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235 results for “Lattices”
Datasets for "Flavour-selective localization in interacting lattice fermions"
<p>This submission includes the datasets shown in the figures of journal article</p> <p>"Flavour-selective localization in interacting lattice fermions" by D. Tusi et al.<br> DOI: 10.1038/s41567-022-01726-5</p> <p>The naming of the files corresponds to the figure numbering in the original article.</p>
Confs N_f=2+1 physical point fullQCD T=230 MeV, lattice = 48^3 x 12
<p>README (Written by Claudio Bonanno: claudio.bonanno@fi.infn.it)</p> <p>Archives of thermalized and well-decorrelated gauge configurations</p> <p>Discretization: Symanzik-improved gauge action & N_f = 2+1 flavors of rooted stout staggered fermions with physical quark masses<br> and physical pion mass</p> <p>Theory details: T=230 MeV, 48^3 x 12 lattice with a lattice spacing a = 0.07149 fm.</p> <p>Algorithm: standard RHMC in the presence of a topological bias potential. For the details about the topological bias potential see https://arxiv.org/abs/1807.07954</p> <p>The topological bias potential can be removed afterwards through standard reweighting.</p> <p>Conf name: stored_conf.${conf_ID} where conf_ID is equal to the RHMC step the conf has been saved.</p> <p>Conf have been saved every 30 RHMC seps in binary files according to the ILDG format for standard C programs.</p> <p>For more details about the ILDG format see, e.g., https://www-zeuthen.desy.de/~pleiter/ildg/ildg-file-format-1.1.pdf</p> <p>The auto-correlation time of Q is about 8 RHMC steps in our setup.</p>
Determinant Quantum Monte Carlo data for the Hubbard model on the square and honeycomb lattice.
<p>Data generated with QUEST 1.4.9. For documentation see these two homepages:<br> Original homepage: http://quest.ucdavis.edu/<br> Newest version available at: https://code.google.com/archive/p/quest-qmc/</p> <p>Available data from equal time measurements:</p> <ul> <li>up-up charge correlation function</li> <li>up-dn charge correlation function</li> <li>sz-sz spin correlation function</li> <li>pair correlation function</li> <li>kinetic energy</li> <li>total energy</li> <li>chi thermal</li> <li>squared magnetization</li> <li>ZZ AF structure factor</li> </ul> <p>Data for the square lattice calculated for</p> <ul> <li>lattice sizes 8x8, 10x10, 12x12</li> <li>trotter discretizations 0.05, 0.1, 0.2</li> <li>U 0.0 to 7.1 in steps of 0.1</li> </ul> <p>Data for the honeycomb lattice calculated for</p> <ul> <li>lattice sizes 6x6, 9x9, 12x12</li> <li>trotter discretizations 0.05, 0.1, 0.2</li> <li>U 0.0 to 7.1 in steps of 0.1</li> </ul> <p>All energies are in units of the hopping parameters which is set to t=1. All simulations are done for half filling.</p> <p>The data are used in the publication "First-order metal-insulator transitions in the extended Hubbard model due to self-consistent screening of the effective interaction" available on the arXiv (arXiv:1706.09644). There it is used to do an extrapolation of finite size and finite trotter errors and finally calculate derivatives of charge correlation functions w.r.t. the interaction U.</p> <p>The data are available in hdf5 archives and can easily be accessed, e.g., with python and h5py. An example python script is included. Relevant input parameters are included in the h5 files.</p> <p>All calculated quantities are averaged over multiple consecutive simulations, which is why the data is not presented in the usual QUEST output. This was necessary due to limited walltime on the used supercomputer.</p> <p>This version (v2) includes the number of bins used in each simulation and a slighlty changed python script to read the data.</p>
Lattice diagrams of elastic maps
<p>This collection of figures is a supplement to the presentation by Gupta and Tape (2024) and builds upon the work of Tape and Tape (2021, 2022, 2024). The collection contains this file, 28 composite pdf files, and three additional composite pdf files. We examine 28 elastic maps, each of which is represented by a 6 x 6 symmetric matrix having 21 parameters (in general). For each map we calculate the closest elastic map in each of 8 symmetry classes, and we depict these 8 elastic maps within a lattice diagram.</p>
Correlator data for determination of the I=1 pion-pion scattering amplitude and timelike pion form factor from Nf=2+1 lattice QCD
<p>Bootstrap samples of all correlation functions involved in the analysis of pion-pion scattering data and the timelike pion form factor described in "The I =1 pion-pion scattering amplitude and timelike pion form factor from N f = 2 + 1 lattice QCD". Additionally, an analysis file is provided for each ensemble which stored the analysis choices made in that work. These data are intended for use with the Jupyter notebook located in https://github.com/ebatz/jupan, which provides an interface. This notebook performs the entire analysis chain discussed in the above paper. </p>
The complex non-collinear magnetic orderings in Ba2YOsO6: A new approach to tuning spin-lattice interactions and controlling magnetic orderings in frustrated complex oxides
<p><strong>Project abstract</strong>: Frustrated magnets are one class of fascinating materials that host many intriguing phases such as spin ice, spin liquid and complex long-range magnetic orderings at low temperatures. In this work we use first-principles calculations to find that in a wide range of magnetically frustrated oxides, at zero temperature a number of non-collinear magnetic orderings are more stable than the type-I collinear ordering that is observed at finite temperatures. The emergence of non-collinear orderings in those complex oxides is due to higher-order exchange interactions that originate from second-row and third-row transition metal elements. This implies a collinear-to-noncollinear spin transition at sufficiently low temperatures in those frustrated complex oxides. Furthermore, we find that in a particular oxide Ba2YOsO6, experimentally feasible uniaxial strain can tune the material between two different non-collinear magnetic orderings. Our work predicts new non- collinear magnetic orderings in frustrated complex oxides at very low temperatures and provides a mechanical route to tuning complex non-collinear magnetic orderings in those materials. <br> <br> <strong>About this entry</strong>: We provide the input files of our DFT calculations for the studied complex oxides. The structures in POSCAR format and the INCAR files for all stabilized magnetic orderings in our study are all included. These files can be directly used into DFT calculations with VASP. Only the versions of PAW potentials are included in POT.info files owing to the VASP license restrictions.</p>
On the mixing between flavor singlets in lattice gauge theories coupled to matter fields in multiple representations - data release
<p>This release contains all data and metadata used to prepare the publication <a href="https://arxiv.org/abs/2405.05765"><em>On the mixing between flavor singlets in lattice gauge theories coupled to matter fields in multiple representations</em> [2405.05765].</a> </p> <p>If you encounter difficulties downloading the large files, we recommend using <a href="../records/11142962">zenodo-get</a>. This provides a command-line downloader for any Zenodo record. For unstable connections we recommend using it with the -w flag to generate a list all files in this Zenodo record. This can then be used with tools such as <a href="https://www.gnu.org/software/wget/">wget</a> to resume partial downloads as</p> <p><code>zenodo_get RECORD_ID_OR_DOI -w - | xargs wget -c<br>zenodo_get RECORD_ID_OR_DOI </code></p> <p>(The second line ensures that the downloads completed correctly, and that the md5 hashes match)<br><br>Further details are given in the file README.md.</p> <p>The work of EB and BL is supported in part by the EPSRC ExCALIBUR programme ExaTEPP (project EP/X017168/1). The work of EB, BL, MP, and FZ has been supported by the STFC Consolidated Grant No. ST/X000648. The work of EB has also been supported by the UKRI Science and Technology Facilities Council (STFC) Research Software Engineering Fellowship EP/V052489/1. The work of NF has been supported by the STFC Consolidated Grant No. ST/X508834/1. The work of DKH was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B06033701). The work of DKH was further supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1A4A5031460). The work of JWL is supported by IBS under the project code, IBS-R018-D1. The work of HH and CJDL is supported by the Taiwanese MoST grant 109-2112-M-009-006-MY3 and NSTC grant 112-2112-M-A49-021-MY3. The work of CJDL is also supported by Grants No. 112-2639-M-002-006-ASP and No. 113-2119-M-007-013. The work of BL and MP has been further supported in part by the STFC Consolidated Grant No. ST/T000813/1.<br>BL and MP received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under Grant Agreement No.~813942. The work of DV is supported by STFC under Consolidated Grant No. ST/X000680/1.</p> <p>Numerical simulations have been performed on the DiRAC Extreme Scaling service at the University of Edinburgh, and on the DiRAC Data Intensive service at Leicester.<br>The DiRAC Extreme Scaling service is operated by the Edinburgh Parallel Computing Centre on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). This equipment was funded by BEIS capital funding via STFC capital grant ST/R00238X/1 and STFC DiRAC Operations grant ST/R001006/1. DiRAC is part of the National e-Infrastructure</p>
Fast calculation methods for the magnetic field of particle lattices: Datasets and scripts
<div>*********************************************** README.txt **************************************************</div> <div> </div> <div>Title: Fast calculation methods for the magnetic field of particle lattices: </div> <div>Datasets and scripts</div> <div>Version: 1.0</div> <div>Date of Release: 2024/10/11</div> <div>Identifier: doi:10.5281/zenodo.13930969</div> <div>Permalink: http://dx.doi.org/10.5281/zenodo.13930969</div> <div> </div> <div>*************************************************************************************************************</div> <div> </div> <div>Associated publication: I. Royo-Silvestre, D. Gandia, J. J. Beato-López, E. Garaio, C. Gómez-Polo </div> <div>"Fast calculation methods for the magnetic field of particle lattices" </div> <div>(paper yet to be published)</div> <div> </div> <div>Link to publication: (paper yet to be published)</div> <div> </div> <div>Suggested citation: Please reference the associated publication above when using any datasets or</div> <div> materials described in this README file.</div> <div> </div> <div>Contact information: Isaac Royo Silvestre, </div> <div>Universidad Pública de Navarra, </div> <div>Pamplona, Spain, </div> <div>isaac.royo@unavarra.es</div> <div> </div> <div>License: CC BY 4.0</div> <div> </div> <div>------------------------------------------------------------------------------------------------------------</div> <div> </div> <div>This directory contains the following datasets and supplementary materials:</div> <div> </div> <div> ------------------------------</div> <div> SCRIPTS</div> <div> ------------------------------</div> <div> </div> <div> - scripts.zip Matlab scripts (compressed zip file) used to calculate the magnetic field of </div> <div>lattices of magnetic particles by analytical and semianalytical methods (more information in the associated paper) </div> <div> </div> <div> --------------------------------</div> <div> DATASETS</div> <div> --------------------------------</div> <div> </div> <div> - data.zip: Tabular data required to plot curves (compressed zip file) in csv format,</div> <div>also data used to obtain average values</div> <div> </div> <div> </div> <div>Specific documentation of each file is described in readme files.</div> <div> </div> <div>Refer to the original manuscript (see above) for additional information regarding the collection and generation of these data.</div> <div> </div> <div>------------------------------------------------------------------------------------------------------------</div> <div> </div> <div> ---------------------------------------------------------------------</div> <div> DOCUMENTATION FOR 'scripts.zip'</div> <div> ---------------------------------------------------------------------</div> <div> </div> <div> The zip file contains another readme.txt file (that explains the content of the zip file in detail), </div> <div>and multiple .m files. m files are Matlab scripts, text files that can be read using any text editor. However it has to be executed via Matlab, scripts contain documentation as comments.</div> <div> </div> <div> ---------------------------------------------------------------</div> <div> DOCUMENTATION FOR 'data.zip'</div> <div> ---------------------------------------------------------------</div> <div> </div> <div> The zip file contains another readme.txt file (that explains the content of the zip file in detail), </div> <div>multiple .dat files with data used to obtain averaged valus (see format in the readme.txt </div> <div>contained in the zip), and a folder "curves".</div> <div>The curves folder contains tabular data in .csv files, these files can be used to plot the curves</div> <div>in the manuscript.</div> <p> </p>
Lattice subpolygons of a square with given sidelength
<p>This record contains all lattice subpolygons of the square of sidelength \(m\) for \(1 \leq m \leq 10\). There is one file for each sidelength. We use the <a href="https://www.hdfgroup.org/solutions/hdf5/" target="_blank" rel="noopener">HDF5</a> file format to store the polygons. The polygons have been obtained using <a href="https://github.com/justus-springer/RationalPolygons.jl" target="_blank" rel="noopener">RationalPolygons.jl</a>.</p> <h3>Structure of the HDF5 files</h3> <p>In each file, the polygons are split into different datasets according to their normalized area and number of vertices. We use "a" to denote the normalized area (twice the euclidian area) and "n" to denote the number of vertices. For example, the subpolygons of the square of sidelength \(4\) with normalized area \(20\) having \(6\) vertices are located in "m4.h5" under the dataset "/a20/n6". Each dataset of polygons is one-dimensional with one entry per polygon. A polygon is stored using a compound datatype with \(2 \cdot n\) fields of integers. These integers are the vertices of the polygon stored in column major layout. For example, a triangle with vertices \((x_1,y_1), (x_2,y_2)\) and \((x_3,y_3)\) is stored as the tuple \((x_1,y_1,x_2,y_2,x_3,y_3)\). Moreover, each file contains the special two-dimensional dataset "numbers_of_polygons" that stores the numbers of polygons for a given normalized volume and number of vertices.</p>
Rational polygons with exactly one interior lattice point
<p>This record contains</p> <ul> <li>maximal \(k\)-rational polygons with exactly one interior lattice point for \(1 \leq k \leq 10\),</li> <li>all \(k\)-rational polygons with exactly one interior lattice point for \(1 \leq k \leq 4\),</li> <li>all Ehrhart quasipolynomials of these polygons for \(1 \leq k \leq 5\).</li> <li>all \(k\)-rational LDP polygons (=almost \(k\)-hollow LDP polygons) for \(1 \leq k \leq 5\).</li> </ul> <p>The maximal polygons are stored as text files with one polygon per line. Each line contains the vertices of the polygon multiplied by \(k\) (so that they are integral). The other polygons as well as the Ehrhart quasipolynomials are stored using the <a href="https://www.hdfgroup.org/solutions/hdf5/">HDF5</a> file format. All polygons and Ehrhart quasipolynomials have been obtained using <a href="https://github.com/justus-springer/RationalPolygons.jl">RationalPolygons.jl</a>.</p> <h3>Structure of the HDF5 files for polygons</h3> <p>In the HDF5 files "all_k<k>.h5", the polygons are split into different datasets according to their normalized area and number of vertices. We use "a" to denote the normalized area (twice the euclidian area) and "n" to denote the number of vertices. For example, the \(3\)-rational polygons with normalized area \(30\) having \(5\) vertices are located in "k3_all.h5" under the dataset "/a30/n5". Each dataset of polygons is one-dimensional with one entry per polygon. A polygon is stored using a compound datatype with \(2 \cdot n\) fields of integers. These integers are the vertices of the polygon multiplied by \(k\), stored in column major layout. For example, a triangle with vertices \((x_1,y_1), (x_2,y_2)\) and \((x_3,y_3)\) is stored as the tuple \((k\cdot x_1,k\cdot y_1,k \cdot x_2,k\cdot y_2,k \cdot x_3,k \cdot y_3)\). Moreover, each file contains the special two-dimensional dataset "numbers_of_polygons" that stores the numbers of polygons for a given normalized volume and number of vertices.</p> <h3>Structure of the HDF5 files for Ehrhart quasipolynomials</h3> <p>In the HDF5 files "ehrhart_k<k>.h5", the Ehrhart quasipolynomials are split into different datasets according to the normalized area of the associated polygon. We use "a" for the normalized area (twice the euclidian area). For example, the Ehrhart quasipolynomials of all \(3\)-rational polygons with normalized area \(30\) are located in "ehrhart_k3.h5" under the dataset "/a30". Each Ehrhart quasipolynomial is stored as a \(3 \times k\) integral matrix. Hence each dataset is three-dimensional, with the first dimension to enumerate the quasipolynomials and the latter two dimensions to store each quasipolynomial itself. For the way Ehrhart quasipolynomials are encoded as \(3 \times k\) integral matrices, we refer to the <a href="https://justus-springer.github.io/RationalPolygons.jl/dev/polygons/#Ehrhart-Theory">relevant documentation of RationalPolygons.jl</a>.</p>
Rational polygons with no interior lattice points
<p>This record contains</p> <ul> <li>maximal \(k\)-rational polygons with no interior lattice points for \(1 \leq k \leq 21\),</li> <li>all \(k\)-rational polygons with no interior lattice points, except those that can be realized in the strip \( \mathbb{R} \times [0,1] \) for \(1 \leq k \leq 6\),</li> <li>all Ehrhart quasipolynomials of these polygons for \(1 \leq k \leq 6\).</li> </ul> <p>The maximal polygons are stored as text files with one polygon per line. Each line contains the vertices of the polygon multiplied by \(k\) (so that they are integral). For \(1 \leq k \leq 6\), the files that contain all polygons as well as the Ehrhart quasipolynomials are stored using the <a href="https://www.hdfgroup.org/solutions/hdf5/">HDF5</a> file format. All polygons and Ehrhart quasipolynomials have been obtained using <a href="https://github.com/justus-springer/RationalPolygons.jl">RationalPolygons.jl</a>.</p> <h3>Structure of the HDF5 files for polygons</h3> <p>In the HDF5 files "all_k<k>.h5", the polygons are split into different datasets according to their normalized area and number of vertices. We use "a" to denote the normalized area (twice the euclidian area) and "n" to denote the number of vertices. For example, the \(3\)-rational polygons with normalized area \(30\) having \(5\) vertices are located in "all_k3.h5" under the dataset "/a30/n5". Each dataset of polygons is one-dimensional with one entry per polygon. A polygon is stored using a compound datatype with \(2 \cdot n\) fields of integers. These integers are the vertices of the polygon multiplied by \(k\), stored in column major layout. For example, a triangle with vertices \((x_1,y_1), (x_2,y_2)\) and \((x_3,y_3)\) is stored as the tuple \((k\cdot x_1,k\cdot y_1,k \cdot x_2,k\cdot y_2,k \cdot x_3,k \cdot y_3)\). Moreover, each file contains the special two-dimensional dataset "numbers_of_polygons" that stores the numbers of polygons for a given normalized volume and number of vertices.</p> <h3>Structure of the HDF5 files for Ehrhart quasipolynomials</h3> <p>In the HDF5 files "ehrhart_k<k>.h5", the Ehrhart quasipolynomials are split into different datasets according to the normalized area of the associated polygon. We use "a" for the normalized area (twice the euclidian area). For example, the Ehrhart quasipolynomials of all \(3\)-rational polygons with normalized area \(30\) are located in "ehrhart_k3.h5" under the dataset "/a30". Each Ehrhart quasipolynomial is stored as a \(3 \times k\) integral matrix. Hence each dataset is three-dimensional, with the first dimension to enumerate the quasipolynomials and the latter two dimensions to store each quasipolynomial itself. For the way Ehrhart quasipolynomials are encoded as \(3 \times k\) integral matrices, we refer to the <a href="https://justus-springer.github.io/RationalPolygons.jl/dev/polygons/#Ehrhart-Theory">relevant documentation of RationalPolygons.jl</a>.</p>
Half-integral polygons with few interior lattice points
<p>This record contains</p> <ul> <li>maximal half-integral polygons with up to 40 interior lattice points,</li> <li>all half-integral polygons with up to 12 interior lattice points,</li> <li>all Ehrhart quasipolynomials of half-integral polygons with up to 16 interior lattice points,</li> <li>all half-integral polygons with up to 15 collinear interior lattice points.</li> </ul> <p>The maximal polygons are stored as text files with one polygon per line. Each line contains the vertices of the polygon multiplied by two (so that they are integral). The other polygons as well as the Ehrhart quasipolynomials are stored using the <a href="https://www.hdfgroup.org/solutions/hdf5/">HDF5</a> file format. All polygons and Ehrhart quasipolynomials have been obtained using <a href="https://github.com/justus-springer/RationalPolygons.jl">RationalPolygons.jl</a>.</p> <h3>Structure of the HDF5 files for polygons</h3> <p>In the HDF5 files "all_i<i>.h5", the polygons are split into different datasets according to their normalized area and number of vertices. We use "a" to denote the normalized area (twice the euclidian area) and "n" to denote the number of vertices. For example, the half-integral polygons with \(8\) interior lattice points having normalized area \(60\) with \(5\) vertices are located in "all_i8.h5" under the dataset "/a60/n5". Each dataset of polygons is one-dimensional with one entry per polygon. A polygon is stored using a compound datatype with \(2 \cdot n\) fields of integers. These integers are the vertices of the polygon multiplied by two, stored in column major layout. For example, a triangle with vertices \((x_1,y_1), (x_2,y_2)\) and \((x_3,y_3)\) is stored as the tuple \((2\cdot x_1,2\cdot y_1,2 \cdot x_2,2\cdot y_2,2 \cdot x_3,2 \cdot y_3)\). Moreover, each file contains the special two-dimensional dataset "numbers_of_polygons" that stores the numbers of polygons for a given normalized volume and number of vertices.</p> <h3>Structure of the HDF5 files for Ehrhart quasipolynomials</h3> <p>In the HDF5 files "ehrhart_k<k>.h5", the Ehrhart quasipolynomials are split into different datasets according to the normalized area of the associated polygon. We use "a" for the normalized area (twice the euclidian area). For example, the Ehrhart quasipolynomials of all half-integral polygons \(8\) interior lattice points having normalized area \(60\) are located in "ehrhart_i8.h5" under the dataset "/a60". Each Ehrhart quasipolynomial is stored as a \(3 \times 2\) integral matrix. Hence each dataset is three-dimensional, with the first dimension to enumerate the quasipolynomials and the latter two dimensions to store each quasipolynomial itself. For the way Ehrhart quasipolynomials are encoded as \(3 \times 2\) integral matrices, we refer to the <a href="https://justus-springer.github.io/RationalPolygons.jl/dev/polygons/#Ehrhart-Theory">relevant documentation of RationalPolygons.jl</a>.</p>
3-rational polygons with few interior lattice points
<p>This record contains</p> <ul> <li>maximal 3-rational polygons with up to 5 interior lattice points,</li> <li>all 3-rational polygons with up to 4 interior lattice points,</li> <li>all Ehrhart quasipolynomials of 3-rational polygons with up to 5 interior lattice points.</li> </ul> <p>The maximal polygons are stored as text files with one polygon per line. Each line contains the vertices of the polygon multiplied by two (so that they are integral). The other polygons as well as the Ehrhart quasipolynomials are stored using the <a href="https://www.hdfgroup.org/solutions/hdf5/">HDF5</a> file format. All polygons and Ehrhart quasipolynomials have been obtained using <a href="https://github.com/justus-springer/RationalPolygons.jl">RationalPolygons.jl</a>.</p> <h3>Structure of the HDF5 files for polygons</h3> <p>In the HDF5 files "all_i<i>.h5", the polygons are split into different datasets according to their normalized area and number of vertices. We use "a" to denote the normalized area (twice the euclidian area) and "n" to denote the number of vertices. For example, the 3-rational polygons with \(8\) interior lattice points having normalized area \(60\) with \(5\) vertices are located in "all_i8.h5" under the dataset "/a60/n5". Each dataset of polygons is one-dimensional with one entry per polygon. A polygon is stored using a compound datatype with \(2 \cdot n\) fields of integers. These integers are the vertices of the polygon multiplied by three, stored in column major layout. For example, a triangle with vertices \((x_1,y_1), (x_2,y_2)\) and \((x_3,y_3)\) is stored as the tuple \((3\cdot x_1,3\cdot y_1,3 \cdot x_2,3\cdot y_2,3 \cdot x_3,3 \cdot y_3)\). Moreover, each file contains the special two-dimensional dataset "numbers_of_polygons" that stores the numbers of polygons for a given normalized volume and number of vertices.</p> <h3>Structure of the HDF5 files for Ehrhart quasipolynomials</h3> <p>In the HDF5 files "ehrhart_k<k>.h5", the Ehrhart quasipolynomials are split into different datasets according to the normalized area of the associated polygon. We use "a" for the normalized area (twice the euclidian area). For example, the Ehrhart quasipolynomials of all 3-rational polygons \(8\) interior lattice points having normalized area \(60\) are located in "ehrhart_i8.h5" under the dataset "/a60". Each Ehrhart quasipolynomial is stored as a \(3 \times 3\) integral matrix. Hence each dataset is three-dimensional, with the first dimension to enumerate the quasipolynomials and the latter two dimensions to store each quasipolynomial itself. For the way Ehrhart quasipolynomials are encoded as \(3 \times 3\) integral matrices, we refer to the <a href="https://justus-springer.github.io/RationalPolygons.jl/dev/polygons/#Ehrhart-Theory">relevant documentation of RationalPolygons.jl</a>.</p>
Spectral response of disorder-free localized lattice gauge theories
<p>Raw data for all figures in the manuscript "Spectral response of disorder-free localized lattice gauge theories"</p>
Unit cells and resummed interactions for the calculations in "Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions"
<p>This directory contains all the unit cells with the respective resummed interactions used for the optimisation procedure to obtain the results in the work "Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions[1]".</p> <p>To get an overview of the organization of the directory and a description of the data we recommend the README file.</p> <p>[1]: J. A. Koziol et al., Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions, <a href="https://10.21468/SciPostPhys.14.5.136">10.21468/SciPostPhys.14.5.136</a>, 2023</p>
Nonlinear THz Control of the Lead Halide Perovskite Lattice - Experimental data
<p>Experimental data for the paper "<strong>Nonlinear THz Control of the Lead Halide Perovskite Lattice</strong>", published with open-access in <em>Science Advances</em> under <a href="https://doi.org/10.1126/sciadv.adg3856">https://doi.org/10.1126/sciadv.adg3856</a></p> <p>The data was measured at the Department of Physical Chemistry, Fritz Haber Institute of the Max Planck Society in Berlin.</p> <p>Contents:</p> <ul> <li>THz E-field data from Fig. 1</li> <li>THz-induced Kerr effect time domain data, fluence dependence, azimuthal angle dependence, and corresponding THz fields for MAPbBr3 and CsPbBr3 at room temperature from Fig. 2.</li> <li>THz-induced Kerr effect time domain data for MAPbBr3 single crystals and thin films for room temperature, 180K and 80K from Fig. 3.</li> <li>THz-induced Kerr effect experimental data and simulated Kerr signals from Fig. 4.</li> <li>THz-induced Kerr effect time domain data for MAPbBr3 single crystal at different THz fluences from Fig. 5a.</li> </ul> <p>Raw data and data of the Supplementary Materials (SM) will be provided upon request. Please contact Maximilian Frenzel (frenzel@fhi-berlin.mpg.de) and Sebastian F. Maehrlein (maehrlein@fhi-berlin.mpg.de) for such a request or for general questions.</p>
Dataset and scripts for "Non-zero temperature study of spin 1/2 charmed baryons using lattice gauge theory"
<p><strong>charmJ12Scripts</strong></p> <p>A set of scripts and folders to reproduce the analysis and plots in the spin 1/2 charm baryon paper which can be found at <a href="https://doi.org/10.1140/epja/s10050-024-01261-2">EPJA</a></p> <p> </p> <p>This repository includes the raw correlator data, the scripts and software used to analyse them as well as a script which can be run in order to reproduce the entire analysis, and particularly the figures in the manuscript.</p> <p> </p> <p><strong>correlators</strong></p> <p>Correlators from openqcd-fastsum-hadspec are zipped in the correlators folder. These are unzipped automatically by the script. The correlators are plain text files.</p> <p> </p> <p><strong>output</strong></p> <p>Analysis output is placed here. You do not need to look here in order to see the figures in the paper</p> <p> </p> <p><strong>code</strong></p> <p>The python code and scripts that do the analysis. There is some modularity here with the libraries in the lib folder</p> <p> </p> <p><strong>paperPlots</strong></p> <p>The plots from the paper will be generated here. They are not supplied with this repo as they can be found in the paper</p> <p> </p> <p><strong>plotXYData</strong></p> <p>The x-y and y-error data of each plot in the paper. Only 'scatter' style data is included. This is generated by the run script, but also supplied herein. It will be overwritten by the runscript</p> <p> </p> <p><strong>run</strong></p> <p>The folder where the main script needed to run all the analysis is.</p> <p> </p> <p><strong>Conda Notes</strong></p> <p>Install your favourite conda solution, such as <a href="https://docs.conda.io/en/latest/miniconda.html">https://docs.conda.io/en/latest/miniconda.html</a></p> <p> </p> <p>Switch to a faster environment solver</p> <p>This is optional, but likely will solve the dependencies much much faster. See <a href="https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community">https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community</a> <code>conda update -n base conda</code> <code>conda install -n base conda-libmamba-solver</code> <code>conda config --set solver libmamba</code></p> <p> </p> <p>Install Environment</p> <p><code>conda env create -f environment.yml</code></p> <p> </p> <p>Activate/Use</p> <p><code>conda activate charm</code></p> <p> </p> <p>Update (w. new packages)</p> <ol> <li>Edit <code>environment.yml</code></li> <li>Deactivate conda environment with <code>conda deactivate</code></li> <li>Update conda environment with <code>conda env update -f=environment.yml</code></li> </ol>
Thermal Phase Diagram of the Square Lattice Ferro-antiferromagnetic J1−J2 Heisenberg Model Data
<p>This repository contains raw data for the article "Thermal Phase Diagram of the Square Lattice Ferro-antiferromagnetic J1-J2 Heisenberg Model", Olivier Gauthé and Frédéric Mila, 2023.</p><p>Raw data is provided as json files into the archive data_PEPS_ferroJ1-J2/ subdirectory.zip. The file "data_mswt_ferroJ1-J2.json" contains modified spin wave theory data.</p><p><br>The jupyter notebook "plot_ferroJ1-J2.ipynb" provides scripts to load and visualize data, as well as reproducing figures from the paper.<br>It can be executed using<br>python version 3.9.17<br>numpy version 1.24.3<br>scipy version 1.10.1</p><p>All the data was generated using finite temperature PEPS. Refer to the paper for a complete methodological discussion. The source code to produce PEPS data is available upon reasonable request.</p><p>Olivier Gauthé<br>October 2023</p>
An association sequence suitable for producing ground-state RbCs molecules in optical lattices
<p>The data that support the findings of this study are uploaded here. All the data files are self-explanatory. They contain individual column names. </p> <p>The experimental data for Fig. 4(a) are in Fig4a_experimental_data.csv under the folders Fig_4>Fig_4a. To convert to binding energy there is a fitting algorithm in the Python code Feshbach_Fit.py .</p>
Spatial Tournament Data on a Periodic Lattice Tournament Size 5 - MSc Dissertation
<p>A data set that contains the results of 1000 spatial games, for the iterated prisoner's dilemma. The spatial topology used is a periodic lattice network, and the tournament size set is 5.</p>
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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.