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447 results for “Model validation”
The validation of new phase-dependent gait stability measures: a modelling approach
<p><span><span><span><span><span><span><span><span><span><span><span>Identification of individuals at risk of falling is important when designing fall prevention methods. Current stability measures that estimate gait stability and robustness appear limited in predicting falls in older adults. Inspired by recent findings of phase-dependent local stability changes within a gait cycle, we used compass-walker models to test several phase-dependent stability metrics for their usefulness to predict gait robustness. These metrics are closely related to the often-employed maximum finite-time Lyapunov exponent and maximum Floquet multiplier. They entail linearizing the system in a rotating hypersurface orthogonal to the period-one solution, and estimating the local divergence rate of the swing phases and the foot strikes. We correlated the metrics with the gait robustness of two compass walker models with either point or circular feet to estimate their prediction accuracy. To also test for the metrics' invariance under coordinate transform, we represented the point-feet walker in both Euler-Lagrange and Hamiltonian canonical form. Our simulations revealed that for most of the metrics, correlations differ between models and also change under coordinate transforms, severely limiting the prediction accuracy of gait robustness. The only exception that consistently correlated with gait robustness is the divergence of foot strikes. These results admit challenges of using phase-dependent stability metrics as objective measure to quantify gait robustness.</span></span></span></span></span></span></span></span></span></span></span></p>
Comparing simulations and experiments of positive streamers in air: steps toward model validation
<p>This dataset consists of files used to produce the data presented in the article "Comparing simulations and experiments of positive streamers in air: steps toward model validation". This dataset includes 1) source code for the simulations; 2) transport data files, which contain a list of included reactions, their reaction rate coefficients, and transport coefficients; 3) configuration files for running the simulations; 4) outputted log files of the simulations and 5) original experimental images.</p> <p>This dataset is for the revised paper.</p>
Development and validation of the Health Belief Model questionnaire to promote smoking cessation for nasopharyngeal cancer prevention: a cross-sectional study
<p>Objective: Nasopharyngeal cancer (NPC) risk factors caused by lifestyle choices are substantial yet avoidable. Using the Health Belief Model as a conceptual framework, this study develops and validates a questionnaire to predict smokers' intentions to quit in Sarawak, Malaysia (HBM).</p> <p>Design: A cross sectional study.</p> <p>Setting: Urban and suburban areas in Sarawak, Malaysia.</p> <p>Participants: Following a thorough literature study, the preliminary items for the instrument were created. Prior to beginning the content validity by a panel of 10 experts, the instrument was translated into the Malay language utilising the forward-backwards approach. 10 smokers carried out face validity in both quantitative and qualitative ways. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to assess the instrument's concept validity. Phase 1 of the EFA involved 100 smokers, whereas phase 2 of the CFA involved 171 smokers. Cronbach's alpha coefficients were used to measure internal consistency and assess reliability.</p> <p>Results: The factor loading of each item remained within the acceptable threshold in the exploratory stage. The final revised CFA yielded the following model fit indices that suitably fitted the seven-factor model: Chi Square: 641.705; df= 500; P< 0.001; CFI = 0.953; TLI: 0.948; RMSEA= 0.041. With the exception of one pairwise construct, satisfactory convergent validity and divergent validity were demonstrated. Phases 1 and 2 both have Cronbach's alpha values over 0.7, suggesting acceptable internal reliability.</p> <p>Conclusions: The study confirmed the validity and reliability of the instrument in predicting smokers' tendency to adopt healthy behaviour of smoking cessation to lower risk of developing cancer. The instrument consists of 34 items, dividing into two sections: 6 HBM components and health behavioural intention. The instrument is regarded as innovation and can be applicable in other smoking-related malignancies in different susceptible populations and geographical locations.</p>
Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America : Dataset
<p>This dataset is linked to the paper “Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-16-1661-2023). It contains the installer of the model, its user guide, as well as all the input files (inventory, thinning, meteorology and soil horizons files for each stand used in the evaluation and calibration steps), the R scripts and associated data used to analyse the model outputs.</p>
UTC STRIDE Project G2: Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation
<p>This repository contains the extracted traffic data used in the case study for UTC STRIDE project G2 "Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation". The traffic data was extracted using manual or AI-based methods (presented in the project final report) from two 30-minute videos with high and low traffic density. </p> <p>Below are the description of each data file:</p> <ul> <li><strong>high_density_both_lane_raw_AI_speed.csv</strong> <ul> <li><strong>Description: </strong>extracted individual vehicle speed data of the high traffic density video using the AI-based method.</li> <li><strong>Data Fields:</strong> <ul> <li>object_id: unique id for each detected and tracked vehicle</li> <li>frame_index: frame index of the video when other tracked vehicle exit the virtual speed loop.</li> <li>lane_id: lane id (1=outer lane, 2=inner lane)</li> <li>speed: average speed traveling through the virtual speed loop (kph)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> </ul>
Experimental Validation of Cryobot Thermal Models for the Exploration of Ocean Worlds
<p>The tables in this repository represent the data used in the figures and analyses of the paper "Experimental Validation of Cryobot Thermal Models for the Exploration of Ocean Worlds", published in the Planetary Science Journal. The provided data was collected between 2020 and 2022.</p> <ul> <li>AllResults.xlsx: compilation of tables 4, 5, 6, and 7 on the paper.</li> <li>WarmA1.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Total Power [W], H6 Power [W], H5 Power [W], H4 Power [W], H3 Power [W], H2 Power [W], H1 Power [W]".</li> <li>WarmA2.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Total Power [W], H6 Power [W], H5 Power [W], H4 Power [W], H3 Power [W], H2 Power [W], H1 Power [W]".</li> <li>CryoA1.xlsx: data presented in tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth Estimation [m], Power [W]".</li> <li>CryoB1.xlsx: data presented in figures 9 and 10, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoB2.xlsx: data presented in figures 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoB3.xlsx: data presented in figures 6, 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC1.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC2.xlsx: data presented in figures 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC3.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Truncated Coarse Depth [m], Power [W]".</li> </ul> <p> </p>
Raw Data for the article: Mortality after transjugular intrahepatic portosystemic shunt in older adult patients with cirrhosis: A validated prediction model
<p><strong>Background and aims: </strong>Implantation of a transjugular intrahepatic portosystemic shunt (TIPS) improves survival in patients with cirrhosis with refractory ascites and portal hypertensive bleeding. However, the indication for TIPS in older adult patients (greater than or equal to 70 years) is debated, and a specific prediction model developed in this particular setting is lacking. The aim of this study was to develop and validate a multivariable model for an accurate prediction of mortality in older adults.</p> <p><strong>Approach and results: </strong>We prospectively enrolled 411 consecutive patients observed at four referral centers with de novo TIPS implantation for refractory ascites or secondary prophylaxis of variceal bleeding (derivation cohort) and an external cohort of 415 patients with similar indications for TIPS (validation cohort). Older adult patients in the two cohorts were 99 and 76, respectively. A cause-specific Cox competing risks model was used to predict liver-related mortality, with orthotopic liver transplant and death for extrahepatic causes as competing events. Age, alcoholic etiology, creatinine levels, and international normalized ratio in the overall cohort, and creatinine and sodium levels in older adults were independent risk factors for liver-related death by multivariable analysis.</p> <p><strong>Conclusions: </strong>After TIPS implantation, mortality is increased by aging, but TIPS placement should not be precluded in patients older than 70 years. In older adults, creatinine and sodium levels are useful predictors for decision making. Further efforts to update the prediction model with larger sample size are warranted.</p>
Identifying the best approximating model in Bayesian phylogenetics: Bayes factors, cross-validation or wAIC?
<p>There is still no consensus as to how to select models in Bayesian phylogenetics, and more generally in applied Bayesian statistics. Bayes factors are often presented as the method of choice, yet other approaches have been proposed, such as cross-validation or information criteria. Each of these paradigms raises specific computational challenges, but they also differ in their statistical meaning, being motivated by different objectives: either testing hypotheses or finding the best-approximating model. These alternative goals entail different compromises, and as a result, Bayes factors, cross-validation and information criteria may be valid for addressing different questions. Here, the question of Bayesian model selection is revisited, with a focus on the problem of finding the best-approximating model. Several model selection approaches were re-implemented, numerically assessed and compared: Bayes factors, cross-validation (CV), in its different forms (k-fold or leave-one-out), and the widely applicable information criterion (wAIC), which is asymptotically equivalent to leave-one-out cross validation (LOO-CV). Using a combination of analytical results and empirical and simulation analyses, it is shown that Bayes factors are unduly conservative. In contrast, cross-validation represents a more adequate formalism for selecting the model returning the best approximation of the data-generating process and the most accurate estimates of the parameters of interest. Among alternative CV schemes, LOO-CV and its asymptotic equivalent represented by the wAIC, stand out as the best choices, conceptually and computationally, given that both can be simultaneously computed based on standard MCMC runs under the posterior distribution.</p>
RT Dataset -- Updated radiative transfer model for Titan in the near-infrared wavelength range: Validation against Huygens atmospheric and surface measurements and application to the Cassini/VIMS observations of the Dragonfly landing area
<p>This dataset contains all Radiative Transfer (RT) results made for the paper.</p> <p>The data are stored in 5 zipped-folders names with the Cassini/VIMS cube flyby and id, or explicitly for Huygens/ULIS calibrated observations:</p> <ul> <li>TB_C1481624349_1</li> <li>T40_C1578266417_1</li> <li>T38_C1575509158_1</li> <li>T40_C1578263500_1</li> <li>T40_C1578263152_1</li> <li>ULIS_observations</li> </ul> <p>The TB_C1481624349_1 folder contains the Cassini/VIMS cube over HLS, the HLS end-member (End_member.txt), the surface albedo retrieved by Karkoschka et al. (2016) corrected for the photometry (HLS_Karkoschka_2016_spectrum.txt), and the inverted surface albedo (Surface_albedo.txt).</p> <p>In these folders, each VIMS pixel is stored in a .txt file with the following pattern:</p> <p><CUBE_ID>_<PIXEL_SAMPLE>_<PIXEL_LINE> .txt</p> <p>It starts with a header describing the observation: </p> <ul> <li>CUBE_ID: the VIMS cube id (`C1234567890_1` format)</li> <li>SAMPLE: the pixel sample number.</li> <li>LINE: the pixel line number.</li> <li>LONG: the pixel longitude (in degree).</li> <li>LAT: the pixel latitude (in degree).</li> <li>INC: the surface incident angle (in degree).</li> <li>EMI: the surface emergent angle (in degree).</li> <li>PHASE: the surface phase angle (in degree).</li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), the header also contains the spatial sampling and the radiative transfer model outputs: </p> <ul> <li>Spatial sampling (km/pix).</li> <li>Fh: the haze scaling factor.</li> <li>Fm: the mist scaling factor.</li> <li>1-sigma (Fh): the 1-sigma uncertainty on Fh.</li> <li>1-sigma (Fm): the 1-sigma uncertainty on Fm.</li> <li>Reduced chi2: the reduced chi2. </li> </ul> <p>Then contains the observed spectra:</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers).</li> <li>Column 2: the VIMS pixel I/F.</li> <li>Column 3: the VIMS pixel I/F 1-sigma uncertainty. </li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), 3 columns are added for: </p> <ul> <li>Column 4: the surface albedo.</li> <li>Column 5: the upper 1-sigma uncertainty on the surface albedo.</li> <li>Column 6 : the lower 1-sigma uncertainty on the surface albedo.</li> </ul> <p>The ULIS folder contains the Huygens/ULIS calibrated observations (in I/F) and the simulations with 1-sigma uncertainties as a function of the altitude (in km):</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers), stopped at the end of the Huygens/ULIS wavelength range.</li> <li>Column 2: the ULIS I/F.</li> <li>Column 3 : the simulated I/F.</li> <li>Column 4: the lower 1-sigma uncertainty on the simulation.</li> <li>Column 5 : the upper 1-sigma uncertainty on the simulation.</li> </ul>
LiftWEC deliverable 3.6 - Part I: Dataset from 3D validation simulations of LiftWEC device using a high-fidelity RANS model
<p>This dataset contains numerical simulation results obtained from 3D-validation studies of the high-fidelity RANS model employed in the LiftWEC project. The case identifiers (ID) correspond to the case numbering employed in the experimental reference cases defined by École Centrale de Nantes. It is highly recommended to read the corresponding project reports on numerical modelling (D3.6) and on experimental modelling (D4.5, D4.6, D4.7, D4.8) which are also available in the LiftWEC community on zenodo (https://zenodo.org/communities/liftwec/).</p> <p>The cases comprise simulations of a rotor at constant velocity in calm water and regular waves in full 3D simulations. It further includes 2D simulation results of a rotor at constant rotational velocity in irregular waves and at variable velocity in monochromatic waves.</p> <p>All loads in the data set are given in force per unit span length (N/m), torque and power output is given as values per unit span as well. Wave elevation data up and down-wave of the rotor is given in (m).</p> <p> </p> <p> </p> <p> </p>
LiftWEC deliverable 3.3 - Dataset from 2D validation simulations of LiftWEC device using a high-fidelity RANS model
<p>This dataset contains results obtained from numerical simulations of the LiftWEC model scale device in a two-dimensional setting. The simulations were done based on the experimental validation campaign conducted in the scope of the LiftWEC project and documented in deliverables D4.2, D4.3 and D.4. The corresponding experimental datasets are also available within the LiftWEC community on zenodo.</p> <p>The numerical setup as well as a presentation and discussion of obtained results is available in LiftWEC deliverable D3.3 Tool Validation and Extension report, which also contains information on the potential flow model. All forces presented in this document are given as forces per unit span length. As the 2D RANS model was found to be rather sensitive to high fluctuations at this preliminary investigation stage, results are presented as mean forces and force fluctuations at rotation period, analysed by means of an FFT post-processing routine. The case identifiers correspond to the case numbering employed in the experimental model tests.</p>
Development and validation of metabolic models for predicting survival and immune status of hepatocellular carcinoma patients
<p>Supplementary materials for the article titled “Development and validation of metabolic models for predicting survival and immune status of hepatocellular carcinoma patients”</p>
ISOLDE model and validation statistics to support: Guanine-containing ssDNA and RNA induce dimeric and tetrameric SAMHD1 in cryo-EM and binding studies
<p>These files provide the pdb atom coordinates and structural validation of the ISOLDE structural model (Fig. 6) contained in the manuscript "Guanine-containing ssDNA and RNA induce dimeric and tetrameric SAMHD1 in cryo-EM and binding studies" </p>
Is my model fit for purpose? Validating a population model for predicting freshwater fish responses to flow management
<p>Models based on ecological processes ("process-explicit models") are often used to predict ecosystem responses to environmental changes or management scenarios. However, models are imperfect and need to be validated, ideally by testing their assumptions and outputs against independent empirical data sets. Examples of validation of process-explicit models are rare. Recently, stochastic population models have been developed to predict the likely responses (over 10-120 years) of a riverine fish (golden perch, Macquaria ambigua) to flow management in the Murray-Darling Basin (MDB) in eastern Australia, one of the world's most regulated river basins. Declines of golden perch (and other species) are a direct consequence of altered hydrology, and managers require information to predict how fish will respond to possible future hydrological conditions to guide the substantial investments in flow management. Here, we use two independent field data sets to validate our population model. We compared model predictions to observed trends to ask: (1) how do predicted population sizes and growth rates compare to observed data? (2) does the correlation between predicted and observed population sizes and growth rates vary among populations? (3) does the correlation between predicted and observed population sizes and growth rates vary across observed hydrological conditions? and (4) how do modelled and observed fish movement rates compare? We found reasonable correlations between fish population sizes and growth rates as predicted by the model and observed in independent data sets for several populations (Aim 1) but the strength of these correlations varied among populations (Aim 2) and hydrological conditions (Aim 3). Predicted and observed fish movement rates were strongly correlated (Aim 4). Population models are frequently used in conservation decision-making but are rarely validated. We demonstrate that: (1) validation can identify model strengths and weaknesses; (2) observed data sets often have inherent limitations that can preclude robust validations; (3) validation is likely be more common if appropriate observed data sets are available; and (4) validation should consider the purpose of modelling. Wider consideration of these messages would contribute to more critical examinations of models so they can be most appropriately used in conservation decision-making.</p>
Validation of Fracture Caging to Contain Hydraulic Fractures: Timeseries, Videos, and Model Script
<p>The data file include an Excel spreadsheet and two videos for each experimental test.</p> <p>You can start with reading the ReadMeFirst.txt file to understand the whole structure of the dataset.</p> <p>The caging_model.txt file includes python codes to calculate critical flow rates and uncaged fracture radius according to the theory that the authors developed and will be published soon.</p>
Data for: A catalytic model for SARS-CoV-2 reinfections: Performing simulation-based validation and extending the model to include nth infections
<p>For code and more details see: </p> <ul> <li><code>inf_for_sbv.RDS</code> - simluated timeseries of primary infections used in the simulation-based validation of reinfections. </li> <li><code>inf_for_sbv_third.RDS</code> - simluated timeseries of primary infections used in the simulation-based validation of third infections. </li> <li><code>3_posterior_90_null_correctdata.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript) when not considering a second lambda parameter (to third infections)</li> <li><code>3_posterior_90_null_l2_correctdata.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript) when considering a second lambda parameter (to third infections)</li> <li><code>3_sim_90_null_correctdata.RDS</code> - simulation results when not considering a second lambda parameter for third infections (as used in the manuscript)</li> <li><code>3_sim_90_null_l2_correctdata.RDS</code> - simulation results when considering a second lambda parameter for third infections (as used in the manuscript)</li> </ul> <p> </p>
Data set for model validation in "Simulating ice segregation and thaw consolidation in permafrost environments with the CryoGrid community model"
<p>This upload contains the data set for model validation in the manuscript "Simulating ice segregation and thaw consolidation in permafrost environments with the CryoGrid community model".</p>
Collocated model and observation datasets for the validation of the Copernicus Mediterranean Sea Waves Analysis and Forecast for the period 2018-2020.
<p>The collocated values are used for skill evaluation of the Mediterranean Sea Waves Analysis and Forecast system for a three-year-long period (Korres et al., 2022). The list of datasets includes:</p> <ol> <li>The collocated model (analysis) – buoy values for significant wave height Hs (Insitu_Hs.mat)</li> <li>The collocated model (analysis) – buoy values for spectral moments (0,2) wave period Tm (Insitu_Tm.mat)</li> <li>The collocated model (first – guess) – satellite values for significant wave height Hs (Satellite_Hs.mat)</li> <li>The collocated model – satellite values for wind speed U10 (Satellite_U10.mat)</li> </ol> <p>Each .mat file contains a header for the variables included. Collocations can be used to estimate standard quality metrics (e.g. scatter index, bias, root-mean-squared-difference). Procedures to produce these model-observation collocated datasets and determine the overall skill assessment are described in detail in Ravdas et al. (2018) and Oikonomou et al. (2022). The buoy (in-situ) measurements are obtained from the product INSITU_GLO_WAV_DISCRETE_MY_013_045 (EU Copernicus Marine Service Product, 2022a), and associated variables contain a quality flag (“time_qc”, “position_qc”, “buoy_Hs_qc”, “buoy_Tm_qc”) (de Alfonso et al., 2022a,b). In addition, the model first-guess significant wave height and the wind speed forcing (Hersbach et al., 2023) are collocated with available satellite observations (EU Copernicus Marine Service Product, 2022b) over the entire model domain.</p> <p>References</p> <p>de Alfonso, M., Manzano, F., and Gallardo, A. (2022a): EU Copernicus Marine Service Quality Information Document for the In Situ TAC Product, INSITU_GLO_WAV_DISCRETE_MY_013_045, Issue 5.0, Mercator Ocean International, <a href="https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-045.pdf">https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-045.pdf</a></p> <p>de Alfonso, M., Manzano, F., Gallardo, A., and In Situ TAC (2022b): EU Copernicus Marine Service Product User Manual for Multi-Year WAVE In Situ Product, INSITU_GLO_WAV_DISCRETE_MY_013_045, Issue 2.0, Mercator Ocean International, <a href="https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013-045.pdf">https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013-045.pdf</a></p> <p>EU Copernicus Marine Service Product (2022a): Multi-Year WAVE In Situ Product, Mercator Ocean International, [dataset], <a href="https://doi.org/10.17882/70345">https://doi.org/10.17882/70345</a></p> <p>EU Copernicus Marine Service Product (2022b): Global Ocean L 3 Significant Wave Height From Reprocessed Satellite Measurements, Mercator Ocean International, [dataset], <a href="https://doi.org/10.48670/moi-00176">https://doi.org/10.48670/moi-00176</a></p> <p>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 (Accessed on 26-09-2023)</p> <p>Korres, G., Oikonomou, C., Denaxa, D., & Sotiropoulou, M. (2022): Mediterranean Sea Waves Analysis and Forecast (CMEMS MED-Waves, MEDWAΜ4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). <a href="https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_WAV_006_017_MEDWAM4">https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_WAV_006_017_MEDWAM4</a></p> <p>Oikonomou, C., Denaxa D., and Korres, G. (2022): EU Copernicus Marine Service Quality Information Document for the Mediterranean Sea Waves Reanalysis, MEDSEA_ANALYSISFORECAST_WAV_006_017, Issue 2.2, Mercator Ocean International, <a href="https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-MED-QUID-006-017.pdf">https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-MED-QUID-006-017.pdf</a>.</p> <p>Ravdas, M., Zacharioudaki, A., and Korres, G. (2018): Implementation and validation of a new operational wave forecasting system of the Mediterranean Monitoring and Forecasting Centre in the framework of the Copernicus Marine Environment Monitoring Service, Nat. Hazards Earth Syst. Sci., 18, 2675–2695, <a href="https://doi.org/10.5194/nhess-18-2675-2018">https://doi.org/10.5194/nhess-18-2675-2018</a></p> <p> </p>
Supplementary Materials: ImpactX Modeling of Benchmark Tests for Space Charge Validation
<p>Supplementary materials (aka data artifact or data archive) for our HB2023 publication: " ImpactX Modeling of Benchmark Tests for Space Charge Validation" (Paper ID: THBP44).</p> <p>This work was supported by the Director, Office of Science of the U.S. Department of Energy under Contracts No. DE-AC02-05CH11231 and DE-AC02-07CH11359. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719.</p>
Validated CFD Model for Multimode Gasoline Compression Ignition Engine
<p>A validated CFD model for the multimode combustion engine developed under the DOE funded project DE-EE0008478 (Co-optimized Mixed-Mode Engine and Fuel Demonstrator for Improved Fuel Economy while Meeting Emissions Requirements). It incorporates advanced physics-based fuel surrogate models for thermophysical properties and reaction kinetics. The combustion modes include spark ignition (SI), low temperature combustion (LTC), and compression ignition (CI). The real fuel model was validated for RON60, RON70, RON80, RON90, and two biofuel blends. The validation cases can be found in <a href="https://doi.org/10.2172/1887341">https://doi.org/10.2172/1887341</a>. </p>
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