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5,805 results for “Data model”
Growth Dynamics and System Models for the Restaurant Industry: Data and Analysis from Taiwanese Chains
<p><span>This dataset includes raw and curated data, system dynamics models, and feedback loop diagrams used in the study of growth dynamics in the restaurant industry, focusing on Taiwanese chains. The data supports the findings presented in the paper "The Growth Dynamics of the Restaurant Industry from Single Store to Chain Store in Taiwan: A Systems Thinking Perspective.”</span></p>
Data for "Revisiting the reanalysis-model discrepancy in Southern Hemisphere winter storm track trends"
<p>The dataset supporting the conclusion of the submitted paper is uploaded here.</p> <p>The data are labeled after each figure. The npz files include data required to reproduce our results in python arrays.</p>
Article: Modeling Mouse ICM - Partial Data Bank
<p>Our study presents a spatial-stochastic model for the gene regulatory network (GRN) and the signaling pathway governing cell-fate differentiation during early mouse embryogenesis, specifically at the blastocyst stage. Departing from biophysics-based models of gene regulation, we perform stochastic simulations of the biochemical processes driving early mouse embryogenesis both at the cell and tissue level. Combining these simulations with state-of-the-art AI-aided inference techniques, we successfully parameterize our model, replicating key experimental observations and providing mechanistic insights into the biochemical interactions giving rise to them. Thanks to the stochastic nature of our approach, we quantify the high robustness of ICM specification to various kinds of noise, and provide quantitative predictions for the effects of diverse experimentally testable perturbations. Altogether, we provide a deeper understanding of the intricate mechanisms driving early cell-fate decisions in mouse embryogenesis, highlighting the synergy of local cellular and broader tissue-scale interactions that shape development.</p>
Model and observation data for paper "Variability of the bottom boundary layer induced by the dynamics of the cross-isobath transport over a variable shelf"
<p>Model and observation data for paper "Variability of the bottom boundary layer induced by the dynamics of the cross-isobath transport over a variable shelf"</p>
Pylake example: Data to fit the Twistable Worm-Like Chain Model
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Data from: Phylogenomic inference and demographic model selection suggest peripatric separation of the cryptic steppe ant species Plagiolepis pyrenaica stat. rev.
<p>The ant <em>Plagiolepis taurica</em> Santschi, 1920 (Hymenoptera, Formicidae) is a typical species of the Eurasian steppes, a large grassland-dominated biome that stretches continuously from Central Asia to Eastern Europe and is represented by disjunct outposts also in Central and Western Europe. The extent of this biome has been influenced by the Pleistocene climate, and steppes expanded recurrently during cold stages and contracted in warm stages. Consequently, stenotopic steppe species such as <em>P. taurica</em> repeatedly went through periods of demographic expansion and severe isolation. Here, we explore the impact of these dynamics on the genetic diversification within <em>P. taurica</em>. Delimitation of <em>P. taurica</em> from other Plagiolepis species has been unclear since its initial description, which raised questions on both its classification and its spatiotemporal diversification early on. We re‐evaluate species limits and explore underlying mechanisms driving speciation by using an integrative approach based on genomic and morphometric data. We found large intraspecific divergence within <em>P. taurica</em> and resolved geographically coherent western and eastern genetic groups, which likewise differed morphologically. A morphometric survey of type material showed that Plagiolepis from the western group were more similar to <em>P. barbara</em> pyrenaica Emery, 1921 than to <em>P. taurica</em>; we thus lift the former from synonymy and establish it as separate species, <em>P. pyrenaica</em> stat. rev. Explicit evolutionary model testing based on genomic data supported a peripatric speciation for the species pair, probably as a consequence of steppe contraction and isolation during the mid‐Pleistocene. We speculate that this scenario could be exemplary for many stenotopic steppe species, given the emphasized dynamics of Eurasian steppes.</p>
Data for: Global GPP estimates at 8-day/monthly/annual temporal resolution generated by the PTEC model
<p>PTEC provides spatiotemporally estimates of Gross Primary Productivity based on a two-leaf light use efficiency model incorporating plant water status and phenology. PTEC integrates a set of satellite and climate variables within a parsimonious modeling framework to be simple yet robust and grounded on eco-physiological principles. Available at 8-day/monthly/annual and 0.05° resolution from 2001 to 2021, PTEC shows superior performance compared to benchmark products.</p>
Abundance Trend Indicator - Models, Prediction, Stacked Environmental Data and Training Set Similarity
<p># Readme</p> <p>These trained models can be used to predict the abundance trends of New Zealand's forest species and can be used together with the code in https://github.com/lnilya/abundance-trend-indicator</p> <p>Since the process of using the models requires coding expertise and some setting up, please make sure to reach out to ilya.shabanov@vuw.ac.nz for any questions. All files will require the code in the repository to be read and used. </p> <p>If you want to explore the results generated with these models, please visit https://ati-nz-predictions-7e6f3d514735.herokuapp.com/ for a user-friendly, interactive UI.</p> <p>## Contents</p> <p>_models: Contains the trained models (Artificial Neural Network (ANN), Random Forest (RF), SVMW (Support vector machine) and GLM (logistic regression)) at different degrees of noise filtering, different datasets and variable sets. The model files also contain test and training scores. To load the files please refer to the readme in the code repository: ttps://github.com/lnilya/abundance-trend-indicator</p> <p><br>_predictions/_environment: Contains the predictor variables for the study area (New Zealand, 1950-2019) that are needed by the models to make predictions. </p> <p>_predictions/_similarity: Contains the masks of areas that can be predicted by models and are similar to the training set.</p> <p>_predictions/_ati: Contain the predicted results for the abundance trend. These can be explored on https://ati-nz-predictions-7e6f3d514735.herokuapp.com/ </p> <p> </p>
ARC³N: A Collaborative Uncertainty Catalog to Address the Awareness Problem of Model-Based Confidentiality Analysis - Data Set
<p>Data set of the Paper "ARC³N: A Collaborative Uncertainty Catalog to Address the Awareness Problem of Model-Based Confidentiality Analysis". For more information, please see the README.md. For even more information please visit https://abunai.dev</p>
Box model data and figure script in support of Nature Communications Comment
<p>Contained within the zip file are model outputs from the Sonke et al. 2022 box model described in the associated manuscript. File names describe the different model runs. The Science_fig.jnl file is a pyferret script to plot the data. Model data from Azimrayat Andrews et al. 2024 are found by following this link: https://iopscience.iop.org/article/10.1088/1748-9326/ad472c</p>
In situ observations used in GMD Manuscript "A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0"
<p>Observations taken by</p> <p>Mika Aurela, Tarek S. El-Madany, Marika Honkanen, Anna Kontu, Juha Lemmetyinen, and Susan C. Steele-Dunne</p> <p>and used in the GMD Manuscript "A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0"</p>
On Inter-dataset Code Duplication and Data Leakage in Large Language Models
<p>This dataset encompasses the sparse graph referenced in the publication titled "On Inter-dataset Code Duplication and Data Leakage in Large Language Models."</p> <p>This resource is a snapshot of the original <a href="https://github.com/Antolin1/code-inter-dataset-duplication">repository</a>, and the graph is preserved in the <em>interduplication.db</em> database. The schema of this database is easily understandable and is available in the original repository. Each code snippet is identified by a unique identifier (id_within_dataset) that corresponds to its identification within the dataset from which it was extracted. The complete datasets are stored in .jsonl files within their respective folders (e.g., python-150/data.jsonl, codetrans/data.jsonl, etc.).</p> <p> </p> <p> </p> <p> </p>
flood modeling datas for Catastrophic outburst floods along the middle Yarlung Tsangpo River: responses to coupled fault and glacial activity on the southern Tibetan Plateau
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Model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model"
<p>This folder contains the model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model"</p> <p>The code for plotting the figures is the notebook Plot_figures.ipynb</p> <p>Fig1/simulation_output/ : Model output necessary for plotting the first figure </p> <p>The last timestep of each simulation is provided. There is one file for 1D variables (ice volume, ice volume above flotation), and one file for 2D variables (ice sheet thickness for instance).</p> <ul> <li><span>melt_insoPI_output/ : melt branch, pre-industrial insolation. Results for different CO2 levels</span></li> <li><span>growth_insoPI_output/ : growth branch, pre-industrial insolation. Results for different CO2 levels</span></li> <li><span>melt_insoMAX_output/ : melt branch, maximum insolation. Results for different CO2 levels</span></li> <li><span>growth_insoMIN_output/ : growth branch, minimum insolation. Results for different CO2 levels</span></li> </ul> <p><span>compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output</span></p> <p><span>Fig1/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level</span></p> <p> </p> <p>Fig2/simulation_output/ : Model output necessary for plotting the second figure </p> <p>The last timestep of each simulation is provided. </p> <ul> <li><span>melt_insoPI_enhancedmelt_albfb/ : melt branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</span></li> <li><span>growth_insoPI_enhancedmelt_albfb/ : growth branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</span></li> <li><span>melt_insoPI_enhancedmelt_fixedalb/ : melt branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</span></li> <li><span>growth_insoPI_enhancedmelt_fixedalb/: growth branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</span></li> </ul> <p><span>compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output</span></p> <p><span>Fig2/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level</span></p> <p> </p> <p><span>Fig3/simulation_output/ : Model output necessary for plotting the third figure </span></p> <ul> <li><span>1xCO2_nocoupling/ : simulation with pre-industrial CO2 levels and insolation and no coupling to the ice sheet model</span></li> <li><span>8xCO2_nocoupling/ : simulation with 8xpiCO2 (pre-industrial CO2) levels, pre-industrial insolation and no coupling to the ice sheet model</span></li> <li><span>8xCO2_transient_albfb/ : quasi transient simulation, 8xpiCO2 levels, pre-industrial insolation, coupling with the ice sheet model </span></li> <li><span>8xCO2_transient_fixedalb/ : quasi transient simulation, 8xpiCO2 levels, pre-industrial insolation, coupling with the ice sheet model excluding the albedo-melt feedback</span></li> </ul> <p> </p>
The data for the work "Verification of the modified Bixon-Jortner-Plotnikov model by calculating rates of non-adiabatic transitions in aromatic compounds"
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Data for "SPH modelling of AGB wind morphology in hierarchical triple systems & comparison to observation of R Aql"
<div> <p>Additional material to Malfait et al. 2024, subm. "SPH modelling of AGB wind morphology in hierarchical triple systems & comparison to observation of R Aql"</p> <p>This contains input files and final output dumps of the Phantom simulations of this paper.</p> <p>The code used to perform the simulations is available at: <a href="https://github.com/danieljprice/phantom">https://github.com/danieljprice/phantom.</a></p> <p>Splash (<a href="https://github.com/danieljprice/splash">https://github.com/danieljprice/splash</a> ) and Plons (<a href="https://github.com/Ensor-code/plons">https://github.com/Ensor-code/plons</a> ) were used to create figures and plots from this data.</p> <p> </p> </div>
Data for Modeling Snow on Sea Ice using Physics Guided Machine Learning
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Electronic structure properties of the SmartNanoTox data set (nanomaterials) for the use of meta models assesing cytotoxicity
<p>Important set of electronic structure properties data on the SmartNanoTox dataset consisting of large molecular systems representing coated materials. The data were used to study lung inflammation within a service offered through EU Horizon 2020 NanoCommons project.</p>
Data Supplement for: "Gradient dynamics model for drops of volatile liquid on a porous substrate"
<p>This dataset contains supplementary data for the following preprint:</p> <p>Hartmann, S. & Thiele, U.<br>Gradient dynamics model for drops of volatile liquid on a porous substrate.<br>(submitted 2024)</p> <p>We provide the data and sources necessary to generate all figures in the paper.</p> <p>The figures are built either with LaTeX/TikZ (Figure 1) or Python/Matplotlib (all other figures).<br>Each subfolder contains the full source code and data for one figure each.</p>
Dataset for Improved differential expression analysis of miRNA-seq data by modeling competition to be counted
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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.