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279 results for “model comparison”
Depressurization of CO2 in a pipe: High-resolution pressure and temperature data and comparison with model predictions – dataset
<p>This dataset contains data from depressurization of pure CO<sub>2</sub> and nitrogen in a tube from a gaseous and a dense-liquid state. The data are described in the accompanying paper (DOI: <a href="https://doi.org/10.1016/j.energy.2020.118560">10.1016/j.energy.2020.118560</a>).</p> <p>Test number; fluid; pressure (MPa); temperature (deg C):<br> 3; CO2; 4.04; 10.2<br> 4; CO2; 12.54; 21.1<br> 6; CO2; 10.40; 40.0<br> 8; CO2; 12.22; 24.6<br> 11; N2; 5.13; 10.0</p> <p><br> </p>
Exp 2 Model Data Comparison
<p>Model results for the base case of comparing with the MODEX flume data</p>
Exp 7 Model Data Comparison
<p>Model results specifically for comparison to MODEX observations</p>
Exp 8 Model Data Comparison
<p>Model results for the base case of comparing with the MODEX flume data</p>
Exp 9 Model Data Comparison
<p>Model output used to validate with Exp 9 data</p>
Data from: Multiresponse algorithms for community-level modeling: review of theory, applications, and comparison to species distribution models
1.Community-level models (CLMs) consider multiple, co-occurring species in model fitting and are lesser known alternatives to species distribution models (SDMs) for analyzing and predicting biodiversity patterns. CLMs simultaneously model multiple species, including rare species, while reducing overfitting and implicitly considering drivers of co-occurrence. Many CLMs are direct extensions of well-known SDMs and therefore should be familiar to ecologists. However, CLMs remain underutilized, and there have been few tests of their potential benefits and no systematic reviews of their assumptions and implementations. Here we review this emerging field and provide examples in R to fit common CLMs. Our goal is to introduce CLMs to a broader audience, and discuss their attributes, benefits, and limitations relative to SDMs. 2.We review i) statistical implementations and applications of CLMs, ii) their advantages and limitations, and iii) comparative analyses of CLMs and SDMs. We also suggest directions for future research. 3.We identify seven CLM algorithms with similar data structures and predictive outputs as SDMs that should be most accessible to ecologists familiar with species-level modeling, including five methods that predict assemblage composition and individual species distributions and two methods that model compositional turnover along environmental gradients. CLMs have been applied to numerous taxa, regions, and spatial scales, and a variety of topics (e.g., studying drivers of community structure or assessing relationships between community composition and functional traits). Studies suggest that the relative benefits of CLMs and SDMs may be case specific, especially in terms of predicting species distributions and community composition. However, CLMs may offer advantages in terms of computational efficiency, modeling rare species, and projecting to no-analog climates. A major shortcoming of CLMs is their reliance on presence-absence community composition data. 4.Studies are needed to assess the relative merits of SDMs and CLMs, and different CLM algorithms, with a focus on three key areas: i) under which circumstances CLMs improve predictions for rare species, ii) how CLMs perform under different community compositions (e.g. relative abundance of rare vs. common species), including the extent to which co-occurrence patterns are structured by biotic interactions, and iii) ability to project across time/space.
Model Data for Doubly Periodic SCREAM Comparison with ARM Observations
<p>Includes the SAM LES, E3SM SCM, and DP-SCREAM simulations used in the study.</p>
raw data - Comparison of High-Flow Nasal Cannula Oxygen and Conventional Oxygen Therapy in a Rat Model of Severe Carbon Monoxide Poisoning
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FIGURE 3 in Comparisons of two cryptic Ampedus species (Coleoptera: Elateridae) by using classical systematics, ecological niche modeling, and DNA barcoding
FIGURE 3. Evolutionary relationships optimal tree of examined and outgroup taxa.
FIGURE 4 in Comparisons of two cryptic Ampedus species (Coleoptera: Elateridae) by using classical systematics, ecological niche modeling, and DNA barcoding
FIGURE 4. Evolutionary relationships bootstrap consensus tree of examined and outgroup taxa.
ICESat, ERS1, ERS2, Envisat Laser and Radar Altimetry Datasets for the Cryosphere model Comparison Tool (CmCt) Input for Greenland and Antarctica
<div> <p>These datasets contain the ICESat, ERS1, ERS2, Envisat Laser and Radar Altimetry Datasets for CmCt Input data for Greenland and Antarctica. These reference observational datasets are used in the CmCt to compare ice sheet models with.</p> <p>The <strong>ICESat/GLAS</strong> instrument was a lidar altimeter and the primary instrument on the NASA ICESat mission. It took point elevation measurements approximately every 170 meters along its track, and each shot had a footprint of approximately 70 meters in diameter.</p> <p>The GLAS instrument contained 3 lasers, but due to some instrumentation issues, it was decided to turn the lasers on and off during predetermined time periods. For more detailed information about GLAS and the ICESat mission, visit the <a href="http://icesat.gsfc.nasa.gov/icesat/" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">ICESat website</a>.</p> <p>For use with the CmCt project, the Greenland elevation data from ICESat/GLAS (Zwally et al, 2002) were preprocessed. The data were cleaned and limited to the ice sheets. At the time of creation the 634 release of the <a href="https://nsidc.org/data/GLA12/versions/34" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">GLAS12</a> product was used (<em>Zwally et al, 2014</em>). </p> <p>The processing was accomplished by:</p> <ol> <li>restricting the data to GLAS data points only on the ice surface</li> <li>applying two data quality filters we required the GLAS surface reflectivity to be > 0.0375 and we required the uncertainty associated with the GLAS fitting procedure to be < 0.0375 (the numerical coincidence is in fact a coincidence). These are the same quality criteria that were used for <a href="http://imbie.org/" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">IMBIE2</a> and thus are being implemented for the CmCt.</li> <li>checking the data against the reerence DEM (GIMP 90-m DEM for Greenland or Bamber 1-km DEM for Antarctica), requiring the elevation difference to be < 200m.</li> </ol> <p>Please find more details on the data preprocessing in the Supporting Docs tab.</p> <p>The<a href="https://www.esa.int/Applications/Observing_the_Earth/Envisat" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer"> <strong>Envisat</strong></a> (Environmental Satellite), <strong><a href="https://eoportal.org/web/eoportal/satellite-missions/e/ers-1" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">ERS1</a></strong>, <strong><a href="https://eoportal.org/web/eoportal/satellite-missions/e/ers-2" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">ERS2</a></strong> (European Remote Sensing Satellites 1 and 2) radar altimeter datasets were also preprocessed to prepare the data to generate a comparison data set for the CmCt. Several filters were used to remove data that are not on the ice sheet or have questionable elevations. Please see the detailed processing descriptions in the Supporting Docs.</p> <p>Radar and laser altimeters measure similar parameters. They measure the time of flight of photons from the spacecraft to the reflection point and back to the spacecraft. The time of flight is then used to calculate an elevation. Accurate elevations require precise knowledge of the spacecraft orbit, corrections for atmospheric scattering, and other factors.</p> <p>There are several differences between the radar and laser altimetry data available here that should be noted:</p> <ul> <li>The accuracy of the elevations calculated from the radar data is generally lower than the accuracy of elevations based on laser data, primarily because <ul> <li>the radar beam is much broader (several km by the time it reaches the ground vs < 100 m for the laser beam).</li> <li>the radar photons penetrate snow and ice a significant amount (cm to m), whereas the laser photons from ICESat penetrate minimally if at all.</li> </ul> </li> <li>ERS and Envisat worked at a lower pulse rate than ICESat, and had a shorter repeat period, so the data are sparser on the ground (but repeat approximately monthly). On the other hand, the radar satellites worked continuously, whereas ICESat only operated for 2-3 months per year. </li> <li>The radar data collectively cover a longer period of time, starting more than a decade earlier and extending past the end of the ICESat data.</li> <li>ERS and Envisat were in orbits that left larger holes at the poles than ICESat (8.5 degrees for the radar satellites vs 4 degrees for ICESat).</li> <li>Radar beams penetrate clouds, whereas the ICESat laser beam was scattered by clouds, with returns becoming unusable if the optical depth was much greater than 1.</li> </ul> <h4> </h4> <h4>Laser and Radar Altimetry Available Data Time Range:</h4> <h4> </h4> <table> <tbody> <tr> <td>ERS1:</td> <td>1991-1995</td> </tr> <tr> <td>ERS2:</td> <td>1996-2002</td> </tr> <tr> <td>Envisat:</td> <td>2003-2012</td> </tr> <tr> <td>ICESat/GLAS:</td> <td>2003-2009</td> </tr> </tbody> </table> <h4> </h4> <h4>Downloading Data</h4> <p><a href="https://theghub.org/resources?id=4737"><strong>The data can be downloaded from the Globus GHub-CmCt endpoint. Please log in and click on the Download tab to receive the Download instructions.</strong></a></p> </div> <h4>References</h4> <div> <p>Howat, I. M., A. Negrete, and B. E. Smith, The Greenland Ice Mapping Project (GIMP) land classification and surface elevation data sets, The Cryosphere 8.4 (2014): 1509-1518.</p> <p> </p> <p>Howat, I. M., A. Negrete, and B. E. Smith. MEaSUREs Greenland Ice Sheet Mapping Project (GIMP) Digital Elevation Model, Boulder, Colorado USA: NASA National Snow and Ice Data Center Distributed Active Archive Center (2015).</p> <p> </p> <p>Zwally, H. J., et al. ICESat's laser measurements of polar ice, atmosphere, ocean, and land, Journal of Geodynamics 34.3 (2002):405-445.</p> <p> </p> <p>Zwally, H. J., et al. GLAS/ICESat L2 Antarctic and Greenland ice sheet altimetry data V034, National Snow and Ice Data Center, Boulder, Colorado (2014).</p> </div>
Fig. 2 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 2: A: Confidence level along depth for Argo-model average temperature profile differences in South Adriatic (blue) & Otranto Strait (green). B: Confidence level along depth for Argo-model average temperature profile differences in Northern (yellow) and Southern (red) Ionian. C: Confidence level along depth for Argo-model average salinity profile differences in South Adriatic (blue) & Otranto Strait (green). D: Confidence level along depth for Argo-model average salinity profile differences in Northern (yellow) and Southern (red) Ionian. E: Confidence level along depth for Argo-model average temperature (green) and salinity (brown) profile differences in the whole study area. The shaded rectangular denotes the area of statistical significant differences with a confidence of 95% between the Argo and model distributions (null-hypothesis rejected, p <0.05).
Comparison of Control and Phage-treated Groups in Mouse Model
<p>Control mice (left) became lethargic at ~36 hours after intraperitoneal injection with <em>P. aeruginosa </em>PAO1. Phage-treated mice (right) showed increased vitality. Further analysis revealed phage-treated mice had completely cleared infection thanks to phage cocktail application.</p>
Models, scripts, simulated data, and results from the article "Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework."
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Data from: Comparison of non-Gaussian quantitative genetic models for migration and stabilizing selection
The balance between stabilizing selection and migration of maladapted individuals has formerly been modeled using a variety of quantitative genetic models of increasing complexity, including models based on a constant expressed genetic variance and models based on normality. The infinitesimal model can accommodate non-normality and a non-constant genetic variance as a result of linkage disequilibrium. It can be seen as a parsimonious one-parameter model which approximates the underlying genetic details well when a large number of loci are involved. Here, the performance of this model is compared to several more realistic explicit multilocus models, with either two, several or a large number of alleles per locus with unequal effect sizes. Predictions for the deviation of the population mean from the optimum are highly similar across the different models, so that the non-Gaussian infinitesimal model forms a good approximation. It does however generally estimate a higher genetic variance than the multilocus models, with the difference decreasing with an increasing number of loci. The difference between multilocus models depends more strongly on the effective number of loci, accounting for relative contributions of loci to the variance, than on the number of alleles per locus.
Data from: Comparison of infinitesimal and finite locus models for long-term breeding simulations with direct and maternal effects at the example of honeybees
Stochastic simulation studies of animal breeding have mostly relied on either the infinitesimal genetic model or finite polygenic models. In this study, we investigated the long-term effects of the chosen model on honeybee breeding schemes. We implemented the infinitesimal model, as well as finite locus models, with 200 and 400 gene loci and simulated populations of 300 and 1000 colonies per year over the course of 100 years. The selection was of a directly and maternally influenced trait with maternal heritability of h²_m = 0.42, direct heritability of h² d = 0.27, and a negative correlation between the effects of r_md = −0.18. Another set of simulations was run with parameters h²_m = 0.53, h²_d = 0.34, and r_md = −0.53. All models showed similar behavior for the first 20 years. Throughout the study, we observed a higher genetic gain in the direct than in the maternal effects and a smaller gain with a stronger negative covariance. In thelong-term, however, only the infinitesimal model predicted sustainable linear genetic progress, while the finite locus models showed sublinear behavior and, after 100 years, only reached between 58% and 62% of the mean breeding values in the infinitesimal model. While the infinitesimal model suggested a reduction of genetic variance by 33% to 49% after 100 years, the finite locus models saw a more drastic loss of 76% to 92%. When designing sustainable breeding strategies, one should, therefore, not blindly trust the infinitesimal model as the predictions may be overly optimistic. Instead, the more conservative choice of the finite locus model should be favored.
Replication Data for: Interpretable machine learning prediction of fire emission and comparison with FireMIP process-based models
<p>The target and predictor variables used in the developed ML model.</p>
Data for thesis: Online Discovery and Model-to-Model Comparison of DCR Models from Event Streams
<p>This is the collection of data referenced in the Thesis</p>
Data from: Comparison of seven simple loss models for runoff prediction at the plot, hillslope and catchment scale in the semiarid southwestern U.S.
<p>Infiltration excess overland flow is the dominant mechanism for runoff generation in many dryland watersheds. Event-based rainfall-runoff models therefore partition precipitation into two components: loss and excess precipitation. The latter is then transformed into a runoff hydrograph. Numerous loss models have been developed over the past century ranging from simple empirical to sophisticated physically based methods. Complex models can lead to equifinality and associated uncertainty at larger spatial scales with varying soil and cover conditions. Simple models are therefore widely used in hydrologic practice. In the absence of measured data in many arid and semiarid regions, model parameters are often estimated based on laboratory or field infiltrometer tests. Given the documented importance of spatial scale on the runoff response in dryland catchments, it is not clear how models parameterized at the point or soil column scale will perform at the hillslope or catchment scale under real-world conditions. In this study, we compared the performance of seven simple loss models with three or less parameters: the Philip, Smith-Parlange, Horton, Kostiakov, curve number (CN), initial and constant (IC) and the linear and constant (LC) models. The latter is a modification of the IC model introduced in this study. We estimated parameters at the plot scale (2.8 m<sup>2</sup><span><span><span><span><span><span><span><span><span>) using rainfall simulation and then tested model performance at the hillslope (1.5–3.7 ha) and catchment scale (2.4–2.8 km</span></span></span></span></span></span></span></span></span><sup>2</sup><span><span><span><span><span><span><span><span><span>) based on measured rainfall-runoff data at two sites in New Mexico and Arizona, U.S. Results show that rainfall simulation can be used successfully to parameterize loss models at the hillslope scale. At the catchment scale, most models showed positive bias, suggesting that other losses (such as channel or transmission losses) play an important role in determining the catchment runoff response. Rainfall intensity and temporal distribution were found to be crucial for accurate runoff prediction. Models that are sensitive to rainfall intensity during the entire simulation (Philip, Smith-Parlange, Horton, Kostiakov, LC) therefore performed better than those with an initial abstraction term (CN, IC). During intermittent rain, the best results were achieved by methods expressing infiltration capacity as a function of cumulative infiltration (LC, Smith-Parlange). </span></span></span></span></span></span></span></span></span></p>
Validity of Digital Models Obtained With iTero® and Lava Digital® in Comparison With Plaster Models.
ClinicalTrials.gov study NCT01934517. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.