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447 results for “Model validation”
Computational modeling and analytical validation of singular geometric effects in fault data using a combinatorial approach - Input and processed data
<p>The archive contains the input and processed data for the companion manuscript.</p> <p>The input data contains XYZ coordinates of points documenting the investigated interfaces. The output datasets contain directional data from applying the combinatorial algorithm to point data sets.</p> <p>We have also included .VTU and .PVSM files for visualization of the geological settings in ParaView.</p>
Abstraction-based Trace Generation to Validate Semantics of Formal Verifiers: Validation Model Suite
<p>Dataset of the Scientific Students’ Association Report titled Abstraction-based Trace Generation to Validate Semantics of Formal Verifiers.</p> <p>These files contain the validation model test suite and the generated traces. The models and traces are in the format of the Gamma modeling tool.</p> <p><em>validation-model-suite/model/package<Letter>/model<Number> </em>contains the files for a given model:<br> - stm.gcd is the statemachine,<br> - default.ggen (and in Package F also abstraction.ggen) is the Gamma script executing trace generation and the generated traces can be found in the default (and abstraction) directories.<br> The report of Theta on possible coverage violation is in the traces directory (report.txt).</p> <p> </p> <p>The prototype implementation of trace generation can be found at: https://github.com/AdamZsofi/gamma/tree/dev-tracegen</p>
Data supplement for: Validating the Nernst--Planck transport model under reaction-driven flow conditions using RetroPy v1.0
<p>This is the repository for the publication's supplementary data and plotting scripts: Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0.</p> <p>The dependency of the scripts can be installed using conda and pip:</p> <pre><code>conda create -n plot numpy matplotlib==3.6.1 h5py python=3.9 conda activate plot pip install palettable</code></pre> <p>To reproduce the figures, execute the files using python:</p> <pre><code>python figure03.py</code></pre> <p> </p>
Reference LES Simulations for Ocean Models Validation
<p>All Large-Eddy Simulations (LES) computations are conducted with the open-source PALM solver (<a href="https://palm.muk.uni-hannover.de/trac">https://palm.muk.uni-hannover.de/trac</a>) and version 5.0 of the Los Alamos National Laboratory (<a href="https://github.com/lanl/palm_lanl">https://github.com/lanl/palm_lanl</a>) branch. The simulations use a cubical domain with length L=128m, three grid resolutions (N=128<sup>3</sup>, 256<sup>3</sup>, or 512<sup>3</sup>), and mimic four single-column canonical oceanic regimes in which the surface flux and background stratification profiles are imposed. These simulations are named cooling (c), stratification (s), evaporation (e), and mixed (m):</p> <ul> <li><strong>cooling cases</strong> test different surface heat fluxes: Q<sub>h</sub>=-1.185x10<sup>-5</sup> (c<sub>01</sub>), -2.371x10<sup>-5</sup> (c<sub>02</sub>), -4.742x10<sup>-5</sup> (c<sub>04</sub>), and -18.966x10<sup>-5</sup> (c<sub>16</sub>) [K.m/s]. The background stratification is set through a vertical temperature gradient (T<sub>z</sub>) equal to 0.1 [K/m], and both the salinity surface flux (Q<sub>s</sub>) and the background stratification due to vertical salinity gradient (S<sub>z</sub>) are equal to zero.</li> <li><strong>stratification cases</strong> are similar to the cooling c<sub>02</sub> case, but Q<sub>h</sub>=-2.371x10<sup>-5</sup> [K.m/s], and T<sub>z</sub>=0.01 (s<sub>01</sub>), 0.1 (s<sub>10</sub>), or 0.2 (s<sub>20</sub>) [K/m].</li> <li><strong>evaporation cases</strong> define Q<sub>h</sub>=T<sub>z</sub>=0, S<sub>z</sub>=-0.025 [PSU/m], and Q<sub>s</sub>=3.115x10<sup>-6</sup> (e<sub>01</sub>) or 1.225x10<sup>-5</sup> (e<sub>04</sub>) [PSU/(m<sup>2</sup>.s)].</li> <li><strong>mixed cases</strong> combine heat and salinity surface fluxes, and the background stratification is imposed by both vertical temperature and salinity gradients. There are four mixed cases in which Q<sub>h</sub>=-1.185x10<sup>-6</sup> [K.m/s], T<sub>z</sub>=0.05 [K/m], S<sub>z</sub>=-0.025 [PSU/m], and Q<sub>s</sub> is either 0 (m<sub>01</sub>), 3.115x10<sup>-6</sup> (m<sub>02</sub>), 9.10x10<sup>-6</sup> (m<sub>03</sub>), or 4.55x10<sup>-5</sup> (m<sub>04</sub>) [PSU/(m<sup>2</sup>.s)].</li> </ul> <p>The simulations run 96h at a latitude of 43.29<sup>o</sup>, and utilize a linear equation of state where the reference temperature and salinity are 293.15 K and 35 PSU. The subgrid turbulent scales are modeled through the Moeng and Wyngaard SGS closure (see PALM documentation), and the flow is perturbed during the first 150 s of simulation with normally distributed fluctuations with a maximum amplitude of 10<sup>-4</sup>. The dataset provides 1D and 3D statistics. The 1D profiles are time-averaged during 3600 s and written every 3600 s. The 3D fields are not time-averaged and written every 3600 s, except those obtained with the grid having 512<sup>3</sup> points. In this case, the data are written every 21,600 s. The 3D fields are not in the tar file but can be requested by emailing the authors (fmsoarespereira@lanl.gov). The PALM input decks of the simulations are included in the shared files.<br> <br> <strong>Note:</strong> the PALM version used in this work requires the 1D salinity flux profiles to be normalized by the product of N<sub>x </sub>and<sub> </sub>N<sub>y </sub>(number of grid points in x and y)</p> <p> </p> <p><strong>LA-UR-22-32996</strong></p>
Case studies from doubleHelix: nucleic acid sequence identification, assignment and validation tool for cryo-EM and crystal structure models
<p>Case studies from "doubleHelix: nucleic acid sequence identification, assignment and validation tool for cryo-EM and crystal structure models"</p>
Case studies from: Sequence assignment validation in protein crystal structure models with checkMySequence
<p>Case studies from "Sequence assignment validation in protein crystal structure models with checkMySequence"</p>
Validation of a new global irrigation scheme in the ORCHIDEE land surface model - Dataset
<p>Datasets used in the paper 'Validation of a new global irrigation scheme in the ORCHIDEE land surface model', submitted to GMD</p>
Analysis datasets for NEMO_validation workflow Byrne et al 2023 GMD. "Using the COAsT Python package to develop a standardised validation workflow for ocean physics models"
<p>Analysis datasets in support of Byrne et al. (2023) "Using the COAsT Python package to develop a standardised validation workflow for ocean physics models", <em>Geoscientific Model Development</em>.</p> <p> </p> <p>The datasets are from a comparative analysis of two versions of the European shelf sea AMM15 (Atlantic Margin Model at 1.5km horizontal resolution) configuration. These are NEMO ocean model configurations with different code base versions. The configurations are CO7, which is based on NEMOv3.6, and CO9p0 (also referred to as P0.0), which is based on NEMOv4.0.4.</p>
Construct Validity of a Large Loop Excision of the Transformation Zone (LLETZ) Training Model
ClinicalTrials.gov study NCT02476500. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Development and Validation of a Prediction Model for AKI Following Cisplatin-Based HIPEC in Patients With Ovarian Cancer
ClinicalTrials.gov study NCT06697613. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Validation of a Prediction Model for Inadequate Bowel Preparation
ClinicalTrials.gov study NCT06438237. IPD Sharing: NO. Countries: 1. Publications: 1.
Validation of a Predictive Model to Estimate the Risk of Conversion to Clinically Significant Macular Edema and/or Vision Loss in Mild Nonproliferative Diabetic Retinopathy in Diabetes Type 2
ClinicalTrials.gov study NCT00763802. IPD Sharing: Not stated. Countries: 1. Publications: 10.
Validation of Insulin Dose Prediction Model Based on Artificial Intelligence Algorithm
ClinicalTrials.gov study NCT07066891. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Development and Validation of DM and Pre-DM Risk Prediction Model
ClinicalTrials.gov study NCT04881383. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Validation of Existing Diabetes Risk Models in a Swedish Population
ClinicalTrials.gov study NCT05609266. IPD Sharing: NO. Countries: 0. Publications: 30.
Validation of a Red Blood Cell Transfusion Prediction Model in a Low Transfusion Rate Population.
ClinicalTrials.gov study NCT05581238. IPD Sharing: YES. Countries: 1. Publications: 7.
Prospective Validation of the ADNEX Model for Discrimination Between Benign and Malignant Adnexal Masses in Pregnancy: International Ovarian Tumour Analysis in Pregnancy Study (p-IOTA)
ClinicalTrials.gov study NCT05974618. IPD Sharing: NO. Countries: 1. Publications: 47.
Refinement and Validation of a Diagnostic Model (GAMAD) for Early Detection of Hepatocellular Carcinoma
ClinicalTrials.gov study NCT05626985. IPD Sharing: NO. Countries: 1. Publications: 3.
Outcomes of Traumatic Brain Injury and External Validation of CRASH Prognostic Model
ClinicalTrials.gov study NCT03932500. IPD Sharing: NO. Countries: 1. Publications: 7.
Development and Validation of The Post-RT LARS Prediction Model (PORTLARS)
ClinicalTrials.gov study NCT05129215. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
ScienceDex guides
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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.