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575 results for “Modeling Codes”
Source codes for preprint Newmark algorithm for dynamic analysis with generalized Maxwell model
<p>Snapshot of a GitLab repository, containing Python source codes of results presented in the preprint by the same authors.</p>
A Weighting Function Model for Unsteady Open Channel Friction: Raw code and output
<p>Matlab source code and Raw Origin Lab file (.opj) for a weighting function model simulating unsteady open channel friction slopes. This material is provided as is. Methods (formulas) and Figures are explained further in the related article "A Weighting Function Model for Unsteady Open Channel Friction" by Junwei Zhou; Weimin Bao; Geoffrey R. Tick; Qing Cao; and Fanghong Ye.</p>
The source code for a new capillary and adsorption‒force model predicting hydraulic conductivity of soil during freeze‒thaw processes
<p>The source code is related to "A New Capillary and Adsorption‒Force Model Predicting Hydraulic Conductivity of Soil during Freeze‒thaw Processes" (Shufeng Qiao, Rui Ma, Yunquan Wang, Ziyong Sun, Helen Kristine French, Yanxin Wang)</p>
Reproduction package for the paper "The open-source sunbather code: Modeling escaping planetary atmospheres and their transit spectra"
<p>This is a reproduction package for the paper "The open-source sunbather code: modeling escaping planetary atmospheres and their transit spectra" by Dion Linssen, Jim Shih, Morgan MacLeod & Antonija Oklopčić (2024). It provides a front-to-end reproduction script to reproduce the results and Figures 1-5 of the paper. Figures 6&7 can be reproduced with the example notebook found in the sunbather installation.</p>
Data and code from: Improving accuracy and reproducibility of cartilage T2 mapping in the OAI dataset through extended phase graph modeling
<p>The repository contains the OAI data used to run the experiments reported in the study "Improving accuracy and reproducibility of cartilage T2 mapping in the OAI dataset through extended phase graph modeling", along with their segmentation and processed T2 maps with the different fitting algorithms.</p> <p>The <em><strong>code_repository</strong></em> folder contains a snapshot of the <a href="https://github.com/barma7/EPGfit_for_cartilage_T2_mapping">GitHub repository</a> at the time of the submission of the manuscript. Please visit the GitHub repository for the latest version. </p>
Non-hydrostatic MASNUM Ocean General Circulation Model codes and results
<p>non-hydro-masnum.rar: codes for Non-hydrostatic MASNUM Ocean General Circulation Model.</p> <p>analyze_test_section.mat: velocities in the section for the ideal experiment.</p> <p>analyze_test_variation.mat: variation of the velocity for the ideal experiment.</p> <p>res_hs_all_1.mat, res_hs_all_2.mat and res_hs_all_3.mat: hydrostatic MASNUM model simulation for the internal tides in the Northern South Chine Sea.</p> <p>res_it_all_1.mat, res_it_all_2.mat and res_it_all_3.mat: non-hydrostatic MASNUM model simulation for the internal tides in the Northern South Chine Sea.</p> <p>sst_01.mat, sst_02.mat and sst_03.mat: sea surface temperature simulation.</p>
The Dataset of Cross-projcet Code Context Model
<p>A dataset of the cross-project code context model for three open-source Eclipse projects: Mylyn, Platform, and PDE.</p>
Dataset and codes used in the manuscript entitled "A rainfall-tracking travel time distribution model to quantify mixing and storage release preference in a large shallow lake by two-year stable isotopic data"
<p>This contains the codes and dataset for the manuscript entitled "A rainfall-tracking travel time distribution model to quantify mixing and storage release preference in a large shallow lake by two-year stable isotopic data". Detailed information about the dataset is described in the Readme.txt file.</p>
Artifacts for "On Hardware Security Bug Code Fixes By Querying Large Language Models""
<p>This repository contains the benchmarks and results obtained for the work "On Hardware Security Bug Code Fixes<br> By Querying Large Language Models".<br> Follow the README.md file for more information on how to use the tools yourself.</p>
MATLAB Code for "Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients"
<p>This MATLAB code is part of the study titled <em>"Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients"</em>, which has been accepted for publication in the <em>Journal of Imaging (MDPI)</em>. The code supports image processing, feature extraction, and deep learning model training (including LSTM and RexNet) to classify pediatric patients as anemic or non-anemic based on palm, conjunctival, and fingernail images. Full study details are available in this paper:</p> <p>Berghout T. Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients. <em>Journal of Imaging</em>. 2024; 10(10):245. <a href="https://doi.org/10.3390/jimaging10100245">https://doi.org/10.3390/jimaging10100245 </a></p> <p>The datsets use in this work are:</p> <p>Asare, J. W., Appiahene, P. & Donkoh, E. (2022). Anemia Detection using Palpable Palm Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/ccr8cm22vz.1<br>Asare, J. W., Appiahene, P. & Donkoh, E. (2023). CP-AnemiC (A Conjunctival Pallor) Dataset from Ghana. Mendeley Data. https://doi.org/10.17632/m53vz6b7fx.1<br>Asare, J. W., Appiahene, P. & Donkoh, E. (2020). Detection of Anemia using Colour of the Fingernails Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/2xx4j3kjg2.1</p>
Biomod2 codes and CSV data for the ensemble model of C. marmorata
Open the record for dataset details and reuse information.
Code für die Bearbeitung der Verfügbaren Speleothem and Permafrost-Model Data für die Masterarbeit: "Speläotheme als globale Permafrostproxies"
<p>Der benutzte Code für die Bearbeitung und Darstellung der Speläothem-Datensätze und die Permafrost-Modelle. Die Datengrundlage der Speläothem-Daten und der Permafrost Modelle AWI-ESM und S21 werden für die Bearbeitung benötigt. Authoren, bzw. Ansprechpartner für die jeweilgen Daten sind im Literaturverzeichnis der Arbeit zu finden.</p>
ORCHIDEE-NLAT model code and evaluation data
<p>ORCHIDEE-NLAT model code and model evaluation data (including water discharge and total nitrogen flow)</p>
Supporting Data and Code for "Managing to Climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network"
<p>Without reliable seasonal climate forecasts, farmers and managers in other weather-sensitive sectors might adopt practices that are optimal for recent climate conditions. To demonstrate this principle, crop simulation models driven by a dense meteorological network were used to identify climate-optimal planting dates for U.S. Southern High Plains (SHP) un-irrigated agriculture. This method converted large samples of SHP growing season weather outcomes into climate-representative cotton and sorghum yield distributions over a range of planting dates. Best planting dates were defined as those that maximized median cotton lint (April 24) and sorghum grain (July 1) yields. Those optimal yield distributions were then converted into corresponding profit distributions reflecting 2005-2019 commodity prices and fixed production costs. Both crop's profitability under variable price conditions and current SHP climate conditions were then compared based on median profits and loss probability, and through stochastic dominance analyses that assumed a slightly risk-averse producer.</p>
Air parcel trajectories data generated by MIMICA code and simulation results generated by a trajectory box model
<p>The dataset includes trajectories of air parcels extracted from the large-eddy (cloud-resolving model) simulations of the deep convective clouds from the Amazon based on the soundings retrieved on April 8, 2020, April 23, 2020, and April 27, 2020, over Manaus, Brazil, as well as the results of the chemical box model simulations quantifying the transport of some atmospheric trace gases abundant in the Amazon.</p>
Code and Data: WRF v.3.9 sensitivity to land surface model and horizontal resolution changes over North America
<p>Data and code used to obtain results published in Garcia-Garcia et al 2021: WRF v.3.9 sensitivity to land surface model and horizontal resolution changes over North America. Submited to<em> Geosci. Model Dev</em>.</p>
Codes and dataset used in the manuscript entitled "Quantifying time-variant travel time distribution by multi-fidelity model in hillslope under nonstationary hydrologic conditions"
<p>This contains the codes and dataset for the manuscript entitled "Quantifying time-variant travel time distribution by multi-fidelity model in hillslope under nonstationary hydrologic conditions". Detailed information about the dataset is described in the Readme.txt file.</p>
Predicting readers' prototypical eye-movement behavior using MASC, a model of Attention in the Superior Colliculus: Stimulus materials, model code, data, and statistical analyses.
<p>The goal of the present research was to determine the role of rudimentary visuo-motor pathways, from the retina and the primary visual cortex to the superior colliculus (SC), in the guidance of human eye movement during reading. To this end, we used MASC, our model of Attention in the Superior Colliculus (Adeli et al., Journal of Neuroscience 2017), a model that relies on well-established saccade-programming principles in the SC. MASC predicts sequences of fixations over an input image by spatially integrating incoming signals in the space of the SC.</p> <p>Here, MASC computed the distribution of luminance contrast over sentences' images (visual-saliency map), after blurring it proportional to retinal eccentricity (retina transformation). It then projected the visual-saliency map into SC space, using a logarithmic afferent-mapping function (magnification factor). Input signals were averaged over retinotopically organized populations of neurons (point images) of constant size, first in the visual map and then in a spatially-registered motor map. The most active population was identified through a winner-take-all process. After jitter applied to the winning population, the next fixation location was determined using inverse efferent mapping. This sequence of events was then repeated to predict following fixation locations, but inserting after each saccade an inhibitory spatial tag (Inhibition of Saccade Return; ISR -referred to as IOR in the uploaded files). All MASC's parameters, but one, were biologically determined, using electrophysiological data in macaque; the ISR window was the one fit parameter.</p> <p>MASC was tested by comparing its predicted sequences of fixations over sentences from the French-Sentence Corpus (FSC) to the eye-movement behavior of 40 French-native speakers reading the same sentences for comprehension (Albrengues et al., Plos One 2019). Then, MASC was dissected to determine the crucial processing steps enabling prediction of human behavior (10 comparison models -see the general README file). Finally, to address crucial issues in the reading literature, i.e., the role of inter-word spacing and character print size in eye-movement guidance, MASC was additionally tested in four additional display conditions: the same sentences from the FSC, but with blank spaces between words being either filled or removed, or with the screen width angle being multiplied by 2 or 4, such that characters were larger in angular size (0.5° and 1°) than in the original experiment (0.25°). MASC's predicted effects of inter-word spacing and print size were compared to previously published data.</p> <p>All material relevant to the project is reported here, including the FSC materials (bitmap and information text files), the Matlab code for our MASC model, raw simulation data for MASC and all our comparison models, as well as MASC's simulations in the different display conditions, the scripts we developed in R to transform raw simulation data into data matrices for statistical analyses of (word-based) eye-movement behavior, the resulting data matrices for all models as well as the data matrix for FSC readers, the R-scripts for statistical comparison of oculomotor behavior between data sets and conditions, literature-review tables of previously published data (for comparison with MASC's predictions), and the R-scripts generating the figures summarizing our results.</p> <p>Further information can be found in the general README file as well as in the README files attached to each folder. The authors' respective contributions to the project, the licence attached to the included materials and their condition of use are listed in the general README file.</p> <p>A manuscript reporting and discussing these modeling data is in preparation (Vitu, F., Adeli, H. & Zelinsky, G. J.); A reference will be provided here when the manuscript appears in a journal.</p> <p>Other references to be cited:</p> <p>- For the model code: Adeli, H., Vitu, F., & Zelinsky, G. J. (2017). A model of the superior colliculus predicts fixation locations during scene viewing and visual search. Journal of Neuroscience, 37(6), 1453-1467. http://www.jneurosci.org/content/37/6/1453</p> <p>- For FSC materials and data: Albrengues, C., Lavigne, F., Aguilar, C., Castet, E., & Vitu, F. (2019). Linguistic processes do not beat visuo-motor constraints, but they modulate where the eyes move regardless of word boundaries: Evidence against top-down word-based eye-movement control during reading. PLoS ONE 14(7): e0219666. https://doi.org/10.1371/journal.pone.0219666<br> </p>
Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding
<p>Datasets for the paper "Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding"</p>
Automating Code Review Activities 2.0 (datasets, models and results)
<p>Resources related by the research work <em>"Automating Code Review Activities 2.0".</em></p> <ul> <li><strong>automating_code_review.zip</strong> contains the material to successfully run our Colab notebooks;</li> <li><strong>dataset.zip </strong>contains all the preprocessed datasets used in our work;</li> <li><strong>generate_prediction.zip </strong>contains the material to successfully generate predictions using a T5 model chekpoint;</li> <li><strong>models.zip</strong> contains the (best) checkpoints of the fine-tuned T5 models;</li> <li><strong>results.zip</strong> contains our results;</li> <li><strong>tokenizer.zip</strong> contains the Sentencepiece model and vocabulary trained on our pre-training dataset.</li> </ul> <p>More information in the replication package of our work: <a href="https://github.com/CodeReviewAutomation/code_review_automation">code_review_autmoation</a></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.