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310 results for “State Model”
Validation of the predictive accuracy of health-state utility values based on the Lloyd model for metastatic or recurrent breast cancer in Japan
<p>Although there is a lack of data on health-state utility values (HSUVs) for calculating quality-adjusted life years in Japan, Cost-utility analysis has been introduced by the Japanese government to inform decision-making in the medical field since 2016. This study aimed to determine whether the Lloyd model which was a predictive model of HSUVs for metastatic breast cancer (MBC) patients in the United Kingdom can accurately predict actual HSUVs for Japanese patients with MBC. The prospective observational study, followed by the validation study of the clinical predictive model.<b> </b>Forty-four Japanese patients with MBC were studied at 336 survey points. This study consisted of two phases. In the first phase, we constructed a database of clinical data prospectively and HSUVs for Japanese patients with MBC to evaluate the predictive accuracy of HSUVs calculated using the Lloyd model. In the second phase, Bland-Altman analysis was used to determine how accurately predicted HSUVs (based on the Lloyd model) correlated with actual HSUVs obtained using the EuroQol 5-Dimension 5-Level questionnaire, a preference-based measure of HSUVs in patients with MBC. In the Bland-Altman analysis, the mean difference between HSUVs estimated by the Lloyd model and actual HSUVs, or systematic error, was -0.106. The precision was 0.165. The 95% limits of agreement ranged from -0.436 to 0.225. The t value was 4.6972, which was greater than the t value with 2 degrees of freedom at the 5% significance level (p=0.425). There were acceptable degrees of fixed and proportional errors associated with the prediction of HSUVs based on the Lloyd model for Japanese patients with MBC. We recommend that sensitivity analysis be performed when conducting cost-effectiveness analyses with HSUVs calculated using the Lloyd model.</p>
Videos of dynamic rupture in models of the 2004 Sumatra-Andaman earthquake published in Madden et al. (2022) "The state of pore fluid pressure and 3D megathrust earthquake dynamics" JGR-Solid Earth
<p>Videos of dynamic rupture from models based on the 2004 Sumatra-Andaman earthquake presented in Madden, E. H., T. Ulrich, A.-A. Gabriel (2022). The state of pore fluid pressure and 3D megathrust earthquake dynamics, Journal of Geophysical Research - Solid Earth, <a href="https://doi.org/10.1029/2021JB023382">https://doi.org/10.1029/2021JB023382</a>. (Previous preprint available at: <a href="https://doi.org/10.1002/essoar.10508297.1">https://doi.org/10.1002/essoar.10508297.2</a>.)</p> <p> </p>
United States Environmentally-Extended Input-Output (USEEIO) Modeling Framework
<p>This is a snapshot of the <a href="https://github.com/USEPA/useeio">USEEIO modeling framework</a> repository that is compilation of a README, versioning scheme, and a python script supplemented with a to convert a USEEIO model generated from <a href="https://github.com/USEPA/useeior">useeior </a>in the API format into openLCA schema JSON-LD files. This is not source code for generating the USEEIO v2.0 model.</p>
Intercomparison of Convective-Aggregation States with two Cloud Resolving Models: DATASET
<p>Radiative-Convective Equilibrium (RCE) is an important modeling paradigm for the tropical atmosphere. In this paradigm, cloud clustering can occur spontaneously, affecting the energy budget of the atmosphere. Here, two models, run in RCE, exhibiting this convective aggregation have been compared with each other and with the results of the Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP). The two models studied, the SAM (System for Atmospheric Modeling) and the ARPS (Advanced Regional Prediction System), are different in the physical and numerical formulation, allowing us to compare the sensitivity to processes related to the phenomenon of convective organization. In General, the two models present similarities in what concerns precipitation, warming, and drying of the atmosphere and anvil cloud area reduction. All these factors are also within the spread of the RCEMIP values. However, the two models differ both in the convective organization feedback and in the degree of organization. SAM is strongly organized and ARPS is weakly organized. SAM achieves convective organization through clouds-radiative feedback and ARPS achieves it through moisture-convection feedback. These differences can be traced back to the interaction between the microphysics and the sub-cloud layer properties. We suggest that when studying climate sensitivity, climate models should include both types of convective organization mechanisms.</p>
Intercomparison of Convective-Aggregation States with two Cloud Resolving Models: DATASET
<p>Radiative-Convective Equilibrium (RCE) is an important modeling paradigm for the tropical atmosphere. In this paradigm, cloud clustering can occur spontaneously, affecting the energy budget of the atmosphere. Here, two models, run in RCE, exhibiting this convective aggregation have been compared with each other and with the results of the Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP). The two models studied, the SAM (System for Atmospheric Modeling) and the ARPS (Advanced Regional Prediction System), are different in the physical and numerical formulation, allowing us to compare the sensitivity to processes related to the phenomenon of convective organization. In General, the two models present similarities in what concerns precipitation, warming, and drying of the atmosphere and anvil cloud area reduction. All these factors are also within the spread of the RCEMIP values. However, the two models differ both in the convective organization feedback and in the degree of organization. SAM is strongly organized and ARPS is weakly organized. SAM achieves convective organization through clouds-radiative feedback and ARPS achieves it through moisture-convection feedback. These differences can be traced back to the interaction between the microphysics and the sub-cloud layer properties. We suggest that when studying climate sensitivity, climate models should include both types of convective organization mechanisms.</p>
Data access - Operando characterization and theoretical modelling of metal|electrolyte interphase growth kinetics in solid-state-batteries - part I: experiments
<p>The zip file contains XPS and EIS data used in parts 1 and 2 of the publication entitled: "New insights into the kinetics of metal|electrolyte interphase growth in solid-state-batteries via an <em>operando</em> XPS analysis"</p> <p>Folders description: </p> <p>- "XPS" contains three subfolders with the XPS data and fitting models (in .vms format, CasaXPS) corresponding to the reference Na metal sample, the Na|NZSPas interface and Na|NZSPpolished interface</p> <p>- "EIS" contains two subfolders with the EIS data from the Na|NZSPas and Na|NZSPpolished symmetrical cells. The raw data is stored as .mpr files (EC-lab), and the fitted data is stored as .eis3 files (RelaxIS)</p>
Prediction of the ground state for indenofluorene-type systems with Clar's π-sextet model
<p>This dataset contains the computational data associated with "Prediction of the ground state for indenofluorene-type systems with Clar's π-sextet model". </p>
Artifact for 'Unfolding State Variables Improves Model Checking'
<p>This is a reproduction package for the experiments that were performed as part of the work 'Unfolding State Variables Improves Model Checking'.</p>
Model simulation output for New Configuration for Impact of microphysics and convection schemes on the mean-state and variability of clouds and precipitation in the E3SM Atmosphere Model
<p>Simulation output from the new configuration model used in the manuscript Impact of microphysics and convection schemes on the mean-state and variability of clouds and precipitation in the E3SM Atmosphere Model</p>
Data and materials for the "Steady-State Mixing State of Black Carbon Aerosols from a Particle-Resolved Model"
<p>Data and sripts for the "Steady-State Mixing State of Black Carbon Aerosols from a Particle-Resolved Model"</p>
Data from: Central place foragers and moving stimuli: a hidden-state model to discriminate the processes affecting movement
1. Human activities can influence the movement of organisms, either repelling or attracting individuals depending on whether they interfere with natural behavioural patterns or enhance access to food. To discern the processes affecting such interactions, an appropriate analytical approach must reflect the motivations driving behavioural decisions at multiple scales. 2. In this study, we developed a modelling framework for the analysis of foraging trips by central place foragers. By recognising the distinction between movement phases at a larger scale and movement steps at a finer scale, our model can identify periods when animals are actively following moving attractors in their landscape. 3. We applied the framework to GPS tracking data of northern fulmars Fulmarus glacialis, paired with contemporaneous fishing boat locations, to quantify the putative scavenging activity of these seabirds on discarded fish and offal. We estimated the rate and scale of interaction between individual birds and fishing boats and the interplay with other aspects of a foraging trip. 4. The model classified periods when birds were heading out to sea, returning towards the colony or following the closest boat. The probability of switching towards a boat declined with distance and varied depending on the phase of the trip. The maximum distance at which a bird switched towards the closest boat was estimated around 35 km, suggesting the use of olfactory information to locate food. Individuals spent a quarter of a foraging trip, on average, following fishing boats, with marked heterogeneity among trips and individuals. 5. Our approach can be used to characterise interactions between central place foragers and different anthropogenic or natural stimuli. The model identifies the processes influencing central place foraging at multiple scales, which can improve our understanding of the mechanisms underlying movement behaviour and characterise individual variation in interactions with a range of human activities that may attract or repel these species. Therefore, it can be adapted to explore the movement of other species that are subject to multiple dynamic drivers.
Data from: Developing state and transition models of floodplain vegetation dynamics as a tool for conservation decision-making: a case study of the Macquarie Marshes Ramsar wetland
1. Floodplain vegetation states (communities) exhibit spatiotemporal dynamics in vegetation structure and composition, which reflect unique hydrological and connectivity patterns. Shifts in inundation regimes can drive succession and establish new stable states, determined by the magnitude and duration of the hydrological perturbation. 2. We aimed to develop a modelling approach that is able to capture ecosystem dynamics, identify and quantify the main drivers of change, and provide a tool for conservation decision-making. We developed state and transition models for floodplain vegetation states based on surveys in 1991 and 2008 in the Macquarie Marshes (Australia), a Ramsar wetland of international importance. We used a Bayesian logistic regression approach to model state and transitions between vegetation states and investigated how flood frequency, distance to stream and fire frequency were associated with vegetation dynamics during this period. 3. During 1991–2008, significant transitions have occurred towards drier states. Semi-permanent wetland vegetation had the lowest persistence probability (ppsis = 0·456) and a significant threshold response of transitioning to terrestrial vegetation (ptran = 0·505). Transition to drier states was driven by lower inundation probabilities followed by increased fire probability, and distance to nearest stream. 4. Using developed models, we predicted persistence probabilities of vegetation states under an unregulated (i.e. no dams or diversions) and regulated water availability system. Under a regulated system, semi-permanent wetland vegetation had an average persistence of ppsis = 0. 67 and 0·08 in the northern and southern sections of the nature reserve, respectively. Under an unregulated system, the predicted persistence of semi-permanent wetland vegetation was considerably higher: ppsis = 0·87 and 0·38, respectively. 5. Synthesis and applications. Developing quantitative models of state transitions significantly improved our understanding of ecosystem dynamics, identifying sensitive indicators for monitoring and thus supporting conservation decision-making. This helps managers understand potential trajectories of change in ecosystems in response to management options. For example, increasing environmental flows in the Macquarie Marshes is predicted to shift the community towards more of a wetland than the terrestrial state, resulting from river regulation. State and transition models identified how key ecological assets respond to drivers of change, particularly where these can be managed. This is critical for ensuring that all ecosystem components are managed and that these do not shift into undesirable states.
Optimal Stopover Model: a state dependent habitat selection model for staging passerines during migration
<p>1. During their seasonal migration, birds stage in areas comprising stopover sites of varying quality. Given that migrating birds have a limited information about their environments, they may land at a low-quality stopover site in which their fuel deposition rate (FDR) is low. Birds landing at such sites, should decide either to extend their stopover duration or to quickly depart in search for a better site. These decisions, however, strongly depend on their body condition upon landing.</p> <p>2. To understand the decision-making process of passerines within a stopover area, comprising stopover sites of varying quality, prior to the crossing of a large ecological barrier, we constructed a state-dependent habitat selection model. The model assumes that even if migrating birds have an expectation of encountered area quality, they cannot control for their initial landing site. Once landing, movement between a low to high quality stopover site will occur only if the body condition of these birds is high to the extent that they can entail the energetic cost of movement. Birds in the model aim to maximize their fuel load at the end of the stopover period, to suffice for successfully crossing a large ecological barrier.</p> <p>3. The model is based on empirical data on autumn migrating Blackcaps (Sylvia atricapilla), collected at two important stopover sites in the Negev desert of Israel. Migrating passerines staging at these two sites differ in their fuel deposition rate and body condition. The model shows that the optimal behavior when arriving at a low-quality stopover site is to abandon it quickly. However, as lean individuals cannot entail the costs of searching for an alternative site, they have no other choice but to stay there even if their chances to successfully cross the Sahara Desert ahead are low.</p> <p>4. Our model can be applied to other ecological systems. Proper use of this model may allow good assessment of stopover site quality, as indicated by the bird's fuel deposition rate, regardless of specific site characteristics. Hence it can help applying targeted management decisions regarding the maintenance of stopover sites or establishment of new ones.</p>
Modeling pulsed evolution and time-independent variation improves the confidence level of ancestral and hidden state predictions
<p><span><span><span><span><span><span><span><span><span><span>Ancestral state reconstruction is not only a fundamental tool for studying trait evolution, but also very useful for predicting the unknown trait values (hidden states) of extant species. A well-known problem in ancestral and hidden state predictions is that the uncertainty associated with predictions can be so large that predictions themselves are of little use. Therefore, for meaningful interpretation of predicted traits and hypothesis testing, it is prudent to accurately assess the uncertainty of the predictions. Commonly used constant-rate Brownian motion (BM) model fails to capture the complexity of tempo and mode of trait evolution in nature, making predictions under the BM model vulnerable to lack-of-fit errors from model misspecification. Using empirical data (mammalian body size and bacterial genome size), we show that the distribution of residual Z-scores under the BM model is neither homoscedastic nor normal as expected. Consequently, the 95% confidence intervals (CIs) of predicted traits are so unreliable that the actual coverage probability ranges from 33% (strongly permissive) to 100% (strongly conservative). Alternative methods such as BayesTraits and StableTraits that allow variable rates in evolution improve the predictions but are computationally expensive. Here we develop RasperGade, a method of ancestral and hidden state prediction that uses the Levy process to explicitly model gradual evolution, pulsed evolution and time-independent variation. Using the same empirical data, we show that RasperGade outperforms both BayesTraits and StableTraits and is orders-of-magnitude faster. Our results suggest that, when predicting the ancestral and hidden states of continuous traits, the tempo and mode of evolution should always be assessed and the quality of confidence estimates should always be examined.</span></span></span></span></span></span></span></span></span></span></p>
Intercomparison of Convective-Aggregation States with two Cloud Resolving Models: DATASET
<p>Radiative-Convective Equilibrium (RCE) is an important modeling paradigm for the tropical atmosphere. In this paradigm, cloud clustering can occur spontaneously, affecting the energy budget of the atmosphere. Here, two models, run in RCE, exhibiting this convective aggregation have been compared with each other and with the results of the Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP). The two models studied, the SAM (System for Atmospheric Modeling) and the ARPS (Advanced Regional Prediction System), are different in the physical and numerical formulation, allowing us to compare the sensitivity to processes related to the phenomenon of convective organization. In General, the two models present similarities in what concerns precipitation, warming, and drying of the atmosphere and anvil cloud area reduction. All these factors are also within the spread of the RCEMIP values. However, the two models differ both in the convective organization feedback and in the degree of organization. SAM is strongly organized and ARPS is weakly organized. SAM achieves convective organization through clouds-radiative feedback and ARPS achieves it through moisture-convection feedback. These differences can be traced back to the interaction between the microphysics and the sub-cloud layer properties. We suggest that when studying climate sensitivity, climate models should include both types of convective organization mechanisms.</p>
Dataset of Flow Velocity Prediction in Vegetated Alluvial Channels Comparing Empirical and State-of-the-art Hybrid Machine Learning Models
<p>We compiled 447 datasets from different sources and lab- and field-based measurements. These datasets included Einstein and Banks (1950), Fenzl (1962), Kouwen et al. (1969), Ree and Crow (1977), Murota (1984), Tsujimoto and Kitamura (1990), Tsujimoto (1991), Tsujimoto (1993), Shimizu (1994), Dunn et al. (1996), Ikeda and Kanazawa (1996), Meijer (1998), Jarvela (2002), Rowinski and Kubrak (2002), Stone and Shen (2002), Poggi et al. (2004), Carollo et al. (2005), and Murphy et al. (2007).</p>
Data associated with Cell Reports publication: Dura-Bernal et al. 2023, "Multiscale model of primary motor cortex circuits predicts in vivo cell type-specific, behavioral state-dependent dynamics"
<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data. The source code for the associated M1 model and data analysis can be found here: https://github.com/suny-downstate-medical-center/M1_NetPyNE_CellReports_2023</p> <p>Please download the data_v2.zip file, which contains the most updated and complete version of the data.</p> <p>For more information please contact: salvador.dura-bernal@downstate.edu </p>
Mixed model-based deconvolution of cell-state abundances along a one-dimensional trajectory [csd-eQTL]
<p><strong>README:</strong></p> <p>The full summary data of the cell-state-dependent eQTLs for GTEx Esophagus Mucosa (n=497) are stored in the .parquet format.</p> <p>An example of the file name:</p> <p><strong>"GTEx_Esophagus_Mucosa_bin1.cis_qtl_pairs.1.parquet.gz"</strong> means the summary data of csd-eQTLs for bin1 of chromosome 1.</p>
A sea state dependent gas transfer velocity for CO$_2$ unifying theory, model and field data
<p>Dataset for "A sea state dependent gas transfer velocity for CO2 unifying theory, model and field data"</p> <p>WaveWatch III simulated significant wave height (Hs, unit:m), volume of entrained air ('wva', unit m/s), 10-meter wind vector ( 'uwnd','vwnd', unit, m/s) for 9 datasets from 11 cruises.</p> <p>The information of dataset is shown in name of each file.</p>
Simulations of 7Be and 10Be with the GEOS-Chem global model v14.0.2 using state-of-the-art production rates
<p>Data repository for the paper: "Simulations of <sup>7</sup>Be and <sup>10</sup>Be with the GEOS-Chem global model v14.0.2 using state-of-the-art production rates"</p> <p>Created by Minjie Zheng (minjie.zheng@env.ethz.ch)</p> <p><strong>The files, model_output_LP67.zip, model_output_P16.zip and model_output_P16spa.zip are the model outputs based on the LP67 production rate, P16 production rate and P16spa production rate. </strong></p> <p><strong>GEOS-Chem14.0.2_Be7Be10.zip includes following folders</strong></p> <p><strong>-> "Model_modified" folder</strong><br> - GEOS-Chem 14.0.2: The folder contains a complete code directory of GEOS-Chem v14.0.2. Details for this see the website: https://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_14.0.2<br> - hcox_gc_RnPbBe_mod_P16.F90: This file replaces HEMCO/Extensions/hcox_gc_RnPbBe_mod.F90 under GEOS-Chem v14.0.2 code directory to read the updated Be7 and Be10 production file. Search 'mzheng' in the file for modifications.</p> <p><strong>-> "ZHENG_BE7BE10" folder includes updated global <sup>7</sup>Be and <sup>10</sup>Be production rates</strong><br> - BE7BE10_P16spa_4x5: Netcdf files for the <sup>7</sup>Be and <sup>10</sup>Be production rates from Poluianov et al., (2016) production model using the solar modulations from Herbst et al., (2017) and geomagnetic cut-off rigidity from Copeland (2018)<br> - BE7BE10_P16_4x5: Netcdf files for the <sup>7</sup>Be and <sup>10</sup>Be production rates from Poluianov et al., (2016) production model using the solar modulations from Herbst et al., (2017) and geomagnetic cut-off rigidity approximated by the Stoermer equation</p> <p> </p> <p> </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.