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575 results for “Modeling Codes”
Precise measurements of chromatin diffusion dynamics by modeling using Gaussian processes (code)
<p>GP-FBM and GP-Tool codes and algorithms as in publication day. For updated version: https://github.com/guilmont/GP-Tool</p>
Data and model code for study: Mechanistic modelling of marsh seedling establishment provides a positive outlook for coastal wetland restoration under global climate change
<p>This folder will include data and model code for study: Mechanistic modelling of marsh seedling establishment provides a positive outlook for coastal wetland restoration under global climate change.</p>
Bayesian N-mixture and occupancy modeling code for songbirds
<p>The proliferation of energy rights-of-way (pipelines and powerlines; ROWs) in the central Appalachian region has prompted wildlife management agencies to consider ways to use these features to manage and conserve at-risk songbird species. However, little empirical evidence exists regarding best management strategies to enhance habitat surrounding ROWs for the songbird community during stopover or breeding periods. We used a before-after-control-impact design to study cut-back border (linear tree cuttings along abrupt forest edges) harvest width (15 m, 30 m, and 45 m wide into the forest) and harvest intensity (14 m<sup>2</sup>/ha and 4.5 m<sup>2</sup>/ha basal area retention) prescriptions along ROWs and assessed their effects on mature forest and young forest songbird species and avian guilds (forest gap habitat, forest interior habitat, young forest habitat, and species of regional conservation priority) up to two years after treatment throughout West Virginia. Species richness during the spring stopover period initially decreased at one-year post-treatment but returned to pre-treatment levels by two-year post-treatment. Breeding season responses to cut-back border treatments varied across harvest width, harvest intensity, and time, but all responses of focal species abundance and guild richness were neutral or positive. Cut-back border harvest intensity had a stronger influence (i.e., more positive responses) than harvest width on breeding focal species abundances and guild richness. For harvest intensity, the more intense, 4.5 m<sup>2</sup>/ha retention treatment had a stronger influence (i.e., more positive responses) than the less intense, 14 m<sup>2</sup>/ha retention treatment. For harvest width, the narrowest treatment (15-m wide) had the strongest influence (i.e., more positive responses) of all width treatments, followed by the widest (45-m wide treatment) with the least influence from the 30-m wide treatment. Abundances and richness increased from pre-treatment to two-year post-treatment across all species and guilds that exhibited a response. These results suggest that cut-back borders increase breeding season habitat suitability along ROWs for the mature forest and young forest songbird community as well as for species of regional conservation priority in the short-term. These findings can aid development of management guidelines for the forest songbird community along abrupt forest edges of man-made habitat features in forest-dominated landscapes.</p>
Code and dataset for publication "Laser Wakefield Accelerator modelling with Variational Neural Networks"
<p>Data and code for reproducing figures in published work.</p> <p> </p> <p>High Power Laser Science and Engineering</p> <p><a href="https://doi.org/10.1017/hpl.2022.47">https://doi.org/10.1017/hpl.2022.47</a></p> <p>Code used various python packages including tensorflow.</p> <p>Conda environment was created with (on 6th Jan 2022)<br> conda create --name tf tensorflow notebook tensorflow-probability pandas tqdm scikit-learn matplotlib seaborn protobuf opencv scipy scikit-image scikit-optimize Pillow PyAbel libclang flatbuffers gast --channel conda-forge</p>
A Predictive Coding Approach to Modelling Perceived Drum Pattern Complexity
<p>Commented <em>R</em> script and matrices to carry out drum pattern complexity prediction.</p>
Detection of the Fire Drill anti-pattern: 15 real-world projects with ground truth, issue-tracking data, source code density, models and code
<p>This package contains artifacts for <strong>15</strong> real-world software projects. The data is supposed to aid the detection of the presence of the Fire Drill anti-pattern. We include original data, ground truth, code (experimental setups and models), and notebooks. The data supports two distinct methods of detecting the AP: a) through issue-tracking data, and b) through the underlying source code. This version of the dataset corresponds to <strong>v8</strong> of the <a href="https://arxiv.org/abs/2104.15090v8">technical report</a> and the <a href="https://github.com/MrShoenel/anti-pattern-models/releases/tag/arxiv-v8">GitHub repository</a>. The package includes the following:</p> <p>Original data:</p> <ul> <li>For each project, its <strong>original</strong> artifacts (e.g., wikis, meeting minutes, mentor's notes, etc.)</li> <li>Evaluation of raters' notes by the assessor</li> </ul> <p>Fire Drill in issue-tracking data:</p> <ul> <li><strong>Ground truth</strong> for whether and how strong each project exhibits the Fire Drill AP, on a scale from [0,10]. This was determined by two individual raters, who also reached a consensus.</li> <li>Coefficients for indicators for the first method, per project.</li> <li>Detailed issue-tracing data for each project: what occurred and when.</li> <li>Time logs for each project.</li> </ul> <p>Fire Drill in source-code data:</p> <ul> <li><strong>Four</strong> technical reports that document the developed method of how to translate a description into a detectable pattern, and to use the pattern to detect the presence and to score it (similar to the rating). Also includes a report for how activities were assigned to individual commits.</li> <li>Source code density data (metrics) for each commit in each of the nine projects as a separate dataset.</li> <li>Code: a snapshot of the repository that holds all code, models, notebooks, and pre-computed results, for utmost reproducibility (the code is written in R).</li> </ul>
Magnetic braking with MESA evolutionary models in the single star and LMXB regimes (Gossage et al. 2023) - Inlists, source code, history files
<p>Associated MESA (r11701) inlists, source files (run_star_extras.f90 and run_binary_extras.f), and outputs (history files for single star models --but the same for binary star models is available upon request) used in producing models featured in <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv221212037G/abstract">Gossage et al. 2023 - Magnetic braking with MESA evolutionary models in the single star and LMXB regimes</a>). README files are included, and please contact to alert me of any issues.</p>
EvoAug Model Weights and Code
<p>This repository provides model weights for analyses in main figures from "EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations" by Nicholas Keone Lee, Ziqi (Amber) Tang, Shushan Toneyan, and Peter K Koo.</p>
Code and model outputs for article "Assessing the potential of backscattering as a proxy for phytoplankton carbon biomass"
<p>Scripts from the scientific article "Assessing the potential of backscattering as a proxy for phytoplankton carbon biomass"<br> Submitted to Global Biogeochemical Cycles<br> Camila Serra-Pompei, Anna Hickman, Gregory L. Britten, Stephanie Dutkiewicz</p> <p>This repository contains all the files needed to run the MITgcm Biogeochemical and Optical model (MITgcmBgc, a.k.a. The Darwin model) and generate the figures in the paper.<br> Model outputs are also provided, so there is no need to run the entire model to generate the figures.</p> <p>The main folder "codes_phyto_bbp_paper.zip" contains all the files to process model output and reproduce the figures from the paper. The main files are:<br> - "Script_main.m" loads and processes model outputs and generates figures 2 to 9.<br> - "Script_new_optical_params" generates the optical parameters that are used as inputs in the MITgcmBgc model. It also generates figure 1.<br> - "Script_sensitivity_analysis" Performs offline sensitivity analysis and figure 10.</p> <p>The "mitgcm_darwin_3.zip" folder contain the code of the MITgcm with the Biogeochemical and Optical modules (Darwin package), with the configurations used in this study. Explanations of the model can be found in https://darwin3.readthedocs.io/en/latest/phys_pkgs/darwin.html </p> <p>These folders also contain the BGC-Argo floats data and climatological bbp and Chl NASA MODIS-Aqua GIOP data used in this study.</p> <p>The BGC-Argo data used in this article (download date: 27/10/2021) is available in "Other_extra_files/Argo_matrix.mat". The BGC-Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org, https://doi.org/10.17882/42182). The Argo Program is part of the Global Ocean Observing System. BGC-Argo float data was extracted using the BGC-Argo-Mat Matlab toolbox (Frenzel et al. 2021).</p> <p>The MODIS-Aqua satellite remote sensing data used in this article (download date: 23/12/2022) is available here in the folder "nasa_data". Satellite remote sensing data was extracted from NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group; (2014): MODIS-Aqua Ocean Color Data; NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. http://dx.doi.org/10.5067/AQUA/MODIS_OC.2014.0</p> <p> </p>
Matlab code and datasets for: Auditory model-based parameter estimation and selection of the most informative experimental conditions
<p>The dataset and the matlab code used for the publication: Auditory model-based parameter estimation and selection of the most informative experimental conditions</p>
The dataset of ESEC/FSE 2023 paper titled "CCT5: A Code-Change-Oriented Pre-Trained Model"
<p>This is the dataset of ESEC/FSE 2023 paper titled <strong>CCT5: A Code-Change-Oriented Pre-Trained Model.</strong></p> <p>Please refer to this <a href="https://github.com/Ringbo/CCT5">link</a> for a detailed implementation guide.</p>
Evolutionary adaptation of trees and modelled future larch forest extent in Siberia. Code and simulation data
<p>Code and datset used for the publication: "Evolutionary adaptation of trees and modelled future larch forest extent in Siberia" 2023 Gloy et al.</p>
Data and codes for Landslide hazard spatiotemporal modelling: a unified and data-driven framework
<p>Data and codes for Landslide hazard spatiotemporal modelling: a unified and data-driven framework</p>
Replication Package for "An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code"
<p>This repository contains the replication package for the paper <em>"</em>An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code" by Max Hort, Anastasiia Grishina, and Leon Moonen, accepted for publication in the 17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2023).</p> <p>The paper is deposited on arXiv, will be available later at the publisher's site (<a href="https://ieeexplore.ieee.org/Xplore/home.jsp">IEEE</a>), and a copy is included in this repository.</p> <p>The replication package is archived on Zenodo with DOI: <a href="https://doi.org/10.5281/zenodo.8058667">10.5281/zenodo.8058667</a>. The data is distributed under the CC BY 4.0 license.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you build on this data or code, please cite this work by referring to the paper:</p> <pre><code>@inproceedings{hort2023:sharing, title = {An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code}, author = {Max Hort and Anastasiia Grishina and Leon Moonen}, booktitle = {17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2023)}, year = {2023}, publisher = {IEEE} note = {To appear. Pre-print on arXiv.} }</code></pre>
Data and code for "An open-source alignment method for multichannel infinite-conjugate microscopes using a ray transfer matrix analysis model"
<p>Original data and code associated with the paper "An open-source alignment method for multichannel infinite-conjugate microscopes using a ray transfer matrix analysis model".<br> <br> Further details on the data are available in the readme.txt files.</p>
No More In-Context Learning? Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models
<p>Data and models part of the replication package of the ICSE 24 submission entitled "<em>No More In-Context Learning? Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models</em>".</p>
Datasets and code for "Phylogenetic modeling of enhancer shifts in mole-rats reveals regulatory changes associated with tissue-specific traits"
<p>This archive contains the code and datasets generated in the associated manuscript: "Phylogenetic modeling of enhancer shifts in mole-rats reveals regulatory changes associated with tissue-specific traits". We provide instructions, environments and scripts to reproduce the presented analyses.</p>
Model and code for "On the influence of hydroxyl radical and ocean sinks on estimated HCFC and HFC emissions and banks"
<p>This archive contains the following contents for the manuscript "On the influence of hydroxyl radical and ocean sinks on estimated HCFC and HFC emissions and banks":</p> <p>plotting_code.zip: <br> This directory contains python code that plot all the figures in the paper</p> <p>residence_time_tables.zip: <br> This directory contains csv files for each species on their atmospheric residence times under different OH and ocean scenarios discussed in the paper (a list of scenarios is in the header of each file)</p> <p>Agage12box_outputs: <br> This directory contains AGAGE 12 box model inputs (observed atmospheric mole fraction) and outputs (12-box model simulated atmospheric mole fractions and inferred emissions). This data is used in the WMO (2022) report (Laube & Tegtmeier, 2022; Liang & Rigby, 2022). </p> <p>HCFC-22.tar.gz (similar structure for the other 5 species):<br> Folders with prefix 'run_' contain the output from the MITgcm ocean and the 12-box coupled model simulation. Different OH and ocean scenarios are followed after the prefix 'run_'. <br> Folders with prefix 'code_' contain the modified MITgcm code. The AGAGE 12-box atmosphere model is coded in these fortran files. <br> Folder named 'Input' contains the input emission, atmospheric initial conditions, other lifetime (mainly stratospheric photochemistry), advection and diffusion timescales in the atmosphere, OH concentration, and atmospheric temperature. <br> These species-specific directories are placed under the folder 'MITgcm/verification/' after installing the MITgcm ocean general circulation model. A detailed instruction on how to install and run the MITgcm can be found at https://mitgcm.readthedocs.io/en/latest/.<br> Note that the plotting_codes can run without installing MITgcm, since the run folders already have all the model outputs.</p> <p>References:<br> Laube, J., & Tegtmeier, S. (2022). Scientific Assessment of Ozone Depletion: 2022, Chapter 1: Update on ozone-depleting substances (ODSs) and other gases of interest to the Montreal Protocol (No. 278) (p. 509). Geneva: World Meteorological Organization.<br> Liang, Q., & Rigby, M. (2022). Scientific Assessment of Ozone Depletion: 2022, Chapter 2: Hydrofluorocarbons (No. 278) (p. 509). Geneva: World Meteorological Organization.</p>
Data and model code: Assessing the Implications of Hydrogen Blending on the European Energy System towards 2050
<p>Dataset for <em>Assessing the Implications of Hydrogen Blending on the European Energy System towards 2050</em></p>
Dataset and Model Files for the Paper "Concrete Type Inference for Code Optimization using Machine Learning with SMT Solving"
<p>Dataset and model files for the paper "Concrete Type Inference for Code Optimization using Machine Learning with SMT Solving".</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.