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163 results for “superconductivity”
Data for Reducing leakage of single-qubit gates for superconducting quantum processors using analytical control pulse envelopes
<p>This dataset contains the experimental data used in the figures of the paper "Reducing leakage of single-qubit gates for superconducting quantum processors using analytical control pulse envelopes" by E. Hyyppä, A. Vepsäläinen, ..., and J. Heinsoo published in PRX Quantum 5, 030353 (2024): https://doi.org/10.1103/PRXQuantum.5.030353.</p> <p>The data is stored mostly as csv-files, the contents of which are explained in the readme-files. Each subfolder corresponds to one figure of the paper and also contains a Jupyter Notebook for plotting the data. The subfolders S1-S10 correspond to the supplementary figures, i.e., figures 6-15 in the Appendix of the paper.</p> <p>Furthermore, we provide a Jupyter notebook in the folder Code_to_plot_FAST_and_HD_DRAG_pulses/ that provides Python functions for evaluating and plotting the proposed FAST DRAG and HD DRAG pulses in time domain and frequency domain. Please cite our paper if you use the Python code for your published research.</p> <p>The notebooks have been tested using the following Python package versions<br>Python 3.11<br>scipy 1.14.1<br>numpy 2.1.0<br>matplotlib 3.9.2</p>
A chip-based superconducting magnetic trap for levitating superconducting microparticles
<p>Video files (mp4 format) showing levitation of a spherical 50μm diameter superconducting microparticle at a temperature of 4K (levitation_4K.mp4) and 40mK (levitation_40mK.mp4).<br> Data file for Figure 6: Frequency spectrum of particle motion.</p>
Dataset supporting the paper "Superconducting Scanning Tunneling Microscope Tip to Reveal Sub-millielectronvolt Magnetic Energy Variations on Surfaces. J. Phys. Chem Lett. 12, 2983 (2021)"
<p>Dataset corresponding to theoretical calculations in the supporting information of the paper "Superconducting Scanning Tunneling Microscope Tip to Reveal Sub-millielectronvolt Magnetic Energy Variations on Surfaces" J. Phys. Chem Lett. 12, 2983 (2021), <a href="https://doi.org/10.1021/acs.jpclett.1c00328">https://doi.org/10.1021/acs.jpclett.1c00328</a></p> <p>List of files:</p> <p>Several folders corresponding to the figures of the supporting information. They contain:</p> <ul> <li>.siesta files: STM images in WsXM format (http://www.wsxm.eu/) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).</li> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>).</li> <li>.agr files: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).</li> </ul>
Measuring Magnetic 1/f Noise in Superconducting Microstructures and the Fluctuation-Dissipation Theorem - Data
<p>Figures and corresponding data associated with the manuscript 'Measuring Magnetic 1/f Noise in Superconducting Microstructures and the Fluctuation-Dissipation Theorem' by Herbst et al.</p>
Interstitial null-distance time-domain diffuse optical spectroscopy using a superconducting nanowire detector
<p>We demonstrate a novel realization of Interstitial fiber, broadband, Time Domain Diffuse Optical Spectroscopy (TD-DOS) in Null Source-Detector separation (NSDS) approach without temporal gating, by using a Superconducting Nanowire single photon detector (SNSPD) for acquisition. As per the MEDPHOT protocol, we test experimentally, the absorption linearity of the system on tissue-equivalent liquid phantoms, and demonstrate the scattering-independent retrieval of the absorption spectrum of water using Intralipid phantoms in the wavelength range of 600-1100 nm.</p> <p>This work has been published in the Journal of Biomedical Optics - https://doi.org/10.1117/1.JBO.28.12.121202. Here, we present the dataset containing the acquired data pertaining to the aforementioned publication, including a brief overview, the tools to read it and the analysis corresponding to the figures in the article.</p>
On the Constraints on Superconducting Cosmic Strings from 21-cm Cosmology (supplementary inference products)
<p>These are the nested sampling inference products that were used to compute the results for <a href="https://arxiv.org/abs/2312.08828">arXiv:2312.08828</a>.</p> <p>The python script, and utility functions, required to produce most of the figures in the paper are included to demonstrate usage. Plotting script for functional posteriors are not included as these require emulators that are not part of this data release. </p> <p>All the included chains were computed using <a href="https://github.com/PolyChord/PolyChordLite">PolyChordLite</a> .</p> <p>Chain foldername conventions:</p> <ul> <li>HERA: Constraints from HERA Phase I 21-cm power spectrum upper limits</li> <li>SARAS_3: Constraints from the SARAS 3 21-cm global signal null detecetion</li> <li>Xray_Background: Constraints from collated measurements of the unresolved X-ray background</li> <li>HERA_SARAS_3_Xray_Background: Joint analysis of the above</li> </ul> <p>Chains have rootnames that are of the form 'foldername_constraints'. See <a href="https://arxiv.org/abs/2312.08828">arXiv:2312.08828</a> for additional details on each of the model parameters. </p> <p>Software used: <a href="https://numpy.org/">numpy</a>, <a href="https://pandas.pydata.org/">pandas</a>, <a href="https://pyyaml.org/">pyYAML</a>, <a href="https://scipy.org/">scipy</a>, <a href="https://github.com/htjb/globalemu">globalemu</a>, <a href="https://matplotlib.org/stable/">matplotlib</a>, <a href="https://www.tensorflow.org/">tensorflow</a>, <a href="https://scikit-learn.org/stable/">scikit-learn</a>, <a href="https://joblib.readthedocs.io/en/stable/">joblib</a>, <a href="https://github.com/PolyChord/PolyChordLite">pypolychord</a>, <a href="https://github.com/handley-lab/anesthetic">anesthetic</a>,<a href="https://github.com/HERA-Team/hera_pspec"> hera-pspec</a>, <a href="https://seaborn.pydata.org/">seaborn</a>, <a href="https://github.com/htjb/margarine">margarine</a>, <a href="https://github.com/handley-lab/fgivenx">fgivenx</a>, <a href="https://github.com/tqdm/tqdm">tqdm</a></p> <p>Exact software versions are specified in an included requirements.txt file for reproducibility. </p>
Data and codes for "A cryogenic electro-optic interconnect for superconducting devices"
<p>Here you find all raw data files and processing Python scripts for plots presented in "A cryogenic electro-optic interconnect for superconducting devices" Amir Youssefi, et.al. Nature Electronics 2021</p> <p> </p>
Demonstrating real-time and low-latency quantum error correction with superconducting qubits
<p>Data associated with results presented in "Demonstrating real-time and low-latency quantum error correction with superconducting qubits".</p> <p>HDF5 files include raw data collected during experiments. Datasets for experiments performed with different number of measurement rounds are saved in separate groups. The group attributes contain information including the total number of measurement rounds. Groups also contain the stim circuits associated with each experiment, which are used for software decoding, and qubit_mappings, which maps each stim coordinate to the corresponding qubit ID on the Ankaa-2 device. Each group has a hard_measurements and soft_measurements group containing the hard and soft measurement results. Measurement results are grouped in datasets per qubit, storing results in the order of measurement execution during the experiment, and with each row representing a separate repetition of the experiment.</p> <p>When decoding with the FPGA decoder we also store the decoder register outcomes in decoder_shot_results. In particular, the first column indicates the logical correction computed by the FPGA decoder – values 0 and 2 correspond to no logical error detected and 1 corresponds to logical error being detected by the decoder.</p> <p>The HDF5 file with data for the fast-feedback experiment ("fast_feedback_raw_data.h5") includes the reference_data group storing reference data. It contains the "delays" group (used to measure T1 in FigS4(d)), "measurement_fidelity" group (used to calculate measurement confusion matrix in Fig S4e, and "double_measurement" group (used to compute post-measurement state distribution in Fig S4f).</p> <p>Also included are files containing the logical error probabilities (LEPs), and CSV files containing timings, both containing data used to plot figures.</p>
Out-of-equilibrium phonons in gated superconducting switches
<p>Raw data and processed data for the paper with the same title. Data is grouped in relation to figures.</p> <p> </p>
Data for article "Theory of superconductivity mediated by Rashba coupling in incipient ferroelectrics"
<p>Ab initio frozen-phonon splitting of the electronic bands, normalized with the polar displacement. The data is plotted in figure 3 (b) of the article Phys. Rev. B <strong>105</strong>, 224503 (2022). </p>
Data for the article "Mechanically induced correlated errors on superconducting qubits with relaxation times exceeding 0.4 milliseconds"
<p>Here you will find all the raw data and data processing scripts for the plots presented in the article "Mechanically induced correlated errors on superconducting qubits with relaxation times exceeding 0.4 milliseconds."</p>
Data and code for figures: Temperature dependence of microwave losses in lumped-element resonators made from superconducting nanowires with high kinetic inductance
<p>This directory contains the datasets and code (if applicable) for generating the figures in the research article: Temperature dependence of microwave losses in lumped-element resonators made from superconducting nanowires with high kinetic inductance, <em>Supercond. Sci. Technol.</em> <strong>37</strong> 075013</p>
Data associated with Altermagnetic superconducting diode effect
<p>Dataset for paper: Altermagnetic Diode Effect (https://arxiv.org/abs/2402.14071)</p>
Dataset for the publication "Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site"
<p>This datasset contains data to reproduce the following figures of the paper <em>Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site</em>:</p> <ul> <li> <p>Time series data of Figures 1c and 2</p> </li> <li> <p>Data (*.asc) used for plotting Figures 1d and 1e (as well as Figure S3 and S4)</p> </li> <li>Pléiades snow depth map (Figure S1)</li> <li> <p>Data used for plotting Figure S2</p> </li> </ul> <p> </p>
Dataset for "Quantum bath suppression in a superconducting circuit by immersion cooling"
<p>Dataset supporting the findings in the manuscript "Quantum bath suppression in a superconducting circuit by immersion cooling" by M Lucas et al. (<em>Nature Communications</em> <strong>14</strong>, 3522 (2023), arxiv:2210.03816) Containing: description of files & figures, raw datasets obtained from superconducting resonator noise and quality factor measurements, electron spin resonance measurements.</p>
Data Analysis files for "Coherent optical control of a superconducting microwave cavity via electro-optical dynamical back-action"
<p>Data analysis files for the manuscript "Coherent optical control of a superconducting microwave cavity via electro-optical dynamical back-action", <a href="https://www.nature.com/articles/s41467-023-39493-3#data-availability">Nature Communications <strong>14</strong>, 3784 (2023)</a>, or <a href="https://arxiv.org/abs/2210.12443">arXiv:2210.12443 (2022)</a></p> <p>This contains the raw data, the data analysis files, and the figure generation files of the manuscript, which includes the following three parts,</p> <p>0. Data preparation</p> <p>The total size of the raw dataset is around 120GB. The raw data is pre-processed via digital down-conversion at 40MHz to obtain the optical/microwave transient response of the electro-optical device in the presence of strong optical pulses at different powers and frequencies.</p> <p>The processed data is adopted for data analysis of the response measurements for convenience.</p> <p>1. Data Analysis</p> <ul> <li>Detailed data analysis of the electro-optical (microwave and optical) responses in presence of the optical pump pulses for different mode and probing configurations.</li> </ul> <p>2. Figures for the manuscripts.</p> <ol> <li>Figures for the optical characterizations</li> <li>Figures for the coherent responses for different configurations</li> <li>Figures for the excess back-action</li> <li>Figures for the theoretical curves in the Supplementary Information </li> </ol>
Enhanced Molecular Spin-Photon Coupling at Superconducting Nanoconstrictions. Open data sets
<p>Includes data relevant for publication with DOI <a href="https://doi.org/10.1021/acsnano.0c03167">10.1021/acsnano.0c03167</a> plus a table with information on how the data were obtained and processed.</p>
data for article "Enhancement of Superconductivity by Amorphizing Molybdenum Silicide Films Using a Focused Ion Beam"
<p>This dataset is the compilation of data used in the article "Enhancement of Superconductivity by Amorphizing Molybdenum Silicide Films Using a Focused Ion Beam" (<a href="https://doi.org/10.3390/nano10050950">https://doi.org/10.3390/nano10050950</a>). For more info of samples see metadata2.txt</p>
Dataset for Superconducting 'twin qubit' , PRB 102, 115422 (2020)
<p>Dataset for manuscript "Superconducting 'twin qubit'", Phys. Rev. B 102, 115422 (2020). Dataset includes experimental data text files of Fig. 2 and Fig. 7 in the manuscript. The dataset describes energy spectrum of superconducting 'twin qubit' (Fig. 2) and its Rabi oscillations (Fig. 7). The paper studies the superconducting double loop system, 'twin qubit'.</p>
Raw data of "Proximity-Induced Superconductivity in Atomically Precise Nanographene on Ag/Nb(110)"
<p>E.M., R.P., and W.W. designed the experiments. P.Z., S.-X.L., R.H., and SD synthesized the molecule. J.-C.L. performed STM/AFM experiments and analyzed the data. W.W. provided the dilution STM and H.C. assisted the measurement. X.W. and U.A. performed the DFT calculations. J.-C.L. wrote the manuscript with the help of R.P. All authors discussed the results and revised the manuscript.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.