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281 results for “source code”
Source code and simulation datasets for the paper 'Migration and accumulation of bacteria with chemotaxis and chemokinesis'
<p>Source code and simulation data files for the paper 'Migration and accumulation of bacteria with chemotaxis and chemokinesis'</p> <p>The zipped folder 'SimulationCode.zip' contains Matlab source code files which have been used to generate the simulations in the paper: fixed attractant gradient, axisymmetric agar-plate like migration and transient attractant source. The file 'main.m' controls all simulations run with initial conditions specified in the files with suffix '_ic'. The file 'PDEsolver.m' specifies the finite difference solver used so solve the model PDEs, while 'FourthOrderFD.m' creates the matrices that are required<br> for the finite differnce solver. The chosen scheme is of fourth order accuracy.</p> <p>The zipped folder 'SimulationData.zip' contains Matlab data files generated by running the simulation code. The results correspont to the figures in the paper.</p>
On the Co-evolution of ML Pipelines and Source Code - Empirical Study of DVC Projects
<p>This is a replication package of our paper submission to the Saner 2021 entitled:</p> <p>On the Co-evolution of ML Pipelines and Source Code - Empirical Study of DVC Projects</p>
Code and Source Data for "Knowledge-Guided Machine Learning can improve C cycle quantification in agroecosystems"
<p>Datasets for code and Source Data for the study "Knowledge-Guided Machine Learning can improve C cycle quantification in agroecosystems" https://doi.org/10.1038/s41467-023-43860-5. All files belong to Licheng Liu and Zhenong Jin at University of Minnesota. deposit_code_v2.zip contains packaged codes and sample runs for KGML-ag-Carbon training, validation and implementations. Source Data.zip contains data for generating the figures inside the study. </p> <p>Note: We used Pytorch 1.6.0 (<a href="https://pytorch.org/get-started/previous-versions/">https://pytorch.org/get-started/previous-versions/</a>, last access: 21 Oct 2023) and Python 3.7.11 (<a href="https://www.python.org/downloads/release/python-3711/">https://www.python.org/downloads/release/python-3711/</a>, last access: 21 Oct 2023) as the programming environment for model development. Statistical analysis, such as linear regression, was conducted using Statsmodels 0.14.0 (<a href="https://github.com/statsmodels/statsmodels/">https://github.com/statsmodels/statsmodels/</a>, last access: 21 Oct 2023) In order to use a GPU to speed-up the training process, we installed the CUDA Toolkit 10.1.243 (<a href="https://developer.nvidia.com/cuda-toolkit">https://developer.nvidia.com/cuda-toolkit</a>, last access: 21 Oct 2023). </p> <p><strong>To use the full kgml_lib function, please create a new environment with the same python and libs above.</strong></p>
Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China"
<p>Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China".</p><p>Transformations among the parameters of the moment tensor model refer to the code package from Tape and Tape (https://github.com/carltape/mtbeach/; https://github.com/carltape/surfacevel2strain; Tape and Tape, 2009, 2012, 2013, 2015).</p><p>Tape, C., P. Muse, M. Simons, D. Dong, and F. Webb (2009). Multiscale estimation of GPS velocity fields, Geophys. J. Int. 179, no.2, 945-971, doi: 10.1111/j.1365-246X.2009.04337.x.</p><p>Tape, W., and C. Tape (2012). A geometric setting for moment tensors, Geophys. J. Int. 190, no. 1, 476–498, doi: 10.1111/j.1365-246X.2012.05491.x.</p><p>Tape, W., and C. Tape (2013). The classical model for moment tensors, Geophys. J. Int. 195, no. 3, 1701–1720, doi: 10.1093/gji/ggt302.</p><p>Tape, W., and C. Tape (2015). A uniform parametrization of moment tensors, Geophys. J. Int. 202, no. 3, 2074–2081, doi: 10.1093/gji/ggv262.</p>
Source code and simulation results: Poles and zeros of electromagnetic quantities in photonic systems
<h4><strong>Summary</strong></h4> <p>This publication supplements the article "Poles and zeros of electromagnetic quantities in photonic systems" with tabulated data and matlab code that allows to reproduce the results. The article elaborates how evaluating resonances based on contour integrals of scalar electromagnetic quantities extends to computing zeros. Furthermore, direct differentiation of underlying scattering problems is used to compute sensitivities with respect to design parameters.</p> <h4><strong>Structure</strong></h4> <p>The script 'main_text.m' can be used to reproduce the results provided in the paper. In tabulated form the results are contained in the directory <strong>tabulated</strong>. Furthermore, the script 'supplement.m' can be used to reproduce results presented in the supplement. The directory <strong>RPExpand </strong>contains the software RPExpand v2, which is available on <a href="https://doi.org/10.5281/zenodo.10371002">Zenodo</a> with additional examples. </p> <h4><strong>Compute residues</strong></h4> <p>The modal expansion of the Fourier transform is based on its residues at the dominant resonances. If the poles are simple, which often is the case, the residues can be obtained directly from the eigenvectors of the generalized eigenvalue problem used to obtain the poles or the zeros. Introducing the Vandermonde matrix</p> <p>\(V = \begin{bmatrix} 1 & \dots & 1 \\ w_1 & \dots & w_M \\ \vdots & & \vdots \\ w_1^{M-1} &\dots & w_M^{M-1} \end{bmatrix}\),</p> <p>the Hankel matrix \(H\) can be written as \(H = V A V^T\) with \(A\) being the diagonal matrix \(\mathrm{diag}(a_1,\dots,a_M)\) containing the residues \(a_m \). This decomposition is a consequence of the Cauchy's reisdue theorem if the poles are simple. Furthermore, we now that \(V^{-T}\) solves the generalized eigenproblem \(H^<X = HX\Omega\) (Eq. 2 in the original paper) and hence the eigenvectors we get from Matlabs eig routine are \(X = V^{-T}D\) where \(D\) is some scaling. It follows that we obtain the residues using \(A = X^T H X (X V^T)^{-2}\)</p> <h4><strong>Derivatives</strong></h4> <p>Similarly, our framework provides a straight forward approach to the derivatives of zeros and poles if they are simple. Using direct differentiation we have access to partial derivatives of the quantity \(q(\omega)\) and hence the derivatives of the moments \(s_k = \frac{1}{2\pi i} \oint_C \omega^k q(\omega) \mathrm{d}\omega\). For the zeros the inverse \(1/q(\omega)\) and the respective derivative are considered. Using Cauchy's residue theorem the derivatives \(\frac{\partial w_m}{\partial p}\)are solutions of the linear system of equations \(\frac{\partial s_k}{\partial p} = \sum_{m = 1}^{M}\left[k\omega_m^{k-1}\frac{\partial w_m}{\partial p} a_m + \omega_m^k\frac{\partial a_m}{\partial p} \right]\).</p> <h4><strong>Higher order singularities</strong></h4> <p>Finding higher order poles and zeros is possible without further adaptation. Computing derivatives and residues requires some special care. The moments are then given by \(s_k = \sum_{m=1}^{M} \sum_{n = 1}^{N_m} a_{m,n} \frac{k! \, \omega^{k-n+1}}{(k-n+1)!(n-1)!}\)with \(a_{m,n}\) being the residue of the pole \(\omega_m\) and \(n \) refers to the order. Accordingly expressions for the derivatives are available.</p> <h4><strong>Error estimates</strong></h4> <p>The estimated errors in Table 1 refer to the number of integration points, i.e. we are interested in the question how close we get with a given number of integration points to the exact solution of the chosen approximate model of the physical system. Due to propagation of the error the convergence of the derivatives is shifted towards a larger number of integration points.</p> <h4><strong>Requirements</strong></h4> <ul> <li>JCMsuite (version 5.4.3 or newer)</li> <li>MATLAB (tested with version R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holder in 'zeros_poles.m' by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>.</p> <h4><strong>References</strong></h4> <p>[1] Felix Binkowski, Fridtjof Betz, Rémi Colom, Patrice Genevet, Sven Burger, Poles and zeros of electromagnetic quantities in photonic systems, https://doi.org/10.48550/arXiv.2307.04654</p> <p>[2] Anthony P. Austin, Peter Kravanja, Lloyd N. Trefethen, Numerical algorithms based on analytic function values at roots of unity, SIAM Journal of Numerical Analysis 52, 1795 (2014), https://doi.org/10.1137/130931035</p> <p>[3] Felix Binkowski, Fridtjof Betz, Martin Hammerschmidt, Philipp-Immanuel Schneider, Lin Zschiedrich, Sven Burger, Computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators, Communications Physics <strong>5</strong>, 202 (2022), https://doi.org/10.1038/s42005-022-00977-1</p>
Reproducibility code and data - Understanding cetacean habitats in the Eastern Caribbean: a study combining data from multiple sources
<p>This R project reproduces the analyses carried out in the article entitled “Modelling cetacean habitats in the Eastern Caribbean: a study combining data from multiple sources” submitted to PCI Ecology. Analyses involve two steps (modelling 1: exploratory; and modelling 2: inferential).</p>
Source code and simulation results: Efficient rational approximation of optical response functions with the AAA algorithm
<p>This publication provides data published in the article "Efficient rational approximation of optical response functions with the AAA algorithm" [1] in tabulated form along with the Matlab scripts that have been used to produce them. These scripts interface the finite element method solver JCMsuite [2,3]. The article presents rational approximations of optical response functions based on an extended version of the AAA algorithm [4] that allows to efficiently reconstruct sensitivty spectra and gives access to sensitivities of poles, residues, and zeros. Furthermore, the rational approximation of a scalar observalbe is used to construct solutions of the source free Maxwell's equation, i.e., a nonlinear eigenvalue problem. </p> <p><strong>The physical Structure</strong></p> <p>The example is based on the chiral metasurface introduced in [5]. For the sake of simplicity we added infinite layers of SiO\(_2\) to the top and the bottom of the structure. The original structure has a SiO\(_2\) substrate and a layer of PMMA polymethyl methacrylate (PMMA) deposited on top. PMMA can be modelled with the same refractive index of 1.45 as SiO\(_2\). Furthermore, our simulations include the 13 nm indium tin oxide (ITO) coating which drastically reduces the Q-factor as it is slightly absorbing. The accuracy of the discrete model is verified by assessing reflection, transmission, and absorption at 241 evenly spaced points within the specified range. Energy conservation requires that the discrepancy between their sum and the energy entering the system is zero. The numerical discretization is chosen such that the maximum relative error is less than \(3\times10^{−5}\).</p> <p><strong>Dispersion</strong></p> <p>Tabulated data for ITO has been taken from the <a href="https://refractiveindex.info/?shelf=other&book=In2O3-SnO2&page=Konig">refractiveindex.info</a> database (T. A. F. König et al., 2014, https://doi.org/10.1021/nn501601e) and the data for TiO2 was kindly provided the authors of [5]. The permittivity \(\varepsilon = (n+ik)^2\) is locally approximated as a rational function, i.e., only data in a vicinity of the frequency range of interest is considered. As we aim for a function with the symmetry \(f^\ast(\omega) = f(-\omega^\ast)\) we add the complex conjugated data at negative frequencies and enforce the symmetry in a second step. The partial fraction decomposition of the required function is of the form: \(\varepsilon(\omega) = \varepsilon_\infty + \sum_{j=1}^{4}a_j/(\omega-\omega_j) - a_j^\ast/(\omega+\omega_j^\ast)\) with the residues \(a_j\) and the poles \(\omega_j\). We expect 4 pairs of poles to sufficiently approximate the data within the range of interest (4 with positive and 4 with negative real parts).</p> <h4><strong>Requirements</strong></h4> <ul> <li>JCMsuite (at least 6.2.0)</li> <li>MATLAB (tested with version R2023b)</li> </ul> <p>In order to run the simulations with JCMsuite you must replace corresponding place holders with a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>.</p> <p><strong>Usage</strong></p> <p>With the content of 'spectra.zip' you can reproduce results presented in the paper. Running the script 'plots.m' will not start any expensive simulation but use the provided data. With 'dispersion.m' the fits to the material data can be reproduced. Additionally, tabulated data is contained in 'data/ascii'. The archive 'eigenmodes.zip' must be extracted in the same directory as 'spectra.zip'.</p> <p><strong>References</strong></p> <p>[1] Fridtjof Betz, Martin Hammerschmidt, Lin Zschiedrich, Sven Burger, Felix Binkowski: Efficient rational approximation of optical response functions<br>with the AAA algorithm, https://doi.org/10.48550/arXiv.2403.19404.</p> <p>[2] Jan Pomplun, Sven Burger, Lin Zschiedrich, Frank Schmidt, Adaptive finite element method for simulation of optical nano structures, Physica Status Solidi B <strong>244</strong>, 3419 (2007), http://dx.doi.org/10.1002/pssb.200743192.</p> <p>[3] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763.</p> <p>[4] Y. Nakatsukasa, O. Sète, and L. N. Trefethen, The AAA Algorithm for Rational Approximation, SIAM Journal on Scientific Computing <strong>40</strong>, A1494 (2018), http://dx.doi.org/10.1137/16M1106122.</p> <p>[5] X. Zhang, Y. Liu, J. Han, Y. Kivshar, and Q. Song, Chiral emission from resonant metasurfaces, Science <strong>377</strong>, 1215 (2022), http://dx.doi.org/%2010.1126/science.abq7870.</p>
Data and source code for Automatic generation of a large dictionary with concreteness/abstractness ratings based on a small human dictionary
<p>We present a method for automatic ranking concreteness of words and propose an approach to significantly decrease amount of expert assessment. The method has been evaluated on a large test set for English. The quality of the constructed dictionaries is comparable to the expert ones. The correlation between predicted and expert ratings is higher comparing to the state-of-the-art methods.</p>
Research data and code for Numerical investigation of the influence of the source and detector position for optical measurement of lung volume and oxygen content in preterm infants
<p># Research data repository</p> <p>## Introduction</p> <p>This repository contains the research data, scripts and codes to process the simulation and generate the figures in research article: <br> <em>"Numerical investigation of the influence of the source and detector position for optical measurement of lung volume and oxygen content in preterm infants"</em></p> <p>This work uses the discrete 3D mesh of the thorax of a newborn that is available at: http://doi.org/10.5281/zenodo.4916863</p> <p>This article has been submitted and publied in Journal of Biophotonics:<br> - DOI: 10.1002/jbio.202200041<br> - Link: <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/jbio.202200041">https://onlinelibrary.wiley.com/doi/abs/10.1002/jbio.202200041</a></p> <p>## Article status</p> <p> [X] Submitted <br> [X] Under review <br> [X] Corrections <br> [X] Published<br> <br> ## Content</p> <p>- Folder "data": This folder needs to be unzipped and contains the raw data from the simulation, as well as some processed data needed to generate the figures. This folder contains all the data necessary to generate the figures. However some intermediate data files (the Photon Hitting Density values interpolated on the elements of the mesh) are not given here because the files are too big. These can be created using the convertPHD2element script.<br> - Folder "function": This folder needs to be unzipped and contains some functions that are used by the scripts to process data or generate the figures<br> - Matlab .m files: The .m files are scripts that are used to generate the figures (generateFigX.m) or to process the data computeYYY.m. The description of each script and function is given in the comment section at the beginning of each file.</p> <p>## Licence<br> This data is published under the creative common CC-BY licence. You are free to use this data as long as you cite this dataset and the article (when DOI available)</p> <p>## Digital Object Identifier<br> DOI: 10.5281/zenodo.5996855</p> <p>## Authors<br> Simulation: Andrea Pacheco<br> Article writing: Andrea Pacheco<br> Data processing and figure generation: Andrea Pacheco and Baptiste Jayet<br> Conceptualisation, investigation, review and editing: Emilie Krite Svanberg, Hamid Dehghani and Eugene Dempsey<br> Project supervision: Stefan Andersson-Engels</p> <p>## Funding<br> The research leading to these results was funded by Science Fundation Ireland project no. SFI/15/RP/2828</p>
Source data and code for: Existing fossil fuel extraction would warm the world beyond 1.5°C
<p>Source data and code for the study, "Existing fossil fuel extraction would warm the world beyond 1.5°C." Datasets 1-4 include mine-level data collected for China (Dataset 1), India (Dataset 2), and five other countries (Dataset 3) that are among the world's top nine coal producers - the United States, Indonesia, Australia, South Africa, and Poland. Dataset 4 includes global and country-level output data from the 1,000-run Monte Carlo simulation. <Committed_Reserves_Monte_Carlo_Input_Data.zip> includes data and code to replicate the Monte Carlo simulation.</p>
LipidMS v3.0.3: source code and example of lipidomics dataset for human serum
<p>Source code and example dataset for LipidMS v3.0.3: a commercially available pooled human serum sample was analyzed in positive and negative detection modes and using MS1, DIA and DDA approaches. The obtained datasets were processed using LipidMS v3.0, MS-DIAL v4.80 or a combination of data pre-processing in XCMS v3.16 and lipid annotation in LipidMS v3.0.</p> <p>This repository contains:</p> <p>- Raw data for positive and negative polarities using MS scan, DIA and DDA acquisition modes.</p> <p>- R scripts for processing with LipidMS v3.0.3 and XCMS v3.16.1 and parameters used for processing with MS-DIAL v4.80.</p> <p>- Source code for LipidMS v3.0.3.</p> <p>- Results obtained for the 3 different softwares employed.</p> <p>- Tutorials for LipidMS R package and online application.</p> <p>- Human pooled serum analysis</p> <ul> <li>Raw data for positive and negative polarities using MS scan, DIA and DDA acquisition modes for a human pooled serum sample with or without the addition of 68 lipid standars</li> <li>Results for the data processing and annotation of the lipid standards using LipidMS 3.0, XCMS 3.16 and MS-DIAL 4.80</li> <li>Results for the manual curation of the total lipid annotations provided by both LipidMS 3.0 and MS-DIAL 4.80</li> </ul>
Source codes and datasets for the paper "DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learning"
<p>This contains the<strong> reproducible source code and dataset </strong>for the paper "DRLComplex : Reconstruction of protein quaternary structures using deep reinforcement learning paper"</p>
Source code and data from: Foraging personalities modify effects of habitat fragmentation on biodiversity
<p><span>Habitat loss undeniably poses a substantial threat to biodiversity, but whether fragmentation per se drives the loss of species is still widely debated. While negative consequences from fragmentation are often anticipated, many empirical studies report positive effects. However, the intrinsic mechanisms governing species' persistence in fragmented landscapes are not yet understood. In this study, we investigated consistent personality-dependent differences in foraging behavior among individuals as a possible mechanism underlying the discrepancy of reported fragmentation effects. </span><span>We </span><span>devised a mechanistic individual-based model simulating the home range behavior of a competitive small mammal community based on the availability of a shared resource. Thereby, an individual's risk-taking behavior dictates its foraging decisions at risky habitat edges, an inherent property of fragmentation per se. Our simulations show that differences in risk-taking while foraging are potentially a further mechanism contributing to reconciling the fragmentation debate. The first scenario considering risk-seeking communities showed a neutral response towards fragmentation, while the second scenario featuring risk-avoiding communities confirmed the negative effects of fragmentation. Notably, the third scenario, simulating behaviorally diverse communities including risk-avoiding and risk-seeking individuals, demonstrated a positive influence of fragmentation on biodiversity. Intraspecific differences in behavior could also enhance the temporal species coexistence (coviability) of communities threatened by an ongoing habitat loss. Our study highlights the importance of recognizing the behavioral composition of populations and communities for estimating fragmentation effects, because differences in risk-taking can influence the coping abilities of animal communities in light of fragmentation.</span></p>
Data and Source codes: Ancestral sex-role plasticity facilitates the evolution of same-sex sexual behavior
<p>This repository provides access to the tracking data and analysis code used for the manuscript:</p> <p>Ancestral sex-role plasticity facilitates the evolution of same-sex sexual behavior</p> <p>by Nobuaki Mizumoto<sup>1</sup>, Thomas Bourguignon<sup>1</sup>, and Nathan W. Bailey<sup>2</sup></p> <p><sup>1</sup> Okinawa Institute of Science & Technology Graduate University, Onna-son, Okinawa, Japan <br /><br> <sup>2</sup> School of Biology, University of St Andrews, St Andrews, U.K. <br /></p> <p>published in the Proceedings of the National Academy of Sciences of the United States of America.</p>
Source Code Archiving to the Rescue of Reproducible Deployment — Replication Package
<p>Replication package for the paper:</p> <p>Ludovic Courtès, Timothy Sample, Simon Tournier, Stefano Zacchiroli.<br><em>Source Code Archiving to the Rescue of Reproducible Deployment</em><br><a href="https://acm-rep.github.io/2024/">ACM REP'24</a>, June 18-20, 2024, Rennes, France<br><a href="https://doi.org/10.1145/3641525.3663622">https://doi.org/10.1145/3641525.3663622</a></p> <h2>Generating the paper</h2> <p>The paper can be generated using the following command:</p> <pre><code>guix time-machine -C channels.scm \ -- shell -C -m manifest.scm \ -- make </code></pre> <p>This uses GNU Guix to run <code>make</code> in the exact same computational environment used when preparing the paper. The computational environment is described by two files. The <code>channels.scm</code> file specifies the exact version of the Guix package collection to use. The <code>manifest.scm</code> file selects a subset of those packages to include in the environment.</p> <p>It may be possible to generate the paper without Guix. To do so, you will need the following software (on top of a Unix-like environment):</p> <ul> <li>GNU Make</li> <li>SQLite 3</li> <li>GNU AWK</li> <li>Rubber</li> <li>Graphviz</li> <li>TeXLive</li> </ul> <h2>Structure</h2> <ul> <li><code>data/</code> contains the data examined in the paper</li> <li><code>scripts/</code> contains dedicated code for the paper</li> <li><code>logs/</code> contains logs generated during certain computations</li> </ul> <h2>Preservation of Guix</h2> <p>Some of the claims in the paper come from analyzing the Preservation of Guix (PoG) database as published on January 26, 2024. This database is the result of years of monitoring the extent to which the source code referenced by Guix packages is archived. This monitoring has been carried out by Timothy Sample who occasionally publishes reports on his personal website: <a href="https://ngyro.com/pog-reports/latest/">https://ngyro.com/pog-reports/latest/</a>. The database included in this package (<code>data/pog.sql</code>) was downloaded from <a href="https://ngyro.com/pog-reports/2024-01-26/pog.db">https://ngyro.com/pog-reports/2024-01-26/pog.db</a> and then exported to SQL format. In addition to the SQL file, the database schema is also included in this package as <code>data/schema.sql</code>.</p> <p>The database itself is largely the result of scripts, but also of manual adjustments (where necessary or convenient). The scripts are available at <a href="https://git.ngyro.com/preservation-of-guix/">https://git.ngyro.com/preservation-of-guix/</a>, which is preserved in the Software Heritage archive as well: <a href="https://archive.softwareheritage.org/swh:1:snp:efba3456a4aff0bc25b271e128aa8340ae2bc816;origin=https://git.ngyro.com/preservation-of-guix">https://archive.softwareheritage.org/swh:1:snp:efba3456a4aff0bc25b271e128aa8340ae2bc816;origin=https://git.ngyro.com/preservation-of-guix</a>. These scripts rely on the availability of source code in certain locations on the Internet, and therefore will not yield exactly the same result when run again.</p> <h3>Analysis</h3> <p>Here is an overview of how we use the PoG database in the paper. The exact way it is queried to produce graphs and tables for the paper is laid out in the Makefile.</p> <p>The <code>pog-types.sql</code> query gives the counts of each source type (e.g. “git” or “tar-gz”) for each commit covered by the database.</p> <p>The <code>pog-status.sql</code> query gives the archival status of the sources by commit. For each commit, it produces a count of how many sources are <em>stored</em> in the Software Heritage archive, <em>missing</em> from it, or <em>unknown</em> if stored or missing. The <code>pog-status-total.sql</code> query does the same thing but over all sources without sorting them into individual commits.</p> <p>The <code>disarchive-ratio.sql</code> query estimates the success rate of Disarchive disassembly.</p> <p>Finally, the <code>swhid-ratio.sql</code> query gives the proportion of sources for which the PoG database has an SWHID.</p> <h3>Estimating missing sources</h3> <p>The Preservation of Guix database only covers sources from a sample of commits to the Guix repository. This greatly simplifies the process of collecting the sources at the risk of missing a few. We estimate how many are missed by searching Guix’s Git history for Nix-style base-32 hashes. The result of this search is compared to the hashes in the PoG database.</p> <p>A naïve search of Git history results in an over estimate due to Guix’s branch development model. We find hashes that were never exposed to users of ‘guix pull’. To work around this, we also approximate the history of commits available to ‘guix pull’. We do this by scraping push events from the guix-commits mailing list archives (<code>data/guix-commits.mbox</code>). Unfortunately, those archives are not quite complete. Missing history is reconstructed in the <code>data/missing-links.txt</code> file.</p> <p>This estimate requires a copy of the Guix Git repository (not included in this package). The repository can be obtained from GNU at <a href="https://git.savannah.gnu.org/git/guix.git">https://git.savannah.gnu.org/git/guix.git</a> or from the Software Heritage archive: <a href="https://archive.softwareheritage.org/swh:1:snp:9d7b8dcf5625c17e42d51357848baa226b70e4bb;origin=https://git.savannah.gnu.org/git/guix.git">https://archive.softwareheritage.org/swh:1:snp:9d7b8dcf5625c17e42d51357848baa226b70e4bb;origin=https://git.savannah.gnu.org/git/guix.git</a>. Once obtained, its location must be specified in the Makefile.</p> <p>To generate the estimate, use:</p> <pre><code>guix time-machine -C channels.scm \ -- shell -C -m manifest.scm \ -- make data/missing-sources.txt </code></pre> <p>If not using Guix, you will need additional software beyond what is used to generate the paper:</p> <ul> <li>GNU Guile</li> <li>GNU Bash</li> <li>GNU Mailutils</li> <li>GNU Parallel</li> </ul> <h2>Measuring link rot</h2> <p>In order to measure link rot, we ran Guix Scheme scripts, i.e., scripts that exploit Guix as a Scheme library. The scripts depend on the state of world at the very specific moment when they ran. Hence, it is not possible to reproduce the exact same outputs. However, their tendency over the passing of time should be very similar. For running them, you need an installation of <a href="https://guix.gnu.org/manual/deve/en/html_node/Installation.html">Guix</a>. For instance,</p> <pre><code>guix repl -q scripts/table-per-origin.scm </code></pre> <p>When running these scripts for the paper, we tracked their output and saved it inside the <code>logs</code> directory.</p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part III: Extra runs)
<p>This is Part III of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files for the extra simulations used in the paper (Figures S3, S8-S10).</p> <p>Each zip file corresponds to a model run. </p> <p>TIGER_XX.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 0-100.<br>TIGER_XX_100.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 100-200.<br>TIGER_XX_HYYY.zip: Scenario XX, hydro-morphodynamics only, year YYY.</p> <p>Main scenarios:<br>- 01: Spartina (Figures 1-5, S3-S10)<br>- 02: Salicornia (Figures 1-5, S3-S10)<br>- 83: No vegetation (Figures 1-5, S3, S8-S10)</p> <p>Additional scenarios:<br>- 146: Spartina, low bulk drag coefficient (Figure S3)<br>- 147: Spartina, very low bulk drag coefficient (Figure S3)<br>- 148: Salicornia, low bulk drag coefficient (Figure S3)<br>- 149: Salicornia, very low bulk drag coefficient (Figure S3)<br>- 122: Spartina, low settling velocity (Figure S8)<br>- 123: Spartina, high settling velocity (Figure S8)<br>- 124: Salicornia, low settling velocity (Figure S8)<br>- 125: Salicornia, high settling velocity (Figure S8)<br>- 126: No vegetation, low settling velocity (Figure S8)<br>- 127: No vegetation, high settling velocity (Figure S8)<br>- 128: Spartina, low critical bed erosion shear stress (Figure S8)<br>- 129: Spartina, high critical bed erosion shear stress (Figure S8)<br>- 130: Salicornia, low critical bed erosion shear stress (Figure S8)<br>- 131: Salicornia, high critical bed erosion shear stress (Figure S8)<br>- 132: No vegetation, low critical bed erosion shear stress (Figure S8)<br>- 133: No vegetation, high critical bed erosion shear stress (Figure S8)<br>- 134: Spartina, low Partheniades constant (Figure S8)<br>- 143: Spartina, high Partheniades constant (Figure S8)<br>- 136: Salicornia, low Partheniades constant (Figure S8)<br>- 144: Salicornia, high Partheniades constant (Figure S8)<br>- 138: No vegetation, low Partheniades constant (Figure S8)<br>- 145: No vegetation, high Partheniades constant (Figure S8)<br>- 150: Spartina, low sediment dry bulk density (Figure S8)<br>- 151: Spartina, high sediment dry bulk density (Figure S8)<br>- 152: Salicornia, low sediment dry bulk density (Figure S8)<br>- 153: Salicornia, high sediment dry bulk density (Figure S8)<br>- 154: No vegetation, low sediment dry bulk density (Figure S8)<br>- 155: No vegetation, high sediment dry bulk density (Figure S8)<br>- 76: Spartina, replicate #1 (Figures S9-S10)<br>- 77: Spartina, replicate #2 (Figures S9-S10)<br>- 78: Spartina, replicate #3 (Figures S9-S10)<br>- 88: Spartina, replicate #4 (Figures S9-S10)<br>- 80: Salicornia, replicate #1 (Figures S9-S10)<br>- 81: Salicornia, replicate #2 (Figures S9-S10)<br>- 82: Salicornia, replicate #3 (Figures S9-S10)<br>- 89: Salicornia, replicate #4 (Figures S9-S10)<br>- 85: No vegetation, replicate #1 (Figures S9-S10)<br>- 86: No vegetation, replicate #2 (Figures S9-S10)<br>- 87: No vegetation, replicate #3 (Figures S9-S10)<br>- 90: No vegetation, replicate #4 (Figures S9-S10)</p> <p> </p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part V: Figures)
<p>This is Part V of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files to generate the figures of the paper.</p> <p>To be able to run the scripts as is, the path (at the beginning of each script) to the following folders must be updated:</p> <p>Runs (includes all model run folders from Part II and Part III)<br>Post (includes all post-processing folders from Part IV)</p>
Source Code and Simulation Results: Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna
<h3>Summary</h3> <p>This publication supplements the article "Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna" with tabulated data and Matlab code that allows the reproduction of the results. Within the article, the chiral behavior of single and double plasmonic nano antennas made from silver is numerically investigated with a focus on the coupling of a linear polarized dipole as an excitation source to the helix.</p> <h3>Simulation Setup - FEM Simulations</h3> <p>The script "run_wavlengthscan.m" allows to reproduce all simulations of the article. It can be chosen between the single and double helices, by specifying the keys parameter "keys.doppelhelix" where 0 gives a single and 1 a double helix. The number of turns can be specified by choosing "keys.case". The dipol is located within a 20nm thick hBN substrate layer, on glass (BK7). Results of the Purcell enhancement can be plotted using the scripts "display_results_single_helix.m" and "display_results_doublehelix.m" in the folder "results". The far-field plots can be reproduced using the scripts "display_farfiel_polarization_single_helix.m" and "display_farfiel_polarization_double_helix.m" of the folder "FunctionsAndScripts".</p> <p>The template for the mesh is contained in the folder "generate_grid_file", where the parameters of the helix (for example: radius, tube radius, and pitch height) can be modified.</p> <p>Within the folder "project3D" all required .jcm files are stored. Copy the "grid.jcm" file with the geometry of interest to this folder to perform simulations.</p> <p>All required keys parameters for the JCM template files (.jcmt, jcmpt) are set within the functions "set_numerical_parameter.m" and "set_physical_parameters.m", contained in the folder "FunctionsAndScripts". Therein, the function "set_sources.m" specifies the parameters for the dipole excitation, such as the position, and the strength (equivalent to the polarization).</p> <h3>Semi-Analytical Model</h3> <p>The Jupyter notebook "Semi_Analytical_Plasmonic_Helix.ipynb" contains the commented Python script for the semi-analytical design tool used to obtain far-field radiation patterns of the single helix. This semi-analytical design tool is based on an analytical model developed in [4]. The script can be divided into three parts. First, the single helix is defined, and a linear wavelength scaling law [5] is used to determine the illuminating wavelengths at which Fabry-Pérot resonances occur. Second, the overlap integral between the mode current on the helix and the incident electric field is evaluated for a given direction of incident light. Thirdly, the direction of incidence is varied to obtain the far-field radiation patterns. The script allows for the radiation patterns to be exported as a .csv file. Alternatively, the radiation patterns can be plotted directly using the provided single_plot functions.</p> <h3>Material</h3> <p>The material data has been taken from the <a href="https://refractiveindex.info/" target="_blank" rel="noopener">refractiveindex.info</a> database. For silver the data is taken from tabulated data from Johnson and Christy [1] . The dispersion relation for hBN comes from [2] and tabulated data for glass (BK7) from [3]. The MATLAB script "material_properties_plot.m" plots the material fits above the wavelengths of interest. The required tabulated data is given in the folder "material_data".</p> <p>With 'material_properties_plot.m' the fits to the material data can be reproduced and plotted.</p> <h3>Usage</h3> <p>The .zip folder Helix_FEM contains all data and scripts to reproduce the plots from the 3D FEM simulations.</p> <p>The Jupyter Notebook Semi_Analytical_Plasmonic_Helix reprouces the results from the semi-analytical model.</p> <h3>Requirements</h3> <ul> <li>JCMsuite (at least 5.4.0)</li> <li>MATLAB (tested with version R2023b)</li> <li>Python (tested with Version 3.10.9)</li> <li>Jupyter Notebook (tested with 6.5.2) </li> </ul> <p>To run the simulations with JCMsuite you must replace corresponding placeholders with a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>.</p> <h3>References</h3> <p>[1] P. B. Johnson and R.-W. Christy, “Optical constants of the noble metals,” Phys. Rev. B 6, 4370 (1972).</p> <p>[2] S.-Y. Lee, T.-Y. Jeong, S. Jung, and K.-J. Yee, “Refractive index dispersion of hexagonal boron nitride in the visible and near-infrared,” Phys. Status Solidi B 256, 1800417 (2019).</p> <p>[3] “SCHOTT Zemax catalogue 2017-01-20b,” (2017).</p> <div>[4] K. Höflich et al., "Resonant behavior of a single plasmonic helix." Optica 6, 1098(2019).</div> <div> </div> <div>[5]L. Novotny, "Effective wavelength scaling for optical antennas", Phys. Rev. Lett. 98,266802 (2007).</div>
Data and source code for: Recent adaptation in a threatened salmonid revealed by museum genomics
<p>Steelhead/rainbow trout (Oncorhynchus mykiss) is an imperiled salmonid with two main life history strategies: migrate to the ocean or remain in freshwater. Domesticated hatchery forms of this species have been stocked into almost all California waterbodies, possibly resulting in introgression into natural populations and altered population structure. </p> <p>We compared whole-genome sequence data from contemporary populations against a set of museum population samples of steelhead from the same locations that were collected prior to most hatchery stocking. </p> <p>We observed minimal introgression and few steelhead-hatchery trout hybrids despite a century of extensive stocking. Our historical data show signals of introgression with a sister species and indications of an early hatchery facility. Finally, we found that migration-associated haplotypes have become less frequent over time, a likely adaptation to decreased opportunities for migration. Since contemporary migration-associated haplotype frequencies have been used to guide species management, we consider this to be a rare example of shifting baseline syndrome that has been validated with historical data. </p> <p>We suggest cautious optimism that a century of hatchery stocking has had minimal impact on California steelhead population genetic structure, but we note that continued shifts in life history may lead to further declines in the ocean-going form of the species. </p>
Source Code—Motorized chain models of the ideal chromosome
<p>Code and data for reproducing the findings in "Motorized chain models of the ideal chromosome" by Zhiyu Cao & Peter G Wolynes</p> <p>We carried out hybrid Brownian-Gillespie simulations of the polymer dynamics for the swimming motors. The programs can run in parallel on Cuda software. We use periodic boundary conditions without considering the self-avoiding and excluded volume effect. In addition, we solve the self-consistent equation by an iterative method.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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