to be adapted to PKM input requirements.</p>
BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance
<p>This Zenodo repository contains 100 copies of the model BERT fine-tuned on the MNLI dataset, created for the paper "BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance." Please see the project GitHub page for more details about using these models and how to cite any such usage: https://github.com/tommccoy1/hans/tree/master/berts_of_a_feather</p>
Data from "Testing models of peripheral encoding using metamerism in an oddity paradigm"
<p>Raw data and stimuli from the experiments reported in Wallis, Bethge & Wichmann (under review). "Testing models of peripheral encoding using metamerism in an oddity paradigm". Journal of Vision.</p> <p>For the code, see http://doi.org/10.5281/zenodo.34218.</p> <p>Please consult the README file in the archive for detailed information on reproducing the results of the paper.</p> <p>Stimuli are modified from the "Judd" dataset (https://people.csail.mit.edu/tjudd/WherePeopleLook/index.html). The citation is </p> <p>Judd, T., Ehinger, K., Durand, F., & Torralba, A. (2009). Learning to predict where humans look. In <em>Computer Vision, 2009 IEEE 12th international conference on</em> (pp. 2106–2113). IEEE.</p> <p>We have been granted permission to reshare these images by Tilke Judd (thanks!).</p>
Testing sky brightness models against radial dependency: A dense two dimensional survey around the city of Madrid, Spain: SQM data
<p>We present a study of the night sky brightness around the extended<br /> metropolitan area of Madrid using Sky Quality Meter (SQM) photometers. The map is the first to cover the spatial distribution of the sky brightness in the<br /> center of the Iberian peninsula. These surveys are neccessary to test the light<br /> pollution models that predict night sky brightness as a function of the<br /> location and brightness of the sources of light pollution and the scattering of<br /> light in the atmosphere. We describe the data-retrieval methodology, which<br /> includes an automated procedure to measure from a moving vehicle in order to<br /> speed up the data collection, providing a denser and wider survey than previous<br /> works with similar time frames. We compare the night sky brightness map to the<br /> nocturnal radiance measured from space by the DMSP satellite. We find that i) a<br /> single source model is not enough to explain the radial evolution of the night<br /> sky brightness, despite the predominance of Madrid in size and population, and<br /> ii) that the orography of the region should be taken into account when deriving<br /> geo-specific models from general first-principles models. We show the tight<br /> relationship between these two luminance measures. This finding sets up an<br /> alternative roadmap to extended studies over the globe that will not require<br /> the local deployment of photometers or trained personnel.</p>
WESSBAS: Extraction of Probabilistic Workload Specifications for Load Testing and Performance Prediction - A Model-Driven Approach for Session-Based Application Systems.
<p>Supplementary material for the paper: "WESSBAS: Extraction of Probabilistic Workload Specifications for Load Testing and Performance Prediction".</p> <p>Included in the supplementary material are the evaluation results.</p> <p>The WESSBAS software relevant to the paper is available via https://github.com/Wessbas/</p> <p>The WESSBAS UI is available as a password-protected (password: wessbasui) ZIP file:</p> <p>https://dl.dropboxusercontent.com/u/81621779/wessbas.ui.zip (--- WESSBAS GUI (license confirmation pending, i.e., not on GitHub, yet))</p>
Dataset for Verification and Validation Tests of the Spalart-Allmaras Model in Moltres
<p>This repository contains input files, raw output data, data analysis scripts, and plots for verification and validation tests of the Spalart-Allmaras turbulence model in Moltres. These V&V tests consist of numerical simulations of turbulent channel, pipe, and backward-facing step flows based on the works by Moser et al. (1999), Laufer (1954), and Driver & Seegmiller (1985), respectively.</p>
NorSand4AI: A Comprehensive Triaxial Test Simulation Database for NorSand Constitutive Model Materials
<p>Dataset of the homonymous paper: a first-of-its-kind database that addresses the size and complexity issues of creating synthetic datasets for nonlinear constitutive modeling of soils by simulating both drained and undrained triaxial tests. Two datasets are provided: the first one considers a nested Latin Hypercube Sampling of input parameters encompassing 2000 soil types, each subjected to 40 initial test configurations, resulting in a total of 160000 triaxial test results (NorSandTXL_H5.zip). The second one considers nested quasi-Monte Carlos sampling techniques (Sobol and Halton) of input parameters encompassing 2048 soil types, each subjected to 42 initial test configurations, resulting in a total of 172032 triaxial test results (NorSand_2048_42_Drained.zip and NorSand_2048_42_Undrained.zip). Each simulation dataset comprises a 4000 by 10 matrix that can be used for general multivariate forecasting benchmarks, in addition to direct geotechnical and soil science applications.</p>
FS1114 test model room 3
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
A multi-omics systems vaccinology resource to develop and test computational models of immunity: 1st challenge dataset and submissions
<p>The goal of the CMI-PB prediction contest is to foster a collaborative research community that can collectively tackle challenges and accelerates scientific progress beyond the capabilities of individual researchers or groups. The CMI-PB consortium has curated multi-source data from multiple individuals, encompassing Ab titers (around four antibodies/features), cell frequency (approximately 20 cell types/features), gene expression (roughly 50,000 RNA transcripts/features), and plasma proteomics (around 50 proteins/features). The challenge requires integrating these diverse data sources to predict different immune responses or tasks. Specifically, you will utilize multi-source data from several individuals on day 0 (baseline) to predict specific immune responses at later time points (1, 3, 7, and 14 days post-booster vaccination).</p> <p>The first CMI-PB challenge, which is an internal challenge, was conducted using datasets from 2020 (train) and 2021 (test). In the following sections, we provide detailed information on the datasets, challenge tasks, submission format, descriptions, and access to the necessary data files for participants to develop their models and make predictions.</p> <p><br><strong>A) Multiomics CMI-PB dataset:</strong></p> <p>We propose a study design that enables a systems-level understanding of the immune responses through computational modeling. Our cohort comprises aP vs. wP infancy-primed subjects boosted with Tdap. We recruit individuals born before 1995 (wP) and after 1996 (aP), collect baseline plasma and blood samples, and then at 1, 3, 7 and 14 days post booster vaccination.</p> <p>With the obtained samples processed, we generated omics data by:</p> <ul> <li> <p>Bulk PBMCs transcriptomics,</p> </li> <li> <p>Plasma proteomics using Olink, which provides a quantitative readout of cytokines, chemokines, and other immune factors,</p> </li> <li> <p>Cell frequency in PBMCs using flow cytometry,</p> </li> <li> <p>Tdap-specific antibodies levels</p> </li> </ul> <p><strong>B) List of tasks can be accessed using the “List of tasks for challenge 1.docx” file, and submissions need to submit in provided format here: “submission template challenge 1.tsv”</strong></p> <p><strong>C) Datasets for model building and making predictions:</strong></p> <p> Data files are divided into two categories: 1) raw dataset and 2) computable matrices.</p> <ol> <li> <p><strong>Raw dataset: </strong>This raw-most dataset is divided into training and test datasets. </p> </li> <li> <p><strong>Computable matrices: </strong>There are three different types of computable matrices. a) Full: These files are generated by dividing raw files into sub-files specific to planned days specific to vaccination. b) harmonized: These are generated by preserving only overlapping features between train and test datasets. b) imputed: MICE imputation is performed to impute missing values in the dataset.</p> </li> </ol> <p><strong>D) Submission evaluation</strong></p> <p>This folder contains all submitted models with ranking files and code for evaluating these models.</p> <p><strong>To learn more about the CMI-PB prediction challenge, visit our website at www.cmi-pb.org.</strong></p>
Experiments for a Model-Based Approach to Complete Property-Oriented Testing
<p>The experiments1.tar.zstd archive contains the scripts, yaml-files, software and model files to create Docker containers that run the experiments.</p> <p>The data1.tar.zstd archive contains the collected log files.</p>
Dataset: Testing for effects of growth rate on isotope trophic discrimination factors and evaluating the performance of Bayesian stable isotope mixing models experimentally: a moment of truth?
<p><span>Discerning assimilated diets of wild animals using stable isotopes is well established where potential dietary items in food webs are isotopically distinct. With the advent of mixing models, and Bayesian extensions of such models (Bayesian Stable Isotope Mixing Models, BSIMMs), statistical techniques available for these efforts have been rapidly increasing. The accuracy with which BSIMMs quantify diet, however, depends on several factors including uncertainty in tissue discrimination factors (TDFs; <em>Δ</em>) and identification of appropriate error structures. Whereas performance of BSIMMs has mostly been evaluated with simulations, here we test the efficacy of BSIMMs by raising domestic broiler chicks (<em>Gallus gallus domesticus</em>) on four isotopically distinct diets under controlled environmental conditions, ideal for evaluating factors that affect TDFs and testing how BSIMMs allocate individual birds to diets that vary in isotopic similarity. For both liver and feather tissues,<em> δ</em><sup>13</sup>C and <em>δ </em><sup>15</sup>N values differed among dietary groups. <em>Δ</em><sup>13</sup>C of liver, but not feather, was negatively related to the rate at which individuals gained body mass. For <em>Δ</em><sup>15</sup>N, we identified effects of dietary group, sex, and tissue type, as well as an interaction between sex and tissue type</span><span><span>, </span></span><span><span>with f</span></span><span>emales having higher liver <em>Δ</em><sup>15</sup>N relative to males. For both tissues, BSIMMs allocated most chicks to correct dietary groups, especially for models using combined TDFs rather than diet specific TDFs, and those applying a multiplicative error structure. These findings provide new information on how biological processes affect TDFs and confirm that adequately accounting for variability in consumer isotopes is necessary to optimize performance of BSIMMs. Moreover, they demonstrate experimentally that these types of models reliably characterize consumed diets when appropriately parameterized.<span> </span></span></p>
Test models and test results to evaluate CAD assembly modules capabilities to generate component interfaces
<p>Set of 3D CAD assembly models in STEP AP 203 format.</p> <p>Assembly test models are devoted to evaluations of interfaces between components. The interfaces can be of type surface, rectilinear contacts, circular contacts, or point contacts.</p> <p>Test results obtained from some commercially available CAD assembly modules are given as a set of tables organized in accordancce with contact categories (surface, rectilinear, circular, point).</p> <p>The content and use of the test models are described into the pdf document: Test models and test results to evaluate CAD assembly modules capabilities to generate component interfaces.</p> <p> </p>
Data from: N-mixture models estimate abundance reliably: a field test on Marsh Tit using time-for-space substitution
<p>Imperfect detection in field studies on animal abundance, including birds, is common and can be corrected for in various ways. The binomial N-mixture (hereafter binmix) model developed for this task is widely used in ecological studies owing to its simplicity: it requires replicated count results as the input. However, it may overestimate abundance and be sensitive to even small violations of its assumptions. We used a 33-year dataset on the Marsh Tit, Poecile palustris, a sedentary forest passerine, from Białowieża Forest, Poland to validate inference from binmix models by comparing model-estimated abundances to the true number of breeding pairs within the plots, determined by exhaustive population study. The abundance estimates, derived from six springtime (April-May) counts of males on each plot in each year, were highly reliable: 116 out of 132 year-plot estimates (88%) included the true number of pairs within the 95% confidence intervals. Over- and underestimations were thus rare and similarly frequent (9 and 12 cases, respectively), with a tendency to overestimate at low densities and underestimate at high densities. Marsh Tits sing rarely but the frequency of countersinging increases with abundance, leading to non-independence in detections. When accounted for in a submodel for detection, the per-survey number of countersinging events positively affected detection probability but only weakly affected abundance estimates. Simulations further demonstrate that this property, overestimation at low densities and underestimation at high densities, may be a systematic bias of binmix model even if density-dependent detection is absent. While the behaviour of binmix models in specific situations requires more study, we conclude that these models are a valid tool to estimate abundance reliably when intensive population monitoring is not feasible.</p>
A Test of Energetic Particle Precipitation Models Using Simultaneous Incoherent Scatter Radar and Van Allen Probes Observations
<p>BERI modeling reuslts for "A Test of Energetic Particle Precipitation Models Using Simultaneous Incoherent Scatter Radar and Van Allen Probes Observations"</p>
mCRL2 models, requirements and test logs for the EULYNX Point interface case study
<p>mCRL2 models and mu-calculus formulas for the EULYNX Point Interface. Models and requirements are made in the context of the FormaSig project. Data is made available for replication purposes.</p> <p>REQ_P_001, REQ_P_001_1 and REQ_P_002 are requirements for the point specific mCRL2 model point_spec.mcrl2</p> <p>Remaining .mcf files are requirements for the generic PDI interface pdi_spec.mcrl2</p> <p>Artifacts relating to testing are:</p> <ul> <li>An mCRL2 model, mbt.mcrl2</li> <li>A rename file to rename internal actions to tau, rename_file.re</li> <li>Partial state space associated to mbt.mcrl2, partial_state_space.aut</li> <li>The weak-trace bisim reduced version of the state space, partial_state_space_reduced.aut</li> <li>Testing logs, test-logs.zip</li> <li>The source code of the simulator and the testing tools, Simulator code.zip</li> </ul>
A model of digestive tooth corrosion in lizards: experimental tests and taphonomic implications
<p>Abstract </p><p>Corrosion patterns induced by gastric fluids on the skeleton of prey animals may depend on the nature of the corrosive agents (acid, enzymes) as well as on the composition of the hard parts and the soft tissues that surround them. We propose a framework for predicting and interpreting corrosion patterns on lizard teeth, our model system, drawing on the different digestive pathways of avian and non-avian vertebrate predators. We propose that high-acid, low-enzyme systems (embodied by mammalian carnivores) will lead to corrosion of the tooth crowns, whereas low-acid, high-enzyme systems (embodied by owls) will lead to corrosion of the tooth shafts. We test our model experimentally using artificial gastric fluids (with HCl and pepsin) and feeding experiments, and phenomenologically using wild-collected owl pellets with lizard remains. Finding an association between the predictions and the experimental results, we then examine corrosion patterns on nearly 900 fossil lizard jaws. Given an appropriate phylogenetic background, our focus on physiological rather than taxonomic classes of predators allows the extension of the approach into Deep Time.</p><p></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.