Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
750
datasets available to search
ShareScore release 0.7.1
Dataset results
750 results for “coherence”
Weak probe readout of coherent impurity orbital superpositions in silicon
<p>Each file contains data which is similar to the one shown in Fig.3.b for different laser bandwidths: 001 and 002 - 0.078THz; 004 and 005 - 0.127THz; 007 and 009 - 0.223THz. The data files 001, 004, and 007 are the probe transmission changes as functions of the delay between one of the pumps and the probe in picoseconds (The other pump arrives a fixed time of 50ps before the probe). The data files 002, 005, and 009 are the same as 001, 004, and 007 but with the probe beam blocked. Please, see the paper for details. </p>
Data for "Coherent control of a few-channel hole type gatemon qubit"
<p>The files contain the raw and processed data used for the publication "Coherent control of a few-channel hole type gatemon qubit" in ASCII format. See readme.txt for details.</p><p> </p>
Data from: "Navigating through Complexity: Optimizing Cathodes for Organic Electrohydrogenation through Coherent Workflows"
<p>The data used in "<strong>Navigating through Complexity: Optimizing Cathodes for Organic Electrohydrogenation through Coherent Workflows</strong>". </p>
Mirror of "ENSPRESO - an open data, EU-28 wide, transparent and coherent database of wind, solar and biomass energy potentials"
<h2>Mirrored from Joint Research Centre Data Catalogue</h2><p><a href="https://data.jrc.ec.europa.eu/collection/id-00138#datasets">https://data.jrc.ec.europa.eu/collection/id-00138#datasets</a></p><blockquote><p>This collection contains datasets from ENSPRESO, an EU-28 wide, open dataset for energy models on renewable energy potentials, at national (NUTS0) and regional levels (NUTS2) for the 2010-2050 period. Within ENSPRESO, ENergy Systems Potential Renewable Energy SOurces, technical potentials are provided for wind, solar and biomass, based on coherent GIS-based land-restriction scenarios. For wind, resource evaluation also considers setback distances as well as high resolution geo-spatial wind speed data. For solar, potentials are derived from irradiation data and available area for solar applications. For biomass, agriculture, forestry and waste sectors are considered. The temporal resolution for wind and solar is both annual and year fractions (timeslices as used by JRC-EU-TIMES). ENSPRESO complements the EMHIRES collection, that provides meteorologically derived power time series at high temporal and spatial resolution. ENSPRESO can impact the results of any energy model by improving its analyses of the competition and complementarity of energy technologies.</p></blockquote><p><a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:RUIZ%20CASTELLO%20Pablo">RUIZ CASTELLO Pablo</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:NIJS%20Wouter">NIJS Wouter</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:TARVYDAS%20Dalius">TARVYDAS Dalius</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:SGOBBI%20Alessandra">SGOBBI Alessandra</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:ZUCKER%20Andreas">ZUCKER Andreas</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:PILLI%20Roberto">PILLI Roberto</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:CAMIA%20Andrea">CAMIA Andrea</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:THIEL%20Christian">THIEL Christian</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:HOYER-KLICK%20Carsten">HOYER-KLICK Carsten</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:DALLA%20LONGA%20Francesco">DALLA LONGA Francesco</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:KOBER%20Tom">KOBER Tom</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:BADGER%20Jake">BADGER Jake</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:VOLKER%20Patrick">VOLKER Patrick</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:ELBERSEN%20Berien">ELBERSEN Berien</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:BROSOWSKI%20Andre">BROSOWSKI Andre</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:THR%C3%84N%20Daniela">THRÄN Daniela</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:JONSSON%20Klas">JONSSON Klas</a></p><h3>How to cite</h3><p>Ruiz Castello, P., Nijs, W., Tarvydas, D., Sgobbi, A., Zucker, A., Pilli, R., Camia, A., Thiel, C., Hoyer-Klick, C., Dalla Longa, F., Kober, T., Badger, J., Volker, P., Elbersen, B., Brosowski, A., Thrän, D. and Jonsson, K., ENSPRESO - an open data, EU-28 wide, transparent and coherent database of wind, solar and biomass energy potentials, European Commission, 2019, JRC116900.</p><p>European Commission</p><p>JRC116900</p><h3>Remarks</h3><p>The originator of this mirror requires stable and reliable URLs due to an integration of the dataset into an automated workflow. The data catalogue has frequent outages.</p>
Dataset for 'Comparison of coherence scanning interferometry, focus variation and confocal microscopy for surface topography measurement'
<p>The original measurement data shown in Figures 1 to 3 of the Euspen conference publication: Comparison of coherence scanning interferometry, focus variation and confocal microscopy for surface topography measurement, https://www.euspen.eu/knowledge-base/ICE23170.pdf. </p><p><strong>Acknowledgements </strong></p><p>The authors would like to thank the UKRI Research England Development (RED) Fund for funding this work via the Midlands Centre for Data-Drive Metrology. This work was supported by the European Metrology Programme for Innovation and Research (EMPIR) project (TracOptic, 20IND07) and the European Union (ERC, AI-SURF, 101054454).</p>
Datasets underlying manuscript "Earthquake observatory with coherent laser interferometry on the telecom fiber network"
<p>Refers to the paper: "Seismic monitoring using the telecom fiber network" by S. Donadello et al.., Communications Earth and Environment, 5,178 (2024). DOI: 10.1038/s43247-024-01338-2</p> <p>Data are structured in folders corresponding to manuscript figures 1 to 4 and Extended Figures S1 to S6.</p> <p>Headers unambiguously indicate the listed quantities.</p> <p>Minimal python scripts are provided to support interpretation and visualization</p>
Dataset for "Controlling the photon number coherence of solid-state quantum light sources for quantum cryptography"
<p>Dataset for <strong>"Controlling the Photon Number Coherence of Solid-state Quantum Light Sources for Quantum Cryptography"</strong></p> <p>This dataset contains data for</p> <p>journal website: <a href="https://www.nature.com/articles/s41534-024-00811-2">https://www.nature.com/articles/s41534-024-00811-2 </a></p> <p>DOI: <a href="https://doi.org/10.1038/s41534-024-00811-2">https://doi.org/10.1038/s41534-024-00811-2</a></p> <p> </p> <p>The data is in either <strong>.txt or .csv f</strong>ormat. The zip file <strong>"PNCDataSet"</strong> includes the following folders and data:</p> <ul> <li>Dataset for Indistinguishability: <ul> <li>Folder <strong>.\HOM</strong> <ul> <li><strong>"HOM_Distinguishable.csv"</strong> as HOM data of Distinguishable case</li> <li><strong> "HOM_stim.csv"</strong> as HOM data of stiX.</li> <li><strong>"HOM_TPE.csv"</strong> as HOM data of reX.</li> </ul> </li> </ul> </li> <li>Dataset for g2 <ul> <li> folder <strong>.\g2</strong> <ul> <li><strong>"Stim_G2.csv"</strong> as g2 data of stiX.</li> <li><strong>"TPE_G2.csv"</strong> as g2 datra of reX.</li> </ul> </li> </ul> </li> <li>Dateset for Spectra <ul> <li>folder <strong>.\Spectra</strong> <ul> <li><strong>"TPE_SPECTRA.csv"</strong> as the spectra of reX.</li> <li><strong>"Stim_SPECTRA.csv" </strong>as the spectra of stiX</li> </ul> </li> </ul> </li> <li>Dataset for reX PNC <ul> <li>folder <strong>.\TPEPhaseScan</strong> <ul> <li>The folder contains 49 CSV files in alphabetical order. Each CSV file contains a 30-second count trace of 2 detectors for different TPE powers specified in the paper.</li> </ul> </li> </ul> </li> <li>Dataset for stiX PNC <ul> <li>folder <strong>.\StimTPE-PhaseScan</strong> <ul> <li>The folder contains 50 CSV files in alphabetical order. Each CSV file contains a 30-second count trace of 2 detectors for different TPE powers and a stimulation pulse power specified in the paper.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Additional data for the supplementary materials is available upon a reasonable request.</p> <p> </p> <p>How to extract data:</p> <p><strong>Windows:</strong></p> <ol> <li> <p>Locate the .zip file on your computer. In this case, the zip file is named "SUPERDataset".</p> </li> <li> <p>Right-click on the .zip file and select "Extract All" from the context menu. This will open the extraction wizard.</p> </li> </ol> <p><strong>macOS:</strong></p> <ol> <li> <p>Locate the .zip file on your computer. In this case, the zip file is named "SUPERDataset".</p> </li> <li> <p>Double-click on the .zip file. macOS will automatically extract the contents of the .zip file to the same location.</p> </li> </ol> <p><strong>Linux:</strong></p> <ol> <li> <p>Open a terminal window.</p> </li> <li> <p>Navigate to the directory where the .zip file is located using the <code>cd</code> command.</p> </li> <li> <p>Run the following command to unzip the file:<br> </p> <pre><code>unzip PNCDataSet.zip</code></pre> <p> </p> </li> </ol> <p> </p> <h2>Acknowledgements</h2> <p>Y.K., F.K., R.S., V.R. and G.W. acknowledge financial support through the Austrian Science Fund FWF projects W1259 (DK-ALM Atoms, Light, and Molecules), FG 5, TAI-556N (DarkEneT), F 7114 (BeyondC) and I4380 (AEQuDot). DAV and TH acknowledge financial support by the German Federal Ministry of Education and Research (BMBF) via projects 13N14876 (‘QuSecure’) and 16KISQ087K (tubLAN Q.0). TKB and DER acknowledge financial support from the German Research Foundation DFG through project 428026575 (AEQuDot). A.R. and SFCdS acknowledge the FWF projects FG 5, P 30459, I 4320, the Linz Institute of Technology (LIT) and the European Union’s Horizon 2020 research, and innovation program under Grant Agreement Nos. 899814 (Qurope), 871130 (ASCENT+) and the QauntERA II Program (project QD-E-QKD). L.M.H., P.W. and J.C.L. acknowledge financial support from the European Union’s Horizon 2020 and Horizon Europe research and innovation program under grant agreement No 899368 (EPIQUS), the Marie Skłodowska-Curie grant agreement No 956071 (AppQInfo), and the QuantERA II Program under Grant Agreement No 101017733 (PhoMemtor); FWF through F7113 (BeyondC), and FG5 (Research Group 5); from the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development and the Christian Doppler Research Association. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.</p>
Data set for Coherent Hole Transport in Selective Area Grown Ge Nanowire Networks
<p>This document contains all the data and analysis used in the manuscript titled </p> <h1><span>Coherent Hole Transport in Selective Area Grown Ge Nanowire Networks</span></h1> <p><span><a title="DOI URL" href="https://doi.org/10.1021/acs.nanolett.2c00358">https://doi.org/10.1021/acs.nanolett.2c00358</a></span></p>
Figure 3 in DNA-based species delimitation separates highly divergent populations within morphologically coherent clades of poorly dispersing beetles
Figure 3. Ultrametric tree for Lyponiini. Branches marked by solid lines originate from continental Asia; those by dashed lines from Taiwan, Okinawa, and Japan. The grey dots/squares designate putative species identified using the general mixed Yule-coalescent model and the barcoding threshold, respectively. Terminals without designation were recovered as independent species-level entities.
Figure 2 in DNA-based species delimitation separates highly divergent populations within morphologically coherent clades of poorly dispersing beetles
Figure 2. Phylogenetic hypothesis on Lyponiini inferred from the maximum likelihood (ML) analysis. Numbers at the branches indicate maximum parsimony and ML bootstrap values, and Bayesian posterior probabilities (left to right).
Figure 7 in DNA-based species delimitation separates highly divergent populations within morphologically coherent clades of poorly dispersing beetles
Figure 7. The number of DNA diagnostic characters as a function of time since the split from the closest relative. The black-rimmed dots designate splits supported also by morphological characters, simple dots designate splits within morphologically defined clades.
Figure 1 in DNA-based species delimitation separates highly divergent populations within morphologically coherent clades of poorly dispersing beetles
Figure 1. Sampling sites of Lyponiini in (A) Continental East Asia, Taiwan and Okinawa and (B) Honshu and Shikoku.
Figure 6 in DNA-based species delimitation separates highly divergent populations within morphologically coherent clades of poorly dispersing beetles
Figure 6. Density plots of genetic distances of Lyponiini for (A) intra- and interspecific diversity of Lyponiini (B) intraspecific diversity for species as listed.
Figure 5 in DNA-based species delimitation separates highly divergent populations within morphologically coherent clades of poorly dispersing beetles
Figure 5. Relationships between Kimura-two-parameter genetic and geographical distances of Lyponiini. A–E, interspecific and intraspecific species pairs for five clades; F, intraspecific pairs of Ponyalis quadricollis. The parameters and P-values are listed in Table 1.
Figure 4. A in DNA-based species delimitation separates highly divergent populations within morphologically coherent clades of poorly dispersing beetles
Figure 4. A, intraspecific relationships between maximum genetic and geographical distances of Lyponiini for the Chinese species pairs (blue) and Japanese species pairs (red). B, intraspecific maximum genetic divergence and geographical distances in Lyponiini (red) and Agabini (blue). The data on Agabini diving beetles are from Bergsten et al. (2012).
Phonon-assisted coherent transport of excitations in Rydberg-dressed atom arrays
<p>Data corresponding to the article 'Phonon-assisted coherent transport of excitations in Rydberg-dressed atom arrays'.</p> <p>File description:</p> <ul> <li> <p>data_experimental_single_run.zip - numerical data corresponding to Fig. 2 and Fig. 3</p> </li> <li> <p>data_general_phase_diagram_dense_grid.zip - numerical data corresponding to Fig. 4</p> </li> <li>data_general_phase_diagram_dense_grid_zoom.zip - numerical data corresponding to Fig. 5</li> <li> <p>data_general_single_run_reply.zip - numerical data corresponding to Fig. 6 and Fig. 7</p> </li> </ul>
Source Data for the publication "Sub-100-fs energy transfer in coenzyme NADH is a coherent process assisted by a charge-transfer state"
<p>Molecular Structures for solvated NADH. </p> <p>The folder "QMMM_OPTIMIZED_STRUCTS" contains the pdb files of the six representatives for the three conformational clusters obtained after REMD used in the Supplementary Information.</p> <p>The folder "SOLVENT_ENSEMBLE_AROUND_FIXED_SOLUTE" conatins AMBER RESTART files for 200 solvent configurations around two cluster reps displayed in Figure 2 of main manuscript. </p> <p>The folder "PARAMETERS_FOR_MLMCTDH" contains the input file, operator file and parameters for ML-MCTDH dynamics for the structures shown in Main Manuscript and Supplementary. </p>
A Network-Based Phase-Gradient Stacking Method for Resolving Long-wavelength deformation from low-coherence SAR interferograms
<p><span><span><strong>This dataset includes the key results, primary </strong></span></span><strong>InSAR and GNS observatons </strong><strong> in our study. </strong></p>
Logical coherence in 2D compass codes Supplementary Material
<h1>Logical coherence in 2D compass codes</h1> <p>This repo contains the tools to reproduce and share the data for the paper "Logical Coherence in 2D Compass Codes" by Balint Pato, Will Judd Staples, and Kenneth R. Brown (2025) as well as data for B. Pato, Q. Miao and K. R. Brown, "Optimal Decoding of 2D Compass Codes Under Coherent Noise," 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Montreal, QC, Canada, 2024, pp. 448-449, doi: 10.1109/QCE60285.2024.10349.</p> <h1>Setup</h1> <p>Python 3.x is required. The instructions for a Unix/Linux system:</p> <pre><code>cd coherence-in-compass-codes-paper python3 -m venv ~/.virtualenvs/cicc . ~/.virtualenvs/cicc/bin/activate pip install -r requirements.txt -r msim/requirements.txt -r msim/requirements.dev.txt </code></pre> <p>Before running any Python file, make sure the source folder is in PYTHONPATH, for example:</p> <pre><code>export PYTHONPATH="." </code></pre> <p>A quick end-to-end test that everything works:</p> <pre><code>check/all </code></pre> <h1>Downloading data</h1> <p>Pregenerated data is available in <code>data/codes.db</code> for the Logical Coherence in 2D Compass Codes paper.</p> <p>The number of samples per code family:</p> <table> <tbody><tr> <th>code family</th> <th>samples</th> </tr> </tbody><tbody> <tr> <td>qshor[0.166667]_13x13_0</td> <td>2,178,400</td> </tr> <tr> <td>qshor[0.166667]_17x17_0</td> <td>2,185,800</td> </tr> <tr> <td>qshor[0.166667]_21x21_0</td> <td>2,210,800</td> </tr> <tr> <td>qshor[0.166667]_9x9_0</td> <td>2,176,600</td> </tr> <tr> <td>qshor[0.333333]_13x13_0</td> <td>3,310,800</td> </tr> <tr> <td>qshor[0.333333]_17x17_0</td> <td>3,317,870</td> </tr> <tr> <td>qshor[0.333333]_21x21_0</td> <td>3,344,800</td> </tr> <tr> <td>qshor[0.333333]_9x9_0</td> <td>3,308,000</td> </tr> <tr> <td>qshor[0.5]_13x13_0</td> <td>2,381,400</td> </tr> <tr> <td>qshor[0.5]_17x17_0</td> <td>2,388,000</td> </tr> <tr> <td>qshor[0.5]_21x21_0</td> <td>2,398,600</td> </tr> <tr> <td>qshor[0.5]_9x9_0</td> <td>2,379,400</td> </tr> <tr> <td>qshor[0.666667]_13x13_0</td> <td>2,808,400</td> </tr> <tr> <td>qshor[0.666667]_17x17_0</td> <td>2,827,000</td> </tr> <tr> <td>qshor[0.666667]_21x21_0</td> <td>2,876,600</td> </tr> <tr> <td>qshor[0.666667]_9x9_0</td> <td>2,805,600</td> </tr> <tr> <td>qshor[0.833333]_13x13_0</td> <td>3,869,600</td> </tr> <tr> <td>qshor[0.833333]_17x17_0</td> <td>3,883,200</td> </tr> <tr> <td>qshor[0.833333]_21x21_0</td> <td>3,913,400</td> </tr> <tr> <td>qshor[0.833333]_9x9_0</td> <td>3,861,400</td> </tr> <tr> <td>surface_13x13</td> <td>2,004,800</td> </tr> <tr> <td>surface_17x17</td> <td>2,004,800</td> </tr> <tr> <td>surface_21x21</td> <td>2,004,800</td> </tr> <tr> <td>surface_9x9</td> <td>2,004,800</td> </tr> <tr> <td>zstackedshor[h11]_11x11_0</td> <td>612,250</td> </tr> <tr> <td>zstackedshor[h1]_11x11_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x11_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x5_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x7_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x9_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_5x5_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_7x7_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_9x9_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h2]_11x11_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h2]_5x5_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h2]_7x7_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h2]_9x9_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_11x11_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_5x5_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_7x7_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_9x9_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h5]_5x5_0</td> <td>700,000</td> </tr> <tr> <td>zstackedshor[h7]_7x7_0</td> <td>700,000</td> </tr> </tbody> </table> <p>For the surface code ML database:</p> <table> <tbody><tr> <th>code family</th> <th>samples</th> </tr> </tbody><tbody> <tr> <td>surface_101x101</td> <td>40,000</td> </tr> <tr> <td>surface_13x13</td> <td>1,599,200</td> </tr> <tr> <td>surface_151x151</td> <td>40,000</td> </tr> <tr> <td>surface_17x17</td> <td>1,599,200</td> </tr> <tr> <td>surface_201x201</td> <td>40,000</td> </tr> <tr> <td>surface_21x21</td> <td>1,599,200</td> </tr> <tr> <td>surface_23x23</td> <td>1,600,000</td> </tr> <tr> <td>surface_251x251</td> <td>40,000</td> </tr> <tr> <td>surface_25x25</td> <td>1,600,000</td> </tr> <tr> <td>surface_27x27</td> <td>1,600,000</td> </tr> <tr> <td>surface_29x29</td> <td>1,600,000</td> </tr> <tr> <td>surface_33x33</td> <td>1,600,000</td> </tr> <tr> <td>surface_37x37</td> <td>1,600,000</td> </tr> <tr> <td>surface_45x45</td> <td>2,000,000</td> </tr> <tr> <td>surface_53x53</td> <td>2,000,000</td> </tr> <tr> <td>surface_61x61</td> <td>2,000,000</td> </tr> <tr> <td>surface_69x69</td> <td>2,000,000</td> </tr> <tr> <td>surface_9x9</td> <td>1,599,380</td> </tr> </tbody> </table> <p>Sample queries:</p> <ul> <li>to find the number of samples above:</li> </ul> <pre><code><span>select</span> code_id, theta_phys, <span>count</span>(<span>*</span>) <span>from</span> logical_angle_samples <span>group</span> <span>by</span> code_id, theta_phys <span>order</span> <span>by</span> theta_phys <span>asc</span>; </code></pre> <ul> <li>to find the number of samples per datapoint for surface codes</li> </ul> <pre><code><span>select</span> code_id, theta_phys, <span>count</span>(<span>*</span>) <span>from</span> logical_angle_samples <span>group</span> <span>by</span> code_id, theta_phys <span>having</span> code_id <span>like</span> <span>'surface%'</span> <span>order</span> <span>by</span> theta_phys <span>asc</span> ; </code></pre> <ul> <li>to query data underlying the plots for infidelity metrics for the d=9 qshor=5/6 compass codes:</li> </ul> <pre><code><span>select</span> <span>*</span> <span>from</span> qshor_loaf <span>where</span> code_id <span>like</span> <span>'qshor%0.83%9x9%'</span>; </code></pre> <h2>Database schema</h2> <pre><code><span>-- code families are the roots of parametrized code hierarchies, e.g. surface, qshor. The convention is to also use the code_family as table names describing the parametrized members. </span> <span>CREATE</span> <span>TABLE</span> CODE_FAMILIES(CODE_FAMILY, <span>UNIQUE</span>(CODE_FAMILY)); <span>-- surface code members. Technically possible to create non-square surface codes, but in this paper we only explored square ones. </span> <span>CREATE</span> <span>TABLE</span> SURFACE(CODE_ID, DX, DZ, <span>UNIQUE</span>(CODE_ID)); <span>-- families of qshor (x check density) parametrized random compass codes. </span> <span>CREATE</span> <span>TABLE</span> qshor(CODE_ID, QSHOR, DX, DZ, <span>UNIQUE</span>(CODE_ID)); <span>--the main samples table, for a given code, and physical rotation angle the logical rotation angle is recorded</span> <span>CREATE</span> <span>TABLE</span> LOGICAL_ANGLE_SAMPLES(CODE_ID,THETA_PHYS TEXT, THETA_LOGICAL TEXT); <span>-- diamond distance metrics aggregated from the logical angle samples table for qshor</span> <span>CREATE</span> <span>TABLE</span> qshor_dd(code_id, theta_phys, mean, std, num_records); <span>-- loss of average fidelity metrics aggregated from the logical angle samples table for qshor</span> <span>CREATE</span> <span>TABLE</span> qshor_loaf(code_id, theta_phys, mean, std, num_records); <span>-- diamond distance metrics aggregated from the logical angle samples table for surface codes</span> <span>CREATE</span> <span>TABLE</span> surface_dd(code_id, theta_phys, mean, std, num_records); <span>-- loss of average fidelity metrics aggregated from the logical angle samples table for surface codes</span> <span>CREATE</span> <span>TABLE</span> surface_loaf(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> pub.zstackedshor(CODE_ID, ZSHOR_HEIGHT, DX, DZ, <span>UNIQUE</span>(CODE_ID)); <span>CREATE</span> <span>TABLE</span> pub.zstackedshor_loaf(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> pub.zstackedshor_loaf_ml(code_id, theta_phys, mean, std, num_records); <span>-- ML decoder version </span> <span>CREATE</span> <span>TABLE</span> qshor_dd_ml(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> qshor_loaf_ml(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> surface_dd_ml(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> surface_loaf_ml(code_id, theta_phys, mean, std, num_records); </code></pre> <h1>Plots</h1> <p>Make sure that you have the data under <code>data/codes.db</code> - this needs to be a SQLite database. See the previous section on how to create it from the published data.</p> <p>Plots are generated under the folder <code>figures</code>. There are separate entry points for each figure in the paper:</p> <ul> <li>combined thresholds in the appendix resulting in <code>figures/threshold_plot-combined_full.pdf</code> and <code>figures/threshold_plot-combined_zoom.pdf</code>:</li> </ul> <pre><code>python cicc/plot/combined_threshold.py </code></pre> <ul> <li>the main plot containing the manually extracted thresholds for the random compass codes and the surface code:</li> </ul> <pre><code>python cicc/plot/qshor_family.py </code></pre> <ul> <li>the QCE2024 poster / extended abstract plots</li> </ul> <pre><code>python cicc/plot/qce2024_figures.py </code></pre> <ul> <li>the repetition code and Z-stacked Shor code plots matching the formulae plots</li> </ul> <pre><code>python cicc/plot/zstack_zshor_thresholds.py </code></pre> <h1>A note on angle conventions</h1> <p>All rotations in this project are around the Z-axis. There are two ways one can interpret the rotation angle based on the unitary rotation:</p> <ul> <li>spin angles: Rz(theta_spin) = exp(-i theta_spin/2 Z)</li> <li>direct angles: Rz(theta_direct) = exp(i theta_direct Z)</li> </ul> <p>Thus, for the same rotation unitary, theta_spin = - 2 * theta_direct. The paper by Bravyi, Engelbrecht, Konig and Peard [^bravyi2018] for the rotated surface code uses the direct angles convention, and similarly, msim uses direct angles. However, this paper uses spin angles closer to the physics convention.</p> <p>[^bravyi2018]:Bravyi, Sergey, Matthias Englbrecht, Robert König, and Nolan Peard, ‘Correcting Coherent Errors with Surface Codes’, Npj Quantum Information, 4.1 (2018), 55 <a href="https://doi.org/10.1038/s41534-018-0106-y">https://doi.org/10.1038/s41534-018-0106-y</a></p> <p>The table below summarizes the angle conventions in different parts of the codebase to avoid confusion:</p> <table> <tbody><tr> <th>Place</th> <th>convention</th> </tr> </tbody><tbody> <tr> <td>physical angles for the samplers in this project</td> <td>direct angles / pi</td> </tr> <tr> <td>msim simulator</td> <td>direct angles</td> </tr> <tr> <td>msim coeffs framework</td> <td>spin angles</td> </tr> <tr> <td>database logical angles</td> <td>direct angles</td> </tr> <tr> <td>database physical angles</td> <td>direct angles</td> </tr> <tr> <td>plots</td> <td>spin angles / pi</td> </tr> </tbody> </table> <h1>Generating your own data</h1> <h2>Random Compass Codes</h2> <p>Generating data for random compass codes for a given set of qshor values, distances and physical theta values:</p> <pre><code> python cicc/sample_angles/qshor_angles.py --help usage: qshor_angles.py [-h] [--db DB] --dz DZ --q Q --theta_phys THETA_PHYS [--num_runs NUM_RUNS] options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --q Q Qshor probability metric --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --num_runs NUM_RUNS number of iterations: the total number of samples, which is divided across sqrt(num_runs) randomly generated codes </code></pre> <p>For example:</p> <pre><code>python cicc/sample_angles/qshor_angles.py --dz "[9, 13, 17, 21]" --theta_phys="np.linspace(0.01,0.25, 10)" --qshor="[1/6, 2/6, 3/6, 4/6, 5/6]" </code></pre> <h2>Surface code</h2> <p>Generating data for the rotated surface code:</p> <pre><code>python cicc/sample_angles/rsc_angles.py --help usage: rsc_angles.py [-h] [--db DB] --dz DZ --theta_phys THETA_PHYS [--batch_size BATCH_SIZE] [--num_workers NUM_WORKERS] [--num_runs NUM_RUNS] options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --batch_size BATCH_SIZE batch size for writing to the db --num_workers NUM_WORKERS parallelism --num_runs NUM_RUNS number of iterations: the total number of samples </code></pre> <p>For example</p> <pre><code>python cicc/sample_angles/rsc_angles.py --dz "[5, 9, 13, 17, 21, 25, 29]" --theta_phys="np.linspace(0.01,0.25, 10)" </code></pre> <h2>Repetition codes</h2> <p>Generating data for the repetition codes:</p> <pre><code>python cicc/sample_angles/repcodes_angles.py --help usage: repcodes_angles.py [-h] [--db DB] --dz DZ --theta_phys THETA_PHYS [--num_runs NUM_RUNS] [--batch_size BATCH_SIZE] options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --num_runs NUM_RUNS number of iterations: the total number of samples --batch_size BATCH_SIZE batch size </code></pre> <p>For example</p> <pre><code>python cicc/sample_angles/repcodes_angles.py --dz "[5,7,9,11]" --theta_phys "list(reversed(list(np.linspace(0.04,0.18,20))))" --num_runs 10000 </code></pre> <h2>Z-stacked Shor codes</h2> <p>Generating data for the repetition codes:</p> <pre><code>python cicc/sample_angles/repcodes_angles.py --help usage: zstacked_shor_angles.py [-h] [--db DB] --dz DZ [--height HEIGHT] [--zshor ZSHOR] --theta_phys THETA_PHYS [--num_runs NUM_RUNS] [--batch_size BATCH_SIZE] Sample angles for ZStackedShor codes. Example usage: Run 10,000 samples for height-2 and height-3 ZStackedShor codes with Z distance 3, 5, and 7, and angles 0.1, 0.15, and 0.17: python -m cicc.sample_angles.zstacked_shor_angles --dz '[3,5,7]' --height [2,3] --theta_phys '[0.1,0.15,0.17]' --num_runs 10000 --batch_size 10 Run 10,000 samples for Z-Shor codes with Z distance 3, 5, and 7, and angles 0.1, 0.15, and 0.17: python -m cicc.sample_angles.zstacked_shor_angles --dz '[3,5,7]' --zshor --theta_phys '[0.1,0.15,0.17]' --num_runs 10000 --batch_size 10 Run 10,000 samples for X-Shor codes with Z distance 3, 5, and 7, and angles 0.1, 0.15, and 0.17: python -m cicc.sample_angles.zstacked_shor_angles --dz '[3,5,7]' --height 1 --theta_phys '[0.1,0.15,0.17]' --num_runs 10000 --batch_size 10 options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --height HEIGHT height of the Z-Shor code block --zshor ZSHOR Z-shor code - Z-Shor height equals to dz --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --num_runs NUM_RUNS number of iterations: the total number of samples --batch_size BATCH_SIZE batch size </code></pre> <h2>Regenerating aggregated statistics</h2> <p>In the combined plot (<code>cicc/plot/combined_threshold.py</code>), by default, the already computed statistics are plotted. Hence, when new data is generated, these need to be recalculated. In <code>cicc/plot/combined_threshold.py</code> there is a section that can be modified to trigger the required recalculation (slow):</p> <pre><code> recompute( conn, <span># set to True to recompute qshor statistics </span> recompute_qshor=<span>False</span>, <span># set to True to recompute surface code statistics </span> recompute_rsc=<span>False</span>, <span># set to True to recompute qshor statistics with ML decoding </span> recompute_ml_qshor=<span>False</span>, <span># set to True to recompute surface code statistics with ML decoding</span> recompute_ml_rsc=<span>False</span>, )</code></pre>
Automatic exhaustive calculations of large material space by Korringa-Kohn-Rostoker coherent approximation method --- Applied to equiatomic quaternary high entropy alloys
<p>Calculated data of equiatomic quaternary solid solution phase (high-entropy alloys) on local magnetic moment, total magnetization, magnetic phase transition temperature and residual resistivity.</p> <p>The data was added on October 28.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.