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20 results for “coding theory”
Network data and script accompanying the paper "Operationalizing anthropological theory: four techniques to simplify networks of co-occurring ethnographic codes"
<p>This repository accompanies the paper<a href="https://rdcu.be/dbuhi"> "Operationalizing anthropological theory: four techniques to simplify networks of co-occurring ethnographic codes"</a>, by Cottica et al. It contains:</p> <ol> <li>A data file, containing networks of co-occurrence of ethnographic codes from three ethnographies. Data are pseudonymized (see the paper for details).</li> <li>A script that, when run on the data, produces simplified versions of each network. Simplifications follow four different techniques, described in the paper. Each technique relies on a tuning parameter, so that, for each network and each techniques, the script produces several simplified networks, each one associated with a unique value of the tuning parameter.</li> </ol> <p>The data file format is that of a Tulip perspective. To open, download Tulip (https://tulip.labri.fr), launch it and open the file from within the Tulip GUI.</p> <p>The script file is in Python. To run, open it from within the Tulip IDE first.</p> <p> </p>
Supplementary data and code for: Non-Abelian Hyperbolic Band Theory from Supercells
<p>This data and code collection contains all necessary code and data required to reproduce the results of the accompanied publication. To run the code contained in this collection GAP, the HyperCells package, Mathematica, the NCAlgebra (version >= 6.0) and the HyperBloch package are required. Please refer to the included Readme file and the links therein for more details.</p> <p>Changes: add NCAlgebra to list of dependencies.</p>
Data and code for Nettle and Frankenhuis, 'The evolution of life history theory'
<p>Data and code for 'The evolution of life history theory: Bibliometric analysis of an interdisciplinary research area', by Daniel Nettle and Willem E Frankenhuis.</p> <p>Version of March 4 2019</p> <p>This archive contains the raw data (Web of Science records), plus VOS Viewer files and R code for performing the analyses and making the bibliometric maps.</p> <p>Please see 'Files read me.txt' for explanation of the different files.</p>
On the Practitioners' Understanding of Coupling Smells -- A Grey Literature Based Grounded-Theory Study: Dataset and Code
<p>This is the dataset and related code artifact for the article "On the Practitioners' Understanding of Coupling Smells -- A Grey Literature Based Grounded-Theory Study".</p> <p> </p> <p>Abstract of the article:</p> <p> </p> <p>Context: Code and design smells, such as the coupling smells examined in this article, are widely studied. Existing empirical studies reveal gaps between the scientific theory and practice, not yet explained by the scientific literature. Only basic coupling smell detection approaches and metrics seem to have been transferred to practice so far.</p> <p> </p> <p>Objective: This article aims to study the current practitioner's understanding of coupling smells.</p> <p> </p> <p>Method: Based on grey literature sources containing practitioner views on coupling smells, we performed a Grounded Theory (GT) study. We used UML-based modeling to precisely encode our findings and performed a rigorous analysis of our codes and models.</p> <p> </p> <p>Results: Our results are defining factors of coupling smells, as well as smell impacts, trade-offs, relationships to other smells, relationships to practices and patterns, and fix options as perceived by practitioners. We further identified gaps in the understanding of coupling smells between science and practice, and derived opportunities and challenges for future scientific work.</p> <p> </p> <p>Conclusions: Five lessons are presented as opportunities and challenges for future research. Our results can help scientists to get a better understanding of practitioner concerns, and practitioners to get an overview of the current perception of other practitioners on coupling smells.</p> <p> </p> <p> </p>
Theory and implementation of inelastic Constitutive Artificial Neural Networks: Source code and data
<p>This dataset contains the source code of the inelastic Constitutive Artificial Neural Network (iCANN) as well as the data for the examples from the publication:</p> <p>Holthusen, H., Lamm, L., Brepols, T., Reese, S., & E. Kuhl.<em> Theory and implementation of inelastic Constitutive Artificial Neural Networks.</em></p> <p>arXiv: <a href="https://doi.org/10.48550/arXiv.2311.06380">https://doi.org/10.48550/arXiv.2311.06380</a></p> <p>Computer Methods in Applied Mechanics and Engineering: <a href="https://doi.org/10.1016/j.cma.2024.117063">https://doi.org/10.1016/j.cma.2024.117063</a></p> <p> </p> <p><strong>01_Example01: </strong> Artificially generated data</p> <p>This example investigates whether the iCANN is able to discover a model for the data generated by a continuum mechanical model.</p> <p> </p> <p><strong>02_Example02:</strong> Discovering a model for the polymer VHB 4910 subjected to cyclic loading</p> <p>Here, we investigate the ability of iCANN to discover and learn a model for the material response of VHB 4910 polymer subjected to cyclic loading at different stretch rates.</p> <p>The experimental data are taken from the literature:</p> <p>Hossain, M., Vu, D. K., & Steinmann, P. (2012). Experimental study and numerical modelling of VHB 4910 polymer. <em>Computational Materials Science</em>, <em>59</em>, 65-74.</p> <p><a href="https://doi.org/10.1016/j.commatsci.2012.02.027">https://doi.org/10.1016/j.commatsci.2012.02.027</a></p> <p> </p> <p><strong>03_Example03: </strong>Discovering a model for passive skeletal muscle subjected to relaxation</p> <p>In this example, we investigate whether the iCANN is able to discover a model for the material behavior of passive skeletal muscles. A total of five independent experiments are carried out in which the maximum applied compression stretch and the stretch rate are varied. In addition, the learning performance of the iCANN is investigated. Training is first carried out in each of the five experiments and then in each of four of the five experiments.</p> <p>The experimental data are taken from the literature:</p> <p>Van Loocke, M., Lyons, C. G., & Simms, C. K. (2008). Viscoelastic properties of passive skeletal muscle in compression: stress-relaxation behaviour and constitutive modelling. <em>Journal of biomechanics</em>, <em>41</em>(7), 1555-1566.</p> <p><a href="https://doi.org/10.1016/j.jbiomech.2008.02.007">https://doi.org/10.1016/j.jbiomech.2008.02.007</a></p> <p> </p> <p><strong>python_requirements.txt: </strong>File containing a list of installed Python modules used to implement the iCANN</p>
Dataset and source code for "Explanation and optimizing multi-model blending algorithm using random variables theory"
<p>this dataset contain: </p> <ol> <li>2m temperature de-biased model forecast data on station location, ECMWF, NCEP, JP and CMA</li> <li>2m temperature observaton data, obs_t2m</li> <li>24H QPF model forecast data on station location, ECMWF, NCEP, CMA-GFS, in raw_data_r24.zip</li> <li>24H precipitation data, in raw_data_r24.zip</li> <li>source code (in python)</li> </ol> <p> </p> <p>how to use it: </p> <p>1. prepare data and python environment<br> 1.1 if you want to run [Station_FCST_MMWB.py] or [Station_FCST_MMWB_r24.py] , please download the station forecast and observation data<br> 1.2 neet meteva package to read/write micaps-3 format data: https://github.com/nmcdev/meteva<br> 1.3 need cartopy to draw picture FigS01. </p> <p>2. try the 2m temperature blending methods <optional><br> 2.1 unzip the [CMA.zip, ECMWF.zip, jp.zip, NCEP.zip, obs_t2m.zip] file into ./raw_data/<br> 2.2 run the Station_FCST_MMWB.py in python environment </p> <p>3. try the 24h QPF multi blending methods <optional><br> 3.1 unzip the [raw_data_r24.zip] file into ./raw_data_r24/<br> 3.2 run the Station_FCST_MMWB_r24.py in python environment</p> <p>4. draw figures<br> 4.1 run Fig01.py in python environment <br> 4.2 run Fig02.py in python environment <br> 4.3 run Fig03.py in python environment <br> 4.4 run FigA01.py in python environment <br> 4.5 run FigS01.py in python environment </p>
Replication data and theory code for: Observation of a Majorana zero mode in a topologically protected edge channel
<p>Replication Data for: Observation of a Majorana zero mode in a topologically protected edge channel</p>
Code and data from: Towards a more dynamic metabolic theory of ecology to predict climate change effects on biological systems
<p>This repository contains data and code used to produce Figures 1, 2, and S1 in <em>Towards a more dynamic metabolic theory of ecology to predict climate change effects on biological systems.</em></p> <p>The file "empirical_mte_database.csv" contains a database of peer-reviewed articles pulled from a Web of Science search of papers that empirically tested the temperature dependence predictions of the metabolic theory of ecology (Brown et al. 2004) between 2004 and 2024. </p> <p>The .zip file contains a jupyter notebook, julia project toml file and a README in order to reproduce the simulations illustrating how different temperature dynamics can lead to different inferred thermal performance curves in populations.</p> <p> </p>
First-order phase transitions in Yang-Mills theories and the density of state method---data and analysis code release
<p>Data release for paper: Lucini, B., Mason, D., Piai, M., Rinaldi, E., & Vadacchino, D. (2023). First-order phase transitions in Yang-Mills theories and the density of state method. arXiv preprint arXiv:2305.07463.</p> <p>This data release comprises of:</p> <p>Importance sampling results: Input and output files for PureGauge file of HiRep (https://github.com/claudiopica/HiRep) and csv files contains analysis of results.</p> <p>LLR results: Input files for LLR_HB for a modified version of HiRep for the heat bath LLR algorthim with umbrella sampling (https://github.com/dave452/Hirep-LLR-SU) and some csv files containing analysis of output.</p> <p>Analysis code within the LLRAnalysis.zip, it contains the code and the conda environment.</p>
Data and code for the manuscript "Internal vs Forced Variability Metrics for General Circulation Models Using Information Theory"
<p>Data and code for the manuscript "Internal vs Forced Variability Metrics for General Circulation Models Using Information Theory" published in the Journal of Geophysical Research Oceans. <br>URL of the manuscript: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JC020101<br>DOI of the manuscript: https://doi.org/10.1029/2023JC020101</p>
Code for Atmospheric Research publication - Height correction method based on the Monin–Obukhov similarity theory for better prediction of near-surface wind fields
<p>In this repository, we include the source codes for WRF namelist, figures, and height correction used in the Atmospheric Research publication "Height correction method based on the Monin–Obukhov similarity theory for better prediction of near-surface wind fields"</p> <p>The namelist.wps and namelist.input in WRF namelist are using for making input and running simulation, and Fig scripts in Figure scripts are using for plotting the figures in the paper.</p> <p>hgt_corr in Height correction method is a code to correct the disparity of the 10-m height definition between the model and observation by applying the developed the height correction algorithm based on the Monin-Obukhov similarity theory.</p>
Data & codes for: Evolution of virulence in emerging epidemics: from theory to experimental evolution and back
<p>Data & Codes for:</p> <p><strong>Evolution of virulence in emerging epidemics: from theory to experimental evolution and back</strong><br><em>Wakinyan Benhamou, François Blanquart, Marc Choisy, Thomas W. Berngruber, Rémi Choquet and Sylvain Gandon</em></p> <p>___________________________________________________________________________________</p> <p>Data that were used in this study, along with the scripts for the analyses (in R), are available in the file <strong><em>Data_and_codes.zip</em></strong><em>. </em>For full description, please see the file <strong><em>README.md</em></strong>. All this is also available in the following GitHub repositery: <a href="https://github.com/WakinyanB/LAMBDA">https://github.com/WakinyanB/LAMBDA</a>.</p>
Data and code for "Tensor product random matrix theory"
<p>The data files and python scripts to generate Figure 3 of the manuscript "Tensor product random matrix theory" are uploaded.</p> <ul> <li>In the 'scripts' folder there are two python files. The file 'SFF.py' was used to generate the dataset. The file 'plot.py' generates Figure 3.</li> <li>In the 'processed data' folder there are .csv files with the data used to generate Figure 3.</li> <li>In the 'figures' folder the generated figure is included.</li> <li>In the 'additional plots' folder, three data sets and corresponding plots not incorporated in the paper are included.</li> </ul>
Raw neural codes and binsizes for the paper 'Robust and consistent measures of pattern separation based on information theory and demonstrated in the dentate gyrus'
<p>Raw optimal spiking codes and binsizes that maximise information theoretic quantities for the figures of the paper 'Robust and consistent measures of pattern separation based on information theory and demonstrated in the dentate gyrus' (PLoS Comput Biol. 2024 Feb 20;20(2):e1010706) . Additional data will be added in the coming months.</p>
Replication data and theory code for: Discovery of segmented Fermi surface induced by Cooper pair momentum
<p>Replication data and theory code for: Discovery of segmented Fermi surface induced by Cooper pair momentum</p>
Supplementary data and code for "Higher order theory of quasi-isodynamicity near the magnetic axis of stellarators"
<p>VMEC equilibria and plotting code for Figure 3 in "Higher order theory of quasi-isodynamicity near the magnetic axis of stellarators".</p>
Data and code from: Network theory predicts ecosystem robustness across environmental conditions
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Simulation Data and postprocessing code for "Two-Dimensional Nonlocal Eshelby's Inclusion Theory: Eigenstress-Driven Formulation and Applications"
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The Theory of Mind and Human-Robot Trust Repair | Data + Code
<p>This repository is the home of for the data and code used in </p> <blockquote>The theory of mind and human–robot trust repair.</blockquote> <p>Details on the structure of the repository are visible in the readme attached and the process and analysis the code and data was used in is detailed fully in the associated publication and pre-print at the following DOIs:</p> <ul> <li>Publisher [open access] Copy: <a href="https://rdcu.be/deQ8o">https://rdcu.be/deQ8o</a></li> <li>Pre-Print: 10.7302/7741</li> </ul> <p>To restate elements form the readme, the repository contains:</p> <p>This repository contains the following:</p> <ol> <li>`Data_Tall.csv` --> Data collected and analyzed in tall format.</li> <li>`Data_Wide.csv` --> Data collected and analyzed in wide format.</li> <li>`Manipulation Check.Rmd` --> r markdown file of our analysis used for manipulation checks.</li> <li>`Main Analysis.Rmd` --> r markdown file of our analysis used for our main analysis.</li> </ol> <p>Questions or concerns regarding the data contained in this repository should be directed to the corresponding author:</p> <p>Connor Esterwood | cte<at>umich.edu</p>
DATA, CODE: Theory and simulation for low frequency interfacial polarization of plant root cell
<p>The data and simulation codes used in the study.</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.