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279 results for “model comparison”
Fig. 11 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 11: RMSE profiles for temperature (A) and salinity (C). All associated profiles differences (Argo-model) for temperature (B) and salinity (D) in 6 discrete depths (10 m dark blue, 20 m light blue, 30 m red, 40 m pink, 50 m green, 60 m yellow).
Fig. 10 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 10: Temperature (A) and salinity (C) average profiles with the associated STD for model (red) and Argo (blue), calculated from all the associated profiles of the study area (Fig. 1). Profile differences (Argo – model) of the average temperature (green line) and salinity (brown line) (B). T-S diagram of all Argo and model associated profiles for two depth layer zones (Argo: 200-800m light blue, 800-2000 m dark blue) (Model: 200-800 m pink, 800-2000 m red) (D).
Fig. 9 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 9: Temperature (A) and salinity (C) average profiles with the associated STD for model (red) and Argo (blue), calculated from the associated profiles during the "winter" periods (November – April). The associated profiles for the "summer" periods (May – October) are shown in (B) and (D) for the temperature and salinity respectively.
Fig. 7 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 7: A: Argo salinity average profiles in Southern Adriatic (SA) and Otranto Strait (OS) for the years 2010 (green) and 2012 (purple). B: Argo salinity average profiles in the Northern Ionian (NI) for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple). C: Model salinity average profiles in Southern Adriatic (SA) and Otranto Strait (OS) for the years 2010 (green) and 2012 (purple). D: Argo salinity average profiles in the Northern Ionian (NI) for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple).
Fig. 8 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 8: A: Argo salinity average profiles in the south-eastern Ionian for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple). B: Model salinity average profiles in the south-eastern Ionian for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple).
Fig. 6 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 6: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the southern Ionian region. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the southern Ionian.
Fig. 5 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 5: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the northern Ionian region. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the northern Ionian.
Fig. 3 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 3: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the southern Adriatic region. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the south Adriatic.
Fig. 4 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 4: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the Otranto Strait. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the Otranto Strait.
Fig. 1 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 1: SANI model bathymetry (A). The geographical area covered by SANI model (red rectangular) and the divided sub-regions SA (Southern Adiatic - yellow), OS (Otranto Strait - green), NI (Northern Ionian – brown) and SI (Southern Ionian – blue). All the available (966) Argo profiles for the period 2008-2012 from 21 individual floats denoted with different colours according to their WMO number (B).
Figure 3. Comparison between potential ant model and membracid mimic. Both specimens have the same body length. A–B in First reports of species-specific ant resemblance in heteronotine treehoppers (Hemiptera: Membracidae: Heteronotinae)
Figure 3. Comparison between potential ant model and membracid mimic. Both specimens have the same body length. A–B) Cephalotes atratus (Linnaeus, 1758), worker. A) Habitus, dorsal. B) Head and anterior part of mesothorax, dorsal. C–D) Heteronotus fabulosus Boulard, 1981. C) Habitus, dorsal. CS = Cornus suprahumeralis; L = length between apex of CS and apex of NT; NPS = Spina nodus primus; NT = Nodus terminalis; PS = Spina pedunculus. D) Anterior part of pronotum; head, wings and legs omitted. E) Cephalotes atratus worker habitus, lateral. F) Heteronotus fabulosus habitus, lateral. Numbered structures in the figures reference the numbering system used for morphological comparisons in Figure 4.
Figure 4. Comparison of ASS and PSS for multilayer model R-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>TDNN models with single and multi layers were developed taking soluble nitrogen, pH,<br> standard plate count, yeast & mould count, spore count as input parameters, and sensory score as<br> output parameter for predicting the shelf life of processed cheese stored at 30o C. Mean Square<br> Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were<br> used in order to compare the prediction ability of the developed TDNN models. Regression<br> equations were developed for predicting the shelf life of processed cheese, which came out as 28.25<br> days. Since, predicted value is close to the experimentally determined shelf life of 30 days, hence<br> from the study it can be concluded that TDNN artificial neural network models are quite efficient in<br> predicting shelf life of processed cheese.</p>
Figure 3. Comparison of ASS and PSS single layer model-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>TDNN models with single and multi layers were developed taking soluble nitrogen, pH,<br> standard plate count, yeast & mould count, spore count as input parameters, and sensory score as<br> output parameter for predicting the shelf life of processed cheese stored at 30o C. Mean Square<br> Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were<br> used in order to compare the prediction ability of the developed TDNN models. Regression<br> equations were developed for predicting the shelf life of processed cheese, which came out as 28.25<br> days. Since, predicted value is close to the experimentally determined shelf life of 30 days, hence<br> from the study it can be concluded that TDNN artificial neural network models are quite efficient in<br> predicting shelf life of processed cheese.</p>
Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures
<p>This dataset comes from the following paper:</p> <p>Chiara Pasini, Oscar Ramponi, Stefano Pandini, Luciana Sartore, Giulia Scalet, Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures, J. of Materi Eng and Perform, 2024. <a href="https://doi.org/10.1007/s11665-024-10199-x">https://doi.org/10.1007/s11665-024-10199-x</a></p> <p>It contains:</p> <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>. m of the neural network</li> <li>. inp of the Abaqus finite element simulations</li> </ul>
Time Series Comparisons, Model Code, and a Demo Dataset for SIBaR: A New Method for Background Quantification and Removal from Mobile Air Pollution Measurements
<p>Time series comparisons between SIBaR, Brantley, and Apte background signals for all 312 time series in the Houston mobile monitoring campaign. Additionally, a R script demo (DemoData.R) of the SIBaR partitioning step on the demo datatset (DemoData.csv).</p>
Model fields supporting the publication "Integrated Assessment of the Risks to Ocean Acidification in the Northern High Latitudes: Regional Comparison of Exposure, Sensitivity and Adaptive Capacity of Pelagic Calcifiers"
<p>These are the model outputs supporting the described manuscript. They include monthly averaged output of aragonite saturation state for each year during the 10-year hindcast. Also included is the particle tracking output, for both the Bering Sea and the Gulf of Alaska, as described in the manuscript.</p>
Fast mvSLOUCH: Model comparison for multivariate Ornstein--Uhlenbeck-based models of trait evolution on large phylogenies
<p>These are the Supplementary Material, R scripts and numerical results accompanying Bartoszek, Fuentes Gonzalez, Mitov, Pienaar, Piwczyński, Puchałka, Spalik and Voje "Model Selection Performance in Phylogenetic Comparative Methods under multivariate Ornstein–Uhlenbeck Models of Trait Evolution".</p> <p>The four data files concern two datasets. Ungulates: measurements of muzzle width, unworn lower third molar crown height, unworn lower third molar crown width and feeding style and their phylogeny; Ferula: measurements of ratio of canals, periderm thickness, wing area, wing thickness, and fruit mass, and their phylogeny.</p>
Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle"
<p>Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle". </p> <p>Clerc, C., Bopp, L., Benedetti, F., Vogt, M., and Aumont, O.: Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1282, 2022.</p> <p>Three directories can be downloaded:</p> <p><strong>DataOBS</strong> : AtlantECO [WP2] – Traditional microscopy dataset – Thaliacea (Salpida+Doliolida+Pyromosomatida) abundance and biomass concentration data, presented in Clerc et al. (2022). </p> <p><strong>FigPaper </strong>: Source code and .nc files for the figures presented in Clerc et al. (2022) (https://doi.org/10.5194/egusphere-2022-1282). </p> <p><strong>MY_SRC_PISCES_NEMO_3.6 :</strong> Additional fortran routines for the compilation of PISCES-FFGM, the model developed for Clerc et al. (2022), from NEMO-3.6 (https://www.nemo-ocean.eu)</p>
An Empirical Comparison of Pre-Trained Models of Source Code
<p>The replication package of the paper "An Empirical Comparison of Pre-Trained Models of Source Code". For the source code, please refer to <a href="https://github.com/NougatCA/FineTuner">https://github.com/NougatCA/FineTuner</a>.</p>
Supporting Material for "Lunar Surface Model Age Derivation: Comparisons Between Automatic and Human Crater Counting Using LRO-NAC And Kaguya TC Images"
<p>Supporting Material for "Lunar Surface Model Age Derivation: Comparisons Between Automatic and Human Crater Counting Using LRO-NAC And Kaguya TC Images"</p> <p>Contents of this material</p> <ul> <li>Supplemental Text S1 and Text S2.</li> <li>Figures S1, S2, S2, S4, S5.</li> <li>Tables S1, S2</li> </ul> <p>For any questions email JHF (john.h.fairweaher@gmail.com).</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.