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307 results for “Model combination”
Representation of model error in convective-scale data assimilation: additive noise, relaxation methods and combinations
<p>ModelError_1_dpsdt.tar.xz for Fig. 11<br> <br> ModelError_1_innovstat.tar.xz for Fig. 4, 10 and 13<br> <br> ModelError_1_KESpectrum.tar.xz for Fig. 1<br> <br> ModelError_1_pre_veri.tar.xz for Fig. 8 and 9<br> <br> ModelError_1_precipobs_tau.tar.xz for Fig. 2<br> <br> ModelError_1_refl_veri.tar.xz for Fig. 6, 12 and 14</p> <p>For plotting, MATLAB (R2017b) and python are used</p> <p> </p>
A global 0.05° gross primary productivity of sunlit and shaded leaves dataset via combining two-leaf light use efficiency model with random forest over 2002~2020
<p>The TL-CRF model generated a global 0.05´0.05° product for eight-day gross primary productivity (GPP) of sunlit and shaded canopies from 2002 to 2020 by embedding the random forest (RF) submodule into the two-leaf light use efficiency (TL-LUE) model while considering the seasonal differences in the clumping index. The RF technique was used to integrate various environmental stress factors including meteorological, hydrological, soil properties, and elevation, thereby improving the overall scale of the complex environmental conditions to the maximum LUE. This novel GPP product could support further research on spatial and temporal patterns of the carbon cycle and its association with climate change.</p> <p> </p> <p> </p> <p>Variable: GPP, GPP<sub>sh</sub>, and GPP<sub>su</sub></p> <p>Spatial coverage: global</p> <p>Temporal coverage: 2002 to 2020</p> <p>Spatial resolution: 0.05×0.05°</p> <p>Temporal resolution: eight-day</p> <p>Unite: g C m<sup>−2</sup> d<sup>−1</sup></p> <p>Data format: raster (.tif)</p>
A Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model
<p>Data used to generate the figures in "A Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model" 2021. The zip files contains the hybrid forecasts and regridded ERA5 data used to verify the forecasts as well as the SPEEDY-LLR and ML-only runs. </p>
Data accompanying the article "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020"
<p>The .zip file contains temporal-spatial averaged metrics for evaluating simulations against observed ice thickness, concentration, volume, and drift. These quantities are presented in the manuscript "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020"</p> <p>Subfolders are named by the experiment IDs, including metrics obtained from the relevant experimental results and observations.</p> <p>In case information is missing, do not hesitate to contact chengsukun@hotmail.com</p> <p>We thank Pavel Sakov for helpful discussions and improvement regarding the EnKF-C code and Jiping Xie for contributing the TOPAZ interface to sea ice observations. We are grateful for the support from Timothy Williams and Anton Korosov regarding the environments of neXtSIM and its analysis tools. The work is funded by the DASIM-II grant from ONR (grant nos. N00014-18-1-2493 and N00014-18-1-2204). Alberto Carrassi, Christopher K. R. T. Jones, Ali Aydo ̆gdu, and Pierre Rampal acknowledge the support of the project SASIP funded by Schmidt Futures – a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologies. Sukun Cheng and Laurent Bertino were co-funded by the FOCUS project from the Research Council of Norway (grant no. 301450), and Alberto Carrassi and Yumeng Chen are also supported by the UK National Centre for Earth Observation (grant no. NCEO02004). Computations were carried out on the Norwegian Supercomputing InfrastructureSigma2 (grants nn2993k for computing and NS2993K for data storage)</p>
Highly variable. Mid-portion of the dorsum used frequently (although other areas as well). Tip of the snout used often, but area around the ear and throat are also relevant. P. carbonelli Variable. In the dorsal view, the tip of the snout is frequently used. In the head lateral view, the tip of the snout is also com- monly used, as well as the most posterior region of the head. P. guadarramae Variable. Mid portion of the dorsum and tip of the snout are the regions used more frequently in dorsal and head lateral views, respectively. P. hispanicus Variable. The head and most anterior part of the dorsum are frequently used in the dorsal view. Snout and/or top of posterior region of head used. P. liolepis Variable. Different parts of the dorsum are used, whereas the tip of the snout is used in most head lateral images. P. lusitanicus Anterior dorsum, in the dorsal view, and both snout and posterior side of the head (in head lateral views) frequently used. P. tunesiacus Variable. Tip of the snout and posterior part of the trunk more used than in other species; snout and top head region behind the eye used with some frequency. P. Ʋaucheri Highly variable. All parts of the dorsum used in dorsal images, various parts of the head (but frequently snout and throat combined) used in head lateral images. P. Ʋirescens Highly variable. All portions of the dorsum used in dorsal images, region around and behind the ear more used than in other species for head lateral images. in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
Highly variable. Mid-portion of the dorsum used frequently (although other areas as well). Tip of the snout used often, but area around the ear and throat are also relevant. P. carbonelli Variable. In the dorsal view, the tip of the snout is frequently used. In the head lateral view, the tip of the snout is also com- monly used, as well as the most posterior region of the head. P. guadarramae Variable. Mid portion of the dorsum and tip of the snout are the regions used more frequently in dorsal and head lateral views, respectively. P. hispanicus Variable. The head and most anterior part of the dorsum are frequently used in the dorsal view. Snout and/or top of posterior region of head used. P. liolepis Variable. Different parts of the dorsum are used, whereas the tip of the snout is used in most head lateral images. P. lusitanicus Anterior dorsum, in the dorsal view, and both snout and posterior side of the head (in head lateral views) frequently used. P. tunesiacus Variable. Tip of the snout and posterior part of the trunk more used than in other species; snout and top head region behind the eye used with some frequency. P. Ʋaucheri Highly variable. All parts of the dorsum used in dorsal images, various parts of the head (but frequently snout and throat combined) used in head lateral images. P. Ʋirescens Highly variable. All portions of the dorsum used in dorsal images, region around and behind the ear more used than in other species for head lateral images.
Data for: Combining stable isotopes, trace elements, and distribution models to assess the geographic origins of migratory bats
<p>The expansion of industrial-scale wind-energy facilities has increased the production of low-carbon emission energy but has also resulted in mortality of wildlife, including migratory bats. Management decisions can be limited by a lack of understanding of the geographic impact of bats killed at wind-energy facilities. Several studies have leveraged stable hydrogen isotope ratios (δ<sup>2</sup>H) of bat fur to illuminate this issue but are limited in the precision of conclusion because δ<sup>2</sup>H values vary primarily across latitudinal and elevational bands. One approach to increase the precision of geographic assignment is to combine independent inferences about spatial location from additional biomarkers and other related information. To test this possibility, we assigned known-origin individuals of three bat species commonly killed at on-shore wind-energy facilities in North America (<em>Lasiurus</em> <em>borealis</em>, <em>L</em>. <em>cinereus</em>, and <em>Lasionycteris</em> <em>noctivagans</em>) to probable origin using δ<sup>2</sup>H values, trace element concentrations, and species distribution models. We used cross-validation calibrated combined model tuning to determine the degree to which assignment probabilities improved when combining datasets. We found that combining markers typically performed better than single approaches. For <em>L. borealis</em> and <em>L. cinereus</em>, combining all three data sources outperformed any single or other combined approach. With an accuracy set at 80%, an average of 39.7% and 36.0% of each species' total geographic range was considered a potential origin, respectively; stable hydrogen alone included 51.8% and 50.6% of the total geographic area. In contrast, for <em>L. noctivagans</em>, including trace elements did not increase precision, and adding distribution data to δ<sup>2</sup>H values only improved precision by 0.6%. Thus, we found that a combination of multiple biomarkers typically, but not always, outperforms single marker approaches, and optimized combinations of different markers outperform equal weighting of each marker. From a practical perspective, δ<sup>2</sup>H values performed better than trace elements alone; in cases where cost is a limiting factor, the stable hydrogen should be the single biomarker used in conjunction with species distribution models. Overall, these results highlight the importance of validating methods for each species they are applied to and show that combining information from intrinsic biomarker approaches is a useful tool to document bat movements.</p>
Data for: Spatial heterogeneity and infection patterns on epidemic transmission disclosed by a combined contact-dependent dynamics and compartmental model
<p>Epidemics, such as COVID-19, have caused significant harm to human society worldwide. A better understanding of epidemic transmission dynamics can contribute to more efficient prevention and control measures. Compartmental models, which assume homogeneous mixing of the population, have been widely used in the study of epidemic transmission dynamics, while agent-based models rely on a network definition for individuals. In this study, we developed a real-scale contact-dependent dynamic (CDD) model and combined it with the traditional susceptible-exposed-infectious-recovered (SEIR) compartment model. </p>
Best Learned Models: End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes
<p>Best learned model learned with the dataset available <a href="http://https://doi.org/10.5281/zenodo.8033058">here</a> for the mTAN-GP, mTAN-MLP, mTAN-LTAE, and raw-LTAE.</p> <p>For further details see the pre-print article "End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes ". This article is available : <a href="https://hal.science/hal-04112115">here</a>.</p> <p>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_mtan_gp_irregular_sits">open source repository</a>.</p>
Object detection for graphical user interface: old fashioned or deep learning or a combination? - Model&Datasets
<p>This repo contains the datasets, trained models, and data splitting in ESEC/FSE 2020 "Object detection for graphical user interface: old fashioned or deep learning or a combination?" paper.</p>
A tightly coupled river-ocean model for simulating combined flood due to storm surge and river flow in coastal-urban areas
<p>Coastal flooding, resulting from storm surges or extreme river flows, causes significant causalities and damage to properties in low-lying areas. The simultaneous occurrence of river flows and storm surges, termed combined/compound events, exacerbates the flood risk compared to independent occurrences. Combined flood events are simulated with the help of hydraulic and hydrodynamic models using a loosely or tightly coupled approach. In the loosely coupled approach, a hydrodynamic model simulates storm surges first, and a hydraulic model then simulates inland flood due to river overflow considering surge as the boundary condition at the river mouth/estuary. Conversely, the tightly coupled approach involves simultaneous simulation of both river flow and storm surge by coding the mathematical representation of river and ocean flow dynamics in the same numerical model. This allows the interaction between river and ocean flows to be simulated anywhere in the combined river-ocean computational domain, making it highly relevant for simulating combined floods. However, existing models based on this approach encounter numerical instability, especially in inland regions where topography variation is steep and highly uneven. Also, such combined models are highly limited for large scale applications. Therefore, this research focuses on developing a tightly coupled 2D finite volume river-ocean model called IROMS-C2D. The developed model intends to address the limitations of the previous models and provide a stable solution framework for the simulation of combined flooding resulting from the interaction of storm surges and river flows in coastal urban areas. Further, it enhances our understanding of flood risks in coastal areas, particularly in urban settings, and facilitates the formulation of effective measures for flood control and adaptation of coastal infrastructure.</p>
A tightly coupled river-ocean model for simulating combined flood due to storm surge and river flow in coastal-urban areas
<p>Coastal flooding, resulting from storm surges or extreme river flows, causes significant causalities and damage to properties in low-lying areas. The simultaneous occurrence of river flows and storm surges, termed combined/compound events, exacerbates the flood risk compared to independent occurrences. Combined flood events are simulated with the help of hydraulic and hydrodynamic models using a loosely or tightly coupled approach. In the loosely coupled approach, a hydrodynamic model simulates storm surges first, and a hydraulic model then simulates inland flood due to river overflow considering surge as the boundary condition at the river mouth/estuary. Conversely, the tightly coupled approach involves simultaneous simulation of both river flow and storm surge by coding the mathematical representation of river and ocean flow dynamics in the same numerical model. This allows the interaction between river and ocean flows to be simulated anywhere in the combined river-ocean computational domain, making it highly relevant for simulating combined floods. However, existing models based on this approach encounter numerical instability, especially in inland regions where topography variation is steep and highly uneven. Also, such combined models are highly limited for large scale applications. Therefore, this research focuses on developing a tightly coupled 2D finite volume river-ocean model called IROMS-C2D. The developed model intends to address the limitations of the previous models and provide a stable solution framework for the simulation of combined flooding resulting from the interaction of storm surges and river flows in coastal urban areas. Further, it enhances our understanding of flood risks in coastal areas, particularly in urban settings, and facilitates the formulation of effective measures for flood control and adaptation of coastal infrastructure.</p>
Primary Organoid Models and Combined Nucleic Acids Therapeutics for Anti-HPV Treatments
ClinicalTrials.gov study NCT04278326. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Combined Model of Online Remote Interventions for Cancer-Related Cognitive Impairment
ClinicalTrials.gov study NCT04210778. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Construction of Clinical Model of Primary Sjogren's Syndrome Combined Traditional Chinese and Western Medicine
ClinicalTrials.gov study NCT06528197. IPD Sharing: NO. Countries: 1. Publications: 1.
A Combined Model Based on Spleen Stiffness, Liver Stiffness and Platelets for Assessing Portal Hypertension in Compensated Cirrhosis (CHESS2202)
ClinicalTrials.gov study NCT05251272. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.
A Human Experimental Model for Neuropathic Pain Using Combined Application of Capsaicin and Local Anesthetics
ClinicalTrials.gov study NCT01540877. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Combined Relapse Prediction Model for Resectable Non-Small Cell Patients - a Prospective Clinical Feasibility Trial
ClinicalTrials.gov study NCT06262386. IPD Sharing: Not stated. Countries: 1. Publications: 35.
Gabapentin and Donepezil Combination on Experimental Human Pain Models
ClinicalTrials.gov study NCT01485185. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Epidural Catheter Retention Depth on Labor Analgesia Performed in DPE Combined With PIEB Model
ClinicalTrials.gov study NCT06602440. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Multi-omics Model Predicts Efficacy of Preoperative Neoadjuvant Chemoradiotherapy Combined PD-1 Antibody Therapy for Locally Advanced Rectal Cancer
ClinicalTrials.gov study NCT05368051. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
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.