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575 results for “Modeling Codes”
Data and code for: Fixed effects or random effects in statistical models? fewer than five levels of a grouping factor
<p>This is code (and simulated data from that code) to assess how sample size and the numbers of levels of random effects influence parameter estimates of fixed effects in linear mixed-effects models. </p>
Source code for dynamic models and simulations of mate sampling behavior
<p>Theory predicts that the strength of sexual selection (i.e., how well a trait predicts mating or fertilization success) should increase with population density, yet empirical support remains mixed. We explore how this discrepancy might reflect a disconnect between current theory and our understanding of the strategies individuals use to choose mates. We demonstrate that the density-dependence of sexual selection predicted by previous theory arises from the assumption that individuals automatically sample more potential mates at higher densities. We provide an updated theoretical framework for the density-dependence of sexual selection by (1) developing models that clarify the mechanisms through which density-dependent mate sampling strategies might be favored by selection and (2) using simulations to determine how sexual selection changes with population density when individuals use those strategies. We find that sexual selection may increase strongly with density if sampling strategies change adaptively in response to density-dependent sampling costs, whereas within-individual plasticity in sampling over time (e.g., due to adaptation to increasing sampling costs as the breeding season progresses) produces weaker density-dependent sexual selection. Our findings suggest that density-dependence of sexual selection depends on the ecological context in which mate sampling has evolved.</p>
Forecasting suppression of invasive Sea Lamprey in Lake Superior: data and code for Bayesian forecast model
<p>Resource managers frequently are tasked with mitigating or reversing adverse effects of invasive species through management policies and actions. In Lake Superior, of the Laurentian Great Lakes, invasive sea lamprey populations are suppressed to protect valuable fish stocks. However, the relationship between choice of long-term control strategy and the future chance of achieving the suppression target is unclear.</p> <p>Using a 60+ year time-series of suppression effort and monitoring data from 50 assessment sites located on Lake Superior tributaries, we developed a Bayesian state-space model to forecast the probability of suppressing lamprey below the suppression target.</p> <p>With annual application of lampricide (i.e., lamprey-specific pesticide) at historical mean levels, we forecasted a 15% chance of achieving the Lake Superior sea lamprey suppression target in 2040.</p> <p>Increasing lampricide effort and/or supplementing lampricide control with age-1 recruitment reduction increased suppression chance. Annual application of the maximum historical lampricide effort resulted in a 50% predicted chance of achieving the target, annual application of the mean historic lampricide effort plus a 40% reduction in recruitment resulted in a 54% chance, and the maximum amount of effort considered (maximum historic lampricide and 60% reduction in recruitment) resulted in a 94% chance.</p> <p><em><a>Policy </a>implications</em>. <a>We</a> developed a simulation model from a robust, long-term monitoring dataset that improves understanding of why long-term sea lamprey suppression objectives have been difficult to achieve in Lake Superior. Furthermore, the model provides a means to gauge efficacy of sea lamprey control policy and action scenarios based on forecasted chance of achieving the suppression target. Creating processes for iteratively refining our forecasting model with stakeholder and technical-expert input and integration with a decision analysis framework could strengthen the link between ecological knowledge obtained from long-term monitoring and invasive sea lamprey management.</p>
Code of "Inclusion of flood diversion canal operation in the H08 hydrological model with a case study from the Chao Phraya River basin: model development and validation"
<p>Code used to prepare a paper entitled "Inclusion of flood diversion canal operation in the H08 hydrological model with a case study from the Chao Phraya River basin: model development and validation" which was submitted to Hydrology and Earth System Sciences.</p>
Exploring Hierarchy and Dependency of Rules for Consistency Checking Between Code and Model (Evaluation Data)
<p>This repository contains the data related to the protocol and results of the evaluation conducted on the HiDeoCR approach. </p>
Datasets and Code for "A Gaussian process model-guided surface polishing process in additive manufacturing"
<p>These are the datasets and computer code for reproducing the results in Jin, Iquebal, Bukkapatnam, Gaynor, and Ding, 2020, “A Gaussian process model-guided surface polishing process in additive manufacturing.” <em>ASME Transactions, Journal of Manufacturing Science and Engineering</em>, Vol. 142(1), pp. 011003.1–011003.12.</p>
Datasets and code of the manuscript 'Insights into the Aerodynamic versus Radiometric Surface Temperature Debate in Thermal-based Evaporation Modeling'
<p>This contains the datasets and codes that were used to generate the results and discussions in the manuscript</p>
Supplementary data and code of the manuscript: "Strong evidence for the adaptive walk model of gene evolution in Drosophila and Arabidopsis"
<p>This repository contains all data tables ad code to reproduce the analysis performed in "Strong evidence for the adaptive walk model of gene evolution in Drosophila and Arabidopsis". </p>
Dataset and code for "Modelling the potential for peat-block transplants to restore industrially contaminated Sphagnum peatlands"
<p>Code to run simulations and dataset from HYDRUS-1D simulations used in the revised manuscript titled "Modelling the potential for peat-block transplants to restore industrially contaminated Sphagnum peatlands" submitted to Ecological Engineering</p>
Trained models and code accompanying 'Downscaling using Deep Convolutional Autoencoders, a case study for South East Asia'
<p>Trained models and code accompanying 'Downscaling using Deep Convolutional Autoencoders, a case study for South East Asia'.</p>
Data and code: Bayesian Multi-level model calibration of the SPASS phenology model for silage maize
<p>Data and code supporting the research article: Bayesian Multi-level model calibration of the SPASS phenology model for silage maize - M. Viswanathan, A. Scheidegger, T. Streck, S. Gayler, T.K.D. Weber. This includes R code for the implementation of the SPASS phenology maize model; Jags in R to implement the Bayesian multi-level models .</p>
Data and code availibility for the paper "Ice-nucleating agents in sea spray aerosol identified and quantified with a holistic multi-modal freezing model" by Alpert et al.
<p>The data and codes used in the paper "Ice-nucleating agents in sea spray aerosol identified and quantified with a holistic multi-modal freezing model" by Alpert et al., published in <em>Science Advances</em> are included in this collection. Detailed descriptions of the files are given in the readme file.</p>
LaMEM source code and input files corresponding to Present‐day upper‐mantle architecture of the Alps: Insights from data‐driven dynamic modelling
<p>This repository contains LaMEM source code and input files for the models presented in Kumar, A., Cacace, M., Scheck-Wenderoth, M., Götze, H.-J., & Kaus, B. J. P. (2022). Present-day upper-mantle architecture of the Alps: Insights from data-driven dynamic modeling. Geophysical Research Letters, 49, e2022GL099476. https://doi. org/10.1029/2022GL099476</p>
Data and codes for: A link model approach to identify congestion hotspots
<p>Congestion emerges when high demand peaks put transportation systems under stress. Understanding the interplay between the spatial organization of demand, the route choices of citizens, and the underlying infrastructures is thus crucial to locate congestion hotspots and mitigate the delay. Here we develop a model where links are responsible for the processing of vehicles, which can be solved analytically before and after the onset of congestion, and provide insights into the global and local congestion. We apply our method to synthetic and real transportation networks, observing a strong agreement between the analytical solutions and the Monte Carlo simulations, and a reasonable agreement with the travel times observed in 12 cities under congested phase. Our framework can incorporate any type of routing extracted from real trajectory data to provide a more detailed description of congestion phenomena and could be used to dynamically adapt the capacity of road segments according to the flow of vehicles, or reduce congestion through hotspot pricing.</p>
Additonal material for the dissertation "An Accelerated Solution Method for Two-Stage Stochastic Models in Disaster Management": Data, MATLAB codes and results
<p>File "DataImport" contains a "ReadMe" file, raw data for all case studies in Excel and the MATLAB code "ImportData.m" importing Excel data into MATLAB</p> <p>File "LShaped" contains a "ReadMe" file, all data in the form of matrices and the MATLAB code "LShaped_MultiCut.m" solving all case studies via the standard or accelerated L-shaped method using a multi-cut approach</p> <p>File "Results" contains a "ReadMe" file, results of all case studies and computation time required by Gurobi, der standard L-shaped method and accelerated L-shaped method</p>
Variability of Global Fire Emissions - Data and Model Code
<p>Netcdf files including all relevant data for the manuscript entitled "Trends and variability of global fire emissions due to historical anthropogenic activities", submitted to Global Biogeochemical Cycles in 2017. </p> <p>FINALv2_presentday_2002-2009.nc: Monthly fire area burned and carbon emissions data from FINAL.2 for the years 2002 through 2009</p> <p>FINALv2C_*_1700-2009.ts.nc: Historical time series of monthly area burned and carbon emissions for natural, secondary, crop and pasture land cover for years 1700 to 2009</p> <p>vegn_fire.F90: The main module of FINAL.2 in the GFDL LM3</p>
Polytopic autoencoders for very low-dimensional parametrizations of fluid flow models [source code]
<p>J. Heiland & Y. Kim, 'Polytopic autoencoders for very low-dimensional parametrizations of fluid flow models', GAMM 2024</p>
The code and data for the paper entitled 'Facilitating Efficient Discovery: A GUI-Oriented Approach for Exploring Functionality using Machine Learning Model'
<h2><strong>Code</strong></h2> <p><strong><span>dataCollection.py</span></strong></p> <p><span>The collection of app data is primarily accomplished by processing and storing XML files and screenshots of the app. Firstly, the uiautomator is utilized to connect to the smart device and obtain screenshots and XML files. Subsequently, the XML files are analyzed to identify clickable functions within the GUI, and the textual information contained within the functions, along with their specific locations, is extracted. Finally, the Python file also includes handling of interface elements, such as determining if elements are obscured and verifying the legitimacy of the text.</span></p> <p><strong><span>tagData.py</span></strong></p> <p><span>The main implementation involves volunteers annotating app functionalities, including HTML generation, user data analysis, and retrieval. Flask framework is employed, presenting one GUI to the user each time while randomly prompting them to click on three functionalities. Ultimately, the time taken by users to locate these three functionalities is collected.</span></p> <p><strong><span>userPersonalization.py</span></strong></p> <p><span>Separating out the data annotated by each user facilitates personalized analysis. This process involves extraction, storage, and loading of individual user annotations.</span></p> <p><strong><span>dataPreprocessing.py</span></strong></p> <p><span>For a user-annotated functionality, completing the conversion from user time to either "hard-to-find" or "easy-to-find" involves several steps. First, the functionalities are vectorized, extracting relevant parameters from the XML files and computing their correlation with the time users spent searching for the functionalities. These parameters are then normalized to obtain feature vectors for the functionalities. Additionally, an initial determination is made regarding whether the annotated functionalities are "hard-to-find" or "easy-to-find" for each user. Subsequently, clustering is performed on all annotated data from users, and based on the clustering results, the outcomes are filtered and adjusted.</span></p> <p><strong><span>difficultFindClassifier.py</span></strong></p> <p><span>Train the classifier and use it to predict "hard-to-find" functionalities, then display the results.</span></p> <h2><span>Data</span></h2> <p><span>The data is located in the "static" folder:</span></p> <p><span>- The "persistentData" folder contains the trained classifier.</span></p> <p><span>- The "picture" folder contains screenshots of the app.</span></p> <p><span>- The "requestTime" folder stores data for when volunteers annotate only one function in a GUI.</span></p> <p><span>- The "threeResponseTime" folder saves data for when volunteers annotate three functions in a GUI.</span></p> <p><span>- The "userData" folder stores personalized user data.</span></p> <p><span><span>- The "xml_information" folder stores XML files of the app.</span></span></p>
Artifacts for paper "Code Refinement with the Assistance of Conversation-Aided Large Language Models"
<p>All the conversation data collected and the source code for our method are inside the uploaded zip file.</p>
Enhancing Climate Model Performance through Improving Volcanic Aerosol Representation Dataset and model source codes
<p>Enhancing Climate Model Performance through Improving Volcanic Aerosol Representation: Dataset and codes used in the study</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.