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547 results for “Model Tests”
Limitations of current in vitro models for testing the clinical potential of epigenetic inhibitors for treatment of pediatric ependymoma
GEO Series GSE112067. Homo sapiens. 16 samples. Type: Methylation profiling by genome tiling array.
A Spheroid Model that Recapitulates the Protective Role of the Lymph Node Microenvironment and Serves as a Platform for Drug Testing in Chronic Lymphocytic Leukemia
GEO Series GSE299467. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Repeated behavioral testing and the use of summary measures reveal trait anxiety in preclinical rodent models
GEO Series GSE240493. Rattus norvegicus. 27 samples. Type: Expression profiling by high throughput sequencing.
Comprehensive testing of chemotherapy and immune checkpoint blockade in preclinical cancer models identifies additive combinations
GEO Series GSE188481. Mus musculus. 28 samples. Type: Expression profiling by high throughput sequencing.
Testing different rRNA removal methods in 4 species of model halophilic archaea
GEO Series GSE200776. Halobacterium salinarum; Haloferax volcanii; Haloferax mediterranei; Haloarcula hispanica. 39 samples. Type: Expression profiling by high throughput sequencing.
Development of a Cellular Assay as a Personalized Model for Testing Chronic Wound Therapeutics (NS79)
GEO Series GSE268639. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Developing human radiation biodosimetry models: Testing cross-species conversion approaches using an ex vivo model system
GEO Series GSE102971. Macaca mulatta; Homo sapiens. 167 samples. Type: Expression profiling by array.
Origin-Dependent Inverted-Repeat Amplification: Tests of a Model for Inverted DNA Amplification
GEO Series GSE74443. Saccharomyces cerevisiae. 15 samples. Type: Genome variation profiling by genome tiling array.
Time‑course analysis of cisplatin induced AKI in preclinical models: implications for testing different sources of MSCs
GEO Series GSE263678. Mus musculus. 32 samples. Type: Expression profiling by high throughput sequencing.
Modelling results for the analytical assessment (Sec. 2.3) and EA benchmark tests (Sec. 3)
<p>These datasets contains the flow profiles and time histories produced by the FV1, MUSCL-FV2, DG2-NL and DG2-LL for the following tests:</p> <ul> <li><strong>Sec. 2.3: Analytical assessments of model conservation properties:</strong> <ul> <li>Time histories of normalised total energy and mass error from FV1, MUSCL-FV2, DG2-NL and DG2-LL at 20m and 40m grid resolution.</li> <li>Water level profiles at 7tau/2 and 8tau from FV1, MUSCL-FV2, DG2-NL and DG2-LL at 40m grid resolution, along with the analytical solutions.</li> </ul> </li> <li><strong>Sec. 3: Comparison against industrial flood model outputs</strong> <ul> <li><strong><em>Sec. 3.1.1: Flooding and drying cycle over a sloping topography:</em></strong> <ul> <li>Time histories of water level from DG2-LL and DG2-NL at 10m resolution, DG2-NL and MUSCL-FV2 at 20m resolution, and the reference solution.</li> </ul> </li> <li><em><strong>Sec. 3.1.2: Symmetrical flow propagation over a flat bed:</strong></em> <ul> <li>Time histories of water level and velocity from from DG2-LL and DG2-NL at 5m resolution, DG2-NL and MUSCL-FV2 at 10m resolution, and the reference solution.</li> </ul> </li> <li><em><strong>Sec. 3.1.3: Slow filling of multiple ponds:</strong></em> <ul> <li>Time histories of water level from from DG2-LL and DG2-NL at 20m resolution, DG2-NL and MUSCL-FV2 at 40m resolution, and the reference solution.</li> </ul> </li> <li><strong><em>Sec. 3.2.1: Momentum conservation over an obstruction:</em></strong> <ul> <li>Time histories of water level and velocity from from DG2-LL and DG2-NL at 5m resolution, DG2-NL and MUSCL-FV2 at 10m resolution, and the reference solution.</li> </ul> </li> <li><em><strong>Sec. 3.2.2: Torrential flooding over a rural catchment:</strong></em> <ul> <li>Time histories of water level and velocity from from DG2-LL and DG2-NL at 50m resolution, DG2-NL and MUSCL-FV2 at 100m resolution, and the reference solution.</li> </ul> </li> <li><em><strong>Sec. 3.2.3: Dam-break over an oblique building:</strong></em> <ul> <li>Time histories of water level and velocity from from DG2-LL and DG2-NL at 0.1m resolution, and DG2-LL and MUSCL-FV2 at 0.2m resolution.</li> </ul> </li> </ul> </li> </ul>
Dataset for Residual Study: Testing Jupiter Atmosphere Models Against Juno MWR Observations
<p>This dataset includes all data values for all 14 figures in paper "Residual study: Testing Jupiter atmosphere models against Juno MWR observations".</p>
Aquaplanet experiment data for Webb, M. J., & Lock, A. P. (2020). Testing a physical hypothesis for the relationship between climate sen-sitivity and double-ITCZ bias in climate models.Journal of Advances in Modeling Earth Systems, 12,e2019MS001999.https://doi.org/10.1029/2019MS001999
<p><strong>Aquaplanet experiment data from Webb and Lock (2020)</strong></p> <p><br> Webb, M. J., & Lock, A. P. (2020). Testing a physical hypothesis for the relationship between climate sen-sitivity and double-ITCZ bias in climate models.Journal of Advances in Modeling Earth Systems, 12,e2019MS001999.https://doi.org/10.1029/2019MS001999</p> <p>CSV files containing data from Figs 1(b) and 2(a-d)</p> <p>Figure 2b:</p> <p>APEQ.Precipitation_mmperday.zonal.csv<br> APEQ_2LW_Cloud.Precipitation_mmperday.zonal.csv<br> APEQ_3LW_Cloud.Precipitation_mmperday.zonal.csv</p> <p>Figure 3a:</p> <p>APEQ.w700.zonal.csv<br> APEQ_3LW_Cloud.w700.zonal.csv<br> APEQ_2LW_Cloud.w700.zonal.csv</p> <p>Figure 3b:</p> <p>APEQ.Estimated_Inversion_Strength_K.zonal.csv<br> APEQ_2LW_Cloud.Estimated_Inversion_Strength_K.zonal.csv<br> APEQ_3LW_Cloud.Estimated_Inversion_Strength_K.zonal.csv</p> <p>Figure 3c:</p> <p>APEQ.Net_CRE_Wperm2.zonal.csv<br> APEQ_2LW_Cloud.Net_CRE_Wperm2.zonal.csv<br> APEQ_3LW_Cloud.Net_CRE_Wperm2.zonal.csv</p> <p>Figure 3d:</p> <p>APEQ4K-APEQ.Net_CRE_Feedback_Wperm2perK.zonal.csv<br> APEQ4K_2LW_Cloud-APEQ_2LW_Cloud.Net_CRE_Feedback_Wperm2perK.zonal.csv<br> APEQ4K_3LW_Cloud-APEQ_3LW_Cloud.Net_CRE_Feedback_Wperm2perK.zonal.csv</p> <p>Any queries please contact Mark Webb mark.webb@metoffice.gov.uk</p> <p> </p>
Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests
<p>The .xlsx dataset includes all patients used for training, internal-external and external validation: these can be distinguished by looking at the ID (first column) in the dataset: those in format Axxxx-<Date> are the data used for the training, those in the format 20xx are the data used for the internal-external validation, while the remaining data were used for external validation.</p> <p>As regards the features: for the Target feature the value 1 stands for "Positive to COVID-19" while the value 0 stands for "Negative to COVID-19"; while for the Sex feature the value 1 stands for "Male" while the value 0 stands for "Female".</p> <p>The full article is available at: https://www.degruyter.com/view/journals/cclm/ahead-of-print/article-10.1515-cclm-2020-1294/article-10.1515-cclm-2020-1294.xml.</p> <p>A pre-print version of the article is also available on MedrXiv: https://www.medrxiv.org/content/10.1101/2020.10.02.20205070v1</p> <p><strong>ABSTRACT</strong></p> <p><strong>Background</strong> The rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15–20%, and expensive equipment. The hematochemical values of routine blood exams could represent a faster and less expensive alternative. </p> <p><strong>Methods</strong> Three different training data set of hematochemical values from 1,624 patients (52% COVID-19 positive), admitted at San Raphael Hospital (OSR) from February to May 2020, were used for developing machine learning (ML) models: the complete OSR dataset (72 features: complete blood count (CBC), biochemical, coagulation, hemogasanalysis and CO-Oxymetry values, age, sex and specific symptoms at triage) and two sub datasets (COVID-specific and CBC dataset, 32 and 21 features respectively). 58 cases (50% COVID-19 positive) from another hospital, and 54 negative patients collected in 2018 at OSR, were used for internal-external and external validation.</p> <p><strong>Results</strong> We developed five ML models: for the complete OSR dataset, the area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.83 to 0.90; for the COVID-specific dataset from 0.83 15 to 0.87; and for the CBC dataset from 0.74 to 0.86. The validations also achieved good results: respectively, AUC 16 from 0.75 to 0.78; and specificity from 0.92 to 0.96. </p> <p><strong>Conclusions</strong> ML can be applied to blood tests as both an adjunct and alternative method to rRT-PCR for the fast and cost-effective identification of COVID-19-positive patients. This is especially useful in developing countries, or in countries facing an increase in contagions.</p>
Subjective Test Dataset and Meta-data-based Models for 360° Streaming Video Quality
<p>During the last years, the number of 360° videos available for streaming has rapidly increased, leading to the<br> need for 360° streaming video quality assessment. In this paper, we report and publish results of three subjective 360° video<br> quality tests, with conditions used to reflect real-world bitrates and resolutions including 4K, 6K and 8K, resulting in 64 stimuli<br> each for the first two tests and 63 for the third. As playout device we used the HTC Vive for the first and HTC Vive Pro<br> for the remaining two tests. Video-quality ratings were collected using the 5-point Absolute Category Rating scale. The 360°<br> dataset provided with the paper contains the links of the used source videos, the raw subjective scores, video-related meta-data,<br> head rotation data and Simulator Sickness Questionnaire results per stimulus and per subject to enable reproducibility of the<br> provided results. Moreover, we use our dataset to compare the performance of state-of-the-art full-reference quality metrics such<br> as VMAF, PSNR, SSIM, ADM2, WS-PSNR and WS-SSIM. Out of all metrics, VMAF was found to show the highest correlation<br> with the subjective scores. Further, we evaluated a center-cropped version of VMAF ("VMAF-cc") that showed to provide a similar<br> performance as the full VMAF. In addition to the dataset and the objective metric evaluation, we propose two new video-quality<br> prediction models, a bitstream meta-data-based model and a hybrid no-reference model using bitrate, resolution and pixel<br> information of the video as input. The new lightweight models provide similar performance as the full-reference models while<br> enabling fast calculations.</p>
Data for tests of the NicheMapR transient heat budget modelling code
<p>This data set and code is used to test the ability of the transient heat budget modelling code in the R package NicheMapR (functions 'onelump_var', 'twolump' and 'trans_behav') against inanimate objects (fruit, pipes) and real lizards (a small and a large lizard who's activity and body temperatures were observed on the same day at an arid locality in central Australia). It generates the figures and statistics reported as supplementary information for the paper:</p> <p>Kearney, M. R. et al. 2020. Modeling the joint effects of body size and microclimate on heat budgets and foraging opportunities of ectotherms. - Methods in Ecology and Evolution in press.</p> <p>See the latter paper for more details.</p> <p>To run the code the package NicheMapR must be installed.</p>
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"
<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" includes two animations showing a full Hudson Strait surge cycle with the default GSM heat flux.</p>
Data and code for training and testing a ResMLP model with experience replay for machine-learning physics parameterization
<p>This directory contains the training data and code for training and testing a ResMLP with experience replay for creating a machine-learning physics parameterization for the Community Atmospheric Model. </p> <p>The directory is structured as follows:</p> <p>1. Download training and testing data: https://portal.nersc.gov/archive/home/z/zhangtao/www/hybird_GCM_ML</p> <p>2. Unzip nncam_training.zip</p> <p>nncam_training</p> <p> - models</p> <p> model definition of ResMLP and other models for comparison purposes</p> <p> - dataloader </p> <p> utility scripts to load data into pytorch dataset</p> <p> - training_scripts</p> <p> scripts to train ResMLP model with/without experience replay</p> <p> - offline_test</p> <p> scripts to perform offline test (Table 2, Figure 2)</p> <p>3. Unzip nncam_coupling.zip</p> <p>nncam_srcmods</p> <p> - SourceMods</p> <p> SourceMods to be used with CAM modules for coupling with neural network</p> <p> - otherfiles</p> <p> additional configuration files to setup and run SPCAM with neural network</p> <p> - pythonfiles</p> <p> python scripts to run neural network and couple with CAM</p> <p> - ClimAnalysis</p> <p> - paper_plots.ipynb</p> <p> scripts to produce online evaluation figures (Figure 1, Figure 3-10)</p> <p> </p>
Tissue-engineered oral epithelial barrier for dental material testing: towards establishing in vitro biomimetic models - Underlying data
<p>Underlying CT data of "<strong>Tissue-engineered oral epithelial barrier for dental material testing: towards establishing <em>in vitro </em>biomimetic models</strong>"<br><a href="https://doi.org/10.1089/ten.tec.2024.0154">https://doi.org/10.1089/ten.tec.2024.0154</a></p> <p>Foteini Machla a, Paraskevi Kyriaki Monou b, c, Chrysanthi Bekiari d, Dimitrios Andreadis e Evangelia Kofidou d, , Emmanouel Panteris f, Orestis L. Katsamenis g, h, Maria Kokoti a, Petros Koidis a, Imad About i, Dimitrios Fatourosb, c, Athina Bakopoulou a</p> <p>a Department of Prosthodontics, Tissue Engineering Core Unit, School of Dentistry, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece</p> <p>b Department of Pharmaceutical Technology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece</p> <p>c Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Thessaloniki 57001, Greece</p> <p>d Laboratory of Anatomy and Histology, Veterinary School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece</p> <p>e Department of Oral Medicine/Pathology, School of Dentistry, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece</p> <p>f Department of Botany, School of Biology, Faculty of Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece</p> <p>g μ-VIS X-ray Imaging Centre, Faculty of Engineering and the Environment, University of Southampton, Southampton SO17 1BJ, United Kingdom</p> <p>h Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom UK</p> <p>i Centre National de la Recherche Scientifique, Institute of Movement Sciences, Aix Marseille University, Marseille 13385, France</p> <p> </p> <p><strong>Measurement of ΤΕΟΕ thickness</strong></p> <p>X-ray computed micro-tomography (μCT) as employed to examine the microstructure of the paraffin-embedded tissue engineered oral epithelium (TEOE), enabling comprehensive 3D assessment of thickness using volumetric analysis (cf. supplementary for imaging parameters) (11). Imaging was conducted using an isotropic voxel-edge of 6.0 μm. Local Thickness was carried out in 3D using the “Volume Thickness Map” tool within Dragonfly software (cf. supplementary), allowing for the visualization and quantification of the spatial distribution and variability of tissue thickness.</p> <p>Imaging was conducted at the University of Southampton’s μ-VIS X-ray Imaging Centre ( https://muvis.org ) / 3D X-ray Histology facility, utilizing a customised μCT scanner optimisedfor intricate histological analyses(1), based on Nikon’s XTH225ST system (Nikon Metrology, UK). Operating parameters were set at 80 kVp / 86 μA (6.88 W), with a source-to-object distance of 37.5 mm and a source-to-detector distance of 937.4 mm, resulting in a magnification factor of 25x and an isotropic voxel-edge of 6.0 μm. Imaging acquisition involved the collection of 4001 projections using a 2850 x 2850 dexels detector, by averaging 4 frames per projection, with an exposure time of 500 ms per projection.</p> <p>Visualisation and analysis of the reconstructed dataset was done using Dragonfly software (Comet Technologies Canada Inc.; software accessible at http://www.theobjects.com/dragonfly). Assessment of Local Thickness was carried out in 3D using the “Volume Thickness Map” tool within Dragonfly software, following segmentation of the tissue layer. Local thickness analysis allowed for the visualization and quantification of the spatial distribution and variability of thickness within the tissue engineered oral epithelium (TEOE).</p> <p>Visual representation of local thickness histograms was employed to elucidate the distribution of thickness throughout the TEOE. These histograms effectively illustrate the number of voxels associated with specific cross-sectional thickness, offering both a graphical depiction of the variation in thickness across the tissue sample, and a quantitative measure of the average thickness of the specimen.</p> <p>It's worth noting, the volumetric and non-destructive nature of the technique enabled whole-block imaging, which proved crucial in addressing challenges arising from tissue sample shrinkage. This shrinkage, a consequence of dehydration during the fixation process, can occur in some cases and lead led to the specimen wrapping. While wrapping is not a common occurrence, this analysis method allowed for the evaluation of challenging-shaped specimens, such as the wrapped one presented in Figure 4. Unlike conventional 2D methods such as classical histology, which rely on the angle of slicing and encounter limitations when dealing with non-perfectly perpendicular slicing, μCT-based XRH enables analysis of all specimens, including those with complex shapes.</p> <p> </p>
Automated Fairness Testing of Large Language Models
<p>This directory contains the evaluation data for the proposal presented in the Master's Thesis "Automated Fairness Testing of Large Language Models". Specifically, it includes the following:</p> <ul> <li><em>base-experiment/</em>: Contains the test cases (<em>test-cases/</em>) generated to address RQ1 and RQ2, along with the results obtained (<em>executions/</em>) after running them on the models under test.</li> <li><em>stability-experiment/</em>: Contains the test cases (<em>test-cases/</em>) generated to address RQ3, along with the results obtained (<em>executions/</em>) after running them a total of 30 times on the models under test.</li> </ul>
Test 4 FaceGen Modeller
**Lucky Luciano**  [**Anaglyph**](https://example3972.files.wordpress.com/2020/02/stereo-2.jpg)  Source: Objaverse 1.0 / Sketchfab
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.