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575 results for “Modeling Codes”
3D Models and Code for the Analysis of Later Acheulean Southern Levantine Handaxes
<p>3D models (.wrl / .vrml) of handaxes from four southern Levantine later Acheulean sites (Ma'ayan Barukh, Nahal Zihor, Revadim, Jaljulia).</p> <p>+</p> <p>MATLAB scripts for computing 3D sinuosity, asymmetry, and edge properties. See the CEAM, SINUOSITY, and ASYMMETRY .m files for instructions and citations to dependencies. </p> <p>The SINUOSITY and ASYMMETRY scripts accompany the submitted manuscript "The Skills of Handaxe Making: Quantifying and Explaining Variability in 3D Sinuosity and Bifacial Asymmetry" by Antoine Muller, Gonen Sharon, and Leore Grosman.</p> <p>The CEAM script accompanies Muller, A., Sharon, G., & Grosman, L. (2024). Automatic analysis of the continuous edges of stone tools reveals fundamental handaxe variability. Scientific Reports, 14, 7422. https://doi.org/10.1038/s41598-024-57450-y.</p>
Codes and data for ARA model
<h5>Codes and data for the Adhesion-Repulsion-Attraction model</h5> <div>by Hideki Murakawa, Jose A. Carrillo, Makoto Sato, Miaoxing Wang</div>
Including population and environmental dynamic heterogeneities in continuum models of collective behaviour with applications to locust foraging and group structure Data and Code
<p>This dataset includes all data used for the creation of "Including dynamic population and environmental heterogeneity in continuum models of collective behaviour with applications to locust foraging and group structure" as well as a snapshot of the code used.<br><br>Each zip should be unzippable and the code should operate with only the contents of the zip file.</p>
Compendium of R code and data for Mycenaean roads in the Peloponnese, Greece: least-cost path modelling using R and Movecost
<p><strong>Abstract</strong></p> <p><em>This study evaluates multiple methodologies and their variants for Least Cost Path (LCP) modelling, applied in combination with different Digital Elevation Models (DEMs), to explore the broader applicability of the Movecost package, using the Mycenaean road networks of the Peloponnese (Greece) as case studies. Using a geographic information system (GIS) and the R programming environment, this paper employs the Movecost package for the R statistical package to simulate ancient routes based on existing road segments. By integrating a variety of functions and parameters, this study evaluates their effectiveness across different DEMs, including both Shuttle Radar Topography Mission DEM (SRTM-DEM) and Copernicus DEM (COP-DEM) at 30 m spatial resolution. The study also examines how varying these parameters can lead to different modelling outcomes, underscoring the necessity of calibrating least-cost analysis to specific regional contexts. The road segments around Nichoria (Messenia), Ayios Ioannis Kazarma (Argolis), and in the Berbati Valley (Argolis), provide a historical canvas against which these methodological innovations are tested with the ultimate aim of exploring the capabilities of the Movecost package and how different combinations of DEM, function, parameter, and path points can effectively model the route through the existing road remains, highlighting the variability and context-specific nature of LCP modelling. The results suggest that the 'Wheeled-vehicle critical cost function' (WCS) was effective in modelling the roads through the extant remains based on start and endpoints suggested by previous research and posited by this paper. These results further suggest that Mycenaean roads likely served as key infrastructure links between major centres and ports or harbours, underscoring their role in facilitating regional trade and communication. However, this outcome represents one of several possible results, as the appropriateness of functions and the parameters tested depend on the specific landscape and archaeological context. This underscores the importance of careful parameter selection, providing insights into the economic and social landscapes of Mycenaean Greece, while also highlighting the potential of integrating spatial data with robust computational tools to enhance our understanding of ancient infrastructure.</em></p>
Code and datasets of Socio-environmental modelling shows physics-like confidence with water modelling surpassing it in numerical claims
Open the record for dataset details and reuse information.
Pre-trained DNN model data for pruning example code
<p>Pre-trained DNN model datasets for example codes of neural network pruning.</p> <p>Example pruning codes are published in "https://github.com/FujitsuLaboratories/CAC/tree/main/cac/pruning".</p>
A code implementing the unified activities-centered approach to the modelling of viral epidemics
<p>A new approach to formulating mathematical models of increasing complexity to describe the dynamics of viral epidemics is proposed. Unifying the compartmental and stochastic approaches, it focuses on daily communicative activities of different groups of population (commuting, work, shopping, socializing) viewing them as the channels through which infection spreads. In order to describe these activities, we introduce a map of social interactions characterizing the structure of the population to which the model is applied and the patterns of behaviour typical to the social groups it is made of. By employing the mathematics of difference equations, the new approach makes it possible to incorporate the clinical picture of a particular viral infection and the complications it causes directly, in the way this picture is reported by medical professionals. As an illustration of the new approach, we consider the simplest model formulated in its framework and apply it to the ongoing pandemic of SARS-CoV-2 (COVID-19), using the UK as a representative country, to assess the impact of non-pharmaceutical measures of social distancing imposed to control its course. Although the purpose of this application is merely to illustrate the approach which is open to further development, the simplest model nevertheless allows one to make some predictions and an a posteriori assessment of the measures already taken.</p>
Data and Codes of the Research 《An Analytical Spectral Model for Infragravity Waves over Topography in Intermediate and Shallow Water》
<p>This is the dataset and codes prepared for the submission of the research in the title.</p> <p>Please run the script Main.m directly after unzipping the package. For a detailed guidance of how to use it, please see the Guide.html in the UserGuide folder.</p>
Code for GMD publication - Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in WRF (v4.1.3) model during ICE-POP 2018
<p>In this repository, we include the source codes for WRF microphysics parameterization used in the GMD publication "Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in WRF (v4.1.3) model during ICE-POP 2018."</p> <p>The four 2-moment bulk microphysical parameterization codes, WDM6, WDM7, Thompson, and Morrison, are divided into 3 WDM, Thompson, and Morrison codes, and each code has been modified so that detailed microphysical processes can be checked in wrfout.</p> <p>The WDM6 and WDM7 schemes include numerical errors for ice microphysical parameterization (Kim and lim, 2021) and for cloud evaporation and melting processes (Lei et al., 2020).</p> <p>Namelist files shows the namelist.input for each case.</p> <p> </p> <p> </p>
Dataset and model codes for "Large daytime molecular chlorine missing source at a suburban site in East China"
<p>This dataset is for measurements of trace gases (Cl<sub>2</sub>, ClNO<sub>2</sub>, N<sub>2</sub>O<sub>5</sub>, HONO, NO<sub>2</sub>, NO, O<sub>3</sub>, NH<sub>3</sub>, SO<sub>2</sub>, CO, VOCs), aerosols (Cl<sup>−</sup>, NO<sub>3</sub><sup>−</sup>, SO<sub>4</sub><sup>2−</sup>, NH<sub>4</sub><sup>+</sup>, organics), and meteorological parameters (<em>T</em>, RH, <em>j</em><sub>NO2</sub>) at a suburban site (32.12°N, 118.95°E) in Nanjing, China during April 13-20, 2018.</p>
Pre-trained neural network model for pruning example code
<p>Pre-trained neural network models for example codes of neural network pruning.</p> <p>Example pruning codes are published in "https://github.com/FujitsuResearch/automatic_pruning".</p>
Code for: Comparison and interpretability of machine learning models to predict severity of chest injury
<p><span><span><span><span><span><span><span><span><span><span><span><b>Objective:</b> Trauma quality improvement programs and registries improve care and outcomes for injured patients. Designated trauma centers calculate injury scores using dedicated trauma registrars; however, many injuries arrive at non-trauma centers, leaving a substantial amount of data uncaptured. We propose automated methods to identify severe chest injury using machine learning (ML) and natural language processing (NLP) methods from the electronic health record (EHR) for quality reporting.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Materials and Methods:</b> A level I trauma center was queried for patients presenting after injury between 2014 and 2018. Prediction modeling was performed to classify severe chest injury using a reference dataset labeled by certified registrars. Clinical documents from trauma encounters were processed into concept unique identifiers for inputs to ML models: logistic regression with elastic net regularization (EN), extreme gradient boosted machines (XGB), and convolutional neural networks (CNN). The optimal model was identified by examining predictive and face validity metrics using global explanations.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results:</b> Of 8,952 encounters, 542 (6.1%) had a severe chest injury. CNN and EN had the highest discrimination, with an area under the receiver operating characteristic curve of 0.93 and calibration slopes between 0.88 and 0.97. CNN had better performance across risk thresholds with fewer discordant cases. Examination of global explanations demonstrated the CNN model had better face validity, with top features including "contusion of lung" and "hemopneumothorax." </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Discussion: </b>The CNN model featured optimal discrimination, calibration, and clinically relevant features selected. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusion:</b> NLP and ML methods to populate trauma registries for quality analyses are feasible.</span></span></span></span></span></span></span></span></span></span></span></p>
R code and data for running models in "Rapid Growth of the Swainson's Hawk Population in California since 2005"
<p>By 1979 Swainson's Hawks (<em>Buteo swainsoni)</em> had declined to as low as 375 breeding pairs throughout their summer range in California. Shortly thereafter the species was listed as threatened in the state. To evaluate the hawk's population trend since then, we analyzed data from 1,038 locations surveyed throughout California in either 2005, 2006, 2016, or 2018. We estimated a total statewide population of 18,810 breeding pairs (95CI: 11,353–37,228) in 2018, and found that alfalfa (<em>Medicago sativa</em>, lucerne) cultivation, agricultural crop diversity, and the occurrence of non-agricultural trees for nesting were positively associated with hawk density. We also concluded that California's Swainson's Hawk summering population grew rapidly between 2005 and 2018 at a rate of 13.9% per year (95CI: 7.8–19.2%). Despite strong evidence that the species has rebounded overall in California, Swainson's Hawks remain largely extirpated from Southern California where they were historically common. Further, we note that the increase in Swainson's Hawks has been coincident with expanded orchard and vineyard cultivation which is not considered suitable for nesting. Therefore, we recommend more frequent, improved surveys to monitor the stability of the species' potential recovery and to better understand the causes. Our results are consistent with increasing raptor populations in North America and Europe that contrast with overall global declines, especially in the tropics.</p>
Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".
<p>The model codes, data, and plot scripts used in the paper, "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".</p> <ul> <li>7_experiments.zip contains modified model code and output data of each experiment in this study.</li> <li>off-line test.zip contains off-line test code and output data.</li> <li>plot_scripts.zip are the NCL scripts used for figures in the paper.</li> </ul>
A Systematic Evaluation of Large Language Models of Code
<p>These are datasets for the paper:</p> <p>"A Systematic Evaluation of Large Language Models of Code"</p> <p><a href="https://arxiv.org/pdf/2202.13169.pdf">https://arxiv.org/pdf/2202.13169.pdf</a></p> <p>The code is available at: <a href="https://github.com/VHellendoorn/Code-LMs">https://github.com/VHellendoorn/Code-LMs</a></p> <p> </p> <p>The file "<a href="https://zenodo.org/record/6338015/files/unseen_test_sets.tar.gz">unseen_test_sets.tar.gz</a>" contains test sets of ~100 files in each of 12 programming languages.</p> <p>These files are not included in The Pile, and thus models such as GPT-Neo, GPT-J, GPT-NeoX were not trained on them.</p> <p>In the paper, we use these test sets to compare a variety of language models of code including OpenAI's Codex, GPT-J, GPT-Neo, GPT-NeoX-20B, and CodeParrot and our PolyCoder model.</p> <p> </p> <p>The file "<a href="https://zenodo.org/record/6341643/files/index.zip?download=1">index.zip</a>" includes an index of the <strong>training set</strong> file paths and commit SHAs.</p> <p> </p> <p>The other files, such as "<a href="https://zenodo.org/record/6344914/files/2-7B-150K.tar">2-7B-150K.tar</a>", are trained model checkpoints, as explained at <a href="https://github.com/VHellendoorn/Code-LMs">https://github.com/VHellendoorn/Code-LMs</a> .</p>
Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".
<p>The model codes, data, and plot scripts used in the paper, "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".</p> <ul> <li>Figs&Table are the NCL scripts used for figures and table in the paper.</li> <li>Model_Results contains output data of each experiment in this study.</li> <li>Mods_Scripts contains modified model code.</li> <li>Offline_Code contains off-line test code.</li> </ul>
Data and Codes of Characterizing Uncertainties of Earth System Modeling with Heterogeneous Many-core Architecture Computing
<p>These are the supporting information to verify the results in the paper, including input data, model outputs, the postprocessing scripts and the source codes.</p>
Source data and codes for the paper "Inviting atomic mechanics to macro-continua: A study on monocrystalline Si using a spatial multilevel coarsening model"
<p>Source data and codes for the paper "Inviting atomic mechanics to macro-continua: A study on monocrystalline Si using a spatial multilevel coarsening model"</p> <p>This file includes </p> <p>- Source data for Figs 1-5 and Supplementary Materials</p> <p>- LAMMPS codes and raw log files used to produce the results of this study</p> <p> </p>
Data and code from: A spectral three-dimensional color space model of tree crown health
<p>Protecting the future of forests in the United States and other countries depends in part on our ability to monitor and map forest health conditions in a timely fashion to facilitate management of emerging threats and disturbances over a multitude of spatial scales. Remote sensing data and technologies have contributed to our ability to meet these needs, but existing methods relying on supervised classification are often limited to specific areas by the availability of imagery or training data, as well as model transferability. Scaling up and operationalizing these methods for general broadscale monitoring and mapping may be promoted by using simple models that are easily trained and projected across space and time with widely available imagery. Here, we describe a new model that classifies high resolution (~1 m<sup>2</sup>) 3-band red, green, blue (RGB) imagery from a single point in time into one of four color classes corresponding to tree crown condition or health: green healthy crowns, red damaged or dying crowns, gray damaged or dead crowns, and shadowed crowns where the condition status is unknown. These Tree Crown Health (TCH) models trained on data from the United States (US) Department of Agriculture, National Agriculture Imagery Program (NAIP), for all 48 States in the contiguous US and spanning years 2012 to 2019, exhibited high measures of model performance and transferability when evaluated using randomly withheld testing data (<em>n</em> = 122 NAIP state x year combinations; median overall accuracy 0.89-0.90; median Kappa 0.85-0.86). We present examples of how TCH models can detect and map individual tree mortality resulting from a variety of nationally significant native and invasive forest insects and diseases in the US. We conclude with discussion of opportunities and challenges for extending and implementing TCH models in support of broadscale monitoring and mapping of forest health.</p>
Code and data for results and figures of the manuscript "Multi-million year cycles in modelled δ13C as a response to astronomical forcing of organic matter fluxes." submitted to Earth System Dynamics
<p>This dataset contains the code of the model used in the manuscript submitted to Earth System Dynamics "Multi-million year cycles in modelled δ13C as a response to astronomical forcing of organic matter fluxes.". It also contains some model outputs and code to draw the figures.</p>
ScienceDex guides
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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.