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447 results for “Model Validation”
Data from: Validating dispersal distances inferred from autoregressive occupancy models with genetic parentage assignments
1.Dispersal distances are commonly inferred from occupancy data but have rarely been validated. Estimating dispersal from occupancy data is further complicated by imperfect detection and the presence of unsurveyed patches. 2.We compared dispersal distances inferred from seven years of occupancy data for 212 wetlands in a metapopulation of the secretive and threatened California black rail (Laterallus jamaicensis coturniculus) to distances between parent-offspring dyads identified with 16 microsatellites. 3.We used a novel autoregressive multi-season occupancy model that accounted for both unsurveyed patches and imperfect detection to quantify patch isolation using buffer radius (BRM) and incidence function (IFM) connectivity measures at 15 scales (1–10, 15, 20, 25, and 30 km). Connectivity measures were then fit as colonization covariates in occupancy models to estimate a model-averaged dispersal distance. 4.As predicted, colonization was more strongly related to connectivity at small spatial scales (< 10 km). AIC weights were greatest at 7 km for BRM and at 4 km for IFM. 5.Model-averaged dispersal distances (BRM = 7.46 km; IFM = 5.48 km) showed good agreement with the mean (± SE) dispersal distance from 23 parent-offspring dyads (5.58 ± 1.92 km), indicating reasonably accurate mean dispersal distances can be inferred from occupancy data when isolation strongly affects colonization.
Screen Capture & Audior Recordings - Empirical Study "Business Process Model Validation Through Virtual Enactment"
<p>Screen captures of task completion and audio recordings of interviews of the empirical study which has been conducted within the context of the master thesis "Business Process Model Validation Through Virtual Enactment"</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>
Data Sets for Evaluation of the Psychometric Properties and Validity of the German Version of the Process Model of Emotion Regulation Scale (PMERQ)
<p>Data files relate to an investigation of the psychometric properties of the German Version of the Process Model of Emotion Regulation Scale (PMERQ). Data set 1 (pmerq_1) contains information regarding the age, gender, ethnicity, and educational status of participants. In addition, responses to the 45 items of the initial translation of the 10-scale PMERQ are included. Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the revised translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the 16-item German Interpersonal Emotion Regulation Questionnaire (IERQ), the 10-item German Emotion Regulation Questionnaire (ERQ), , the German version of the 10-item Big Five Inventory-10 (BFI-10), the 4-item German version of the Patient Health Questionnaire-4 (PHQ-4), the German version of the Satisfaction with Life Scale (SWLS), and the 17-item German Social Desirability Scale-17 (SES-17). Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the readability-improved translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the German version of the Satisfaction with Life Scale (SWLS) and the German version of the 9-items UCLA Loneliness Scale (UCLA).</p>
Modeling and validating a SuperDARN radar's Poynting flux profile
<p>Numerical ray trace modelingl output corresponding to Radio Science manuscript "Modeling and validating a SuperDARN radar's Poynting flux profile" by G. W. Perry.</p>
Using VLF Transmitter Signals at LEO for Plasmasphere Model Validation
<p><strong>VPM_survey_data_2020-03-12.xml </strong>survey electric field data collected between 2020-03-12 00:13:35UT and 2020-03-12 23:59:15 UT</p> <p><strong>vpm_map.cdf.zip</strong> processed VPM survey mode data containing longitude, latitude and power spectral density across the entire mission</p> <p> </p><p> </p> <p></p> <p> </p> <p> </p> <p> </p>
In-vitro Major Arterial Cardiovascular Simulator: Benchmark Data Set for in-silico Model Validation
<p><strong>Background</strong><br> <br> The data described here supplements the paper "In-vitro Major Arterial Cardiovascular Simulator to generate Benchmark Data Sets for in-silico Model Validation" (to be submitted). It was created at Technische Hochschule Mittelhessen (THM) in Germany and uploaded to Zenodo. Please cite the paper M. Wisotzki, A. Mair, P. Schlett, B. Lindner, M. Oberhardt, S. Bernhard, In Vitro Major Arterial Cardiovascular Simulator to Generate Benchmark Data Sets for In Silico Model Validation (2022), Data 7(11), DOI: 10.3390/data7110145 and the Zenodo doi when using this dataset.</p> <p><strong>General description / Dataset Structure</strong></p> <p>Each mat-File describes a different stenosis degree at the popliteal artery of the in-vitro simulator MACSim (details can be found in the paper). There are 17 pressure signals for different positions, one flow sensor close to the stenosis location and one monitor signal of the proportional valve use to control the input curve. Total duration of each signal is 60s with a sampling rate of 1000 Hz. Each mat-file contains a header structure with metadata and struct array for signals of each sensor. Signals in each mat-File are aligned with respect to a common time axis, but this is not guaranteed between different measurements/files. The file format can either be loaded directly in Matlab or in Python with scipy's loadmat function.</p> <p>The different stenosis degrees for each degree are:<br> ScenarioI: 100 % Area fraction (no stenosis)<br> ScenarioII: 37,5 % Area fraction<br> ScenarioIII: 23,4 % Area fraction<br> ScenarioIV: 6,56 % Area fraction</p> <p><strong>Data fields for each file</strong></p> <table> <caption>headerStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>rate</td> <td>sampling rate in Hz</td> </tr> <tr> <td>description</td> <td>name of the scenario according to the paper, corresponds to filename</td> </tr> <tr> <td>configuration</td> <td>parameters of the trapezoidal input curve (offset and amplitude in mmHg, ascend times and descend times and smoothing window in a fraction the time period (1.2s))</td> </tr> </tbody> </table> <p> </p> <table> <caption>signalStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>nodeId</td> <td>corresponds to numbered nodes at which the sensor is placed, the corresponding location can be found in the paper (node numbering, not sensor numbers) or in the software SISCA (https://gitlab.com/agbernhard.lse.thm/sisca) in the example database.</td> </tr> <tr> <td>type</td> <td>'p' ... pressure or 'q' ... flow</td> </tr> <tr> <td>data</td> <td>double array, time series of each sensor, unit mmHg for type 'p' and ml/s for type 'q' </td> </tr> <tr> <td>anatomicalPosition</td> <td> <p>name of the corresponding anatomical position</p> </td> </tr> </tbody> </table>
Training and validation datasets for "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network"
<p>This is training and validation datasets used in manuscript "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network". In this manuscript, we propose an efficient deep learning method using a Convolutional Neural Network (CNN) to predict a scalar field from sparse structural data associated with multiple distinct stratigraphic layers and faults. The CNN architecture is beneficial for the flexible incorporation of empirical geological knowledge when trained with numerous and realistic structural models that are automatically generated from a data simulation workflow. It also presents an expressive characteristic of integrating various types of structural constraints by optimally minimizing a hybrid loss function to compare predicted and reference structural models, opening new opportunities for further improving geological modeling. </p>
Code of "Inclusion of flood diversion canal operation in the H08 hydrological model with a case study from the Chao Phraya River basin: model development and validation"
<p>Code used to prepare a paper entitled "Inclusion of flood diversion canal operation in the H08 hydrological model with a case study from the Chao Phraya River basin: model development and validation" which was submitted to Hydrology and Earth System Sciences.</p>
Dataset Validation Images for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)
<p>Dataset for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)</p> <p>This is the Validation Image part. </p> <p>If you use this dataset in your research, you must cite the papers in the References below !!!</p>
Supplemental Material to Article "Validation of crack initiation model by means of cyclic full-scale blade test"
<p>This set supplements the figure data to the article "Validation of crack initiation model by means of cyclic full-scale blade test", DOI: <a href="https://doi.org/10.1088/1742-6596/2265/3/032045">https://doi.org/10.1088/1742-6596/2265/3/032045</a>.</p>
Data from: Hindcast-validated species distribution models reveal future vulnerabilities of mangroves and salt marsh species
<p>Rapid climate change threatens biodiversity via habitat loss, range shifts, increases in invasive species, novel species interactions, and other unforeseen changes. Coastal and estuarine species are especially vulnerable to the impacts of climate change due to sea level rise and may be severely impacted in the next several decades. Species distribution modeling can project the potential future distributions of species under scenarios of climate change using bioclimatic data and georeferenced occurrence data. However, models projecting suitable habitat into the future are impossible to ground truth. One solution is to develop species distribution models for the present and project them to periods in the recent past where distributions are known to test model performance before making projections into the future. Here, we develop models using abiotic environmental variables to quantify the current suitable habitat available to eight Neotropical coastal species: four mangrove species and four salt marsh species. Using a novel model validation approach that leverages newly available monthly climatic data from 1960-2018, we project these niche models into two time periods in the recent past (i.e., within the past half-century) when either mangrove or salt marsh dominance was documented via other data sources. Models were hindcast-validated and then used to project the suitable habitat of all species at four time periods in the future under a model of climate change. For all future time periods, the projected suitable habitat of mangrove species decreased, and suitable habitat declined more severely in salt marsh species.</p>
Research data for "Indirect learning and physically guided validation of interatomic potential models"
<p>This dataset contains structural data, potential parameter files, and data shown in the plots for the publication "Indirect learning and physically guided validation of interatomic potential models". Details of the contents can be found in README.txt.</p>
Validation of ATC NewsAsset performance model
<p>This file corresponds to the validation results of the performance model used for studying the NewsAsset application, branded by ATC.</p>
Wide post-common envelope binaries from Gaia: orbit validation and formation models
<p>Inlists and run_star_extras used in the models of Yamaguchi et al. (2024, submitted to PASP, arXiv:2405.06020<span>) ("Wide post-common envelope binaries from Gaia: orbit validation and formation models"). See Rees et al. (2024) for details about TP-AGB routines.</span></p> <p>MESA r22.05.1, measdk 22.6.1</p>
Model for random atmospheric inhomogeneities in engine noise auralization: Audio files for validation
<p>Illustration of engine noise auralization by DLR Institute of Propulsion Technology obtained with the framework PropNoise, VIOLIN, CORAL. Data associated with the following publication: A. Prescher, A. Moreau, S. Schade, "<a href="https://doi.org/10.1007/s13272-024-00764-4" target="_blank" rel="noopener"><em>Model for random atmospheric inhomogeneities in engine noise auralization</em></a>", CEAS Aeronautical Journal, 2024.</p> <p>Selected binaural audio files to illustrate the impact of random atmospheric inhomogenities on the noise characteristics of a turbofan engine.</p> <p>The corresponding time signals and spectrograms are available in the associated paper in Figure 7.</p>
Model and Data for the T&C-CROP Validation Paper: T&C-CROP: Representing mechanistic crop growth with a terrestrial biosphere model (T&C,v1.5): Model formulation and validation.
<p>Here included is the code used to run T&C-CROP as used for the GMD paper submission alongside with the necessary weather data and raw field data used as part of the validation exercise. </p> <p> </p>
Data/Code for: Sediment dynamics in the energetic nearshore zone: Acoustic remote sensing and model validation
<p>This archive contains data and postprocessed results used in the article "Sediment dynamics in the energetic nearshore zone: Acoustic remote sensing and model validation" by G. Wilson, P. Dickhudt & J. Aldrich. All data and code in this archive is copyright of the authors. Please contact the authors prior to publishing new results or derivative works based on data/code from this archive.</p>
Generative AI for designing and validating easily synthesizable and structurally novel antibiotics: Data and Models
<p>This repository contains data and models used in the following paper.</p> <p>Swanson, K., Liu, G., Catacutan, D., Zou, J. & Stokes, J. <a href="https://www.nature.com/articles/s42256-024-00809-7">Generative AI for designing and validating easily synthesizable and structurally novel antibiotics</a>. <em>Nature Machine Intelligence, </em>2024.</p> <p>The data and models are meant to be used with the <a href="https://github.com/swansonk14/SyntheMol">SyntheMol</a> code. More details about how to use the data and models with the code are available <a href="https://github.com/swansonk14/SyntheMol/tree/main/docs">here</a>.</p> <p>The Data.zip file has the following structure. Note that the numbers for the Data subdirectories correspond to the supplementary data numbers in the paper (e.g., 1_training_data corresponds to Supplementary Data 1).</p> <p>Data</p> <p> 1_training_data: The <em>Acinetobacter baumannii</em> inhibition data used to train antibiotic property prediction models.</p> <p> 2_chembl: Known antibiotic and antibacterial molecules from <a href="https://www.ebi.ac.uk/chembl/">ChEMBL</a>, which are used to compute the novelty of generated antibiotic candidates.</p> <p> 4_real_space: Data files and statistics for the <a href="https://enamine.net/compound-collections/real-compounds/real-space-navigator">Enamine REAL Space</a>. The molecular building blocks file is version 2021 q3-4 while all other REAL Space details are computed from the full enumerated REAL space version 2022 q1-2 (downloaded on August 30, 2022).</p> <p> 5_generations_clogp: Compounds generated by SyntheMol using Chemprop models trained to predict cLogP.</p> <p> 6_generations_chemprop: Compounds generated by SyntheMol using Chemprop models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 7_generations_chemprop_rdkit: Compounds generated by SyntheMol using Chemprop-RDKit models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 8_generations_random_forest: Compounds generated by SyntheMol using random forest models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 9_synthesized: Information on the 58 SyntheMol-generated compounds that were successfully synthesized by Enamine.</p> <p>The Models.zip file contains one folder for each model used in the paper. Note that each model is technically an ensemble of ten individual models, so each directory contains ten model files.</p>
Definition of the terms verification, validation, evaluation and benchmarking for use in the climate model context
<p>Schematic definition of the terms Verification, Validation, Evaluation and Benchmarking for use in the climate model context. Note that although through benchmarking some kind of ranking can be performed based on the chosen metric and selected observations, this is by far not a generally applicable ranking valid for all metrics, all realms and all possible observational references.</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.