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547 results for “Model Tests”
A blind test on wind turbine wake modelling based on wind tunnel experiments: Phase I – The benchmark case
<p>This data set ("Data files.zip") contains the wind tunnel measurement data from Phase I of the Blind test on wind turbine wake modelling based on wind tunnel experiments organised during the TWEET-IE project (www.tweet-ie.eu).</p> <p>This updated version <strong>replaces</strong> the older versions 1.0.0 (https://doi.org/10.5281/zenodo.10566401), 1.1.0 (https://doi.org/10.5281/zenodo.11370112), 2.0 (https://doi.org/10.5281/zenodo.12188194) and 2.1 (https://doi.org/ 10.5281/zenodo.13918935). In comparison to the previous version 2.1 the data documentation has been updated to follow the template of the TWEET-IE project documents, indicating the Grant Agreement Number with the European Union and the Call Topic of the project.</p> <p>All tests were conducted in the closed-loop, low-speed boundary layer wind tunnel of the Chair of Aerodynamics and Fluid Mechanics at Technische Universität München (TUM). The experiments concerned two wind turbines, aligned with the flow, one downstream of the other, at a distance of 5 diameters. For Phase I, no control was applied to the wind turbine models, which were operating at constant RPM. The turbine models, designed and manufactured by TUM, were instrumented with multiple sensors and actuators and had a diameter of 1.1M. Measurements include velocity, power and loads on the turbines. A detailed description of the experimental set up can be found in the accompanying document ("Data documentation.pdf"). </p> <p>File "Submission procedure.zip" includes the format description and the templates of the output data that should be submitted by the participants in the blind test comparison.</p>
Analogue models testing the interaction between a propagating continental rift and inherited crustal fabrics
<p>This dataset presents the results of an experimental series of analogue models performed to investigate the interaction between a propagating continental rift and inherited crustal fabrics. Our experimental series was designed adopting a parametric approach, which consisted in the systematic variation of the orientation of various kinds of brittle discontinuities (e.g., faults, fractures, foliations, etc.). Structures of models have been analysed quantitatively by means of photogrammetric digital elevation model reconstruction and semi-automatic fault pattern quantification. In this dataset, we show the row data and specific elaborations supporting the interpretation of results.</p>
An inflamed human alveolar model for testing the efficiency of anti-inflammatory drugs in vitro
<p>The data set accompanies the study where we developed an inflamed human alveolar epithelium model and to test the resolution<strong><em> </em></strong>lipopolysaccharide (LPS)-induced inflammation <em>in vitro</em> with a corticosteroid, methylprednisolone (MP). A specific focus of the study was in macrophage phenotype shifts in response to these stimuli.</p> <p>The data set includes:</p> <p>- Pro-inflammatory marker (interleukin (IL)-8, tumor necrosis factor α (TNFα), IL1β) secretion data, analysed via ELISA, and cell viability (analysed via lactate dehydrogenase assay) of both monocultures (human monocyte-derived macrophages) and of the multicellular human alveolar model, composed of macrophages, dendritic cells, and epithelial cells. </p> <p>- Barrier permeability data of the multicellular model, assessed via labeled-dextran permeability assay. </p> <p>All the above-stated data is joined in the file: DraslerB_Frontiers 2020_Inflammatory model. Sample codes are explained int he first tabs. </p> <p>- Pro-inflammatory marker gene expression data of the multicellular model, assessed via real time RT-qPCR. The data prepared for analysis and analysed is joined to the above-mentioned folder, whereas the direct PCR runs are joined in the file: DraslerB_Frontiers 2020_Inflammatory model_PCR runs. </p> <p>- Confocal laser scanning microscopy raw files (lsm) of the multicellular model can be opened with an open source software Fiji, based on ImageJ. </p>
Testing 3D modelling software. Modelling charging pads for WPT of electric vehicles for EM emissions simulation.
<p>Even for the experienced 3D FEM modelers it may not be obvious which geometry discretization is the most appropriate and suitable for this type of problem. It may be a conservative approach to test the computation tool on a simplified geometry, on which the magnetic field distribution is known. As part of the “Metrology for inductive charging of electric vehicles” (MICEV) project (www.micev.eu), an axisymmetric geometry was used, with the results reported.</p>
Model outputs from the study "A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce region"
<p>Model outputs from the study "A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce region". The study is published as open access and can be found at the following link: <a href="https://www.sciencedirect.com/science/article/pii/S2950289625000326">https://www.sciencedirect.com/science/article/pii/S2950289625000326</a></p> <p> </p> <p>The file "SWAT_USERSOIL.csv" was included to facilitate the assimilation of the soil mapping data into the Soil & Water Assessment Tool (SWAT, https://swat.tamu.edu/) for hydrological modeling. </p> <p> </p> <p>Regarding the raster files, please note:</p> <p>a) All values in these datasets have been multiplied by 10,000 to optimize file sizes.</p> <p>b) Files are named using the variable acronym, followed by the corresponding soil layer. For outputs derived from pedotransfer functions (PTFs), the PTF reference is appended after the variable acronym.</p> <p>c) Available data decrease with increasing soil layer number. This occurs because not all locations (grid cells) have the same soil depth or number of soil layers.</p> <p> </p> <p>If you have any questions about the dataset or its use, please don't hesitate to contact us.</p> <p> </p> <p> </p>
ConFiRMa dataset_01: simulation of CRM characterization tests with the OOFEM code (detailed level modelling)
<p>The Dataset collects the input files developed for the simulation of characterization tests performed on Composite Reinforced Mortar samples with the free open-source code OOFEM (detailed level modelling). The description of the numerical models and the analysis and comparison of the results can be found in paper "Characterization of Textile Reinforced Mortar: state of the art and detailed modelling with a free open source finite element code" (<a href="https://doi.org/10.1061/(ASCE)CC.1943-5614.0001240">https://doi.org/10.1061/(ASCE)CC.1943-5614.0001240</a>).</p> <p>OOFEM Version 2.5 (https://doi.org/10.5281/zenodo.4339630) was used for running the analyzes.</p> <p>ReadMe file provide a description of the different input files.</p> <p> </p>
ConFiRMa dataset_02: simulation of tests on CRM strengthened masonry elements with the OOFEM code (detailed level modelling)
<p>The Dataset collects the input files developed for the simulation of tests on masonry elements strengthened through Composite Reinforced Mortar with the free open-source code OOFEM (detailed level modelling). The description of the numerical models and the analysis and comparison of the results can be found in paper "Masonry elements strengthened through Textile-Reinforced Mortar:application of the detailed level modelling with a free open-source Finite-Element code".</p> <p>OOFEM Version 2.5 (https://doi.org/10.5281/zenodo.4339630) was used for running the analyzes.</p> <p>ReadMe file provide a description of the different input files.</p>
Digital models of test objects captured by RFSAT Ltd using 3D photogrammetry
<p>This data set contains a number of digital models produced via 3D photogrammetric scanning as part of the SCAN4RECO project, funded by the European Horizon'2020 program. Scanning and processing of models was done with Pix4D Mapper and Autodesk ReMake software from images captured with Canon 5DS camera in 50 Megapixel image resolution. Example objects include Byzantine icons painted on wood, oil paintings on canvas and painted Venetian carnival paper masks.</p> <p>Second version of the data set includes historical icons of Saint DImitrios and Saint Archangel Michael, an icon of Saint Mary painted specially for testing SCAN4RECO technologies, as well as models of an original high-relief sculpture from OPD and of its 3D printed copy (made by Fraunhofer-IGD and hand painted by RFSAT)..</p> <p>Selected models can be also seen in the SCAN4RECO Virtual Museum developed by CERTH-ITI:<br> http://scan4reco.eu/scan4reco/content/scan4reco-virtual-museum</p>
Metrics for two-sample tests: results on Mixture of Gaussians and Correlated Gaussians models
<p>The repository includes version 1.0 (v1.0) of the code and results corresponding to the GitHub repository <a href="https://github.com/TwoSampleTests/GenerativeModelsMetrics">GenerativeModelsMetrics</a>.</p> <p>Publishing information and arXiv identifier will be added after publication of the main manuscript related to the data.</p>
ConFiRMa dataset_04: simulation of tests on CRM strengthened masonry buildings with the OOFEM code (intermediate, multi-layer level modelling)
<p>The Dataset collects the input files developed for the simulation of tests on one, two and three stroeys masonry buildings strengthened through Composite Reinforced Mortar with the free open-source code OOFEM (intermediate, multi-layer level modelling).</p> <p>OOFEM Version 2.5 (https://doi.org/10.5281/zenodo.4339630) was used for running the analyzes.</p> <p>ReadMe file provides a description of the different input files.</p>
Detecting coarse beach sediment using remotely sensed imagery at the FRF, Duck, NC, USA: Labeled images, deep learning model, testing data, and predictions.
<p>This data record contains 5 zip files all used to build and use a semantic segmentation model to operate on beach imagery taken at the Field Research Facility (FRF) in Duck, North Carolina, USA. All data is from 2015-2021</p> <p>The `training_data.zip` contains all data used to train the ML model. All images come from the north facing (c1) camera. This zip file includes: a list of classes used to label the imagery, and folders of 107 images, 107 sparse annotations (doodles), 107 labels, and 107 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021).</p> <p>The `model.zip` file contains the ML model, and associated metadata. This includes: a JSON model configuration file, a figure showing model training statistics, an `.npz` file of model training output, a list of training and validation files, the model as an h5 file and in the Tensorflow ‘saved model’ format. All modeling was done with Segmentation Gym (Buscombe & Goldstein 2022).</p> <p>The `test_data_c6.zip` file contains all data from the south facing (c6) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `test_data_c1.zip` file contains all data from the north facing (c1) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with an open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `predictions.zip` file contains 4418 images from the north facing (c1) camera that were run through the trained segmentation model as well as the resulting output (presented as side-by-side image and overlays). These images were created using codes in Segmentation Gym (Buscombe & Goldstein 2022).</p>
The SPOTS Models: A Grid of Theoretical Stellar Evolution Tracks and Isochrones For Testing The Effects of Starspots on Structure and Colors
<p><strong>The SPOTS Models: A Grid of Theoretical Stellar Evolution Tracks and Isochrones For Testing The Effects of Starspots on Structure and Colors</strong></p> <p>This repository contains the Stellar Parameters of Tracks with Starspots (SPOTS) grid of theoretical stellar evolutionary tracks and isochrones, presented in Somers, Pinsonneault, and Cao (2020, in prep). Our models were calculated with the Yale Rotating Evolution Code (e.g. van Saders & Pinsonneault, 2013, ApJ 776, 67), including updated which incorporate a treatment of surface starspots (Somers & Pinsonneault, 2015, ApJ 807, 174S). Modelling details can be found in these references. The purpose of this evolutionary suite is to provide the community with state-of-the-art predictions for the influence of starspots and magnetic activity on the structure of stars.</p> <p>The grid includes both isochrones and tracks. They can be downloaded individually from this repository, or in bulk by downloading the .zip files.</p> <p><strong>Isochrones (.isoc):</strong></p> <p>Each isochrone file contains a series of isochrones (stellar properties for a range of masses at fixed age) for ages between 1 Myrs and 4 Gyrs. Each file contains these isochrones for a different surface starspot covering fraction, given by the name of the file -- f000.isoc = 0% covering fraction, f017.isoc = 17% covering fraction, etc. Each isochrone contains several columns with different information, including,</p> <ol> <li>Fundamental properties: mass, age, luminosity, radius, logg, Teff, convective overturn timescale (TauCZ), lithium abundance relative to initial (Li/Li0).</li> <li>Starspot properties: Covering fraction (Fspot), ratio of spot temperature to ambient temperature (Xspot), the temperatures of hot and cool regions (T_hot, T_cool).</li> <li>Two-temperature colors, including Johnson BV, Cousins RI, 2MASS JHK, WISE W1, and Gaia G, BP, RP.</li> </ol> <p>Colors that fell outside of the calibrated range are listed as -99.0.</p> <p><strong>Tracks (.track):</strong></p> <p>We also include individual tracks for every combination of Fspot and Mass considered in the paper. Each .track file lists the mass and starspot covering fraction in the filename -- i.e. m055_f034.track is the model of mass 0.55Msun and with a 34% surface covering fraction. In addition to all the properties included in the isochrones, the track files also include:</p> <ol> <li>The total moment of interia of the model (total_I) and the moment of interia of the surface convection zone (CZ_I)</li> <li>The central and surface hydrogen abundances (X_cen, X_surf) and the surface metallicity (Z/X_surf)</li> <li>The deuterium abundance relative to initial (H2/H2_0)</li> </ol>
Data from: Exploring rainforest diversification using demographic model testing in the African foam-nest treefrog (Chiromantis rufescens)
Aim: Species with wide distributions spanning the African Guinean and Congolian rainforests are often composed of genetically distinct populations or cryptic species with geographic distributions that mirror the locations of the remaining forest habitats. We used phylogeographic inference and demographic model testing to evaluate diversification models in a widespread rainforest species, the African Foam-nest Treefrog (Chiromantis rufescens). Location: Guinean and Congolian rainforests, West and Central Africa. Taxon: Chiromantis rufescens. Methods: We collected mitochondrial DNA (mtDNA) and single nucleotide polymorphism (SNP) data for 130 samples of Chiromantis rufescens. After estimating population structure and inferring species trees using coalescent methods, we tested demographic models to evaluate alternative population divergence histories that varied with respect to gene flow, population size change, and periods of isolation and secondary contact. Species distribution models were used to identify regions of climatic stability that could have served as forest refugia since the Last Interglacial. Results: Population structure within Chiromantis rufescens resembles the major biogeographic regions of the Guinean and Congolian forests. Coalescent-based phylogenetic analyses provide strong support for an early divergence between the western Upper Guinean forest and the remaining populations. Demographic inferences support diversification models with gene flow and population size changes even in cases where contemporary populations are currently allopatric, which provides support for forest refugia and barrier models. Species distribution models suggest that forest refugia were available for each of the populations throughout the Pleistocene. Main conclusions: Considering historical demography is essential for understanding population diversification, especially in complex landscapes such as those found in the Guineo-Congolian forest. Population demographic inferences help connect patterns of genetic variation to diversification model predictions. The diversification history of Chiromantis rufescens was shaped by a variety of processes, including vicariance from river barriers, forest fragmentation, and adaptive evolution along environmental gradients.
Training and test data, plus saved models for the upcoming paper `Top-down perceptual inference shaping the activity of early visual cortex'
<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. These are 40px images that 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. These are 40px images that 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>The .zip file contains a saved model snapshot and various intermediate evaluative data. Details on these are coming soon.</p>
Artifact for "Challenges in Empirically Testing Memory Persistency Models"
<p>Presented here are the litmus tests, auxiliary scripts, and output data constituting the artifact accompanying the paper titled 'Challenges in Empirically Testing Memory Persistency Models,' published in ICSE NIER'24. For detailed information, please refer to the README.</p>
Tuning-less Object Naming with a Foundation Model - Data recorded during testing
<p>We implement a real-time object naming system that enables learning a set of named entities never seen. Our approach employs an existing foundation model that we consider ready to see anything before starting. It turns seen images into relatively small feature vectors that we associate with index to a gradually built vocabulary without any training of fine-tuning of the model. Our contribution is using the association mechanism known from transformers as attention. It has features that support generalization from irrelevant information for distinguishing the entities and potentially enable associating with much more than indices to vocabulary. As a result, the system can work in a one-shot manner and correctly name objects named in different contents. We also outline implementation details of the system modules integrated by a blackboard architecture. Finally, we investigate the<br>system's quality, mainly how many objects it can handle in this way.</p>
Train and Evaluation Code, Road Classification Models and Test set of the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road classification models corresponding to the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification". The scripts make use of the Tensorflow with Keras framework and the additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 546 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area of 28.5 km * 18.5 km and features binary road labels. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
Experimental absorption spectra used in "Spectral profile of ro-vibrational transitions of HCl broadened by He, Ar and SF6: testing the β-correction to the Hartmann-Tran profile and the speed dependent (complex) hard collision model"
<p>Experimental absorption spectra used in the article entitled <strong>"Spectral profile of ro-vibrational transitions of HCl broadened by He, Ar and SF<sub>6</sub>: testing the β-</strong><strong>correction to the Hartmann-Tran profile and the speed dependent (complex) hard collision model", </strong>to be published in the Journal of Quantitative Spectroscopy and Radiative Transfer. Accepted 19 March 2024.</p> <div> <div> <div> <div> <h3><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jqsrt.2024.108977" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jqsrt.2024.108977</a></h3> </div> </div> </div> </div> <p> </p> <p>Comma separated ASCII files with 1 header line.</p> <p>First column is wavenumber in cm-1</p> <p>The rest of the columns contain the napierian absorbance at the total pressure given in the header. </p> <p>Mind the units!: pressure of Ar-mixtures is expressed in Torr, pressure of He- and SF6-mixtures is expressed in mbar</p> <p> </p>
Simulation and laboratory eddy current testing data - modelling compound defects via perturbation theory
<p>This dataset serves to fit and validate a perturbation approach to the modelling eddy current signals of compound defects. It was obtained during the AIFRI project (Artificial Intelligence for Rail Inspection). The simulation data was generated with the Faraday software by INTEGRATED Engineering Software, using its BEM Solver. The simulation data is supplied as csv. The laboratory data was gathered by Rainer Pohl in the eddy current laboratory of BAM, section 8.4. It is supplied in the DICONDE data format. The first frame of the pixel array in the DICONDE files corresponds to the real part and the second frame corresponds to the imaginary part of the signal. The data set is analyzed in an upcoming article.</p> <p><span> </span></p>
Xenopus tissue data for testing segmentation models
<pre>This dataset is of xenopus tissue imaged with the following settings and it comes with a trained UNET model for performing the segmentation of such tissues. In order to use the segmentation model please install the vollseg-napari plugin from the napari hub and the model will be automatically downloaded for usage. Dataset was acquired by Mari Tolonen and Jakub Sedzinski, (0000-0002-4395-9022,0000-0002-1788-0329) at the university of Copenhagen and the model was trained by Varun Kapoor at Kapoorlabs. A Z projection of 21 Z slices acquired by the ImageJ Z Projection plugin was performed on the original acquired data. ObjectiveSettings ID="Objective:0" Medium="Water" RefractiveIndex="1.333"</pre> <pre>LensNA="1.2000000000000002" Model="C-Apochromat 40x/1.2 W AutoCorr M27" NominalMagnification="40.0"</pre> <pre>Physical Size X="0.6918881841365326" Physical Size X Unit="µm" </pre> <pre>Physical Size Y="0.6918881841365326" Physical Size Y Unit="µm" </pre> <pre>Physical Size Z="2.0" Physical Size Z Unit="µm"</pre> <pre>Time interval frames 1-160: 182 sec Time interval frames 161-262: 283 sec</pre> <pre>SignificantBits="8" Type="uint8"></pre> <pre>Channel AcquisitionMode="LaserScanningConfocalMicroscopy" ExcitationWavelength="488.0" ExcitationWavelengthUnit="nm" Fluor="EGFP"</pre>
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.